diff --git a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/daily_robust_gru-checkpoint.ipynb b/.ipynb_checkpoints/daily_robust_gru-checkpoint.ipynb new file mode 100644 index 0000000..b39215f --- /dev/null +++ b/.ipynb_checkpoints/daily_robust_gru-checkpoint.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + "# print(salmon_copy)\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", + " king_all_copy, king_data= load_data(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "max val king_train:\n", + "67521\n", + "max val king_test:\n", + "32446\n", + "(1824, 1)\n", + "(1644, 1)\n", + "(1644, 1)\n", + "(22365, 1)\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def create_GRU_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create GRU model trained on X_train and y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " # The GRU architecture\n", + " regressorGRU = Sequential()\n", + " # First GRU layer \n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=1, activation='tanh'))\n", + " #regressorGRU.add(Dense(units=1))\n", + "\n", + " # Compiling the RNN\n", + " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + " # Fitting to the training set\n", + " history = regressorGRU.fit(x_train, y_train, epochs=1, batch_size=150)\n", + " \n", + " # Predictions \n", + " GRU_train_predict = regressorGRU.predict(x_train)\n", + " GRU_test_predict = regressorGRU.predict(x_test)\n", + "\n", + " # Descale \n", + " GRU_train_predict = scaler.inverse_transform(GRU_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " GRU_test_predict = scaler.inverse_transform(GRU_test_predict)\n", + " GRU_test_predict = GRU_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return regressorGRU, GRU_train_predict, GRU_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "150/150 [==============================] - 51s 306ms/step - loss: 9.6425e-04\n" + ] + } + ], + "source": [ + "regressorGRU, GRU_train_day, GRU_test_day, history_GRU, y_train, y_test = create_GRU_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_chris_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "# print(traditional)\n", + "y_test_year = y_test_year.astype(np.int64)\n", + "# print(y_test_year)\n", + "# print(GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1428.1983548353.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, GRU_test_day)\n", + "return_rmse(y_test, GRU_test_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1428.1983548353.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, GRU_test_day)\n", + "return_rmse(y_test, GRU_test_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Count\n", + "0 462777\n", + "1 327175\n", + "2 369265\n", + "3 514269" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GRU_test_year = day_to_year(GRU_test_day)\n", + "GRU_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 18593.568531887577.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and GRU\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/daily_robust_lstm-checkpoint.ipynb b/.ipynb_checkpoints/daily_robust_lstm-checkpoint.ipynb new file mode 100644 index 0000000..f4aacde --- /dev/null +++ b/.ipynb_checkpoints/daily_robust_lstm-checkpoint.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + "# print(salmon_copy)\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", + " king_all_copy, king_data= load_data(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "max val king_train:\n", + "67521\n", + "max val king_test:\n", + "32446\n", + "(1824, 1)\n", + "(1644, 1)\n", + "(1644, 1)\n", + "(22365, 1)\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(22365, 180, 1)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def create_LSTM_model(x_train, y_train, x_test, y_test): \n", + " '''\n", + " Create LSTM model trained on X_train and Y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " LSTM_model = Sequential()\n", + " LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(1))\n", + " #LSTM_model.add(Dense(1))\n", + " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=5, batch_size=150, verbose=2)\n", + " \n", + " train_preds = LSTM_model.predict(x_train)\n", + " test_preds = LSTM_model.predict(x_test)\n", + " train_preds = scaler.inverse_transform(train_preds)\n", + " test_preds = scaler.inverse_transform(test_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return LSTM_model, test_preds, train_preds, y_test, y_train, history_LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "150/150 - 29s - loss: 0.0016\n", + "Epoch 2/5\n", + "150/150 - 20s - loss: 9.7357e-04\n", + "Epoch 3/5\n", + "150/150 - 20s - loss: 8.0802e-04\n", + "Epoch 4/5\n", + "150/150 - 23s - loss: 7.4978e-04\n", + "Epoch 5/5\n", + "150/150 - 19s - loss: 6.8307e-04\n" + ] + } + ], + "source": [ + "# running LSTM\n", + "LSTM_model, test_preds_LSTM, train_preds_LSTM, y_test, y_train, history_LSTM = create_LSTM_model(x_train, y_train, x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_chris_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "# print(traditional)\n", + "y_test_year = y_test_year.astype(np.int64)\n", + "# print(y_test_year)\n", + "# print(GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 829.1131409311972.\n" + ] + } + ], + "source": [ + "plot_predictions(y_train, train_preds_LSTM)\n", + "return_rmse(y_train, train_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1509.7059285022888.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, test_preds_LSTM)\n", + "return_rmse(y_test, test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparing RMSE to curr Forecasting methods to LSTM\n", + "LSTM_test_year = day_to_year(test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 24899.162873493984.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and LSTM\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, LSTM_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/daily_robust_rnn-checkpoint.ipynb b/.ipynb_checkpoints/daily_robust_rnn-checkpoint.ipynb new file mode 100644 index 0000000..79ef2d4 --- /dev/null +++ b/.ipynb_checkpoints/daily_robust_rnn-checkpoint.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + " \n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "max val king_train:\n", + "67521\n", + "max val king_test:\n", + "32446\n", + "(1824, 1)\n", + "(1644, 1)\n", + "(1644, 1)\n", + "(22365, 1)\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create single layer rnn model trained on x_train and y_train\n", + " and make predictions on the x_test data\n", + " '''\n", + " # create a model\n", + " model = Sequential()\n", + " model.add(SimpleRNN(32))\n", + "# model.add(SimpleRNN(32, return_sequences=True))\n", + "# model.add(SimpleRNN(16))\n", + " model.add(Dense(1))\n", + "\n", + " model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + " # fit the RNN model\n", + " history = model.fit(x_train, y_train, epochs=3, batch_size=64)\n", + "\n", + " print(\"predicting\")\n", + " # Finalizing predictions\n", + " RNN_train_preds = model.predict(x_train)\n", + " RNN_test_preds = model.predict(x_test)\n", + " \n", + " #Descale\n", + " RNN_train_preds = scaler.inverse_transform(RNN_train_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " RNN_test_preds = scaler.inverse_transform(RNN_test_preds)\n", + " RNN_test_preds = RNN_test_preds.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + "# RNN_salmon_count = (RNN_preds * (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"])) + np.min(king_training[\"king\"])).astype(np.int64)\n", + "\n", + "# why are we normalizing the test and train set, then un-normalizing (maybe this can cause problems in the sense tht we are\n", + "# not comparing our preds to the proper y values)\n", + " return model, RNN_train_preds, RNN_test_preds, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/3\n", + "350/350 [==============================] - 8s 21ms/step - loss: 0.0030\n", + "Epoch 2/3\n", + "350/350 [==============================] - 7s 20ms/step - loss: 5.5065e-04\n", + "Epoch 3/3\n", + "350/350 [==============================] - 6s 18ms/step - loss: 3.9362e-04\n", + "predicting\n" + ] + } + ], + "source": [ + "# train single_layer_rnn_model\n", + "model, RNN_train_preds, RNN_test_preds, history_RNN, y_train, y_test = create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "y_test_year = y_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 570.6108536758172.\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "# plot single_layer_rnn_model\n", + "plot_predictions(y_train, RNN_train_preds)\n", + "return_rmse(y_train, RNN_train_preds)\n", + "print(RNN_train_preds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1367.212325791287.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, RNN_test_preds)\n", + "return_rmse(y_test, RNN_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_RNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Count
0479454
1343823
2384598
3524886
\n", + "
" + ], + "text/plain": [ + " Count\n", + "0 479454\n", + "1 343823\n", + "2 384598\n", + "3 524886" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RNN_test_year = day_to_year(RNN_test_preds)\n", + "RNN_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 8328.45420831501.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, RNN_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/daily_simple_gru-checkpoint.ipynb b/.ipynb_checkpoints/daily_simple_gru-checkpoint.ipynb index 5da32cc..c7639fc 100644 --- a/.ipynb_checkpoints/daily_simple_gru-checkpoint.ipynb +++ b/.ipynb_checkpoints/daily_simple_gru-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -13,40 +13,30 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error\n", - "from sklearn import model_selection\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.linear_model import ElasticNet\n", "# plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy = salmon_data\n", " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", " inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", @@ -60,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -102,13 +92,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -122,24 +112,16 @@ " king_test = king_all[king_test_parse]\n", " king_test = king_test.reset_index()\n", " king_test = king_test.drop('index', axis=1)\n", - " print(king_test.shape)\n", " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - " print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - " print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", - " print(king_test_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + " \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -147,7 +129,7 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", + " # Set up train and test (train is 180 day series, y val is 181st day etc.)\n", " \n", " for i in range(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", @@ -167,26 +149,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1824, 2)\n", - "max val king_train:\n", - "67521\n", - "max val king_test:\n", - "32446\n", - "(1824, 1)\n", - "(1644, 1)\n", - "(1644, 1)\n", - "(22365, 1)\n", - "(22365, 1)\n" - ] - } - ], + "outputs": [], "source": [ "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", "x_train = np.array(x_train)\n", @@ -196,18 +161,15 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" + "\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -231,69 +193,19 @@ " rmse = math.sqrt(mean_squared_error(test, predicted))\n", " print(\"The root mean squared error is {}.\".format(rmse))\n", " \n", - "# def day_to_year(day_preds):\n", - "# day_preds = day_preds[183:]\n", - "# year_preds = []\n", - "# for i in range(365, len(day_preds), 365): \n", - "# salmon_count = np.sum(day_preds[i - 365:i])\n", - "# year_preds.append(salmon_count)\n", - "# year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", - "# return year_preds" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def create_linear_model(x_train, y_train, x_test, y_test, scaler): \n", - " lr = LinearRegression()\n", - " x_train = x_train.to_frame()\n", - " lr.fit(x_train.shape[1], y_train)\n", - " \n", - " train_preds_lr = lr.predict(x_train)\n", - " test_preds_lr = lr.predict(x_test)\n", - " \n", - " #Descale \n", - " \n", - " train_preds_lr = scaler.inverse_transform(train_preds_lr)\n", - " y_train = scaler.inverse_transform(y_train)\n", - " test_preds_lr = scaler.inverse_transform(test_preds_lr)\n", - " test_preds_lr = GRU_test_predict.astype(np.int64)\n", - " y_test = scaler.inverse_transform(y_test)\n", - " \n", - " return train_preds_lr, test_preds_lr" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'numpy.ndarray' object has no attribute 'to_frame'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlr_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_linear_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mreturn_rmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mreturn_rmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mcreate_linear_model\u001b[0;34m(x_train, y_train, x_test, y_test, scaler)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_linear_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'to_frame'" - ] - } - ], - "source": [ - "lr_train, lr_test = create_linear_model(x_train, y_train, x_test, y_test, scaler)\n", - "\n", - "return_rmse(y_train, lr_train)\n", - "return_rmse(y_test, lr_test)" + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -309,12 +221,12 @@ " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", " regressorGRU.add(GRU(units=1, activation='tanh'))\n", - " #regressorGRU.add(Dense(units=1))\n", + " regressorGRU.add(Dense(units=1))\n", "\n", " # Compiling the RNN\n", " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", " # Fitting to the training set\n", - " history = regressorGRU.fit(x_train, y_train, epochs=1, batch_size=150)\n", + " history = regressorGRU.fit(x_train, y_train, epochs=5, batch_size=150)\n", " \n", " # Predictions \n", " GRU_train_predict = regressorGRU.predict(x_train)\n", @@ -332,14 +244,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "150/150 [==============================] - 76s 467ms/step - loss: 8.5667e-04\n" + "Epoch 1/5\n", + " 35/150 [======>.......................] - ETA: 36s - loss: 0.0018" ] } ], @@ -349,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -367,69 +280,26 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "# print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "# print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 1482.919743387506.\n" - ] - } - ], + "outputs": [], "source": [ - "plot_predictions(y_test, GRU_test_day)\n", - "return_rmse(y_test, GRU_test_day)" + "plot_predictions(y_train, GRU_train_day)\n", + "return_rmse(y_train, GRU_train_day)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 1482.919743387506.\n" - ] - } - ], + "outputs": [], "source": [ "plot_predictions(y_test, GRU_test_day)\n", "return_rmse(y_test, GRU_test_day)" @@ -437,144 +307,18 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " Count\n", - "0 458768\n", - "1 345403\n", - "2 380981\n", - "3 504885" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GRU_test_year = day_to_year(GRU_test_day)\n", - "GRU_test_year" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 22101.086602246505.\n" - ] - } - ], "source": [ "# test RMSE with baseline and GRU\n", "return_rmse(y_test_year, traditional)\n", @@ -619,7 +363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/daily_simple_lstm-checkpoint.ipynb b/.ipynb_checkpoints/daily_simple_lstm-checkpoint.ipynb index f4aacde..b771392 100644 --- a/.ipynb_checkpoints/daily_simple_lstm-checkpoint.ipynb +++ b/.ipynb_checkpoints/daily_simple_lstm-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -16,34 +16,27 @@ "from keras.optimizers import SGD\n", "from keras.models import Sequential\n", "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error\n", - "plt.style.use('fivethirtyeight')" + "# plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -55,27 +48,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " date king\n", - "0 1938-05-01 201\n", - "1 1938-05-02 227\n", - "2 1938-05-03 78\n", - "3 1938-05-04 37\n", - "4 1938-05-05 29\n", - "... ... ...\n", - "24729 2021-04-28 2433\n", - "24730 2021-04-29 4782\n", - "24731 2021-04-30 4641\n", - "24732 2021-05-01 2087\n", - "24733 2021-05-02 2517\n", - "\n", - "[24734 rows x 2 columns]\n", " date king\n", "0 1939-01-01 0\n", "1 1939-01-02 0\n", @@ -94,16 +73,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -121,20 +100,14 @@ " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - " print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - " print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", " print(king_test_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + "\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -142,8 +115,6 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", " for i in range(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", " y_train.append(king_train_norm[i])\n", @@ -154,7 +125,7 @@ " # make y_test_not_norm\n", " for i in range(180, 1824):\n", " y_test_not_norm.append(king_test['king'][i])\n", - " for i in range(180,22545): # 30\n", + " for i in range(180,22545): \n", " y_train_not_norm.append(king_training['king'][i])\n", " \n", " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" @@ -162,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -170,15 +141,7 @@ "output_type": "stream", "text": [ "(1824, 2)\n", - "max val king_train:\n", - "67521\n", - "max val king_test:\n", - "32446\n", - "(1824, 1)\n", - "(1644, 1)\n", - "(1644, 1)\n", - "(22365, 1)\n", - "(22365, 1)\n" + "(1824, 1)\n" ] } ], @@ -191,18 +154,14 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -247,7 +206,7 @@ "(22365, 180, 1)" ] }, - "execution_count": 26, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -258,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -288,23 +247,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/5\n", - "150/150 - 29s - loss: 0.0016\n", - "Epoch 2/5\n", - "150/150 - 20s - loss: 9.7357e-04\n", - "Epoch 3/5\n", - "150/150 - 20s - loss: 8.0802e-04\n", - "Epoch 4/5\n", - "150/150 - 23s - loss: 7.4978e-04\n", - "Epoch 5/5\n", - "150/150 - 19s - loss: 6.8307e-04\n" + "Epoch 1/5\n" ] } ], @@ -315,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -325,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,37 +283,16 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_chris_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "# print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "# print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 829.1131409311972.\n" - ] - } - ], + "outputs": [], "source": [ "plot_predictions(y_train, train_preds_LSTM)\n", "return_rmse(y_train, train_preds_LSTM)" @@ -371,27 +300,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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sDRs2xM3NjUuXLuHv719ksLt7965SOfMHtq1cuVKpNpmVlcXChQtVfratrS2dOnXi8uXLRU4zSk9PV2pdyH+vbt++rfIz8n9/FyxYoNSsnJ2dzaxZs4CS/85qItGHWcNMnjyZFy9esHjxYj766CM++OAD2rZti46ODsnJycTGxnL58uUqswKLTCZj/fr19O/fHw8PD/bv3y/Nw8wPaoGBgdKUEsibKB8REUFERAR2dnY4OzuTnZ1NWFgYf//9N8OGDSt2Skk+W1tbDhw4QP/+/Rk+fDhbtmxRCsrl4XXzaWRkRFZWFnZ2dvTq1YusrCxCQ0N58OAB48ePVxoQpKGhwZdffsmiRYvo0qULffv2BfJqcAqFgtatW/P777+/Mq9t27alY8eOhIWF0aNHDzp27EhSUhIRERE0a9asyADWtWtX1NTUCAwM5PHjx1I/n7u7e7Fz/CwsLFi6dClTpkzB0dGR/v37Y2hoyJkzZzh58iSmpqasXLlSpde3Kli2bBk3btxg6dKlBAcHY2dnh6GhIQ8ePODatWucPXuWRYsWSSv1dOzYEXd3dzZs2ECnTp3o16+fNA+zfv36GBkZqTy1JCgoiD59+rBs2TLCw8Pp0qULcrmcW7ducfz4cXbt2iX1szs6OrJ//34mTZqEq6sr2tra1K9fH3d392LvP2jQIA4fPszevXvp2LEjLi4u0jzMa9eu0bVr12qz+UJ5EQGzBpo2bRr9+/dn06ZN/PLLL+zevZunT5+iq6tLq1atWLp0aaEpHJWpbdu2REZGsnz5ciIjIzl27Bj169fHxcWFqVOnKjVBQt6w9n379rF+/Xr27NnDpk2bUFNTo2XLlsyYMUP6ZvwqrVq1Ijw8HFdXV0aNGsWGDRvKPJevPPKpoaHB/v37WbhwISEhIfzzzz+8++67TJ06tcg1TqdNm4aWlhZbtmxh27ZtvPPOO7i4uDBnzhw++eQTlfIql8vZtWsXfn5+/PzzzwQFBWFsbMyoUaOYNm1akaN2LS0t2bx5M2vWrGHHjh1SbXbo0KElToofM2YMTZo0Ye3atRw8eJCMjAyMjY1xd3dn2rRp5b6AREXS0dHhwIEDbN++nb1793LgwAEyMzPR19fHwsKCuXPnFvpStHTpUiwtLdm0aZP0fvXp00eaq6gqCwsLoqKi+Oabbzhw4ADffvstGhoamJqaMnLkSKUpOJ988gl3795lz549BAQEkJ2djbm5eYkBE/KCsp2dHdu3b2f79u3k5ubStGlTFixYgIeHh8rzsmsKWUpKiurriAmCUKF0dXUxNzcvdklAQRAqj+jDFARBEAQViIApCIIgCCoQAVMQBEEQVCAG/QhCFVKdlw0ThJpO1DAFQRAEQQUiYAqCIAiCCkTAFARBEAQViIBZSUqzt2R1IMpTtYnyVG2iPNWDCJiCIAiCoAIRMAVBEARBBSJgCoIgCIIKRMAUBEEQBBWIhQsEoZK8ePGCjIyMN/Ks2rVrK+2PWN2J8lRtVbk82traqKu/XugTAbMakT18iPziRXJsbFBUoy2QhMJevHjBkydP0NXVRSaTVfjzNDU1qV27doU/500R5anaqmp5FAoFKSkp6OjovFbQFAGzmpDdvk3dbt1Qe/iQ3IYNSY+IQNG4cWVnS3hNGRkZbyxYCoKQRyaToaurS1paWol7thZH9GFWE7WXLEHt4UMA1B49ovbixZWcI6GsRLAUhDevLH93ImBWExq7din9XGvPnkrKiSAIwttJBExBEARBUIEImIIgVDnR0dHo6uqSnJxcpvvcunULXV1dzp8/X6Y0FWHnzp2Ympq+0WcKZSMCpiAIKvP09ERXVxddXV309PRo3bo1U6ZMqbR9PG/evMkXX3yBtbU1BgYG2NjYMGrUKM6cOaPyPczMzIiPj8fGxqYCc1p+kpKS8PHxwdbWFgMDA1q2bMngwYP5+eef33hePD09cXNze+PPrSxilKwgCKXi4OBAUFAQL168ID4+ni+++ILU1FQ2b978RvNx/vx5XF1dad68OStWrKBFixZkZGTw888/4+3tTVRUlEr3kcvlGBoaVnBuy8etW7fo2bMndevWZd68ebRu3Zrc3FyioqKYMmUKv//+e2VnsUYTNUxBEEpFU1MTQ0NDTE1N6datGwMGDOD48eNKaXbs2EGHDh0wNDSkXbt2BAQEkJubK53/5ptvsLOzw8TEhJYtW/Lll1+WqpaqUCiYMGECjRo14siRI/Tq1Yt3331XqvH+9NNPSukTExPp378/xsbGdOjQgRMnTkjnCjbJ5jcHR0VF4eTkhLGxMQ4ODsTFxSndMzQ0FDs7OwwMDLC2tmbFihUoFArpfEpKCh4eHjRq1AgjIyNcXV25cuVKsWVKSUnB2dmZgQMHFrugxbRp01AoFJw4cYIBAwbQrFkzrKyscHd3JyYmRkp3+/ZtRowYgZmZGWZmZnzyySfcvXtXOu/v70+nTp2U7l2wiTg/zQ8//ICtrS1mZmYMHz5caib39/dn165dHDlyRGp1iI6OLrZ8NYEImNWFmILwVqivq1sh/wyNjIo8XlZ//fUXx44dQ0NDQzq2bds2Fi5cyMyZMzlz5gx+fn6sWbOGTZs2SWnU1NTw9/fn1KlTbNy4kXPnzuHt7a3yc3///XeuXLnCxIkTkcvlhc7rFiibn58f48ePJyYmhvfff59///vfpKenl/iM+fPnM2/ePKKionjnnXdwd3eXAmJcXByjR4+mT58+/Prrr8ybN4/Vq1ezYcMG6XpPT0/OnTvH999/z7Fjx9DS0mLw4ME8e/as0LMePHhA7969MTY2Zvfu3WhraxdK8/jxYyIiIhg3bhx169YttswKhYIRI0bw8OFDQkNDCQsL4/79+4wYMUIpoKsiMTGRffv2sWPHDvbt28fFixdZuHAhAF9++SUDBgzAwcGB+Ph44uPj6dChQ6nuX92IJllBEEolIiICU1NTcnJyyMzMBGDRokXS+eXLlzN//nxcXV0BaNy4MTdv3mTz5s24u7sDMGHCBCl9o0aNWLBgAcOHDycwMBA1tVd/j79x4wYAzZs3VynPEyZMoFevXgDMnTuX3bt3c+nSpUK1rJfNmjWLLl26AODt7U3Pnj35+++/MTU1JSAgAHt7e2bOnAmApaUl169fZ82aNYwfP57r169z6NAhDh48iL29PQBBQUHY2Niwd+9eRo0apVSW/v370717d1asWFFs+W/cuIFCoXhlmSMjI/n99985f/48jRo1AmDTpk28//77REVF4eDgoNJrBnkrUq1bt06a5D969Gh27twJQN26daldu7bU4vA2EAFTEIRSsbOzY82aNTx79oxt27bx119/4eHhAcCjR4+4c+cOXl5eTJ06VbrmxYsXSrWbqKgoVq9ezdWrV0lLSyMnJ4fnz5/z4MEDjI2NX5mH0taUrK2tpf/n3//h/xYCUeUaIyMj6RpTU1Pi4+Pp0aOHUvpOnTqxdOlS0tLSiI+PR01Njfbt20vn69evT6tWrfjzzz+lY8+fP6dnz5707t2bVatWlZgfVcscHx+PsbGxFCwh70uLsbExf/75Z6kCprm5udKKOEZGRjx69Ejl62sa0SQrCEKp1KlThyZNmmBtbc2yZct4+vQpy5YtA5D6KVetWkV0dLT079SpU5w+fRrIa+Zzc3OjefPmbN26lcjISL755hsgL4CoomnTpgBcvXpVpfQvNxnnr/TyqgBU0jUKhaLYFWNkMlmJ9375Og0NDRwdHTl27BiJiYkl5qdp06bIZLJXlvlVeYO8JvGCeXzx4kWh9C+/BvnXv9wX/bYRNUxBqEJSK2h6RmZmZoUthu3j48OQIUMYPXo0xsbGmJiYcPPmTT7++OMi058/f57nz5/j7+8v9T8ePny4VM9s3bo1LVq04Ouvv2bgwIGF+jFTUlIK9WOWpxYtWkhfAPKdOnUKU1NTdHR0aNGiBbm5ucTGxkpNsmlpaVy+fJnhw4dL18hkMtavX4+7uzt9+/blwIEDmJubF/nMBg0a4OTkxMaNGxk/fnyhfsz8Mrdo0YK///6bW7duSbXMv/76i3v37tGiRQsAGjZsSFJSklJwvXTpUqlfh1q1apGTk1Pq66qrSqthbty4ETs7O8zNzTE3N+ejjz7iyJEj0nmFQoG/vz8tWrTAyMgIFxeXQiPMsrKymD59Ok2aNMHExIRhw4YpjQSDvF8id3d3LCwssLCwwN3dvdBovNu3b+Pm5oaJiQlNmjTB29tb5W+6b4wY9CNUUR9++CEtWrRgxYoVAMyYMYOvv/6agIAAEhISuHz5Mrt27ZKaHJs2bUpubi7r1q3jr7/+IiQkhMDAwFI9UyaTERAQwF9//YWzszOHDx/m5s2b/PHHH6xZs4b+/fuXezlf9vnnn3Py5En8/f25du0ae/bsISAggIkTJwJ5ZezduzdeXl78+uuv/PHHH7i7u6Ojo8OQIUOU7qWmpsbXX39Nhw4d6NOnD7dv3y72ufkjcR0dHfnxxx9JSEjg6tWrbN68mc6dOwN5035at26Nu7s7cXFxnD9/nnHjxtGmTRupT7Zz5848fvyYlStXcvPmTb777rtCI4tVYWFhwZUrV0hISCA5OZns7OxS36M6qbSAaWJiwvz584mKiuLEiRN06dKFESNGSPOI1qxZQ0BAAEuXLuX48ePo6+szYMAAnjx5It3D19eXsLAwNm/eTHh4OE+ePMHNzU3pG8/YsWO5ePEie/fuJSQkhIsXLzJ+/HjpfE5ODm5ubqSnpxMeHs7mzZsJDQ1l1qxZb+7FEIRq7vPPP2f79u0kJiYyatQovvnmG4KDg+ncuTO9evVi27ZtUm2ndevWLFmyhHXr1tGxY0e+++47aeRlabRr147IyEiaN2/OlClTaN++PW5ubpw7d47ly5eXdxGV2NrasnXrVsLCwujUqRPz589n8uTJ0qAmgHXr1tG2bVs+/vhjnJycePbsGSEhIWhpaRW6n5qaGuvXr6dDhw707du32KDZuHFjaeDOvHnzsLe3p1+/fhw6dIjVq1cDeV8mdu7ciZ6eHn369KFv374YGBiwc+dOqTZpZWXFqlWr2Lp1K/b29kRGRjJlypRSvw6ffvopzZs3x9HRkaZNmxaqddc0spSUlNL1nlegxo0bM2/ePEaPHk2LFi0YN24c06ZNA+DZs2c0a9aMhQsXMmbMGFJTU7G0tCQgIIChQ4cCcOfOHWxsbAgJCcHJyUka5nz48GE6duwI5DWb9OrVi7Nnz9KsWTOOHj3K0KFDuXTpEmZmZgAEBwczceJEEhISqFevXoWUNSEhgWbNmqmcvl7DhsgK9DFUVPPd6yhteaq6ii5Pamrqa20v9Loqskm2MojyVG1VvTyv+/dXJQb95OTk8MMPP5CRkUH79u25desWDx48oFu3blIaLS0t7OzspCWv4uLiyM7OVkpjZmaGlZWVlCY2Npa6desqzQ3q2LEj2traSmmsrKykYAng5OREVlZWoYnKgiAIwturUgf9/PHHH/To0YPMzEy0tbXZsWMH1tbWUjDT19dXSq+vr8+9e/eAvPUU5XI5enp6hdIkJSVJafT09JRGjMlkMqnDOz9Nwefo6ekhl8ulNIIgCIJQqQGzWbNmREdHk5qaSmhoKJ6enhw4cEA6X3BodEnDpYtLU1R6VdKUdDxfQkJCiedfpTTXt1UoKJibsj6/vFW1/JRVRZYnf8L3m5S/yEBNIcpTtVXl8qSlpRVZIXpVN0ylBsxatWrRpEkTAN5//31+++031q1bJ/VbJiUlKTWVPnr0SKoNGhgYkJOTQ3JyMg0bNlRKY2dnJ6V59OiRUoBUKBQkJycr3afgzgbJycnk5OQUqnkWVJY+rtL2kcnU1KDA8O2q1Gco+jBLJzU19Y328VT1PqXSEuWp2qp6eerVq1fs9J2SVIk+zHy5ubk8f/6cRo0aYWhoqLRAcmZmJqdOnZL6I21tbdHQ0FBKc/fuXaX1DNu3b096ejqxsbFSmtjYWDIyMpTSxMfHK01HOXHiBJqamtja2lZoeQVBEITqo9JqmF999RU9evTA1NSU9PR0QkJCiImJYc+ePchkMjw9PVm5ciXNmjXD0tKSFStWoK2tzeDBg4G8ZaZGjhzJ3Llz0dfXp0GDBsyaNZvKMjsAACAASURBVAtra2tp6ScrKyu6d++Ol5cXa9asQaFQ4OXlhbOzs1R76NatGy1btsTDwwM/Pz8eP37M3LlzGTVqVIWNkBUEQRCqn0oLmA8ePMDd3Z2kpCTq1auHtbW1NB0EYNKkSTx79ozp06eTkpJCu3bt2LdvHzo6OtI9Fi9ejFwuZ8yYMWRmZtKlSxcCAwOVVv3YuHEjPj4+DBw4EIBevXpJy3hB3l54wcHBTJs2jZ49e1K7dm0GDx6Mn5/fG3olBEEQhOqgSs3DfJuUeh6mvj6yAqtoiHmYFUfMw6zaRHmqtqpenmo9D1MQBEEQqjoRMAVBqJJ++uknpQXUd+7ciampaaXkxc3NDU9PzzLfx9PTEzc3tzKnqQg2NjasXbv2jT+3OhEBs7oQi68LVYCnpye6urro6urSsGFD2rRpw+zZs8nIyKjwZw8cOLBUq2+96QCgUCj47rvv+OijjzAzM8Pc3JwuXbqwZs0a0tLSVL7PkiVLCAoKqsCclq/Q0FD69u2LhYUFJiYm2NnZ4e/v/8r9RsvbrVu30NXV5fz58xX2DBEwBUEoFQcHB+Lj44mLi2P27Nls3ryZOXPmFJm24MbRZaGlpfXKudGVafz48Xh7e/PRRx/x008/ERMTw6xZs4iOjiYsLEzl+9SvX79CtyYrTwsXLmT06NHY2NgQHBzM6dOn8ff35/bt22zevLmys1fuRMAUBKFUNDU1MTQ0xMzMjCFDhjBkyBAOHjwIgL+/P506dWLnzp3Y2tpiYGBARkYGqampTJo0CUtLS8zMzOjdu3ehmsCuXbto3bo1xsbGuLm5FVqJpagm2SNHjuDk5ISRkRHvvvsubm5uZGZm4uLiwu3bt5kzZ45UI8535swZevfujbGxMS1btmTKlClKNcCnT5/i6emJqakpzZo1Y+XKla98Tfbv38+ePXvYsGED3t7etGvXjkaNGuHs7ExISAguLi5K6devX0/Lli1p1KgREyZM4OnTp9K5gk2yLi4uTJ06lQULFtCkSRMsLS2ZPXu20kbOKSkpeHh40KhRI4yMjHB1dS20HWJoaCh2dnYYGBhgbW0tbRVWnODgYMzNzQkPDy/y/Llz51i5ciULFixg8eLFdOrUCQsLC7p27cq6devw8PCQ0m7ZsoX3338ffX193n//fbZt26Z0L11d3ULbixVsIdDV1WXr1q18+umnmJiY0KZNG4KDg6Xzbdq0AcDR0RFdXd1Cr3l5EAFTEKoQXd36FfLPyMiwyOPloXbt2kr7IN66dYuQkBC2bt1KTEwMmpqauLm5ce/ePYKDg/nll1+ws7OjX79+3L9/H4D//ve/TJgwgdGjRxMdHU3Pnj1ZvHhxic+NiIhg+PDhODo6EhkZSVhYGJ07dyY3N5cdO3ZgamqKt7c38fHxxMfHA3nrVw8cOJBevXoRExPD9u3buXTpEl988YV03zlz5hAZGSntEXnx4kV+/fXXEvOyZ88eLC0t6devX5HnXw7Yp06d4sqVK/z4449s2bKFAwcOsGnTphLvv3fvXuRyOT///DPLly9n/fr17Nu3Tzrv6enJuXPn+P777zl27BhaWloMHjyYZ8+eAXmbVYwePZo+ffrw66+/Mm/ePFavXs2GDRuKfF5gYCDe3t7s3r2b3r17F1tmbW1tpe0SiypzWFgY06dPx9PTk1OnTuHh4cHUqVM5dOhQiWUuyrJly+jduzcxMTEMHDiQL774gsTERACOHz8OwA8//EB8fDw7duwo9f1fpVKXxhMEoXo7d+4cISEhdO3aVTr2/PlzgoKCMDAwACAqKopLly5x7do1aS/I2bNnc/jwYYKDg5k0aRKBgYF07dpVWhbT0tKS3377je3btxf77OXLl+Pq6srs2bOlY61btwagTp06qKmpoaOjg6GhoXT+66+/ZsCAAXz55ZfSsZUrV9KlSxcePnyIlpYW27dv55tvvpHmhAcEBNCqVasSX4cbN26oPA1JR0eHVatWoa6ujpWVFf379yc6Ohpvb+9ir7GyspL26LW0tGTbtm1ERUUxePBgrl+/zqFDhzh48CD29vYABAUFYWNjw969exk1ahQBAQHY29szc+ZM6R7Xr19nzZo1hQLeokWL2Lp1K6GhoVKtrbgyN27cGA0NjRLL+8033+Dm5ibtFWppaUlcXBxr1qyhV69er37BXuLm5ibVvmfNmkVgYCCnTp3CwsJC2ojjnXfeUXrPy5MImNWFGPQjVBERERGYmpry4sULsrOz6d27t9JiICYmJlKwBLhw4QJPnz7F0tJS6T6ZmZncvHkTgPj4eHr27Kl0/l//+leJAfPixYsMHz68VHm/cOECN27cYP/+/dKx/GbJmzdvoqWlxfPnz2nfvr10vm7dulhbW5d439L001pZWaGu/v8fvUZGRpw9e7bEawo+38jISBpUEx8fj5qamlKe69evT6tWrfjzzz+lND169FC6R6dOnVi6dClpaWnSqmaBgYE8efKEEydO0LRp0xLzpGqZ4+PjGTFiRKFnv04N8+XXQV1dHT09vTc6uEgETEEQSsXOzo41a9agrq6OsbFxoRqGtra20s+5ubkYGBgU+QGZv3JXeQ0MepXc3FxGjRrFhAkTCp0zNjZ+7R1qmjZtytWrV1VKW/D1kslkSv2Rql6T/5qV9Nq9vOmEKrsydezYkYiICEJCQvDx8SkxT02bNuXUqVM8f/6cWrVqlZi2qGcX3DGqYDlevHhR6JqSXoc3QfRhCjWW7O+/qTNoEHXbtkXju+8qOzsqSUlJrZB/9+8/KPL466hTpw5NmjTBwsLilc1xkDcYIykpCTU1NZo0aaL0L3/Ua4sWLfjvf/+rdF3Bnwt67733iIqKKvZ8rVq1yCmww0+bNm24cuVKoXw0adIELS0tmjRpgoaGhlKNLyMjg8uXL5eYlyFDhnD9+nVCQ0OLPJ9SgatytWjRgtzcXKVNJtLS0rh8+TJWVlZSmtOnTytdd+rUKUxNTZWWG7W1tWX//v0EBAQotRoUZciQIWRkZBTbD5pfZisrqyKf3aJFC+nnhg0bSv3ZkLdT1cs/qyI/aBd8z8uTCJhCjaW5ciUax44hv3EDrcmTkT16VNlZeis5ODjQsWNHhg8fztGjR/nrr7+IjY1l8eLF0mCa8ePHExkZyapVq7h+/Trbtm1T2hu3KFOnTuXHH3/Ez8+PP//8kytXrhAQECCNOLWwsODUqVP8/fffJCcnA3lrVP/22294eXlJzbOHDx9m8uTJQF7z68iRI/nqq684ceIEV65c4YsvvnhlDXDAgAEMGjQId3d3li1bxm+//UZiYiIREREMHTpUGkVcEZo2bUrv3r3x8vLi119/5Y8//sDd3R0dHR2GDBkCwOeff87Jkyfx9/fn2rVr7Nmzh4CAACZOnFjofm3btmX//v188803LF++vNjnfvDBB0yaNIm5c+cyc+ZMTp8+TWJiItHR0XzxxRcEBgYC8OWXXxIcHMzGjRu5fv06QUFB7N27V+nZXbp0YdOmTZw/f54LFy4wYcKEUi+tp6+vj5aWFseOHSMpKYnU1Nf7QlgSETCFGkvzpXlgstxcalWTWmZNI5PJ2LNnDx9++CGTJk3iX//6F2PGjOHatWsYGxsDef2Va9eu5dtvv8Xe3p6wsDBmzJhR4n179OjBjh07OHr0KF26dMHFxYXo6GjU1PI+1mbOnMmdO3d4//33pf641q1bEx4eTmJiIn369KFz584sWLBAaX7nwoUL6dy5M5988gl9+/alZcuW0h67JZVx06ZNLFmyhCNHjtC3b1/s7e2ZP38+9vb2xY6eLS/r1q2jbdu2fPzxxzg5OfHs2TNCQkKkQVa2trZs3bqVsLAwOnXqxPz585k8ebI0EKegdu3asX//ftauXVti0Jw/fz7ffvstFy5cYOjQoXTs2BFvb29MTU0ZO3YsAH369GHZsmWsW7eODh06EBgYyMqVK5UG/Pj5+dG4cWP69OnDp59+ysiRI5X2OVaFuro6S5cuZfv27bRo0aLU/duqEIuvV5JSL75uaIgsK0vpmFh8vWT1C0z+zpw1i6zp01W6Viy+XrWJ8lRtVb08YvF1QXiVNzg4QBCEmkcETEEQBEFQgQiY1YWYh1l2ooYpCEIZiIApCIIgCCoQAVN4e4gapiAIZSACpiBUkje5QokgCHnK8ncnAqbw9qhCAUpbW5uUlBQRNAXhDVIoFKSkpBRavlFVYi3Z6kIM+im7KhSc1NXV0dHRUdqHsSK9vMB2TSDKU7VV5fLo6OgoLX5fGiJgCm+PKhQwIS9ovqnFC5KSkjA3N38jz3oTRHmqtppWnnyV1iS7atUqHB0dMTc3p2nTpri5uRVa4NjT01PaLT3/X/fu3ZXSZGVlMX36dJo0aYKJiQnDhg3j7t27SmlSUlJwd3fHwsICCwsL3N3dCy2GfPv2bdzc3DAxMaFJkyZ4e3vz/Pnziim8IAiCUO2oHDDbtGlDeHh4secPHz5c4majBcXExPDZZ59x5MgRQkNDUVdXp3///jx+/FgpnYODg7Rjenx8PHv37lU67+vrS1hYGJs3byY8PJwnT57g5uamtGL92LFjuXjxInv37iUkJISLFy8qbZqak5ODm5sb6enphIeHs3nzZkJDQ6UNW4UaoorVMAVBqF5UbpJNTEwkIyOj2PMZGRncvn1b5Qfv27dP6eegoCAsLCw4ffq00qK8mpqaxe6enZqayvbt2wkICMDR0VG6j42NDZGRkTg5OREfH09ERASHDx+mQ4cOAKxevZpevXpJ64UeP36cK1eucOnSJczMzIC8RYUnTpzInDlzqmxbvCAIgvDmlKpJtrgNSAGuXbumtK9aaaWnp5Obm4tugQWzT506haWlJe3atWPixIlKu2vHxcWRnZ1Nt27dpGNmZmZYWVlx5swZAGJjY6lbt64ULCFvk1RtbW2lNFZWVlKwBHByciIrK4u4uLjXLpMgCIJQc5RYw/z+++/ZtWuX9POKFSvYtm1boXQpKSlcvnwZZ2fn187IjBkzsLGxoX379tKx7t2707dvXxo1akRiYiJ+fn7069ePyMhINDU1SUpKQi6Xo6enp3QvfX19kpKSgLzOZz09vUK7ezds2FApzcvb+wDo6ekhl8ulNEV53d3ZX+f693NzkZfz88tbVcvPBwV+/ueff/i7FHmsauUpK1Geqk2Up/K9aoeiEgNmRkYGDx48kH5OTU0ttJGqTCajTp06fPrpp6/cv644+ZuPHj58GLn8/8PCoEGDpP9bW1tja2uLjY0NR44cKXF/OYVCUShAvk6ako7Dq1/ckpR2+6j8Pf7K6/nlrSpu71XQOw0aoK1iHqtDeUpDlKdqE+WpHkoMmOPGjWPcuHEAvPfeeyxZsoTevXuXawZ8fX3Zt28fYWFhNG7cuMS0xsbGmJiYcOPGDQAMDAzIyckhOTlZabPRR48eSRu+GhgY8OjRI6UAqVAoSE5OlmqVBgYGUvNsvuTkZHJycgrVPCuNmIcpCIJQqVTuw7x48WK5B0sfHx9CQkIIDQ2lefPmr0yfnJzMvXv3pEFAtra2aGhocOLECSnN3bt3iY+Pl/os27dvT3p6OrGxsVKa2NhYMjIylNLEx8crTUc5ceIEmpqa2NralktZhSpAjJIVBKEMSr1wwZMnT7hz5w6PHz8uclkve3t7le4zbdo0goOD2bFjB7q6ulLTr7a2NnXr1iU9PZ0lS5bQr18/DA0NSUxMZMGCBejr69OnTx8A6tevz8iRI5k7dy76+vo0aNCAWbNmYW1tjYODAwBWVlZ0794dLy8v1qxZg0KhwMvLC2dnZ6nJoFu3brRs2RIPDw/8/Px4/Pgxc+fOZdSoUWKErCAIggCUImA+fvwYHx8f9u/frzTHMV9+k+c///yj0v02bdoEgKurq9JxHx8ffH19kcvlXL58md27d5OamoqhoSEffvghW7ZsURqNu3jxYuRyOWPGjCEzM5MuXboQGBio1Be6ceNGfHx8GDhwIAC9evVi2bJl0nm5XE5wcDDTpk2jZ8+e1K5dm8GDB+Pn56fqyyNUB6KGKQhCGagcML28vDhw4ADjxo3D3t6+0PSP0iq40k5BWlpaheZqFqV27dosX76c5cuXF5umQYMGbNiwocT7mJubExwc/MrnCdWYCJiCIJSBygEzIiKC8ePHs2jRoorMj1AcMeinzGQiYAqCUAYqD/qpVasWTZs2rci8CIIgCEKVpXLAdHV15ejRoxWZF0GoWKKGKQhCGagcML/88kvu37+Ph4cHZ8+e5f79+zx8+LDQP0EQBEGoiVTuw2zXrh0ymYy4uDj27NlTbDpVR8kKwhsnapiCIJSBygHT29u7xGXihAomXntBEIRKpXLA9PX1rch8CELFEzVMQRDKoFTbewmCIAjC20rlGubSpUtfmUYmk+Ht7V2mDAlChRE1TEEQykDlgLlkyZJiz8lkMmlpPBEwhSpLBExBEMqgVGvJFpSbm0tiYiJBQUGcOXOGkJCQcs2cIJQrETAFQSiDMvVhqqmp0bhxY/z9/WnUqNFrbyAtCIIgCFVduQ36+fDDDzly5Eh53U4Qyp+oYQqCUAblFjATEhKK3B9TKCdiHqYgCEKlUrkP8+TJk0UeT01NJTo6mo0bN9K/f/9yy5gglDvxhU4QhDJQOWD26dOnyJV+FAoFcrmcQYMGqTT1RBAEQRCqI5UDZlhYWKFjMpkMXV1dLCws0NHRKdeMCS/JzUX25Ell56L6EzVMQRDKQOWA2blz54rMh1CCWmvXVnYWBEEQ3noqB8x8T548ISYmhsTERAAsLCzo3LmzqGFWIK158yo7C4IgCG+9UgXMoKAg/Pz8yMjIUBoRq62tzZw5cxg/fny5Z1AQyo1okhUEoQxUDpi7d+9mxowZtGvXDk9PT6ysrFAoFFy9epXAwEB8fX1p0KABQ4cOrcj8CoIgCEKlUDlgBgQE0KFDBw4cOIC6+v9fZmNjg6urK3369GHt2rUiYApVl6hhCoJQBiovXJCQkMDAgQOVgmU+dXV1Bg4cyLVr11R+8KpVq3B0dMTc3JymTZvi5ubG5cuXldIoFAr8/f1p0aIFRkZGuLi4cOXKFaU0WVlZTJ8+nSZNmmBiYsKwYcO4e/euUpqUlBTc3d2xsLDAwsICd3d3UlJSlNLcvn0bNzc3TExMaNKkCd7e3jx//lzl8giCIAg1m8oBU1tbmwcPHhR7/sGDB9SpU0flB8fExPDZZ59x5MgRQkNDUVdXp3///kqLvK9Zs4aAgACWLl3K8ePH0dfXZ8CAATx5aYqFr68vYWFhbN68mfDwcJ48eYKbmxs5OTlSmrFjx3Lx4kX27t1LSEgIFy9eVOpvzcnJwc3NjfT0dMLDw9m8eTOhoaHMmjVL5fII1YCoYQqCUAYqB8xu3boRFBREdHR0oXMxMTFs2LABJycnlR+8b98+PvnkE1q1aoW1tTVBQUE8evSI06dPA3m1y/Xr1zN58mRcXV1p1aoV69evJz09XdoVJTU1le3bt7NgwQIcHR2xtbUlKCiIP/74g8jISADi4+OJiIjgP//5Dx06dKB9+/asXr2aI0eOkJCQAMDx48e5cuUKQUFB2Nra4ujoyPz58/nuu+9IS0tTuUyCIAhCzaVywJw3bx7169fH1dUVBwcH3N3dcXd3x8HBgX79+lGvXj3mlWH6Q3p6Orm5uejq6gJw69YtHjx4QLdu3aQ0Wlpa2NnZcebMGQDi4uLIzs5WSmNmZoaVlZWUJjY2lrp169KhQwcpTceOHdHW1lZKY2VlhZmZmZTGycmJrKws4uLiXrtMQhUjapiCIJSByoN+zMzMiI6OZtWqVfz888+EhoYCefMwP//8c7y8vHjnnXdeOyMzZszAxsaG9u3bA0jNv/r6+krp9PX1uXfvHgBJSUnI5XL09PQKpUlKSpLS6OnpKS3rJ5PJaNiwoVKags/R09NDLpdLaYqSX0N9Xape/0EFPb+8VbX8FHzdUlNTSSxFHqtaecpKlKdqE+WpfM2aNSvxfKnmYb7zzjv4+fnh5+dXpkwVNHPmTE6fPs3hw4eRy+VK5wquX6tQKIpc07akNMWtgfuqNCUdh1e/uCVJSEgo0/VlfX55K4/yVLT69eqpnMfqUJ7SEOWp2kR5qodXNsmePXuW8+fPl5jm/Pnz/Pe//32tDPj6+vLDDz8QGhpK48aNpeOGhoYAhWp4jx49kmqDBgYG5OTkkJycXGKaR48eKS20oFAoSE5OVkpT8DnJycnk5OQUqnkKgiAIb6cSA2Z0dDTOzs7Ex8eXeJP4+Hh69Ogh9QmqysfHh5CQEEJDQ2nevLnSuUaNGmFoaMiJEyekY5mZmZw6dUrqj7S1tUVDQ0Mpzd27d4mPj5fStG/fnvT0dGJjY6U0sbGxZGRkKKWJj49Xmo5y4sQJNDU1sbW1LVWZhCpM9GEKglAGJQbMrVu3YmNjw7Bhw0q8ybBhw2jTpg2bNm1S+cHTpk3j+++/Z9OmTejq6vLgwQMePHhAeno6kNcU6unpyX/+8x9CQ0O5fPkyEyZMQFtbm8GDBwNQv359Ro4cydy5c4mMjOTChQuMHz8ea2trHBwcALCysqJ79+54eXlx9uxZYmNj8fLywtnZWWoy6NatGy1btsTDw4MLFy4QGRnJ3LlzGTVqFPXq1VO5TELVJhMBUxCEMiixD/P06dOMGTNGpRu5uLiwdetWlR+cH1xdXV2Vjvv4+ODr6wvApEmTePbsGdOnTyclJYV27dqxb98+pYXeFy9ejFwuZ8yYMWRmZtKlSxcCAwOV+kI3btyIj48PAwcOBKBXr14sW7ZMOi+XywkODmbatGn07NmT2rVrM3jw4HLvqxUqmQiYgiCUQYkB8+HDhxgbG6t0I2Nj4xJHlBZUcKWdoshkMnx9faUAWpTatWuzfPlyli9fXmyaBg0asGHDhhKfZW5uTnBw8CvzJAiCILydSmySrVu3Lv/8849KN/rnn3+oW7duuWRKUI1aKZYiFBA1TEEQyqTEgGljY0N4eLhKNwoPD6d169blkilBNbW/+qqysyAIgvDWKDFgDhs2jNOnT/PNN9+UeJOAgADOnDnDiBEjyjVzQsk0Dhyo7CxUL6KGKZQz9UOHqO3rizwqqrKzIrwBJfZhDhs2jP379zN37lyOHz+Om5sb1tbW1K1bl/T0dC5fvszu3buJjIzko48+ws3N7U3lWxAEoVLJY2LQ/vhjAGoFBpIeFUXue+9Vcq6EilRiwJTJZGzfvp1Zs2axbds2aUHzfAqFAnV1dT777DMWLlxYkfkUhLITNUyhHGl9+aX0f5lCgZavLxkHD1ZijoSK9sql8TQ1NVmxYgVTp07l6NGjxMfH8+TJE3R0dKQ5jiYmJm8ir4IgCFWG/OZNpZ/V/vijknIivCkqryVrbGzMqFGjKjIvglCxRA1TqEivWONaqP5U3t5LEKo9ETCFiiQCZo0nAqbw9hABU6hIImDWeCJgCoIglAcRMGs8ETCFt4eoYQoVSU18nBYrJwfNpUup27UrtWfPhufPKztHr6VUG0gLgiAIxRA1zGLJo6Op7e+f9/8LF8h5/32yBw2q5FyVnspfiV7ec7I4/v97QQShShI1TKEiiYBZrDoTJyr//NlnlZSTslE5YH7yySf8+uuvxZ6fPXt2iTuGCBVDVoodYt4qIjgKb5oImEVS//ln1BITKzsb5ULlgOns7MywYcM4d+5coXNTpkwhICCAr8Ri4G+c5sqVlZ2FqqmogCmCqFCRRB9mIbJbt6jzv+UDawKV3+GNGzdib2/PoEGDuHjxIpC3NJ6Hhwdbt25l+fLlTCxQ7RYqnvx/74VQwP+C4zWa0ocwHDnOmYdNKzlTQo0mapiFqEdFIcvJqexslBuVA6ZcLmfbtm20bduWgQMHcuHCBT799FP27t3L2rVrGTt2bEXmUxBK538B05P1HKQPkTgyMnYyubmVnC9BeIvIVNxPuboo1SjZWrVqsXPnTgYNGoSTkxNqamps3ryZ/v37V1T+BKFMIvhI+v+tp4bcuvWEd98VUVOoAKKGWeMVGzD3799f7EXDhg3j0qVL9O7dG4VCoZR2wIAB5ZtDoWSiX65oCgVFvTI1qHVIqGpEwCxEVsM+n4oNmP/+97+RyWQoiihw/vE9e/awZ88epeMiYL5hNewXstwoFDynVqHDL15UQl6Et4MImDVesQEzLCzsTeZDeF0iYBZNoSAD7UKHnz0TH2pCxVCIgFnjFRswO3fuXOEPP3nyJGvXruXChQvcu3ePgIAARowYIZ339PRk165dStd88MEHRERESD9nZWUxe/ZsfvjhBzIzM+nSpQsrV67E1NRUSpOSkoK3tzeHDx8GoGfPnixbtgxdXV0pze3bt5k2bRrR0dHUrl2bwYMH4+fnR61ahWspVYoImEVTKHhKnUKHnz2rhLwIbwcxraTGe62l8f744w8S/zcR1cLCAmtr69d6eEZGBq1ateLjjz/Gw8OjyDQODg4EBQVJPxcMYL6+voSHh7N582YaNGjArFmzcHNzIyoqCrlcDsDYsWO5c+cOe/fuRSaTMXHiRMaPH09wcDAAOTk5uLm50aBBA8LDw3n8+DGenp4oFIqqvxiDCJhFUyjIpHahw5mZohYgVBBRw6zxShUwDx48iK+vL3fu3AHy5mHKZDLMzc1ZvHgxLi4upXp4jx496NGjBwATJkwoMo2mpiaGhoZFnktNTWX79u0EBATg6OgIQFBQEDY2NkRGRuLk5ER8fDwREREcPnyYDh06ALB69Wp69epFQkICzZo14/jx41y5coVLly5hZmYGwPz585k4cSJz5syhXr16pSrXGyUCZtEUCrLRKHQ4O7sS8iK8HUTArPFUbkOIiIhg1KhRKBQK5syZw44dO9ixYwdz5sxBoVDw6aefcuzYsXLP4KlTp7C0tKRdu3ZMnDiRhw8fSufi4uLIzs6mW7du0jEzMzOsrKw4c+YMPh/BogAAIABJREFUALGxsdStW1cKlgAdO3ZEW1tbKY2VlZUULAGcnJzIysoiLi6u3MskvAEKBS+K+D4oBv0IFUYEzBpP5RrmsmXLsLKy4siRI+jo6EjHXVxcGDt2LM7OzixfvhwnJ6dyy1z37t3p27cvjRo1IjExET8/P/r160dkZCSampokJSUhl8vR09NTuk5fX5+k/62xmpSUhJ6eHrKXfpllMhkNGzZUSqOvr690Dz09PeRyuZRGqGZEwBTeNBEwC6thLWAqB8zff/+dWbNmKQXLfDo6OowYMYJFixaVa+YGvbT9i7W1Nba2ttjY2HDkyBH69etX7HX5TcX5ZEX8IquSpqTjAAkJCSXm/1VUvf6DEs5lPXtW5nyUl6qSDwC1jAxyivj1vnPnPgkJj1W6R1UqT3kQ5SlfBf8un2dllSlPlV2e8paQkIDRo0eYlXC+qmnWrFmJ51UOmBoaGjx9+rTY8xkZGWhoFO4zKk/GxsaYmJhw48YNAAwMDMjJySE5OZmGDRtK6R49eoSdnZ2U5tGjR0oBUqFQkJycLNUqDQwMpObZfMnJyeTk5BSqeb7sVS9uSfL7T8tKU1OzXO5TVuVVnnKTlsYfRfRhNmxoTLNmDYu4QFmVK08ZifJUvFpl+FusiuUpi/zyaBZo/XtZdSyvyn2YnTp1YuPGjVy/fr3QuRs3brBp0yYpSFWU5ORk7t27Jw0CsrW1RUNDQ2mvzrt37xIfHy/1WbZv35709HRiY2OlNLGxsWRkZCiliY+P5+7du1KaEydOoKmpia2tbYWWqcxqWJNHeRJNsoIglCeVa5jz5s3D2dmZTp060atXL+nbwdWrVzly5Ai1a9dm3rx5pXp4enq6VFvMzc3lzp07XLx4kQYNGtCgQQOWLFlCv379MDQ0JDExkQULFqCvr0+fPn0AqF+/PiNHjmTu3Lno6+tL00qsra1xcHAAwMrKiu7du+Pl5cWaNWtQKBR4eXnh7OwslaFbt260bNkSDw8P/Pz8ePz4MXPnzmXUqFFVe4QsNW/pqXJTTB+mWBpPEN6gGtavq3LAbNmyJSdOnGD+/PkcO3aM0NBQALS1tenZsydz5szB0tKyVA8/f/48ffv2lX729/fH39+fjz/+mFWrVnH58mV2795NamoqhoaGfPjhh2zZskWpH3Xx4sXI5XLGjBkjLVwQGBgozcGEvK3JfHx8GDhwIAC9evVi2bJl0nm5XE5wcDDTpk2jZ8+eSgsXVHkiYBat2EE/NesPWBCqtBq2PVCp5mE2bdqU7777jtzcXB49egRAw4YNUXvNFS4+/PBDUlJSij2/b9++V96jdu3aLF++vMQFBho0aMCGDRtKvI+5ubm0kEFVoh4eXtlZqLaKmocpmmQF4Q2qYQHztSKdmpoaWlpaaGlpvXawFFRT56UVkO5jiAfrGcIe4mhTibmq+mRiWonwponWnsJqWB9IqWqYiYmJLFq0iKNHj0o1Q11dXXr06MHMmTOxsLCokEy+zWRpadL/XfmJWPIGKp3EnkQsRB9mcUQfpiBUvhpWw1Q5YCYkJODs7ExqaioODg5YWVmhUChISEhg7969HD16lCNHjpS6H1NQzWN0pWAJcA8TrtIcKxEwiyb6MAWh8r2tAXP+/PkoFApOnDjBe++9p3Tu0qVLuLq6Mn/+fLZv317umRQocueNJAywUjyqhNxUAwoFOcgLHRZryQoVRnx5LayGBUyVOyBjYmIYP358oWAJYGNjw7hx44iOji7XzAn/r6jNkB+iL/5Ii6NQkFvEr7fowxSEN0f2tgbM58+flzgnsX79+jx//rxcMiUUVlTAfIZWJeSkmiimhin6MAXhDfq/9s48vImq7cP3JF3SvYWWspRFpAJFsAICLyqyyeKCgigi8iqI8OKCoMgiCArIVhRQKi64giiyfCqKoCggslVFQECwiEWWUmihpS1Nm2W+P9qmncwkDTRNUjj3dfVqcuZkcs5kZn7znPOc5yknmFaq/3SIy4KZkJDAihUrKNDIwFtYWMiKFStISEhwa+MEZWgJZiGBXmhJ9UHbwqz+F63ARxGjPWpKjsk45mDAyA3sIY2GXm7U5ePyHOazzz7Lww8/TJcuXXjssccUkX7ef/99UlNTWbZsWZU19GpHSxyNGMRF6ggHFqYYkhUIPIgss4cbSGIcAPu4gadYxNfcXcEHfROXBfOOO+7gnXfeYfLkyYwbN04RyDw2NpZ33nmH3r17V1lDr3a0LEwhmE4Qc5gCgfeRZbZyq6LoG+7yUmMqzyWtw+zfvz/33nsve/bs4d9//wWgQYMGJCYm4ud3SbsSXCJiSPYScSCYYg5TIPAgssxpaquKLZcXM8frXLLK+fn50bZtW9q2dZalUeBuhIV5iYghWYGnEdeiGlkuvk/ZkUOExtXp+1yyYB4+fJi0tDTOnz+PrHGCDBw40C0NEyjRsiYLCBIXqSMcWJgmk3D6EQg8hixrriHPIYIaXmhOZXFZMI8dO8aIESNISUnRFEoASZKEYFYRWhZmDhFCMB0hLEyBwPs4EEwtq7M64LJgjhkzhn379vHKK69w8803ExkZWZXtEtihJZjnifJCS6oJYg5T4GnEw6saWSafEFXxFS+YO3bsYNSoUYwcObIq2yNwgNaQbDaR4iJ1hLAwBQKfQCvASnUVTJddlSIiIqhZs2ZVtkXgBC0L8yLBQjAd4XBZiZjDFAg8iVZe2iteMB966CG++OKLqmyLwAnC6ecSEeswBZ5GXItqZPmKEkyXh2Rvv/12Nm3axN13382QIUOIi4tDr1cPebVp08atDRQUs4inVGXV9aTzFJqxZIVgCgSew0GaveoaB/uSIv2Usm3bNtV2WZaRJIlz5865p2UCBX/RVFUmLEznCAtTIPAyDgSzuj7suyyYycnJVdkOwWUgAhc4RhJOPwKB93E2JJudDdVstYXLgvnQQw9VZTsEl0F1fUrzCGIOU+BhJPHwqsaJhWmYORPj3LleaNTlUz0D+l1lmNEjoU7EWkAQuqwsL7SoGuBIME1eaItAcJUiORHMwHfe8UKLKodDC3POnDlIksTYsWPR6XTMmTOnwp1JksS4ceNc/vJt27bxxhtvsHfvXtLT00lOTmbQoEG27bIsM3v2bD766COys7Np06YN8+bNo3nz5rY6hYWFTJ48mdWrV2M0GunUqROvvvoq9erVs9XJzs5m3LhxrF+/HoBevXoxd+5cRfCF48ePM3bsWLZu3YrBYKB///7MmDGDgAD1cg5Pc5YYZK0wbwRgzc33QouqAY6GZEXgAoHAc1TkJWsygb96u6/iUDBnz56NJEmMHj2agIAAZs+eXeHOLlUw8/PzSUhIYODAgfzvf/9TbV+4cCHJyckkJycTHx/P3Llz6du3L7/88gthYWEATJw4kXXr1vHee+8RFRXFpEmTGDBgAFu2bLF58Q4bNowTJ06wcuVKJEli1KhRjBgxghUrVgBgsVgYMGAAUVFRrFu3jvPnzzNy5EhkWSYpKcnl/ridkiGeC4Q7rGLEUJzVXCcGCxSIIVmBpxFDspo4c/rxW7cO8z33eLpJl41DwTx//rzT9+6gR48e9OjRA4AnnnhCsU2WZRYvXszo0aO5p+SALl68mPj4eFatWsWQIUPIyclh6dKlJCcn06VLFwDefvttWrZsyebNm+nWrRuHDx9m48aNrF+/nvbt2wMwf/58evfuTWpqKvHx8fz444/8+eef/PHHH8TFxQHw8ssvM2rUKF588UXCwx0LVpViLR6G1QotVYoRA37iQlXjwMIUy0oEAg9SgYWpq2arKnzWLDl27BgZGRl07drVVhYUFETHjh3ZtWsXAHv27MFkMinqxMXF0bRpU1udlJQUQkNDbWIJ0KFDB0JCQhR1mjZtahNLgG7dulFYWMiePXuqtJ9OKRHCPEIdVhFLSxwgIv0IPIzu338hX0yRKLha12F6moyMDABiYmIU5TExMaSnpwNw5swZ9Hq9KmRfTEwMZ86csdWpWbMmklR2o5QkiejoaEUd+++pWbMmer3eVkeL1NTUy+yda5+XzGbaAJvp7LCOEQMZqanIPpDAu7LHw50Y0tI0LUxjTr7L7fSl/rgD0R/30hbYzG08xHJyCeN1RtH5nXfIuuuuy9qft/vjblJTU2mUk+N0SDZozBiyjhzh9KOPerh12sTHxzvdfkl32Y8//piPPvrIlg/THkmSyHKz12Z5oYOyAAnOsK+jVd+VOs7KoeKD64zS4WCnFBVhQcdUpjmsUkAQTRo3hkB16DxP4lJ/PIjOYsGK+mFHl53rUjt9rT+VRfTHzZSM6jxPEunUBWA0Czj9cm1qjBlzybvzen/cTGl/gsLCKgyNF5ecTPjjjyM3auTBFl4eLgvmtGnTWLBgAS1atOD++++v8vResbGxQLH1V36oNDMz02YN1qpVC4vFQlZWFtHR0Yo6HTt2tNXJzMxUCKQsy2RlZSn2Uzo8W0pWVhYWi0VleXoUWeYojZ1WEcELHOAwgbQX2iK44ggaORIZ+JWbbGUXiOAgCTTxXrN8Dxcj/QS++Wa1WJPpsmAuW7aMO+64g2XLllVle2w0bNiQ2NhYNm3aROvWrQEwGo3s2LGDadOKLa7ExET8/f3ZtGkT999/PwAnT57k8OHDtjnLdu3akZeXR0pKiq0sJSWF/Px8RZ158+Zx8uRJ23KUTZs2ERgYSGJiokf6q4nVqpl8tTw2L1mBEkfLSnx3FkJQTdAdPkzAZ59xTiMfbRY1hWCWx9XQeBWMGvoKLt898vPz6d69u1u/PC8vj6NHjwJgtVo5ceIE+/btIyoqivr16zNy5EheffVV4uPjadKkCfPmzSMkJIT+/fsDxSnHBg8ezJQpU4iJibEtK2nRogWdO3cGoGnTpnTv3p0xY8awcOFCZFlmzJgx9OzZ0zYE0rVrV5o3b87//vc/ZsyYwfnz55kyZQr//e9/vechCw49zMojnH4c4MjpRwimoJLod+4EIIcI1bZcwjzdHN/GwT3sinf66dChAwcOHHDrl//+++/cfffdtvezZs1i1qxZDBw4kMWLF/PMM89QUFDA888/bwtcsGbNGtsaTICZM2ei1+sZMmSILXDBW2+9pcik8u677zJ+/Hj69esHQO/evZlbzvzX6/WsWLGCsWPH0qtXL0XgAq/igmDupjXthYWpRliYgirmShKCqkK2ylg0rrlsqlcM2VJcvnskJSVx7733csMNNzBo0KAKHW9c4dZbbyU7O9vhdkmSmDhxIhMnTnRYx2AwkJSU5DTAQFRUFO9UEIapfv36tkAGPoMLgjmR2YyUj3uoQdUIq9WJhSnC/Qgqz5WU57GqMMvqh1aAM9RSFlSTUTKHgll+3WIpRUVFjBo1inHjxlG3bl1VPkxJkthZMlwhcAMuCCaA1awlDVc5Ti1MIZiCSlBycxcWZsWYrdp3pgxikYHqMXNZhkPBjI6OVlmRMTExNGkiprQ9htVKERXHsv33hB+NanigPdUJp3OYhZ5vj+CKQwhmxTgSzCICuUA4EVzwcIsqh0PB/OabbzzZDoEWLlqYZzIkGlV9a6oVutOnNQXTgh+yXG2c8gQ+jBDMijFZtYdkAU5Rt9oJphjJ83G0LsrObFK8Nxo91ZrqQ/DQoZpDsgDmIuEkJag8Yg6zYswWx0+mf3Fd2ZtqMofpVDAzMjK46aabmD59utOdTJ8+nXbt2pGZmenWxl3tSBoW5gOsIIgCRVmhEEwFur17kS5e1LQwAcyFYg5TUHmEhVkxjoZkAU5Sz+E2X8WpYL711lucO3eO0aNHO93JM888Q1ZWFm+//bZbG3fVoyGY/pgwoFRIYWGWodu/n7DbbgNwbGEKwVQgpaURsGABfj/+6O2mVCuEYFaMsyFZladsNcCpYH733Xf069dPse5Ri/DwcO677z6+/fZbtzbuqsdqFYJ5iQSNGmV77cjCtOSJA2YjO5uwW28l6KWXCOnXD7+1a73domqDGJKtGOlnx6smzuLFsKOXiVPB/Oeff7j++utd2lGLFi1sUXsEbkKWVV6yARRpCKbwYCnFb/du22uHi23Wb/RQa3yfwORkpNxc2/vgoUO92JrqhbAwnSOlpWHOd+yRfsUJpiRJWF2MImO1Wt0SzEBQDgdDsqo5zEJx3LVwNCQr/fiTh1viu+j37VO8l0R0epcRgukcQ1KS08hap0qyvFQnnApmgwYN+O2331za0e7du2nQoIFbGiUowYFgBtqtIxRDsto4sjBNodUrLJf/0qWEduhA8EMPITnJzyrwLGJI1jm6EyecCuY2bsFSzRZqOG1tz549Wb16NX/99ZfTnfz111+sWrWKXr16ubVxVz2OnH4ke8EUFqYWjizMooRWHm7J5SNlZBA0ahT6Q4fwX7eOwAULvN2kqx7/r78GhIVZEbKfX4XryLdyq4da4x6cCuZTTz1FSEgId999N6tWrcJsNiu2m81mVq1aRZ8+fQgLC+Opp56q0sZedThy+rEXTBG4RhOHFmawFzPQXCIB77+PVG6NWuCbb7r3CzSmUYIefRRJLBFziP/G4jnwfEJU24RglkOvrzDZwSa6eKgx7sGpYEZHR7Ny5Ur0ej3Dhw+nQYMGdOrUiTvuuINOnTrRoEEDhg8fjl6v5/PPP6dmzZqeavfVgaM5TMnO6UfMYWriyMKsVtN0XmhswBdfEHLXXdVmMbk3SOEmRvGGqryAIHSHD3uhRT6ITlehYO5CHbPcl6lwAPnGG29kx44dTJkyhVatWnH8+HFSUlI4fvw4rVq1YurUqWzfvt27iZavVBx5yeqKFGXC6Ucbh4ELnEQfERSjP3QIvYv+C1cVJaNsM5isudmIgcA5czzZIt9FY0jWT1KOUiqi/VQDXErvFRERwejRoysMYCBwMy7PYVavifPy6PbuJXjYMHRnzmCcOpUiNy5rcBi4wKxZ7Jt40fNcOnvWa9/tq0hZWQCspY/m9gKCCFizhoL33/dks3wTjSHZVtGn2H22zDk0k2hPt6pSVN877dWAy0OynmyUezHMnIk+NRUpJwfDxIlwwX3BmB0NB1mEYLqGGJJVIeXnY3WSlErMYZYh63Sq+1fN4Hz0lF2AuYRTSEC1OdeEYPowwUOHaluYdkOy1c3C1O/YgWHsWPw/+QT/DRts5VJhIX5bt7rtexxZmH5Jr2F4/nkoKNDcflVRIsgykE2EcpuLa7CvKi5ccGoVGTEgi/XoAOj371c9tAborUSQoyi7QPVxwqted9qrCP3vv+P366/aFmbBOUVZYbryvS8jHT9OyJ13ErhkCcFPPqmuoHcce/JSOE0sB2ihuc2MH4HvvkvA8uVu+a4qRZIwo2c+oxnMx3zH7W7/ijPE0IGdRJFNW37hZOmC8mry1O9JpNxcjlPf4fZCDFgNwR5skW8S9/rr6FNTVYLpp7cSbpfSSwimoNL4lbiu2wtmAEWqSD9Fh//1WLsqi2HePCRnloufS9PqTvmSPjQiDaOD4bHSizjouecq/V1VjiTxAUN4lvksYzC9WM+xY+61YBYwmpQSb8XfaMvLTC3eIARThWQycYI4p3UKC65yy7ywkNpLlwKonBb9hWAKqgRLcUYN1QmnFXy9GkUX0f39t/MKbrAw5zCeQifHpCJXd19jOO/aXsvoWLYswEntS8RqZQM9FUXvMpwi/IVgalFUVKFgGjEQ8IZ6ycnVgpSfb3ttf28y+Ju1h2SrybkmBNNXKRFMV2LJVifBRFfBKVdU5Hy7C+ygo9PtFUUf8Sk05sOOHXPjZWsyaS7A/4lO1eYm5lEKCysUzAKCCHrxRXH80BJMi8rCzCECyY3OflWJEExfpWTY0pX0XtXJM092YEHmE0wBBo8Exq1WFqaGYLppmrd490VFXEQ95/YP1ygiDAmKkUymCoOG267Hq/X4lTtn7QUz0F97SDZg5UqPNK2y+LRgzpo1i8jISMXfddeVLXSVZZlZs2bRrFkzateuzZ133smff/6p2EdhYSHPP/88jRs3pm7dujz44IOcPHlSUSc7O9sWyag0elF2drZH+uiQq8jCfJvhRJNJFOf5bItjhwp3YT/M7cv4f/GFqqzQncuITCZy7L1jKckkcbXe8J0gnThRYeJjm2BerV7G5c4blYUZJIk5zKokPj6ew4cP2/62b99u27Zw4UKSk5OZM2cOP/74IzExMfTt25fccvn9Jk6cyNq1a3nvvfdYt24dubm5DBgwAEuJIAEMGzaMffv2sXLlSlatWsW+ffsYMWKER/tpj+RAMLXyYVYnC9PePMohnDHMx0gQhRiYsuamSu3elewHeYRW6js8if7QIVWZO3VMLizSPB7p1KmegnnxIvqUlKoJulBYSNBLL1UomDaRqI7Hzx04EczAMH8hmFWJn58fsbGxtr/o6OI1ULIss3jxYkaPHs0999xDQkICixcvJi8vj1WrVgGQk5PD0qVLmTZtGl26dCExMZG3336bAwcOsHnzZgAOHz7Mxo0bWbBgAe3bt6ddu3bMnz+fDRs2kJqa6q1uX7kWpt0Q4x+0pKDckOCp7NBKWVBaw4v25BJ2+V/gA1it7vOSLSoEq8Z61VPUJfixxwi79lr0v/zitu+rUnJzCe3ShdAePQht3x6d3WhTZfH77jug4sTHV72FWa7f2ShT6QWG+Yl1mFVJWloazZs3p1WrVgwdOpS0tDQAjh07RkZGBl27drXVDQoKomPHjuzatQuAPXv2YDKZFHXi4uJo2rSprU5KSgqhoaG0b18WBLhDhw6EhITY6niFkvht9vMl1X0O035IVkvgcnMvXxC0HFjsqU4XqJaN4s778IX6zTXLS887XVYWhhdfdN8XViEBH32EviTwue7cOcL+8x+3Hizd338jg8rCbM9OxXshmGX9tl8LfU3dgmptYfq090Pbtm158803iY+PJzMzk6SkJHr06MHOnTvJyMgAICZG+bQXExNDeno6AGfOnEGv16uyqMTExHCmJBHvmTNnqFmzJlI5y0eSJKKjo211HFFZC9TZ5xucO0cAEfxFU0V5IIUEUISEFbnkecdEAIcO7XOrM8jl4MrxuLaggKhy77XE/o8/jhEXd3lmZqQLw63lLUxnbfbqCEMJ116bAHYrcbKzcklNPXrJ+9LsT2ANzbrlH9T8du70iWNhj32bmpes/SvPufnzyeqjHff1UqlptVKDcMWSJQMF1EJ5nygd8fk7NRVrkOsPs754jC8H/7NnuaHk9TmU51ewdAyrA8H0hf7Hx8c73e7Tgnn77cqoJm3btiUxMZHly5dz003Fc12S3RCfLMuqMnvs62jVd2U/FR1cZ6Smpjr9vCEykk+5V1UeSTYSYMCoGMqsXz+ekIqNqyqjov6UEhyufJrUsjBr1mxEfPzlPZ2nXaKF6ajNrvanqjFH1laVBfx2gOsCIpAbNnR5P476kyZr39DPEoMMtqipvnAsyqPVH4NBPTVxzfTp1HBTgAr/evU4jvL3qEO6aoqk9CHw2saNIdS1+XJfOd/cgRRcdk3bD8k2aRTF3wYT5QfJSp3OqkP/fX5ItjyhoaE0a9aMo0ePEhsbC6CyAjMzM21WZ61atbBYLGSVZBhwVCczMxO53ES1LMtkZWWprFdPo5UrrnT8335Ytrqk+JLthmS1LMyqHpKtTnOYWrlOCwp1BC5Y4Jb9XzygHSXKjL+m9+xVjSQVO0OVozanHU+RiCFZ1TkUYTASLos5TI9gNBpJTU0lNjaWhg0bEhsby6ZNmxTbd+zYYZuPTExMxN/fX1Hn5MmTHD582FanXbt25OXlkZKSYquTkpJCfn6+Yl7T40gSYeSqiksFU+X4U/XLF91Dybjxdv7DLCZoPhRURjCvNKefwiL1JVpAEIEffOCW/Rv/yXC4bR13uOU7rhh0Ok67YGHanPCucsGU0RDMQCMhXdsoyqqTYPr0kOzkyZPp1asXcXFxtjnMixcvMnDgQCRJYuTIkbz66qvEx8fTpEkT5s2bR0hICP379weK83gOHjyYKVOmEBMTQ1RUFJMmTaJFixZ07twZgKZNm9K9e3fGjBnDwoULkWWZMWPG0LNnT68PEWgtsA+kOBKOKjyeUULbRcTH0OnYQQduZaumdyZUTjALCaywTnW6QAtN2oIJFEdFCqjcmtJcKdzhabOR7jzEp5Xavyep8iwhGhZmHdLxx6QoK/19JFmuDlek+ykZrcsnBEu5e5iBAgKz0gnq0QG+LatuE1VZ9m46OxfwacE8deoUw4YNIysri+joaNq2bcv3339PgwbFCUifeeYZCgoKeP7558nOzqZNmzasWbOGsLAyC2LmzJno9XqGDBmC0WikU6dOvPXWW+jLeci8++67jB8/nn79+gHQu3dv5s6d69nOamC/Pm56uSzv1dbC1OlYxFMOxRIqJ5iuLLGpTham0YGFCRD8yCNc/LQSgma1ki87tsgPl3c4qwY3syqPTGS1qizM2pxWTQNc7ZF+SpMrZBCrKI8kG0vr1oQEKufeSx9g/TZswNyrl2caeZn4tGC+X0HWckmSmDhxIhMnTnRYx2AwkJSURFJSksM6UVFRvPPOO5fdzqrC/sZ+Df/YXqsszJwifPznLEanYwUDnFbJy7v83V95Fqb6waL0ocD/22+Rzp9HjopS1XEJk0nllFEexXrDoiIIrPjYXtE4EEz7pV9iDrO434/yoaJYjwXr9dcTlqGOJQsQ9MQT5B69dO9vT1Kt5jCvKmRZZWGWzmma27VTO/2c9nIoP1fR6YjEeVsrNYcZHlthnepkYRaa1YKpcJSqTNBqk4nzOBZbhWC6NR5f1VDlQ7IWi+oBI5pMQlE+4dnWaV7FgikDP3OrovgkcSBJGIJ1+JUbxi4iECOB6M75fl5fIZi+iiyrbuylF6Zx+nTVkKz82mKPNa1S6PXosTitkpdXiTlMfcVOP+eogRXfHl4spUDDwlQIZmWG/cxmp4KZTRRjeI3DXIdkMjmsd9Vgsah1iWmcAAAgAElEQVRGJ8K5QHOUEYV+oFvx3OVVLJjOvNUlP70q2s98xlR1q9yCEExfxWJRWZilgmlp1kxlYZr++MtjTasMsk6HH2andSolmFZtJxj7J1r7+RVfxWhSpyIzEWCLmes0GXcFSEVFqoXl9ixgDK3ZzekTzh9yfIlcQpnBJCYznXTU61gvG1nWFMybUIYOPMq17Kb1VTuHidWqOYrzZO/iKEzodCpv9heYVS0cpIRg+ipBQaqTrvwyE0eLpX0eSarYwsy6/JyYhVa1wNzAHlryh6Jsq91wka9y0aI9b/ghj5JBrcrlDzUanVqYtjYQwpsfVJ81mT3ZwIvM4BUm05f/c5tuSQ4szGiy6M73ivKfueXqtTAtFk3BHNq1JJKPXq8IulJKdXiIFYLpq1itDi1MJEnt9FNNArBLFkuFgpl/pNzawAsXkE6ccHn/RrPa8ekDhtCNHxRlP9DN5X16kwKzdrLrYbxHQ46x67fLd8TxX7fOJcEE+OPP6uHw8x23KxKI76IDe/e66TanIZilD7Hd2ago/4vrqo1g6nfvJjwujrCEBAJfe03bMpZlAt5+m+C+fQlYuNCWHEITDQvzWo7QNPZ88RsHSeQziHVLAvmqRAimpzEakY4dI2TfPvzWrgVHeTc1BPOKsDBdEMy8km76f/wxEQ0aEH799QSNHOnS7osKlPueyURuZA9d+VFR/guVSyPmESwWjLJjoSrEwJsr6jjcXhGGadNUglkf7cg/ugp+M19hNfepyg4edFOQZatV08IEaMgxRflpalcLwQycNo3Qrl2R8vLQnTqFYdo09Hv2qOrpf/qJoPHj8d+0iaCpU/Fbt87xTjX8L+qQXjYPrtPxFG+oPnaWGM38r76EEEwPE3LHHYTfcAPNH3uMkMGDNfMdAshmx3OYksVSbS1MzGYsTtZgAuQV6JFOnyZ41CiyiWAdvfn7098rTNek+/NPdf49ir077eeZ/qCl7zv+mEwVRi76v58ufxhLunhRJZhNOaxdOVsddcoXSaORquzIEffc5nRTX8FY7sFUhwW/gfdSMH8+Mc2Vc8Gnqe3zc5j63bsxvPaaqjzgww9VZQa7denBTz7pcL+ShoUZRi6UCqYkMYHZqs9lEo1/ZdYVewAhmB5Gji27wRkJhJPpmvUKTH6Kxf0GCvArecqXJYlgLirquxJD1SewWiu0hvMKAwj45BNyCKcj27mTdSTwJ8vnOA7jBmCYPVu1DtOAEUt8PFFNoggv55lnxr9ChxevYzJV6ciBqWdPlWDexhbNurmH050Pw/kCkqSZ3PnIETdYmLm5XChQDo+HcwHj4jcpGjKEWqHKpSWnqV31gRQqiWHcOADOEs1TvMFNpHCEa22pBcvjt20bAMdoQAo3UXDBide0o9Gxcp7W9TjF/1B69p8lxueDYwjB9DDW2FhOUYfJTKc+x9n2mPYTVZ5ReXGWWpfmdu0gMlIVZzaXMJ9/ogXAYqnQaso1BxE4bx6LGcmfJNjK52y+xenn/L79VtvCDAoib/NmVRomrZurLyG5YGHC5U/7FITUJLfcEKMeM234TbNuDhHo9+27vC/yIFrOJqeOOffKdgXp3DkyiVaURQeXPbTWDstXbDtNbWSLbw/J+v36K2b03MpWknmKX7mJeI5wyHStol7AokUAfMYAmnCE9qTQiZ/I+eWI9o4dWJj2S5OiyVS8zyQa/x+VUye+hhBMD/PW8btpyDFeYTKZxPAW/1M8eZWSV6gUzFKBLA2FpiWY0r/a808+hdk1wZQLjCziKUX5P9k1OXvWyROowaBpYWIyQUgIsSgtVJ/3ynPRwszKuryn8jNnlJ+L9ctSRJMqTzaRSJmZmtt8Br1eUzDT0yq/hlQymVSCWdOvbMQiIthEYLlpkouEVCpilafYyq0cppmibMbvdyveB02ejAUdY5mHmeL70m+0ZcHT2qNjjgTT0rKloiyGs4r39sfXFxGC6WFuaJBlO+kANtEF6/FTijpSRgaFazcpykotTLkkGbZW1vKAjz6qiia7lSKTpAjIrIVRNrCdjsWRQez4/ecCjU8UI/v5qS7UIAqKTTBJUgnmGWoR+Oqrl9B6D1NU5JKFmZluJmjoUCIiIzFMnepaYGGrlayflQl7YwPO0cCB008OEUiViSrkCSRJNRQIcPpCiNYo46WhIZgx+nJpAw2B1EEpIGd2OxAUH2IH/1GV/ZFVdt1Jp08DsJiRquvxq0NKobXhSDDbtlWU2VuYtshSPuwpKwTTw7Srf4qa5U6UHCI5uO6kok7gG284dF939D6XMHR2uUF9kYsaC/G1cBRv9q8hrzv+kL8/J6mnKKpDOlLJBaglmIbp0302cr1kNrtkYeYPfpaANWsACFy4kPB69TRHLcqj37FDHRc14DzBaD+QFBDs89HxLOi5qDGXb5H1KmtaOn/esYe6FkVFylCBQE1duVBuAQEqwcx69jXN+UBf4ke6qspOnQtCOnuWwFdeIbxZM9KpzfOoY3H/TRMyjmiY0RrrMMPIVc1Pag3JAj79YCYE08NIfjo68ZOi7MfJ2xXvAxct4jmUlo/9zV5zDtPHL05wXTCX85Bm+W5aIx0/rrlNysjgH65RlMVxArkkA3wNlLEqR5W4tkvnz7vUJk/j9+WXLjlznTtZSB4hjCWJG9nNQ5alZH3yg9PP6P/6SyWYsRHFc3INSdP8TE6ub98u8qyOrfH09LK2B7z9NmFNmhB+3XX4r1jh0r4ls1llYdZo28j2Wg4KUg1n/8M1BI0a5dL+vcVeblCVXSACXXwihqQkLOiYx1iFd3B5DszZqCqTzGanQVdKsR+SLX0g8f/8c5fb72l8+wq4ApEjI+nFekXZ5zyAZBd4OBVlLs44lIv3NYdkP/vM5x1/XBXMc9TULP+dG/HbvFlVrtu3j0yiOV/O8zWIi9TjJHJJujf7J1qALGr47ING0EsvOc0mUspZYujORl5lLHu4kc8YyGOjazk9FeTQUJVgRrcuzrrxtMYaOYDcHM1inyHX6vjh4uTJEuumqAjD+PG8axnKwKKPeG/8cdecf4uKVIIZ1aGJ7bUcFkZjlJk2jtKYgOXLXW6/p7lAGJl2VnMpJ6lHNhF0ZDuv8ZzDfez/4pi6sKioQsEsfOwxh0OyQS+84LP3MSGYHsZ077305f/Ql4unup+WnGh8p+19AQbVkOy9fIGpe3fb+5AopfCU1g+91bdDvl00VS4F2T805sSUj1XlQZMnK/M3AvGkokOmcExxYOcb2Kv63F5uQP/775VqU5VQcsNwZenLM7zOLjooyjbTha1rnFjOZrMqLVWtkHwsrVrxLK/xmcaQ+KWMYHoDZ4J56lTxrc5v3To+YAgjeIcVPMio7Okkv+HCQ5zJpBqSrRFTbohRQzD/RultKqWl4f/550jHNETG0+Tnc5TGDjefpjZJPE8K7Z3uZqPpNnWhyeQ4SlkJxnnzqMUZRUCMDGpzrnSZU65vrvsVgulpwsMJXpGsijyzhGGE3H47WK0coQmy3U/TlU0Yp02zvQ+eNVax/Tj1sSKh378fyYfnMgtM2sHRL4U550eoyvx++knlwNCMQ5h697Ylpb0t+BfV547REMNLL1W6TW6nZF7V3gq8FD7/2PGyBik/XzV8Xb95MMbnnkMCBvA5XezO0QsXfPt2kVvo+Nw6vbE46EVgcjLJKBfdv/9SBobhI6DAsUOZlpdsdHSZFWRJSFAJZvnjq0tNJeyWWwgePpywW25B94+2N7Kn0KWnOxXMP2jJTCZVuJ+t3Mr5V5cpoxppDMkaOrVWflCSCKSIZigDt9zK1uIXPholybevgCsUc8+ePBD+taJsMSP54xcTgdOnqyylnqwn/8MPsSaUrUms8UBnaviXDcsWEMweEgHQ79pVha2vHAUuDsmWEoiRF3hFUfYx/+XECfVSCvt5z2v7JXBx+XLQFy9cNyYvYrxdhJFjNETvg0lrpZwc0mjImUosffk+pabjka28PJVgNritPuY77sBcMkphP8d0NEPtgepL2K9dLs+ZjX+i+/tvUn/JZTdtFNvSuAbD558x8uajjtOLaliYNWuWHVxzz56ac5gAWK0EvvIKUsk6Eyk3l8BZs1zsVRVRUOBUMJ9mkWb52A6buZHdtvcyOr6fvoeADz6wlUkmk0owA4fer9qXqUcPWpfbF8BBWvAjXZB8dJpECKaXaDc7kVhO294bCaI1v/PQ/FvYgnKYoxmHkOspvT8lnUTzpkpPyDmML36hd1PszCrgovnSLMzZd/7I5B7baULZEggz/qy7fjqGZ5+1RZ/JI0TlwNDvrnyFZ56pTx9VzM/9XH+pXfAIUk6Opsv/pZBREMHfkz7R3Ga9kK8KI9ewkQz+/uT/3/9hrVGDVigDFez5y7cFM/+i49vZCeIobNOD2+2yipTn06MdmTRBfX7q9uwh5MEHVYJZ3sJEp6MeJ/GnbElEJjHkEkpEjRoE2MVI9S/xavYWktGoGjKuiFlMYMLKZvRD2fYJzKbouZfLCjQEMyShvmp/xpkzeZJkVfkrTPLZpSVCML1E0U2tSKo9T1X+f/RjEU8ryppxSPME6ttFOWn+f/Qll1B0J0+q6voK+WbXs15c73+IIQuaYp7yAk/ZPfGu5j4C338fv5LIIAdooRjGjucvml5n582h16vWGa6mP6/wwiX2ouqRcnIu+YYG0J6divePvdmRi9nqc+fMm2sV64FrheQRWqqHfn4UjhlDW35VfGbzsWuR85UhGX2J3ALH8+M/cRt9+EpzbW95ln8WSHp6udGLggJC+vblB7ryLw0VdWvVUg4byh070MjOwzieVM5rOW5526nl4sXijCoucpRrGFdzCX5hQSrBPEdNYsngg7F/FxdoDMmGhqr7a23ShBte6s0glinKd/AfzBeFYArsuGfDfxnGuxXWu5HfNS+wIY8YiaNsiYWJAJpxCOMS33XLzjW5HiT+0en1ICYa6/XXcx+rFdt20YHG/M1nY/Yhy2r3+BvYC4FqcdZamD+ZVzi0x7cu0MsRzNX04wGUv/1eErml0QUyTpeJiZSWxkajMszgNTHKsciiwYO5mW0EULb48ijXcqbebYS2b1+cacfHyDY6P7fKp/1yhMWqY/WMsqFVfUoKqedjuANldo7aYbmE2PkYGSdNognKcHEZ1GZSyZRCDuEM5206sIP3rEMqbEtVIl24wB+0rLgicIim1B3WndyDBwFo/GgHEjigqFOIgTFLWrOixXxYsNglwQRAp2MpgxVFBQSz/8/K+zpUBUIwvYjcsCELJx1nnhO37Q7soG3EX1jatVNt0zdppBKSU9Rj8OEp+G1Ur4/yBfZfaKRZHhVl5Yknym7O3bubGDyk7Ak+ZmQflfX0D40ZdmIana/NYT5jFNtasU8z7159tNdwbu08BwoK+OtPmS1b9BQVeTcI9KUKZjg53BH7qyrvJxQL3YT7Atnzm0x2NgR+8IHqJtU1UTlfSWQk0qdLVMHYv+FOth+O4cnBZuZGLiLrW+UclNcoKiLdpL1EQotEfudRPtDcNvmTG2jfMJ9xT8t8+mUECRykyC7k4h3t1IkALDffTDsNTV7ME1wkiNEs4F2Gs4sOPC6/y8aNlfMYrwxHBidx1oVYyv1ZSe0TP2KcN8/2AFo47WWmMUWz/oiTLxFy9CD59l6yDkbz5Zo1kYC+dlbr9q+Fl2y1YMmSJbRq1YrY2Fhuu+02tm/fXvGHKoHpmad5akQ+BRhoiTq49RzGY/zgfQjQfuLq+vEDqrJvuIsu/euTEJlH0/p6pkwxOHMA9AjZ2TBz9AWSsx9WbdPrZRYsKGDmTCM//JDH11/nsXz5RYWBaBw/noFoB6rfe64Bh2iuKOtgJ66lGEYOogZZqvLnmcdjdX6m3X8iueeeUAYNSuDvv713eUg5Oaq1uAP4jGv0x+jfLYN27ZROEf+9M4PCXVtp+khrumqI5v6i5nTuFkmjRhEYFi5gK50U2+9/XL0w3dK2LT34TlH2LPPpxFY+4lFmMokmA29jxpiLXj+/jm1LZzYTFWUPd0kjyi5YBYCElSWNpvE+Q/mN1iQxVlXncE5d3lkayfD3b1NkDQKowyleTNK+HjtP104QEMJFPkRpVb73nnesqNNbjvAY7ynKbmMz/ewevgEeeqGOWu3Cw+n9ZnfeZjiJVLwkKzygwFHOaEz9+gHQmc2K8m0rnGcm8hZSdna2b64Q9QJr1qxh+PDhvPrqq3To0IElS5awfPlydu7cSf366knrypCamkp8fNkNUf/zz/x114vczDZbVI2BLOfdlQGYb7/d4X5kGR7sms+G3+s6rAMQFCRz881mIiJkZLnYa1uWpZL/lCtT/im3S5rbAPLzjRgMBs3PZWToFJFWyvP66xfp29dEmDpmtgrT/8bR5rMXVHNJ9tQmnX/qdqRw/26Vlanbu5dZt/3MLBfnLW+6yUxIiIyfX7Evlf2F7yA5fYXIcrG/ksUCZrNU8r/s/bm0XE5cKJv7CqCQ09/vRJfYAvz92bNHx913h5KbK3HttRa+/z6fGjXk4nyjdw9i6o47VXPhjvgP2/n2fIJmaqULre+iydGNKgvLngYNLPTsaeb8eYnsbMnpMbD/mvLvy78uO0aS7VhZrcX/8/ML8fcvPt8C/S0c3G1ShcXbvDmXRpuWMvHlSD4t50H9eP8MkpYYkI4fR5eZSWBSEkPXPcgyO6tbC3+KuFA/gcI/tLO6ALx00xYWpPapcF8AN95oxt8fZPkiNWoE4edX3Mfy15bWsSndXvZfUpWVvi5fXlQkcehgcbjA8nw+8DPu+fS/PMZ7tuMwhPd57Vw/JJ3j0Rb/pUtZ/vQeHmeJwzpt6p7gh4OOL/CgJ57g8PJ9tOIPRXlHtnEwqC3BUf7Ur2+lTh2r7VqBsuNT+r9+fSvz5lVtmEshmOXo1q0bLVq04PXXy+KVtm7dmnvuuYepU6e69bvsBRPAMGYMez/Yz6cMpE5EPkPXdMG/TcVenMYCmcl1VrKEx93axqqmTdTfbEiNxs/FkSnp/Hn+HbeEF1e24Qv6Oqw3ncmM/vYWLP/R9jK1HExlaceljGbh5TTbK7SPOsSGf+ooyrKyJI4c0dGmjUV1DPVbtmC5ZzD9WcVGHD9wAawesZZuczppbpNOn2ZGsy9JYlyl2u9pmkeeYtvREHQ68Pv0M76csJtVwf+l5f3XMmpKkOJ46bdvR7qjP0N5n6+5SzMebSnv8DgD/ngG2ckDdODMmWydu5seTjxyfYlEfmfTvzHozp8j9PbbsZ7JIlcXge7oboisONJURGQkr/ACM3lB89jNG/M3w6Y6zkTiv3QphqdHEUuGw8hDrtC8wQV27KtaOROCWUJRURF16tThvffe495777WVjx07loMHD7Ju3Tonn750tAQTii0gAGvLlppzcI4wjB1LzyWD2EJndzWxygghj3HM5clFjfB7+L5L34HZTER0NGk0ZBpT+IChtk0JHOD7E/UICXU+Bxn8wAO88t1/eJmXLv37vcCHT/zEvTPVcT+doduzh+DOXZnFRN7kCfIIVeS/jOIcSXEL6L/f8Rw6gOWZF7jxo3EVWva+Qnt28vbY/TSarF775wgpLQ3Da6/h//HHXCCcTXThW3qzjZvRYyGeVEbwNh1+fgnr9c4fYvUpKYT26ME6ejOZGfxOa6f1vUlt0vmy9RSa/ljisZ+djT41FUuLFhBccaYcAOnMGcKvuw4LOgoJZD29+D/6cpJ69GI9j58Ygy7Uyb6ys4lo1IiBLOczBl52XxICjrD9zOULrisIwSwhPT2d5s2b880333DzzTfbyufMmcPKlSv59ddfnXz60nEkmJeN1UpGfG/uy3qX/bQkmrOMZDFpNGIl9zsMnuwp/DDRmKN0ZyMvMp3oxDrkb9ig6cnq8j7XryfkwQc5R1Rx9JaAAB7eMpAazSu+aHR792K4px9vZP+X53hNsS2cHGI4y980cfBpz3EtR5ion0v/4y+7fAMrj37XLoKHDrUtNfqN1nzBvTTkGA/yGXLKJqzXOV9eoNu/n123TKU339qWotTnXzbQk36sUc0fe4sAChnHXF5mKvk7d2Bt5iD9VAXof/+d0C5dVOWFTz+Ncfp0l/bht349/qtWEbBqFXmEsJHuLOQZdvAfCnHdU7yqaMVehvMOg+r+iHRQe77/cghr1gzd6bL15QVz51I0fHiFn/Nftoy/nnqXdqRg4vLmdoVgepBSwVy3bh0dO5a5us2ePZvVq1fzyy/qsGqpqamqMq8iywSmHUP/0TfUMGZQ+4dvATDhx2Ga8ifNKSIAHVZ0WJGQK/xf+lf+/aW+DuYi8aTiXxI/98icOWR36oTLY7FO8M/IoN7ixfhnZXH6kUfItcu55wx9bi5xr79O9BdfsIIB7KI9t/M9d/AtMnCYppyiLhb0mPHDjB8W9EgoLxn7966W6bGU7NVse136P5BCGnKMoHAdR159lbzERJf75YiAU6doNGMGUlERuW3bcva++zDFuHaDCTxxgpy+80jmSQwYmcI0riENI4F8xCP8wzXU4ByxZFCTLPzQjtQiIzl8b78Nio+Rsz8JGSMGrOi4nv1EksM/U6eSddddl3BknCDLhO7di6zTkd+ypeY8b0WEb9/Odc88Y3u/np6M4nVyiOBOvuFeviCIAgoIwopOde2B+jjZX8Na16+ja7oup6hHcQ7efWvXUlT78sMvaqHPziY8JYX8hASK4pyvey2PZDSSc+sU3uJ/nCeKMHL5Lx/Tin0cJIGLBOOH2dYPQPHfv3YY0WufrVTbKzJihGCW4CtDsm7HZMLv22/x++knsFiQrFbIy0O6WG4BuiyrbwSlZfZ/Oh2yRnlubi5hERHFn9XpbOV+W7ZAYSFFjz+OqV8/rJ7o86VgteL33Xfof/2VoocfRq5XDykzk7ykJOq+/z7mG28sngstKEC6eBGpoKDY66S895M9pcdF67VdmVx+O6A7fRo5MBCCgpDDwjDOno1co+IA7BXhtvMtPx9dairW+Hh06ekYJk/Gf/16ZH9/TPfdB7JcnM+w9BzQ8l4B9fkFmu9lB9tzc3MJCwuz7cfauDFFgwc7nVv0JtK//+L3yy9YmjfH7/vv8d+4Ef3PP2Pu2hWpqIg8WSYkPLysr+U9WkqPVekxLfkv2723v07t6+rS09EfPIipe3eML78M4eGOG1xJKnO+6f7+G/22begyMtD/8gtyZCRyjRpI584VX3+SpM73KstY4+KKl79UIUIwy9GtWzeuv/56Fi4scwZp06YNffr08YjTT3VG9Me3Ef3xbUR/qgfeWznrgzz55JOMGDGCNm3a0L59e95//31Onz7NkCHejcohEAgEAu8jBLMc/fr149y5cyQlJZGRkUHz5s35/PPPadCggbebJhAIBAIvIwTTjmHDhjFs2DBvN0MgEAgEPoYIjScQCAQCgQsIwRQIBAKBwAWEYAoEAoFA4AJiWYlAIBAIBC4gLEyBQCAQCFxACKZAIBAIBC4gBFMgEAgEAhcQgikQCAQCgQsIwRQIBAKBwAWEYHqBJUuW0KpVK2JjY7ntttvYvn27t5uk4rXXXqNLly7Ur1+fa6+9lgEDBnDw4EFFnZEjRxIZGan46969u6JOYWEhzz//PI0bN6Zu3bo8+OCDnCzJzehJZs2apWrrdeXyQMqyzKxZs2jWrBm1a9fmzjvv5M8//1Tsw1f6AtCyZUtVfyIjI3nggQcA3/9ttm3bxoMPPkjz5s2JjIzkk08+UWx31++RnZ3N8OHDadCgAQ0aNGD48OFkZ2d7tD8mk4mpU6fSsWNH6tatS9OmTRk2bBjHjx9X7OPOO+9U/WZDhw5V1PGF/oD7zi9P9cddCMH0MGvWrGHChAk899xz/PTTT7Rr1477779fdfF4m59//pnHHnuMDRs28NVXX+Hn58e9997L+fPnFfU6d+7M4cOHbX8rV65UbJ84cSJr167lvffeY926deTm5jJgwAAsFosnuwMU57or39byDyoLFy4kOTmZOXPm8OOPPxITE0Pfvn3Jzc31yb5s2rRJ0ZctW7YgSZIiNZ0v/zb5+fkkJCQwe/ZsgoLUyc3d9XsMGzaMffv2sXLlSlatWsW+ffsYMWKER/tz8eJF9u7dy9ixY9myZQvLly/n5MmT9O/fH7NZmTd00KBBit9s/vz5iu2+0J9S3HF+eao/7kKsw/Qw3bp1o0WLFrz++uu2statW3PPPfe4PYWYO8nLy6NBgwZ88skn9O7dGyh+yjx37hwrVqzQ/ExOTg5NmjQhOTnZZvmcOHGCli1bsmrVKrp16+ax9s+aNYuvvvqKHTt2qLbJskyzZs14/PHHGTt2LAAFBQXEx8czffp0hgwZ4lN90WLevHm8/vrrHDp0iODg4Gr129SrV4+5c+cyaNAgwH2/x+HDh2nfvj3r16+nQ4cOAOzYsYPevXvzyy+/VFn6Kfv+aHHo0CE6dOjAtm3baNGiBVBsYSYkJJCUlKT5GV/qjzvOL2/1pzIIC9ODFBUVsWfPHrp27aoo79q1K7t27fJSq1wjLy8Pq9VKZGSkonzHjh00adKENm3aMGrUKM6ePWvbtmfPHkwmk6K/cXFxNG3a1Cv9TUtLo3nz5rRq1YqhQ4eSlpYGwLFjx8jIyFC0MygoiI4dO9ra6Wt9KY8syyxdupQBAwYQHBxsK69Ov0153PV7pKSkEBoaSvv27W11OnToQEhIiNf7WGop219Pq1evpnHjxnTo0IHJkycrLGpf609lzy9f648riGwlHiQrKwuLxUJMTIyiPCYmhjNnznipVa4xYcIEWrZsSbt27Wxl3bt35+6776Zhw4b8+++/zJgxgz59+rB582YCAwM5c+YMer2emjVrKvbljf62bduWN998k/j4eDIzM0lKSqJHjx7s3LmTjIwMW7vs25meng7gU32xZ9OmTRw7dozBgwfbyqrTb2OPu36PM2fOULNmTSRJsm2XJIno6GPlMOUAAAfDSURBVGiv9rGoqIjJkyfTq1cv6tWrZyu///77qV+/PrVr1+bQoUO8/PLL7N+/ny+++ALwrf644/zypf64ihBML1D+BIFiC8G+zJd44YUX2LlzJ+vXr0ev19vK77vvPtvrFi1akJiYSMuWLdmwYQN9+vRxuD9v9Pf2229XvG/bti2JiYksX76cm266Cbi838UXfruPPvqI1q1b06pVK1tZdfptHOGO30Orvjf7aDabGT58ODk5OXz66aeKbY8++qjtdYsWLWjUqBHdunVjz549JCYmAr7TH3edX77SH1cRQ7IepGbNmuj1etXTU2Zmpupp2leYOHEiq1ev5quvvqJRo0ZO69apU4e6dety9OhRAGrVqoXFYiErK0tRzxf6GxoaSrNmzTh69CixsbEATn8XX+3L2bNnWbduHY888ojTetXpt3HX71GrVi0yMzOR5TI3DVmWycrK8kofzWYzjz32GAcOHODLL7+kRo0aTuvfeOON6PV6xW/mS/0pz+WcX77cH0cIwfQgAQEBJCYmsmnTJkX5pk2bFOP4vsL48eNZtWoVX331lWIJhiOysrJIT0+33fASExPx9/dX9PfkyZO2yX5vYjQaSU1NJTY2loYNGxIbG6top9FoZMeOHbZ2+mpfli9fTmBgIP369XNarzr9Nu76Pdq1a0deXh4pKSm2OikpKeTn53u8jyaTiSFDhnDgwAHWrl1r+x2cceDAASwWi62uL/XHnss5v3y5P47QT5gw4SVvN+JqIiwsjFmzZlG7dm0MBgNJSUls376dRYsWERER4e3m2Rg7diyfffYZH374IXFxceTn55Ofnw8UC39eXh7Tpk0jNDQUs9nMH3/8wdNPP43FYiEpKYnAwEAMBgOnT5/m3Xff5frrrycnJ4cxY8YQHh7Oyy+/jE7nuee1yZMnExAQgNVq5ciRIzz//PMcPXqU+fPnExkZicViYf78+TRp0gSLxcKkSZPIyMhgwYIFPteXUmRZ5sknn6Rnz56K5STV4bfJy8vj0KFDZGRksHTpUhISEggPD6eoqIiIiAi3/B7R0dH8+uuvrFq1ilatWnHy5EnGjBlD69at3b50wVl/QkJCeOSRR9i9ezcff/wxYWFhtutJr9fj7+/PP//8wzvvvENISAhFRUWkpKQwevRo6tWrx+TJk32qP3q93i3nlyf74y7EshIvsGTJEhYuXEhGRgbNmzdn5syZ3Hzzzd5ulgJ7771Sxo8fz8SJEykoKGDQoEHs27ePnJwcYmNjufXWW5k0aRJxcXG2+kajkRdffJFVq1ZhNBrp1KkTr776qqKOJxg6dCjbt28nKyuL6Oho2rZty6RJk2jWrBlQLD6zZ8/mww8/JDs7mzZt2jBv3jwSEhJ8ri+l/PTTT/Tp04cffviBNm3a2Mqrw2+zdetW7r77blX5wIEDWbx4sdt+j/PnzzN+/Hi+/fZbAHr37s3cuXMdnt9V0Z8JEyZwww03aH4uOTmZQYMGceLECYYPH86ff/5Jfn4+9erVo0ePHkyYMIGoqCif6s9rr73mtvPLU/1xF0IwBQKBQCBwATGHKRAIBAKBCwjBFAgEAoHABYRgCgQCgUDgAkIwBQKBQCBwASGYAoFAIBC4gBBMgUAgEAhcQAimQHCVUppUWyAQuIYIvi4QXEG4KoDJyclV3BKB4MpDBC4QCK4g7BP6fvjhh/z6668sWrRIUd6+fXvi4uIwm80YDAZPNlEgqLYIwRQIrmBGjhzJmjVrbDkmBQLB5SPmMAWCqxStOcyWLVty3333sWPHDrp160bt2rXp0KGDLevExo0b6dSpE7GxsXTs2JFdu3ap9nv69GmeeeYZmjVrRq1atWjdujULFy5UpHESCKojQjAFAoGCY8eOMWTIELp27crUqVPJz89n4MCBrFmzhlGjRtGnTx8mT57MmTNnGDx4MIWFhbbPnj17lu7du7NhwwYeeeQR5syZQ9u2bZk6dSoTJ070Yq8EgsojnH4EAoGCI0eO8M0339gy6LRq1Yo777yTESNGsH37duLj4wGIi4tjyJAhrF+/nnvuuQeAGTNmUFhYyLZt26hVqxYAQ4YMoXbt2ixatIiRI0fSsGFD73RMIKgkwsIUCAQKmjRpokg317ZtW6A44W+pWAK2lGJpaWlAcYq0L7/8kp49e6LX68nKyrL9devWDavVyrZt2zzXEYHAzQgLUyAQKLDPhxkYGEhgYCD16tVTlIeHhwOQnZ0NQGZmJtnZ2Sxbtoxly5Zp7jszM7MKWiwQeAYhmAKBQIFer7+k8lJnHqvVCkD//v15+OGHNes2btzYDS0UCLyDEEyBQOAWoqOjCQ8Px2w207lzZ283RyBwO2IOUyAQuAW9Xk+fPn34+uuv2bNnj2p7Tk4OJpPJCy0TCNyDsDAFAoHbeOmll9i2bRu9evVi8ODBJCQkkJuby8GDB1m7di27d+8mNjbW280UCC4LIZgCgcBtREdH88MPP5CUlMQ333zDhx9+SEREBE2aNGHChAlERUV5u4kCwWUjQuMJBAKBQOACYg5TIBAIBAIXEIIpEAgEAoELCMEUCAQCgcAFhGAKBAKBQOACQjAFAoFAIHABIZgCgUAgELiAEEyBQCAQCFxACKZAIBAIBC4gBFMgEAgEAhcQgikQCAQCgQv8P/9JqelQ9M3wAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 1509.7059285022888.\n" - ] - } - ], + "outputs": [], "source": [ "plot_predictions(y_test, test_preds_LSTM)\n", "return_rmse(y_test, test_preds_LSTM)" @@ -399,27 +310,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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vv/1mdM6ePXvC0tKy2u178skncenSJUHZpUuX0L59+2qf41E9KbfC6A42grLFf+RBpeY0LhGRuZl1G0t4eDi2bt2KzZs3IyUlBXPnzkVGRgbCwsIAAIsWLcKwYcMM9UePHg0bGxtMmzYNycnJ2LVrF1asWIFp06YZpmfDwsJw69YtREREICUlBZs3b8bWrVsxffp0w3nUajWSkpKQlJSEkpISKJVKJCUlIS0tzVBn2rRpOH78OD7++GOkpaXhxx9/xPr16/Haa681UO+Ue7+3I2zElVO2WSU6fPxnfoO2gYiIjJn1O9CRI0ciJycHkZGRyMzMhI+PD2JiYuDu7g6g/EIGV65cMdR3cnLCjh07MHv2bAwaNAgymQzh4eGCcPT09ERMTAzmz5+P6OhoKBQKLF++3LCFBQDS09MxYMAAw+MrV65g48aN6N+/P/bu3QugfKvLli1bsHjxYkRGRqJdu3aYP39+gwdoO3sJ3u5uj2WnK0Pzi+QC/LOzHTo6mf0rbCKiFkuUm5vLi602Eg/6wrtIo0PAdiVuFlbe4mxwe2t8+7RLQzavSeLCjLrHPq0f7Ne612wXEVH12UossOgJ4d1a9t0owYG/Sh5wBBER1TcGaBMx8nEbPOkmFZTNT1ChTMcJBCIic2CANhGiu9ta7t0BmqLSIPpCodnaRETUkjFAm5Aej0nxipetoOzDU3nILtE+4AgiIqovDNAm5t+9HOFgWTkOVan1WHqK21qIiBoaA7SJkduKMdvfQVAWnVKIcznVv1A+ERE9OgZoEzTF1x6PO4gNj3V6YH6iqkHuFENEROUYoE2QlViEJX2E18n9Pb0UP13nthYioobCAG2ihrhbY2AbK0HZu8dVKNVyFEpE1BAYoE2USCTChwFOuOcyubiar8XacwXmaxQRUQvCAG3CfJ0tMcnbTlD28Z/5yCzithYiovrGAG3i5vV0gExaOQwt0Oix+GSeGVtERNQyMECbuFbWYszrKbxO7pbUIpzKUpupRURELQMDtBmY1MUOXWTCW5tFJHBbCxFRfWKANgOWFiIsDRBua0lQqvHDlWIztYiIqPljgDYTg9paY3B7a0HZwuN5KNLozNQiIqLmjQHajHzQxwmW9/yN/lWkxWdnuK2FiKg+MECbkY5OEkz1tReUfXYmHzcKNGZqERFR88UAbWZm+zvA1bryr7VEC7x/gttaiIjqGgO0mXGUWuDfvYXbWn64Uoz4jFIztYiIqHligDZDr3SyhV8rS0HZvEQVdNzWQkRUZxigzZDYQoRlgcJtLX9ml2FLapGZWkRE1PwwQJupfgorjHzcRlC2+I885Km5rYWIqC4wQJuxRU84wrryvtu4XaLDJ3/mm69BRETNCAO0GWtvL8Fb3R0EZWuSC3BZxW0tRESPigHazL3dzR5tbSuHoWU6YMFxlRlbRETUPDBAmzk7Swu8/4RwW8vPN0oQ+1eJmVpERNQ8MEBbgNEdbBDoJhWUzUtUQaPjthYiotpigLYAIpHx3Vou5GoQfaHQTC0iImr6GKAtRC9XKcZ3shWUfXgqDzklWjO1iIioaWOAtiDv9XaEvURkeJyr1mPpaW5rISKqDQZoC6KwFWOWv3BbS/SFQpy/U2amFhERNV0M0BZmqq89PB0qt7Vo9cD8RBX0vE4uEVGNmD1Ao6Ki4OfnB7lcjqCgIMTHx1dZ/9y5cxgyZAgUCgV8fHywfPlyo1/+cXFxCAoKglwuh7+/P6KjowXPnz9/HhMmTIC/vz9kMhmWLl1a5Wt+8sknkMlkeOedd2r3JhsRa4kIS/oIFxTF3irFzze4rYWIqCbMGqDbt29HREQEZs2ahUOHDiEgIABjxozBjRs3TNbPy8vDiBEj4ObmhoMHD2LZsmVYuXIlVq1aZahz9epVjB07FgEBATh06BBmzpyJOXPmYOfOnYY6xcXFcHd3x4IFC+Dh4VFlG48fP45Nmzaha9eudfOmG4Gh7tYY0NpKULYgUYVSLUehRETVZdYAXb16NcaPH4+JEyfC29sbkZGRkMvlRiPGCtu2bUNxcTHWrl0LX19fhIaG4u2338aaNWsMo9CNGzdCoVAgMjIS3t7emDhxIl5++WVByPbq1QtLlizBmDFjYGtra/K1AEClUmHy5MlYuXIlZDJZ3b55M6rY1mJRuZ4IaflarEsuMF+jiIiaGLMFqFqtxunTpxEcHCwoDw4ORkJCgsljEhMT0bdvX9jYVN5lJCQkBOnp6bh27Zqhzv3nDAkJwalTp1BWVrPFMjNmzEBoaCiCgoJqdFxT0LWVJcK87QRlkX/mQ1nMbS1ERNUhMdcLZ2dnQ6vVwtXVVVDu6uoKpVJp8hilUok2bdoY1a94ztPTE0qlEgMHDjSqo9FokJ2dDYVCUa32bdq0CWlpaVi3bl0131G51NTUGtWv6+NrYpwTECO2Qb62fCiaX6bHO7E3scBL3WBtaAgN2actBfu0frBf696j9KmXl1eVz5stQCuIRCLBY71eb1T2sPr3l1enTlVSU1OxePFi/Pzzz5BKpQ8/4B4P6/CHve6jHF8b76IAEQmVF5fflSnBvwJao8djNXvfjZU5+rS5Y5/WD/Zr3avvPjXbFK6LiwvEYrHRaDMrK8toVFrBzc3NZH2gciT6oDoSiQStWrWqVtsSExORnZ2Nvn37wsXFBS4uLjhy5AiioqLg4uKC0tLSap2nKXi1ix28nSr/HaVH+XVyua2FiKhqZgtQqVSKHj16IDY2VlAeGxuLwMBAk8cEBATg6NGjKCkpEdRv3bq1YTVtQEAAfvvtN6Nz9uzZE5aWltVq29ChQxEfH4/Dhw8b/vTs2ROjRo3C4cOHazwqbcwsLUT4MFC4reVopho7rhSbqUVERE2DWVfhhoeHY+vWrdi8eTNSUlIwd+5cZGRkICwsDACwaNEiDBs2zFB/9OjRsLGxwbRp05CcnIxdu3ZhxYoVmDZtmmF6NiwsDLdu3UJERARSUlKwefNmbN26FdOnTzecR61WIykpCUlJSSgpKYFSqURSUhLS0tIAADKZDL6+voI/tra2cHZ2hq+vb7WngpuKkLbWeK6dcFvLeyfyUKTRmalFRESNn1m/Ax05ciRycnIQGRmJzMxM+Pj4ICYmBu7u7gCAjIwMXLlyxVDfyckJO3bswOzZszFo0CDIZDKEh4cLwtHT0xMxMTGYP38+oqOjoVAosHz5coSGhhrqpKenY8CAAYbHV65cwcaNG9G/f3/s3bu3Ad554/NBgBMO/KWE5u7M7c1CLVaeLcDcHo5VH0hE1EKJcnNz+WVXI2HuRQQLElVYda5yL6iNWITjI93Qzt7sa81qzdx92hyxT+sH+7XuNdtFRNT4vNPDAY9ZV34kirV6vP9HnhlbRETUeDFAycBJaoF/9xJO2X6fVoxjmc1n1TERUV1hgJLA371s0b2VcLVyRIIKOm5rISISYICSgNhChGX3bWs5nV2Gby4VmalFRESNEwOUjPRXWGG4p42gbNEfechTc1sLEVEFBiiZtOgJR1hV3ncbymIdPk3KN1+DiIgaGQYomeThIMGb3RwEZWvOFeBKnsZMLSIialwYoPRA/+pujza2lR8RtQ5YcFxVxRFERC0HA5QeyM7SAgufEC4o2nu9BL/fKnnAEURELQcDlKo0poMN+rgKt7XMS1BBo+O2FiJq2RigVCULkQjLAmWCsuRcDf6bUmimFhERNQ4MUHqo3q5SjOso3Nby4al83CnlthYiarkYoFQtC59wgp2k8jZuOaU6LDvF6+QSUcvFAKVqaW0rxkw/4baWqAuFuJBbZqYWERGZFwOUqi28qz3c7SuvrqDVA/MTVNDzOrlE1AIxQKnarCUiLOkj3NZy8FYp9t/kthYiankYoFQjL3pY4ymFVFD2bqIKai1HoUTUsjBAqUZEIhGWBspgUbmeCJfztFh3vsB8jSIiMgMGKNVY91aW+GdnO0FZ5Ol83C7WmqlFREQNjwFKtTK/lwMcpZXD0LwyPZac5LYWImo5GKBUK49ZixHRw1FQtvliEf7MVpupRUREDYsBSrU22ccOXk4Sw2M9gAhuayGiFoIBSrVmaSHChwHCbS1HM9XYeZXbWoio+WOA0iN5pp01nm1nJShbcFyFYg1HoUTUvDFA6ZF9EOCEey6Ti5uFWqw8m2++BhERNQAGKD0yLydLvO4r3Nay4kwB/irkthYiar4YoFQn5vg7wsWq8uNUpNFj0QmVGVtERFS/GKBUJ2RWFljQS7itJSatGInKUjO1iIiofjFAqc5M6GyLrs4SQVlEggo6bmshomaIAUp1RmwhwrJAmaDsZFYZvr1UZKYWERHVHwYo1am/tbbCMA9rQdniP/KQX6YzU4uIiOoHA5Tq3OI+TrCqvO82Mop1+E8St7UQUfNS4wDNyMjAyZMnBWUpKSmYMWMG/vnPf2L37t111jhqmjwdJJje1V5QtvpcAa7ma8zUIiKiulfjAI2IiMCCBQsMj3NycjBkyBBs2bIFBw8exMSJE7Fv375qny8qKgp+fn6Qy+UICgpCfHx8lfXPnTuHIUOGQKFQwMfHB8uXLze69mpcXByCgoIgl8vh7++P6OhowfPnz5/HhAkT4O/vD5lMhqVLlxq9zqeffopBgwahffv26NixI1566SUkJydX+321dP/yc4DCpvLjVaoF/n2c21qIqPmocYCeOHECISEhhsffffcdVCoVfv/9d1y+fBmBgYH4/PPPq3Wu7du3IyIiArNmzcKhQ4cQEBCAMWPG4MaNGybr5+XlYcSIEXBzc8PBgwexbNkyrFy5EqtWrTLUuXr1KsaOHYuAgAAcOnQIM2fOxJw5c7Bz505DneLiYri7u2PBggXw8PAw+VpxcXF49dVXsX//fuzatQsSiQTDhw/HnTt3qvXeWjp7SwssfEJ4ndzd10pwKJ3bWoioeahxgGZlZUEulxse79+/H/369YOvry8sLS0xatQoXLhwoVrnWr16NcaPH4+JEyfC29sbkZGRkMvlRiPGCtu2bUNxcTHWrl0LX19fhIaG4u2338aaNWsMo9CNGzdCoVAgMjIS3t7emDhxIl5++WVByPbq1QtLlizBmDFjYGtra/K1tm/fjr///e/w9fVF165dsW7dOmRlZeHYsWPV7aoW76WONuj9mKWgbF5CLjQ6bmshoqavxgEqk8mQmZkJACgqKkJCQgKCg4MNz4tEIpSWPnyUoVarcfr0acGxABAcHIyEhASTxyQmJqJv376wsbExlIWEhCA9PR3Xrl0z1Ln/nCEhITh16hTKysqq9yZNKCgogE6ng0wme3hlAgBYiIy3tZy7o8Hmi9zWQkRNn+ThVYSefPJJfPnll+jcuTMOHDiA0tJSPP/884bnU1NT0bp164eeJzs7G1qtFq6uroJyV1dXKJVKk8colUq0adPGqH7Fc56enlAqlRg4cKBRHY1Gg+zsbCgUiuq8TSMRERHo3r07AgICqqyXmppaq/PX1fGNjQzA865S/Hy78qO26Pgd+OtvwbHGn77aaW592hiwT+sH+7XuPUqfenl5Vfl8jX+FLVy4ECNGjMCECRMAAFOnToW3tzcAQKvVYteuXXjmmWeqfT6RSCR4rNfrjcoeVv/+8urUqYn58+fj2LFj2LdvH8RicZV1H9bhVUlNTX2k4xurT9po8fv2TBTdvcWZSiPC93muWBpY/6P55tqn5sQ+rR/s17pX331a4wB9/PHHceLECVy4cAEODg6CRThFRUWIjIxEt27dHnoeFxcXiMVio9FmVlaW0ai0gpubm8n6QOVI9EF1JBIJWrVq9fA3eJ958+Zh+/bt2L17Nzw9PWt8PAFt7MSY6eeAJSfzDGUbzhcizNsOnWWWVRxJRNR41epCChKJBN26dTNawerg4IChQ4c+cGXrvaRSKXr06IHY2FhBeWxsLAIDA00eExAQgKNHj6KkpERQv3Xr1obXDAgIwG+//WZ0zp49e8LSsma/rOfOnYvvv/8eu3btQufOnWt0LAmFd7VHe/vK0btGD7ybyG0tRNR01ThAjxw5gi+++EJQtm3bNjzxxBPo1KkT5s6dC52uepdtCw8Px9atW7F582akpKRg7ty5yMjIQFhYGABg0aJFGDZsmKH+6NGjYWNjg2nTpiE5ORrlOfUAACAASURBVBm7du3CihUrMG3aNMP0bFhYGG7duoWIiAikpKRg8+bN2Lp1K6ZPn244j1qtRlJSEpKSklBSUgKlUomkpCSkpaUZ6syePRtbt25FVFSUYeFUZmYmCgoKatplBMBGIsKSPsJtLb/8VYr/3Sh5wBFERI1bjadwly9fDhcXF0yZMgUAcPHiRUybNg2PP/44evbsiQ0bNsDDwwPTpk176LlGjhyJnJwcREZGIjMzEz4+PoiJiYG7uzuA8qseXblyxVDfyckJO3bswOzZszFo0CDIZDKEh4cLwtHT0xMxMTGYP38+oqOjoVAosHz5coSGhhrqpKenY8CAAYbHV65cwcaNG9G/f3/s3bsXQPkFHgAIjgPKR6Xz5s2rabcRgGEe1ugnlyI+U20om5+owsA2VpCKa/f9NBGRuYhyc3NrtCmvc+fOeOuttwyhtWTJEqxfvx5nz56Fo6Mjpk6dij///POhVxQiYy1hEUFSthpBu27j3g/dBwFOCL/v0n91pSX0aUNjn9YP9mvdq+8+rfEUbl5enmAv5IEDBzBo0CA4OpbfTLlv3764fv163bWQmhU/FykmdhZevGL56TzcLtaaqUVERLVT4wCVy+VISUkBUD4VmpSUJLhwQV5e3kO3elDL9m4vRzhaVk7Z5qn1+OCeFbpERE1Bjb8DffHFF7FhwwaUlpbi5MmTsLKyElxI4ezZs9zuQVVytRFjTg8HLDheGZqbLhZhUhc7+LlIzdgyIqLqq/EIdN68eRg2bBhiYmKQmZmJVatWwc3NDUD56HP37t0YNGhQnTeUmpfXfezR6Z5LEekBzEtUGd1Zh4iosarxCNTOzg7r1683+Zy9vT2Sk5MfeIF2ogpSsQgfBDjhpV+zDWVHMtTYda0EoZ42VRxJRNQ41OpCCvfKz89Hfn5++cksLODk5FTjCxZQy/RsOys83dZKUPbv4yoUazgKJaLGr1YBev36dbzxxhvo0KEDPDw84OHhgQ4dOmDKlClcgUvVJhKVj0Lv3QJ6vUCL1ed4sQoiavxqPIWbmpqK5557DiqVCgMHDoS3tzf0ej1SU1Oxbds2/PLLL9i/fz86depUH+2lZsZbZonJPnb4IrnQUPafpHyM72SLNnZczU1EjVeNA3TRokXQ6/WIjY2Fn5+f4LkzZ84gNDQUixYtwldffVVnjaTmLaKHI2IuFyOntPwSkIUaPRb9ocK6ATW/+D8RUUOp8RRuXFwc3njjDaPwBIDu3btj8uTJOHz4cJ00jloGmZUF3u3lICj77nIxjivVDziCiMj8ahygarXacNUhU5ycnKBW8xcf1czEznbwdRZOiMxLzIWO21qIqJGqcYD6+vriu+++Q3FxsdFzpaWl+O677+Dr61snjaOWQ2IhwtIA4Q22T9wuQ8xl488ZEVFjUOPvQGfOnIm///3vGDRoEF599VXDhXovXryI6OhopKam4uuvv67zhlLzF9TGCi+4W2PP9cpbnC36Q4UXPKxhb/nIO66IiOpUjQN0yJAhWL9+Pd59913MmTPHcB9OvV4PuVyO9evXCy7tR1QTSwKc8L+bJVDfvaVsepEOK5IKsKD3g782ICIyhxoHKFB+Y+vhw4fj9OnThn2f7u7u6NGjBySSWp2SCADg6SBBeFd7/OdM5V7Qlefy8ffOtvB04GeLiBqPh/5GunHjxgOfk8vlkMvlhsfp6emG/2/fvv0jNo1aqpn+Dth6qQiZxeXD0FItsPCECpsGuZi5ZURElR4aoH5+foZp2prIycmpVYOIHCwtsLC3I6bF5RrKdl4tweH0UvyttVUVRxIRNZyHBuiqVatqFaBEj2JcJ1tEXSjEyawyQ9m8RBV+f9EVYgt+HonI/B4aoK+88kpDtINIwEIkwrJAJzy7N8tQdjanDF+lFuGf3nZmbBkRUTnuDaBGK8DNCmM6CG9t9n9/5CH37iX/iIjMiQFKjdr7TzjBVlI5ZZtdqkPkn/lmbBERUTkGKDVqbe3EmNHdXlC2LrkAqaqyBxxBRNQwGKDU6L3ZzQHt7rm1mUYPvJuoMmOLiIgYoNQE2EhE+L8+wisR/e9mKX65WfKAI4iI6h8DlJqE4Z426CuXCsrmJ6pQpuPdWojIPBig1CSI7m5ruXcHaKpKgw3nC83WJiJq2Rig1GT4u0jxj862grJlp/OQVaI1U4uIqCVjgFKTsqCXIxwsK8eheWo9PjzJbS1E1PAYoNSkuNmIMcffQVD234uFOJvDbS1E1LAYoNTkvOFrj46OldtadHpgXkIu9HouKCKihsMApSZHKhZhSR8nQdnhDDV2X+O2FiJqOAxQapIGt7dGcBvhrc3+fVyFEg1HoUTUMBig1CSJRCJ8GOgE8T37Wq4VaLEmucB8jSKiFsXsARoVFQU/Pz/I5XIEBQUhPj6+yvrnzp3DkCFDoFAo4OPjg+XLlxt99xUXF4egoCDI5XL4+/sjOjpa8Pz58+cxYcIE+Pv7QyaTYenSpXXSNmpYXWSWeLWL8NZmn/yZj/Qibmshovpn1gDdvn07IiIiMGvWLBw6dAgBAQEYM2YMbty4YbJ+Xl4eRowYATc3Nxw8eBDLli3DypUrsWrVKkOdq1evYuzYsQgICMChQ4cwc+ZMzJkzBzt37jTUKS4uhru7OxYsWAAPD486aRuZx7yejnC2qhyGFmr0WPxHnhlbREQthVkDdPXq1Rg/fjwmTpwIb29vREZGQi6XG40YK2zbtg3FxcVYu3YtfH19ERoairfffhtr1qwxjEI3btwIhUKByMhIeHt7Y+LEiXj55ZcFIdurVy8sWbIEY8aMga2trcnXqmnbyDycrSwwv6fwOrnfXCrCH7fVZmoREbUUZgtQtVqN06dPIzg4WFAeHByMhIQEk8ckJiaib9++sLGpvMlySEgI0tPTce3aNUOd+88ZEhKCU6dOoaysensFa9M2Mp8wbzv4yCSCsghuayGieiZ5eJX6kZ2dDa1WC1dXV0G5q6srlEqlyWOUSiXatGljVL/iOU9PTyiVSgwcONCojkajQXZ2NhQKRb20rUJqaupDz1+fx7dU09tZIDzX2vD4+O0yrIy/gufd2Kf1gX1aP9ivde9R+tTLy6vK580WoBVEIpHgsV6vNyp7WP37y6tTpz7aBjy8w6uSmpr6SMe3ZF4A9uZl46frlXtBv7hpg4EuBfDrwj6tS/yc1g/2a92r7z412xSui4sLxGKx0YguKyvLaORXwc3NzWR9oHIk+qA6EokErVq1qre2kfkt6eME6T2f6FtFOmy6aWm+BhFRs2a2AJVKpejRowdiY2MF5bGxsQgMDDR5TEBAAI4ePYqSkhJB/datWxtW0wYEBOC3334zOmfPnj1haVm9X6a1aRuZXwdHCaZ1tReUff2XBNfyNWZqERE1Z2ZdhRseHo6tW7di8+bNSElJwdy5c5GRkYGwsDAAwKJFizBs2DBD/dGjR8PGxgbTpk1DcnIydu3ahRUrVmDatGmGqdWwsDDcunULERERSElJwebNm7F161ZMnz7dcB61Wo2kpCQkJSWhpKQESqUSSUlJSEtLq3bbqHGa5e8AN5vKj3WpToTRv2Rj88VCFGl0ZmwZETU3Zv0OdOTIkcjJyUFkZCQyMzPh4+ODmJgYuLu7AwAyMjJw5coVQ30nJyfs2LEDs2fPxqBBgyCTyRAeHi4IR09PT8TExGD+/PmIjo6GQqHA8uXLERoaaqiTnp6OAQMGGB5fuXIFGzduRP/+/bF3795qtY0aJwdLC7zX2xHT43INZakqDd46kouFJ1SY4GWHV33s4G5v9q//iaiJE+Xm5nKtfyPBRQR1Q6fX47m9t3H8tultSxYi4Pn21njdxx4DWktrvLispePntH6wX+tes11ERFRfLEQibHvmMQz3tIEFjP99qNMDe6+XIHR/Fvr9qMTGC4UoLOP0LhHVDAOUmiWZlQX+O6gVdjxRgre72UMmNT3KPJ+rwb+O5sI3JgPvJqpwlQuOiKiaGKDUrLWx1mNRHyckv6TA5/1l6Ops+rtPlVqP1ecK0PP7TIz7NRuxf5XwSkZEVCUGKLUIthILTOhsh7hQN+x9/jGEeloLboVWQQ9g340SjPhfNgJ3KLHhfAHyOb1LRCYwQKlFEYlE6K+wwqZBLvhztBwz/ezhYmX6x+CiSoN3jqnQ9bsMRCTk4rKK07tEVIkBSi1WO3sJ3uvthHNjFVj9lAz+LqYvtJFXpscXyYXovT0TY/6XhV9ulkDH6V2iFo+b4ajFs5aI8IqXHcZ3skWiUo115wux62oxNCYy8pe/SvHLX6Xo6CjGa13sMd7LFk5S/juUqCXiTz7RXSKRCIFyK0QPbIUzYxV4x98Brtamf0Qu52kxL7F8evedo7m4mFu9W+URUfPBACUyobWtGO/2csTZsQqsG+CMXo+Znt4t0Oix4UIhAnYoMWJ/FvbdKIZWx+ldopaAU7hEVbASi/BSR1u81NEWJ26rsT65ADuuFsPUwtzYW6WIvVUKTwcxXutih7972UH2gAVKRNT08aebqJqecJVifVArnB2jQEQPB8htTP/4XM3XYsHxPPjGZGBmfC7O3+H0LlFzxAAlqiG5rRgRPR1xZowCUUHOCHCVmqxXpNEjOqUQfX9UYti+LOy5xuldouaEU7hEtSQVizC6gy1Gd7DFqSw11iUXYPuVYqhNTO8eSi/FofRStLcXY3IXO/yjsx2cOb1L1KTxJ5ioDvR8TIovBrTCubEKLOjliDa2pn+0bhRo8d6JPPh+l4G3jtzB2RxO7xI1VQxQojrkaiPGbH8H/DlGgY0DndFXbnp6t1irx+aLRXhqpxJDf76NnVeLoeH0LlGTwilconpgaSHCiMdtMeJxW/yZrcaG84XYllaEUq1x3SMZahzJyEE7OzEmdbHDxM62cLEWN3yjiahGOAIlqmf+LlKsesoZyWMVWNjbEe3sTIfjzUItFv9Rvno3PO4O/sxWN3BLiagmGKBEDcTFWox/+Tng9Gg5Ng9qhacUpqd3S7XAltQiBO26jcF7b2N7WhHKOL1L1OhwCpeogUksRBjmaYNhnjY4m1OGDecLEHO5GMVa45A8plTjmFKN1sdVmORth39628HVhtO7RI0BR6BEZtStlSU+6++M5JcU+L8nHOFubzoc04t0+OBUPrrGZOCNQzk4eZvTu0TmxgAlagScrSzwZncHnBolx5bgVghqbWWynloHfHe5GMF7buOZPUpsu1wEtYmRKxHVPwYoUSMithBhqIcNdg5+DMdGuOHVLnawk4hM1j1+uwyTD91B920ZWHoqDxlFJpb4ElG9YYASNVJdZJb4pK8M58Yq8EGAEx53MD29m1msw/LT+ei+LQOTf8/BcaUaet7wm6jeMUCJGjmZlQXCu9rjj1FyfPe0C0Lamp7eLdMB29KK8cze2wjecxvfXCpCKad3ieoNA5SoibAQifBce2v88OxjOD7SDZN97GD/gOndU1llmHr4DrrGZGDJyTzcKuT0LlFdY4ASNUFeTpaIfFKG5JcUWB7ohE6OpnekZZXo8PGf+fDbloGw2BwczSzl9C5RHWGAEjVhjlILvOFrj8SRbvj+GRc828709K5GD+y4Woznf8rCgF238XVqIYo1DFKiR8EAJWoGLEQiPN3OGjHPPIaTo+SY6msHR0vT07tncsowPS4XXWMysOiECjcKNA3cWqLmgQFK1Mx0cJRgaWD59O7HTzrB28n09G5OqQ7/OVMA/+8zMeFgNuIyOL1LVBMMUKJmyt7SAq/52OPYCDf8+JwLnm9vDVNjUp0e2HWtBC/8nIX+O5XYlFKIIo2Ju4ITkQADlKiZE4lEGNjGGt887YJTo+WY3tUeTlLT07vJdzR4Oz4Xvt9l4L3jKlzL5/Qu0YMwQIlaEE8HCZYEOCF5rAIr+sngIzM9vZur1uPzswXo+UMmxh/Ixu+3Sji9S3Qf3o2FqAWys7TAP73Lb959OEON9ckF+OlGCe6/a5pOD/x0vQQ/XS+Bj0yCyT72eMI8TSZqdMw+Ao2KioKfnx/kcjmCgoIQHx9fZf1z585hyJAhUCgU8PHxwfLly43+ZRwXF4egoCDI5XL4+/sjOjra6Dw7d+5EYGAg3NzcEBgYiN27dwue12q1WLJkiaFtfn5+WLJkCTQaTmlR8yESiTCgtRW+DnHB6dFyzOhuD2cr09O753M1mHk0F0MSbfD3A9l4/4QKX6cW4lhmKbJLtByhUotj1hHo9u3bERERgU8++QRPPvkkoqKiMGbMGBw7dgzt27c3qp+Xl4cRI0agX79+OHjwIFJTUxEeHg5bW1u8+eabAICrV69i7NixeOWVV7B+/XocO3YMs2bNgouLC0JDQwEAiYmJmDRpEubNm4cXX3wRu3fvxj//+U/s378fTzxR/u/rFStWICoqCmvXroWvry/OnTuHqVOnQiqVYs6cOQ3XSUQNxN1egvefcMLcHo7YllaE9ecLcTanzKhegVaEPddLjMplUhE6OUnQyVGCTk6W8HKSoKNj+R+bB1wxiagpE+Xm5prtn40hISHo2rUrPv/8c0NZr169EBoaioULFxrV//LLL/H+++/j4sWLsLGxAQBERkYiOjoaycnJEIlEWLhwIXbv3o2TJ08ajnvzzTdx4cIF/PLLLwCAsLAw3LlzBz/++KOhTmhoKB577DF8+eWXAICXXnoJzs7O+OKLLwx1pkyZgjt37uC7776r2464KzU1FV5eXvVy7paKfVp7er0eRzPVWHe+AHuuleBRLqvbzk6MTk4SeDlK0NFJYgjX9nZiiC0YrgA/q/WhvvvUbCNQtVqN06dPG0aOFYKDg5GQkGDymMTERPTt29cQnkB5CH/wwQe4du0aPD09kZiYiODgYMFxISEh+Oabb1BWVgZLS0scP34cr7/+ulGd9evXGx4/+eST+PLLL3Hx4kV07twZFy5cwOHDh/Gvf/3rUd86UZMgEonQT2GFfgor3CzQYGNKIf6bUoTs0ppvcblZqMXNQi1+u1UqKLcSAx0cysPUy+luuDpK0MlJAhdr03efIWoszBag2dnZ0Gq1cHV1FZS7urpCqVSaPEapVKJNmzZG9Sue8/T0hFKpxMCBA43qaDQaZGdnQ6FQIDMz86GvO2PGDBQUFCAwMBBisRgajQazZ8/Ga6+9VuX7Sk1NrfL5h3nU48kY+7RujHMERvUGUgotcK1IhOsld/9bbIHrJSKU6mo+kizVln+3ej7XeG2Bk0QPdxsd3G308Ljnv+2s9Wiu2crPat17lD592OjV7KtwRSLhD51erzcqe1j9+8trW+fesu3bt+Pbb79FVFQUunTpgjNnziAiIgLu7u6YMGHCA9v3KNMFnMKpe+zTumeZmooRvYR9qtPr8VehFpfzNEhVaXBJpcGlvPL/Xi/QojazvyqNCGfyxTiTLywXAWhrJ4aX4fvW8j9NfUqYn9W612yncF1cXCAWi41Gm1lZWUajwwpubm4m6wOVI9EH1ZFIJGjVqhUAQC6XP/R133vvPUyfPh2jRo0CAHTt2hU3btzAf/7znyoDlKglshCJ0N5egvb2EgwUThKhRKPHlfzKQDX8V6Wp1XSwHpVTwrEPmBLudG+4ckqY6onZAlQqlaJHjx6IjY3F8OHDDeWxsbEYNmyYyWMCAgLw/vvvo6SkBNbW1ob6rVu3hoeHh6HO3r17BcfFxsaiZ8+esLS0BAD06dMHsbGxeOuttwR1AgMDDY+LioogFgt/4MRiMXQ6XuKMqCasJSL4OFvCx9nS6Lk7pbp7QrXMEK6X8zQoqcUtTKuaEna2EhlWCN8brh24SphqyaxTuOHh4XjjjTfQu3dvBAYGIjo6GhkZGQgLCwMALFq0CH/88Qd27doFABg9ejSWL1+OadOmYfbs2bh06RJWrFiBOXPmGKZfw8LCsGHDBkRERCAsLAwJCQnYunUroqKiDK87ZcoUDBkyBJ9++ileeOEF7NmzB4cPH8a+ffsMdQYPHowVK1bAw8MDXbp0QVJSElavXo1x48Y1YA8RNW/OVhbo4yZFHzepoLxiSrgiXFPvhuqjTAnfKdXj+O0yHL8t3JojAtDOXiwI1YpVwu2a8JQw1T+zBujIkSORk5ODyMhIZGZmwsfHBzExMXB3dwcAZGRk4MqVK4b6Tk5O2LFjB2bPno1BgwZBJpMhPDwc06dPN9Tx9PRETEwM5s+fj+joaCgUCixfvtywBxSAIayXLFmCpUuX4vHHH0d0dLRhDygAfPTRR/jggw8wa9YsZGVlQS6XY+LEidwDStQA7p0SHtRW+FzFlLAhVOtgSvhGgRY3CkxPCXd0EG69qQjYVpwSbvHMug+UhLiIoO6xT+teY+7TiinhVFWZIVxTVRqk1XJKuCrOViJ4OVoahWttp4Qbc782Vc12ERERUV2rakr4ZqEWl1V3VwnnaQwrhm88wpRw4m01Em+rBeUVU8KGi0Y43rNK2F4Miyp2GVDTwgAlombPQiSCu70E7g+YEk7LF64QrgjXnEecEj5435Sw9b2rhJ0qLyDRyZG/ipsi/q0RUYtmLRHB19kSviZWCeeUaI1C9VJe7aeES7RAcq4GySZWCcskNuh26TZ8nC3hK7OEj7MEXWSWkFmZ/Z4f9AAMUCKiB2hlLUaAtRgBblaC8oop4Uv3XTTiUl7tp4RzNSLEZagRlyGcEm5rK4aPs6R8K5BMAl9nS3SWSWArYbCaGwOUiKiG7p0SDr5vSrj4/lXC94RsbaaE/yrS4q8iLX79q3I6WATgcQcxfO/ur/W9G7AdHSWw5LabBsMAJSKqQzbVnBK+d/vN5XwNSmswJawHkJavRVq+VnBrOUsLwMupfJTqc3ca2NfZEu5cvFQvGKBERA3kQVPCWp0ev5+9jCKntjh/pwzJdzQ4n1uGSyoNNDWYDy7TAcl3NEi+owFQbCi3lYjQRSacBvZxtoTCxqLKa49T1RigRERmJrYQob2NHl4eNnjBo/J2jaVaPS6pysP03mC9ml+zFUxFGj1OZpXhZJbwKkwyqejuFHB5sFYELC8SUT0MUCKiRspKLELXVpbo2ko4HVxYpkNKrgbJuWU4f0eD83fKcD63DOlFNfuONVddftP0o5nChUsKG4u71y+WwEdWHrDeMgnsLblw6V4MUCKiJsbO0gK9XKXo5Sq8YMSdUp0hTM/f0SD57v/fKa3ZuuCMYh0yikuNLm3oYS+uXLQkK58G9nKSwErcMqeBGaBERM2Es5UF+ims0E9R+R2rXq9HZnF5sCbn3h2t3inDhVwNCmvyBSuAawVaXCvQYt+NyjKxCOjkKLlvxCrB4w6SZn8hfgYoEVEzJhKJoLAVQ2ErFlyFSafX43qB9u6IVXP3O9YypKo0UNdgJlirB1JUGqSoNPjxamW5tRjo7FS5ErhiVXA7O3GzWbjEACUiaoEsRCJ4Okjg6SDB8+6V5WU6PdLyNOVTwHcXL52/o0Favga6GgxYS7RAUk4ZknLKcO+KYAdLkSFMfe4Gq6+zBK42TW/hEgOUiIgMLC1E8JZZwltmieGoXBFcrNHjokq4aCn5jgY3C2u2Iji/zPRF+B+ztjCsBK5YFdzF2RJO0sa7cIkBSkRED2UjEcHfRQp/F+HCJZVahxTBoqXygL1dUrMVwVklOhzOUOPwfZcybGcnrtxiczdYvWWWtbplXF1jgBIRUa05SS0Q4GZldHGI28VaQ5je+z1rXlnNFi7dLNTiZqEWv9xzKUMLUfmlDCtWApvrUoYMUCIiqnOuNmK42ogxoLVwRfBfhVrBoqXzuRqk5JbV6O42Oj1wOU+Ly3lVX8rwcY0F6vMW5QxQIiJqECKRCO3sJWhnL8Ez7awN5VqdHlfztYJFS+dzy1cEax/hUoYT2ooxsu7fhgEDlIiIzEpsIUJHJwk6OknwYh1eyrCjXc3vflMTDFAiImqUHnQpw4IyHS5W41KGHW0ZoERERAb21biUYfIdDTxsiuq1HQxQIiJqFu6/lGFq6u16fb3Gu0OViIioEWOAEhER1QIDlIiIqBYYoERERLXAACUiIqoFBigREVEtiHJzc2t2ZV8iIiLiCJSIiKg2GKBERES1wAAlIiKqBQYoERFRLTBAiYiIaoEB2ghERUXBz88PcrkcQUFBiI+PN3eTmrQjR45g3Lhx8PHxgUwmw5YtW8zdpCbv008/xaBBg9C+fXt07NgRL730EpKTk83drCZvw4YN6NevH9q3b4/27dvjmWeewf79+83drGblk08+gUwmwzvvvFPn52aAmtn27dsRERGBWbNm4dChQwgICMCYMWNw48YNczetySosLISvry+WLVsGGxubhx9ADxUXF4dXX30V+/fvx65duyCRSDB8+HDcuXPH3E1r0tq0aYNFixbh999/R2xsLAYMGIBXXnkFZ8+eNXfTmoXjx49j06ZN6Nq1a72cn/tAzSwkJARdu3bF559/bijr1asXQkNDsXDhQjO2rHlo27YtPvroI7zyyivmbkqzUlBQAHd3d2zZsgXPP/+8uZvTrHh6emLhwoUICwszd1OaNJVKhaCgIHz22Wf46KOP4Ovri8jIyDp9DY5AzUitVuP06dMIDg4WlAcHByMhIcFMrSJ6uIKCAuh0OshkMnM3pdnQarX44YcfUFhYiICAAHM3p8mbMWMGQkNDERQUVG+vwRtqm1F2dja0Wi1cXV0F5a6urlAqlWZqFdHDRUREoHv37vxFXwfOnTuHZ599FiUlJbCzs8PXX39db1OOLcWmTZuQlpaGdevW1evrMEAbAZFIJHis1+uNyogai/nz5+PYsWPYt28fxGKxuZvT5Hl5eeHw4cNQqVTYtWsXpk6dij179sDX19fcTWuSUlNTsXjxYvz888+QSqX1+loMUDNycXGBWCw2Gm1mZWUZjUqJGoN58+Zh+/bt2L17Nzw9Pc3dnGZBKpWiQ4cOAICePXvi5MmTWLNmDVatWmXmljVNiYmJyM7ORt++fQ1lWq0WH96MbgAABo5JREFU8fHxiI6Oxq1bt2BlZVUnr8UANSOpVIoePXogNjYWw4cPN5THxsZi2LBhZmwZkbG5c+di+/bt2LNnDzp37mzu5jRbOp0OarXa3M1osoYOHYqePXsKysLDw9GxY0fMnDmzTkelDFAzCw8PxxtvvIHevXsjMDAQ0dHRyMjI4Aq8R1BQUIC0tDQA5b+Mbt68iaSkJDg7O6N9+/Zmbl3TNHv2bHz33Xf4+uuvIZPJkJmZCQCws7ODvb29mVvXdL3//vt49tln0bZtWxQUFOD7779HXFwcYmJizN20JksmkxktbrO1tYWzs3OdT4tzG0sjEBUVhc8++wyZmZnw8fHBhx9+iP79+5u7WU3W4cOH8eKLLxqVv/zyy1i7dq0ZWtT0PWi17dy5czFv3rwGbk3zMXXqVBw+fBhKpRKOjo7o2rUr3nrrLYSEhJi7ac3K0KFD62UbCwOUiIioFrgPlIiIqBYYoERERLXAACUiIqoFBigREVEtMECJiIhqgQFKRERUCwxQImpQ165dg0wmw3/+8x9zN4XokTBAiYiIaoEBSkREVAsMUCIiolpggBI1UxkZGXj77bfRpUsXuLm5oVevXvjss8+g15dfvfPe7yLXrVsHPz8/KBQKPP300zhx4oTR+ZKTkzFu3Di4u7ujdevWeOaZZ/DLL78Y1VOr1YiMjESfPn3g5uYGLy8vvPzyyzh//rxR3W+++cZQr1+/fvjtt9/qvB+I6guvhUvUDN2+fRuDBg2CRqPBxIkToVAocPToUcTExGDKlClYtmwZrl27Bn9/f/j6+kKlUuHVV1+FTqdDVFQUCgoK8Ntvv6FTp04AgEuXLiE4OBhSqRSvvfYa7OzssHXrVqSkpGDTpk2Gi/frdDqMGTMGBw4cwLBhw/DUU0+huLgYhw8fxqhRo/Dyyy8bXrdHjx7Izs5GWFgYrK2tsXbtWty5cwdnzpyBs7OzObuPqFoYoETN0Ntvv42ffvoJR44cgZubm6H8vffew6pVq3Dq1CkAgL+/P6RSKY4fPw4PDw8A5WH55JNPYvjw4YiKigIATJgwAT/99BPi4+MN9wLNy8tDv379AABJSUmwsLDAli1bEB4ejgULFmD27NmCNun1eohEIkOAOjk54Y8//sBjjz1mOMeAAQMQGRmJyZMn128HEdUBTuESNTN6vR47d+7Ec889B7FYjOzsbMOfkJAQ6HQ6HDlyxFD/+eefN4QnAHTq1AkhISGG6VmtVosDBw5g8ODBghtpOzo6YtKkSbh58ybOnTsHANi1axecnJzw5ptvGrVLJBIJHg8fPtwQngDg5+cHR0dHXL16tU76gai+8YbaRM1MVlYWcnNz8fXXX+Prr79+YJ0KHTt2NHq+Y8eO2L9/P1QqFUpKSlBYWCgIzwre3t4AgOvXr6N79/9v7+5dWofiMI5/MWBBi6HQURw0XbQFBzcXQRF1CAgKHXTzH+jULkWzuIhjJgc7qrhpoA6FDjq4iC4dFCxCoThp6SJtKXcoBny5KOHq9fY+ny3kl5PkLE9OTg5JUC6XsSyLUCj04XW+93Nz0zR5eHj48FiRn0ABKtJl2u02AEtLS6ysrLxbMzw87H9M9HpkCPj7PvK67vk17WcYhvGpNkV+KgWoSJeJRqMMDAzQarWYmpr6bd3d3R3QmfN87fb2FtM0MU2TcDhMf38/19fXb+pubm4AGBoaAjrBfH5+TqPRoLe39w/cjcjPpTlQkS5jGAa2bXN8fMzl5eWb/bVajWaz6W/n83k/TKETqIVCgZmZGb+96elpTk5OXoRtvV5nd3eXwcFBxsbGALBtm8fHR1zXfXNejSyl22gEKtKFNjY2ODs7Y25ujtXVVUZHR6nX65RKJY6Ojri4uPBrR0ZGWFhYYG1tjXa7zc7ODqFQiHQ67ddks1mKxSLz8/MvlrFUKhVyuRw9PZ1n8WQyycHBAY7jcHV1xeTkJE9PT5yenrK4uEgymfz2vhD5KgpQkS4UjUYpFApsbW3heR65XA7TNLEsi0wmQyQSoVqtArC8vExfXx+u63J/f088Hmdzc/PFR0OxWIx8Po/jOLiuS6PRIJFIsLe3x+zsrF9nGAb7+/tsb29zeHiI53lEIhEmJiYYHx//9n4Q+UpaByryn3pej7m+vk4qlfrblyPyz9EcqIiISAAKUBERkQAUoCIiIgFoDlRERCQAjUBFREQCUICKiIgEoAAVEREJQAEqIiISgAJUREQkAAWoiIhIAL8A65B/sDiyDrwAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_loss(history_LSTM)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -429,31 +329,15 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 24899.162873493984.\n" - ] - } - ], + "outputs": [], "source": [ "# test RMSE with baseline and LSTM\n", "return_rmse(y_test_year, traditional)\n", "return_rmse(y_test_year, LSTM_test_year)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -478,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/monthly_robust_gru-checkpoint.ipynb b/.ipynb_checkpoints/monthly_robust_gru-checkpoint.ipynb new file mode 100644 index 0000000..27c3bd8 --- /dev/null +++ b/.ipynb_checkpoints/monthly_robust_gru-checkpoint.ipynb @@ -0,0 +1,2831 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Simple GRU with Monthly Dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + "# print(salmon_copy)\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + "# print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", + " king_all_copy, king_data= load_data(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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king
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......
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2020-09-30254930
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + "# print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + "# print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + "# print(king_train_norm)\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print('king_test_norm')\n", + " print(king_test_norm.shape)\n", + " print('king_train_norm')\n", + " print(king_train_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "717915\n", + "294611\n", + "king_test_norm\n", + "(60, 1)\n", + "king_train_norm\n", + "(924, 1)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def create_GRU_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create GRU model trained on X_train and y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " # The GRU architecture\n", + " regressorGRU = Sequential()\n", + " # First GRU layer \n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=1, activation='tanh'))\n", + " #regressorGRU.add(Dense(units=1))\n", + "\n", + " # Compiling the RNN\n", + " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + " # Fitting to the training set\n", + " history = regressorGRU.fit(x_train, y_train, epochs=1000, batch_size=150)\n", + " \n", + " # Predictions \n", + " GRU_train_predict = regressorGRU.predict(x_train)\n", + " GRU_test_predict = regressorGRU.predict(x_test)\n", + "\n", + " # Descale \n", + " GRU_train_predict = scaler.inverse_transform(GRU_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " GRU_test_predict = scaler.inverse_transform(GRU_test_predict)\n", + " GRU_test_predict = GRU_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return regressorGRU, GRU_train_predict, GRU_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "7/7 [==============================] - 13s 47ms/step - loss: 0.0129\n", + "Epoch 2/1000\n", + "7/7 [==============================] - 0s 36ms/step - loss: 0.0099\n", + "Epoch 3/1000\n", + "7/7 [==============================] - 0s 48ms/step - loss: 0.0083\n", + "Epoch 4/1000\n", + "7/7 [==============================] - 0s 37ms/step - loss: 0.0091\n", + "Epoch 5/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 0.0094\n", + "Epoch 6/1000\n", + "7/7 [==============================] - 0s 33ms/step - loss: 0.0103\n", + "Epoch 7/1000\n", + "7/7 [==============================] - 0s 32ms/step - loss: 0.0105\n", + "Epoch 8/1000\n", + "7/7 [==============================] - 0s 37ms/step - loss: 0.0099\n", + "Epoch 9/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 0.0111\n", + "Epoch 10/1000\n", + "7/7 [==============================] - 0s 30ms/step - loss: 0.0115\n", + "Epoch 11/1000\n", + "7/7 [==============================] - 0s 32ms/step - loss: 0.0111\n", + "Epoch 12/1000\n", + "7/7 [==============================] - 0s 30ms/step - loss: 0.0092\n", + "Epoch 13/1000\n", + "7/7 [==============================] - 0s 26ms/step - loss: 0.0083\n", + "Epoch 14/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 0.0076\n", + "Epoch 15/1000\n", + "7/7 [==============================] - 0s 27ms/step - loss: 0.0089\n", + "Epoch 16/1000\n", + "7/7 [==============================] - 0s 30ms/step - loss: 0.0085\n", + "Epoch 17/1000\n", + "7/7 [==============================] - 0s 28ms/step - loss: 0.0109\n", + "Epoch 18/1000\n", + "7/7 [==============================] - 0s 28ms/step - loss: 0.0083\n", + "Epoch 19/1000\n", + "7/7 [==============================] - 0s 29ms/step - loss: 0.0078\n", + "Epoch 20/1000\n", + "7/7 [==============================] - 0s 32ms/step - loss: 0.0096\n", + "Epoch 21/1000\n", + "7/7 [==============================] - 0s 45ms/step - loss: 0.0084\n", + "Epoch 22/1000\n", + "7/7 [==============================] - 0s 30ms/step - loss: 0.0100\n", + "Epoch 23/1000\n", + "7/7 [==============================] - 0s 27ms/step - loss: 0.0087\n", + "Epoch 24/1000\n", + "7/7 [==============================] - 0s 28ms/step - loss: 0.0086\n", + "Epoch 25/1000\n", + "7/7 [==============================] - 0s 26ms/step - loss: 0.0091\n", + "Epoch 26/1000\n", + "7/7 [==============================] - 0s 36ms/step - loss: 0.0105\n", + "Epoch 27/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 0.0095\n", + "Epoch 28/1000\n", + "7/7 [==============================] - 0s 41ms/step - loss: 0.0103\n", + "Epoch 29/1000\n", + "7/7 [==============================] - 0s 33ms/step - loss: 0.0069\n", + "Epoch 30/1000\n", + "7/7 [==============================] - 0s 28ms/step - loss: 0.0093\n", + "Epoch 31/1000\n", + "7/7 [==============================] - 0s 23ms/step - loss: 0.0082\n", + "Epoch 32/1000\n", + "7/7 [==============================] - 0s 19ms/step - loss: 0.0088\n", + "Epoch 33/1000\n", + "7/7 [==============================] - 0s 21ms/step - loss: 0.0102\n", + "Epoch 34/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 0.0082\n", + "Epoch 35/1000\n", + "7/7 [==============================] - 0s 25ms/step - loss: 0.0084\n", + "Epoch 36/1000\n", + "7/7 [==============================] - 0s 20ms/step - loss: 0.0112\n", + "Epoch 37/1000\n", + "7/7 [==============================] - 0s 29ms/step - loss: 0.0085\n", + "Epoch 38/1000\n", + "7/7 [==============================] - 0s 27ms/step - loss: 0.0100\n", + "Epoch 39/1000\n", + "7/7 [==============================] - 0s 19ms/step - loss: 0.0083\n", + "Epoch 40/1000\n", + "7/7 [==============================] - 0s 20ms/step - loss: 0.0090\n", + "Epoch 41/1000\n", + "7/7 [==============================] - ETA: 0s - loss: 0.012 - 0s 19ms/step - loss: 0.0105\n", + "Epoch 42/1000\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0076\n", + "Epoch 43/1000\n", + "7/7 [==============================] - 0s 27ms/step - loss: 0.0082: 0s - loss: 0.008\n", + "Epoch 44/1000\n", + "7/7 [==============================] - 0s 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+ "7/7 [==============================] - 0s 17ms/step - loss: 0.0093\n", + "Epoch 56/1000\n", + "7/7 [==============================] - 0s 19ms/step - loss: 0.0084\n", + "Epoch 57/1000\n", + "7/7 [==============================] - 0s 25ms/step - loss: 0.0096\n", + "Epoch 58/1000\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0097\n", + "Epoch 59/1000\n", + "7/7 [==============================] - 0s 20ms/step - loss: 0.0089\n", + "Epoch 60/1000\n", + "7/7 [==============================] - 0s 22ms/step - loss: 0.0086\n", + "Epoch 61/1000\n", + "7/7 [==============================] - 0s 24ms/step - loss: 0.0082\n", + "Epoch 62/1000\n", + "7/7 [==============================] - 0s 23ms/step - loss: 0.0103\n", + "Epoch 63/1000\n", + "7/7 [==============================] - 0s 29ms/step - loss: 0.0101\n", + "Epoch 64/1000\n", + "7/7 [==============================] - 0s 25ms/step - loss: 0.0083\n", + "Epoch 65/1000\n", + "7/7 [==============================] - 0s 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[==============================] - 0s 22ms/step - loss: 7.6316e-04\n", + "Epoch 988/1000\n", + "7/7 [==============================] - 0s 17ms/step - loss: 8.8623e-04\n", + "Epoch 989/1000\n", + "7/7 [==============================] - 0s 16ms/step - loss: 8.4782e-04\n", + "Epoch 990/1000\n", + "7/7 [==============================] - 0s 17ms/step - loss: 8.7639e-04\n", + "Epoch 991/1000\n", + "7/7 [==============================] - 0s 16ms/step - loss: 7.0559e-04\n", + "Epoch 992/1000\n", + "7/7 [==============================] - 0s 16ms/step - loss: 7.2847e-04\n", + "Epoch 993/1000\n", + "7/7 [==============================] - 0s 21ms/step - loss: 7.2675e-04\n", + "Epoch 994/1000\n", + "7/7 [==============================] - 0s 24ms/step - loss: 7.2092e-04\n", + "Epoch 995/1000\n", + "7/7 [==============================] - 0s 18ms/step - loss: 8.2554e-04\n", + "Epoch 996/1000\n", + "7/7 [==============================] - 0s 29ms/step - loss: 6.7251e-04\n", + "Epoch 997/1000\n", + "7/7 [==============================] - 0s 22ms/step - loss: 9.1755e-04\n", + "Epoch 998/1000\n", + "7/7 [==============================] - 0s 25ms/step - loss: 7.3192e-04\n", + "Epoch 999/1000\n", + "7/7 [==============================] - 0s 34ms/step - loss: 9.0213e-04\n", + "Epoch 1000/1000\n", + "7/7 [==============================] - 0s 27ms/step - loss: 8.3379e-04\n" + ] + } + ], + "source": [ + "regressorGRU, GRU_train_day, GRU_test_day, history_GRU, y_train, y_test = create_GRU_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 16652.956231051456.\n" + ] + } + ], + "source": [ + "# plot training results\n", + "plot_predictions(y_train, GRU_train_day)\n", + "return_rmse(y_train, GRU_train_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 41196.05830614093.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, GRU_test_day)\n", + "return_rmse(y_test, GRU_test_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Count\n", + "0 375497\n", + "1 363115\n", + "2 290129\n", + "3 366017" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GRU_test_year = month_to_year(GRU_test_day)\n", + "GRU_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115854.5707848853.\n", + "The root mean squared error is 112727.77054146862.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "# after 200 epochs\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/monthly_robust_lstm-checkpoint.ipynb b/.ipynb_checkpoints/monthly_robust_lstm-checkpoint.ipynb new file mode 100644 index 0000000..eae5ff3 --- /dev/null +++ b/.ipynb_checkpoints/monthly_robust_lstm-checkpoint.ipynb @@ -0,0 +1,6884 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Simple LSTM with Monthly Dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + "# print(salmon_copy)\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + "# print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", + " king_all_copy, king_data= load_data(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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......
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + "# print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + "# print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + "# print(king_train_norm)\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print('king_test_norm')\n", + " print(king_test_norm.shape)\n", + " print('king_train_norm')\n", + " print(king_train_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "717915\n", + "294611\n", + "king_test_norm\n", + "(60, 1)\n", + "king_train_norm\n", + "(924, 1)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def create_LSTM_model(x_train, y_train, x_test, y_test): \n", + " '''\n", + " Create LSTM model trained on X_train and Y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " LSTM_model = Sequential()\n", + " LSTM_model.add(LSTM(5, input_shape=(x_train.shape[1],1), return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(1))\n", + " LSTM_model.add(Dense(1))\n", + " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=3000, batch_size=300, verbose=2)\n", + " \n", + " train_preds = LSTM_model.predict(x_train)\n", + " test_preds = LSTM_model.predict(x_test)\n", + " train_preds = scaler.inverse_transform(train_preds)\n", + " test_preds = scaler.inverse_transform(test_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return LSTM_model, test_preds, train_preds, y_test, y_train, history_LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/3000\n", + "4/4 - 5s - loss: 0.0113\n", + "Epoch 2/3000\n", + "4/4 - 0s - loss: 0.0105\n", + "Epoch 3/3000\n", + "4/4 - 0s - loss: 0.0098\n", + "Epoch 4/3000\n", + "4/4 - 0s - loss: 0.0094\n", + "Epoch 5/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 6/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 7/3000\n", + "4/4 - 0s - loss: 0.0093\n", + "Epoch 8/3000\n", + "4/4 - 0s - loss: 0.0093\n", + "Epoch 9/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 10/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 11/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 12/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 13/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 14/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 15/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 16/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 17/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 18/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 19/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 20/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 21/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 22/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 23/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 24/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 25/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 26/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 27/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 28/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 29/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 30/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 31/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 32/3000\n", + "4/4 - 0s - loss: 0.0092\n", + "Epoch 33/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 34/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 35/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 36/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 37/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 38/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 39/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 40/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 41/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 42/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 43/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 44/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 45/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 46/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 47/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 48/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 49/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 50/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 51/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 52/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 53/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 54/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 55/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 56/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 57/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 58/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 59/3000\n", + "4/4 - 0s - loss: 0.0091\n", + "Epoch 60/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 61/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 62/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 63/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 64/3000\n", + "4/4 - 0s - loss: 0.0090\n", + "Epoch 65/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 66/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 67/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 68/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 69/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 70/3000\n", + "4/4 - 0s - loss: 0.0089\n", + "Epoch 71/3000\n", + "4/4 - 0s 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+ "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2900/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2901/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2902/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2903/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2904/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2905/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2906/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2907/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2908/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2909/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2910/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2911/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2912/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2913/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2914/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2915/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2916/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2917/3000\n", 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2954/3000\n", + "4/4 - 0s - loss: 0.0039\n", + "Epoch 2955/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2956/3000\n", + "4/4 - 0s - loss: 0.0037\n", + "Epoch 2957/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2958/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2959/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2960/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2961/3000\n", + "4/4 - 0s - loss: 0.0034\n", + "Epoch 2962/3000\n", + "4/4 - 0s - loss: 0.0034\n", + "Epoch 2963/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2964/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2965/3000\n", + "4/4 - 0s - loss: 0.0037\n", + "Epoch 2966/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2967/3000\n", + "4/4 - 0s - loss: 0.0042\n", + "Epoch 2968/3000\n", + "4/4 - 0s - loss: 0.0034\n", + "Epoch 2969/3000\n", + "4/4 - 0s - loss: 0.0044\n", + "Epoch 2970/3000\n", + "4/4 - 0s - loss: 0.0063\n", + "Epoch 2971/3000\n", + "4/4 - 0s - loss: 0.0143\n", + "Epoch 2972/3000\n", + "4/4 - 0s - loss: 0.0077\n", + "Epoch 2973/3000\n", + "4/4 - 0s - loss: 0.0058\n", + "Epoch 2974/3000\n", + "4/4 - 0s - loss: 0.0079\n", + "Epoch 2975/3000\n", + "4/4 - 0s - loss: 0.0082\n", + "Epoch 2976/3000\n", + "4/4 - 0s - loss: 0.0075\n", + "Epoch 2977/3000\n", + "4/4 - 0s - loss: 0.0069\n", + "Epoch 2978/3000\n", + "4/4 - 0s - loss: 0.0061\n", + "Epoch 2979/3000\n", + "4/4 - 0s - loss: 0.0058\n", + "Epoch 2980/3000\n", + "4/4 - 0s - loss: 0.0056\n", + "Epoch 2981/3000\n", + "4/4 - 0s - loss: 0.0054\n", + "Epoch 2982/3000\n", + "4/4 - 0s - loss: 0.0051\n", + "Epoch 2983/3000\n", + "4/4 - 0s - loss: 0.0049\n", + "Epoch 2984/3000\n", + "4/4 - 0s - loss: 0.0047\n", + "Epoch 2985/3000\n", + "4/4 - 0s - loss: 0.0044\n", + "Epoch 2986/3000\n", + "4/4 - 0s - loss: 0.0041\n", + "Epoch 2987/3000\n", + "4/4 - 0s - loss: 0.0039\n", + "Epoch 2988/3000\n", + "4/4 - 0s - loss: 0.0038\n", + "Epoch 2989/3000\n", + "4/4 - 0s - loss: 0.0039\n", + "Epoch 2990/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2991/3000\n", + "4/4 - 0s - loss: 0.0037\n", + "Epoch 2992/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2993/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2994/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2995/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2996/3000\n", + "4/4 - 0s - loss: 0.0036\n", + "Epoch 2997/3000\n", + "4/4 - 0s - loss: 0.0035\n", + "Epoch 2998/3000\n", + "4/4 - 0s - loss: 0.0037\n", + "Epoch 2999/3000\n", + "4/4 - 0s - loss: 0.0038\n", + "Epoch 3000/3000\n", + "4/4 - 0s - loss: 0.0036\n" + ] + } + ], + "source": [ + "# running LSTM\n", + "LSTM_model, test_preds_LSTM, train_preds_LSTM, y_test, y_train, history_LSTM = create_LSTM_model(x_train, y_train, x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 17875.7577855883.\n" + ] + } + ], + "source": [ + "plot_predictions(y_train, train_preds_LSTM)\n", + "return_rmse(y_train, train_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 60732.446796151184.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, test_preds_LSTM)\n", + "return_rmse(y_test, test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + }, + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# global var for baseline\n", + "y_test_year = month_to_year(y_test)\n", + "len(y_test)\n", + "len(y_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n", + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n", + " Count\n", + "0 488943\n", + "1 336031\n", + "2 381766\n", + "3 535809\n" + ] + } + ], + "source": [ + "y_test_year = month_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_chris_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)\n", + "y_test_year = y_test_year.astype(np.int64)\n", + "print(y_test_year)\n", + "# print(GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + } + ], + "source": [ + "# Comparing RMSE to curr Forecasting methods to LSTM\n", + "LSTM_test_year = month_to_year(test_preds_LSTM)\n", + "LSTM_test_year = LSTM_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Simple Single Layer RNN with Monthly dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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984 rows × 1 columns

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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + "\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create single layer rnn model trained on x_train and y_train\n", + " and make predictions on the x_test data\n", + " '''\n", + " # create a model\n", + " model = Sequential()\n", + " model.add(SimpleRNN(32))\n", + " #model.add(SimpleRNN(32, return_sequences=True))\n", + " #model.add(SimpleRNN(32, return_sequences=True))\n", + " #model.add(SimpleRNN(1))\n", + " model.add(Dense(1))\n", + "\n", + " model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + " # fit the RNN model\n", + " history = model.fit(x_train, y_train, epochs=300, batch_size=64)\n", + "\n", + " print(\"predicting\")\n", + " # Finalizing predictions\n", + " RNN_train_preds = model.predict(x_train)\n", + " RNN_test_preds = model.predict(x_test)\n", + " \n", + " #Descale\n", + " RNN_train_preds = scaler.inverse_transform(RNN_train_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " RNN_test_preds = scaler.inverse_transform(RNN_test_preds)\n", + " RNN_test_preds = RNN_test_preds.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + "\n", + " return model, RNN_train_preds, RNN_test_preds, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + "15/15 [==============================] - 1s 2ms/step - loss: 0.0153\n", + "Epoch 2/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0099\n", + "Epoch 3/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0093\n", + "Epoch 4/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "Epoch 5/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0110\n", + "Epoch 6/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0080\n", + "Epoch 7/300\n", + "15/15 [==============================] - 0s 3ms/step - loss: 0.0075\n", + "Epoch 8/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0077\n", + "Epoch 9/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0091\n", + "Epoch 10/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0086\n", + "Epoch 11/300\n", + "15/15 [==============================] - 0s 3ms/step - loss: 0.0080\n", + "Epoch 12/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "Epoch 13/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0056\n", + "Epoch 14/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0096\n", + "Epoch 15/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0068\n", + "Epoch 16/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "Epoch 17/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0081\n", + "Epoch 18/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "Epoch 19/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0082\n", + "Epoch 20/300\n", + "15/15 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loss: 0.0045\n", + "Epoch 94/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0054\n", + "Epoch 95/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 96/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "Epoch 97/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0066\n", + "Epoch 98/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "Epoch 99/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0056\n", + "Epoch 100/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0050\n", + "Epoch 101/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "Epoch 102/300\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15/15 [==============================] - 0s 2ms/step - loss: 0.0050\n", + "Epoch 103/300\n", + "15/15 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2ms/step - loss: 0.0041\n", + "Epoch 289/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 290/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "Epoch 291/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", + "Epoch 292/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0040\n", + "Epoch 293/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", + "Epoch 294/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 295/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0040\n", + "Epoch 296/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0033\n", + "Epoch 297/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "Epoch 298/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "Epoch 299/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0028\n", + "Epoch 300/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", + "predicting\n" + ] + } + ], + "source": [ + "model, RNN_train_preds, RNN_test_preds, history_RNN, y_train, y_test = create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 17280.786003235695.\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "# plot single_layer_rnn_model\n", + "plot_predictions(y_train, RNN_train_preds)\n", + "return_rmse(y_train, RNN_train_preds)\n", + "print(RNN_train_preds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 53707.14979473472.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, RNN_test_preds)\n", + "return_rmse(y_test, RNN_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Count\n", + "0 431033\n", + "1 305159\n", + "2 288853\n", + "3 376991" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RNN_test_year = month_to_year(RNN_test_preds)\n", + "RNN_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115854.5707848853.\n", + "The root mean squared error is 97676.29085018534.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, RNN_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/monthly_simple_gru-checkpoint.ipynb b/.ipynb_checkpoints/monthly_simple_gru-checkpoint.ipynb index 8763107..fc46cf8 100644 --- a/.ipynb_checkpoints/monthly_simple_gru-checkpoint.ipynb +++ b/.ipynb_checkpoints/monthly_simple_gru-checkpoint.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Simple GRU with Monthly Dataset

" + "

Robust GRU with Monthly Dataset

" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -20,10 +20,9 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -34,23 +33,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy = salmon_data\n", " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", " inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", + " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -62,13 +57,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", " date king\n", "0 1939-01-01 0\n", "1 1939-01-02 0\n", @@ -90,13 +99,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -195,7 +204,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 6, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -234,34 +243,26 @@ "\n", "[984 rows x 1 columns]\n" ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "print(data_copy)\n", - "data_copy.shape\n", - "forecast_set = data_copy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def create_MA(data):\n", - " " + "data_copy.shape" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -301,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -405,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -442,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -458,12 +459,12 @@ " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", " regressorGRU.add(GRU(units=1, activation='tanh'))\n", - " #regressorGRU.add(Dense(units=1))\n", + " regressorGRU.add(Dense(units=1))\n", "\n", " # Compiling the RNN\n", " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", " # Fitting to the training set\n", - " history = regressorGRU.fit(x_train, y_train, epochs=50000, batch_size=150)\n", + " history = regressorGRU.fit(x_train, y_train, epochs=400, batch_size=150)\n", " \n", " # Predictions \n", " GRU_train_predict = regressorGRU.predict(x_train)\n", @@ -481,101717 +482,831 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/50000\n", - "7/7 [==============================] - 5s 12ms/step - loss: 0.0100\n", - "Epoch 2/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0095\n", - "Epoch 3/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0094\n", - "Epoch 4/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 5/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0091\n", - "Epoch 6/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 7/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 8/50000\n", + "Epoch 1/400\n", + "7/7 [==============================] - 5s 14ms/step - loss: 0.0130\n", + "Epoch 2/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0076\n", + "Epoch 3/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0079\n", + "Epoch 4/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0086\n", + "Epoch 5/400\n", + "7/7 [==============================] - 0s 20ms/step - loss: 0.0084\n", + "Epoch 6/400\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0098\n", + "Epoch 7/400\n", + "7/7 [==============================] - 0s 15ms/step - loss: 0.0087\n", + "Epoch 8/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0083\n", + "Epoch 9/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0074\n", + "Epoch 10/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0080\n", + "Epoch 11/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 12/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0094\n", + "Epoch 13/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0090\n", + "Epoch 14/400\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0082\n", + "Epoch 15/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0097\n", + "Epoch 16/400\n", + "7/7 [==============================] - 0s 26ms/step - loss: 0.0101\n", + "Epoch 17/400\n", + "7/7 [==============================] - 0s 19ms/step - loss: 0.0082\n", + "Epoch 18/400\n", + "7/7 [==============================] - 0s 17ms/step - loss: 0.0079\n", + "Epoch 19/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0086\n", + "Epoch 20/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0081\n", + "Epoch 21/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0075\n", + "Epoch 22/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0098\n", + "Epoch 23/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0091\n", + "Epoch 24/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", + "Epoch 25/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", + "Epoch 26/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", + "Epoch 27/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0106\n", + "Epoch 28/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 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- loss: 0.0089\n", - "Epoch 60/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 61/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 62/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 63/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 64/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 65/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 66/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 67/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 68/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 69/50000\n", + "Epoch 31/400\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 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[==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 40/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0078\n", + "Epoch 41/400\n", + "7/7 [==============================] - 0s 15ms/step - loss: 0.0073\n", + "Epoch 42/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 43/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0113\n", + "Epoch 44/400\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 74/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 75/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 76/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 77/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 78/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0087\n", - "Epoch 79/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0088\n", - "Epoch 80/50000\n", + "Epoch 45/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0092\n", + "Epoch 46/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0103\n", + "Epoch 47/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0083\n", + "Epoch 48/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0082\n", + "Epoch 49/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0079\n", + "Epoch 50/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0103\n", + "Epoch 51/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0099\n", + "Epoch 52/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0091\n", + "Epoch 53/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0077\n", + "Epoch 54/400\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0090\n", - "Epoch 81/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 82/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 83/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 84/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 85/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 86/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 87/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 88/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 89/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 90/50000\n", - "7/7 [==============================] - 0s 9ms/step 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1955/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4896e-04\n", - "Epoch 1956/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.8779e-04\n", - "Epoch 1957/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.5417e-04\n", - "Epoch 1958/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4379e-04\n", - "Epoch 1959/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.4122e-04\n", - "Epoch 1960/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3914e-04\n", - "Epoch 1961/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 5.2328e-04\n", - "Epoch 1962/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4970e-04\n", - "Epoch 1963/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.6286e-04\n", - "Epoch 1964/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 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1994/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2049e-04\n", - "Epoch 1995/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1917e-04\n", - "Epoch 1996/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4606e-04\n", - "Epoch 1997/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.2904e-04\n", - "Epoch 1998/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1866e-04\n", - "Epoch 1999/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2580e-04\n", - "Epoch 2000/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5327e-04\n", - "Epoch 2001/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4247e-04\n", - "Epoch 2002/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.2318e-04\n", - "Epoch 2003/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 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14ms/step - loss: 3.4706e-05\n", - "Epoch 19614/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7644e-05\n", - "Epoch 19615/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9066e-05\n", - "Epoch 19616/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1441e-04\n", - "Epoch 19617/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.2794e-05\n", - "Epoch 19618/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.4153e-05\n", - "Epoch 19619/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.5082e-05\n", - "Epoch 19620/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3740e-05\n", - "Epoch 19621/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4310e-05\n", - "Epoch 19622/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0896e-05\n", - "Epoch 19623/50000\n", - "7/7 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11ms/step - loss: 1.0101e-04\n", - "Epoch 19643/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.0091e-05\n", - "Epoch 19644/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2268e-04\n", - "Epoch 19645/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0025e-04\n", - "Epoch 19646/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 9.2407e-05\n", - "Epoch 19647/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.2866e-05\n", - "Epoch 19648/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.1500e-05\n", - "Epoch 19649/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.1109e-05\n", - "Epoch 19650/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.7712e-05\n", - "Epoch 19651/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.6781e-05\n", - "Epoch 19652/50000\n", - "7/7 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[==============================] - 0s 13ms/step - loss: 1.3913e-04\n", - "Epoch 19778/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.9770e-05\n", - "Epoch 19779/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.1298e-05\n", - "Epoch 19780/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.1831e-05\n", - "Epoch 19781/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.2651e-05\n", - "Epoch 19782/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4150e-05\n", - "Epoch 19783/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.6905e-05\n", - "Epoch 19784/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.2801e-05\n", - "Epoch 19785/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.4255e-05\n", - "Epoch 19786/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1919e-05\n", - "Epoch 19787/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.7680e-05\n", - "Epoch 19788/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3787e-05\n", - "Epoch 19789/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1979e-05\n", - "Epoch 19790/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.8419e-05\n", - "Epoch 19791/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.8763e-05\n", - "Epoch 19792/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8167e-05\n", - "Epoch 19793/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.6169e-05\n", - "Epoch 19794/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.5272e-05\n", - "Epoch 19795/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1737e-05\n", - "Epoch 19796/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0403e-05\n", - "Epoch 19797/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8953e-05\n", - "Epoch 19798/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7759e-05\n", - "Epoch 19799/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8308e-05\n", - "Epoch 19800/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7253e-05\n", - "Epoch 19801/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5372e-05\n", - "Epoch 19802/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3535e-05\n", - "Epoch 19803/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3665e-05\n", - "Epoch 19804/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4556e-05\n", - "Epoch 19805/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0452e-05\n", - "Epoch 19806/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1132e-05\n", - "Epoch 19807/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1322e-05\n", - "Epoch 19808/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7266e-05\n", - "Epoch 19809/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7346e-05\n", - "Epoch 19810/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7892e-05\n", - "Epoch 19811/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5241e-05\n", - "Epoch 19812/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3357e-05\n", - "Epoch 19813/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5763e-05\n", - "Epoch 19814/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1584e-05\n", - "Epoch 19815/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4088e-05\n", - "Epoch 19816/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1496e-05\n", - "Epoch 19817/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.0615e-05\n", - "Epoch 19818/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8272e-05\n", - "Epoch 19819/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.3576e-05\n", - "Epoch 19820/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6986e-05\n", - "Epoch 19821/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2957e-05\n", - "Epoch 19822/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2766e-05\n", - "Epoch 19823/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.4636e-05\n", - "Epoch 19824/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8781e-05\n", - "Epoch 19825/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2292e-05\n", - "Epoch 19826/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4951e-05\n", - "Epoch 19827/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3696e-05\n", - "Epoch 19828/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.2333e-05\n", - "Epoch 19829/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.1525e-05\n", - "Epoch 19830/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1870e-05\n", - "Epoch 19831/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0212e-05\n", - "Epoch 19832/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9785e-05\n", - "Epoch 19833/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1089e-05\n", - "Epoch 19834/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9214e-05\n", - "Epoch 19835/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.0178e-05\n", - "Epoch 19836/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8142e-05\n", - "Epoch 19837/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7970e-05\n", - "Epoch 19838/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7858e-05\n", - "Epoch 19839/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.5101e-05\n", - "Epoch 19840/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.8075e-05\n", - "Epoch 19841/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4313e-05\n", - "Epoch 19842/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3656e-05\n", - "Epoch 19843/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2711e-05\n", - "Epoch 19844/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0038e-05\n", - "Epoch 19845/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 2.8391e-05\n", - "Epoch 19846/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0548e-05\n", - "Epoch 19847/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7020e-05\n", - "Epoch 19848/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1362e-05\n", - "Epoch 19849/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5708e-05\n", - "Epoch 19850/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1243e-05\n", - "Epoch 19851/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4188e-05\n", - "Epoch 19852/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0943e-05\n", - "Epoch 19853/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8316e-05\n", - "Epoch 19854/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8953e-05\n", - "Epoch 19855/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9953e-05\n", - "Epoch 19856/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0953e-05\n", - "Epoch 19857/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8978e-05\n", - "Epoch 19858/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8310e-05\n", - "Epoch 19859/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0332e-05\n", - "Epoch 19860/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8098e-05\n", - "Epoch 19861/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5938e-05\n", - "Epoch 19862/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6720e-05\n", - "Epoch 19863/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5726e-05\n", - "Epoch 19864/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6639e-05\n", - "Epoch 19865/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8434e-05\n", - "Epoch 19866/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7275e-05\n", - "Epoch 19867/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5937e-05\n", - "Epoch 19868/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6671e-05\n", - "Epoch 19869/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7777e-05\n", - "Epoch 19870/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7721e-05\n", - "Epoch 19871/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0844e-05\n", - "Epoch 19872/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5492e-05\n", - "Epoch 19873/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4778e-05\n", - "Epoch 19874/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5549e-05\n", - "Epoch 19875/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6213e-05\n", - "Epoch 19876/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0020e-05\n", - "Epoch 19877/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3472e-05\n", - "Epoch 19878/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7020e-05\n", - "Epoch 19879/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7247e-05\n", - "Epoch 19880/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.9983e-05\n", - "Epoch 19881/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4895e-05\n", - "Epoch 19882/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6541e-05\n", - "Epoch 19883/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9694e-05\n", - "Epoch 19884/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2751e-05\n", - "Epoch 19885/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5745e-05\n", - "Epoch 19886/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5051e-05\n", - "Epoch 19887/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8104e-05\n", - "Epoch 19888/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7414e-05\n", - "Epoch 19889/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.0936e-05\n", - "Epoch 19890/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.4394e-05\n", - "Epoch 19891/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 8.1643e-05\n", - "Epoch 19892/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4778e-05\n", - "Epoch 19893/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.1243e-05\n", - "Epoch 19894/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3561e-05\n", - "Epoch 19895/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3074e-05\n", - "Epoch 19896/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3150e-05\n", - "Epoch 19897/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5327e-04\n", - "Epoch 19898/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4351e-04\n", - "Epoch 19899/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0699e-04\n", - "Epoch 19900/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.1196e-05\n", - "Epoch 19901/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.3795e-05\n", - "Epoch 19902/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8369e-05\n", - "Epoch 19903/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6336e-05\n", - "Epoch 19904/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1095e-05\n", - "Epoch 19905/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6060e-05\n", - "Epoch 19906/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4496e-05\n", - "Epoch 19907/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4652e-05\n", - "Epoch 19908/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4721e-05\n", - "Epoch 19909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1129e-05\n", - "Epoch 19910/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.0788e-05\n", - "Epoch 19911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9580e-05\n", - "Epoch 19912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8539e-05\n", - "Epoch 19913/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8825e-05\n", - "Epoch 19914/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8610e-05\n", - "Epoch 19915/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1507e-05\n", - "Epoch 19916/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9149e-05\n", - "Epoch 19917/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1521e-05\n", - "Epoch 19918/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.5481e-05\n", - "Epoch 19919/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4004e-05\n", - "Epoch 19920/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8972e-05\n", - "Epoch 19921/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6710e-05\n", - "Epoch 19922/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7500e-05\n", - "Epoch 19923/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.2198e-05\n", - "Epoch 19924/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4236e-05\n", - "Epoch 19925/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1049e-05\n", - "Epoch 19926/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9835e-05\n", - "Epoch 19927/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.5283e-05\n", - "Epoch 19928/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.0171e-05\n", - "Epoch 19929/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.7182e-05\n", - "Epoch 19930/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7837e-05\n", - "Epoch 19931/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5037e-05\n", - "Epoch 19932/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5811e-05\n", - "Epoch 19933/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4857e-05\n", - "Epoch 19934/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4594e-05\n", - "Epoch 19935/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4452e-05\n", - "Epoch 19936/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3507e-05\n", - "Epoch 19937/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2983e-05\n", - "Epoch 19938/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2555e-05\n", - "Epoch 19939/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1412e-05\n", - "Epoch 19940/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2948e-05\n", - 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"stream", - "text": [ - "7/7 [==============================] - 0s 14ms/step - loss: 3.7326e-05\n", - "Epoch 21317/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.2361e-05\n", - "Epoch 21318/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 3.0085e-05\n", - "Epoch 21319/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.9112e-05\n", - "Epoch 21320/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.7642e-05\n", - "Epoch 21321/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5559e-05\n", - "Epoch 21322/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.5933e-05\n", - "Epoch 21323/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.4945e-05\n", - "Epoch 21324/50000\n", - "7/7 [==============================] - 0s 16ms/step - loss: 2.4683e-05\n", - "Epoch 21325/50000\n", - "7/7 [==============================] - 0s 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"Epoch 23249/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0301e-05\n", - "Epoch 23250/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3532e-05\n", - "Epoch 23251/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0271e-05\n", - "Epoch 23252/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2168e-05\n", - "Epoch 23253/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.8924e-05\n", - "Epoch 23254/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9144e-05\n", - "Epoch 23255/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5124e-05\n", - "Epoch 23256/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.4480e-05\n", - "Epoch 23257/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1337e-05\n", - "Epoch 23258/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 4.6010e-05\n", - "Epoch 23433/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.6978e-05\n", - "Epoch 23434/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3599e-05\n", - "Epoch 23435/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1078e-05\n", - "Epoch 23436/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2430e-05\n", - "Epoch 23437/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0820e-05\n", - "Epoch 23438/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0635e-05\n", - "Epoch 23439/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9129e-05\n", - "Epoch 23440/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9187e-05\n", - "Epoch 23441/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1008e-05\n", - "Epoch 23442/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2127e-05\n", - "Epoch 23443/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9776e-05\n", - "Epoch 23444/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8567e-05\n", - "Epoch 23445/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.7090e-05\n", - "Epoch 23446/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.7415e-05\n", - "Epoch 23447/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2396e-05\n", - "Epoch 23448/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0975e-05\n", - "Epoch 23449/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7933e-05\n", - "Epoch 23450/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.7699e-05\n", - "Epoch 23451/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2400e-05\n", - "Epoch 23452/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0709e-05\n", - "Epoch 23453/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7598e-05\n", - "Epoch 23454/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3560e-05\n", - "Epoch 23455/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2733e-05\n", - "Epoch 23456/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2517e-05\n", - "Epoch 23457/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9840e-05\n", - "Epoch 23458/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8508e-05\n", - "Epoch 23459/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.3410e-05\n", - "Epoch 23460/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4771e-05\n", - "Epoch 23461/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2814e-05\n", - "Epoch 23462/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0014e-05\n", - "Epoch 23463/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8732e-05\n", - "Epoch 23464/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.5860e-05\n", - "Epoch 23465/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6192e-05\n", - "Epoch 23466/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.6549e-05\n", - "Epoch 23467/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.7352e-05\n", - "Epoch 23468/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.5906e-05\n", - "Epoch 23469/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6240e-05\n", - "Epoch 23470/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5368e-05\n", - "Epoch 23471/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.2620e-05\n", - "Epoch 23472/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8842e-05\n", - "Epoch 23473/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.7303e-05\n", - "Epoch 23474/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8750e-05\n", - "Epoch 23475/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.3939e-05\n", - "Epoch 23476/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.9546e-05\n", - "Epoch 23477/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.5309e-05\n", - "Epoch 23478/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1923e-05\n", - "Epoch 23479/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8150e-05\n", - "Epoch 23480/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.9208e-05\n", - "Epoch 23481/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4498e-05\n", - "Epoch 23482/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2063e-05\n", - "Epoch 23483/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7811e-05\n", - "Epoch 23484/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7583e-05\n", - "Epoch 23485/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5951e-05\n", - "Epoch 23486/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5151e-05\n", - "Epoch 23487/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6067e-05\n", - "Epoch 23488/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1394e-05\n", - "Epoch 23489/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7233e-05\n", - "Epoch 23490/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4651e-05\n", - "Epoch 23491/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5156e-05\n", - "Epoch 23492/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4007e-05\n", - "Epoch 23493/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4684e-05\n", - "Epoch 23494/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3928e-05\n", - "Epoch 23495/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4022e-05\n", - "Epoch 23496/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.8431e-05\n", - "Epoch 23497/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8279e-05\n", - "Epoch 23498/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6713e-05\n", - "Epoch 23499/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5680e-05\n", - "Epoch 23500/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6259e-05\n", - "Epoch 23501/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7480e-05\n", - "Epoch 23502/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.3655e-05\n", - "Epoch 23503/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1550e-05\n", - "Epoch 23504/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.4370e-05\n", - "Epoch 23505/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9314e-05\n", - "Epoch 23506/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6549e-05\n", - "Epoch 23507/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4755e-05\n", - "Epoch 23508/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3518e-05\n", - "Epoch 23509/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4187e-05\n", - "Epoch 23510/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3798e-05\n", - "Epoch 23511/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5226e-05\n", - "Epoch 23512/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5348e-05\n", - "Epoch 23513/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.9022e-05\n", - "Epoch 23514/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0759e-05\n", - "Epoch 23515/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.0604e-05\n", - "Epoch 23516/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7109e-05\n", - "Epoch 23517/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0146e-05\n", - "Epoch 23518/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7829e-05\n", - "Epoch 23519/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.0784e-05\n", - "Epoch 23520/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5770e-05\n", - "Epoch 23521/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2886e-05\n", - "Epoch 23522/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5721e-05\n", - "Epoch 23523/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.8824e-05\n", - "Epoch 23524/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8668e-05\n", - "Epoch 23525/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4161e-05\n", - "Epoch 23526/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4468e-05\n", - "Epoch 23527/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5839e-05\n", - "Epoch 23528/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4512e-05\n", - "Epoch 23529/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.2637e-05\n", - "Epoch 23530/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2154e-05\n", - "Epoch 23531/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1768e-05\n", - "Epoch 23532/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3506e-05\n", - "Epoch 23533/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0778e-05\n", - "Epoch 23534/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5901e-05\n", - "Epoch 23535/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0218e-05\n", - "Epoch 23536/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0550e-05\n", - "Epoch 23537/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3878e-05\n", - "Epoch 23538/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5844e-05\n", - "Epoch 23539/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4375e-05\n", - "Epoch 23540/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.7345e-05\n", - "Epoch 23541/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9480e-05\n", - "Epoch 23542/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1917e-05\n", - "Epoch 23543/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1697e-05\n", - "Epoch 23544/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7104e-05\n", - "Epoch 23545/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9606e-05\n", - "Epoch 23546/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9767e-05\n", - "Epoch 23547/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6691e-05\n", - "Epoch 23548/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3007e-05\n", - "Epoch 23549/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4046e-05\n", - "Epoch 23550/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5667e-05\n", - "Epoch 23551/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1777e-05\n", - "Epoch 23552/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8845e-05\n", - "Epoch 23553/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1837e-05\n", - "Epoch 23554/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8923e-05\n", - "Epoch 23555/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9526e-05\n", - "Epoch 23556/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8342e-05\n", - "Epoch 23557/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8063e-05\n", - "Epoch 23558/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6172e-05\n", - "Epoch 23559/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2864e-05\n", - "Epoch 23560/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8676e-05\n", - "Epoch 23561/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.3519e-05\n", - "Epoch 23562/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8222e-05\n", - "Epoch 23563/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4155e-05\n", - "Epoch 23564/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9131e-05\n", - "Epoch 23565/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3653e-05\n", - "Epoch 23566/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6954e-05\n", - "Epoch 23567/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8022e-05\n", - "Epoch 23568/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6864e-05\n", - "Epoch 23569/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2261e-05\n", - "Epoch 23570/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.6646e-05\n", - "Epoch 23571/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7534e-05\n", - "Epoch 23572/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8265e-05\n", - "Epoch 23573/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8859e-05\n", - "Epoch 23574/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2807e-05\n", - "Epoch 23575/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.2236e-05\n", - "Epoch 23576/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4960e-05\n", - "Epoch 23577/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0156e-05\n", - "Epoch 23578/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6483e-05\n", - "Epoch 23579/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9604e-05\n", - "Epoch 23580/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5583e-05\n", - "Epoch 23581/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4188e-05\n", - "Epoch 23582/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2398e-05\n", - "Epoch 23583/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8665e-05\n", - "Epoch 23584/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8440e-05\n", - "Epoch 23585/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.6623e-05\n", - "Epoch 23586/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9043e-05\n", - "Epoch 23587/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8429e-05\n", - "Epoch 23588/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6642e-05\n", - "Epoch 23589/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9073e-05\n", - "Epoch 23590/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9482e-05\n", - "Epoch 23591/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4810e-05\n", - "Epoch 23592/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1569e-05\n", - "Epoch 23593/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.9189e-05\n", - "Epoch 23594/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6146e-05\n", - "Epoch 23595/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5590e-05\n", - "Epoch 23596/50000\n", - "7/7 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"Epoch 23606/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5704e-05\n", - "Epoch 23607/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4365e-05\n", - "Epoch 23608/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4373e-05\n", - "Epoch 23609/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4142e-05\n", - "Epoch 23610/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2904e-05\n", - "Epoch 23611/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6730e-05\n", - "Epoch 23612/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5792e-05\n", - "Epoch 23613/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4164e-05\n", - "Epoch 23614/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9055e-05\n", - "Epoch 23615/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6864e-05\n", - "Epoch 23616/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 3.4038e-05\n", - "Epoch 23617/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.1968e-05\n", - "Epoch 23618/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7091e-05\n", - "Epoch 23619/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7266e-05\n", - "Epoch 23620/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3614e-05\n", - "Epoch 23621/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7237e-05\n", - "Epoch 23622/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7758e-05\n", - "Epoch 23623/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5257e-05\n", - "Epoch 23624/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0767e-05\n", - "Epoch 23625/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7683e-05\n", - "Epoch 23626/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0619e-05\n", - "Epoch 23627/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0319e-05\n", - "Epoch 23628/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7991e-05\n", - "Epoch 23629/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8876e-05\n", - "Epoch 23630/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8809e-05\n", - "Epoch 23631/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.2010e-05\n", - "Epoch 23632/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.7894e-05\n", - "Epoch 23633/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3718e-05\n", - "Epoch 23634/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5204e-05\n", - "Epoch 23635/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2014e-05\n", - "Epoch 23636/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2880e-05\n", - "Epoch 23637/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5564e-05\n", - "Epoch 23638/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9026e-05\n", - "Epoch 23639/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5445e-05\n", - "Epoch 23640/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5169e-05\n", - "Epoch 23641/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8014e-05\n", - "Epoch 23642/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5679e-05\n", - "Epoch 23643/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0406e-05\n", - "Epoch 23644/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3643e-05\n", - "Epoch 23645/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8575e-05\n", - "Epoch 23646/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6185e-05\n", - "Epoch 23647/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4632e-05\n", - "Epoch 23648/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4687e-05\n", - "Epoch 23649/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4658e-05\n", - "Epoch 23650/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4483e-05\n", - "Epoch 23651/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4009e-05\n", - "Epoch 23652/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2351e-05\n", - "Epoch 23653/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4488e-05\n", - "Epoch 23654/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4639e-05\n", - "Epoch 23655/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4955e-05\n", - "Epoch 23656/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0767e-05\n", - "Epoch 23657/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8016e-05\n", - "Epoch 23658/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8854e-05\n", - "Epoch 23659/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5960e-05\n", - "Epoch 23660/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4098e-05\n", - "Epoch 23661/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4636e-05\n", - "Epoch 23662/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2445e-05\n", - "Epoch 23663/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3344e-05\n", - "Epoch 23664/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3481e-05\n", - "Epoch 23665/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1670e-05\n", - "Epoch 23666/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4447e-05\n", - "Epoch 23667/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.1847e-05\n", - "Epoch 23668/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.1552e-05\n", - "Epoch 23669/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5797e-05\n", - "Epoch 23670/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1826e-05\n", - "Epoch 23671/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8771e-05\n", - "Epoch 23672/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4872e-05\n", - "Epoch 23673/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.3653e-05\n", - "Epoch 23674/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.5365e-05\n", - "Epoch 23675/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3012e-05\n", - "Epoch 23676/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3617e-05\n", - "Epoch 23677/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4191e-05\n", - "Epoch 23678/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6538e-05\n", - "Epoch 23679/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 4.2320e-05\n", - "Epoch 23680/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.5275e-05\n", - "Epoch 23681/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.9244e-05\n", - "Epoch 23682/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7300e-05\n", - "Epoch 23683/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6932e-05\n", - "Epoch 23684/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5854e-05\n", - "Epoch 23685/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4326e-05\n", - "Epoch 23686/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5780e-05\n", - "Epoch 23687/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1066e-05\n", - "Epoch 23688/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5521e-05\n", - "Epoch 23689/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3718e-05\n", - "Epoch 23690/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1832e-05\n", - "Epoch 23691/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.1163e-05\n", - "Epoch 23692/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8843e-05\n", - "Epoch 23693/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9241e-05\n", - "Epoch 23694/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9073e-05\n", - "Epoch 23695/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8197e-05\n", - "Epoch 23696/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9026e-05\n", - "Epoch 23697/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8002e-05\n", - "Epoch 23698/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8679e-05\n", - "Epoch 23699/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2048e-05\n", - "Epoch 23700/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2018e-05\n", - "Epoch 23701/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6756e-05\n", - "Epoch 23702/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9201e-05\n", - "Epoch 23703/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6470e-05\n", - "Epoch 23704/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5143e-05\n", - "Epoch 23705/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4812e-05\n", - "Epoch 23706/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0299e-05\n", - "Epoch 23707/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9345e-05\n", - "Epoch 23708/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.6805e-05\n", - "Epoch 23709/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7475e-05\n", - "Epoch 23710/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4365e-05\n", - "Epoch 23711/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3320e-05\n", - "Epoch 23712/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2409e-05\n", - "Epoch 23713/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7743e-05\n", - "Epoch 23714/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6637e-05\n", - "Epoch 23715/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2084e-05\n", - "Epoch 23716/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1979e-05\n", - "Epoch 23717/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3245e-05\n", - "Epoch 23718/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2369e-05\n", - "Epoch 23719/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7115e-05\n", - "Epoch 23720/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3830e-05\n", - "Epoch 23721/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.1199e-05\n", - "Epoch 23722/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.0127e-05\n", - "Epoch 23723/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2311e-05\n", - "Epoch 23724/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3387e-05\n", - "Epoch 23725/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1512e-05\n", - "Epoch 23726/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0352e-05\n", - "Epoch 23727/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8119e-05\n", - "Epoch 23728/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2063e-05\n", - "Epoch 23729/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4763e-05\n", - "Epoch 23730/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4621e-05\n", - "Epoch 23731/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7095e-05\n", - "Epoch 23732/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5056e-05\n", - "Epoch 23733/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6370e-05\n", - "Epoch 23734/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3280e-05\n", - "Epoch 23735/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1391e-05\n", - "Epoch 23736/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0699e-05\n", - "Epoch 23737/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0009e-05\n", - "Epoch 23738/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0589e-05\n", - "Epoch 23739/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2473e-05\n", - "Epoch 23740/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2581e-05\n", - "Epoch 23741/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1752e-05\n", - "Epoch 23742/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5551e-05\n", - "Epoch 23743/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3815e-05\n", - "Epoch 23744/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2509e-05\n", - "Epoch 23745/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3618e-05\n", - "Epoch 23746/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2252e-05\n", - "Epoch 23747/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4351e-05\n", - "Epoch 23748/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0722e-05\n", - "Epoch 23749/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7665e-05\n", - "Epoch 23750/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6781e-05\n", - "Epoch 23751/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6616e-05\n", - "Epoch 23752/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9325e-05\n", - "Epoch 23753/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2813e-05\n", - "Epoch 23754/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3721e-05\n", - "Epoch 23755/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5558e-05\n", - "Epoch 23756/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2096e-05\n", - "Epoch 23757/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2963e-05\n", - "Epoch 23758/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8401e-05\n", - "Epoch 23759/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2599e-05\n", - "Epoch 23760/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2255e-05\n", - "Epoch 23761/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0272e-05\n", - "Epoch 23762/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8055e-05\n", - "Epoch 23763/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5438e-05\n", - "Epoch 23764/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4125e-05\n", - "Epoch 23765/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0397e-05\n", - "Epoch 23766/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4767e-05\n", - "Epoch 23767/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.5200e-05\n", - "Epoch 23768/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7804e-05\n", - "Epoch 23769/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3146e-05\n", - "Epoch 23770/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0784e-05\n", - "Epoch 23771/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0197e-05\n", - "Epoch 23772/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6247e-05\n", - "Epoch 23773/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6731e-05\n", - "Epoch 23774/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4810e-05\n", - "Epoch 23775/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.9114e-05\n", - "Epoch 23776/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7849e-05\n", - "Epoch 23777/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6189e-05\n", - "Epoch 23778/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0369e-05\n", - "Epoch 23779/50000\n", - "7/7 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"Epoch 23789/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9379e-05\n", - "Epoch 23790/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1691e-05\n", - "Epoch 23791/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3722e-05\n", - "Epoch 23792/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2429e-05\n", - "Epoch 23793/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1399e-05\n", - "Epoch 23794/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2518e-05\n", - "Epoch 23795/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.4716e-05\n", - "Epoch 23796/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9540e-05\n", - "Epoch 23797/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7829e-05\n", - "Epoch 23798/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0593e-05\n", - "Epoch 23799/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1591e-05\n", - "Epoch 23800/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 9.4877e-05\n", - "Epoch 23801/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2257e-04\n", - "Epoch 23802/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1930e-04\n", - "Epoch 23803/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 9.2008e-05\n", - "Epoch 23804/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.8575e-05\n", - "Epoch 23805/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.4640e-05\n", - "Epoch 23806/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.5856e-05\n", - "Epoch 23807/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4215e-05\n", - "Epoch 23808/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.5360e-05\n", - "Epoch 23809/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0452e-05\n", - "Epoch 23810/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8842e-05\n", - "Epoch 23811/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7855e-05\n", - "Epoch 23812/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.9154e-05\n", - "Epoch 23813/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5625e-05\n", - "Epoch 23814/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4306e-05\n", - "Epoch 23815/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5513e-05\n", - "Epoch 23816/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6111e-05\n", - "Epoch 23817/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4145e-05\n", - "Epoch 23818/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2804e-05\n", - "Epoch 23819/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5313e-05\n", - "Epoch 23820/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1655e-05\n", - "Epoch 23821/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3518e-05\n", - "Epoch 23822/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4233e-05\n", - "Epoch 23823/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3891e-05\n", - "Epoch 23824/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3367e-05\n", - "Epoch 23825/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.9486e-05\n", - "Epoch 23826/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8639e-05\n", - "Epoch 23827/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0562e-05\n", - "Epoch 23828/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7663e-05\n", - "Epoch 23829/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6418e-05\n", - "Epoch 23830/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6493e-05\n", - "Epoch 23831/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6679e-05\n", - "Epoch 23832/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7336e-05\n", - "Epoch 23833/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5839e-05\n", - "Epoch 23834/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5450e-05\n", - "Epoch 23835/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1636e-05\n", - "Epoch 23836/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2367e-05\n", - "Epoch 23837/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2115e-05\n", - "Epoch 23838/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1604e-05\n", - "Epoch 23839/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8024e-05\n", - "Epoch 23840/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7015e-05\n", - "Epoch 23841/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8219e-05\n", - "Epoch 23842/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0857e-05\n", - "Epoch 23843/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0822e-05\n", - "Epoch 23844/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0770e-05\n", - "Epoch 23845/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8265e-05\n", - "Epoch 23846/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7338e-05\n", - "Epoch 23847/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5964e-05\n", - "Epoch 23848/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6698e-05\n", - "Epoch 23849/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9312e-05\n", - "Epoch 23850/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7258e-05\n", - "Epoch 23851/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7102e-05\n", - "Epoch 23852/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9390e-05\n", - "Epoch 23853/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6908e-05\n", - "Epoch 23854/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6553e-05\n", - "Epoch 23855/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6067e-05\n", - "Epoch 23856/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5755e-05\n", - "Epoch 23857/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6269e-05\n", - "Epoch 23858/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6401e-05\n", - "Epoch 23859/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2663e-05\n", - "Epoch 23860/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7594e-05\n", - "Epoch 23861/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5565e-05\n", - "Epoch 23862/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9669e-05\n", - "Epoch 23863/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7041e-05\n", - "Epoch 23864/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0560e-05\n", - "Epoch 23865/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3824e-05\n", - "Epoch 23866/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6015e-05\n", - "Epoch 23867/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9301e-05\n", - "Epoch 23868/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5457e-05\n", - "Epoch 23869/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6337e-05\n", - "Epoch 23870/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3129e-05\n", - "Epoch 23871/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.4596e-05\n", - "Epoch 23872/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7506e-05\n", - "Epoch 23873/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5586e-05\n", - "Epoch 23874/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.4428e-05\n", - "Epoch 23875/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3919e-05\n", - "Epoch 23876/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2784e-05\n", - "Epoch 23877/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1111e-05\n", - "Epoch 23878/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2117e-05\n", - "Epoch 23879/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7067e-05\n", - "Epoch 23880/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.8795e-05\n", - "Epoch 23881/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4407e-05\n", - "Epoch 23882/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8703e-05\n", - "Epoch 23883/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3880e-05\n", - "Epoch 23884/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7448e-05\n", - "Epoch 23885/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3471e-05\n", - "Epoch 23886/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1339e-05\n", - "Epoch 23887/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.6910e-05\n", - "Epoch 23888/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6920e-05\n", - "Epoch 23889/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.9030e-05\n", - "Epoch 23890/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8489e-05\n", - "Epoch 23891/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8872e-05\n", - "Epoch 23892/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2205e-05\n", - "Epoch 23893/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4243e-05\n", - "Epoch 23894/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8801e-05\n", - "Epoch 23895/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8914e-05\n", - "Epoch 23896/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3483e-05\n", - "Epoch 23897/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2336e-05\n", - "Epoch 23898/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0174e-05\n", - "Epoch 23899/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0452e-05\n", - "Epoch 23900/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1412e-05\n", - "Epoch 23901/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9840e-05\n", - "Epoch 23902/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.8522e-05\n", - "Epoch 23903/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0964e-05\n", - "Epoch 23904/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8856e-05\n", - "Epoch 23905/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.9022e-05\n", - "Epoch 23906/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3402e-05\n", - "Epoch 23907/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1087e-05\n", - "Epoch 23908/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0539e-05\n", - "Epoch 23909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2833e-05\n", - "Epoch 23910/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1388e-05\n", - "Epoch 23911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6788e-05\n", - "Epoch 23912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1430e-05\n", - "Epoch 23913/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2329e-05\n", - "Epoch 23914/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2421e-05\n", - "Epoch 23915/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5395e-05\n", - "Epoch 23916/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6937e-05\n", - "Epoch 23917/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1067e-05\n", - "Epoch 23918/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0419e-05\n", - "Epoch 23919/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9055e-05\n", - "Epoch 23920/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.4457e-05\n", - "Epoch 23921/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1589e-05\n", - "Epoch 23922/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9622e-05\n", - "Epoch 23923/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9513e-05\n", - "Epoch 23924/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.9404e-05\n", - "Epoch 23925/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8674e-05\n", - "Epoch 23926/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8007e-05\n", - "Epoch 23927/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8709e-05\n", - "Epoch 23928/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8611e-05\n", - "Epoch 23929/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3758e-05\n", - "Epoch 23930/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1835e-05\n", - "Epoch 23931/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0782e-05\n", - "Epoch 23932/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0375e-05\n", - "Epoch 23933/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1423e-05\n", - "Epoch 23934/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3013e-05\n", - "Epoch 23935/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1903e-05\n", - "Epoch 23936/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5613e-05\n", - "Epoch 23937/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.9040e-05\n", - "Epoch 23938/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6080e-05\n", - "Epoch 23939/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1538e-05\n", - "Epoch 23940/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0219e-05\n", - "Epoch 23941/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9790e-05\n", - "Epoch 23942/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8817e-05\n", - "Epoch 23943/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7981e-05\n", - "Epoch 23944/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8573e-05\n", - "Epoch 23945/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7411e-05\n", - "Epoch 23946/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9376e-05\n", - "Epoch 23947/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9837e-05\n", - "Epoch 23948/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0239e-05\n", - "Epoch 23949/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1395e-05\n", - "Epoch 23950/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8874e-05\n", - "Epoch 23951/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8331e-05\n", - "Epoch 23952/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7207e-05\n", - "Epoch 23953/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6660e-05\n", - "Epoch 23954/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6541e-05\n", - "Epoch 23955/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6089e-05\n", - "Epoch 23956/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6403e-05\n", - "Epoch 23957/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6542e-05\n", - "Epoch 23958/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2801e-05\n", - "Epoch 23959/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8983e-05\n", - "Epoch 23960/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1468e-05\n", - "Epoch 23961/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2582e-05\n", - "Epoch 23962/50000\n", - "7/7 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"Epoch 23972/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4298e-05\n", - "Epoch 23973/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2131e-05\n", - "Epoch 23974/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0561e-05\n", - "Epoch 23975/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8581e-05\n", - "Epoch 23976/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3475e-05\n", - "Epoch 23977/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2918e-05\n", - "Epoch 23978/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9136e-05\n", - "Epoch 23979/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.9528e-05\n", - "Epoch 23980/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8537e-05\n", - "Epoch 23981/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 1.7529e-05\n", - "Epoch 24733/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7449e-05\n", - "Epoch 24734/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6034e-05\n", - "Epoch 24735/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7467e-05\n", - "Epoch 24736/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6576e-05\n", - "Epoch 24737/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.6335e-05\n", - "Epoch 24738/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6790e-05\n", - "Epoch 24739/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8957e-05\n", - "Epoch 24740/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2986e-05\n", - "Epoch 24741/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.0194e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.7356e-05\n", - "Epoch 24762/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1827e-05\n", - "Epoch 24763/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3802e-05\n", - "Epoch 24764/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4008e-05\n", - "Epoch 24765/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8595e-05\n", - "Epoch 24766/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7949e-05\n", - "Epoch 24767/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6831e-05\n", - "Epoch 24768/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7662e-05\n", - "Epoch 24769/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6373e-05\n", - "Epoch 24770/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5196e-05\n", - 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[==============================] - 0s 11ms/step - loss: 1.3809e-05\n", - "Epoch 24791/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.3920e-05\n", - "Epoch 24792/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.8590e-05\n", - "Epoch 24793/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9114e-05\n", - "Epoch 24794/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7890e-05\n", - "Epoch 24795/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8200e-05\n", - "Epoch 24796/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6155e-05\n", - "Epoch 24797/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7630e-05\n", - "Epoch 24798/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.7232e-05\n", - "Epoch 24799/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6947e-05\n", - 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11ms/step - loss: 5.4158e-05\n", - "Epoch 24810/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6040e-05\n", - "Epoch 24811/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3580e-05\n", - "Epoch 24812/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0186e-05\n", - "Epoch 24813/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6558e-04\n", - "Epoch 24814/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0212e-04\n", - "Epoch 24815/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4144e-05\n", - "Epoch 24816/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.4020e-05\n", - "Epoch 24817/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.8310e-05\n", - "Epoch 24818/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0694e-04\n", - "Epoch 24819/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.9574e-05\n", - "Epoch 24820/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.3052e-05\n", - "Epoch 24821/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0523e-05\n", - "Epoch 24822/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5772e-05\n", - "Epoch 24823/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2507e-05\n", - "Epoch 24824/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6146e-05\n", - "Epoch 24825/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8661e-05\n", - "Epoch 24826/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6872e-05\n", - "Epoch 24827/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0943e-05\n", - "Epoch 24828/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7707e-05\n", - 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11ms/step - loss: 4.4730e-05\n", - "Epoch 24839/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1474e-05\n", - "Epoch 24840/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5355e-05\n", - "Epoch 24841/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1567e-05\n", - "Epoch 24842/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1634e-05\n", - "Epoch 24843/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7046e-05\n", - "Epoch 24844/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5663e-05\n", - "Epoch 24845/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7562e-05\n", - "Epoch 24846/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5789e-05\n", - "Epoch 24847/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3216e-05\n", - "Epoch 24848/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2104e-05\n", - "Epoch 24849/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9442e-05\n", - "Epoch 24850/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4825e-05\n", - "Epoch 24851/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.0871e-05\n", - "Epoch 24852/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0099e-05\n", - "Epoch 24853/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7283e-05\n", - "Epoch 24854/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5392e-05\n", - "Epoch 24855/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5345e-05\n", - "Epoch 24856/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2066e-05\n", - "Epoch 24857/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0134e-05\n", - 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11ms/step - loss: 1.5330e-05\n", - "Epoch 24906/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0475e-05\n", - "Epoch 24907/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.8604e-05\n", - "Epoch 24908/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4510e-05\n", - "Epoch 24909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7617e-05\n", - "Epoch 24910/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2462e-05\n", - "Epoch 24911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3793e-05\n", - "Epoch 24912/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7600e-05\n", - "Epoch 24913/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8930e-05\n", - "Epoch 24914/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1486e-05\n", - "Epoch 24915/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5146e-05\n", - "Epoch 24916/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0859e-05\n", - "Epoch 24917/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1786e-05\n", - "Epoch 24918/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3403e-05\n", - "Epoch 24919/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1328e-05\n", - "Epoch 24920/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7939e-05\n", - "Epoch 24921/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6464e-05\n", - "Epoch 24922/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7295e-05\n", - "Epoch 24923/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9852e-05\n", - "Epoch 24924/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9623e-05\n", - 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[==============================] - 0s 11ms/step - loss: 3.0700e-05\n", - "Epoch 24974/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7797e-05\n", - "Epoch 24975/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.2459e-05\n", - "Epoch 24976/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1090e-05\n", - "Epoch 24977/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0703e-05\n", - "Epoch 24978/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8181e-05\n", - "Epoch 24979/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6166e-05\n", - "Epoch 24980/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4454e-05\n", - "Epoch 24981/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5163e-05\n", - "Epoch 24982/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4280e-05\n", - 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[==============================] - 0s 11ms/step - loss: 3.1634e-05\n", - "Epoch 25822/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7317e-05\n", - "Epoch 25823/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6834e-05\n", - "Epoch 25824/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 10ms/step - loss: 4.7669e-05\n", - "Epoch 25825/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.1422e-05\n", - "Epoch 25826/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2976e-05\n", - "Epoch 25827/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.5574e-05\n", - "Epoch 25828/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0543e-05\n", - "Epoch 25829/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5195e-05\n", - "Epoch 25830/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8536e-05\n", - "Epoch 25831/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2453e-05\n", - "Epoch 25832/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1413e-05\n", - "Epoch 25833/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8930e-05\n", - "Epoch 25834/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8359e-05\n", - "Epoch 25835/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5731e-05\n", - "Epoch 25836/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6551e-05\n", - "Epoch 25837/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.5717e-05\n", - "Epoch 25838/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4901e-05\n", - "Epoch 25839/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6032e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.6276e-05\n", - "Epoch 25860/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3991e-05\n", - "Epoch 25861/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5982e-05\n", - "Epoch 25862/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.6268e-05\n", - "Epoch 25863/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2306e-05\n", - "Epoch 25864/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3560e-05\n", - "Epoch 25865/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8045e-05\n", - "Epoch 25866/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5671e-05\n", - "Epoch 25867/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3549e-05\n", - "Epoch 25868/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2647e-05\n", - 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[==============================] - 0s 12ms/step - loss: 2.1931e-05\n", - "Epoch 25889/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3844e-05\n", - "Epoch 25890/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1107e-05\n", - "Epoch 25891/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9684e-05\n", - "Epoch 25892/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1053e-05\n", - "Epoch 25893/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4040e-05\n", - "Epoch 25894/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2784e-05\n", - "Epoch 25895/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0750e-05\n", - "Epoch 25896/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2402e-05\n", - "Epoch 25897/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.3458e-05\n", - 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[==============================] - 0s 12ms/step - loss: 5.6134e-05\n", - "Epoch 25918/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.5403e-05\n", - "Epoch 25919/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6765e-05\n", - "Epoch 25920/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7890e-05\n", - "Epoch 25921/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0598e-05\n", - "Epoch 25922/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1767e-05\n", - "Epoch 25923/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7404e-05\n", - "Epoch 25924/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2232e-05\n", - "Epoch 25925/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8199e-05\n", - "Epoch 25926/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9346e-05\n", - 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12ms/step - loss: 2.1423e-05\n", - "Epoch 25937/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.1904e-05\n", - "Epoch 25938/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0661e-05\n", - "Epoch 25939/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9313e-05\n", - "Epoch 25940/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7857e-05\n", - "Epoch 25941/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3721e-05\n", - "Epoch 25942/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2280e-05\n", - "Epoch 25943/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5495e-05\n", - "Epoch 25944/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5555e-05\n", - "Epoch 25945/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0446e-05\n", - "Epoch 25946/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3348e-05\n", - "Epoch 25947/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0818e-05\n", - "Epoch 25948/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1979e-05\n", - "Epoch 25949/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0544e-05\n", - "Epoch 25950/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0660e-05\n", - "Epoch 25951/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.6894e-05\n", - "Epoch 25952/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5000e-05\n", - "Epoch 25953/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9693e-05\n", - "Epoch 25954/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0361e-05\n", - "Epoch 25955/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6885e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.5184e-05\n", - "Epoch 26929/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4222e-05\n", - "Epoch 26930/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3310e-05\n", - "Epoch 26931/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3239e-05\n", - "Epoch 26932/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2884e-05\n", - "Epoch 26933/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2390e-05\n", - "Epoch 26934/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3655e-05\n", - "Epoch 26935/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2763e-05\n", - "Epoch 26936/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4072e-05\n", - "Epoch 26937/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3450e-05\n", - "Epoch 26938/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4546e-05\n", - "Epoch 26939/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3715e-05\n", - "Epoch 26940/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4661e-05\n", - "Epoch 26941/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7418e-05\n", - "Epoch 26942/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7488e-05\n", - "Epoch 26943/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3713e-05\n", - "Epoch 26944/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5251e-05\n", - "Epoch 26945/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4530e-05\n", - "Epoch 26946/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7748e-05\n", - "Epoch 26947/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 2.5146e-05\n", - "Epoch 26958/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4216e-05\n", - "Epoch 26959/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6558e-05\n", - "Epoch 26960/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.7741e-05\n", - "Epoch 26961/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8454e-05\n", - "Epoch 26962/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7167e-05\n", - "Epoch 26963/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6216e-05\n", - "Epoch 26964/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.8900e-05\n", - "Epoch 26965/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1199e-04\n", - "Epoch 26966/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7643e-04\n", - "Epoch 26967/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4175e-04\n", - "Epoch 26968/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.8195e-05\n", - "Epoch 26969/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.9880e-05\n", - "Epoch 26970/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.4876e-05\n", - "Epoch 26971/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4510e-05\n", - "Epoch 26972/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4896e-05\n", - "Epoch 26973/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2310e-05\n", - "Epoch 26974/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4270e-05\n", - "Epoch 26975/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2708e-05\n", - "Epoch 26976/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1721e-05\n", - "Epoch 26977/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2142e-05\n", - "Epoch 26978/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7907e-05\n", - "Epoch 26979/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8556e-05\n", - "Epoch 26980/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7438e-05\n", - "Epoch 26981/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7620e-05\n", - "Epoch 26982/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5760e-05\n", - "Epoch 26983/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6755e-05\n", - "Epoch 26984/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5238e-05\n", - "Epoch 26985/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2834e-05\n", - "Epoch 26986/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2678e-05\n", - "Epoch 26987/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2136e-05\n", - "Epoch 26988/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2402e-05\n", - "Epoch 26989/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0936e-05\n", - "Epoch 26990/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1420e-05\n", - "Epoch 26991/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2483e-05\n", - "Epoch 26992/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1144e-05\n", - "Epoch 26993/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1456e-05\n", - "Epoch 26994/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0421e-05\n", - "Epoch 26995/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1660e-05\n", - 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[==============================] - 0s 12ms/step - loss: 2.0525e-05\n", - "Epoch 27016/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0203e-05\n", - "Epoch 27017/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0583e-05\n", - "Epoch 27018/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2282e-05\n", - "Epoch 27019/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2098e-05\n", - "Epoch 27020/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0471e-05\n", - "Epoch 27021/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1555e-05\n", - "Epoch 27022/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8258e-05\n", - "Epoch 27023/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9451e-05\n", - "Epoch 27024/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0709e-05\n", - "Epoch 27025/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8411e-05\n", - "Epoch 27026/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7998e-05\n", - "Epoch 27027/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8093e-05\n", - "Epoch 27028/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7804e-05\n", - "Epoch 27029/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8348e-05\n", - "Epoch 27030/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7999e-05\n", - "Epoch 27031/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8792e-05\n", - "Epoch 27032/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0925e-05\n", - "Epoch 27033/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4775e-05\n", - "Epoch 27034/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3719e-05\n", - "Epoch 27035/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5676e-05\n", - "Epoch 27036/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.2482e-04\n", - "Epoch 27037/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.2644e-04\n", - "Epoch 27038/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.2225e-05\n", - "Epoch 27039/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.1328e-05\n", - "Epoch 27040/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.1129e-05\n", - "Epoch 27041/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2062e-05\n", - "Epoch 27042/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.6720e-05\n", - "Epoch 27043/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1325e-05\n", - "Epoch 27044/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6846e-05\n", - "Epoch 27045/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2841e-05\n", - "Epoch 27046/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2187e-05\n", - "Epoch 27047/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6471e-05\n", - "Epoch 27048/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9936e-05\n", - "Epoch 27049/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1104e-05\n", - "Epoch 27050/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2663e-05\n", - "Epoch 27051/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3426e-05\n", - "Epoch 27052/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.8513e-05\n", - "Epoch 27053/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4129e-05\n", - "Epoch 27054/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.5891e-05\n", - "Epoch 27055/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4580e-05\n", - "Epoch 27056/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5934e-05\n", - "Epoch 27057/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6374e-05\n", - "Epoch 27058/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5131e-05\n", - "Epoch 27059/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4830e-05\n", - "Epoch 27060/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7851e-05\n", - "Epoch 27061/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5814e-05\n", - "Epoch 27062/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8023e-05\n", - "Epoch 27063/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2154e-05\n", - "Epoch 27064/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3298e-05\n", - "Epoch 27065/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0553e-05\n", - "Epoch 27066/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1026e-05\n", - "Epoch 27067/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9357e-05\n", - "Epoch 27068/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8536e-05\n", - "Epoch 27069/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8562e-05\n", - "Epoch 27070/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9393e-05\n", - "Epoch 27071/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0357e-05\n", - "Epoch 27072/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.1576e-05\n", - "Epoch 27073/50000\n", - "7/7 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"stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 4.5241e-05\n", - "Epoch 28769/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3386e-05\n", - "Epoch 28770/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.9402e-05\n", - "Epoch 28771/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1438e-05\n", - "Epoch 28772/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4715e-05\n", - "Epoch 28773/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1774e-05\n", - "Epoch 28774/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4141e-05\n", - "Epoch 28775/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7035e-05\n", - "Epoch 28776/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5148e-05\n", - "Epoch 28777/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 7.3849e-05\n", - "Epoch 30425/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.4921e-05\n", - "Epoch 30426/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4640e-05\n", - "Epoch 30427/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1544e-05\n", - "Epoch 30428/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9716e-05\n", - "Epoch 30429/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3887e-04\n", - "Epoch 30430/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9610e-05\n", - "Epoch 30431/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9877e-05\n", - "Epoch 30432/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.5305e-05\n", - "Epoch 30433/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 5.4866e-05\n", - "Epoch 32909/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.3510e-05\n", - "Epoch 32910/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1850e-05\n", - "Epoch 32911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7481e-05\n", - "Epoch 32912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.8220e-05\n", - "Epoch 32913/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4716e-04\n", - "Epoch 32914/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4908e-04\n", - "Epoch 32915/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4671e-04\n", - "Epoch 32916/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1917e-04\n", - "Epoch 32917/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 2.0073e-05\n", - "Epoch 37233/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1679e-05\n", - "Epoch 37234/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2135e-05\n", - "Epoch 37235/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7731e-05\n", - "Epoch 37236/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.7776e-05\n", - "Epoch 37237/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5839e-05\n", - "Epoch 37238/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.8770e-05\n", - "Epoch 37239/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8998e-05\n", - "Epoch 37240/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6969e-05\n", - "Epoch 37241/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 1.2379e-06\n", - "Epoch 37734/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0061e-06\n", - "Epoch 37735/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0950e-06\n", - "Epoch 37736/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1839e-06\n", - "Epoch 37737/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6224e-06\n", - "Epoch 37738/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3901e-06\n", - "Epoch 37739/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1574e-06\n", - "Epoch 37740/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5711e-06\n", - "Epoch 37741/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4687e-06\n", - "Epoch 37742/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.5395e-06\n", - 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[==============================] - 0s 11ms/step - loss: 4.7016e-06\n", - "Epoch 37763/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0520e-06\n", - "Epoch 37764/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6428e-06\n", - "Epoch 37765/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7166e-06\n", - "Epoch 37766/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3465e-06\n", - "Epoch 37767/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7502e-06\n", - "Epoch 37768/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4421e-06\n", - "Epoch 37769/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5173e-06\n", - "Epoch 37770/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4224e-06\n", - "Epoch 37771/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7219e-06\n", - 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[==============================] - 0s 12ms/step - loss: 6.6544e-06\n", - "Epoch 38437/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.1696e-06\n", - "Epoch 38438/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9033e-06\n", - "Epoch 38439/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4072e-06\n", - "Epoch 38440/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6528e-05\n", - "Epoch 38441/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.7790e-06\n", - "Epoch 38442/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.6262e-06\n", - "Epoch 38443/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.5324e-06\n", - "Epoch 38444/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.2331e-06\n", - "Epoch 38445/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.6563e-06\n", - 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[==============================] - 0s 11ms/step - loss: 7.6524e-06\n", - "Epoch 38466/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0809e-06\n", - "Epoch 38467/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5629e-06\n", - "Epoch 38468/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.0790e-06\n", - "Epoch 38469/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.3602e-06\n", - "Epoch 38470/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6444e-06\n", - "Epoch 38471/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.9360e-06\n", - "Epoch 38472/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6600e-06\n", - "Epoch 38473/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.7517e-06\n", - "Epoch 38474/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9473e-06\n", - 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[==============================] - 0s 11ms/step - loss: 3.0485e-06\n", - "Epoch 38495/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4108e-06\n", - "Epoch 38496/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7857e-06\n", - "Epoch 38497/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0058e-06\n", - "Epoch 38498/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9807e-06\n", - "Epoch 38499/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9307e-06\n", - "Epoch 38500/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4639e-06\n", - "Epoch 38501/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1070e-06\n", - "Epoch 38502/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1334e-06\n", - "Epoch 38503/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.3350e-06\n", - 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[==============================] - 0s 12ms/step - loss: 3.0714e-05\n", - "Epoch 38649/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8884e-05\n", - "Epoch 38650/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4364e-05\n", - "Epoch 38651/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7996e-05\n", - "Epoch 38652/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2901e-05\n", - "Epoch 38653/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2162e-05\n", - "Epoch 38654/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.8286e-05\n", - "Epoch 38655/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8848e-05\n", - "Epoch 38656/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6511e-05\n", - "Epoch 38657/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.6643e-05\n", - 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[==============================] - 0s 13ms/step - loss: 1.9631e-05\n", - "Epoch 39381/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6552e-05\n", - "Epoch 39382/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4381e-05\n", - "Epoch 39383/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2029e-05\n", - "Epoch 39384/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2922e-05\n", - "Epoch 39385/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3103e-05\n", - "Epoch 39386/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1796e-05\n", - "Epoch 39387/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.0172e-05\n", - "Epoch 39388/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1772e-05\n", - "Epoch 39389/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1186e-05\n", - 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[==============================] - 0s 13ms/step - loss: 1.4994e-05\n", - "Epoch 39410/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8309e-05\n", - "Epoch 39411/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4411e-05\n", - "Epoch 39412/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9916e-05\n", - "Epoch 39413/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7863e-05\n", - "Epoch 39414/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5408e-05\n", - "Epoch 39415/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5524e-05\n", - "Epoch 39416/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4642e-05\n", - "Epoch 39417/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2116e-05\n", - "Epoch 39418/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0530e-05\n", - 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[==============================] - 0s 12ms/step - loss: 1.2031e-05\n", - "Epoch 39439/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.5087e-06\n", - "Epoch 39440/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 9.3842e-06\n", - "Epoch 39441/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7309e-06\n", - "Epoch 39442/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.6794e-06\n", - "Epoch 39443/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0357e-05\n", - "Epoch 39444/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8119e-05\n", - "Epoch 39445/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1915e-05\n", - "Epoch 39446/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0549e-05\n", - "Epoch 39447/50000\n", - "7/7 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"name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 1.7322e-06\n", - "Epoch 42844/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7806e-06\n", - "Epoch 42845/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.0464e-06\n", - "Epoch 42846/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2788e-06\n", - "Epoch 42847/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9658e-06\n", - "Epoch 42848/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1947e-06\n", - "Epoch 42849/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5012e-06\n", - "Epoch 42850/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3344e-06\n", - "Epoch 42851/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.0406e-06\n", - "Epoch 42852/50000\n", - "7/7 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[==============================] - 0s 12ms/step - loss: 7.7092e-06\n", - "Epoch 43228/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4165e-05\n", - "Epoch 43229/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0005e-05\n", - "Epoch 43230/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5544e-05\n", - "Epoch 43231/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4702e-05\n", - "Epoch 43232/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1398e-05\n", - "Epoch 43233/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2091e-05\n", - "Epoch 43234/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6230e-05\n", - "Epoch 43235/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6845e-06\n", - "Epoch 43236/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2940e-05\n", - 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 7.6205e-07\n", - "Epoch 45327/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4616e-07\n", - "Epoch 45328/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.8955e-07\n", - "Epoch 45329/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.1970e-07\n", - "Epoch 45330/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.8041e-07\n", - "Epoch 45331/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9274e-07\n", - "Epoch 45332/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5518e-07\n", - "Epoch 45333/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.6990e-06\n", - "Epoch 45334/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.4269e-06\n", - "Epoch 45335/50000\n", - "7/7 [==============================] - 0s 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"Epoch 47994/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 1.2532e-05\n", - "Epoch 47995/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0569e-05\n", - "Epoch 47996/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.8825e-06\n", - "Epoch 47997/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0719e-05\n", - "Epoch 47998/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0923e-05\n", - "Epoch 47999/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0117e-05\n", - "Epoch 48000/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.0401e-06\n", - "Epoch 48001/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.9834e-06\n", - "Epoch 48002/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 8.8612e-06\n", - 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"Epoch 49467/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.6866e-06\n", - "Epoch 49468/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 5.4784e-06\n", - "Epoch 49469/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3431e-06\n", - "Epoch 49470/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0404e-06\n", - "Epoch 49471/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.9062e-06\n", - "Epoch 49472/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1703e-06\n", - "Epoch 49473/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.4531e-06\n", - "Epoch 49474/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.0131e-06\n", - "Epoch 49475/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.6738e-06\n", - "Epoch 49476/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 10ms/step - loss: 1.4975e-05\n", - "Epoch 49651/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1889e-05\n", - "Epoch 49652/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.4433e-06\n", - "Epoch 49653/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0820e-05\n", - "Epoch 49654/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1861e-05\n", - "Epoch 49655/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1899e-04\n", - "Epoch 49656/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9514e-04\n", - "Epoch 49657/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.2698e-04\n", - "Epoch 49658/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.2481e-05\n", - "Epoch 49659/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 12ms/step - loss: 7.1033e-06\n", - "Epoch 49940/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.3151e-06\n", - "Epoch 49941/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.2184e-06\n", - "Epoch 49942/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.4264e-06\n", - "Epoch 49943/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.6738e-06\n", - "Epoch 49944/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.1131e-06\n", - "Epoch 49945/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.0153e-06\n", - "Epoch 49946/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.1032e-06\n", - "Epoch 49947/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.2864e-06\n", - "Epoch 49948/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3000e-06\n", - "Epoch 49949/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.6645e-06\n", - "Epoch 49950/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6711e-06\n", - "Epoch 49951/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.6946e-06\n", - "Epoch 49952/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.4531e-06\n", - "Epoch 49953/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5593e-06\n", - "Epoch 49954/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.7885e-06\n", - "Epoch 49955/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.4208e-06\n", - "Epoch 49956/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.9488e-06\n", - "Epoch 49957/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6610e-06\n", - "Epoch 49958/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0361e-06\n", - "Epoch 49959/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6001e-06\n", - "Epoch 49960/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.6603e-06\n", - "Epoch 49961/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.5209e-06\n", - "Epoch 49962/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.4988e-06\n", - "Epoch 49963/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.3635e-06\n", - "Epoch 49964/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2364e-06\n", - "Epoch 49965/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.9834e-06\n", - "Epoch 49966/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.1732e-06\n", - "Epoch 49967/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4447e-06\n", - "Epoch 49968/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7295e-06\n", - "Epoch 49969/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3647e-06\n", - "Epoch 49970/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7957e-06\n", - "Epoch 49971/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0295e-06\n", - "Epoch 49972/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3650e-06\n", - "Epoch 49973/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2844e-06\n", - "Epoch 49974/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1500e-06\n", - "Epoch 49975/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2084e-06\n", - "Epoch 49976/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1635e-06\n", - "Epoch 49977/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8953e-06\n", - "Epoch 49978/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5371e-06\n", - "Epoch 49979/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.1587e-06\n", - "Epoch 49980/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.7720e-06\n", - "Epoch 49981/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.7413e-06\n", - "Epoch 49982/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1490e-06\n", - "Epoch 49983/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9010e-06\n", - "Epoch 49984/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.5590e-06\n", - "Epoch 49985/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5812e-06\n", - "Epoch 49986/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7308e-06\n", - "Epoch 49987/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1034e-06\n", - "Epoch 49988/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.0472e-06\n", - "Epoch 49989/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9684e-06\n", - "Epoch 49990/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.8573e-06\n", - "Epoch 49991/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3569e-06\n", - "Epoch 49992/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6127e-06\n", - "Epoch 49993/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.2226e-06\n", - "Epoch 49994/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5725e-06\n", - "Epoch 49995/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1977e-06\n", - "Epoch 49996/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7932e-06\n", - "Epoch 49997/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3120e-06\n", - "Epoch 49998/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7087e-06\n", - "Epoch 49999/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.8593e-05\n", - "Epoch 50000/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4367e-05\n" + "Epoch 394/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0036\n", + "Epoch 395/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0027\n", + "Epoch 396/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0024\n", + "Epoch 397/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0029\n", + "Epoch 398/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0030\n", + "Epoch 399/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0031\n", + "Epoch 400/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0027\n" ] } ], @@ -102201,12 +1316,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -102220,7 +1335,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1440.9631330881728.\n" + "The root mean squared error is 15649.68598451201.\n" ] } ], @@ -102232,12 +1347,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 114, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -102251,7 +1366,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 48966.743530875305.\n" + "The root mean squared error is 42762.87195427568.\n" ] } ], @@ -102262,12 +1377,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -102284,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -102300,7 +1415,7 @@ "4" ] }, - "execution_count": 16, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -102314,7 +1429,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -102340,7 +1455,7 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)\n", "y_test_year = y_test_year.astype(np.int64)\n", @@ -102350,7 +1465,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -102387,19 +1502,19 @@ " \n", " \n", " 0\n", - " 237144\n", + " 185775\n", " \n", " \n", " 1\n", - " 223614\n", + " 166868\n", " \n", " \n", " 2\n", - " 187817\n", + " 188893\n", " \n", " \n", " 3\n", - " 265150\n", + " 264217\n", " \n", " \n", "\n", @@ -102407,13 +1522,13 @@ ], "text/plain": [ " Count\n", - "0 237144\n", - "1 223614\n", - "2 187817\n", - "3 265150" + "0 185775\n", + "1 166868\n", + "2 188893\n", + "3 264217" ] }, - "execution_count": 18, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -102425,7 +1540,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -102433,7 +1548,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 216167.08073386198.\n" + "The root mean squared error is 240566.6048031189.\n" ] } ], @@ -102475,7 +1590,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/monthly_simple_lstm-checkpoint.ipynb b/.ipynb_checkpoints/monthly_simple_lstm-checkpoint.ipynb index eae5ff3..4e678f8 100644 --- a/.ipynb_checkpoints/monthly_simple_lstm-checkpoint.ipynb +++ b/.ipynb_checkpoints/monthly_simple_lstm-checkpoint.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -20,10 +20,9 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from keras.optimizers import SGD\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -34,23 +33,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -62,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -87,16 +80,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -195,7 +188,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 4, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -241,7 +234,7 @@ "(984, 1)" ] }, - "execution_count": 5, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -293,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -311,24 +304,13 @@ " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - "# print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - "# print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", - "# print(king_train_norm)\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", - " print('king_test_norm')\n", - " print(king_test_norm.shape)\n", - " print('king_train_norm')\n", - " print(king_train_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + "\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -336,8 +318,6 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", " for i in range(6,924): # 30\n", " x_train.append(king_train_norm[i-6:i])\n", " y_train.append(king_train_norm[i])\n", @@ -356,24 +336,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 130, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(60, 2)\n", - "717915\n", - "294611\n", - "king_test_norm\n", - "(60, 1)\n", - "king_train_norm\n", - "(924, 1)\n", - "(54, 1)\n", - "(54, 1)\n", - "(918, 1)\n", - "(918, 1)\n" + "(60, 2)\n" ] } ], @@ -386,18 +356,14 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ @@ -434,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ @@ -444,13 +410,13 @@ " and make predictions on the X_test data\n", " '''\n", " LSTM_model = Sequential()\n", - " LSTM_model.add(LSTM(5, input_shape=(x_train.shape[1],1), return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", " LSTM_model.add(LSTM(5, return_sequences=True))\n", " LSTM_model.add(LSTM(5, return_sequences=True))\n", " LSTM_model.add(LSTM(1))\n", - " LSTM_model.add(Dense(1))\n", + " #LSTM_model.add(Dense(1))\n", " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", - " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=3000, batch_size=300, verbose=2)\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=2000, batch_size=150, verbose=2)\n", " \n", " train_preds = LSTM_model.predict(x_train)\n", " test_preds = LSTM_model.predict(x_test)\n", @@ -464,6097 +430,396 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/3000\n", - "4/4 - 5s - loss: 0.0113\n", - "Epoch 2/3000\n", - "4/4 - 0s - loss: 0.0105\n", - "Epoch 3/3000\n", - "4/4 - 0s - loss: 0.0098\n", - "Epoch 4/3000\n", - "4/4 - 0s - loss: 0.0094\n", - "Epoch 5/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 6/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 7/3000\n", - "4/4 - 0s - loss: 0.0093\n", - "Epoch 8/3000\n", - "4/4 - 0s - loss: 0.0093\n", - "Epoch 9/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 10/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 11/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 12/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 13/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 14/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 15/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 16/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 17/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 18/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 19/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 20/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 21/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 22/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 23/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 24/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 25/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 26/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 27/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 28/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 29/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 30/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 31/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 32/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 33/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 34/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 35/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 36/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 37/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 38/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 39/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 40/3000\n", - "4/4 - 0s - loss: 0.0091\n", - 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60/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 61/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 62/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 63/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 64/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 65/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 66/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 67/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 68/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 69/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 70/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 71/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 72/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 73/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 74/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 75/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 76/3000\n", - "4/4 - 0s - loss: 0.0088\n", - "Epoch 77/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 78/3000\n", - "4/4 - 0s - loss: 0.0089\n", - "Epoch 79/3000\n", 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2999/3000\n", - "4/4 - 0s - loss: 0.0038\n", - "Epoch 3000/3000\n", - "4/4 - 0s - loss: 0.0036\n" + "Epoch 1/2000\n", + "7/7 - 5s - loss: 0.0112\n", + "Epoch 2/2000\n", + "7/7 - 0s - loss: 0.0100\n", + "Epoch 3/2000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 4/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 5/2000\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 6/2000\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 7/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 8/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 9/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 10/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 11/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 12/2000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 13/2000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 14/2000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 15/2000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 16/2000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 17/2000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 18/2000\n", + "7/7 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"Epoch 57/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 58/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 59/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 60/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 61/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 62/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 63/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 64/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 65/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 66/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 67/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 68/2000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 69/2000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 70/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 71/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 72/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 73/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 74/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 75/2000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 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114/2000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 115/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 116/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 117/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 118/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 119/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 120/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 121/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 122/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 123/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 124/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 125/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 126/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 127/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 128/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 129/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 130/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 131/2000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 132/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 133/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 134/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 135/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 136/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 137/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 138/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 139/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 140/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 141/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 142/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 143/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 144/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 145/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 146/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 147/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 148/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 149/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 150/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 151/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 152/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 153/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 154/2000\n", + "7/7 - 0s - loss: 0.0085\n", + "Epoch 155/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 156/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 157/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 158/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 159/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 160/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 161/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 162/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 163/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 164/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 165/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 166/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 167/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 168/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 169/2000\n", + "7/7 - 0s - loss: 0.0084\n", + "Epoch 170/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 171/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 172/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 173/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 174/2000\n", + "7/7 - 0s - loss: 0.0082\n", + "Epoch 175/2000\n", + "7/7 - 0s - loss: 0.0082\n", + "Epoch 176/2000\n", + "7/7 - 0s - loss: 0.0082\n", + "Epoch 177/2000\n", + "7/7 - 0s - loss: 0.0081\n", + "Epoch 178/2000\n", + "7/7 - 0s - loss: 0.0083\n", + "Epoch 179/2000\n", + "7/7 - 0s - loss: 0.0081\n", + "Epoch 180/2000\n", + "7/7 - 0s - loss: 0.0081\n", + "Epoch 181/2000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 182/2000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 183/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 184/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 185/2000\n", + "7/7 - 0s - loss: 0.0081\n", + "Epoch 186/2000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 187/2000\n", + "7/7 - 0s - loss: 0.0082\n", + "Epoch 188/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 189/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 190/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 191/2000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 192/2000\n" ] } ], @@ -6565,29 +830,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 17875.7577855883.\n" - ] - } - ], + "outputs": [], "source": [ "plot_predictions(y_train, train_preds_LSTM)\n", "return_rmse(y_train, train_preds_LSTM)" @@ -6595,29 +840,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 60732.446796151184.\n" - ] - } - ], + "outputs": [], "source": [ "plot_predictions(y_test, test_preds_LSTM)\n", "return_rmse(y_test, test_preds_LSTM)" @@ -6625,105 +850,43 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_loss(history_LSTM)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "49\n" - ] - }, - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# global var for baseline\n", - "y_test_year = month_to_year(y_test)\n", - "len(y_test)\n", - "len(y_test_year)" + "y_test_year = month_to_year(y_test)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "49\n", - " Count\n", - "0 498710\n", - "1 439060\n", - "2 294840\n", - "3 347600\n", - " Count\n", - "0 488943\n", - "1 336031\n", - "2 381766\n", - "3 535809\n" - ] - } - ], + "outputs": [], "source": [ "y_test_year = month_to_year(y_test)\n", "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_chris_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "49\n" - ] - } - ], + "outputs": [], "source": [ "# Comparing RMSE to curr Forecasting methods to LSTM\n", "LSTM_test_year = month_to_year(test_preds_LSTM)\n", @@ -6732,126 +895,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Count\n", - "0 315542\n", - "1 151571\n", - "2 143755\n", - "3 137088" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "LSTM_test_year" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 264443.31640400743.\n" - ] - } - ], + "outputs": [], "source": [ "# test RMSE with baseline and LSTM\n", "return_rmse(y_test_year, traditional)\n", "return_rmse(y_test_year, LSTM_test_year)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -6876,7 +937,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/multivar_gru-checkpoint.ipynb b/.ipynb_checkpoints/multivar_robust_gru-checkpoint.ipynb similarity index 100% rename from .ipynb_checkpoints/multivar_gru-checkpoint.ipynb rename to .ipynb_checkpoints/multivar_robust_gru-checkpoint.ipynb diff --git a/.ipynb_checkpoints/multivar_lstm-checkpoint.ipynb b/.ipynb_checkpoints/multivar_robust_lstm-checkpoint.ipynb similarity index 100% rename from .ipynb_checkpoints/multivar_lstm-checkpoint.ipynb rename to .ipynb_checkpoints/multivar_robust_lstm-checkpoint.ipynb diff --git a/multivar_gru.ipynb b/.ipynb_checkpoints/multivar_robust_rnn-checkpoint.ipynb similarity index 59% rename from multivar_gru.ipynb rename to .ipynb_checkpoints/multivar_robust_rnn-checkpoint.ipynb index ea84e1f..68cec4f 100644 --- a/multivar_gru.ipynb +++ b/.ipynb_checkpoints/multivar_robust_rnn-checkpoint.ipynb @@ -13,10 +13,10 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "import tensorflow.keras\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from keras.optimizers import SGD\n", + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -93,14 +93,14 @@ "source": [ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", " king_all_copy, king_data= load_data(chris_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "(984, 1)" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -391,7 +391,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -508,7 +508,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -635,7 +635,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -699,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -716,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 21, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -901,14 +901,13 @@ "source": [ "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", - "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", "cov_data = load_cov_set(chris_path_cov)\n", "cov_data" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1026,7 +1025,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1169,7 +1168,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1182,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1324,7 +1323,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1337,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1491,7 +1490,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 25, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1504,7 +1503,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1670,7 +1669,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1850,7 +1849,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1868,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1880,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1889,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1905,7 +1904,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1915,7 +1914,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1924,7 +1923,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1943,7 +1942,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1959,7 +1958,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1974,7 +1973,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1983,7 +1982,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1993,7 +1992,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2007,7 +2006,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2171,7 +2170,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 39, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -2184,7 +2183,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2200,15 +2199,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", - "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=abdul_checkpoint_path,\n", + " filepath=checkpoint_filepath,\n", " save_weights_only=True,\n", " monitor='val_accuracy',\n", " mode='max',\n", @@ -2252,7 +2250,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2293,7 +2291,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2379,38 +2377,46 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], "source": [ "# split into train and test sets\n", "values = reframed.values\n", - "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MONTH TO YEAR CHECK THIS\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", "train = values[:n_train_months, :]\n", "test = values[n_train_months:, :]\n", "# split into input and outputs\n", "n_obs = n_months * n_features\n", - "train_x, train_y = train[:, :n_obs], train[:, -n_features]\n", - "test_x, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", "# print(train_X.shape, len(train_X), train_y.shape)\n", "# reshape input to be 3D [samples, timesteps, features]\n", - "train_x = train_x.reshape((train_X.shape[0], n_months, n_features))\n", - "test_x = test_x.reshape((test_X.shape[0], n_months, n_features))\n", - "# print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "x_train, x_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2427,9 +2433,9 @@ } ], "source": [ - "print(x_dev.shape)\n", + "print(X_dev.shape)\n", "print(y_dev.shape)\n", - "print(x_train.shape)\n", + "print(X_train.shape)\n", "print(y_train.shape)\n", "print(test_X.shape)\n", "print(test_y.shape)" @@ -2437,7 +2443,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -2445,121 +2451,121 @@ "output_type": "stream", "text": [ "Epoch 1/1000\n", - "1/1 - 7s - loss: 0.0143 - root_mean_squared_error: 0.1196 - val_loss: 0.0488 - val_root_mean_squared_error: 0.2209\n", + "8/8 - 4s - loss: 0.0173 - root_mean_squared_error: 0.1316 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 2/1000\n", - "1/1 - 0s - loss: 0.0115 - root_mean_squared_error: 0.1071 - val_loss: 0.0447 - val_root_mean_squared_error: 0.2114\n", + "8/8 - 0s - loss: 0.0137 - root_mean_squared_error: 0.1172 - val_loss: 0.0450 - val_root_mean_squared_error: 0.2121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 3/1000\n", - "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0994 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2046\n", + "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 4/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 5/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "8/8 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 6/1000\n", - "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0994 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 7/1000\n", - "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1006 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 8/1000\n", - "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1005 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 9/1000\n", - "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0995 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 10/1000\n", - "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0983 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 11/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 12/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2031\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 13/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2046\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 14/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2058\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 15/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0428 - val_root_mean_squared_error: 0.2068\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 16/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0431 - val_root_mean_squared_error: 0.2075\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 17/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0432 - val_root_mean_squared_error: 0.2079\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 18/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0970 - val_loss: 0.0433 - val_root_mean_squared_error: 0.2080\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 19/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0970 - val_loss: 0.0432 - val_root_mean_squared_error: 0.2077\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 20/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0430 - val_root_mean_squared_error: 0.2073\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 21/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0967 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2067\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 22/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2059\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 23/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0962 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2052\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 24/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 25/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 26/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 27/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 28/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 29/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 30/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 31/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 32/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 33/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 34/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0797 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1718\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 35/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1711\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 36/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 37/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1698\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 38/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 39/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2568,121 +2574,121 @@ "output_type": "stream", "text": [ "Epoch 40/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 41/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 42/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 43/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0762 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1657\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 44/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2043\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 45/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 46/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1635\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 47/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2036\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 48/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 49/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0737 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1613\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 50/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 51/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2026\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1598\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 52/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 53/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 54/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 55/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1565\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 56/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2026\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1556\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 57/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 58/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1540\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 59/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 60/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1524\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 61/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1515\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 62/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1507\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 63/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1499\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 64/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 65/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1483\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 66/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1475\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 67/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 68/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1459\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 69/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2021\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 70/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1443\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 71/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 72/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 73/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 74/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 75/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1403\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 76/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1395\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 77/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 78/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2691,121 +2697,121 @@ "output_type": "stream", "text": [ "Epoch 79/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 80/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1370\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 81/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0930 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 82/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 83/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 84/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 85/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1338\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 86/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 87/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 88/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0400 - val_root_mean_squared_error: 0.1999\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 89/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1306\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 90/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1299\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 91/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1989\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 92/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 93/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 94/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 95/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 96/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1975\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 97/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1971\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 98/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 99/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 100/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1290\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 101/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 102/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 103/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 104/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1267\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 105/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 106/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 107/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1431\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 108/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 109/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 110/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1912\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 111/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1325\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 112/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 113/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 114/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 115/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 116/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 117/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n" + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n" ] }, { @@ -2814,121 +2820,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 118/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1199\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 119/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 120/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1191\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 121/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 122/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1186\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 123/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 124/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1161\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 125/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1150\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 126/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 127/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 128/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1132\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 129/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1885\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 130/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 131/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 132/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 133/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 134/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 135/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 136/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 137/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 138/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 139/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 140/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 141/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 142/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 143/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1077\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 144/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1082\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 145/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 146/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 147/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 148/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 149/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 150/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1053\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 151/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 152/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0543 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1097\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 153/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1051\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 154/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 155/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1089\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 156/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n" + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n" ] }, { @@ -2937,121 +2943,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 157/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1058\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 158/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 159/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 160/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 161/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1007\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 162/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 163/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 164/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 165/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 166/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1826\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 167/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 168/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0971\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 169/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 170/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 171/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1818\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 172/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1815\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0953\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 173/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 174/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 175/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0935\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 176/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 177/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0948\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 178/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0828 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 179/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 180/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 181/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1796\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 182/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1793\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 183/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 184/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 185/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 186/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 187/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 188/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1776\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0911\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 189/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 190/1000\n", - "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 191/1000\n", - "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0430 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 192/1000\n", - "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 193/1000\n", - "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 194/1000\n", - "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 195/1000\n", - "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0797 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n" + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0923\n" ] }, { @@ -3060,121 +3066,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 196/1000\n", - "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1749\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 197/1000\n", - "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1745\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 198/1000\n", - "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1742\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 199/1000\n", - "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1739\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 200/1000\n", - "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1736\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 201/1000\n", - "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 202/1000\n", - "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0776 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1729\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 203/1000\n", - "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0772 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 204/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1722\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 205/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1718\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 206/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1748\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0816\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 207/1000\n", - "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1716\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0825\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 208/1000\n", - "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1709\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 209/1000\n", - "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1743\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 210/1000\n", - "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1707\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 211/1000\n", - "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1706\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 212/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1694\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 213/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 214/1000\n", - "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1689\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 215/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1685\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0807\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 216/1000\n", - "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0758 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 217/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 218/1000\n", - "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 219/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 220/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0750 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1663\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0817\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 221/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1676\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 222/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0746 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1665\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 223/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1666\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 224/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1655\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 225/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1662\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 226/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1655\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 227/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1652\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 228/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1647\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 229/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1649\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 230/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1645\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 231/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1641\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 232/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1640\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 233/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1640\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 234/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1638\n" + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n" ] }, { @@ -3183,121 +3189,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 235/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1632\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 236/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1633\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 237/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1632\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 238/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1629\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 239/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0722 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1624\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0464 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 240/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0721 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1623\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 241/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1623\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 242/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0719 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1619\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 243/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0718 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1616\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 244/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0717 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1615\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 245/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1615\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 246/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1611\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 247/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1608\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 248/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1608\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 249/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1607\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 250/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1602\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 251/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1600\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 252/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1600\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 253/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1598\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 254/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1594\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 255/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1593\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 256/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1592\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 257/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1589\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 258/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1585\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 259/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1584\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 260/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1583\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 261/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 262/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1576\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 263/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1575\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 264/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1572\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 265/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1569\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 266/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1567\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 267/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1565\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 268/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1561\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 269/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1558\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 270/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1556\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 271/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1553\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 272/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1550\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 273/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n" + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n" ] }, { @@ -3306,2299 +3312,2305 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 274/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1545\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 275/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0238 - val_root_mean_squared_error: 0.1542\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 276/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1540\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 277/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0684 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1537\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 278/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1533\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 279/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 280/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1527\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 281/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1523\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 282/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1521\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 283/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1517\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 284/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1513\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 285/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0676 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1509\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 286/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1505\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 287/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1501\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 288/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1498\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 289/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0672 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1493\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0667\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 290/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1489\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 291/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1485\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 292/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1481\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 293/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1477\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 294/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1473\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 295/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0667 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1468\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 296/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1465\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 297/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 298/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1457\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 299/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1452\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 300/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1449\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 301/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0661 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1444\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 302/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1444\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 303/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0660 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1435\n", + "8/8 - 0s - loss: 9.6715e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0631\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 304/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1450\n", + "8/8 - 0s - loss: 8.5799e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 305/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1425\n", + "8/8 - 0s - loss: 7.4368e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 306/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1504\n", + "8/8 - 0s - loss: 7.8944e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 307/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1438\n", + "8/8 - 0s - loss: 7.1894e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 308/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1428\n", + "8/8 - 0s - loss: 7.6310e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 309/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1487\n", + "8/8 - 0s - loss: 8.7377e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 310/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0672 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1516\n", + "8/8 - 0s - loss: 7.7619e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 311/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1425\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 312/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1428\n" + "8/8 - 0s - loss: 8.7049e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Epoch 312/1000\n", + "8/8 - 0s - loss: 9.2348e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 313/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0682 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1463\n", + "8/8 - 0s - loss: 8.6449e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 314/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1492\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 315/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 316/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", + "8/8 - 0s - loss: 9.5606e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 317/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1449\n", + "8/8 - 0s - loss: 9.6162e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0596\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 318/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1463\n", + "8/8 - 0s - loss: 9.5000e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0634\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 319/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1418\n", + "8/8 - 0s - loss: 9.3189e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 320/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 321/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1440\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 322/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0652 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1432\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 323/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1406\n", + "8/8 - 0s - loss: 9.1583e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 324/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1406\n", + "8/8 - 0s - loss: 7.8489e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 325/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1429\n", + "8/8 - 0s - loss: 8.8173e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 326/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1412\n", + "8/8 - 0s - loss: 9.2407e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 327/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1400\n", + "8/8 - 0s - loss: 9.1302e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 328/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1402\n", + "8/8 - 0s - loss: 9.6200e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 329/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1415\n", + "8/8 - 0s - loss: 9.7697e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 330/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1397\n", + "8/8 - 0s - loss: 7.5199e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 331/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1392\n", + "8/8 - 0s - loss: 7.5368e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 332/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1401\n", + "8/8 - 0s - loss: 7.2867e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 333/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1405\n", + "8/8 - 0s - loss: 7.5344e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 334/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1392\n", + "8/8 - 0s - loss: 8.5836e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0547\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 335/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1389\n", + "8/8 - 0s - loss: 9.4637e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 336/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1402\n", + "8/8 - 0s - loss: 7.9697e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 337/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1389\n", + "8/8 - 0s - loss: 7.2785e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 338/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", + "8/8 - 0s - loss: 6.2342e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 339/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", + "8/8 - 0s - loss: 6.0845e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 340/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0638 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1401\n", + "8/8 - 0s - loss: 6.6577e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 341/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1379\n", + "8/8 - 0s - loss: 7.5679e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 342/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1389\n", + "8/8 - 0s - loss: 6.8130e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 343/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1389\n", + "8/8 - 0s - loss: 6.4722e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 344/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1375\n", + "8/8 - 0s - loss: 5.9530e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 345/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1381\n", + "8/8 - 0s - loss: 5.9113e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 346/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", + "8/8 - 0s - loss: 7.0934e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 347/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1374\n", + "8/8 - 0s - loss: 8.2147e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 348/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1371\n", + "8/8 - 0s - loss: 7.2468e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 349/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1375\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 350/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1371\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 351/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n" + "8/8 - 0s - loss: 7.5941e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Epoch 350/1000\n", + "8/8 - 0s - loss: 7.5998e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "8/8 - 0s - loss: 7.7289e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 352/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1369\n", + "8/8 - 0s - loss: 9.8107e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 353/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1362\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 354/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1360\n", + "8/8 - 0s - loss: 9.0664e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 355/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1365\n", + "8/8 - 0s - loss: 7.8665e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 356/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1356\n", + "8/8 - 0s - loss: 7.3592e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 357/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1356\n", + "8/8 - 0s - loss: 7.1735e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 358/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1358\n", + "8/8 - 0s - loss: 7.9459e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 359/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1351\n", + "8/8 - 0s - loss: 8.8665e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 360/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1353\n", + "8/8 - 0s - loss: 8.2309e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 361/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1351\n", + "8/8 - 0s - loss: 7.6252e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 362/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", + "8/8 - 0s - loss: 6.6454e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 363/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n", + "8/8 - 0s - loss: 6.8247e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 364/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1342\n", + "8/8 - 0s - loss: 7.4920e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 365/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1340\n", + "8/8 - 0s - loss: 7.0340e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 366/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1343\n", + "8/8 - 0s - loss: 7.6945e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 367/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1332\n", + "8/8 - 0s - loss: 7.1307e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 368/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1342\n", + "8/8 - 0s - loss: 5.8773e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 369/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1329\n", + "8/8 - 0s - loss: 5.3162e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 370/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1337\n", + "8/8 - 0s - loss: 4.7439e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 371/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1328\n", + "8/8 - 0s - loss: 4.2849e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 372/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1335\n", + "8/8 - 0s - loss: 4.2583e-04 - root_mean_squared_error: 0.0206 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 373/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", + "8/8 - 0s - loss: 4.0052e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 374/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1342\n", + "8/8 - 0s - loss: 3.6993e-04 - root_mean_squared_error: 0.0192 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 375/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1329\n", + "8/8 - 0s - loss: 3.7999e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 376/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1329\n", + "8/8 - 0s - loss: 3.4556e-04 - root_mean_squared_error: 0.0186 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 377/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0612 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1314\n", + "8/8 - 0s - loss: 3.2869e-04 - root_mean_squared_error: 0.0181 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 378/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1313\n", + "8/8 - 0s - loss: 3.4876e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 379/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", + "8/8 - 0s - loss: 3.4044e-04 - root_mean_squared_error: 0.0185 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 380/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1312\n", + "8/8 - 0s - loss: 3.2293e-04 - root_mean_squared_error: 0.0180 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 381/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n", + "8/8 - 0s - loss: 3.4753e-04 - root_mean_squared_error: 0.0186 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 382/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1312\n", + "8/8 - 0s - loss: 3.5364e-04 - root_mean_squared_error: 0.0188 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 383/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", + "8/8 - 0s - loss: 3.6709e-04 - root_mean_squared_error: 0.0192 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 384/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", + "8/8 - 0s - loss: 4.4280e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 385/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", + "8/8 - 0s - loss: 4.6696e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 386/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", + "8/8 - 0s - loss: 4.4802e-04 - root_mean_squared_error: 0.0212 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 387/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1298\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 388/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 389/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 390/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n" + "8/8 - 0s - loss: 5.8214e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Epoch 388/1000\n", + "8/8 - 0s - loss: 6.4122e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "8/8 - 0s - loss: 6.8686e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "8/8 - 0s - loss: 9.6442e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 391/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n", + "8/8 - 0s - loss: 9.3211e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 392/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n", + "8/8 - 0s - loss: 8.0295e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 393/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1286\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 394/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 395/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 396/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 397/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1273\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 398/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", + "8/8 - 0s - loss: 9.1018e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 399/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n", + "8/8 - 0s - loss: 7.0819e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 400/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1289\n", + "8/8 - 0s - loss: 6.7078e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 401/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", + "8/8 - 0s - loss: 5.7658e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 402/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1287\n", + "8/8 - 0s - loss: 5.5376e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 403/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n", + "8/8 - 0s - loss: 5.2939e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 404/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1265\n", + "8/8 - 0s - loss: 4.9393e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 405/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1259\n", + "8/8 - 0s - loss: 5.0609e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 406/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1262\n", + "8/8 - 0s - loss: 4.6631e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 407/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", + "8/8 - 0s - loss: 4.1723e-04 - root_mean_squared_error: 0.0204 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 408/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1258\n", + "8/8 - 0s - loss: 3.8559e-04 - root_mean_squared_error: 0.0196 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 409/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1266\n", + "8/8 - 0s - loss: 3.4872e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 410/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n", + "8/8 - 0s - loss: 2.9498e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 411/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n", + "8/8 - 0s - loss: 2.9884e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 412/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n", + "8/8 - 0s - loss: 2.9221e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 413/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n", + "8/8 - 0s - loss: 2.7950e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 414/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", + "8/8 - 0s - loss: 2.9001e-04 - root_mean_squared_error: 0.0170 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 415/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n", + "8/8 - 0s - loss: 2.6861e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 416/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n", + "8/8 - 0s - loss: 2.5827e-04 - root_mean_squared_error: 0.0161 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 417/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1231\n", + "8/8 - 0s - loss: 2.7559e-04 - root_mean_squared_error: 0.0166 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 418/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1231\n", + "8/8 - 0s - loss: 2.6797e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 419/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1233\n", + "8/8 - 0s - loss: 2.6366e-04 - root_mean_squared_error: 0.0162 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 420/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n", + "8/8 - 0s - loss: 2.9091e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 421/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1231\n", + "8/8 - 0s - loss: 2.9309e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 422/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n", + "8/8 - 0s - loss: 2.9613e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 423/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1229\n", + "8/8 - 0s - loss: 3.4982e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 424/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1235\n", + "8/8 - 0s - loss: 3.6090e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 425/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 3.7280e-04 - root_mean_squared_error: 0.0193 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 426/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n", + "8/8 - 0s - loss: 4.5479e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 427/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1235\n", + "8/8 - 0s - loss: 4.4346e-04 - root_mean_squared_error: 0.0211 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 428/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1212\n", + "8/8 - 0s - loss: 4.1939e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 429/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 4.7709e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 430/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0570 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n", + "8/8 - 0s - loss: 4.6385e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 431/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n", + "8/8 - 0s - loss: 4.1944e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 432/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n", + "8/8 - 0s - loss: 4.4082e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 433/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1197\n", + "8/8 - 0s - loss: 4.2984e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 434/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n", + "8/8 - 0s - loss: 3.7776e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 435/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", + "8/8 - 0s - loss: 3.8667e-04 - root_mean_squared_error: 0.0197 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 436/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", + "8/8 - 0s - loss: 3.9802e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 437/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", + "8/8 - 0s - loss: 3.6349e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 438/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1188\n", + "8/8 - 0s - loss: 3.9382e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 439/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1194\n", + "8/8 - 0s - loss: 4.1811e-04 - root_mean_squared_error: 0.0204 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 440/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1181\n", + "8/8 - 0s - loss: 3.9386e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 441/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n", + "8/8 - 0s - loss: 4.7010e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 442/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n", + "8/8 - 0s - loss: 5.2306e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 443/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1180\n", + "8/8 - 0s - loss: 5.2691e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 444/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1177\n", + "8/8 - 0s - loss: 6.8996e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 445/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n", + "8/8 - 0s - loss: 6.8401e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 446/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", + "8/8 - 0s - loss: 6.4830e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 447/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", + "8/8 - 0s - loss: 7.5397e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 448/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1173\n", + "8/8 - 0s - loss: 7.1244e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 449/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n", + "8/8 - 0s - loss: 6.0638e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 450/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", + "8/8 - 0s - loss: 5.3113e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 451/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n", + "8/8 - 0s - loss: 4.9536e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 452/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", + "8/8 - 0s - loss: 4.2452e-04 - root_mean_squared_error: 0.0206 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 453/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1161\n", + "8/8 - 0s - loss: 4.4990e-04 - root_mean_squared_error: 0.0212 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 454/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1180\n", + "8/8 - 0s - loss: 4.9485e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 455/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1197\n", + "8/8 - 0s - loss: 3.7840e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 456/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", + "8/8 - 0s - loss: 4.3200e-04 - root_mean_squared_error: 0.0208 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 457/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1188\n", + "8/8 - 0s - loss: 3.9516e-04 - root_mean_squared_error: 0.0199 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 458/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\n", + "8/8 - 0s - loss: 3.5956e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 459/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n", + "8/8 - 0s - loss: 3.7792e-04 - root_mean_squared_error: 0.0194 - val_loss: 9.9502e-04 - val_root_mean_squared_error: 0.0315\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 460/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1157\n", + "8/8 - 0s - loss: 3.2222e-04 - root_mean_squared_error: 0.0180 - val_loss: 9.7789e-04 - val_root_mean_squared_error: 0.0313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 461/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n", + "8/8 - 0s - loss: 3.0630e-04 - root_mean_squared_error: 0.0175 - val_loss: 9.6259e-04 - val_root_mean_squared_error: 0.0310\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 462/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n", + "8/8 - 0s - loss: 2.7725e-04 - root_mean_squared_error: 0.0167 - val_loss: 9.7596e-04 - val_root_mean_squared_error: 0.0312\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 463/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1158\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 2.2451e-04 - root_mean_squared_error: 0.0150 - val_loss: 8.6896e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 464/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n", + "8/8 - 0s - loss: 2.1785e-04 - root_mean_squared_error: 0.0148 - val_loss: 8.3482e-04 - val_root_mean_squared_error: 0.0289\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 465/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n", + "8/8 - 0s - loss: 2.1364e-04 - root_mean_squared_error: 0.0146 - val_loss: 8.5460e-04 - val_root_mean_squared_error: 0.0292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 466/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1135\n", + "8/8 - 0s - loss: 1.9596e-04 - root_mean_squared_error: 0.0140 - val_loss: 8.0568e-04 - val_root_mean_squared_error: 0.0284\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 467/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n", + "8/8 - 0s - loss: 1.9083e-04 - root_mean_squared_error: 0.0138 - val_loss: 8.1381e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 468/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 2.0778e-04 - root_mean_squared_error: 0.0144 - val_loss: 7.4864e-04 - val_root_mean_squared_error: 0.0274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 469/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n", + "8/8 - 0s - loss: 2.1937e-04 - root_mean_squared_error: 0.0148 - val_loss: 8.1518e-04 - val_root_mean_squared_error: 0.0286\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 470/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n", + "8/8 - 0s - loss: 2.4795e-04 - root_mean_squared_error: 0.0157 - val_loss: 9.1599e-04 - val_root_mean_squared_error: 0.0303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 471/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n", + "8/8 - 0s - loss: 2.8756e-04 - root_mean_squared_error: 0.0170 - val_loss: 7.4531e-04 - val_root_mean_squared_error: 0.0273\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 472/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", + "8/8 - 0s - loss: 2.9599e-04 - root_mean_squared_error: 0.0172 - val_loss: 8.5210e-04 - val_root_mean_squared_error: 0.0292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 473/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", + "8/8 - 0s - loss: 3.3062e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 474/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n", + "8/8 - 0s - loss: 3.4449e-04 - root_mean_squared_error: 0.0186 - val_loss: 7.3901e-04 - val_root_mean_squared_error: 0.0272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 475/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1124\n", + "8/8 - 0s - loss: 3.4591e-04 - root_mean_squared_error: 0.0186 - val_loss: 8.7491e-04 - val_root_mean_squared_error: 0.0296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 476/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", + "8/8 - 0s - loss: 3.5486e-04 - root_mean_squared_error: 0.0188 - val_loss: 9.8170e-04 - val_root_mean_squared_error: 0.0313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 477/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1120\n", + "8/8 - 0s - loss: 2.9692e-04 - root_mean_squared_error: 0.0172 - val_loss: 7.2556e-04 - val_root_mean_squared_error: 0.0269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 478/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1111\n", + "8/8 - 0s - loss: 2.2238e-04 - root_mean_squared_error: 0.0149 - val_loss: 7.3739e-04 - val_root_mean_squared_error: 0.0272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 479/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n", + "8/8 - 0s - loss: 2.0870e-04 - root_mean_squared_error: 0.0144 - val_loss: 7.7403e-04 - val_root_mean_squared_error: 0.0278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 480/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1108\n", + "8/8 - 0s - loss: 1.7103e-04 - root_mean_squared_error: 0.0131 - val_loss: 6.5161e-04 - val_root_mean_squared_error: 0.0255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 481/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", + "8/8 - 0s - loss: 1.4065e-04 - root_mean_squared_error: 0.0119 - val_loss: 6.3855e-04 - val_root_mean_squared_error: 0.0253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 482/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", + "8/8 - 0s - loss: 1.3437e-04 - root_mean_squared_error: 0.0116 - val_loss: 6.3875e-04 - val_root_mean_squared_error: 0.0253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 483/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\n", + "8/8 - 0s - loss: 1.1798e-04 - root_mean_squared_error: 0.0109 - val_loss: 6.0623e-04 - val_root_mean_squared_error: 0.0246\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 484/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\n", + "8/8 - 0s - loss: 1.3256e-04 - root_mean_squared_error: 0.0115 - val_loss: 5.8019e-04 - val_root_mean_squared_error: 0.0241\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 485/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1097\n", + "8/8 - 0s - loss: 1.5440e-04 - root_mean_squared_error: 0.0124 - val_loss: 6.0428e-04 - val_root_mean_squared_error: 0.0246\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 486/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1098\n", + "8/8 - 0s - loss: 1.6313e-04 - root_mean_squared_error: 0.0128 - val_loss: 6.4668e-04 - val_root_mean_squared_error: 0.0254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 487/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1093\n", + "8/8 - 0s - loss: 2.2628e-04 - root_mean_squared_error: 0.0150 - val_loss: 5.9709e-04 - val_root_mean_squared_error: 0.0244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 488/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "8/8 - 0s - loss: 3.0404e-04 - root_mean_squared_error: 0.0174 - val_loss: 6.9394e-04 - val_root_mean_squared_error: 0.0263\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 489/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n", + "8/8 - 0s - loss: 3.3873e-04 - root_mean_squared_error: 0.0184 - val_loss: 8.4976e-04 - val_root_mean_squared_error: 0.0292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 490/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1134\n", + "8/8 - 0s - loss: 4.4846e-04 - root_mean_squared_error: 0.0212 - val_loss: 7.4154e-04 - val_root_mean_squared_error: 0.0272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 491/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1187\n", + "8/8 - 0s - loss: 5.5776e-04 - root_mean_squared_error: 0.0236 - val_loss: 9.6730e-04 - val_root_mean_squared_error: 0.0311\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 492/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", + "8/8 - 0s - loss: 5.0629e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 493/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "8/8 - 0s - loss: 4.4137e-04 - root_mean_squared_error: 0.0210 - val_loss: 6.9011e-04 - val_root_mean_squared_error: 0.0263\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 494/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", + "8/8 - 0s - loss: 3.8293e-04 - root_mean_squared_error: 0.0196 - val_loss: 8.3572e-04 - val_root_mean_squared_error: 0.0289\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 495/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "8/8 - 0s - loss: 3.2970e-04 - root_mean_squared_error: 0.0182 - val_loss: 7.3644e-04 - val_root_mean_squared_error: 0.0271\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 496/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1090\n", + "8/8 - 0s - loss: 2.6029e-04 - root_mean_squared_error: 0.0161 - val_loss: 6.4372e-04 - val_root_mean_squared_error: 0.0254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 497/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", + "8/8 - 0s - loss: 2.9394e-04 - root_mean_squared_error: 0.0171 - val_loss: 7.0573e-04 - val_root_mean_squared_error: 0.0266\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 498/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1087\n", + "8/8 - 0s - loss: 3.4970e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.7731e-04 - val_root_mean_squared_error: 0.0260\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 499/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1088\n", + "8/8 - 0s - loss: 3.1088e-04 - root_mean_squared_error: 0.0176 - val_loss: 7.5611e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 500/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n", + "8/8 - 0s - loss: 3.8185e-04 - root_mean_squared_error: 0.0195 - val_loss: 6.4203e-04 - val_root_mean_squared_error: 0.0253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 501/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1074\n", + "8/8 - 0s - loss: 3.6900e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.6096e-04 - val_root_mean_squared_error: 0.0276\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 502/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\n", + "8/8 - 0s - loss: 3.5178e-04 - root_mean_squared_error: 0.0188 - val_loss: 8.6912e-04 - val_root_mean_squared_error: 0.0295\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 503/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1080\n", + "8/8 - 0s - loss: 3.1573e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.5040e-04 - val_root_mean_squared_error: 0.0235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 504/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1069\n", + "8/8 - 0s - loss: 2.7166e-04 - root_mean_squared_error: 0.0165 - val_loss: 6.4850e-04 - val_root_mean_squared_error: 0.0255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 505/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n", + "8/8 - 0s - loss: 2.8281e-04 - root_mean_squared_error: 0.0168 - val_loss: 6.9561e-04 - val_root_mean_squared_error: 0.0264\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 506/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1098\n", + "8/8 - 0s - loss: 2.1290e-04 - root_mean_squared_error: 0.0146 - val_loss: 5.4596e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 507/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1104\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 2.0503e-04 - root_mean_squared_error: 0.0143 - val_loss: 5.3358e-04 - val_root_mean_squared_error: 0.0231\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 508/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", + "8/8 - 0s - loss: 2.8788e-04 - root_mean_squared_error: 0.0170 - val_loss: 6.2024e-04 - val_root_mean_squared_error: 0.0249\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 509/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1092\n", + "8/8 - 0s - loss: 2.3235e-04 - root_mean_squared_error: 0.0152 - val_loss: 6.0799e-04 - val_root_mean_squared_error: 0.0247\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 510/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1070\n", + "8/8 - 0s - loss: 2.5692e-04 - root_mean_squared_error: 0.0160 - val_loss: 5.2059e-04 - val_root_mean_squared_error: 0.0228\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 511/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0524 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", + "8/8 - 0s - loss: 2.6420e-04 - root_mean_squared_error: 0.0163 - val_loss: 5.4534e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 512/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", + "8/8 - 0s - loss: 2.3730e-04 - root_mean_squared_error: 0.0154 - val_loss: 6.2816e-04 - val_root_mean_squared_error: 0.0251\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 513/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\n", + "8/8 - 0s - loss: 2.4161e-04 - root_mean_squared_error: 0.0155 - val_loss: 6.0380e-04 - val_root_mean_squared_error: 0.0246\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 514/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1056\n", + "8/8 - 0s - loss: 1.7235e-04 - root_mean_squared_error: 0.0131 - val_loss: 4.4601e-04 - val_root_mean_squared_error: 0.0211\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 515/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1079\n", + "8/8 - 0s - loss: 1.6396e-04 - root_mean_squared_error: 0.0128 - val_loss: 5.1931e-04 - val_root_mean_squared_error: 0.0228\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 516/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1051\n", + "8/8 - 0s - loss: 2.1498e-04 - root_mean_squared_error: 0.0147 - val_loss: 5.4155e-04 - val_root_mean_squared_error: 0.0233\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 517/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", + "8/8 - 0s - loss: 1.9613e-04 - root_mean_squared_error: 0.0140 - val_loss: 4.6195e-04 - val_root_mean_squared_error: 0.0215\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 518/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\n", + "8/8 - 0s - loss: 2.8673e-04 - root_mean_squared_error: 0.0169 - val_loss: 5.9810e-04 - val_root_mean_squared_error: 0.0245\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 519/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", + "8/8 - 0s - loss: 4.5870e-04 - root_mean_squared_error: 0.0214 - val_loss: 7.2325e-04 - val_root_mean_squared_error: 0.0269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 520/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", + "8/8 - 0s - loss: 5.4648e-04 - root_mean_squared_error: 0.0234 - val_loss: 7.9112e-04 - val_root_mean_squared_error: 0.0281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 521/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n", + "8/8 - 0s - loss: 5.3935e-04 - root_mean_squared_error: 0.0232 - val_loss: 7.6844e-04 - val_root_mean_squared_error: 0.0277\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 522/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n", + "8/8 - 0s - loss: 4.7389e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.3131e-04 - val_root_mean_squared_error: 0.0251\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 523/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n", + "8/8 - 0s - loss: 5.0696e-04 - root_mean_squared_error: 0.0225 - val_loss: 8.9842e-04 - val_root_mean_squared_error: 0.0300\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 524/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1046\n", + "8/8 - 0s - loss: 4.5085e-04 - root_mean_squared_error: 0.0212 - val_loss: 7.1025e-04 - val_root_mean_squared_error: 0.0267\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 525/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1046\n", + "8/8 - 0s - loss: 3.0575e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.7770e-04 - val_root_mean_squared_error: 0.0240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 526/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", + "8/8 - 0s - loss: 3.4315e-04 - root_mean_squared_error: 0.0185 - val_loss: 7.1947e-04 - val_root_mean_squared_error: 0.0268\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 527/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", + "8/8 - 0s - loss: 3.3443e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7517e-04 - val_root_mean_squared_error: 0.0240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 528/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1036\n", + "8/8 - 0s - loss: 2.8961e-04 - root_mean_squared_error: 0.0170 - val_loss: 5.9116e-04 - val_root_mean_squared_error: 0.0243\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 529/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", + "8/8 - 0s - loss: 4.5353e-04 - root_mean_squared_error: 0.0213 - val_loss: 6.5068e-04 - val_root_mean_squared_error: 0.0255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 530/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n", + "8/8 - 0s - loss: 5.2866e-04 - root_mean_squared_error: 0.0230 - val_loss: 6.9204e-04 - val_root_mean_squared_error: 0.0263\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 531/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", + "8/8 - 0s - loss: 4.2837e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.9146e-04 - val_root_mean_squared_error: 0.0281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 532/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", + "8/8 - 0s - loss: 4.4561e-04 - root_mean_squared_error: 0.0211 - val_loss: 5.3124e-04 - val_root_mean_squared_error: 0.0230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 533/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", + "8/8 - 0s - loss: 3.8930e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.5122e-04 - val_root_mean_squared_error: 0.0274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 534/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1037\n", + "8/8 - 0s - loss: 3.7363e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.3352e-04 - val_root_mean_squared_error: 0.0252\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 535/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1028\n", + "8/8 - 0s - loss: 3.4662e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.5312e-04 - val_root_mean_squared_error: 0.0235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 536/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1040\n", + "8/8 - 0s - loss: 3.5728e-04 - root_mean_squared_error: 0.0189 - val_loss: 5.6960e-04 - val_root_mean_squared_error: 0.0239\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 537/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", + "8/8 - 0s - loss: 3.4491e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.4149e-04 - val_root_mean_squared_error: 0.0233\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 538/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 2.6088e-04 - root_mean_squared_error: 0.0162 - val_loss: 5.6926e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 539/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\n", + "8/8 - 0s - loss: 2.7060e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.9709e-04 - val_root_mean_squared_error: 0.0223\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 540/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1072\n", + "8/8 - 0s - loss: 2.6895e-04 - root_mean_squared_error: 0.0164 - val_loss: 5.4883e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 541/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\n", + "8/8 - 0s - loss: 2.2454e-04 - root_mean_squared_error: 0.0150 - val_loss: 4.3685e-04 - val_root_mean_squared_error: 0.0209\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 542/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1070\n", + "8/8 - 0s - loss: 1.8667e-04 - root_mean_squared_error: 0.0137 - val_loss: 4.4620e-04 - val_root_mean_squared_error: 0.0211\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 543/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1023\n", + "8/8 - 0s - loss: 1.7310e-04 - root_mean_squared_error: 0.0132 - val_loss: 4.1411e-04 - val_root_mean_squared_error: 0.0203\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 544/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "8/8 - 0s - loss: 2.1253e-04 - root_mean_squared_error: 0.0146 - val_loss: 4.2685e-04 - val_root_mean_squared_error: 0.0207\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 545/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1075\n", + "8/8 - 0s - loss: 2.1115e-04 - root_mean_squared_error: 0.0145 - val_loss: 4.6501e-04 - val_root_mean_squared_error: 0.0216\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 546/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 1.8423e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.8537e-04 - val_root_mean_squared_error: 0.0196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 547/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", + "8/8 - 0s - loss: 1.6496e-04 - root_mean_squared_error: 0.0128 - val_loss: 4.0038e-04 - val_root_mean_squared_error: 0.0200\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 548/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n", + "8/8 - 0s - loss: 1.4379e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.5765e-04 - val_root_mean_squared_error: 0.0189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 549/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1016\n", + "8/8 - 0s - loss: 1.3108e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.8782e-04 - val_root_mean_squared_error: 0.0197\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 550/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "8/8 - 0s - loss: 1.2438e-04 - root_mean_squared_error: 0.0112 - val_loss: 3.7214e-04 - val_root_mean_squared_error: 0.0193\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 551/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", + "8/8 - 0s - loss: 1.3586e-04 - root_mean_squared_error: 0.0117 - val_loss: 3.3352e-04 - val_root_mean_squared_error: 0.0183\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 552/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", + "8/8 - 0s - loss: 1.4379e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.6135e-04 - val_root_mean_squared_error: 0.0190\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 553/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", + "8/8 - 0s - loss: 1.5171e-04 - root_mean_squared_error: 0.0123 - val_loss: 3.2266e-04 - val_root_mean_squared_error: 0.0180\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 554/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1044\n", + "8/8 - 0s - loss: 1.4822e-04 - root_mean_squared_error: 0.0122 - val_loss: 3.5914e-04 - val_root_mean_squared_error: 0.0190\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 555/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", + "8/8 - 0s - loss: 1.3671e-04 - root_mean_squared_error: 0.0117 - val_loss: 3.3337e-04 - val_root_mean_squared_error: 0.0183\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 556/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1020\n", + "8/8 - 0s - loss: 1.2932e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.6500e-04 - val_root_mean_squared_error: 0.0191\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 557/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", + "8/8 - 0s - loss: 1.3004e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.5273e-04 - val_root_mean_squared_error: 0.0188\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 558/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1003\n", + "8/8 - 0s - loss: 1.4511e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.0121e-04 - val_root_mean_squared_error: 0.0174\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 559/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", + "8/8 - 0s - loss: 1.6231e-04 - root_mean_squared_error: 0.0127 - val_loss: 3.5505e-04 - val_root_mean_squared_error: 0.0188\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 560/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1006\n", + "8/8 - 0s - loss: 2.1139e-04 - root_mean_squared_error: 0.0145 - val_loss: 3.4679e-04 - val_root_mean_squared_error: 0.0186\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 561/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0997\n", + "8/8 - 0s - loss: 2.0150e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.7814e-04 - val_root_mean_squared_error: 0.0194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 562/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0999\n", + "8/8 - 0s - loss: 1.9164e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.6121e-04 - val_root_mean_squared_error: 0.0190\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 563/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "8/8 - 0s - loss: 1.7448e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.5629e-04 - val_root_mean_squared_error: 0.0189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 564/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "8/8 - 0s - loss: 1.8390e-04 - root_mean_squared_error: 0.0136 - val_loss: 4.2245e-04 - val_root_mean_squared_error: 0.0206\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 565/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", + "8/8 - 0s - loss: 2.2930e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.2703e-04 - val_root_mean_squared_error: 0.0181\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 566/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1005\n", + "8/8 - 0s - loss: 2.2982e-04 - root_mean_squared_error: 0.0152 - val_loss: 4.0925e-04 - val_root_mean_squared_error: 0.0202\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 567/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0993\n", + "8/8 - 0s - loss: 3.4805e-04 - root_mean_squared_error: 0.0187 - val_loss: 4.2365e-04 - val_root_mean_squared_error: 0.0206\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 568/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0992\n", + "8/8 - 0s - loss: 3.1265e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.0412e-04 - val_root_mean_squared_error: 0.0201\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 569/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\n", + "8/8 - 0s - loss: 3.1437e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2196e-04 - val_root_mean_squared_error: 0.0228\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 570/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0992\n", + "8/8 - 0s - loss: 3.1147e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.1803e-04 - val_root_mean_squared_error: 0.0178\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 571/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "8/8 - 0s - loss: 2.7474e-04 - root_mean_squared_error: 0.0166 - val_loss: 5.3455e-04 - val_root_mean_squared_error: 0.0231\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 572/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "8/8 - 0s - loss: 4.3481e-04 - root_mean_squared_error: 0.0209 - val_loss: 5.2397e-04 - val_root_mean_squared_error: 0.0229\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 573/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0985\n", + "8/8 - 0s - loss: 3.8626e-04 - root_mean_squared_error: 0.0197 - val_loss: 4.2342e-04 - val_root_mean_squared_error: 0.0206\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 574/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", + "8/8 - 0s - loss: 5.2704e-04 - root_mean_squared_error: 0.0230 - val_loss: 5.7690e-04 - val_root_mean_squared_error: 0.0240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 575/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0982\n", + "8/8 - 0s - loss: 7.1104e-04 - root_mean_squared_error: 0.0267 - val_loss: 8.1341e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 576/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0985\n", + "8/8 - 0s - loss: 5.7404e-04 - root_mean_squared_error: 0.0240 - val_loss: 5.8292e-04 - val_root_mean_squared_error: 0.0241\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 577/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\n", + "8/8 - 0s - loss: 5.6877e-04 - root_mean_squared_error: 0.0238 - val_loss: 5.3892e-04 - val_root_mean_squared_error: 0.0232\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 578/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0972\n", + "8/8 - 0s - loss: 5.4786e-04 - root_mean_squared_error: 0.0234 - val_loss: 7.4032e-04 - val_root_mean_squared_error: 0.0272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 579/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 4.3005e-04 - root_mean_squared_error: 0.0207 - val_loss: 4.3757e-04 - val_root_mean_squared_error: 0.0209\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 580/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\n", + "8/8 - 0s - loss: 2.9275e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.0873e-04 - val_root_mean_squared_error: 0.0202\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 581/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 3.3393e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.8830e-04 - val_root_mean_squared_error: 0.0243\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 582/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0999\n", + "8/8 - 0s - loss: 2.7920e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.8969e-04 - val_root_mean_squared_error: 0.0197\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 583/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1017\n", + "8/8 - 0s - loss: 2.2323e-04 - root_mean_squared_error: 0.0149 - val_loss: 4.0348e-04 - val_root_mean_squared_error: 0.0201\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 584/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1140\n", + "8/8 - 0s - loss: 2.1990e-04 - root_mean_squared_error: 0.0148 - val_loss: 4.6761e-04 - val_root_mean_squared_error: 0.0216\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 585/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0975\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 2.3697e-04 - root_mean_squared_error: 0.0154 - val_loss: 4.1632e-04 - val_root_mean_squared_error: 0.0204\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 586/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1041\n", + "8/8 - 0s - loss: 2.8634e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3085e-04 - val_root_mean_squared_error: 0.0208\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 587/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1099\n", + "8/8 - 0s - loss: 2.6647e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.3402e-04 - val_root_mean_squared_error: 0.0183\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 588/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "8/8 - 0s - loss: 2.8150e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.8572e-04 - val_root_mean_squared_error: 0.0196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 589/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\n", + "8/8 - 0s - loss: 2.9818e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5975e-04 - val_root_mean_squared_error: 0.0214\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 590/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", + "8/8 - 0s - loss: 2.5339e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.8803e-04 - val_root_mean_squared_error: 0.0170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 591/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", + "8/8 - 0s - loss: 2.3655e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.5578e-04 - val_root_mean_squared_error: 0.0189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 592/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0977\n", + "8/8 - 0s - loss: 2.1493e-04 - root_mean_squared_error: 0.0147 - val_loss: 3.9139e-04 - val_root_mean_squared_error: 0.0198\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 593/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1037\n", + "8/8 - 0s - loss: 1.8590e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.4300e-04 - val_root_mean_squared_error: 0.0185\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 594/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "8/8 - 0s - loss: 1.9541e-04 - root_mean_squared_error: 0.0140 - val_loss: 3.3028e-04 - val_root_mean_squared_error: 0.0182\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 595/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\n", + "8/8 - 0s - loss: 1.7216e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.2505e-04 - val_root_mean_squared_error: 0.0180\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 596/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", + "8/8 - 0s - loss: 1.5616e-04 - root_mean_squared_error: 0.0125 - val_loss: 3.1118e-04 - val_root_mean_squared_error: 0.0176\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 597/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\n", + "8/8 - 0s - loss: 1.4647e-04 - root_mean_squared_error: 0.0121 - val_loss: 2.6253e-04 - val_root_mean_squared_error: 0.0162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 598/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1047\n", + "8/8 - 0s - loss: 1.1950e-04 - root_mean_squared_error: 0.0109 - val_loss: 2.6548e-04 - val_root_mean_squared_error: 0.0163\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 599/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", + "8/8 - 0s - loss: 1.2035e-04 - root_mean_squared_error: 0.0110 - val_loss: 2.6251e-04 - val_root_mean_squared_error: 0.0162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 600/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", + "8/8 - 0s - loss: 1.0707e-04 - root_mean_squared_error: 0.0103 - val_loss: 2.3701e-04 - val_root_mean_squared_error: 0.0154\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 601/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1104\n", + "8/8 - 0s - loss: 1.1796e-04 - root_mean_squared_error: 0.0109 - val_loss: 2.6617e-04 - val_root_mean_squared_error: 0.0163\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 602/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1062\n", + "8/8 - 0s - loss: 1.0722e-04 - root_mean_squared_error: 0.0104 - val_loss: 2.2277e-04 - val_root_mean_squared_error: 0.0149\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 603/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0587 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1032\n", + "8/8 - 0s - loss: 9.6768e-05 - root_mean_squared_error: 0.0098 - val_loss: 2.4028e-04 - val_root_mean_squared_error: 0.0155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 604/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n", + "8/8 - 0s - loss: 1.4483e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.4614e-04 - val_root_mean_squared_error: 0.0157\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 605/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n", + "8/8 - 0s - loss: 1.4700e-04 - root_mean_squared_error: 0.0121 - val_loss: 2.5570e-04 - val_root_mean_squared_error: 0.0160\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 606/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n", + "8/8 - 0s - loss: 1.6254e-04 - root_mean_squared_error: 0.0127 - val_loss: 2.6406e-04 - val_root_mean_squared_error: 0.0163\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 607/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n", + "8/8 - 0s - loss: 1.8869e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.2095e-04 - val_root_mean_squared_error: 0.0149\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 608/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", + "8/8 - 0s - loss: 1.8906e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.5818e-04 - val_root_mean_squared_error: 0.0189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 609/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", + "8/8 - 0s - loss: 2.0553e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.6460e-04 - val_root_mean_squared_error: 0.0163\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 610/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1041\n", + "8/8 - 0s - loss: 1.9770e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.4078e-04 - val_root_mean_squared_error: 0.0155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 611/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\n", + "8/8 - 0s - loss: 2.0986e-04 - root_mean_squared_error: 0.0145 - val_loss: 4.0074e-04 - val_root_mean_squared_error: 0.0200\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 612/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", + "8/8 - 0s - loss: 2.1333e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.9032e-04 - val_root_mean_squared_error: 0.0170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 613/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 2.5129e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.0896e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 614/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", + "8/8 - 0s - loss: 2.5034e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.8533e-04 - val_root_mean_squared_error: 0.0169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 615/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1168\n", + "8/8 - 0s - loss: 2.6753e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.3700e-04 - val_root_mean_squared_error: 0.0184\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 616/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", + "8/8 - 0s - loss: 4.4481e-04 - root_mean_squared_error: 0.0211 - val_loss: 4.2790e-04 - val_root_mean_squared_error: 0.0207\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 617/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1025\n", + "8/8 - 0s - loss: 5.1476e-04 - root_mean_squared_error: 0.0227 - val_loss: 3.8293e-04 - val_root_mean_squared_error: 0.0196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 618/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", + "8/8 - 0s - loss: 7.1839e-04 - root_mean_squared_error: 0.0268 - val_loss: 5.1427e-04 - val_root_mean_squared_error: 0.0227\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 619/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "8/8 - 0s - loss: 6.8672e-04 - root_mean_squared_error: 0.0262 - val_loss: 6.8491e-04 - val_root_mean_squared_error: 0.0262\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 620/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1025\n", + "8/8 - 0s - loss: 4.7100e-04 - root_mean_squared_error: 0.0217 - val_loss: 4.7959e-04 - val_root_mean_squared_error: 0.0219\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 621/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1020\n", + "8/8 - 0s - loss: 3.9487e-04 - root_mean_squared_error: 0.0199 - val_loss: 4.6875e-04 - val_root_mean_squared_error: 0.0217\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 622/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", + "8/8 - 0s - loss: 3.2411e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.1077e-04 - val_root_mean_squared_error: 0.0203\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 623/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0994\n", + "8/8 - 0s - loss: 3.2309e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.5378e-04 - val_root_mean_squared_error: 0.0213\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 624/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 4.2074e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.4603e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 625/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1043\n", + "8/8 - 0s - loss: 4.7857e-04 - root_mean_squared_error: 0.0219 - val_loss: 5.1264e-04 - val_root_mean_squared_error: 0.0226\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 626/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1023\n", + "8/8 - 0s - loss: 3.5986e-04 - root_mean_squared_error: 0.0190 - val_loss: 4.4244e-04 - val_root_mean_squared_error: 0.0210\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 627/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", + "8/8 - 0s - loss: 4.4115e-04 - root_mean_squared_error: 0.0210 - val_loss: 4.0894e-04 - val_root_mean_squared_error: 0.0202\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 628/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "8/8 - 0s - loss: 3.9511e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.0206e-04 - val_root_mean_squared_error: 0.0224\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 629/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", + "8/8 - 0s - loss: 4.0544e-04 - root_mean_squared_error: 0.0201 - val_loss: 4.6837e-04 - val_root_mean_squared_error: 0.0216\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 630/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", + "8/8 - 0s - loss: 4.4739e-04 - root_mean_squared_error: 0.0212 - val_loss: 5.3384e-04 - val_root_mean_squared_error: 0.0231\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 631/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1198\n", + "8/8 - 0s - loss: 4.1815e-04 - root_mean_squared_error: 0.0204 - val_loss: 3.6571e-04 - val_root_mean_squared_error: 0.0191\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 632/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\n", + "8/8 - 0s - loss: 3.2656e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.6068e-04 - val_root_mean_squared_error: 0.0190\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 633/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", + "8/8 - 0s - loss: 3.2054e-04 - root_mean_squared_error: 0.0179 - val_loss: 2.7640e-04 - val_root_mean_squared_error: 0.0166\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 634/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", + "8/8 - 0s - loss: 3.0655e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.1974e-04 - val_root_mean_squared_error: 0.0179\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 635/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "8/8 - 0s - loss: 3.0200e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.2097e-04 - val_root_mean_squared_error: 0.0179\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 636/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\n", + "8/8 - 0s - loss: 3.2911e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.5654e-04 - val_root_mean_squared_error: 0.0189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 637/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0543 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", + "8/8 - 0s - loss: 3.0671e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.3115e-04 - val_root_mean_squared_error: 0.0208\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 638/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n", + "8/8 - 0s - loss: 3.4294e-04 - root_mean_squared_error: 0.0185 - val_loss: 4.0184e-04 - val_root_mean_squared_error: 0.0200\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 639/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1203\n", + "8/8 - 0s - loss: 3.2548e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.7136e-04 - val_root_mean_squared_error: 0.0217\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 640/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n", + "8/8 - 0s - loss: 3.6769e-04 - root_mean_squared_error: 0.0192 - val_loss: 4.1572e-04 - val_root_mean_squared_error: 0.0204\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 641/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "8/8 - 0s - loss: 4.1541e-04 - root_mean_squared_error: 0.0204 - val_loss: 5.5906e-04 - val_root_mean_squared_error: 0.0236\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 642/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0995\n", + "8/8 - 0s - loss: 5.2379e-04 - root_mean_squared_error: 0.0229 - val_loss: 5.1515e-04 - val_root_mean_squared_error: 0.0227\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 643/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "8/8 - 0s - loss: 5.2044e-04 - root_mean_squared_error: 0.0228 - val_loss: 7.2496e-04 - val_root_mean_squared_error: 0.0269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 644/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", + "8/8 - 0s - loss: 5.5410e-04 - root_mean_squared_error: 0.0235 - val_loss: 6.2315e-04 - val_root_mean_squared_error: 0.0250\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 645/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", + "8/8 - 0s - loss: 4.5055e-04 - root_mean_squared_error: 0.0212 - val_loss: 5.3077e-04 - val_root_mean_squared_error: 0.0230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 646/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1160\n", + "8/8 - 0s - loss: 4.0954e-04 - root_mean_squared_error: 0.0202 - val_loss: 4.7698e-04 - val_root_mean_squared_error: 0.0218\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 647/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", + "8/8 - 0s - loss: 3.0639e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.8475e-04 - val_root_mean_squared_error: 0.0196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 648/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1041\n", + "8/8 - 0s - loss: 2.2840e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.8712e-04 - val_root_mean_squared_error: 0.0169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 649/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "8/8 - 0s - loss: 2.1552e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8837e-04 - val_root_mean_squared_error: 0.0170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 650/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1002\n", + "8/8 - 0s - loss: 1.9038e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.9335e-04 - val_root_mean_squared_error: 0.0171\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 651/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0979\n", + "8/8 - 0s - loss: 2.4359e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.2853e-04 - val_root_mean_squared_error: 0.0181\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 652/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1056\n", + "8/8 - 0s - loss: 2.7259e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.6410e-04 - val_root_mean_squared_error: 0.0191\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 653/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0979\n", + "8/8 - 0s - loss: 2.8906e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.4386e-04 - val_root_mean_squared_error: 0.0185\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 654/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", + "8/8 - 0s - loss: 2.5061e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.7260e-04 - val_root_mean_squared_error: 0.0193\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 655/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0997\n", + "8/8 - 0s - loss: 1.9924e-04 - root_mean_squared_error: 0.0141 - val_loss: 3.0028e-04 - val_root_mean_squared_error: 0.0173\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 656/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0977\n", + "8/8 - 0s - loss: 1.7463e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.1623e-04 - val_root_mean_squared_error: 0.0178\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 657/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\n", + "8/8 - 0s - loss: 1.6551e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.2089e-04 - val_root_mean_squared_error: 0.0149\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 658/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "8/8 - 0s - loss: 1.8685e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.5421e-04 - val_root_mean_squared_error: 0.0159\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 659/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "8/8 - 0s - loss: 1.9856e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.9400e-04 - val_root_mean_squared_error: 0.0171\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 660/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 2.8635e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.0404e-04 - val_root_mean_squared_error: 0.0174\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 661/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 3.3806e-04 - root_mean_squared_error: 0.0184 - val_loss: 3.2786e-04 - val_root_mean_squared_error: 0.0181\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 662/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0972\n", + "8/8 - 0s - loss: 3.4080e-04 - root_mean_squared_error: 0.0185 - val_loss: 3.2467e-04 - val_root_mean_squared_error: 0.0180\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 663/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1020\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 2.9415e-04 - root_mean_squared_error: 0.0172 - val_loss: 3.7794e-04 - val_root_mean_squared_error: 0.0194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 664/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "8/8 - 0s - loss: 2.1577e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8571e-04 - val_root_mean_squared_error: 0.0169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 665/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0963\n", + "8/8 - 0s - loss: 1.8398e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.6157e-04 - val_root_mean_squared_error: 0.0162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 666/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", + "8/8 - 0s - loss: 1.5084e-04 - root_mean_squared_error: 0.0123 - val_loss: 2.2632e-04 - val_root_mean_squared_error: 0.0150\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 667/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\n", + "8/8 - 0s - loss: 2.1625e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.4206e-04 - val_root_mean_squared_error: 0.0156\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 668/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", + "8/8 - 0s - loss: 2.4729e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.8681e-04 - val_root_mean_squared_error: 0.0169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 669/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0957\n", + "8/8 - 0s - loss: 3.2641e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.1811e-04 - val_root_mean_squared_error: 0.0178\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 670/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", + "8/8 - 0s - loss: 2.9412e-04 - root_mean_squared_error: 0.0171 - val_loss: 2.9759e-04 - val_root_mean_squared_error: 0.0173\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 671/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", + "8/8 - 0s - loss: 2.6658e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.1312e-04 - val_root_mean_squared_error: 0.0177\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 672/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", + "8/8 - 0s - loss: 2.1241e-04 - root_mean_squared_error: 0.0146 - val_loss: 3.2491e-04 - val_root_mean_squared_error: 0.0180\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 673/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0919\n", + "8/8 - 0s - loss: 1.9096e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2855e-04 - val_root_mean_squared_error: 0.0151\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 674/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0935\n", + "8/8 - 0s - loss: 1.7190e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.2709e-04 - val_root_mean_squared_error: 0.0151\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 675/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", + "8/8 - 0s - loss: 1.4480e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.2393e-04 - val_root_mean_squared_error: 0.0150\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 676/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 1.8214e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.2961e-04 - val_root_mean_squared_error: 0.0152\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 677/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0921\n", + "8/8 - 0s - loss: 1.9623e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3445e-04 - val_root_mean_squared_error: 0.0153\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 678/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", + "8/8 - 0s - loss: 2.0643e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.3299e-04 - val_root_mean_squared_error: 0.0153\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 679/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", + "8/8 - 0s - loss: 1.9350e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.3082e-04 - val_root_mean_squared_error: 0.0152\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 680/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1002\n", + "8/8 - 0s - loss: 1.5316e-04 - root_mean_squared_error: 0.0124 - val_loss: 2.1128e-04 - val_root_mean_squared_error: 0.0145\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 681/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", + "8/8 - 0s - loss: 1.2284e-04 - root_mean_squared_error: 0.0111 - val_loss: 2.0593e-04 - val_root_mean_squared_error: 0.0144\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 682/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0951\n", + "8/8 - 0s - loss: 1.0592e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.6620e-04 - val_root_mean_squared_error: 0.0129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 683/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", + "8/8 - 0s - loss: 9.5866e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.5859e-04 - val_root_mean_squared_error: 0.0126\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 684/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", + "8/8 - 0s - loss: 8.6011e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.6141e-04 - val_root_mean_squared_error: 0.0127\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 685/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n", + "8/8 - 0s - loss: 1.0901e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.6565e-04 - val_root_mean_squared_error: 0.0129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 686/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0932\n", + "8/8 - 0s - loss: 1.1830e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.5982e-04 - val_root_mean_squared_error: 0.0126\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 687/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0921\n", + "8/8 - 0s - loss: 1.2311e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.6179e-04 - val_root_mean_squared_error: 0.0127\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 688/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 1.1693e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.6202e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 689/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", + "8/8 - 0s - loss: 9.2394e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.4730e-04 - val_root_mean_squared_error: 0.0121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 690/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", + "8/8 - 0s - loss: 7.8754e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.4943e-04 - val_root_mean_squared_error: 0.0122\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 691/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", + "8/8 - 0s - loss: 7.2523e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.3206e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 692/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0881\n", + "8/8 - 0s - loss: 6.7071e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.2016e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 693/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\n", + "8/8 - 0s - loss: 6.4637e-05 - root_mean_squared_error: 0.0080 - val_loss: 1.2499e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 694/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0911\n", + "8/8 - 0s - loss: 8.2554e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.2201e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 695/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", + "8/8 - 0s - loss: 8.9115e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.1894e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 696/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", + "8/8 - 0s - loss: 8.8789e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.3118e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 697/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\n", + "8/8 - 0s - loss: 8.6189e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.2502e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 698/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 6.5275e-05 - root_mean_squared_error: 0.0081 - val_loss: 1.1547e-04 - val_root_mean_squared_error: 0.0107\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 699/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", + "8/8 - 0s - loss: 5.7332e-05 - root_mean_squared_error: 0.0076 - val_loss: 1.1939e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 700/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0921\n", + "8/8 - 0s - loss: 5.0386e-05 - root_mean_squared_error: 0.0071 - val_loss: 1.0113e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 701/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 4.1299e-05 - root_mean_squared_error: 0.0064 - val_loss: 8.9439e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 702/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0970\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 5.0372e-05 - root_mean_squared_error: 0.0071 - val_loss: 9.8760e-05 - val_root_mean_squared_error: 0.0099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 703/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0914\n", + "8/8 - 0s - loss: 6.2140e-05 - root_mean_squared_error: 0.0079 - val_loss: 9.3990e-05 - val_root_mean_squared_error: 0.0097\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 704/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\n", + "8/8 - 0s - loss: 7.2920e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.0150e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 705/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", + "8/8 - 0s - loss: 7.4662e-05 - root_mean_squared_error: 0.0086 - val_loss: 1.1051e-04 - val_root_mean_squared_error: 0.0105\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 706/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0999\n", + "8/8 - 0s - loss: 7.5887e-05 - root_mean_squared_error: 0.0087 - val_loss: 1.0012e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 707/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", + "8/8 - 0s - loss: 7.1630e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.1229e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 708/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", + "8/8 - 0s - loss: 6.7844e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.1193e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 709/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", + "8/8 - 0s - loss: 5.5791e-05 - root_mean_squared_error: 0.0075 - val_loss: 8.8482e-05 - val_root_mean_squared_error: 0.0094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 710/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n", + "8/8 - 0s - loss: 5.2301e-05 - root_mean_squared_error: 0.0072 - val_loss: 9.3442e-05 - val_root_mean_squared_error: 0.0097\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 711/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", + "8/8 - 0s - loss: 4.8713e-05 - root_mean_squared_error: 0.0070 - val_loss: 8.3300e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 712/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n", + "8/8 - 0s - loss: 3.9518e-05 - root_mean_squared_error: 0.0063 - val_loss: 7.9375e-05 - val_root_mean_squared_error: 0.0089\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 713/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", + "8/8 - 0s - loss: 5.6434e-05 - root_mean_squared_error: 0.0075 - val_loss: 8.1756e-05 - val_root_mean_squared_error: 0.0090\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 714/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0891\n", + "8/8 - 0s - loss: 6.4619e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.8649e-05 - val_root_mean_squared_error: 0.0089\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 715/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0861\n", + "8/8 - 0s - loss: 7.7898e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.0850e-04 - val_root_mean_squared_error: 0.0104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 716/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0875\n", + "8/8 - 0s - loss: 1.1375e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.0137e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 717/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0878\n", + "8/8 - 0s - loss: 1.1320e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.1168e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 718/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0900\n", + "8/8 - 0s - loss: 1.3959e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.7221e-04 - val_root_mean_squared_error: 0.0131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 719/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", + "8/8 - 0s - loss: 1.5763e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.2210e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 720/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", + "8/8 - 0s - loss: 1.5414e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4050e-04 - val_root_mean_squared_error: 0.0119\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 721/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n", + "8/8 - 0s - loss: 1.6626e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.0186e-04 - val_root_mean_squared_error: 0.0142\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 722/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", + "8/8 - 0s - loss: 1.3487e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2148e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 723/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "8/8 - 0s - loss: 1.2021e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.2563e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 724/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0999\n", + "8/8 - 0s - loss: 1.1718e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.5108e-04 - val_root_mean_squared_error: 0.0123\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 725/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "8/8 - 0s - loss: 7.9296e-05 - root_mean_squared_error: 0.0089 - val_loss: 9.7617e-05 - val_root_mean_squared_error: 0.0099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 726/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0896\n", + "8/8 - 0s - loss: 7.0692e-05 - root_mean_squared_error: 0.0084 - val_loss: 9.6296e-05 - val_root_mean_squared_error: 0.0098\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 727/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0863\n", + "8/8 - 0s - loss: 7.1072e-05 - root_mean_squared_error: 0.0084 - val_loss: 8.9326e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 728/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", + "8/8 - 0s - loss: 6.0757e-05 - root_mean_squared_error: 0.0078 - val_loss: 9.1281e-05 - val_root_mean_squared_error: 0.0096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 729/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", + "8/8 - 0s - loss: 9.2821e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.0278e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 730/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0854\n", + "8/8 - 0s - loss: 9.9609e-05 - root_mean_squared_error: 0.0100 - val_loss: 7.1993e-05 - val_root_mean_squared_error: 0.0085\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 731/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0863\n", + "8/8 - 0s - loss: 1.1562e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.5079e-04 - val_root_mean_squared_error: 0.0123\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 732/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", + "8/8 - 0s - loss: 2.1259e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.7073e-04 - val_root_mean_squared_error: 0.0131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 733/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0855\n", + "8/8 - 0s - loss: 2.3345e-04 - root_mean_squared_error: 0.0153 - val_loss: 1.2522e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 734/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "8/8 - 0s - loss: 2.4010e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.7828e-04 - val_root_mean_squared_error: 0.0167\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 735/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", + "8/8 - 0s - loss: 3.5859e-04 - root_mean_squared_error: 0.0189 - val_loss: 2.6722e-04 - val_root_mean_squared_error: 0.0163\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 736/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", + "8/8 - 0s - loss: 3.2947e-04 - root_mean_squared_error: 0.0182 - val_loss: 1.7639e-04 - val_root_mean_squared_error: 0.0133\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 737/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", + "8/8 - 0s - loss: 2.7586e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.0638e-04 - val_root_mean_squared_error: 0.0175\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 738/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", + "8/8 - 0s - loss: 2.5922e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.5905e-04 - val_root_mean_squared_error: 0.0161\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 739/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0825\n", + "8/8 - 0s - loss: 1.7942e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.3348e-04 - val_root_mean_squared_error: 0.0116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 740/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "8/8 - 0s - loss: 1.2228e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.0605e-04 - val_root_mean_squared_error: 0.0103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 741/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 8.9950e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.3134e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 742/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0821\n", + "8/8 - 0s - loss: 6.9278e-05 - root_mean_squared_error: 0.0083 - val_loss: 1.0731e-04 - val_root_mean_squared_error: 0.0104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 743/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", + "8/8 - 0s - loss: 5.9917e-05 - root_mean_squared_error: 0.0077 - val_loss: 8.2995e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 744/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", + "8/8 - 0s - loss: 8.6283e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.2353e-04 - val_root_mean_squared_error: 0.0111\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 745/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n", + "8/8 - 0s - loss: 1.2852e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3380e-04 - val_root_mean_squared_error: 0.0116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 746/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", + "8/8 - 0s - loss: 1.4626e-04 - root_mean_squared_error: 0.0121 - val_loss: 9.9826e-05 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 747/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", + "8/8 - 0s - loss: 1.5724e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.1800e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 748/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", + "8/8 - 0s - loss: 1.2147e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.1629e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 749/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "8/8 - 0s - loss: 1.1508e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.1038e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 750/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0430 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", + "8/8 - 0s - loss: 1.0462e-04 - root_mean_squared_error: 0.0102 - val_loss: 9.6267e-05 - val_root_mean_squared_error: 0.0098\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 751/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0795\n", + "8/8 - 0s - loss: 8.8128e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.1728e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 752/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "8/8 - 0s - loss: 6.7168e-05 - root_mean_squared_error: 0.0082 - val_loss: 9.1075e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 753/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", + "8/8 - 0s - loss: 5.7499e-05 - root_mean_squared_error: 0.0076 - val_loss: 8.3108e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 754/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0795\n", + "8/8 - 0s - loss: 8.9135e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.2445e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 755/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", + "8/8 - 0s - loss: 1.3496e-04 - root_mean_squared_error: 0.0116 - val_loss: 9.9713e-05 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 756/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0795\n", + "8/8 - 0s - loss: 1.4909e-04 - root_mean_squared_error: 0.0122 - val_loss: 8.2712e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 757/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", + "8/8 - 0s - loss: 1.4717e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4664e-04 - val_root_mean_squared_error: 0.0121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 758/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", + "8/8 - 0s - loss: 2.3111e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.9005e-04 - val_root_mean_squared_error: 0.0138\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 759/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", + "8/8 - 0s - loss: 2.4344e-04 - root_mean_squared_error: 0.0156 - val_loss: 1.3699e-04 - val_root_mean_squared_error: 0.0117\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 760/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", + "8/8 - 0s - loss: 1.8397e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8900e-04 - val_root_mean_squared_error: 0.0137\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 761/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", + "8/8 - 0s - loss: 1.7214e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.5667e-04 - val_root_mean_squared_error: 0.0125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 762/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "8/8 - 0s - loss: 1.3038e-04 - root_mean_squared_error: 0.0114 - val_loss: 8.2874e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 763/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 1.1203e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.6289e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 764/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "8/8 - 0s - loss: 1.7359e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.0823e-04 - val_root_mean_squared_error: 0.0144\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 765/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "8/8 - 0s - loss: 1.9406e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.0292e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 766/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "8/8 - 0s - loss: 1.5738e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.2262e-04 - val_root_mean_squared_error: 0.0111\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 767/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0785\n", + "8/8 - 0s - loss: 2.4615e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.4368e-04 - val_root_mean_squared_error: 0.0156\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 768/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", + "8/8 - 0s - loss: 2.8419e-04 - root_mean_squared_error: 0.0169 - val_loss: 1.9570e-04 - val_root_mean_squared_error: 0.0140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 769/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\n", + "8/8 - 0s - loss: 2.5220e-04 - root_mean_squared_error: 0.0159 - val_loss: 1.6198e-04 - val_root_mean_squared_error: 0.0127\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 770/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", + "8/8 - 0s - loss: 2.6920e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.0937e-04 - val_root_mean_squared_error: 0.0145\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 771/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "8/8 - 0s - loss: 1.8746e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.3641e-04 - val_root_mean_squared_error: 0.0117\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 772/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", + "8/8 - 0s - loss: 1.6821e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.0881e-04 - val_root_mean_squared_error: 0.0104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 773/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", + "8/8 - 0s - loss: 1.2822e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.0033e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 774/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", + "8/8 - 0s - loss: 9.4503e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.1629e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 775/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", + "8/8 - 0s - loss: 1.0788e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.5173e-04 - val_root_mean_squared_error: 0.0123\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 776/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", + "8/8 - 0s - loss: 1.0864e-04 - root_mean_squared_error: 0.0104 - val_loss: 9.7324e-05 - val_root_mean_squared_error: 0.0099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 777/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", + "8/8 - 0s - loss: 9.4807e-05 - root_mean_squared_error: 0.0097 - val_loss: 7.7864e-05 - val_root_mean_squared_error: 0.0088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 778/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "8/8 - 0s - loss: 1.0281e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.1206e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 779/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0765\n", + "8/8 - 0s - loss: 1.0962e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0715e-04 - val_root_mean_squared_error: 0.0104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 780/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 9.6726e-05 - root_mean_squared_error: 0.0098 - val_loss: 8.6378e-05 - val_root_mean_squared_error: 0.0093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 781/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\n", + "8/8 - 0s - loss: 8.3861e-05 - root_mean_squared_error: 0.0092 - val_loss: 8.3633e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 782/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", + "8/8 - 0s - loss: 6.2084e-05 - root_mean_squared_error: 0.0079 - val_loss: 8.0212e-05 - val_root_mean_squared_error: 0.0090\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 783/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "8/8 - 0s - loss: 5.3816e-05 - root_mean_squared_error: 0.0073 - val_loss: 7.7485e-05 - val_root_mean_squared_error: 0.0088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 784/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "8/8 - 0s - loss: 4.7799e-05 - root_mean_squared_error: 0.0069 - val_loss: 5.4109e-05 - val_root_mean_squared_error: 0.0074\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 785/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", + "8/8 - 0s - loss: 4.2071e-05 - root_mean_squared_error: 0.0065 - val_loss: 5.2514e-05 - val_root_mean_squared_error: 0.0072\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 786/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", + "8/8 - 0s - loss: 3.8690e-05 - root_mean_squared_error: 0.0062 - val_loss: 6.9593e-05 - val_root_mean_squared_error: 0.0083\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 787/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", + "8/8 - 0s - loss: 3.9697e-05 - root_mean_squared_error: 0.0063 - val_loss: 5.5563e-05 - val_root_mean_squared_error: 0.0075\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 788/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", + "8/8 - 0s - loss: 4.8925e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.2673e-05 - val_root_mean_squared_error: 0.0073\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 789/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", + "8/8 - 0s - loss: 4.9338e-05 - root_mean_squared_error: 0.0070 - val_loss: 6.0850e-05 - val_root_mean_squared_error: 0.0078\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 790/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", + "8/8 - 0s - loss: 6.0285e-05 - root_mean_squared_error: 0.0078 - val_loss: 5.8741e-05 - val_root_mean_squared_error: 0.0077\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 791/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\n", + "8/8 - 0s - loss: 6.9298e-05 - root_mean_squared_error: 0.0083 - val_loss: 5.5784e-05 - val_root_mean_squared_error: 0.0075\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 792/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\n", + "8/8 - 0s - loss: 6.3387e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.2491e-05 - val_root_mean_squared_error: 0.0085\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 793/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", + "8/8 - 0s - loss: 6.8541e-05 - root_mean_squared_error: 0.0083 - val_loss: 7.7701e-05 - val_root_mean_squared_error: 0.0088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 794/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "8/8 - 0s - loss: 7.1279e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.4910e-05 - val_root_mean_squared_error: 0.0074\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 795/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "8/8 - 0s - loss: 6.6401e-05 - root_mean_squared_error: 0.0081 - val_loss: 8.5680e-05 - val_root_mean_squared_error: 0.0093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 796/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", + "8/8 - 0s - loss: 1.3197e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.6160e-04 - val_root_mean_squared_error: 0.0127\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 797/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "8/8 - 0s - loss: 2.2607e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.4185e-04 - val_root_mean_squared_error: 0.0119\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 798/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "8/8 - 0s - loss: 2.4049e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.2481e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 799/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "8/8 - 0s - loss: 3.0773e-04 - root_mean_squared_error: 0.0175 - val_loss: 2.3161e-04 - val_root_mean_squared_error: 0.0152\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 800/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "8/8 - 0s - loss: 3.8935e-04 - root_mean_squared_error: 0.0197 - val_loss: 2.8989e-04 - val_root_mean_squared_error: 0.0170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 801/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0741\n", + "8/8 - 0s - loss: 5.8134e-04 - root_mean_squared_error: 0.0241 - val_loss: 3.6970e-04 - val_root_mean_squared_error: 0.0192\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 802/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", + "8/8 - 0s - loss: 5.1266e-04 - root_mean_squared_error: 0.0226 - val_loss: 3.9748e-04 - val_root_mean_squared_error: 0.0199\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 803/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\n", + "8/8 - 0s - loss: 3.6931e-04 - root_mean_squared_error: 0.0192 - val_loss: 2.6069e-04 - val_root_mean_squared_error: 0.0161\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 804/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 3.0846e-04 - root_mean_squared_error: 0.0176 - val_loss: 2.7883e-04 - val_root_mean_squared_error: 0.0167\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 805/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 2.3945e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.6706e-04 - val_root_mean_squared_error: 0.0129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 806/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", + "8/8 - 0s - loss: 1.9913e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1979e-04 - val_root_mean_squared_error: 0.0148\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 807/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "8/8 - 0s - loss: 1.4901e-04 - root_mean_squared_error: 0.0122 - val_loss: 2.5834e-04 - val_root_mean_squared_error: 0.0161\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 808/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", + "8/8 - 0s - loss: 1.3576e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.6660e-04 - val_root_mean_squared_error: 0.0129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 809/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", + "8/8 - 0s - loss: 1.0963e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0087e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 810/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "8/8 - 0s - loss: 8.3586e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.0062e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 811/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0730\n", + "8/8 - 0s - loss: 6.8630e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.9861e-05 - val_root_mean_squared_error: 0.0084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 812/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "8/8 - 0s - loss: 6.1209e-05 - root_mean_squared_error: 0.0078 - val_loss: 6.7016e-05 - val_root_mean_squared_error: 0.0082\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 813/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0730\n", + "8/8 - 0s - loss: 4.8471e-05 - root_mean_squared_error: 0.0070 - val_loss: 6.7212e-05 - val_root_mean_squared_error: 0.0082\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 814/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 3.6809e-05 - root_mean_squared_error: 0.0061 - val_loss: 6.3040e-05 - val_root_mean_squared_error: 0.0079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 815/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\n", + "8/8 - 0s - loss: 2.9326e-05 - root_mean_squared_error: 0.0054 - val_loss: 5.8909e-05 - val_root_mean_squared_error: 0.0077\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 816/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "8/8 - 0s - loss: 2.3997e-05 - root_mean_squared_error: 0.0049 - val_loss: 4.5806e-05 - val_root_mean_squared_error: 0.0068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 817/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "8/8 - 0s - loss: 2.5177e-05 - root_mean_squared_error: 0.0050 - val_loss: 4.2363e-05 - val_root_mean_squared_error: 0.0065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 818/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "8/8 - 0s - loss: 3.1595e-05 - root_mean_squared_error: 0.0056 - val_loss: 5.0597e-05 - val_root_mean_squared_error: 0.0071\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 819/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 3.7081e-05 - root_mean_squared_error: 0.0061 - val_loss: 5.1813e-05 - val_root_mean_squared_error: 0.0072\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 820/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0771\n", + "8/8 - 0s - loss: 3.2407e-05 - root_mean_squared_error: 0.0057 - val_loss: 4.6544e-05 - val_root_mean_squared_error: 0.0068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 821/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", + "8/8 - 0s - loss: 2.9868e-05 - root_mean_squared_error: 0.0055 - val_loss: 4.4705e-05 - val_root_mean_squared_error: 0.0067\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 822/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "8/8 - 0s - loss: 2.5864e-05 - root_mean_squared_error: 0.0051 - val_loss: 3.8886e-05 - val_root_mean_squared_error: 0.0062\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 823/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", + "8/8 - 0s - loss: 2.4002e-05 - root_mean_squared_error: 0.0049 - val_loss: 3.9315e-05 - val_root_mean_squared_error: 0.0063\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 824/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "8/8 - 0s - loss: 2.4982e-05 - root_mean_squared_error: 0.0050 - val_loss: 4.2243e-05 - val_root_mean_squared_error: 0.0065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 825/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", + "8/8 - 0s - loss: 2.6199e-05 - root_mean_squared_error: 0.0051 - val_loss: 3.5591e-05 - val_root_mean_squared_error: 0.0060\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 826/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "8/8 - 0s - loss: 2.5160e-05 - root_mean_squared_error: 0.0050 - val_loss: 3.8112e-05 - val_root_mean_squared_error: 0.0062\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 827/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", + "8/8 - 0s - loss: 3.5990e-05 - root_mean_squared_error: 0.0060 - val_loss: 4.8295e-05 - val_root_mean_squared_error: 0.0069\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 828/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", + "8/8 - 0s - loss: 4.5836e-05 - root_mean_squared_error: 0.0068 - val_loss: 4.2376e-05 - val_root_mean_squared_error: 0.0065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 829/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", + "8/8 - 0s - loss: 4.2252e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.3244e-05 - val_root_mean_squared_error: 0.0066\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 830/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0758\n", + "8/8 - 0s - loss: 6.1599e-05 - root_mean_squared_error: 0.0078 - val_loss: 7.0279e-05 - val_root_mean_squared_error: 0.0084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 831/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "8/8 - 0s - loss: 8.9628e-05 - root_mean_squared_error: 0.0095 - val_loss: 5.8244e-05 - val_root_mean_squared_error: 0.0076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 832/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", + "8/8 - 0s - loss: 8.6583e-05 - root_mean_squared_error: 0.0093 - val_loss: 5.3778e-05 - val_root_mean_squared_error: 0.0073\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 833/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", + "8/8 - 0s - loss: 1.1643e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.1559e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 834/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "8/8 - 0s - loss: 1.5212e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.0185e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 835/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", + "8/8 - 0s - loss: 1.5428e-04 - root_mean_squared_error: 0.0124 - val_loss: 7.2317e-05 - val_root_mean_squared_error: 0.0085\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 836/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "8/8 - 0s - loss: 1.5821e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4453e-04 - val_root_mean_squared_error: 0.0120\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 837/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "8/8 - 0s - loss: 1.8935e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.4978e-04 - val_root_mean_squared_error: 0.0122\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 838/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 2.6605e-04 - root_mean_squared_error: 0.0163 - val_loss: 1.3306e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 839/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", + "8/8 - 0s - loss: 2.4914e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.3685e-04 - val_root_mean_squared_error: 0.0154\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 840/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", + "8/8 - 0s - loss: 1.7135e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.6981e-04 - val_root_mean_squared_error: 0.0130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 841/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "8/8 - 0s - loss: 2.0720e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.7919e-04 - val_root_mean_squared_error: 0.0134\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 842/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "8/8 - 0s - loss: 2.1254e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.8008e-04 - val_root_mean_squared_error: 0.0134\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 843/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", + "8/8 - 0s - loss: 1.4829e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.3795e-04 - val_root_mean_squared_error: 0.0117\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 844/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "8/8 - 0s - loss: 1.1922e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.3942e-04 - val_root_mean_squared_error: 0.0118\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 845/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", + "8/8 - 0s - loss: 1.2979e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3747e-04 - val_root_mean_squared_error: 0.0117\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 846/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "8/8 - 0s - loss: 1.1790e-04 - root_mean_squared_error: 0.0109 - val_loss: 9.3611e-05 - val_root_mean_squared_error: 0.0097\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 847/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", + "8/8 - 0s - loss: 9.1151e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.0967e-04 - val_root_mean_squared_error: 0.0105\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 848/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "8/8 - 0s - loss: 9.3682e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.0050e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 849/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", + "8/8 - 0s - loss: 1.3606e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.0676e-04 - val_root_mean_squared_error: 0.0103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 850/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", + "8/8 - 0s - loss: 1.4858e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.9580e-04 - val_root_mean_squared_error: 0.0140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 851/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", + "8/8 - 0s - loss: 1.0737e-04 - root_mean_squared_error: 0.0104 - val_loss: 6.4122e-05 - val_root_mean_squared_error: 0.0080\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 852/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "8/8 - 0s - loss: 1.3604e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1637e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 853/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "8/8 - 0s - loss: 1.6617e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.9755e-04 - val_root_mean_squared_error: 0.0141\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 854/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "8/8 - 0s - loss: 1.5457e-04 - root_mean_squared_error: 0.0124 - val_loss: 8.7808e-05 - val_root_mean_squared_error: 0.0094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 855/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "8/8 - 0s - loss: 1.2534e-04 - root_mean_squared_error: 0.0112 - val_loss: 8.4907e-05 - val_root_mean_squared_error: 0.0092\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 856/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "8/8 - 0s - loss: 1.3032e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.5083e-04 - val_root_mean_squared_error: 0.0123\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 857/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "8/8 - 0s - loss: 1.8514e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.5611e-04 - val_root_mean_squared_error: 0.0125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 858/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 1.7111e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8201e-04 - val_root_mean_squared_error: 0.0135\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 859/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "8/8 - 0s - loss: 1.1635e-04 - root_mean_squared_error: 0.0108 - val_loss: 9.5617e-05 - val_root_mean_squared_error: 0.0098\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 860/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "8/8 - 0s - loss: 1.7436e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8297e-04 - val_root_mean_squared_error: 0.0135\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 861/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "8/8 - 0s - loss: 2.2071e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8590e-04 - val_root_mean_squared_error: 0.0169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 862/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 1.9022e-04 - root_mean_squared_error: 0.0138 - val_loss: 9.9335e-05 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 863/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0394 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "8/8 - 0s - loss: 1.5727e-04 - root_mean_squared_error: 0.0125 - val_loss: 8.3490e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 864/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0394 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 1.4158e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.6932e-04 - val_root_mean_squared_error: 0.0130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 865/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "8/8 - 0s - loss: 1.9087e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7040e-04 - val_root_mean_squared_error: 0.0131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 866/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 1.8918e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7799e-04 - val_root_mean_squared_error: 0.0133\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 867/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "8/8 - 0s - loss: 1.2863e-04 - root_mean_squared_error: 0.0113 - val_loss: 7.2960e-05 - val_root_mean_squared_error: 0.0085\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 868/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "8/8 - 0s - loss: 1.2820e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2085e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 869/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "8/8 - 0s - loss: 1.5433e-04 - root_mean_squared_error: 0.0124 - val_loss: 2.2075e-04 - val_root_mean_squared_error: 0.0149\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 870/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "8/8 - 0s - loss: 1.6112e-04 - root_mean_squared_error: 0.0127 - val_loss: 9.9612e-05 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 871/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 1.1171e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.6641e-05 - val_root_mean_squared_error: 0.0088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 872/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 7.4767e-05 - root_mean_squared_error: 0.0086 - val_loss: 9.1160e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 873/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "8/8 - 0s - loss: 1.1533e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.3030e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 874/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "8/8 - 0s - loss: 1.4870e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.8634e-04 - val_root_mean_squared_error: 0.0137\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 875/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "8/8 - 0s - loss: 1.2080e-04 - root_mean_squared_error: 0.0110 - val_loss: 6.3899e-05 - val_root_mean_squared_error: 0.0080\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 876/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "8/8 - 0s - loss: 1.1243e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.9149e-05 - val_root_mean_squared_error: 0.0089\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 877/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "8/8 - 0s - loss: 1.3378e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.6350e-04 - val_root_mean_squared_error: 0.0128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 878/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "8/8 - 0s - loss: 1.9785e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.3042e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 879/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 1.7141e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.2767e-04 - val_root_mean_squared_error: 0.0113\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 880/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0696\n", + "8/8 - 0s - loss: 1.0979e-04 - root_mean_squared_error: 0.0105 - val_loss: 8.2482e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 881/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", + "8/8 - 0s - loss: 1.1491e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.1775e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 882/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", + "8/8 - 0s - loss: 1.4248e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.8826e-04 - val_root_mean_squared_error: 0.0137\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 883/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "8/8 - 0s - loss: 1.6023e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.0706e-04 - val_root_mean_squared_error: 0.0103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 884/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "8/8 - 0s - loss: 1.1554e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.2259e-05 - val_root_mean_squared_error: 0.0096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 885/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "8/8 - 0s - loss: 7.0947e-05 - root_mean_squared_error: 0.0084 - val_loss: 6.8237e-05 - val_root_mean_squared_error: 0.0083\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 886/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "8/8 - 0s - loss: 8.1474e-05 - root_mean_squared_error: 0.0090 - val_loss: 8.3653e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 887/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 1.1484e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.4367e-04 - val_root_mean_squared_error: 0.0120\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 888/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", + "8/8 - 0s - loss: 1.3929e-04 - root_mean_squared_error: 0.0118 - val_loss: 9.2329e-05 - val_root_mean_squared_error: 0.0096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 889/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 1.3643e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1684e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 890/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", + "8/8 - 0s - loss: 1.0543e-04 - root_mean_squared_error: 0.0103 - val_loss: 8.8670e-05 - val_root_mean_squared_error: 0.0094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 891/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "8/8 - 0s - loss: 1.1285e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0008e-04 - val_root_mean_squared_error: 0.0100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 892/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "8/8 - 0s - loss: 1.3351e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.5825e-04 - val_root_mean_squared_error: 0.0126\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 893/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "8/8 - 0s - loss: 1.6460e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.1731e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 894/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "8/8 - 0s - loss: 1.5570e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.3880e-04 - val_root_mean_squared_error: 0.0118\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 895/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", + "8/8 - 0s - loss: 1.1434e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.1354e-05 - val_root_mean_squared_error: 0.0090\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 896/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0726\n", + "8/8 - 0s - loss: 1.2785e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.1100e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 897/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 1.6161e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8008e-04 - val_root_mean_squared_error: 0.0134\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 898/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", + "8/8 - 0s - loss: 2.1913e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.4097e-04 - val_root_mean_squared_error: 0.0119\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 899/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "8/8 - 0s - loss: 2.2737e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.4053e-04 - val_root_mean_squared_error: 0.0155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 900/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", + "8/8 - 0s - loss: 2.0317e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.2289e-04 - val_root_mean_squared_error: 0.0111\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 901/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", + "8/8 - 0s - loss: 2.3230e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.1742e-04 - val_root_mean_squared_error: 0.0147\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 902/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "8/8 - 0s - loss: 2.3945e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.3586e-04 - val_root_mean_squared_error: 0.0154\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 903/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "8/8 - 0s - loss: 2.7463e-04 - root_mean_squared_error: 0.0166 - val_loss: 1.9657e-04 - val_root_mean_squared_error: 0.0140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 904/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "8/8 - 0s - loss: 2.7624e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.0035e-04 - val_root_mean_squared_error: 0.0173\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 905/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 2.3827e-04 - root_mean_squared_error: 0.0154 - val_loss: 1.3229e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 906/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", + "8/8 - 0s - loss: 2.3593e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.0632e-04 - val_root_mean_squared_error: 0.0144\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 907/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", + "8/8 - 0s - loss: 2.3234e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.5061e-04 - val_root_mean_squared_error: 0.0123\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 908/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "8/8 - 0s - loss: 2.8803e-04 - root_mean_squared_error: 0.0170 - val_loss: 1.9394e-04 - val_root_mean_squared_error: 0.0139\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 909/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "8/8 - 0s - loss: 2.8623e-04 - root_mean_squared_error: 0.0169 - val_loss: 1.8946e-04 - val_root_mean_squared_error: 0.0138\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 910/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "8/8 - 0s - loss: 2.5965e-04 - root_mean_squared_error: 0.0161 - val_loss: 1.5600e-04 - val_root_mean_squared_error: 0.0125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 911/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "8/8 - 0s - loss: 1.8558e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.2811e-04 - val_root_mean_squared_error: 0.0151\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 912/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "8/8 - 0s - loss: 2.3228e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.7243e-04 - val_root_mean_squared_error: 0.0131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 913/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 2.5019e-04 - root_mean_squared_error: 0.0158 - val_loss: 1.9305e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 914/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", + "8/8 - 0s - loss: 2.2594e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.9437e-04 - val_root_mean_squared_error: 0.0139\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 915/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "8/8 - 0s - loss: 1.7904e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.4516e-04 - val_root_mean_squared_error: 0.0120\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 916/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "8/8 - 0s - loss: 2.0551e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.1979e-04 - val_root_mean_squared_error: 0.0148\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 917/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "8/8 - 0s - loss: 2.5943e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.9001e-04 - val_root_mean_squared_error: 0.0170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 918/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "8/8 - 0s - loss: 2.3362e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.4497e-04 - val_root_mean_squared_error: 0.0157\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 919/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "8/8 - 0s - loss: 1.2879e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.0761e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 920/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "8/8 - 0s - loss: 1.0259e-04 - root_mean_squared_error: 0.0101 - val_loss: 7.3706e-05 - val_root_mean_squared_error: 0.0086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 921/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "8/8 - 0s - loss: 8.9792e-05 - root_mean_squared_error: 0.0095 - val_loss: 9.7478e-05 - val_root_mean_squared_error: 0.0099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 922/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "8/8 - 0s - loss: 9.7179e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1341e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 923/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "8/8 - 0s - loss: 9.4046e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.3221e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 924/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "8/8 - 0s - loss: 8.2226e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.2919e-05 - val_root_mean_squared_error: 0.0079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 925/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "8/8 - 0s - loss: 9.0594e-05 - root_mean_squared_error: 0.0095 - val_loss: 7.0370e-05 - val_root_mean_squared_error: 0.0084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 926/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "8/8 - 0s - loss: 9.9493e-05 - root_mean_squared_error: 0.0100 - val_loss: 9.7639e-05 - val_root_mean_squared_error: 0.0099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 927/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "8/8 - 0s - loss: 1.2080e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.0105e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 928/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "8/8 - 0s - loss: 1.3884e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3014e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 929/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "8/8 - 0s - loss: 1.3882e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3286e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 930/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "8/8 - 0s - loss: 1.2523e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1835e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 931/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "8/8 - 0s - loss: 1.1752e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.0818e-04 - val_root_mean_squared_error: 0.0104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 932/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "8/8 - 0s - loss: 7.8590e-05 - root_mean_squared_error: 0.0089 - val_loss: 8.5372e-05 - val_root_mean_squared_error: 0.0092\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 933/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "8/8 - 0s - loss: 6.8057e-05 - root_mean_squared_error: 0.0082 - val_loss: 7.9808e-05 - val_root_mean_squared_error: 0.0089\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 934/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "8/8 - 0s - loss: 6.2886e-05 - root_mean_squared_error: 0.0079 - val_loss: 5.9097e-05 - val_root_mean_squared_error: 0.0077\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 935/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "8/8 - 0s - loss: 5.6347e-05 - root_mean_squared_error: 0.0075 - val_loss: 5.3149e-05 - val_root_mean_squared_error: 0.0073\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 936/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 5.5876e-05 - root_mean_squared_error: 0.0075 - val_loss: 5.7601e-05 - val_root_mean_squared_error: 0.0076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 937/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "8/8 - 0s - loss: 4.3466e-05 - root_mean_squared_error: 0.0066 - val_loss: 4.7957e-05 - val_root_mean_squared_error: 0.0069\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 938/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", + "8/8 - 0s - loss: 3.8292e-05 - root_mean_squared_error: 0.0062 - val_loss: 4.9583e-05 - val_root_mean_squared_error: 0.0070\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 939/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n", + "8/8 - 0s - loss: 3.1108e-05 - root_mean_squared_error: 0.0056 - val_loss: 4.2719e-05 - val_root_mean_squared_error: 0.0065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 940/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "8/8 - 0s - loss: 2.7261e-05 - root_mean_squared_error: 0.0052 - val_loss: 3.8458e-05 - val_root_mean_squared_error: 0.0062\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 941/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n", + "8/8 - 0s - loss: 2.2047e-05 - root_mean_squared_error: 0.0047 - val_loss: 3.0742e-05 - val_root_mean_squared_error: 0.0055\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 942/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "8/8 - 0s - loss: 1.9378e-05 - root_mean_squared_error: 0.0044 - val_loss: 3.0574e-05 - val_root_mean_squared_error: 0.0055\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 943/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", + "8/8 - 0s - loss: 1.9781e-05 - root_mean_squared_error: 0.0044 - val_loss: 2.8618e-05 - val_root_mean_squared_error: 0.0053\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 944/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "8/8 - 0s - loss: 2.1801e-05 - root_mean_squared_error: 0.0047 - val_loss: 2.9400e-05 - val_root_mean_squared_error: 0.0054\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 945/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", + "8/8 - 0s - loss: 2.2440e-05 - root_mean_squared_error: 0.0047 - val_loss: 2.9232e-05 - val_root_mean_squared_error: 0.0054\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 946/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "8/8 - 0s - loss: 2.8331e-05 - root_mean_squared_error: 0.0053 - val_loss: 4.1730e-05 - val_root_mean_squared_error: 0.0065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 947/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 4.0255e-05 - root_mean_squared_error: 0.0063 - val_loss: 6.1603e-05 - val_root_mean_squared_error: 0.0078\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 948/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "8/8 - 0s - loss: 5.3308e-05 - root_mean_squared_error: 0.0073 - val_loss: 6.3179e-05 - val_root_mean_squared_error: 0.0079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 949/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "8/8 - 0s - loss: 5.1722e-05 - root_mean_squared_error: 0.0072 - val_loss: 4.5856e-05 - val_root_mean_squared_error: 0.0068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 950/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 4.1932e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.0316e-05 - val_root_mean_squared_error: 0.0063\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 951/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 7.0285e-05 - root_mean_squared_error: 0.0084 - val_loss: 8.0378e-05 - val_root_mean_squared_error: 0.0090\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 952/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", + "8/8 - 0s - loss: 1.1564e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.2018e-04 - val_root_mean_squared_error: 0.0110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 953/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", + "8/8 - 0s - loss: 1.3298e-04 - root_mean_squared_error: 0.0115 - val_loss: 9.2302e-05 - val_root_mean_squared_error: 0.0096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 954/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", + "8/8 - 0s - loss: 1.5451e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4610e-04 - val_root_mean_squared_error: 0.0121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 955/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "8/8 - 0s - loss: 1.3982e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3007e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 956/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", + "8/8 - 0s - loss: 1.8497e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.6301e-04 - val_root_mean_squared_error: 0.0128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 957/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "8/8 - 0s - loss: 1.9531e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.6717e-04 - val_root_mean_squared_error: 0.0129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 958/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "8/8 - 0s - loss: 1.5251e-04 - root_mean_squared_error: 0.0123 - val_loss: 8.5285e-05 - val_root_mean_squared_error: 0.0092\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 959/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 1.3727e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.0554e-04 - val_root_mean_squared_error: 0.0103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 960/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 1.3481e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.0125e-04 - val_root_mean_squared_error: 0.0101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 961/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 1.2541e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.0655e-04 - val_root_mean_squared_error: 0.0103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 962/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "8/8 - 0s - loss: 1.2616e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1832e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 963/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 1.2871e-04 - root_mean_squared_error: 0.0113 - val_loss: 8.7537e-05 - val_root_mean_squared_error: 0.0094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 964/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 1.2592e-04 - root_mean_squared_error: 0.0112 - val_loss: 9.1618e-05 - val_root_mean_squared_error: 0.0096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 965/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "8/8 - 0s - loss: 1.1988e-04 - root_mean_squared_error: 0.0109 - val_loss: 9.0373e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 966/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 1.1389e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.9640e-05 - val_root_mean_squared_error: 0.0095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 967/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "8/8 - 0s - loss: 9.6888e-05 - root_mean_squared_error: 0.0098 - val_loss: 7.4263e-05 - val_root_mean_squared_error: 0.0086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 968/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", + "8/8 - 0s - loss: 9.1415e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.2326e-05 - val_root_mean_squared_error: 0.0079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 969/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", + "8/8 - 0s - loss: 7.2259e-05 - root_mean_squared_error: 0.0085 - val_loss: 5.7104e-05 - val_root_mean_squared_error: 0.0076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 970/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "8/8 - 0s - loss: 6.8295e-05 - root_mean_squared_error: 0.0083 - val_loss: 5.7322e-05 - val_root_mean_squared_error: 0.0076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 971/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 4.6255e-05 - root_mean_squared_error: 0.0068 - val_loss: 5.1797e-05 - val_root_mean_squared_error: 0.0072\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 972/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "8/8 - 0s - loss: 3.7687e-05 - root_mean_squared_error: 0.0061 - val_loss: 4.9673e-05 - val_root_mean_squared_error: 0.0070\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 973/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0656\n", + "8/8 - 0s - loss: 3.0702e-05 - root_mean_squared_error: 0.0055 - val_loss: 4.0822e-05 - val_root_mean_squared_error: 0.0064\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 974/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0659\n", + "8/8 - 0s - loss: 3.3565e-05 - root_mean_squared_error: 0.0058 - val_loss: 4.4309e-05 - val_root_mean_squared_error: 0.0067\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 975/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 3.3553e-05 - root_mean_squared_error: 0.0058 - val_loss: 5.1481e-05 - val_root_mean_squared_error: 0.0072\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 976/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", + "8/8 - 0s - loss: 4.1456e-05 - root_mean_squared_error: 0.0064 - val_loss: 5.6749e-05 - val_root_mean_squared_error: 0.0075\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 977/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", + "8/8 - 0s - loss: 4.1719e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.9498e-05 - val_root_mean_squared_error: 0.0070\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 978/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", + "8/8 - 0s - loss: 3.4179e-05 - root_mean_squared_error: 0.0058 - val_loss: 3.2296e-05 - val_root_mean_squared_error: 0.0057\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 979/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", + "8/8 - 0s - loss: 3.4550e-05 - root_mean_squared_error: 0.0059 - val_loss: 3.1676e-05 - val_root_mean_squared_error: 0.0056\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 980/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", + "8/8 - 0s - loss: 6.6102e-05 - root_mean_squared_error: 0.0081 - val_loss: 7.4812e-05 - val_root_mean_squared_error: 0.0086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 981/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", + "8/8 - 0s - loss: 1.2834e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.0470e-04 - val_root_mean_squared_error: 0.0102\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 982/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0656\n", + "8/8 - 0s - loss: 1.7333e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9454e-04 - val_root_mean_squared_error: 0.0139\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 983/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", + "8/8 - 0s - loss: 1.3208e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2640e-04 - val_root_mean_squared_error: 0.0112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 984/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", + "8/8 - 0s - loss: 1.7959e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.6001e-04 - val_root_mean_squared_error: 0.0126\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 985/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0652\n", + "8/8 - 0s - loss: 2.2595e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.1169e-04 - val_root_mean_squared_error: 0.0145\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 986/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", + "8/8 - 0s - loss: 2.1086e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.6833e-04 - val_root_mean_squared_error: 0.0130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 987/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0651\n", + "8/8 - 0s - loss: 1.4464e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.1271e-04 - val_root_mean_squared_error: 0.0106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 988/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0649\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 1.1132e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.5204e-05 - val_root_mean_squared_error: 0.0087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 989/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", + "8/8 - 0s - loss: 7.1571e-05 - root_mean_squared_error: 0.0085 - val_loss: 8.1473e-05 - val_root_mean_squared_error: 0.0090\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 990/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0650\n", + "8/8 - 0s - loss: 6.7915e-05 - root_mean_squared_error: 0.0082 - val_loss: 6.9879e-05 - val_root_mean_squared_error: 0.0084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 991/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0652\n", + "8/8 - 0s - loss: 6.2029e-05 - root_mean_squared_error: 0.0079 - val_loss: 5.0120e-05 - val_root_mean_squared_error: 0.0071\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 992/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0649\n", + "8/8 - 0s - loss: 5.0002e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.4865e-05 - val_root_mean_squared_error: 0.0067\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 993/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", + "8/8 - 0s - loss: 4.4919e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.6180e-05 - val_root_mean_squared_error: 0.0068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 994/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0649\n", + "8/8 - 0s - loss: 4.7784e-05 - root_mean_squared_error: 0.0069 - val_loss: 5.3897e-05 - val_root_mean_squared_error: 0.0073\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 995/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0647\n", + "8/8 - 0s - loss: 6.0128e-05 - root_mean_squared_error: 0.0078 - val_loss: 3.7373e-05 - val_root_mean_squared_error: 0.0061\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 996/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0650\n", + "8/8 - 0s - loss: 8.9006e-05 - root_mean_squared_error: 0.0094 - val_loss: 7.3439e-05 - val_root_mean_squared_error: 0.0086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 997/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0647\n", + "8/8 - 0s - loss: 1.1618e-04 - root_mean_squared_error: 0.0108 - val_loss: 8.3374e-05 - val_root_mean_squared_error: 0.0091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 998/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0651\n", + "8/8 - 0s - loss: 1.4546e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.7940e-04 - val_root_mean_squared_error: 0.0134\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 999/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 1.1602e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.3071e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 1000/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", + "8/8 - 0s - loss: 1.4286e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3949e-04 - val_root_mean_squared_error: 0.0118\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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\n", 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zELjTPfpmOFBgjDkiIgK8Cuwwxjxbr5U3ptiO0HEUbJrt2+gbpZRqQmoNemNMJTAD+Bjrw9R3jDHbROQ+EbnP3WwxsBfIBF4G7ndvHwl8FxgnIunur8n1fRKNYuCdcHIfHFjp70qUUqpOAn1pZIxZjBXmnttmeTw3wANejvsK7/33zU/PKbD4Edj4H0gdXXNbY0DscdpKqeZP74z1VVAY9JsG2xdAcV7NbV2VjVOTUkr5QIO+LgbdDc5ySH+r5nZOXWtWKdV0aNDXRdtekDQUNrxudc9ciAa9UqoJ0aCvq0F3Q24GHPr63O2ewe/UrhulVNOhQV9XPadAYBh888652yuKzz6fNQqcFY1bl1JKXYCtgv6G51fyyoq9DftDQqKsG6i2zbfCvCQf/nXFuVMknD4KJScbtg6llPKRrYI+41ghRwtKG/4H9b0VinNhz+ewYxEc2Qyf/e7cNmWFDV+HUkr5wFZB7wgQKl01fEhaXzqPtyY72/IOFB51b6z2czXolVJNhK2CPtARQGVjTFEQGAy9brSmLs7NtLblH7Qer/2L9Vh+uuHrUEopH9gr6AMEZ2Nc0QP0nWZ9ALtl7rnbY1KsxzINeqVU02C7oK90NlLQJw+DywZU2ygQ457EszS/cepQSqla2CroHY5G6qMHay6bCX+CyLbWcEuAiHiITQVHCBz9pnHqUEqpWtgq6IMCAhov6MFaeerh3TDkHut18jCr/75Nd2uhEqWUagJsFfSOAMHpj/niU0ZDm15w5S+t19HJUJDV+HUopZQXtgv6Ruuj99RtIty/Ctr3tV5HJ1lX9Cufa/xalFKqGlsFfWBj9tHXpMtV1uPS3/i3DqWUwm5B39h99BeSdjV0nQjBkf6uRCml7Bb0fuqj96bj5dZNU6Wn/F2JUqqFs1XQOwKECn/00XvTKtF6PJHh3zqUUi2erYI+0NGId8bWJvVKCI2GN2/2mA9HKaUan72Cvqn00QNEJsCEJ63pitfOqr29Uko1EJsFfRPqowcY8B1IvQJ2LfF3JUqpFsxWQe+3cfQ16ToRcnbAyf3+rkQp1ULZKuibzDh6T10nWo+7P/ZvHUqpFsteQR8Q0HQ+jD0jrjPEpVlz1yullB/YLOilcRYeqateN8De5fD+A2Ca2B8ipZTt2Srom2QfPcAVj8Cg70H6bMha571NxlL90FYp1SBsFfRNso8eIDAErn4CAoJg+wLvbd68BeZMa9y6lFItgr2Cvin20Z8RGg2dx8KOhed338z9jn9qUkq1CLYKemsKhCbYR39Gj+utRcSPbrHCPv8QVJTCzg/OtjmR6b/6lFK2ZKugb9TFwS9Gt2tBHLB1Hnz6W/hbb9j/1blt/jnIP7UppWwr0N8F1KdARxOaAsGbiDjoNglW/xNclda29Nn+rUkpZXt6Rd/Yrn0WOo05O1d9xlJ/VqOUagFsdUXvcAe9MQYR8Xc53kW1hTves/ron0mDopzz2zgrwWGrfxqllB/Z7ooeaNrdN2eIQGCY+3kA/HI/THrael2a77eylFL241PQi8hEEdklIpkiMtPLfhGR59z7t4jIQI99r4nIcRHZWp+FexPosE6nyXffnDHyQetx8D0QFmt9gTW1sVJK1ZNag15EHMDzwCSgJ3C7iPSs1mwSkOb+mg686LHvdWBifRRbm2Z1RQ8w9Afw2FGY/GfrdVhr6/HYNv/VpJSyHV+u6IcCmcaYvcaYcmAuMLVam6nAG8ayBogRkfYAxpgvgbz6LPpCHGeCvimPpa8uKMzqxgGI7Wg9vnsXVJb7ryallK34EvSJwCGP11nubXVtUyMRmS4i60VkfU6Olw8ofRDoaGZX9NXFp0Hn8dbzd+/yby1KKdvwJei9DV+pnqS+tKmRMeYlY8xgY8zghISEuhxaJTCgmfXRe3P7XAgMtWa7VEqpeuBL0GcByR6vk4DDF9GmwZ3po2/S0yDUJjAYhv8IKorhidY6JYJS6pL5EvTrgDQRSRWRYOA2YGG1NguBO92jb4YDBcaYI/Vca63O9NE36yt6gN43W4/GCe98F041+q9SKWUjtQa9MaYSmAF8DOwA3jHGbBOR+0TkPnezxcBeIBN4Gbj/zPEiMgdYDXQTkSwRuaeez6FKcKB1OmWVzfiKHqBdH3i8wHp+fDs8213voFVKXTSfbr80xizGCnPPbbM8nhvggQsce/ulFFgXcRHBALy19iCPT+nVWD+24bRKhFPZ1vM3b4HEwXDvp9brtf+C2BTo1igjV5VSzZit7oxNiAoB4PVV+/1bSH25ZylM+NPZ19nrYdNsmH8fLPmltVCJs9J/9SmlmgVbBj3YoJ8eIDoRRlR7o7RwBmyZe/Z1/oHGrUkp1ezYKuhjwoOrnv/6/a3N68apmvxsp3V1702ujspRStXMVkEP0CcxGoA5Xx/kF+9t8XM19aRVe0gean1AO2O9tVLVTa9Y+05k+Lc2pVSTZ7ug79A6vOr5vI3ZTH9jPQUlFX6sqJ7Fp8G02dD3W9YkaLka9Eqpmtku6J+8uQ+z7hjEkp+MZsbYLizbdZxp/1rN7mOF/i6t/sV3tT6cLT3l70qUUk2Y7YK+VWgQE3u3o3u7Vjw8oRsv3TmYE6fLuPnFVazdm+vv8urXZQOsJQmfSoa9X8ArV8HJ/TDvhzoDplKqiu2Cvrqx3drw/gMjSYgK4Y5X1/LxtqP+Lqn+jH3s7PM3pkDWOlj2J2tUzuvXQWXZxX9vlxNW/EXfLShlA7YPeoCk2HDm/2gkvROjeeDNjSxIz/Z3SfUjtBU8svfcbVveth5L8mDpb89uNwYKsqwbrUp8WMFq54fw2e/g08frrVyllH+0iKAHiA4P4o3vD2VQx1h+8nY6/11jk/HnEXHw0+1w08vn71v7IrjcQ0zXvQJ/7QUf/QI+fuz8tmeUnoJlT0KZ+0q+TK/olWruWkzQA0SFBvGf7w9lfPc2/Ob9rTy7dDfW7A3NXHQi9L0VRv8cbn0Doj0mEl1wv3X37OKHz24rrDax6NGt8Hg0rH4ePv8DfPEUbHvf2vfNu1Boo+4upVogaYpBN3jwYLN+/foG+/6VThe/mv8N76zP4rYhyfzhht5V683awqnD8NVf4euXLtzm7g8hZRRsfQ/+9/2z23tcDzsWnds2rDX8bAcEhTZMvUqpSyYiG4wxg73t82lSM7sJdATw/27uS9tWofzj80xyCsv4x7cHEB5sk19Hq8usdWg7j4M5t3lv8/q11mPUZedurx7yYPX37/sCuk6o3zqVUo3CRpexdSMi/Pyabvzhht4s23Wc219eS+7pSxil0hR1mwTjfmM9D42B4EjoWy34q3fjXMjOD+q3NqVUo2mRXTfVfbztKA/O2US76FD+ffcQOiVENtrP9ovcPTD325Czs27HPZwJkRe3zKNSqmHV1HXTYq/oPU3o1Y4504dTWFrJTS+uYlXmiTp/D5fLMPO9LXzSHMbpx3WG+1aev73D5RDRBn6dY+2fsR46jjq7/8Qua3nDl8ZAcV6jlauUujR6Re/hQG4R9/xnPXtzTvPopB7cOzoVEW/rnp9v3f48vjVrNQD7n7q2IcusPwXZ4AiCZ9Kg83j4zrtQUQIhHu9oyk7Dk4nnH9u6M4x51JpzRynld3pF76OOcRG8/8BIJvRqxx8X72DGnE0Ulfm2sMfB3OKq52WVzoYqsX5FJ0JkG3g4A257CwIc54Y8WK/v++r8Y/P2wLx7rRuxlFJNmgZ9NZEhgbzwnYHMnNSdj745wo0vrGTfiaJaj1t/4GTV8y925TRkifUvsk3NQyfb9YF7PvW+r+Sk9+1KqSZDg94LEeG+KzvzxveHkVNYxuS/r+CF5ZmUVlz4Sn3O1wernj+xaHtjlNm4kod4X/zk6VTrMWu9ddPVkketqRP0Sl+pJkODvgaj0uL58MHRjE6L5+kluxj/ly9YkJ6Ny2OZwryicv74oRXsV/dsy5huCWTnlzSf7pu6SB4KP1wBN78KA+44u33Ot+GV8dbzNS9Yk6EV1f0DbaVUw9Cgr8VlMWG8dOdg3rx3GNFhQTw0N50bX1jJ5kP5FJdXcsuLq3h5xT4ABnWM5bYh1vQDO474Nv+9MYavMk6QMvND3t/UDCZba98X+twC1z57dtuuD89vt/5VOHWk8epSSl2QBr2PRnaJ54Mfj+Iv3+rH4YJSpj6/kiueXs6+3CLuHNERgK5tI+mXHANA+kHf+q6fWrKTO15dC8BP3k5vmOIbQmAI3PvZhfcvfxKe7Q77voQNr0NF6bn7szfAgVUNWqJSymKTe/4bR0CAcPOgJK7p1ZY3Vh/g6315XNunPbcOSebB8WnERViLk7dtFcJ/1xxgXPe2dIgLr/F7vup+N3CGMcbnIZ1+lzQYfrEPvnwGMj72vlD5f663Hk/nwKC7ISIejAteHmdt/00uSAAE6DWHUg1Fx9E3gCVbj/KjNzcQGx7MOz8cQZc2F77TdvxflrMn5+yontWPjqN9dFhjlFn/CrIgb6/VZbPqOTi29fw2addAxifnbutxvbUOrlLqouk4+kY2sXc7HhyXRl5ROVc9+wVLth4h93TZeVMif7E7pyrkfzW5OwCf7jjeoLVlHCvE6WqgP+7RSZB6BfSbBj9aCVP+4aWAT87ftmMRrPqHjtRRqoFo0DeQO4Z35MYBiSTGhHHf7I0M+sOnzHhrE6UVTl5fuY+xzyznrte+BmDG2C78YHQnOsVH8On2Yw1W09bsAq7+65e8smLvefuMMeQXlwNQXulio4+fMdRo4J3weIHVl9+6c81tP/k1PBFjLWHoTUE2LH/KGtFzZjEVpZRPtI++gSREhfDXaf05fqqU/645wPFTZby9/hDLdh2nuPzcMOsQF46IMCotnnfWH+JQXjHJrc/27RtjWJB+mCGprUmMCeN/G7J4e91Bnrypb43dQtWt3mMtjr7t8PmrRv1p8Q5eXrGPzf93DZOfW0F2fgm/m9oLAb47IuWifgdVkgbDgxuthctfm1TzjJl7l0GXq87dVpAFf+0NuK/4k4ZY7xyUUj7RPvpGtDLzBM8vyyQ0yMG9o1LJL6kg2BHAqLR4QoMc7DtRxNhnlgNw9+UpJMWGISLM25jlNZzT2kSy9GdXnre9tMJJYICcs5jK7DUH+PX7Vp/5jQMS+eu0/lX7Kp0uujz2EQDX9W3PB1vOHRa54ddXERcZcsnnf46KEig8AhvfgNUvgNNjiujJz8Dg71tTMhgDz/aw2p4x+ufWV3BE/dakVDNWUx+9Bn0T89bag/xq/jc+t185cxyJMWc/vN2aXcB1//iK0WnxvHb3EILcYT/xb1+y86g1tj8i2EH6b6+p2vfqV/v4/QcXvpv3udsHMKXfZRfcf8mMsbpsvnoWlv3x7PbYVDjpMSpp0p+tefH3fWG9vmMedBnfcHUp1Yzoh7HNyLeHdWDL49dw54iOdG8XxW+v78nLdw5m0YxR3DQwkX9/bwg/GtOZl++0/j1XuqdUXr0nl7fWHuS6f1gTkK3IOMFv3j876uWEx6IqReVOPnZPp7x2b67XkB+a2ppHJnQD4ME5m1iQ3oA3c4mAIxCu/AXMPAg9pljbPUP+2+/AsOnW6Jy+06xts2+C9a/59iFu4TH48GH42ssi6krZnF7RN1Mul2H008sIdAjTr+jE4wu3UeGs/d/yZ1d35fVV+8krKici2EGRx+cFV/dsy9Ltx7h9aAeevKkPAI/O28Kcrw8BsOIXY4mLDOazHcf5ZPsx+iZG84MrOjXMCZYXQcZSOJUN7fvh6jCSj7cdZUy3NoQFO2DbfHj3bqttbApcNgBiOsDeL+CI+8azbpNh/P9BeTG8Mu7s9773M+tzA6VsRLtubGrDgTzuem0dp8sqaRUayL+/N4Rgh4P2MaE8uXgn723MqmrbvV0UL313MMmtw3hi0XZeX7W/at9Pr+rKgA4x5BSW8ZdPdvHyXYPpdVl01f631x3kl+95707a/NtriA4L4mhBKX//bDe3DelA+qF87hzREWOsm8y8WZl5grJKJ+O6t/XpXN9Zd4hfvLcFgDWPjqdddKg1V/7iR2DP53C6jgu+hMfB8Pth1M/O3qxVchJCos+9eauyHI5+A0mD6vb9lWpkGvQ2dqq0gk0H8+kUH3HOSB0Ap8uwdA8EU4sAAA2CSURBVPtR+ifHWsHoVuF0sTeniN9/sJ34yGD+dtuAWn/O3K8PMnPe+WF/bZ/2hAc7eHdD1nn7HAHCHcM6sPXwKX4/tTdd20ZSUuFk86GCqmkffnpVV/olRzO8UxwBIgQHnt+buHT7MX7wxtn/Hh6/vid3j0w928Dlsla/yvzMGtlTWQK5e+GgxxQLfW7FhEYjxblWP7/TGkpKWGu4/Mew5kUoct/D8N33ofNYqCyDP7Sxtt02xxrtk/4mjJhhdTWdUXjMukGsy1WQMtpaTD0iweqS8tXhdGjTw5paQqmLoEGv6sXenNOEBDlIjAnDGMPP393MvI1n++6TYsMY1SWeBemHKfEypXOQQ2rsXuoYF86iH4+iVWhQ1bbyShffeWUN6/afZGSXOI7kl+Iyhnn3j6S1e8qJ2rhchun/3cCnO6xuqT/d2BspOGSN+PnyGaqGbXoaeJfVPVR2/mgnAMY+Zk3dkDwM/nPd+fvb9oHrnoW2vSG4hmkwjKE46xvCXx1tvb76d3D5g7X+kXC6DK/+84/0iCxi9G2PQFis9YH2mT9AOz+kcsljBNzxHgHxtdzD0ETtOlpIlzaROC7wrlCd65KDXkQmAn8HHMArxpinqu0X9/7JQDFwtzFmoy/HeqNB3zwUllbwu0XbSYgK4a7LU2jbynrXUFLu5NipUorKK/lsx3FiI4J5eslOCkut1boSY8L4v+t74nIZtmQXcDCvmKXbj1Fe6aJb2yhuGphIp4RIyiqdPLFoOzmFZfxoTGdmjO3CF7tzmPHWRoIDA5jcuz13jOhIn8RoghwBFJdXMm9jNvM3ZVNW6aRLQiRJseEs2nKYAx4rgAHERwbz+6m9ubp7HIH7v8Qc20ZeYRHlJw/TLmsxUpIPxvpjlUEyaRy6+F9UhxHWZwjJw6wALy+Gkjxc6XMIOOa9S8yEx0PyUGTYDyFp6Nk/Fsd3wp7P2XykmH5bfn/ecRlDf0/bg4tpddRa1vJEUCKt75pNQG4GJA4ip0yQw5uI7X89uceyaRMTVS8LvjtdhvV7jzMwJY6gwEu/PWfZzmO888bzdOs3gp9Mm3TJ36/OXC7r3+rMH1xj6vYOzQ8uKehFxAHsBq4GsoB1wO3GmO0ebSYDP8YK+mHA340xw3w51hsNevupcLoIcgRwsqic2AtciS/beZzfLtzGwbyzoRwW5ODukSn8cmL3qm1bswuY8/VBFqQf5rR7qcfwYAdlla6q6R2SYsM4dqq06h3Ej8d14cHxafzjswye+/zs5GsJUSH0S4om8/hp9rv/GAzsEMN3hlzGkk17+GxvCS6PwWm9ZS9tJJ9kRy7DQrPpWLmPN8rH8D8zls7mEIUmnN4B+wjAMCNwPp0cOUSY2lcoW+oaQkpgLmmu8+9adgYEURkaj8tZQVjZufP8F5kQAnESIucveTnfOZLrA1YTKOffSXyaMCIpASAzagiVgRHEJHWnNCiW0PAIotqkUOZ00apVDAGhrSgqLoLAMBxh0Rzfv5XiXcsJdJUQ2e8GygOjiJn/HWJMPiWEcGLsn8l3BlMS1JqO7eLZs3Mzzu0fEBUsxJ/eSX5lMLtbXU7PASMIioonLDaRNokpOIJDKSs5zf5vVuH6+Nf0xPpdFIS0p/KGl2ndeSDiCLHurxDBGEPW0RNkbllB5ZZ5BBTnENz5CuKSuhCy+wMqjVBgwhh6dC5ZoWmciuxEYHgMJZEdqSzKI1+iiAsLIjCuA2UVTsoLTxB5fAN9Tiyu+j2lmzSKHK0Y4dpIRvhAwq9+lPLgGEpy9hHephMdu/bH4fGHzVVZya7VC8nO3IIzvA1x7VMIjWlHdEISiW0TCAgQyspK2Db7lwSc2M2J3t9j+NgpVBafImvZS5SEJDBkyn21/vfizaUG/QjgcWPMBPfrRwGMMU96tPkXsNwYM8f9ehcwBkip7VhvNOhbLmMM2fklHMwrJjw4kE4JEed05Xg6XVbJR98c4UhBKadKKgh0BDA0NZbRaQkEOQIoq3RSVOYkJizonA+FXS5DcYWTT7YdZf6mbHJPlxMTHsSk3u1AhL8u3U1eUTmOAOH3U3tzfb/2/OuLvUSHBREdHsR/Vu3naEEpCVEh7DxayO1Dk3liSm8+2noEEWFy73ZsPXyKeRuzWLr9GCXllYSVHCNJcmglRURTxJGQVCKDIK59CkPaBXDd1ePJKSzjz0t2kn+6hFHBuwgsOMA3ucIA5zd0kcMUE8IVAVs4GRDDV3G30mPCvUTGXcbxwjK6to2k7OhOstfOJyvhSlK7D6BH+yjmvP0m+fs2EBoeSc/cT9kf0ZdoKSLkdDYhbToTmLubUOdpWkkRqVLHD7QbWGl8b0JPeJkYDygnkEochFPmdX9jcRqhjGBKJJRAnERz+oJtXUZwigMwBOE853s4xMrhQsII/nU2IYGOOtdyqUF/CzDRGHOv+/V3gWHGmBkebT4AnjLGfOV+/RnwS6ygr/FYj+8xHZgO0KFDh0EHDhyo63kqVS9KK5xk55cQFxFMTHjNnwO4XOaCI4vOMMaQdbKE4nIn4cEOAgKEy6JDfZqO2uUyHMgr5kBuETHhwfRJjL7oPmvPWksrnIQGOXC6DBVOF6fLKtm47xgRAU5OFZykPO8QwYEOSooKCagoJDQ0nEBXGYHl+YRExZPauSvFLgdZuzcTWJZPaq+htO8+gtzsPWzetJro8FDCAw0nTp4kJjyEbv1HUHDsAEEOB9FdR1GQtYODmVsR46KopJTyvIOIswJHaCSOwEDSBl9N67ThIEJmxg5yNiyg8FQ+rspyHKaSAFcloQFOoltFEd86lrhuowjsNIp9OzeRf2gHsZU5tIppjTMuDSrLiaGQwtIKTuXnUVJSQqvQAGiVxOniIpz52YRGJxARJAQER5DQdSgSnQyl+RASBQGBUFHCgTXzyD9xFHEE42iTxumjeyBvD6a8BCqKcboMAWExhMUnkdJnJBQe52RxGSVFp6g4mU1BQQFOZyUhrlJCUwbT7fLr2btyHhW5+ykPCCUqsQdJfa4kum2Hi/r3vdSg/xYwoVpYDzXG/NijzYfAk9WC/hdAp9qO9Uav6JVSqm5qCnpfPjXJApI9XicB1WelulCbYB+OVUop1YB8mQJhHZAmIqkiEgzcBiys1mYhcKdYhgMFxpgjPh6rlFKqAdV6RW+MqRSRGcDHWEMkXzPGbBOR+9z7ZwGLsUbcZGINr/xeTcc2yJkopZTySm+YUkopG9DZK5VSqgXToFdKKZvToFdKKZvToFdKKZtrkh/GikgOcLG3xsYDJ2ptZS96zi2DnrP9Xcr5djTGeJ2hrkkG/aUQkfUX+uTZrvScWwY9Z/trqPPVrhullLI5DXqllLI5Owb9S/4uwA/0nFsGPWf7a5DztV0fvVJKqXPZ8YpeKaWUBw16pZSyOdsEvYhMFJFdIpIpIjP9XU99EZFkEVkmIjtEZJuIPOTe3lpElopIhvsx1uOYR92/h10iMsF/1V8aEXGIyCb3Cma2P2cRiRGR/4nITve/94gWcM4/df93vVVE5ohIqN3OWUReE5HjIrLVY1udz1FEBonIN+59z4kvS5SdYYxp9l9YUyDvwVrRKhjYDPT0d131dG7tgYHu51FYi633BJ4GZrq3zwT+n/t5T/f5hwCp7t+Lw9/ncZHn/jPgLeAD92tbnzPwH+Be9/NgIMbO5wwkAvuAMPfrd4C77XbOwBXAQGCrx7Y6nyPwNTACEOAjYJKvNdjlin4okGmM2WuMKQfmAlP9XFO9MMYcMcZsdD8vBHZg/Q8yFSsYcD/e4H4+FZhrjCkzxuzDWiNgaONWfelEJAm4FnjFY7Ntz1lEWmEFwqsAxphyY0w+Nj5nt0AgTEQCgXCsFehsdc7GmC+BvGqb63SOItIeaGWMWW2s1H/D45ha2SXoE4FDHq+z3NtsRURSgAHAWqCtsVbxwv3Yxt3MLr+Lv2GtO+zy2Gbnc+4E5AD/dndXvSIiEdj4nI0x2cAzwEHgCNbKdJ9g43P2UNdzTHQ/r77dJ3YJem99VbYaNyoikcB7wE+MMadqauplW7P6XYjIdcBxY8wGXw/xsq1ZnTPWle1A4EVjzACgCOst/YU0+3N290tPxeqiuAyIEJE7ajrEy7Zmdc4+uNA5XtK52yXofVnAvNkSkSCskH/TGDPPvfmY++0c7sfj7u12+F2MBKaIyH6sbrhxIjIbe59zFpBljFnrfv0/rOC38zlfBewzxuQYYyqAecDl2Pucz6jrOWa5n1ff7hO7BL1tFyF3f7L+KrDDGPOsx66FwF3u53cBCzy23yYiISKSCqRhfYjTbBhjHjXGJBljUrD+LT83xtyBvc/5KHBIRLq5N40HtmPjc8bqshkuIuHu/87HY30GZedzPqNO5+ju3ikUkeHu39WdHsfUzt+fSNfjJ9uTsUak7AEe83c99Xheo7Deom0B0t1fk4E44DMgw/3Y2uOYx9y/h13U4ZP5pvgFjOHsqBtbnzPQH1jv/rd+H4htAef8BLAT2Ar8F2u0ia3OGZiD9RlEBdaV+T0Xc47AYPfvaQ/wT9wzG/jypVMgKKWUzdml60YppdQFaNArpZTNadArpZTNadArpZTNadArpZTNadArpZTNadArpZTN/X/GTvnrLU7tewAAAABJRU5ErkJggg==\n", 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" ] @@ -5612,15 +5624,15 @@ "source": [ "# design network\n", "model = Sequential()\n", - "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(GRU(1))\n", + "model.add(SimpleRNN(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(SimpleRNN(50, return_sequences=True))\n", + "model.add(SimpleRNN(50, return_sequences=True))\n", + "model.add(SimpleRNN(1))\n", "model.add(Dense(1))\n", "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", "# fit network\n", "# \n", - "history = model.fit(train_X, train_y, epochs=1000, batch_size=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "history = model.fit(train_X, train_y, epochs=1000, batch_size=100, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", "# plot history\n", "plt.plot(history.history['loss'], label='train')\n", "plt.plot(history.history['val_loss'], label='dev')\n", @@ -5630,7 +5642,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -5641,7 +5653,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -5658,7 +5670,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -5678,7 +5690,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -5707,24 +5719,17 @@ "\n", "def return_rmse(test, predicted):\n", " rmse = math.sqrt(mean_squared_error(test, predicted))\n", - " print(\"The root mean squared error is {}.\".format(rmse))" + " print(\"The test root mean squared error is {}.\".format(rmse))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 127, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5738,7 +5743,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 62766.86208502063.\n" + "The test root mean squared error is 67926.15661142621.\n" ] } ], @@ -5749,12 +5754,12 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5771,7 +5776,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -5779,10 +5784,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 318737\n", - "1 299330\n", - "2 82255\n", - "3 212019\n", + "0 636064\n", + "1 455702\n", + "2 452453\n", + "3 703108\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5800,7 +5805,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -5819,14 +5824,14 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_chris_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)" ] }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -5843,14 +5848,14 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 237086.54136675494.\n" + "The test root mean squared error is 131301.65451832663.\n" ] } ], @@ -5860,7 +5865,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -5923,18 +5928,24 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 86, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'create_train_test' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test_not_norm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_not_norm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_train_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_copy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mx_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mx_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'create_train_test' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "717915\n", + "294611\n", + "king_test_norm\n", + "(60, 1)\n", + "king_train_norm\n", + "(924, 1)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" ] } ], @@ -5958,7 +5969,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -5970,7 +5981,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -5980,7 +5991,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -5990,7 +6001,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 179, "metadata": {}, "outputs": [], "source": [ @@ -5999,13 +6010,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 180, "metadata": {}, "outputs": [], "source": [ "# print(data_copy)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/.ipynb_checkpoints/multivar_simple_gru-checkpoint.ipynb b/.ipynb_checkpoints/multivar_simple_gru-checkpoint.ipynb new file mode 100644 index 0000000..777d864 --- /dev/null +++ b/.ipynb_checkpoints/multivar_simple_gru-checkpoint.ipynb @@ -0,0 +1,702 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mismael_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mabdul_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mking_all_copy\u001b[0m\u001b[0;34m,\u001b[0m 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946\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1176\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2009\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(abdul_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy = king_all_copy\n", + "print(data_copy['date'])\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = data_copy\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data[132:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data.drop(labels='index', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(master_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", + "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", + "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", + "cov_data = load_cov_set(ismael_path_cov)\n", + "cov_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "upwelling = cov_data[\"upwelling\"]\n", + "master_data = master_data.join(upwelling)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noi = cov_data[\"noi\"]\n", + "master_data = master_data.join(noi)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "npgo = cov_data[\"npgo\"]\n", + "master_data = master_data.join(npgo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "oni = cov_data[\"oni \"]\n", + "master_data = master_data.join(oni)\n", + "master_data\n", + "# cov_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data.rename(columns={\"oni \": \"oni\"})\n", + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(1))\n", + "model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=1000, batch_size=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/multivar_simple_lstm-checkpoint.ipynb b/.ipynb_checkpoints/multivar_simple_lstm-checkpoint.ipynb new file mode 100644 index 0000000..1f860a4 --- /dev/null +++ b/.ipynb_checkpoints/multivar_simple_lstm-checkpoint.ipynb @@ -0,0 +1,4900 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1939-01-01\n", + "1 1939-01-02\n", + "2 1939-01-03\n", + "3 1939-01-04\n", + "4 1939-01-05\n", + " ... \n", + "24364 2020-12-25\n", + "24365 2020-12-26\n", + "24366 2020-12-27\n", + "24367 2020-12-28\n", + "24368 2020-12-29\n", + "Name: date, Length: 24369, dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwelling
01950-01-310-16
11950-02-280-166
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852 rows × 3 columns

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datekingupwellingnoi
01950-01-310-162.644
11950-02-280-1662.077
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31950-04-306630-41.923
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852 rows × 4 columns

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datekingupwellingnoinpgo
01950-01-310-162.644-2.190
11950-02-280-1662.077-1.450
21950-03-3121-493.091-0.970
31950-04-306630-41.923-0.860
41950-05-3150638492.211-0.630
..................
8472020-08-3110526943-0.463-1.422
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852 rows × 5 columns

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datekingupwellingnoinpgopdo
01950-01-310-162.644-2.190-1.61
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21950-03-3121-493.091-0.970-1.89
31950-04-306630-41.923-0.860-1.99
41950-05-3150638492.211-0.630-3.19
.....................
8472020-08-3110526943-0.463-1.422-1.32
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852 rows × 6 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19\n", + ".. ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
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852 rows × 7 columns

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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kingupwellingnoinpgopdooni
date
1950-01-310-162.644-2.190-1.61-1.40
1950-02-280-1662.077-1.450-2.17-1.20
1950-03-3121-493.091-0.970-1.89-1.10
1950-04-306630-41.923-0.860-1.99-1.20
1950-05-3150638492.211-0.630-3.19-1.10
.....................
2020-08-3110526943-0.463-1.422-1.32-0.57
2020-09-30254930-1-0.276-1.161-1.03-0.89
2020-10-3130917101.612-1.476-0.62-1.17
2020-11-30843-431.998-1.710-1.58-1.27
2020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 6 columns

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" + ], + "text/plain": [ + " king upwelling noi npgo pdo oni\n", + "date \n", + "1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + "... ... ... ... ... ... ...\n", + "2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " var1(t-6) var2(t-6) var3(t-6) var4(t-6) var5(t-6) var6(t-6) \\\n", + "6 0.000006 0.520913 0.710488 0.220877 0.329032 0.119048 \n", + "7 0.000006 0.079848 0.683284 0.332829 0.238710 0.166667 \n", + "8 0.000035 0.399240 0.731936 0.405446 0.283871 0.190476 \n", + "9 0.009241 0.566540 0.675895 0.422088 0.267742 0.166667 \n", + "10 0.070540 0.764259 0.689713 0.456883 0.074194 0.190476 \n", + "\n", + " var1(t-5) var2(t-5) var3(t-5) var4(t-5) ... var3(t-1) var4(t-1) \\\n", + "6 0.000006 0.079848 0.683284 0.332829 ... 0.632281 0.464448 \n", + "7 0.000035 0.399240 0.731936 0.405446 ... 0.567508 0.440242 \n", + "8 0.009241 0.566540 0.675895 0.422088 ... 0.572306 0.468986 \n", + "9 0.070540 0.764259 0.689713 0.456883 ... 0.591786 0.461422 \n", + "10 0.023221 0.703422 0.632281 0.464448 ... 0.461760 0.570348 \n", + "\n", + " var5(t-1) var6(t-1) var1(t) var2(t) var3(t) var4(t) var5(t) \\\n", + "6 0.182258 0.238095 0.045884 0.847909 0.567508 0.440242 0.000000 \n", + "7 0.000000 0.309524 0.056366 0.638783 0.572306 0.468986 0.108065 \n", + "8 0.108065 0.309524 0.286279 0.634981 0.591786 0.461422 0.201613 \n", + "9 0.201613 0.333333 0.006073 0.380228 0.461760 0.570348 0.279032 \n", + "10 0.279032 0.309524 0.000205 0.311787 0.606804 0.512859 0.354839 \n", + "\n", + " var6(t) \n", + "6 0.309524 \n", + "7 0.309524 \n", + "8 0.333333 \n", + "9 0.309524 \n", + "10 0.285714 \n", + "\n", + "[5 rows x 42 columns]\n" + ] + } + ], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "#create train, test, dev split\n", + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 6)\n", + "(80,)\n", + "(712, 6, 6)\n", + "(712,)\n", + "(54, 6, 6)\n", + "(54,)\n" + ] + } + ], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "1/1 - 5s - loss: 0.0112 - root_mean_squared_error: 0.1059 - val_loss: 0.0452 - val_root_mean_squared_error: 0.2126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2/1000\n", + "1/1 - 0s - loss: 0.0100 - root_mean_squared_error: 0.0999 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 5/1000\n", + "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0984 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 6/1000\n", + "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0989 - val_loss: 0.0400 - val_root_mean_squared_error: 0.1999\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 7/1000\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0982 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 8/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 9/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 10/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0962 - val_loss: 0.0422 - val_root_mean_squared_error: 0.2055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 11/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 12/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0430 - val_root_mean_squared_error: 0.2073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 13/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0431 - val_root_mean_squared_error: 0.2076\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 14/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0430 - val_root_mean_squared_error: 0.2074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 15/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0967 - val_loss: 0.0428 - val_root_mean_squared_error: 0.2069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 16/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0425 - val_root_mean_squared_error: 0.2061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 17/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 18/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 19/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 20/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 21/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 22/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 23/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 24/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 25/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 26/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 27/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 28/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2038\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 29/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 30/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 31/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2047\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 32/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 33/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 34/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 35/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 36/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 37/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 38/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 39/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 40/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 41/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 42/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 43/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 44/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 45/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 46/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 47/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 48/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 49/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 50/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 51/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2011\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 52/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 53/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 54/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 55/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 56/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 57/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 58/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 59/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 60/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 61/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 62/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 63/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 64/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 65/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 66/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 67/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 68/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 69/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2011\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 70/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 71/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 72/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 73/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 74/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 75/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 76/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0930 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 77/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 78/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 80/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 81/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 82/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 83/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 84/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1999\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 85/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 86/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 87/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 88/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 89/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 90/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 91/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 92/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 93/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 94/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 95/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1989\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 96/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 97/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 98/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 99/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 100/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1982\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 101/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 102/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0914 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1979\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 103/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 104/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 105/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1973\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 106/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 107/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 108/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 109/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 110/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 111/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 112/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 113/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 114/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 115/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 116/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 117/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 118/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 119/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 120/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 121/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 122/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 123/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 124/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 125/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 126/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 127/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 128/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 129/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 130/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 131/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 132/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 133/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 134/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 135/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 136/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 137/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 138/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 139/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 140/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 141/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 142/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 143/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 144/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 145/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 146/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 147/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 148/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 149/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 150/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 151/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 152/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 153/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 154/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 155/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 156/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 157/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 158/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 159/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 160/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 161/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 162/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 163/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1793\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 164/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 165/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 166/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 167/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 168/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 169/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 170/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 171/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1750\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 172/1000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1742\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 173/1000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1726\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 174/1000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1715\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 175/1000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 176/1000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1688\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 177/1000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 178/1000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0778 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1659\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 179/1000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0772 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1644\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 180/1000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1624\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 181/1000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0761 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1609\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 182/1000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0756 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1594\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 183/1000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1571\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 184/1000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1554\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 185/1000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 186/1000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0738 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1510\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 187/1000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 188/1000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 189/1000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 190/1000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1437\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 191/1000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1499\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 192/1000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 193/1000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 194/1000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0717 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 195/1000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1365\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 196/1000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 197/1000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 198/1000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 199/1000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 200/1000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 201/1000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 202/1000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 203/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 204/1000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 205/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 206/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 207/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 208/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 209/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0676 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1287\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 210/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 211/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 212/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 213/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 214/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 215/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 216/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 217/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 218/1000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 219/1000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 220/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 221/1000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 222/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 223/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 224/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 225/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 226/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 227/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 228/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 229/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 230/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 231/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 232/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 233/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 234/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1120\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 235/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 236/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 237/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 238/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1045\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 239/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 240/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 241/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 242/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1047\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 243/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0635 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 244/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 245/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 246/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 247/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 248/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 249/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 250/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 251/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 252/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 253/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 254/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1027\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 255/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 256/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 257/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1023\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 258/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 259/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 260/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 261/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 262/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 263/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 264/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 265/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1053\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 266/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 267/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 268/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 269/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 270/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0596 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 271/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0953\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 272/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 273/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 274/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 275/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0985\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 276/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 277/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 278/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 279/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 280/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 281/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 282/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 283/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 284/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 285/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 286/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 287/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 288/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 289/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 290/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 291/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 292/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 293/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 294/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 295/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 296/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 297/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 298/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 299/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 300/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 301/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 302/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 303/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 304/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 305/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 306/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 307/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 308/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 309/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 310/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 311/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1120\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 313/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 314/1000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 315/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0676 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 316/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 317/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 318/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 319/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 320/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 321/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 322/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 323/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0587 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 324/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 325/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 326/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 327/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 328/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 329/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 330/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 331/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 332/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 333/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 334/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 335/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 336/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0745\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 337/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 338/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 339/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 340/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 341/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0543 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 342/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 343/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 344/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 345/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 346/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 347/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 348/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 349/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 352/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0741\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 353/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 354/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 355/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 356/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 357/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 358/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0730\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 359/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 360/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 361/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 362/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 363/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 364/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 365/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 366/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 367/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 368/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 369/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 370/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 371/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 372/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 373/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 374/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 375/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 376/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 377/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 378/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 379/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 380/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 381/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 382/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 383/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 384/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 385/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 386/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 387/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 391/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 392/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 393/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 394/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 395/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 396/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 397/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 398/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 399/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 400/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 401/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 402/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 403/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0515 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 404/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 405/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 406/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 407/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 408/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 409/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 410/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 411/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 412/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 413/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 414/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 415/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 416/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 417/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 418/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 419/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 420/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 421/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0667\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 422/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 423/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 424/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 425/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0505 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 426/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0505 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 427/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 428/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 429/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 430/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 431/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 432/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 433/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 434/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1468\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 435/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 436/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 437/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0458 - val_root_mean_squared_error: 0.2140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 438/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2047\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 439/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 440/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 441/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 442/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 443/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 444/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1695\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 445/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 446/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 447/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 448/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 449/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 450/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1653\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 451/1000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1589\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 452/1000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1611\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 453/1000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 454/1000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1704\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 455/1000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1687\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 456/1000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1617\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 457/1000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0746 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1515\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 458/1000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0738 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1451\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 459/1000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 460/1000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 461/1000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 462/1000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 463/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 464/1000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 465/1000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0676 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 466/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 467/1000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 468/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 469/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1305\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 470/1000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0660 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 471/1000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 472/1000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 473/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 474/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 475/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 476/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 477/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 478/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 479/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 480/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 481/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 482/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 483/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 484/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 485/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 486/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 487/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 488/1000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 489/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 490/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 491/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 492/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 493/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 494/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 495/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 496/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 497/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 498/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 499/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 500/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 501/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 502/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 503/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1092\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 504/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 505/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 506/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 507/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 508/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 509/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 510/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 511/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 512/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 513/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 514/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 515/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 516/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 517/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 518/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 519/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 520/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 521/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 522/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 523/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 524/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 525/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 526/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0980\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 527/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0975\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 528/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0969\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 529/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 530/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 531/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 532/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 533/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 534/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 535/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 536/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 537/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 538/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 539/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 540/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 541/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 542/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 543/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 544/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 545/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 546/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 547/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 548/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 549/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 550/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 551/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 552/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 553/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0543 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 554/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 555/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 556/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 557/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 558/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 559/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 560/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 561/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 562/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0552 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 563/1000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 564/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 565/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 566/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 567/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 568/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 569/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 570/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 571/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 572/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 573/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 574/1000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 575/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 576/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 577/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 578/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1084\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 579/1000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 580/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1027\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 581/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 582/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 583/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 584/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 585/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0868\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 586/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 587/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 588/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 589/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 590/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 591/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 592/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 593/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 594/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 595/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 596/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 597/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 598/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 599/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 600/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 601/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 602/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 603/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 604/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 605/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 606/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 607/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 608/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 609/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 610/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 611/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 612/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 613/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 614/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 615/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 616/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 617/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 618/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 619/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 620/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 621/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 622/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 623/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 624/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 625/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0515 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 626/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 627/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 628/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0741\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 629/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 630/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 631/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 632/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 633/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 634/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 635/1000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 636/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 637/1000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 638/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 639/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 640/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 641/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 642/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 643/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 644/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 645/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 646/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 647/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 648/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 649/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 650/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 651/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 652/1000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 653/1000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 654/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 655/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 656/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 657/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 658/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 659/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 660/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 661/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 662/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 663/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 664/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 665/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 666/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 667/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 668/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0745\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 669/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 670/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 671/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 672/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 673/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 674/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 675/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 676/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 677/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 678/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 679/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 680/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 681/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 682/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 683/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 684/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 685/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 686/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 687/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 688/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 689/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 690/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 691/1000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 692/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 693/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 694/1000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 695/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 696/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 697/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 698/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 699/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1027\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 700/1000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 701/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 702/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 703/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 704/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 705/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 706/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 707/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 708/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 709/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 710/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 711/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 712/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 713/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 714/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 715/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 716/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 717/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 718/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 719/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 720/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 721/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 722/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 723/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 724/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0682\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 725/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 726/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 727/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 728/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 729/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 730/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 731/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 732/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 733/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 734/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 735/1000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 736/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 737/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 738/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 739/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 740/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 741/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 742/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 743/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 744/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 745/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 746/1000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 747/1000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 748/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 749/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 750/1000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 751/1000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 752/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 753/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 754/1000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 755/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 756/1000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 757/1000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 758/1000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 759/1000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 760/1000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0596 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 761/1000\n" + ] + } + ], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(1))\n", + "# model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=1000, batch_size=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/multivar_simple_rnn-checkpoint.ipynb b/.ipynb_checkpoints/multivar_simple_rnn-checkpoint.ipynb index 68cec4f..2efe8f2 100644 --- a/.ipynb_checkpoints/multivar_simple_rnn-checkpoint.ipynb +++ b/.ipynb_checkpoints/multivar_simple_rnn-checkpoint.ipynb @@ -13,11 +13,10 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from keras.optimizers import SGD\n", - "import keras\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import LabelEncoder\n", @@ -48,13 +47,10 @@ "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -93,8 +89,8 @@ "source": [ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/data.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, @@ -697,16 +693,6 @@ "print(master_data)" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# type(data_copy['date'])\n", - "# # data_copy['date'].astype(p)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -716,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -893,7 +879,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -901,13 +887,14 @@ "source": [ "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", - "cov_data = load_cov_set(chris_path_cov)\n", + "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", + "cov_data = load_cov_set(ismael_path_cov)\n", "cov_data" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1025,7 +1012,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1038,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1168,7 +1155,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1323,7 +1310,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1336,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1490,7 +1477,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1503,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1669,7 +1656,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1849,7 +1836,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1859,80 +1846,6 @@ "master_data" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_cov_set(ismael_path_pdo)\n", - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = data_copy" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# pdo = pdo[:984]\n", - "# pdo\n", - "# master_data = master_data.join(pdo)\n", - "# # master_data\n", - "# # master_data = master_data[:984]\n", - "# # master_data = master_data.reindex(columns=[\"Date\", \"Month\", \"king\", \"PDO\"])\n", - "# # master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# # master_data.columns = ['year', 'month', 'king', 'pdo']\n", - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data['year']=pd.to_datetime(master_data[['year','month']])\n", - "# master_data.set_index('date', inplace=True)\n", - "# master_data.index = pd.to_datetime(master_data.index)\n", - "# master_data" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1942,71 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_noi = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/noi.csv'\n", - "# noi_data = load_cov_set(ismael_path_noi)\n", - "# noi_data = noi_data[:877]\n", - "# noi_data = noi_data.drop(labels=0, axis=0)\n", - "# noi_data.reset_index()\n", - "# print(noi_data)\n", - "# print(noi_data['noix'])\n", - "# # noi_data = noi_data.drop(columns=\"index\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# noi = noi_data[\"noix\"]\n", - "# # noi\n", - "# print(master_data)\n", - "# master_data = master_data[120:]\n", - "# print(master_data)\n", - "# master_data.reset_index()\n", - "# master_data = master_data.join(noi)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = master_data.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data\n", - "# master_data = master_data.drop(labels=\"index\", axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data.head(700)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2170,7 +2019,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 34, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2183,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -2199,12 +2048,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_filepath,\n", " save_weights_only=True,\n", @@ -2250,12 +2100,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2291,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2332,6 +2182,7 @@ ], "source": [ "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", " n_vars = 1 if type(data) is list else data.shape[1]\n", " df = DataFrame(data)\n", @@ -2377,7 +2228,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2407,7 +2258,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2416,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2443,7 +2294,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2451,121 +2302,121 @@ "output_type": "stream", "text": [ "Epoch 1/1000\n", - "8/8 - 4s - loss: 0.0173 - root_mean_squared_error: 0.1316 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "8/8 - 3s - loss: 0.1804 - root_mean_squared_error: 0.4248 - val_loss: 0.0968 - val_root_mean_squared_error: 0.3112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 2/1000\n", - "8/8 - 0s - loss: 0.0137 - root_mean_squared_error: 0.1172 - val_loss: 0.0450 - val_root_mean_squared_error: 0.2121\n", + "8/8 - 0s - loss: 0.0573 - root_mean_squared_error: 0.2393 - val_loss: 0.0604 - val_root_mean_squared_error: 0.2458\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 3/1000\n", - "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "8/8 - 0s - loss: 0.0301 - root_mean_squared_error: 0.1735 - val_loss: 0.0465 - val_root_mean_squared_error: 0.2157\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 4/1000\n", - "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "8/8 - 0s - loss: 0.0312 - root_mean_squared_error: 0.1766 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 5/1000\n", - "8/8 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "8/8 - 0s - loss: 0.0294 - root_mean_squared_error: 0.1716 - val_loss: 0.0630 - val_root_mean_squared_error: 0.2509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 6/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "8/8 - 0s - loss: 0.0198 - root_mean_squared_error: 0.1406 - val_loss: 0.0577 - val_root_mean_squared_error: 0.2401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 7/1000\n", - "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "8/8 - 0s - loss: 0.0129 - root_mean_squared_error: 0.1137 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 8/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "8/8 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1003 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 9/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "8/8 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1049 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 10/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "8/8 - 0s - loss: 0.0125 - root_mean_squared_error: 0.1118 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 11/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "8/8 - 0s - loss: 0.0127 - root_mean_squared_error: 0.1127 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 12/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "8/8 - 0s - loss: 0.0116 - root_mean_squared_error: 0.1077 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 13/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "8/8 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0997 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 14/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "8/8 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 15/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 16/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 17/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "8/8 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 18/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "8/8 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0981 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 19/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "8/8 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1025 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 20/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "8/8 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1050 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 21/1000\n", - "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "8/8 - 0s - loss: 0.0108 - root_mean_squared_error: 0.1039 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 22/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "8/8 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0994 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 23/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "8/8 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 24/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 25/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 26/1000\n", - "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 27/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 28/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1759\n", + "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 29/1000\n", - "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 30/1000\n", - "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1746\n", + "8/8 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 31/1000\n", - "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n", + "8/8 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 32/1000\n", - "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", + "8/8 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 33/1000\n", - "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", + "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 34/1000\n", - "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0797 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1718\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 35/1000\n", - "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1711\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 36/1000\n", - "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 37/1000\n", - "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1698\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 38/1000\n", - "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 39/1000\n", - "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1684\n", + "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2574,121 +2425,121 @@ "output_type": "stream", "text": [ "Epoch 40/1000\n", - "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1677\n", + "8/8 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 41/1000\n", - "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", + "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 42/1000\n", - "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", + "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 43/1000\n", - "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0762 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1657\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 44/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 45/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1745\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 46/1000\n", - "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1635\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 47/1000\n", - "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0846 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1726\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 48/1000\n", - "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1621\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 49/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0737 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1613\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 50/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1606\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 51/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1598\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 52/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1590\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 53/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 54/1000\n", - "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 55/1000\n", - "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1565\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 56/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1556\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 57/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 58/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1540\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 59/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 60/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1524\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1699\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 61/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1515\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 62/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1507\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 63/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1499\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 64/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1706\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 65/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1483\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 66/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1475\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1701\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 67/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1467\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 68/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1459\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1689\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 69/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1451\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 70/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1443\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1676\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 71/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1435\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1672\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 72/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1426\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0828 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1669\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 73/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1418\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1667\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 74/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1410\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 75/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1403\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 76/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1395\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 77/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 78/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2697,121 +2548,121 @@ "output_type": "stream", "text": [ "Epoch 79/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1376\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1663\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 80/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1370\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1660\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 81/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 82/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1651\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 83/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1351\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1645\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 84/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1345\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1640\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 85/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1338\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1635\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 86/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1631\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 87/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 88/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1313\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1625\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 89/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1306\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 90/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1299\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 91/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1622\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 92/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1285\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1622\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 93/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1622\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 94/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 95/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1619\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 96/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1616\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 97/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1612\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 98/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1607\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 99/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1601\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 100/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1290\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1595\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 101/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1589\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 102/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1253\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1583\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 103/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 104/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1267\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 105/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1570\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 106/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1336\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 107/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1431\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0797 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 108/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 109/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1569\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 110/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1571\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 111/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1325\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 112/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 113/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 114/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 115/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 116/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1560\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 117/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n" + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1551\n" ] }, { @@ -2820,121 +2671,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 118/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1199\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0746 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1541\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 119/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 120/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1191\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1520\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 121/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0743 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1510\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 122/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1186\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1503\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 123/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0755 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1497\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 124/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1161\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1493\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 125/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1150\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0776 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 126/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 127/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0797 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 128/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1132\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1502\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 129/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0803 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1514\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 130/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1130\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 131/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1546\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 132/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1559\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 133/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0765 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1564\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 134/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1096\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1558\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 135/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1540\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 136/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1515\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 137/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1487\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 138/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1461\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 139/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 140/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 141/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 142/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 143/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1077\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 144/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1082\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 145/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 146/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 147/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1508\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 148/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 149/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 150/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1053\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1547\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 151/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1499\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 152/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0543 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1097\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1444\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 153/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1051\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 154/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 155/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1089\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 156/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n" + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0759 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1390\n" ] }, { @@ -2943,121 +2794,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 157/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1058\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 158/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 159/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1479\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 160/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1493\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 161/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1007\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 162/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 163/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 164/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1367\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 165/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 166/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 167/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0711 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 168/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0971\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 169/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 170/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1408\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 171/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 172/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0953\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 173/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 174/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 175/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0935\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 176/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1321\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 177/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0948\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 178/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 179/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1302\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 180/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 181/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1319\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 182/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 183/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 184/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 185/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 186/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 187/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0903\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 188/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0911\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1318\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 189/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1291\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 190/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0893\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 191/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0430 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 192/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 193/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 194/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 195/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0923\n" + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1263\n" ] }, { @@ -3066,121 +2917,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 196/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1271\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 197/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0897\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 198/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1299\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 199/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 200/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 201/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 202/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 203/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1273\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 204/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 205/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 206/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0816\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1199\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 207/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0825\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 208/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0638 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1202\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 209/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0672 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 210/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 211/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 212/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1247\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 213/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 214/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0635 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 215/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0807\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 216/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 217/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 218/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1183\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 219/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1160\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 220/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0817\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1151\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 221/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 222/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 223/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 224/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1186\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 225/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1203\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 226/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 227/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 228/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 229/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 230/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1156\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 231/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 232/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 233/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 234/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n" + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n" ] }, { @@ -3189,121 +3040,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 235/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 236/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0638 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 237/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 238/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1158\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 239/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0464 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 240/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 241/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 242/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1138\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 243/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1105\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 244/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 245/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 246/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 247/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 248/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 249/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 250/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 251/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 252/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 253/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 254/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1124\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 255/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 256/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 257/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 258/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 259/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 260/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 261/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 262/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 263/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1075\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 264/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 265/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 266/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 267/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 268/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 269/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1024\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 270/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1009\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 271/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 272/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 273/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n" + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n" ] }, { @@ -3312,2305 +3163,2299 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 274/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 275/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 276/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1078\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 277/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1058\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 278/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 279/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0785\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 280/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1031\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 281/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1006\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 282/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 283/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 284/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 285/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0982\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 286/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0982\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 287/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 288/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 289/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0667\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 290/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 291/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 292/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0946\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 293/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 294/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 295/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0941\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 296/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 297/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 298/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0943\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 299/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0936\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 300/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 301/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 302/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 303/1000\n", - "8/8 - 0s - loss: 9.6715e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0631\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0935\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 304/1000\n", - "8/8 - 0s - loss: 8.5799e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 305/1000\n", - "8/8 - 0s - loss: 7.4368e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0948\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 306/1000\n", - "8/8 - 0s - loss: 7.8944e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 307/1000\n", - "8/8 - 0s - loss: 7.1894e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 308/1000\n", - "8/8 - 0s - loss: 7.6310e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 309/1000\n", - "8/8 - 0s - loss: 8.7377e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 310/1000\n", - "8/8 - 0s - loss: 7.7619e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0925\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 311/1000\n", - "8/8 - 0s - loss: 8.7049e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/1000\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 312/1000\n", - "8/8 - 0s - loss: 9.2348e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 313/1000\n", - "8/8 - 0s - loss: 8.6449e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 314/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 315/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 316/1000\n", - "8/8 - 0s - loss: 9.5606e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 317/1000\n", - "8/8 - 0s - loss: 9.6162e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0596\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 318/1000\n", - "8/8 - 0s - loss: 9.5000e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0634\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 319/1000\n", - "8/8 - 0s - loss: 9.3189e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 320/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 321/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 322/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 323/1000\n", - "8/8 - 0s - loss: 9.1583e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 324/1000\n", - "8/8 - 0s - loss: 7.8489e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 325/1000\n", - "8/8 - 0s - loss: 8.8173e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 326/1000\n", - "8/8 - 0s - loss: 9.2407e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 327/1000\n", - "8/8 - 0s - loss: 9.1302e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 328/1000\n", - "8/8 - 0s - loss: 9.6200e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 329/1000\n", - "8/8 - 0s - loss: 9.7697e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0505 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 330/1000\n", - "8/8 - 0s - loss: 7.5199e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 331/1000\n", - "8/8 - 0s - loss: 7.5368e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0543\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 332/1000\n", - "8/8 - 0s - loss: 7.2867e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 333/1000\n", - "8/8 - 0s - loss: 7.5344e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 334/1000\n", - "8/8 - 0s - loss: 8.5836e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0547\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 335/1000\n", - "8/8 - 0s - loss: 9.4637e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0855\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 336/1000\n", - "8/8 - 0s - loss: 7.9697e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 337/1000\n", - "8/8 - 0s - loss: 7.2785e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 338/1000\n", - "8/8 - 0s - loss: 6.2342e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 339/1000\n", - "8/8 - 0s - loss: 6.0845e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 340/1000\n", - "8/8 - 0s - loss: 6.6577e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 341/1000\n", - "8/8 - 0s - loss: 7.5679e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 342/1000\n", - "8/8 - 0s - loss: 6.8130e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 343/1000\n", - "8/8 - 0s - loss: 6.4722e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 344/1000\n", - "8/8 - 0s - loss: 5.9530e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 345/1000\n", - "8/8 - 0s - loss: 5.9113e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 346/1000\n", - "8/8 - 0s - loss: 7.0934e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0825\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 347/1000\n", - "8/8 - 0s - loss: 8.2147e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 348/1000\n", - "8/8 - 0s - loss: 7.2468e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 349/1000\n", - "8/8 - 0s - loss: 7.5941e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/1000\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 350/1000\n", - "8/8 - 0s - loss: 7.5998e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 351/1000\n", - "8/8 - 0s - loss: 7.7289e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 352/1000\n", - "8/8 - 0s - loss: 9.8107e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 353/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 354/1000\n", - "8/8 - 0s - loss: 9.0664e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 355/1000\n", - "8/8 - 0s - loss: 7.8665e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 356/1000\n", - "8/8 - 0s - loss: 7.3592e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 357/1000\n", - "8/8 - 0s - loss: 7.1735e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 358/1000\n", - "8/8 - 0s - loss: 7.9459e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 359/1000\n", - "8/8 - 0s - loss: 8.8665e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 360/1000\n", - "8/8 - 0s - loss: 8.2309e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 361/1000\n", - "8/8 - 0s - loss: 7.6252e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0957\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 362/1000\n", - "8/8 - 0s - loss: 6.6454e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 363/1000\n", - "8/8 - 0s - loss: 6.8247e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0947\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 364/1000\n", - "8/8 - 0s - loss: 7.4920e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 365/1000\n", - "8/8 - 0s - loss: 7.0340e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 366/1000\n", - "8/8 - 0s - loss: 7.6945e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 367/1000\n", - "8/8 - 0s - loss: 7.1307e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 368/1000\n", - "8/8 - 0s - loss: 5.8773e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 369/1000\n", - "8/8 - 0s - loss: 5.3162e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 370/1000\n", - "8/8 - 0s - loss: 4.7439e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 371/1000\n", - "8/8 - 0s - loss: 4.2849e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 372/1000\n", - "8/8 - 0s - loss: 4.2583e-04 - root_mean_squared_error: 0.0206 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 373/1000\n", - "8/8 - 0s - loss: 4.0052e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 374/1000\n", - "8/8 - 0s - loss: 3.6993e-04 - root_mean_squared_error: 0.0192 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 375/1000\n", - "8/8 - 0s - loss: 3.7999e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 376/1000\n", - "8/8 - 0s - loss: 3.4556e-04 - root_mean_squared_error: 0.0186 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0919\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 377/1000\n", - "8/8 - 0s - loss: 3.2869e-04 - root_mean_squared_error: 0.0181 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0917\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 378/1000\n", - "8/8 - 0s - loss: 3.4876e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 379/1000\n", - "8/8 - 0s - loss: 3.4044e-04 - root_mean_squared_error: 0.0185 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0817\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 380/1000\n", - "8/8 - 0s - loss: 3.2293e-04 - root_mean_squared_error: 0.0180 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 381/1000\n", - "8/8 - 0s - loss: 3.4753e-04 - root_mean_squared_error: 0.0186 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 382/1000\n", - "8/8 - 0s - loss: 3.5364e-04 - root_mean_squared_error: 0.0188 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 383/1000\n", - "8/8 - 0s - loss: 3.6709e-04 - root_mean_squared_error: 0.0192 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 384/1000\n", - "8/8 - 0s - loss: 4.4280e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 385/1000\n", - "8/8 - 0s - loss: 4.6696e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 386/1000\n", - "8/8 - 0s - loss: 4.4802e-04 - root_mean_squared_error: 0.0212 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0807\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 387/1000\n", - "8/8 - 0s - loss: 5.8214e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/1000\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 388/1000\n", - "8/8 - 0s - loss: 6.4122e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 389/1000\n", - "8/8 - 0s - loss: 6.8686e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 390/1000\n", - "8/8 - 0s - loss: 9.6442e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 391/1000\n", - "8/8 - 0s - loss: 9.3211e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 392/1000\n", - "8/8 - 0s - loss: 8.0295e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 393/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 394/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 395/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 396/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 397/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 398/1000\n", - "8/8 - 0s - loss: 9.1018e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 399/1000\n", - "8/8 - 0s - loss: 7.0819e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 400/1000\n", - "8/8 - 0s - loss: 6.7078e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 401/1000\n", - "8/8 - 0s - loss: 5.7658e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 402/1000\n", - "8/8 - 0s - loss: 5.5376e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 403/1000\n", - "8/8 - 0s - loss: 5.2939e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 404/1000\n", - "8/8 - 0s - loss: 4.9393e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 405/1000\n", - "8/8 - 0s - loss: 5.0609e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 406/1000\n", - "8/8 - 0s - loss: 4.6631e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 407/1000\n", - "8/8 - 0s - loss: 4.1723e-04 - root_mean_squared_error: 0.0204 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 408/1000\n", - "8/8 - 0s - loss: 3.8559e-04 - root_mean_squared_error: 0.0196 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0828\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 409/1000\n", - "8/8 - 0s - loss: 3.4872e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 410/1000\n", - "8/8 - 0s - loss: 2.9498e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 411/1000\n", - "8/8 - 0s - loss: 2.9884e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 412/1000\n", - "8/8 - 0s - loss: 2.9221e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 413/1000\n", - "8/8 - 0s - loss: 2.7950e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 414/1000\n", - "8/8 - 0s - loss: 2.9001e-04 - root_mean_squared_error: 0.0170 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 415/1000\n", - "8/8 - 0s - loss: 2.6861e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 416/1000\n", - "8/8 - 0s - loss: 2.5827e-04 - root_mean_squared_error: 0.0161 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 417/1000\n", - "8/8 - 0s - loss: 2.7559e-04 - root_mean_squared_error: 0.0166 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 418/1000\n", - "8/8 - 0s - loss: 2.6797e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 419/1000\n", - "8/8 - 0s - loss: 2.6366e-04 - root_mean_squared_error: 0.0162 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 420/1000\n", - "8/8 - 0s - loss: 2.9091e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 421/1000\n", - "8/8 - 0s - loss: 2.9309e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 422/1000\n", - "8/8 - 0s - loss: 2.9613e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 423/1000\n", - "8/8 - 0s - loss: 3.4982e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 424/1000\n", - "8/8 - 0s - loss: 3.6090e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 425/1000\n", - "8/8 - 0s - loss: 3.7280e-04 - root_mean_squared_error: 0.0193 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 426/1000\n", - "8/8 - 0s - loss: 4.5479e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 427/1000\n", - "8/8 - 0s - loss: 4.4346e-04 - root_mean_squared_error: 0.0211 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 428/1000\n", - "8/8 - 0s - loss: 4.1939e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 429/1000\n", - "8/8 - 0s - loss: 4.7709e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 430/1000\n", - "8/8 - 0s - loss: 4.6385e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 431/1000\n", - "8/8 - 0s - loss: 4.1944e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 432/1000\n", - "8/8 - 0s - loss: 4.4082e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 433/1000\n", - "8/8 - 0s - loss: 4.2984e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 434/1000\n", - "8/8 - 0s - loss: 3.7776e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 435/1000\n", - "8/8 - 0s - loss: 3.8667e-04 - root_mean_squared_error: 0.0197 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 436/1000\n", - "8/8 - 0s - loss: 3.9802e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 437/1000\n", - "8/8 - 0s - loss: 3.6349e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 438/1000\n", - "8/8 - 0s - loss: 3.9382e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 439/1000\n", - "8/8 - 0s - loss: 4.1811e-04 - root_mean_squared_error: 0.0204 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 440/1000\n", - "8/8 - 0s - loss: 3.9386e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 441/1000\n", - "8/8 - 0s - loss: 4.7010e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 442/1000\n", - "8/8 - 0s - loss: 5.2306e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 443/1000\n", - "8/8 - 0s - loss: 5.2691e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 444/1000\n", - "8/8 - 0s - loss: 6.8996e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 445/1000\n", - "8/8 - 0s - loss: 6.8401e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 446/1000\n", - "8/8 - 0s - loss: 6.4830e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 447/1000\n", - "8/8 - 0s - loss: 7.5397e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 448/1000\n", - "8/8 - 0s - loss: 7.1244e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 449/1000\n", - "8/8 - 0s - loss: 6.0638e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 450/1000\n", - "8/8 - 0s - loss: 5.3113e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 451/1000\n", - "8/8 - 0s - loss: 4.9536e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 452/1000\n", - "8/8 - 0s - loss: 4.2452e-04 - root_mean_squared_error: 0.0206 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 453/1000\n", - "8/8 - 0s - loss: 4.4990e-04 - root_mean_squared_error: 0.0212 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 454/1000\n", - "8/8 - 0s - loss: 4.9485e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 455/1000\n", - "8/8 - 0s - loss: 3.7840e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 456/1000\n", - "8/8 - 0s - loss: 4.3200e-04 - root_mean_squared_error: 0.0208 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0394 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 457/1000\n", - "8/8 - 0s - loss: 3.9516e-04 - root_mean_squared_error: 0.0199 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 458/1000\n", - "8/8 - 0s - loss: 3.5956e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 459/1000\n", - "8/8 - 0s - loss: 3.7792e-04 - root_mean_squared_error: 0.0194 - val_loss: 9.9502e-04 - val_root_mean_squared_error: 0.0315\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 460/1000\n", - "8/8 - 0s - loss: 3.2222e-04 - root_mean_squared_error: 0.0180 - val_loss: 9.7789e-04 - val_root_mean_squared_error: 0.0313\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 461/1000\n", - "8/8 - 0s - loss: 3.0630e-04 - root_mean_squared_error: 0.0175 - val_loss: 9.6259e-04 - val_root_mean_squared_error: 0.0310\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 462/1000\n", - "8/8 - 0s - loss: 2.7725e-04 - root_mean_squared_error: 0.0167 - val_loss: 9.7596e-04 - val_root_mean_squared_error: 0.0312\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 463/1000\n", - "8/8 - 0s - loss: 2.2451e-04 - root_mean_squared_error: 0.0150 - val_loss: 8.6896e-04 - val_root_mean_squared_error: 0.0295\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 464/1000\n", - "8/8 - 0s - loss: 2.1785e-04 - root_mean_squared_error: 0.0148 - val_loss: 8.3482e-04 - val_root_mean_squared_error: 0.0289\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 465/1000\n", - "8/8 - 0s - loss: 2.1364e-04 - root_mean_squared_error: 0.0146 - val_loss: 8.5460e-04 - val_root_mean_squared_error: 0.0292\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 466/1000\n", - "8/8 - 0s - loss: 1.9596e-04 - root_mean_squared_error: 0.0140 - val_loss: 8.0568e-04 - val_root_mean_squared_error: 0.0284\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 467/1000\n", - "8/8 - 0s - loss: 1.9083e-04 - root_mean_squared_error: 0.0138 - val_loss: 8.1381e-04 - val_root_mean_squared_error: 0.0285\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0696\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 468/1000\n", - "8/8 - 0s - loss: 2.0778e-04 - root_mean_squared_error: 0.0144 - val_loss: 7.4864e-04 - val_root_mean_squared_error: 0.0274\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 469/1000\n", - "8/8 - 0s - loss: 2.1937e-04 - root_mean_squared_error: 0.0148 - val_loss: 8.1518e-04 - val_root_mean_squared_error: 0.0286\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 470/1000\n", - "8/8 - 0s - loss: 2.4795e-04 - root_mean_squared_error: 0.0157 - val_loss: 9.1599e-04 - val_root_mean_squared_error: 0.0303\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 471/1000\n", - "8/8 - 0s - loss: 2.8756e-04 - root_mean_squared_error: 0.0170 - val_loss: 7.4531e-04 - val_root_mean_squared_error: 0.0273\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 472/1000\n", - "8/8 - 0s - loss: 2.9599e-04 - root_mean_squared_error: 0.0172 - val_loss: 8.5210e-04 - val_root_mean_squared_error: 0.0292\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0652\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 473/1000\n", - "8/8 - 0s - loss: 3.3062e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 474/1000\n", - "8/8 - 0s - loss: 3.4449e-04 - root_mean_squared_error: 0.0186 - val_loss: 7.3901e-04 - val_root_mean_squared_error: 0.0272\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0658\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 475/1000\n", - "8/8 - 0s - loss: 3.4591e-04 - root_mean_squared_error: 0.0186 - val_loss: 8.7491e-04 - val_root_mean_squared_error: 0.0296\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 476/1000\n", - "8/8 - 0s - loss: 3.5486e-04 - root_mean_squared_error: 0.0188 - val_loss: 9.8170e-04 - val_root_mean_squared_error: 0.0313\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 477/1000\n", - "8/8 - 0s - loss: 2.9692e-04 - root_mean_squared_error: 0.0172 - val_loss: 7.2556e-04 - val_root_mean_squared_error: 0.0269\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0640\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 478/1000\n", - "8/8 - 0s - loss: 2.2238e-04 - root_mean_squared_error: 0.0149 - val_loss: 7.3739e-04 - val_root_mean_squared_error: 0.0272\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0634\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 479/1000\n", - "8/8 - 0s - loss: 2.0870e-04 - root_mean_squared_error: 0.0144 - val_loss: 7.7403e-04 - val_root_mean_squared_error: 0.0278\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 480/1000\n", - "8/8 - 0s - loss: 1.7103e-04 - root_mean_squared_error: 0.0131 - val_loss: 6.5161e-04 - val_root_mean_squared_error: 0.0255\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0635\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 481/1000\n", - "8/8 - 0s - loss: 1.4065e-04 - root_mean_squared_error: 0.0119 - val_loss: 6.3855e-04 - val_root_mean_squared_error: 0.0253\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 482/1000\n", - "8/8 - 0s - loss: 1.3437e-04 - root_mean_squared_error: 0.0116 - val_loss: 6.3875e-04 - val_root_mean_squared_error: 0.0253\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 483/1000\n", - "8/8 - 0s - loss: 1.1798e-04 - root_mean_squared_error: 0.0109 - val_loss: 6.0623e-04 - val_root_mean_squared_error: 0.0246\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 484/1000\n", - "8/8 - 0s - loss: 1.3256e-04 - root_mean_squared_error: 0.0115 - val_loss: 5.8019e-04 - val_root_mean_squared_error: 0.0241\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0631\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 485/1000\n", - "8/8 - 0s - loss: 1.5440e-04 - root_mean_squared_error: 0.0124 - val_loss: 6.0428e-04 - val_root_mean_squared_error: 0.0246\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0616\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 486/1000\n", - "8/8 - 0s - loss: 1.6313e-04 - root_mean_squared_error: 0.0128 - val_loss: 6.4668e-04 - val_root_mean_squared_error: 0.0254\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 487/1000\n", - "8/8 - 0s - loss: 2.2628e-04 - root_mean_squared_error: 0.0150 - val_loss: 5.9709e-04 - val_root_mean_squared_error: 0.0244\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 488/1000\n", - "8/8 - 0s - loss: 3.0404e-04 - root_mean_squared_error: 0.0174 - val_loss: 6.9394e-04 - val_root_mean_squared_error: 0.0263\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0625\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 489/1000\n", - "8/8 - 0s - loss: 3.3873e-04 - root_mean_squared_error: 0.0184 - val_loss: 8.4976e-04 - val_root_mean_squared_error: 0.0292\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0624\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 490/1000\n", - "8/8 - 0s - loss: 4.4846e-04 - root_mean_squared_error: 0.0212 - val_loss: 7.4154e-04 - val_root_mean_squared_error: 0.0272\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 491/1000\n", - "8/8 - 0s - loss: 5.5776e-04 - root_mean_squared_error: 0.0236 - val_loss: 9.6730e-04 - val_root_mean_squared_error: 0.0311\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 492/1000\n", - "8/8 - 0s - loss: 5.0629e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0609\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 493/1000\n", - "8/8 - 0s - loss: 4.4137e-04 - root_mean_squared_error: 0.0210 - val_loss: 6.9011e-04 - val_root_mean_squared_error: 0.0263\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 494/1000\n", - "8/8 - 0s - loss: 3.8293e-04 - root_mean_squared_error: 0.0196 - val_loss: 8.3572e-04 - val_root_mean_squared_error: 0.0289\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0612\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 495/1000\n", - "8/8 - 0s - loss: 3.2970e-04 - root_mean_squared_error: 0.0182 - val_loss: 7.3644e-04 - val_root_mean_squared_error: 0.0271\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 496/1000\n", - "8/8 - 0s - loss: 2.6029e-04 - root_mean_squared_error: 0.0161 - val_loss: 6.4372e-04 - val_root_mean_squared_error: 0.0254\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 497/1000\n", - "8/8 - 0s - loss: 2.9394e-04 - root_mean_squared_error: 0.0171 - val_loss: 7.0573e-04 - val_root_mean_squared_error: 0.0266\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0603\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 498/1000\n", - "8/8 - 0s - loss: 3.4970e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.7731e-04 - val_root_mean_squared_error: 0.0260\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 499/1000\n", - "8/8 - 0s - loss: 3.1088e-04 - root_mean_squared_error: 0.0176 - val_loss: 7.5611e-04 - val_root_mean_squared_error: 0.0275\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 500/1000\n", - "8/8 - 0s - loss: 3.8185e-04 - root_mean_squared_error: 0.0195 - val_loss: 6.4203e-04 - val_root_mean_squared_error: 0.0253\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 501/1000\n", - "8/8 - 0s - loss: 3.6900e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.6096e-04 - val_root_mean_squared_error: 0.0276\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 502/1000\n", - "8/8 - 0s - loss: 3.5178e-04 - root_mean_squared_error: 0.0188 - val_loss: 8.6912e-04 - val_root_mean_squared_error: 0.0295\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 503/1000\n", - "8/8 - 0s - loss: 3.1573e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.5040e-04 - val_root_mean_squared_error: 0.0235\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 504/1000\n", - "8/8 - 0s - loss: 2.7166e-04 - root_mean_squared_error: 0.0165 - val_loss: 6.4850e-04 - val_root_mean_squared_error: 0.0255\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 505/1000\n", - "8/8 - 0s - loss: 2.8281e-04 - root_mean_squared_error: 0.0168 - val_loss: 6.9561e-04 - val_root_mean_squared_error: 0.0264\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 506/1000\n", - "8/8 - 0s - loss: 2.1290e-04 - root_mean_squared_error: 0.0146 - val_loss: 5.4596e-04 - val_root_mean_squared_error: 0.0234\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 507/1000\n", - "8/8 - 0s - loss: 2.0503e-04 - root_mean_squared_error: 0.0143 - val_loss: 5.3358e-04 - val_root_mean_squared_error: 0.0231\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 508/1000\n", - "8/8 - 0s - loss: 2.8788e-04 - root_mean_squared_error: 0.0170 - val_loss: 6.2024e-04 - val_root_mean_squared_error: 0.0249\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 509/1000\n", - "8/8 - 0s - loss: 2.3235e-04 - root_mean_squared_error: 0.0152 - val_loss: 6.0799e-04 - val_root_mean_squared_error: 0.0247\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 510/1000\n", - "8/8 - 0s - loss: 2.5692e-04 - root_mean_squared_error: 0.0160 - val_loss: 5.2059e-04 - val_root_mean_squared_error: 0.0228\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0597\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 511/1000\n", - "8/8 - 0s - loss: 2.6420e-04 - root_mean_squared_error: 0.0163 - val_loss: 5.4534e-04 - val_root_mean_squared_error: 0.0234\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 512/1000\n", - "8/8 - 0s - loss: 2.3730e-04 - root_mean_squared_error: 0.0154 - val_loss: 6.2816e-04 - val_root_mean_squared_error: 0.0251\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 513/1000\n", - "8/8 - 0s - loss: 2.4161e-04 - root_mean_squared_error: 0.0155 - val_loss: 6.0380e-04 - val_root_mean_squared_error: 0.0246\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 514/1000\n", - "8/8 - 0s - loss: 1.7235e-04 - root_mean_squared_error: 0.0131 - val_loss: 4.4601e-04 - val_root_mean_squared_error: 0.0211\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 515/1000\n", - "8/8 - 0s - loss: 1.6396e-04 - root_mean_squared_error: 0.0128 - val_loss: 5.1931e-04 - val_root_mean_squared_error: 0.0228\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 516/1000\n", - "8/8 - 0s - loss: 2.1498e-04 - root_mean_squared_error: 0.0147 - val_loss: 5.4155e-04 - val_root_mean_squared_error: 0.0233\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 517/1000\n", - "8/8 - 0s - loss: 1.9613e-04 - root_mean_squared_error: 0.0140 - val_loss: 4.6195e-04 - val_root_mean_squared_error: 0.0215\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 518/1000\n", - "8/8 - 0s - loss: 2.8673e-04 - root_mean_squared_error: 0.0169 - val_loss: 5.9810e-04 - val_root_mean_squared_error: 0.0245\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 519/1000\n", - "8/8 - 0s - loss: 4.5870e-04 - root_mean_squared_error: 0.0214 - val_loss: 7.2325e-04 - val_root_mean_squared_error: 0.0269\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 520/1000\n", - "8/8 - 0s - loss: 5.4648e-04 - root_mean_squared_error: 0.0234 - val_loss: 7.9112e-04 - val_root_mean_squared_error: 0.0281\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 521/1000\n", - "8/8 - 0s - loss: 5.3935e-04 - root_mean_squared_error: 0.0232 - val_loss: 7.6844e-04 - val_root_mean_squared_error: 0.0277\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0609\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 522/1000\n", - "8/8 - 0s - loss: 4.7389e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.3131e-04 - val_root_mean_squared_error: 0.0251\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0652\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 523/1000\n", - "8/8 - 0s - loss: 5.0696e-04 - root_mean_squared_error: 0.0225 - val_loss: 8.9842e-04 - val_root_mean_squared_error: 0.0300\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 524/1000\n", - "8/8 - 0s - loss: 4.5085e-04 - root_mean_squared_error: 0.0212 - val_loss: 7.1025e-04 - val_root_mean_squared_error: 0.0267\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 525/1000\n", - "8/8 - 0s - loss: 3.0575e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.7770e-04 - val_root_mean_squared_error: 0.0240\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 526/1000\n", - "8/8 - 0s - loss: 3.4315e-04 - root_mean_squared_error: 0.0185 - val_loss: 7.1947e-04 - val_root_mean_squared_error: 0.0268\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0659\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 527/1000\n", - "8/8 - 0s - loss: 3.3443e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7517e-04 - val_root_mean_squared_error: 0.0240\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 528/1000\n", - "8/8 - 0s - loss: 2.8961e-04 - root_mean_squared_error: 0.0170 - val_loss: 5.9116e-04 - val_root_mean_squared_error: 0.0243\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 529/1000\n", - "8/8 - 0s - loss: 4.5353e-04 - root_mean_squared_error: 0.0213 - val_loss: 6.5068e-04 - val_root_mean_squared_error: 0.0255\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 530/1000\n", - "8/8 - 0s - loss: 5.2866e-04 - root_mean_squared_error: 0.0230 - val_loss: 6.9204e-04 - val_root_mean_squared_error: 0.0263\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0641\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 531/1000\n", - "8/8 - 0s - loss: 4.2837e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.9146e-04 - val_root_mean_squared_error: 0.0281\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 532/1000\n", - "8/8 - 0s - loss: 4.4561e-04 - root_mean_squared_error: 0.0211 - val_loss: 5.3124e-04 - val_root_mean_squared_error: 0.0230\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 533/1000\n", - "8/8 - 0s - loss: 3.8930e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.5122e-04 - val_root_mean_squared_error: 0.0274\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 534/1000\n", - "8/8 - 0s - loss: 3.7363e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.3352e-04 - val_root_mean_squared_error: 0.0252\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 535/1000\n", - "8/8 - 0s - loss: 3.4662e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.5312e-04 - val_root_mean_squared_error: 0.0235\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0613\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 536/1000\n", - "8/8 - 0s - loss: 3.5728e-04 - root_mean_squared_error: 0.0189 - val_loss: 5.6960e-04 - val_root_mean_squared_error: 0.0239\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 537/1000\n", - "8/8 - 0s - loss: 3.4491e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.4149e-04 - val_root_mean_squared_error: 0.0233\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 538/1000\n", - "8/8 - 0s - loss: 2.6088e-04 - root_mean_squared_error: 0.0162 - val_loss: 5.6926e-04 - val_root_mean_squared_error: 0.0239\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0600\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 539/1000\n", - "8/8 - 0s - loss: 2.7060e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.9709e-04 - val_root_mean_squared_error: 0.0223\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0631\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 540/1000\n", - "8/8 - 0s - loss: 2.6895e-04 - root_mean_squared_error: 0.0164 - val_loss: 5.4883e-04 - val_root_mean_squared_error: 0.0234\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 541/1000\n", - "8/8 - 0s - loss: 2.2454e-04 - root_mean_squared_error: 0.0150 - val_loss: 4.3685e-04 - val_root_mean_squared_error: 0.0209\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 542/1000\n", - "8/8 - 0s - loss: 1.8667e-04 - root_mean_squared_error: 0.0137 - val_loss: 4.4620e-04 - val_root_mean_squared_error: 0.0211\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 543/1000\n", - "8/8 - 0s - loss: 1.7310e-04 - root_mean_squared_error: 0.0132 - val_loss: 4.1411e-04 - val_root_mean_squared_error: 0.0203\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 544/1000\n", - "8/8 - 0s - loss: 2.1253e-04 - root_mean_squared_error: 0.0146 - val_loss: 4.2685e-04 - val_root_mean_squared_error: 0.0207\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 545/1000\n", - "8/8 - 0s - loss: 2.1115e-04 - root_mean_squared_error: 0.0145 - val_loss: 4.6501e-04 - val_root_mean_squared_error: 0.0216\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0579\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 546/1000\n", - "8/8 - 0s - loss: 1.8423e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.8537e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 547/1000\n", - "8/8 - 0s - loss: 1.6496e-04 - root_mean_squared_error: 0.0128 - val_loss: 4.0038e-04 - val_root_mean_squared_error: 0.0200\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 548/1000\n", - "8/8 - 0s - loss: 1.4379e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.5765e-04 - val_root_mean_squared_error: 0.0189\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 549/1000\n", - "8/8 - 0s - loss: 1.3108e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.8782e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 9.9062e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 550/1000\n", - "8/8 - 0s - loss: 1.2438e-04 - root_mean_squared_error: 0.0112 - val_loss: 3.7214e-04 - val_root_mean_squared_error: 0.0193\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 551/1000\n", - "8/8 - 0s - loss: 1.3586e-04 - root_mean_squared_error: 0.0117 - val_loss: 3.3352e-04 - val_root_mean_squared_error: 0.0183\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 552/1000\n", - "8/8 - 0s - loss: 1.4379e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.6135e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 553/1000\n", - "8/8 - 0s - loss: 1.5171e-04 - root_mean_squared_error: 0.0123 - val_loss: 3.2266e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 554/1000\n", - "8/8 - 0s - loss: 1.4822e-04 - root_mean_squared_error: 0.0122 - val_loss: 3.5914e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0541\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 555/1000\n", - "8/8 - 0s - loss: 1.3671e-04 - root_mean_squared_error: 0.0117 - val_loss: 3.3337e-04 - val_root_mean_squared_error: 0.0183\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 556/1000\n", - "8/8 - 0s - loss: 1.2932e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.6500e-04 - val_root_mean_squared_error: 0.0191\n", + "8/8 - 0s - loss: 9.6792e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 557/1000\n", - "8/8 - 0s - loss: 1.3004e-04 - root_mean_squared_error: 0.0114 - val_loss: 3.5273e-04 - val_root_mean_squared_error: 0.0188\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 558/1000\n", - "8/8 - 0s - loss: 1.4511e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.0121e-04 - val_root_mean_squared_error: 0.0174\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 559/1000\n", - "8/8 - 0s - loss: 1.6231e-04 - root_mean_squared_error: 0.0127 - val_loss: 3.5505e-04 - val_root_mean_squared_error: 0.0188\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 560/1000\n", - "8/8 - 0s - loss: 2.1139e-04 - root_mean_squared_error: 0.0145 - val_loss: 3.4679e-04 - val_root_mean_squared_error: 0.0186\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 561/1000\n", - "8/8 - 0s - loss: 2.0150e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.7814e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 562/1000\n", - "8/8 - 0s - loss: 1.9164e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.6121e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 563/1000\n", - "8/8 - 0s - loss: 1.7448e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.5629e-04 - val_root_mean_squared_error: 0.0189\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 564/1000\n", - "8/8 - 0s - loss: 1.8390e-04 - root_mean_squared_error: 0.0136 - val_loss: 4.2245e-04 - val_root_mean_squared_error: 0.0206\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 565/1000\n", - "8/8 - 0s - loss: 2.2930e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.2703e-04 - val_root_mean_squared_error: 0.0181\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 566/1000\n", - "8/8 - 0s - loss: 2.2982e-04 - root_mean_squared_error: 0.0152 - val_loss: 4.0925e-04 - val_root_mean_squared_error: 0.0202\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 567/1000\n", - "8/8 - 0s - loss: 3.4805e-04 - root_mean_squared_error: 0.0187 - val_loss: 4.2365e-04 - val_root_mean_squared_error: 0.0206\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 568/1000\n", - "8/8 - 0s - loss: 3.1265e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.0412e-04 - val_root_mean_squared_error: 0.0201\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 569/1000\n", - "8/8 - 0s - loss: 3.1437e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2196e-04 - val_root_mean_squared_error: 0.0228\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 570/1000\n", - "8/8 - 0s - loss: 3.1147e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.1803e-04 - val_root_mean_squared_error: 0.0178\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 571/1000\n", - "8/8 - 0s - loss: 2.7474e-04 - root_mean_squared_error: 0.0166 - val_loss: 5.3455e-04 - val_root_mean_squared_error: 0.0231\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 572/1000\n", - "8/8 - 0s - loss: 4.3481e-04 - root_mean_squared_error: 0.0209 - val_loss: 5.2397e-04 - val_root_mean_squared_error: 0.0229\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 573/1000\n", - "8/8 - 0s - loss: 3.8626e-04 - root_mean_squared_error: 0.0197 - val_loss: 4.2342e-04 - val_root_mean_squared_error: 0.0206\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 574/1000\n", - "8/8 - 0s - loss: 5.2704e-04 - root_mean_squared_error: 0.0230 - val_loss: 5.7690e-04 - val_root_mean_squared_error: 0.0240\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 575/1000\n", - "8/8 - 0s - loss: 7.1104e-04 - root_mean_squared_error: 0.0267 - val_loss: 8.1341e-04 - val_root_mean_squared_error: 0.0285\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 576/1000\n", - "8/8 - 0s - loss: 5.7404e-04 - root_mean_squared_error: 0.0240 - val_loss: 5.8292e-04 - val_root_mean_squared_error: 0.0241\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 577/1000\n", - "8/8 - 0s - loss: 5.6877e-04 - root_mean_squared_error: 0.0238 - val_loss: 5.3892e-04 - val_root_mean_squared_error: 0.0232\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0552\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 578/1000\n", - "8/8 - 0s - loss: 5.4786e-04 - root_mean_squared_error: 0.0234 - val_loss: 7.4032e-04 - val_root_mean_squared_error: 0.0272\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 579/1000\n", - "8/8 - 0s - loss: 4.3005e-04 - root_mean_squared_error: 0.0207 - val_loss: 4.3757e-04 - val_root_mean_squared_error: 0.0209\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 580/1000\n", - "8/8 - 0s - loss: 2.9275e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.0873e-04 - val_root_mean_squared_error: 0.0202\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 581/1000\n", - "8/8 - 0s - loss: 3.3393e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.8830e-04 - val_root_mean_squared_error: 0.0243\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 582/1000\n", - "8/8 - 0s - loss: 2.7920e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.8969e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 583/1000\n", - "8/8 - 0s - loss: 2.2323e-04 - root_mean_squared_error: 0.0149 - val_loss: 4.0348e-04 - val_root_mean_squared_error: 0.0201\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 584/1000\n", - "8/8 - 0s - loss: 2.1990e-04 - root_mean_squared_error: 0.0148 - val_loss: 4.6761e-04 - val_root_mean_squared_error: 0.0216\n", + "8/8 - 0s - loss: 9.8885e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 585/1000\n", - "8/8 - 0s - loss: 2.3697e-04 - root_mean_squared_error: 0.0154 - val_loss: 4.1632e-04 - val_root_mean_squared_error: 0.0204\n", + "Epoch 585/1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 586/1000\n", - "8/8 - 0s - loss: 2.8634e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3085e-04 - val_root_mean_squared_error: 0.0208\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 587/1000\n", - "8/8 - 0s - loss: 2.6647e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.3402e-04 - val_root_mean_squared_error: 0.0183\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 588/1000\n", - "8/8 - 0s - loss: 2.8150e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.8572e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 589/1000\n", - "8/8 - 0s - loss: 2.9818e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5975e-04 - val_root_mean_squared_error: 0.0214\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 590/1000\n", - "8/8 - 0s - loss: 2.5339e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.8803e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 9.9169e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 591/1000\n", - "8/8 - 0s - loss: 2.3655e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.5578e-04 - val_root_mean_squared_error: 0.0189\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 592/1000\n", - "8/8 - 0s - loss: 2.1493e-04 - root_mean_squared_error: 0.0147 - val_loss: 3.9139e-04 - val_root_mean_squared_error: 0.0198\n", + "8/8 - 0s - loss: 8.5596e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 593/1000\n", - "8/8 - 0s - loss: 1.8590e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.4300e-04 - val_root_mean_squared_error: 0.0185\n", + "8/8 - 0s - loss: 8.9007e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 594/1000\n", - "8/8 - 0s - loss: 1.9541e-04 - root_mean_squared_error: 0.0140 - val_loss: 3.3028e-04 - val_root_mean_squared_error: 0.0182\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 595/1000\n", - "8/8 - 0s - loss: 1.7216e-04 - root_mean_squared_error: 0.0131 - val_loss: 3.2505e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 8.0961e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 596/1000\n", - "8/8 - 0s - loss: 1.5616e-04 - root_mean_squared_error: 0.0125 - val_loss: 3.1118e-04 - val_root_mean_squared_error: 0.0176\n", + "8/8 - 0s - loss: 8.9548e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 597/1000\n", - "8/8 - 0s - loss: 1.4647e-04 - root_mean_squared_error: 0.0121 - val_loss: 2.6253e-04 - val_root_mean_squared_error: 0.0162\n", + "8/8 - 0s - loss: 9.0129e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 598/1000\n", - "8/8 - 0s - loss: 1.1950e-04 - root_mean_squared_error: 0.0109 - val_loss: 2.6548e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 7.5580e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 599/1000\n", - "8/8 - 0s - loss: 1.2035e-04 - root_mean_squared_error: 0.0110 - val_loss: 2.6251e-04 - val_root_mean_squared_error: 0.0162\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 600/1000\n", - "8/8 - 0s - loss: 1.0707e-04 - root_mean_squared_error: 0.0103 - val_loss: 2.3701e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 9.5379e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 601/1000\n", - "8/8 - 0s - loss: 1.1796e-04 - root_mean_squared_error: 0.0109 - val_loss: 2.6617e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 7.2662e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 602/1000\n", - "8/8 - 0s - loss: 1.0722e-04 - root_mean_squared_error: 0.0104 - val_loss: 2.2277e-04 - val_root_mean_squared_error: 0.0149\n", + "8/8 - 0s - loss: 9.7026e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 603/1000\n", - "8/8 - 0s - loss: 9.6768e-05 - root_mean_squared_error: 0.0098 - val_loss: 2.4028e-04 - val_root_mean_squared_error: 0.0155\n", + "8/8 - 0s - loss: 9.8502e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 604/1000\n", - "8/8 - 0s - loss: 1.4483e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.4614e-04 - val_root_mean_squared_error: 0.0157\n", + "8/8 - 0s - loss: 7.6451e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 605/1000\n", - "8/8 - 0s - loss: 1.4700e-04 - root_mean_squared_error: 0.0121 - val_loss: 2.5570e-04 - val_root_mean_squared_error: 0.0160\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 606/1000\n", - "8/8 - 0s - loss: 1.6254e-04 - root_mean_squared_error: 0.0127 - val_loss: 2.6406e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 607/1000\n", - "8/8 - 0s - loss: 1.8869e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.2095e-04 - val_root_mean_squared_error: 0.0149\n", + "8/8 - 0s - loss: 7.5891e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 608/1000\n", - "8/8 - 0s - loss: 1.8906e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.5818e-04 - val_root_mean_squared_error: 0.0189\n", + "8/8 - 0s - loss: 8.9854e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 609/1000\n", - "8/8 - 0s - loss: 2.0553e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.6460e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 9.4718e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 610/1000\n", - "8/8 - 0s - loss: 1.9770e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.4078e-04 - val_root_mean_squared_error: 0.0155\n", + "8/8 - 0s - loss: 7.0567e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 611/1000\n", - "8/8 - 0s - loss: 2.0986e-04 - root_mean_squared_error: 0.0145 - val_loss: 4.0074e-04 - val_root_mean_squared_error: 0.0200\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 612/1000\n", - "8/8 - 0s - loss: 2.1333e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.9032e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 9.1763e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 613/1000\n", - "8/8 - 0s - loss: 2.5129e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.0896e-04 - val_root_mean_squared_error: 0.0176\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 7.7271e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 614/1000\n", - "8/8 - 0s - loss: 2.5034e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.8533e-04 - val_root_mean_squared_error: 0.0169\n", + "8/8 - 0s - loss: 8.2071e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0453\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 615/1000\n", - "8/8 - 0s - loss: 2.6753e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.3700e-04 - val_root_mean_squared_error: 0.0184\n", + "8/8 - 0s - loss: 8.6782e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0468\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 616/1000\n", - "8/8 - 0s - loss: 4.4481e-04 - root_mean_squared_error: 0.0211 - val_loss: 4.2790e-04 - val_root_mean_squared_error: 0.0207\n", + "8/8 - 0s - loss: 7.5657e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 617/1000\n", - "8/8 - 0s - loss: 5.1476e-04 - root_mean_squared_error: 0.0227 - val_loss: 3.8293e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 8.2107e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 618/1000\n", - "8/8 - 0s - loss: 7.1839e-04 - root_mean_squared_error: 0.0268 - val_loss: 5.1427e-04 - val_root_mean_squared_error: 0.0227\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 619/1000\n", - "8/8 - 0s - loss: 6.8672e-04 - root_mean_squared_error: 0.0262 - val_loss: 6.8491e-04 - val_root_mean_squared_error: 0.0262\n", + "8/8 - 0s - loss: 7.7408e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 620/1000\n", - "8/8 - 0s - loss: 4.7100e-04 - root_mean_squared_error: 0.0217 - val_loss: 4.7959e-04 - val_root_mean_squared_error: 0.0219\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 621/1000\n", - "8/8 - 0s - loss: 3.9487e-04 - root_mean_squared_error: 0.0199 - val_loss: 4.6875e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 622/1000\n", - "8/8 - 0s - loss: 3.2411e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.1077e-04 - val_root_mean_squared_error: 0.0203\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 623/1000\n", - "8/8 - 0s - loss: 3.2309e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.5378e-04 - val_root_mean_squared_error: 0.0213\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 624/1000\n", - "8/8 - 0s - loss: 4.2074e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.4603e-04 - val_root_mean_squared_error: 0.0234\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0453\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 625/1000\n", - "8/8 - 0s - loss: 4.7857e-04 - root_mean_squared_error: 0.0219 - val_loss: 5.1264e-04 - val_root_mean_squared_error: 0.0226\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 626/1000\n", - "8/8 - 0s - loss: 3.5986e-04 - root_mean_squared_error: 0.0190 - val_loss: 4.4244e-04 - val_root_mean_squared_error: 0.0210\n", + "8/8 - 0s - loss: 8.9907e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 627/1000\n", - "8/8 - 0s - loss: 4.4115e-04 - root_mean_squared_error: 0.0210 - val_loss: 4.0894e-04 - val_root_mean_squared_error: 0.0202\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 628/1000\n", - "8/8 - 0s - loss: 3.9511e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.0206e-04 - val_root_mean_squared_error: 0.0224\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 629/1000\n", - "8/8 - 0s - loss: 4.0544e-04 - root_mean_squared_error: 0.0201 - val_loss: 4.6837e-04 - val_root_mean_squared_error: 0.0216\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 630/1000\n", - "8/8 - 0s - loss: 4.4739e-04 - root_mean_squared_error: 0.0212 - val_loss: 5.3384e-04 - val_root_mean_squared_error: 0.0231\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 631/1000\n", - "8/8 - 0s - loss: 4.1815e-04 - root_mean_squared_error: 0.0204 - val_loss: 3.6571e-04 - val_root_mean_squared_error: 0.0191\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 632/1000\n", - "8/8 - 0s - loss: 3.2656e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.6068e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 633/1000\n", - "8/8 - 0s - loss: 3.2054e-04 - root_mean_squared_error: 0.0179 - val_loss: 2.7640e-04 - val_root_mean_squared_error: 0.0166\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 634/1000\n", - "8/8 - 0s - loss: 3.0655e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.1974e-04 - val_root_mean_squared_error: 0.0179\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 635/1000\n", - "8/8 - 0s - loss: 3.0200e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.2097e-04 - val_root_mean_squared_error: 0.0179\n", + "8/8 - 0s - loss: 9.3345e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 636/1000\n", - "8/8 - 0s - loss: 3.2911e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.5654e-04 - val_root_mean_squared_error: 0.0189\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 637/1000\n", - "8/8 - 0s - loss: 3.0671e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.3115e-04 - val_root_mean_squared_error: 0.0208\n", + "8/8 - 0s - loss: 8.7514e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 638/1000\n", - "8/8 - 0s - loss: 3.4294e-04 - root_mean_squared_error: 0.0185 - val_loss: 4.0184e-04 - val_root_mean_squared_error: 0.0200\n", + "8/8 - 0s - loss: 6.9592e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 639/1000\n", - "8/8 - 0s - loss: 3.2548e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.7136e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 6.8979e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 640/1000\n", - "8/8 - 0s - loss: 3.6769e-04 - root_mean_squared_error: 0.0192 - val_loss: 4.1572e-04 - val_root_mean_squared_error: 0.0204\n", + "8/8 - 0s - loss: 6.8597e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 641/1000\n", - "8/8 - 0s - loss: 4.1541e-04 - root_mean_squared_error: 0.0204 - val_loss: 5.5906e-04 - val_root_mean_squared_error: 0.0236\n", + "8/8 - 0s - loss: 6.4530e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 642/1000\n", - "8/8 - 0s - loss: 5.2379e-04 - root_mean_squared_error: 0.0229 - val_loss: 5.1515e-04 - val_root_mean_squared_error: 0.0227\n", + "8/8 - 0s - loss: 6.6964e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 643/1000\n", - "8/8 - 0s - loss: 5.2044e-04 - root_mean_squared_error: 0.0228 - val_loss: 7.2496e-04 - val_root_mean_squared_error: 0.0269\n", + "8/8 - 0s - loss: 7.5649e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 644/1000\n", - "8/8 - 0s - loss: 5.5410e-04 - root_mean_squared_error: 0.0235 - val_loss: 6.2315e-04 - val_root_mean_squared_error: 0.0250\n", + "8/8 - 0s - loss: 6.7981e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0434\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 645/1000\n", - "8/8 - 0s - loss: 4.5055e-04 - root_mean_squared_error: 0.0212 - val_loss: 5.3077e-04 - val_root_mean_squared_error: 0.0230\n", + "8/8 - 0s - loss: 7.3140e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 646/1000\n", - "8/8 - 0s - loss: 4.0954e-04 - root_mean_squared_error: 0.0202 - val_loss: 4.7698e-04 - val_root_mean_squared_error: 0.0218\n", + "8/8 - 0s - loss: 7.9844e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 647/1000\n", - "8/8 - 0s - loss: 3.0639e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.8475e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 7.3196e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 648/1000\n", - "8/8 - 0s - loss: 2.2840e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.8712e-04 - val_root_mean_squared_error: 0.0169\n", + "8/8 - 0s - loss: 8.3855e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 649/1000\n", - "8/8 - 0s - loss: 2.1552e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8837e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 9.4600e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 650/1000\n", - "8/8 - 0s - loss: 1.9038e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.9335e-04 - val_root_mean_squared_error: 0.0171\n", + "8/8 - 0s - loss: 6.6688e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 651/1000\n", - "8/8 - 0s - loss: 2.4359e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.2853e-04 - val_root_mean_squared_error: 0.0181\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 9.9424e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 652/1000\n", - "8/8 - 0s - loss: 2.7259e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.6410e-04 - val_root_mean_squared_error: 0.0191\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 653/1000\n", - "8/8 - 0s - loss: 2.8906e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.4386e-04 - val_root_mean_squared_error: 0.0185\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 654/1000\n", - "8/8 - 0s - loss: 2.5061e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.7260e-04 - val_root_mean_squared_error: 0.0193\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 655/1000\n", - "8/8 - 0s - loss: 1.9924e-04 - root_mean_squared_error: 0.0141 - val_loss: 3.0028e-04 - val_root_mean_squared_error: 0.0173\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 656/1000\n", - "8/8 - 0s - loss: 1.7463e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.1623e-04 - val_root_mean_squared_error: 0.0178\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 657/1000\n", - "8/8 - 0s - loss: 1.6551e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.2089e-04 - val_root_mean_squared_error: 0.0149\n", + "8/8 - 0s - loss: 8.9340e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 658/1000\n", - "8/8 - 0s - loss: 1.8685e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.5421e-04 - val_root_mean_squared_error: 0.0159\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 659/1000\n", - "8/8 - 0s - loss: 1.9856e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.9400e-04 - val_root_mean_squared_error: 0.0171\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 660/1000\n", - "8/8 - 0s - loss: 2.8635e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.0404e-04 - val_root_mean_squared_error: 0.0174\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 661/1000\n", - "8/8 - 0s - loss: 3.3806e-04 - root_mean_squared_error: 0.0184 - val_loss: 3.2786e-04 - val_root_mean_squared_error: 0.0181\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 662/1000\n", - "8/8 - 0s - loss: 3.4080e-04 - root_mean_squared_error: 0.0185 - val_loss: 3.2467e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 663/1000\n", - "8/8 - 0s - loss: 2.9415e-04 - root_mean_squared_error: 0.0172 - val_loss: 3.7794e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 9.2852e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 664/1000\n", - "8/8 - 0s - loss: 2.1577e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8571e-04 - val_root_mean_squared_error: 0.0169\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 665/1000\n", - "8/8 - 0s - loss: 1.8398e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.6157e-04 - val_root_mean_squared_error: 0.0162\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 666/1000\n", - "8/8 - 0s - loss: 1.5084e-04 - root_mean_squared_error: 0.0123 - val_loss: 2.2632e-04 - val_root_mean_squared_error: 0.0150\n", + "8/8 - 0s - loss: 8.4643e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 667/1000\n", - "8/8 - 0s - loss: 2.1625e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.4206e-04 - val_root_mean_squared_error: 0.0156\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0453\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 668/1000\n", - "8/8 - 0s - loss: 2.4729e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.8681e-04 - val_root_mean_squared_error: 0.0169\n", + "8/8 - 0s - loss: 8.3200e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 669/1000\n", - "8/8 - 0s - loss: 3.2641e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.1811e-04 - val_root_mean_squared_error: 0.0178\n", + "8/8 - 0s - loss: 6.3169e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 670/1000\n", - "8/8 - 0s - loss: 2.9412e-04 - root_mean_squared_error: 0.0171 - val_loss: 2.9759e-04 - val_root_mean_squared_error: 0.0173\n", + "8/8 - 0s - loss: 6.4671e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 671/1000\n", - "8/8 - 0s - loss: 2.6658e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.1312e-04 - val_root_mean_squared_error: 0.0177\n", + "8/8 - 0s - loss: 5.7715e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 672/1000\n", - "8/8 - 0s - loss: 2.1241e-04 - root_mean_squared_error: 0.0146 - val_loss: 3.2491e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 5.2000e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 673/1000\n", - "8/8 - 0s - loss: 1.9096e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2855e-04 - val_root_mean_squared_error: 0.0151\n", + "8/8 - 0s - loss: 7.3261e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 674/1000\n", - "8/8 - 0s - loss: 1.7190e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.2709e-04 - val_root_mean_squared_error: 0.0151\n", + "8/8 - 0s - loss: 4.8020e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 675/1000\n", - "8/8 - 0s - loss: 1.4480e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.2393e-04 - val_root_mean_squared_error: 0.0150\n", + "8/8 - 0s - loss: 4.9688e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 676/1000\n", - "8/8 - 0s - loss: 1.8214e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.2961e-04 - val_root_mean_squared_error: 0.0152\n", + "8/8 - 0s - loss: 5.3925e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 677/1000\n", - "8/8 - 0s - loss: 1.9623e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3445e-04 - val_root_mean_squared_error: 0.0153\n", + "8/8 - 0s - loss: 4.9504e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 678/1000\n", - "8/8 - 0s - loss: 2.0643e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.3299e-04 - val_root_mean_squared_error: 0.0153\n", + "8/8 - 0s - loss: 5.6141e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 679/1000\n", - "8/8 - 0s - loss: 1.9350e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.3082e-04 - val_root_mean_squared_error: 0.0152\n", + "8/8 - 0s - loss: 6.0885e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 680/1000\n", - "8/8 - 0s - loss: 1.5316e-04 - root_mean_squared_error: 0.0124 - val_loss: 2.1128e-04 - val_root_mean_squared_error: 0.0145\n", + "8/8 - 0s - loss: 5.1876e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 681/1000\n", - "8/8 - 0s - loss: 1.2284e-04 - root_mean_squared_error: 0.0111 - val_loss: 2.0593e-04 - val_root_mean_squared_error: 0.0144\n", + "8/8 - 0s - loss: 4.9104e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 682/1000\n", - "8/8 - 0s - loss: 1.0592e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.6620e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 6.9941e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 683/1000\n", - "8/8 - 0s - loss: 9.5866e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.5859e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 7.1592e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 684/1000\n", - "8/8 - 0s - loss: 8.6011e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.6141e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 6.4509e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 685/1000\n", - "8/8 - 0s - loss: 1.0901e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.6565e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 9.5879e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 686/1000\n", - "8/8 - 0s - loss: 1.1830e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.5982e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 9.0734e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0409\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 687/1000\n", - "8/8 - 0s - loss: 1.2311e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.6179e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 6.7464e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 688/1000\n", - "8/8 - 0s - loss: 1.1693e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.6202e-04 - val_root_mean_squared_error: 0.0127\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 8.1118e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 689/1000\n", - "8/8 - 0s - loss: 9.2394e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.4730e-04 - val_root_mean_squared_error: 0.0121\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 690/1000\n", - "8/8 - 0s - loss: 7.8754e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.4943e-04 - val_root_mean_squared_error: 0.0122\n", + "8/8 - 0s - loss: 8.6433e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 691/1000\n", - "8/8 - 0s - loss: 7.2523e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.3206e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 692/1000\n", - "8/8 - 0s - loss: 6.7071e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.2016e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 693/1000\n", - "8/8 - 0s - loss: 6.4637e-05 - root_mean_squared_error: 0.0080 - val_loss: 1.2499e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 9.2892e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 694/1000\n", - "8/8 - 0s - loss: 8.2554e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.2201e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 8.8011e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 695/1000\n", - "8/8 - 0s - loss: 8.9115e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.1894e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 696/1000\n", - "8/8 - 0s - loss: 8.8789e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.3118e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 9.9231e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 697/1000\n", - "8/8 - 0s - loss: 8.6189e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.2502e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 8.5761e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 698/1000\n", - "8/8 - 0s - loss: 6.5275e-05 - root_mean_squared_error: 0.0081 - val_loss: 1.1547e-04 - val_root_mean_squared_error: 0.0107\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0430\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 699/1000\n", - "8/8 - 0s - loss: 5.7332e-05 - root_mean_squared_error: 0.0076 - val_loss: 1.1939e-04 - val_root_mean_squared_error: 0.0109\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 700/1000\n", - "8/8 - 0s - loss: 5.0386e-05 - root_mean_squared_error: 0.0071 - val_loss: 1.0113e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 7.7608e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 701/1000\n", - "8/8 - 0s - loss: 4.1299e-05 - root_mean_squared_error: 0.0064 - val_loss: 8.9439e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 8.3258e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 702/1000\n", - "8/8 - 0s - loss: 5.0372e-05 - root_mean_squared_error: 0.0071 - val_loss: 9.8760e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 7.2847e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 703/1000\n", - "8/8 - 0s - loss: 6.2140e-05 - root_mean_squared_error: 0.0079 - val_loss: 9.3990e-05 - val_root_mean_squared_error: 0.0097\n", + "8/8 - 0s - loss: 6.4246e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 704/1000\n", - "8/8 - 0s - loss: 7.2920e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.0150e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 9.8904e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 705/1000\n", - "8/8 - 0s - loss: 7.4662e-05 - root_mean_squared_error: 0.0086 - val_loss: 1.1051e-04 - val_root_mean_squared_error: 0.0105\n", + "8/8 - 0s - loss: 5.5888e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 706/1000\n", - "8/8 - 0s - loss: 7.5887e-05 - root_mean_squared_error: 0.0087 - val_loss: 1.0012e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 5.4454e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 707/1000\n", - "8/8 - 0s - loss: 7.1630e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.1229e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 6.6511e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0367\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 708/1000\n", - "8/8 - 0s - loss: 6.7844e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.1193e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 6.1356e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 709/1000\n", - "8/8 - 0s - loss: 5.5791e-05 - root_mean_squared_error: 0.0075 - val_loss: 8.8482e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 5.9877e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 710/1000\n", - "8/8 - 0s - loss: 5.2301e-05 - root_mean_squared_error: 0.0072 - val_loss: 9.3442e-05 - val_root_mean_squared_error: 0.0097\n", + "8/8 - 0s - loss: 7.0553e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 711/1000\n", - "8/8 - 0s - loss: 4.8713e-05 - root_mean_squared_error: 0.0070 - val_loss: 8.3300e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 6.9182e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 712/1000\n", - "8/8 - 0s - loss: 3.9518e-05 - root_mean_squared_error: 0.0063 - val_loss: 7.9375e-05 - val_root_mean_squared_error: 0.0089\n", + "8/8 - 0s - loss: 5.5769e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 713/1000\n", - "8/8 - 0s - loss: 5.6434e-05 - root_mean_squared_error: 0.0075 - val_loss: 8.1756e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 8.6155e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 714/1000\n", - "8/8 - 0s - loss: 6.4619e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.8649e-05 - val_root_mean_squared_error: 0.0089\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 715/1000\n", - "8/8 - 0s - loss: 7.7898e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.0850e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 7.4126e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 716/1000\n", - "8/8 - 0s - loss: 1.1375e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.0137e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 717/1000\n", - "8/8 - 0s - loss: 1.1320e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.1168e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 718/1000\n", - "8/8 - 0s - loss: 1.3959e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.7221e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 7.2845e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 719/1000\n", - "8/8 - 0s - loss: 1.5763e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.2210e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 9.7422e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 720/1000\n", - "8/8 - 0s - loss: 1.5414e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4050e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 721/1000\n", - "8/8 - 0s - loss: 1.6626e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.0186e-04 - val_root_mean_squared_error: 0.0142\n", + "8/8 - 0s - loss: 9.3763e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 722/1000\n", - "8/8 - 0s - loss: 1.3487e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2148e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 723/1000\n", - "8/8 - 0s - loss: 1.2021e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.2563e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 9.1108e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0402\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 724/1000\n", - "8/8 - 0s - loss: 1.1718e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.5108e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 6.8850e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 725/1000\n", - "8/8 - 0s - loss: 7.9296e-05 - root_mean_squared_error: 0.0089 - val_loss: 9.7617e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 6.5613e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 726/1000\n", - "8/8 - 0s - loss: 7.0692e-05 - root_mean_squared_error: 0.0084 - val_loss: 9.6296e-05 - val_root_mean_squared_error: 0.0098\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 5.9564e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 727/1000\n", - "8/8 - 0s - loss: 7.1072e-05 - root_mean_squared_error: 0.0084 - val_loss: 8.9326e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 5.3555e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 728/1000\n", - "8/8 - 0s - loss: 6.0757e-05 - root_mean_squared_error: 0.0078 - val_loss: 9.1281e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 8.1124e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 729/1000\n", - "8/8 - 0s - loss: 9.2821e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.0278e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 5.5143e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 730/1000\n", - "8/8 - 0s - loss: 9.9609e-05 - root_mean_squared_error: 0.0100 - val_loss: 7.1993e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 5.4727e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 731/1000\n", - "8/8 - 0s - loss: 1.1562e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.5079e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 6.6527e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 732/1000\n", - "8/8 - 0s - loss: 2.1259e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.7073e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 5.9649e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 733/1000\n", - "8/8 - 0s - loss: 2.3345e-04 - root_mean_squared_error: 0.0153 - val_loss: 1.2522e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 6.4615e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 734/1000\n", - "8/8 - 0s - loss: 2.4010e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.7828e-04 - val_root_mean_squared_error: 0.0167\n", + "8/8 - 0s - loss: 7.4269e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 735/1000\n", - "8/8 - 0s - loss: 3.5859e-04 - root_mean_squared_error: 0.0189 - val_loss: 2.6722e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 6.1921e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 736/1000\n", - "8/8 - 0s - loss: 3.2947e-04 - root_mean_squared_error: 0.0182 - val_loss: 1.7639e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 6.1855e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 737/1000\n", - "8/8 - 0s - loss: 2.7586e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.0638e-04 - val_root_mean_squared_error: 0.0175\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 8.2617e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 738/1000\n", - "8/8 - 0s - loss: 2.5922e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.5905e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 8.0024e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 739/1000\n", - "8/8 - 0s - loss: 1.7942e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.3348e-04 - val_root_mean_squared_error: 0.0116\n", + "8/8 - 0s - loss: 8.2579e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 740/1000\n", - "8/8 - 0s - loss: 1.2228e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.0605e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 8.0339e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 741/1000\n", - "8/8 - 0s - loss: 8.9950e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.3134e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 6.3630e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 742/1000\n", - "8/8 - 0s - loss: 6.9278e-05 - root_mean_squared_error: 0.0083 - val_loss: 1.0731e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.1048e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 743/1000\n", - "8/8 - 0s - loss: 5.9917e-05 - root_mean_squared_error: 0.0077 - val_loss: 8.2995e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 8.0150e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 744/1000\n", - "8/8 - 0s - loss: 8.6283e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.2353e-04 - val_root_mean_squared_error: 0.0111\n", + "8/8 - 0s - loss: 7.0910e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 745/1000\n", - "8/8 - 0s - loss: 1.2852e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3380e-04 - val_root_mean_squared_error: 0.0116\n", + "8/8 - 0s - loss: 7.2883e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 746/1000\n", - "8/8 - 0s - loss: 1.4626e-04 - root_mean_squared_error: 0.0121 - val_loss: 9.9826e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 747/1000\n", - "8/8 - 0s - loss: 1.5724e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.1800e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 9.0201e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 748/1000\n", - "8/8 - 0s - loss: 1.2147e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.1629e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 8.2497e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 749/1000\n", - "8/8 - 0s - loss: 1.1508e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.1038e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 8.7548e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 750/1000\n", - "8/8 - 0s - loss: 1.0462e-04 - root_mean_squared_error: 0.0102 - val_loss: 9.6267e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 6.4082e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 751/1000\n", - "8/8 - 0s - loss: 8.8128e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.1728e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 7.1956e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 752/1000\n", - "8/8 - 0s - loss: 6.7168e-05 - root_mean_squared_error: 0.0082 - val_loss: 9.1075e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 8.7582e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 753/1000\n", - "8/8 - 0s - loss: 5.7499e-05 - root_mean_squared_error: 0.0076 - val_loss: 8.3108e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 5.2695e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 754/1000\n", - "8/8 - 0s - loss: 8.9135e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.2445e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 6.5359e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 755/1000\n", - "8/8 - 0s - loss: 1.3496e-04 - root_mean_squared_error: 0.0116 - val_loss: 9.9713e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 8.1538e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 756/1000\n", - "8/8 - 0s - loss: 1.4909e-04 - root_mean_squared_error: 0.0122 - val_loss: 8.2712e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 6.5078e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 757/1000\n", - "8/8 - 0s - loss: 1.4717e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4664e-04 - val_root_mean_squared_error: 0.0121\n", + "8/8 - 0s - loss: 8.4484e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 758/1000\n", - "8/8 - 0s - loss: 2.3111e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.9005e-04 - val_root_mean_squared_error: 0.0138\n", + "8/8 - 0s - loss: 7.2488e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 759/1000\n", - "8/8 - 0s - loss: 2.4344e-04 - root_mean_squared_error: 0.0156 - val_loss: 1.3699e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 6.8540e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 760/1000\n", - "8/8 - 0s - loss: 1.8397e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8900e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 8.8735e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0367\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 761/1000\n", - "8/8 - 0s - loss: 1.7214e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.5667e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 6.9568e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 762/1000\n", - "8/8 - 0s - loss: 1.3038e-04 - root_mean_squared_error: 0.0114 - val_loss: 8.2874e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 7.2407e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 763/1000\n", - "8/8 - 0s - loss: 1.1203e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.6289e-04 - val_root_mean_squared_error: 0.0128\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 764/1000\n", - "8/8 - 0s - loss: 1.7359e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.0823e-04 - val_root_mean_squared_error: 0.0144\n", + "8/8 - 0s - loss: 7.8399e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 765/1000\n", - "8/8 - 0s - loss: 1.9406e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.0292e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 7.4065e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 766/1000\n", - "8/8 - 0s - loss: 1.5738e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.2262e-04 - val_root_mean_squared_error: 0.0111\n", + "8/8 - 0s - loss: 7.7901e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 767/1000\n", - "8/8 - 0s - loss: 2.4615e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.4368e-04 - val_root_mean_squared_error: 0.0156\n", + "8/8 - 0s - loss: 6.0617e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 768/1000\n", - "8/8 - 0s - loss: 2.8419e-04 - root_mean_squared_error: 0.0169 - val_loss: 1.9570e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 7.6797e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 769/1000\n", - "8/8 - 0s - loss: 2.5220e-04 - root_mean_squared_error: 0.0159 - val_loss: 1.6198e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 6.6966e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 770/1000\n", - "8/8 - 0s - loss: 2.6920e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.0937e-04 - val_root_mean_squared_error: 0.0145\n", + "8/8 - 0s - loss: 6.0099e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 771/1000\n", - "8/8 - 0s - loss: 1.8746e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.3641e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 6.7998e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 772/1000\n", - "8/8 - 0s - loss: 1.6821e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.0881e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 5.8296e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0409\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 773/1000\n", - "8/8 - 0s - loss: 1.2822e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.0033e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 8.6551e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 774/1000\n", - "8/8 - 0s - loss: 9.4503e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.1629e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 6.4973e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 775/1000\n", - "8/8 - 0s - loss: 1.0788e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.5173e-04 - val_root_mean_squared_error: 0.0123\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 4.9405e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 776/1000\n", - "8/8 - 0s - loss: 1.0864e-04 - root_mean_squared_error: 0.0104 - val_loss: 9.7324e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 5.5797e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 777/1000\n", - "8/8 - 0s - loss: 9.4807e-05 - root_mean_squared_error: 0.0097 - val_loss: 7.7864e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 5.3626e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 778/1000\n", - "8/8 - 0s - loss: 1.0281e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.1206e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 5.0608e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 779/1000\n", - "8/8 - 0s - loss: 1.0962e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0715e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.9473e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 780/1000\n", - "8/8 - 0s - loss: 9.6726e-05 - root_mean_squared_error: 0.0098 - val_loss: 8.6378e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 5.3221e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 781/1000\n", - "8/8 - 0s - loss: 8.3861e-05 - root_mean_squared_error: 0.0092 - val_loss: 8.3633e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 5.8925e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 782/1000\n", - "8/8 - 0s - loss: 6.2084e-05 - root_mean_squared_error: 0.0079 - val_loss: 8.0212e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 7.6191e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 783/1000\n", - "8/8 - 0s - loss: 5.3816e-05 - root_mean_squared_error: 0.0073 - val_loss: 7.7485e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 6.7035e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 784/1000\n", - "8/8 - 0s - loss: 4.7799e-05 - root_mean_squared_error: 0.0069 - val_loss: 5.4109e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 9.6637e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 785/1000\n", - "8/8 - 0s - loss: 4.2071e-05 - root_mean_squared_error: 0.0065 - val_loss: 5.2514e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 7.2558e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 786/1000\n", - "8/8 - 0s - loss: 3.8690e-05 - root_mean_squared_error: 0.0062 - val_loss: 6.9593e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 5.1863e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 787/1000\n", - "8/8 - 0s - loss: 3.9697e-05 - root_mean_squared_error: 0.0063 - val_loss: 5.5563e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 5.9273e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 788/1000\n", - "8/8 - 0s - loss: 4.8925e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.2673e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 5.8900e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 789/1000\n", - "8/8 - 0s - loss: 4.9338e-05 - root_mean_squared_error: 0.0070 - val_loss: 6.0850e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 4.8070e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 790/1000\n", - "8/8 - 0s - loss: 6.0285e-05 - root_mean_squared_error: 0.0078 - val_loss: 5.8741e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 8.1447e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 791/1000\n", - "8/8 - 0s - loss: 6.9298e-05 - root_mean_squared_error: 0.0083 - val_loss: 5.5784e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 4.9308e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 792/1000\n", - "8/8 - 0s - loss: 6.3387e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.2491e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 4.3004e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 793/1000\n", - "8/8 - 0s - loss: 6.8541e-05 - root_mean_squared_error: 0.0083 - val_loss: 7.7701e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 5.7329e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 794/1000\n", - "8/8 - 0s - loss: 7.1279e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.4910e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 4.7771e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 795/1000\n", - "8/8 - 0s - loss: 6.6401e-05 - root_mean_squared_error: 0.0081 - val_loss: 8.5680e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 6.0620e-04 - root_mean_squared_error: 0.0246 - val_loss: 9.7228e-04 - val_root_mean_squared_error: 0.0312\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 796/1000\n", - "8/8 - 0s - loss: 1.3197e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.6160e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 6.0275e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 797/1000\n", - "8/8 - 0s - loss: 2.2607e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.4185e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 4.5867e-04 - root_mean_squared_error: 0.0214 - val_loss: 9.9721e-04 - val_root_mean_squared_error: 0.0316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 798/1000\n", - "8/8 - 0s - loss: 2.4049e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.2481e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 5.7446e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 799/1000\n", - "8/8 - 0s - loss: 3.0773e-04 - root_mean_squared_error: 0.0175 - val_loss: 2.3161e-04 - val_root_mean_squared_error: 0.0152\n", + "8/8 - 0s - loss: 7.0285e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 800/1000\n", - "8/8 - 0s - loss: 3.8935e-04 - root_mean_squared_error: 0.0197 - val_loss: 2.8989e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 5.5967e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 801/1000\n", - "8/8 - 0s - loss: 5.8134e-04 - root_mean_squared_error: 0.0241 - val_loss: 3.6970e-04 - val_root_mean_squared_error: 0.0192\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 802/1000\n", - "8/8 - 0s - loss: 5.1266e-04 - root_mean_squared_error: 0.0226 - val_loss: 3.9748e-04 - val_root_mean_squared_error: 0.0199\n", + "8/8 - 0s - loss: 6.9698e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 803/1000\n", - "8/8 - 0s - loss: 3.6931e-04 - root_mean_squared_error: 0.0192 - val_loss: 2.6069e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 5.2715e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 804/1000\n", - "8/8 - 0s - loss: 3.0846e-04 - root_mean_squared_error: 0.0176 - val_loss: 2.7883e-04 - val_root_mean_squared_error: 0.0167\n", + "8/8 - 0s - loss: 6.0984e-04 - root_mean_squared_error: 0.0247 - val_loss: 9.5726e-04 - val_root_mean_squared_error: 0.0309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 805/1000\n", - "8/8 - 0s - loss: 2.3945e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.6706e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 5.1578e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 806/1000\n", - "8/8 - 0s - loss: 1.9913e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1979e-04 - val_root_mean_squared_error: 0.0148\n", + "8/8 - 0s - loss: 5.6266e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 807/1000\n", - "8/8 - 0s - loss: 1.4901e-04 - root_mean_squared_error: 0.0122 - val_loss: 2.5834e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 6.9412e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 808/1000\n", - "8/8 - 0s - loss: 1.3576e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.6660e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 4.8203e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 809/1000\n", - "8/8 - 0s - loss: 1.0963e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0087e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 7.1471e-04 - root_mean_squared_error: 0.0267 - val_loss: 9.6284e-04 - val_root_mean_squared_error: 0.0310\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 810/1000\n", - "8/8 - 0s - loss: 8.3586e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.0062e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 9.9701e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 811/1000\n", - "8/8 - 0s - loss: 6.8630e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.9861e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 7.6904e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 812/1000\n", - "8/8 - 0s - loss: 6.1209e-05 - root_mean_squared_error: 0.0078 - val_loss: 6.7016e-05 - val_root_mean_squared_error: 0.0082\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 813/1000\n", - "8/8 - 0s - loss: 4.8471e-05 - root_mean_squared_error: 0.0070 - val_loss: 6.7212e-05 - val_root_mean_squared_error: 0.0082\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 814/1000\n", - "8/8 - 0s - loss: 3.6809e-05 - root_mean_squared_error: 0.0061 - val_loss: 6.3040e-05 - val_root_mean_squared_error: 0.0079\n", + "8/8 - 0s - loss: 8.3650e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 815/1000\n", - "8/8 - 0s - loss: 2.9326e-05 - root_mean_squared_error: 0.0054 - val_loss: 5.8909e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 8.4868e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 816/1000\n", - "8/8 - 0s - loss: 2.3997e-05 - root_mean_squared_error: 0.0049 - val_loss: 4.5806e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 6.3198e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 817/1000\n", - "8/8 - 0s - loss: 2.5177e-05 - root_mean_squared_error: 0.0050 - val_loss: 4.2363e-05 - val_root_mean_squared_error: 0.0065\n", + "8/8 - 0s - loss: 5.8687e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 818/1000\n", - "8/8 - 0s - loss: 3.1595e-05 - root_mean_squared_error: 0.0056 - val_loss: 5.0597e-05 - val_root_mean_squared_error: 0.0071\n", + "8/8 - 0s - loss: 7.3861e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 819/1000\n", - "8/8 - 0s - loss: 3.7081e-05 - root_mean_squared_error: 0.0061 - val_loss: 5.1813e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 4.9231e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 820/1000\n", - "8/8 - 0s - loss: 3.2407e-05 - root_mean_squared_error: 0.0057 - val_loss: 4.6544e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 6.7916e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 821/1000\n", - "8/8 - 0s - loss: 2.9868e-05 - root_mean_squared_error: 0.0055 - val_loss: 4.4705e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 7.3542e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 822/1000\n", - "8/8 - 0s - loss: 2.5864e-05 - root_mean_squared_error: 0.0051 - val_loss: 3.8886e-05 - val_root_mean_squared_error: 0.0062\n", + "8/8 - 0s - loss: 7.8108e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 823/1000\n", - "8/8 - 0s - loss: 2.4002e-05 - root_mean_squared_error: 0.0049 - val_loss: 3.9315e-05 - val_root_mean_squared_error: 0.0063\n", + "8/8 - 0s - loss: 7.6654e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 824/1000\n", - "8/8 - 0s - loss: 2.4982e-05 - root_mean_squared_error: 0.0050 - val_loss: 4.2243e-05 - val_root_mean_squared_error: 0.0065\n", + "8/8 - 0s - loss: 5.9602e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 825/1000\n", - "8/8 - 0s - loss: 2.6199e-05 - root_mean_squared_error: 0.0051 - val_loss: 3.5591e-05 - val_root_mean_squared_error: 0.0060\n", + "8/8 - 0s - loss: 7.5081e-04 - root_mean_squared_error: 0.0274 - val_loss: 9.5766e-04 - val_root_mean_squared_error: 0.0309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 826/1000\n", - "8/8 - 0s - loss: 2.5160e-05 - root_mean_squared_error: 0.0050 - val_loss: 3.8112e-05 - val_root_mean_squared_error: 0.0062\n", + "8/8 - 0s - loss: 7.5678e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 827/1000\n", - "8/8 - 0s - loss: 3.5990e-05 - root_mean_squared_error: 0.0060 - val_loss: 4.8295e-05 - val_root_mean_squared_error: 0.0069\n", + "8/8 - 0s - loss: 6.5443e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 828/1000\n", - "8/8 - 0s - loss: 4.5836e-05 - root_mean_squared_error: 0.0068 - val_loss: 4.2376e-05 - val_root_mean_squared_error: 0.0065\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 829/1000\n", - "8/8 - 0s - loss: 4.2252e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.3244e-05 - val_root_mean_squared_error: 0.0066\n", + "8/8 - 0s - loss: 9.6038e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 830/1000\n", - "8/8 - 0s - loss: 6.1599e-05 - root_mean_squared_error: 0.0078 - val_loss: 7.0279e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 9.8784e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 831/1000\n", - "8/8 - 0s - loss: 8.9628e-05 - root_mean_squared_error: 0.0095 - val_loss: 5.8244e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 8.6321e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 832/1000\n", - "8/8 - 0s - loss: 8.6583e-05 - root_mean_squared_error: 0.0093 - val_loss: 5.3778e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 6.9161e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 833/1000\n", - "8/8 - 0s - loss: 1.1643e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.1559e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 8.7773e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 834/1000\n", - "8/8 - 0s - loss: 1.5212e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.0185e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 7.6993e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 835/1000\n", - "8/8 - 0s - loss: 1.5428e-04 - root_mean_squared_error: 0.0124 - val_loss: 7.2317e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 7.1853e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 836/1000\n", - "8/8 - 0s - loss: 1.5821e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4453e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 6.1588e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 837/1000\n", - "8/8 - 0s - loss: 1.8935e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.4978e-04 - val_root_mean_squared_error: 0.0122\n", + "8/8 - 0s - loss: 5.5294e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 838/1000\n", - "8/8 - 0s - loss: 2.6605e-04 - root_mean_squared_error: 0.0163 - val_loss: 1.3306e-04 - val_root_mean_squared_error: 0.0115\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 5.6000e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 839/1000\n", - "8/8 - 0s - loss: 2.4914e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.3685e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 4.9119e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 840/1000\n", - "8/8 - 0s - loss: 1.7135e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.6981e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 4.9058e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 841/1000\n", - "8/8 - 0s - loss: 2.0720e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.7919e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 5.6255e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 842/1000\n", - "8/8 - 0s - loss: 2.1254e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.8008e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 4.6441e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 843/1000\n", - "8/8 - 0s - loss: 1.4829e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.3795e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 6.8667e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 844/1000\n", - "8/8 - 0s - loss: 1.1922e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.3942e-04 - val_root_mean_squared_error: 0.0118\n", + "8/8 - 0s - loss: 6.5391e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 845/1000\n", - "8/8 - 0s - loss: 1.2979e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3747e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 6.6329e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 846/1000\n", - "8/8 - 0s - loss: 1.1790e-04 - root_mean_squared_error: 0.0109 - val_loss: 9.3611e-05 - val_root_mean_squared_error: 0.0097\n", + "8/8 - 0s - loss: 5.3975e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 847/1000\n", - "8/8 - 0s - loss: 9.1151e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.0967e-04 - val_root_mean_squared_error: 0.0105\n", + "8/8 - 0s - loss: 4.7309e-04 - root_mean_squared_error: 0.0218 - val_loss: 9.0017e-04 - val_root_mean_squared_error: 0.0300\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 848/1000\n", - "8/8 - 0s - loss: 9.3682e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.0050e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 5.1698e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 849/1000\n", - "8/8 - 0s - loss: 1.3606e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.0676e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 5.3219e-04 - root_mean_squared_error: 0.0231 - val_loss: 9.8322e-04 - val_root_mean_squared_error: 0.0314\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 850/1000\n", - "8/8 - 0s - loss: 1.4858e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.9580e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 5.5450e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 851/1000\n", - "8/8 - 0s - loss: 1.0737e-04 - root_mean_squared_error: 0.0104 - val_loss: 6.4122e-05 - val_root_mean_squared_error: 0.0080\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 4.7079e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 852/1000\n", - "8/8 - 0s - loss: 1.3604e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1637e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 4.3778e-04 - root_mean_squared_error: 0.0209 - val_loss: 9.0995e-04 - val_root_mean_squared_error: 0.0302\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 853/1000\n", - "8/8 - 0s - loss: 1.6617e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.9755e-04 - val_root_mean_squared_error: 0.0141\n", + "8/8 - 0s - loss: 4.7170e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.7871e-04 - val_root_mean_squared_error: 0.0313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 854/1000\n", - "8/8 - 0s - loss: 1.5457e-04 - root_mean_squared_error: 0.0124 - val_loss: 8.7808e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 4.2918e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.1141e-04 - val_root_mean_squared_error: 0.0302\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 855/1000\n", - "8/8 - 0s - loss: 1.2534e-04 - root_mean_squared_error: 0.0112 - val_loss: 8.4907e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 4.9944e-04 - root_mean_squared_error: 0.0223 - val_loss: 8.7434e-04 - val_root_mean_squared_error: 0.0296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 856/1000\n", - "8/8 - 0s - loss: 1.3032e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.5083e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 4.3915e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 857/1000\n", - "8/8 - 0s - loss: 1.8514e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.5611e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 5.2614e-04 - root_mean_squared_error: 0.0229 - val_loss: 7.9903e-04 - val_root_mean_squared_error: 0.0283\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 858/1000\n", - "8/8 - 0s - loss: 1.7111e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8201e-04 - val_root_mean_squared_error: 0.0135\n", + "8/8 - 0s - loss: 5.0553e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 859/1000\n", - "8/8 - 0s - loss: 1.1635e-04 - root_mean_squared_error: 0.0108 - val_loss: 9.5617e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 5.2350e-04 - root_mean_squared_error: 0.0229 - val_loss: 8.6958e-04 - val_root_mean_squared_error: 0.0295\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 860/1000\n", - "8/8 - 0s - loss: 1.7436e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8297e-04 - val_root_mean_squared_error: 0.0135\n", + "8/8 - 0s - loss: 5.7241e-04 - root_mean_squared_error: 0.0239 - val_loss: 9.9896e-04 - val_root_mean_squared_error: 0.0316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 861/1000\n", - "8/8 - 0s - loss: 2.2071e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8590e-04 - val_root_mean_squared_error: 0.0169\n", + "8/8 - 0s - loss: 5.0703e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 862/1000\n", - "8/8 - 0s - loss: 1.9022e-04 - root_mean_squared_error: 0.0138 - val_loss: 9.9335e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 4.9802e-04 - root_mean_squared_error: 0.0223 - val_loss: 9.3527e-04 - val_root_mean_squared_error: 0.0306\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 863/1000\n", - "8/8 - 0s - loss: 1.5727e-04 - root_mean_squared_error: 0.0125 - val_loss: 8.3490e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 6.8141e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 864/1000\n", - "8/8 - 0s - loss: 1.4158e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.6932e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 6.3916e-04 - root_mean_squared_error: 0.0253 - val_loss: 9.5798e-04 - val_root_mean_squared_error: 0.0310\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 865/1000\n", - "8/8 - 0s - loss: 1.9087e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7040e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 6.4874e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 866/1000\n", - "8/8 - 0s - loss: 1.8918e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7799e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 5.4697e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 867/1000\n", - "8/8 - 0s - loss: 1.2863e-04 - root_mean_squared_error: 0.0113 - val_loss: 7.2960e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 6.1512e-04 - root_mean_squared_error: 0.0248 - val_loss: 8.1288e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 868/1000\n", - "8/8 - 0s - loss: 1.2820e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2085e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 5.7630e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 869/1000\n", - "8/8 - 0s - loss: 1.5433e-04 - root_mean_squared_error: 0.0124 - val_loss: 2.2075e-04 - val_root_mean_squared_error: 0.0149\n", + "8/8 - 0s - loss: 6.6092e-04 - root_mean_squared_error: 0.0257 - val_loss: 8.7514e-04 - val_root_mean_squared_error: 0.0296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 870/1000\n", - "8/8 - 0s - loss: 1.6112e-04 - root_mean_squared_error: 0.0127 - val_loss: 9.9612e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 6.6668e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 871/1000\n", - "8/8 - 0s - loss: 1.1171e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.6641e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 5.2691e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 872/1000\n", - "8/8 - 0s - loss: 7.4767e-05 - root_mean_squared_error: 0.0086 - val_loss: 9.1160e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 4.9239e-04 - root_mean_squared_error: 0.0222 - val_loss: 8.7620e-04 - val_root_mean_squared_error: 0.0296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 873/1000\n", - "8/8 - 0s - loss: 1.1533e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.3030e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 5.4272e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 874/1000\n", - "8/8 - 0s - loss: 1.4870e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.8634e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 4.6881e-04 - root_mean_squared_error: 0.0217 - val_loss: 8.5119e-04 - val_root_mean_squared_error: 0.0292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 875/1000\n", - "8/8 - 0s - loss: 1.2080e-04 - root_mean_squared_error: 0.0110 - val_loss: 6.3899e-05 - val_root_mean_squared_error: 0.0080\n", + "8/8 - 0s - loss: 5.3982e-04 - root_mean_squared_error: 0.0232 - val_loss: 8.9089e-04 - val_root_mean_squared_error: 0.0298\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 876/1000\n", - "8/8 - 0s - loss: 1.1243e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.9149e-05 - val_root_mean_squared_error: 0.0089\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 4.6406e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 877/1000\n", - "8/8 - 0s - loss: 1.3378e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.6350e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 6.7086e-04 - root_mean_squared_error: 0.0259 - val_loss: 7.7319e-04 - val_root_mean_squared_error: 0.0278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 878/1000\n", - "8/8 - 0s - loss: 1.9785e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.3042e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 5.4714e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 879/1000\n", - "8/8 - 0s - loss: 1.7141e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.2767e-04 - val_root_mean_squared_error: 0.0113\n", + "8/8 - 0s - loss: 5.7052e-04 - root_mean_squared_error: 0.0239 - val_loss: 7.9182e-04 - val_root_mean_squared_error: 0.0281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 880/1000\n", - "8/8 - 0s - loss: 1.0979e-04 - root_mean_squared_error: 0.0105 - val_loss: 8.2482e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 5.4761e-04 - root_mean_squared_error: 0.0234 - val_loss: 9.4503e-04 - val_root_mean_squared_error: 0.0307\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 881/1000\n", - "8/8 - 0s - loss: 1.1491e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.1775e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 4.1541e-04 - root_mean_squared_error: 0.0204 - val_loss: 9.9429e-04 - val_root_mean_squared_error: 0.0315\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 882/1000\n", - "8/8 - 0s - loss: 1.4248e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.8826e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 5.3283e-04 - root_mean_squared_error: 0.0231 - val_loss: 8.2486e-04 - val_root_mean_squared_error: 0.0287\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 883/1000\n", - "8/8 - 0s - loss: 1.6023e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.0706e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 4.8680e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 884/1000\n", - "8/8 - 0s - loss: 1.1554e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.2259e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 4.1848e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.0825e-04 - val_root_mean_squared_error: 0.0284\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 885/1000\n", - "8/8 - 0s - loss: 7.0947e-05 - root_mean_squared_error: 0.0084 - val_loss: 6.8237e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 4.4153e-04 - root_mean_squared_error: 0.0210 - val_loss: 8.0741e-04 - val_root_mean_squared_error: 0.0284\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 886/1000\n", - "8/8 - 0s - loss: 8.1474e-05 - root_mean_squared_error: 0.0090 - val_loss: 8.3653e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 3.9124e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 887/1000\n", - "8/8 - 0s - loss: 1.1484e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.4367e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 6.6816e-04 - root_mean_squared_error: 0.0258 - val_loss: 7.8306e-04 - val_root_mean_squared_error: 0.0280\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 888/1000\n", - "8/8 - 0s - loss: 1.3929e-04 - root_mean_squared_error: 0.0118 - val_loss: 9.2329e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 5.8580e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 889/1000\n", - "8/8 - 0s - loss: 1.3643e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1684e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 5.7418e-04 - root_mean_squared_error: 0.0240 - val_loss: 9.0324e-04 - val_root_mean_squared_error: 0.0301\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 890/1000\n", - "8/8 - 0s - loss: 1.0543e-04 - root_mean_squared_error: 0.0103 - val_loss: 8.8670e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 4.6841e-04 - root_mean_squared_error: 0.0216 - val_loss: 8.6541e-04 - val_root_mean_squared_error: 0.0294\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 891/1000\n", - "8/8 - 0s - loss: 1.1285e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0008e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 3.6153e-04 - root_mean_squared_error: 0.0190 - val_loss: 9.0860e-04 - val_root_mean_squared_error: 0.0301\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 892/1000\n", - "8/8 - 0s - loss: 1.3351e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.5825e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 5.2558e-04 - root_mean_squared_error: 0.0229 - val_loss: 7.5420e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 893/1000\n", - "8/8 - 0s - loss: 1.6460e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.1731e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 4.8337e-04 - root_mean_squared_error: 0.0220 - val_loss: 9.4404e-04 - val_root_mean_squared_error: 0.0307\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 894/1000\n", - "8/8 - 0s - loss: 1.5570e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.3880e-04 - val_root_mean_squared_error: 0.0118\n", + "8/8 - 0s - loss: 4.5927e-04 - root_mean_squared_error: 0.0214 - val_loss: 7.7305e-04 - val_root_mean_squared_error: 0.0278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 895/1000\n", - "8/8 - 0s - loss: 1.1434e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.1354e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 5.0648e-04 - root_mean_squared_error: 0.0225 - val_loss: 8.6223e-04 - val_root_mean_squared_error: 0.0294\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 896/1000\n", - "8/8 - 0s - loss: 1.2785e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.1100e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 4.8759e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 897/1000\n", - "8/8 - 0s - loss: 1.6161e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8008e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 7.8271e-04 - root_mean_squared_error: 0.0280 - val_loss: 8.0954e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 898/1000\n", - "8/8 - 0s - loss: 2.1913e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.4097e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 6.0615e-04 - root_mean_squared_error: 0.0246 - val_loss: 9.8347e-04 - val_root_mean_squared_error: 0.0314\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 899/1000\n", - "8/8 - 0s - loss: 2.2737e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.4053e-04 - val_root_mean_squared_error: 0.0155\n", + "8/8 - 0s - loss: 5.5244e-04 - root_mean_squared_error: 0.0235 - val_loss: 8.2293e-04 - val_root_mean_squared_error: 0.0287\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 900/1000\n", - "8/8 - 0s - loss: 2.0317e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.2289e-04 - val_root_mean_squared_error: 0.0111\n", + "8/8 - 0s - loss: 4.7387e-04 - root_mean_squared_error: 0.0218 - val_loss: 8.7846e-04 - val_root_mean_squared_error: 0.0296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 901/1000\n", - "8/8 - 0s - loss: 2.3230e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.1742e-04 - val_root_mean_squared_error: 0.0147\n", + "8/8 - 0s - loss: 4.0641e-04 - root_mean_squared_error: 0.0202 - val_loss: 9.1930e-04 - val_root_mean_squared_error: 0.0303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 902/1000\n", - "8/8 - 0s - loss: 2.3945e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.3586e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 5.4009e-04 - root_mean_squared_error: 0.0232 - val_loss: 7.2501e-04 - val_root_mean_squared_error: 0.0269\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 903/1000\n", - "8/8 - 0s - loss: 2.7463e-04 - root_mean_squared_error: 0.0166 - val_loss: 1.9657e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 4.7190e-04 - root_mean_squared_error: 0.0217 - val_loss: 8.9438e-04 - val_root_mean_squared_error: 0.0299\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 904/1000\n", - "8/8 - 0s - loss: 2.7624e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.0035e-04 - val_root_mean_squared_error: 0.0173\n", + "8/8 - 0s - loss: 4.6752e-04 - root_mean_squared_error: 0.0216 - val_loss: 7.0264e-04 - val_root_mean_squared_error: 0.0265\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 905/1000\n", - "8/8 - 0s - loss: 2.3827e-04 - root_mean_squared_error: 0.0154 - val_loss: 1.3229e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 5.3000e-04 - root_mean_squared_error: 0.0230 - val_loss: 9.0373e-04 - val_root_mean_squared_error: 0.0301\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 906/1000\n", - "8/8 - 0s - loss: 2.3593e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.0632e-04 - val_root_mean_squared_error: 0.0144\n", + "8/8 - 0s - loss: 5.3050e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 907/1000\n", - "8/8 - 0s - loss: 2.3234e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.5061e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 8.7949e-04 - root_mean_squared_error: 0.0297 - val_loss: 9.2914e-04 - val_root_mean_squared_error: 0.0305\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 908/1000\n", - "8/8 - 0s - loss: 2.8803e-04 - root_mean_squared_error: 0.0170 - val_loss: 1.9394e-04 - val_root_mean_squared_error: 0.0139\n", + "8/8 - 0s - loss: 6.6742e-04 - root_mean_squared_error: 0.0258 - val_loss: 9.7022e-04 - val_root_mean_squared_error: 0.0311\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 909/1000\n", - "8/8 - 0s - loss: 2.8623e-04 - root_mean_squared_error: 0.0169 - val_loss: 1.8946e-04 - val_root_mean_squared_error: 0.0138\n", + "8/8 - 0s - loss: 5.9917e-04 - root_mean_squared_error: 0.0245 - val_loss: 8.8424e-04 - val_root_mean_squared_error: 0.0297\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 910/1000\n", - "8/8 - 0s - loss: 2.5965e-04 - root_mean_squared_error: 0.0161 - val_loss: 1.5600e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 4.4601e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.3237e-04 - val_root_mean_squared_error: 0.0305\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 911/1000\n", - "8/8 - 0s - loss: 1.8558e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.2811e-04 - val_root_mean_squared_error: 0.0151\n", + "8/8 - 0s - loss: 4.2685e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.6532e-04 - val_root_mean_squared_error: 0.0277\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 912/1000\n", - "8/8 - 0s - loss: 2.3228e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.7243e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 4.4456e-04 - root_mean_squared_error: 0.0211 - val_loss: 7.8140e-04 - val_root_mean_squared_error: 0.0280\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 913/1000\n", - "8/8 - 0s - loss: 2.5019e-04 - root_mean_squared_error: 0.0158 - val_loss: 1.9305e-04 - val_root_mean_squared_error: 0.0139\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 4.0473e-04 - root_mean_squared_error: 0.0201 - val_loss: 7.0842e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 914/1000\n", - "8/8 - 0s - loss: 2.2594e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.9437e-04 - val_root_mean_squared_error: 0.0139\n", + "8/8 - 0s - loss: 4.1814e-04 - root_mean_squared_error: 0.0204 - val_loss: 6.7086e-04 - val_root_mean_squared_error: 0.0259\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 915/1000\n", - "8/8 - 0s - loss: 1.7904e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.4516e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 3.5738e-04 - root_mean_squared_error: 0.0189 - val_loss: 8.4749e-04 - val_root_mean_squared_error: 0.0291\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 916/1000\n", - "8/8 - 0s - loss: 2.0551e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.1979e-04 - val_root_mean_squared_error: 0.0148\n", + "8/8 - 0s - loss: 4.4853e-04 - root_mean_squared_error: 0.0212 - val_loss: 7.7752e-04 - val_root_mean_squared_error: 0.0279\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 917/1000\n", - "8/8 - 0s - loss: 2.5943e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.9001e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 5.1527e-04 - root_mean_squared_error: 0.0227 - val_loss: 8.6535e-04 - val_root_mean_squared_error: 0.0294\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 918/1000\n", - "8/8 - 0s - loss: 2.3362e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.4497e-04 - val_root_mean_squared_error: 0.0157\n", + "8/8 - 0s - loss: 4.0143e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.2763e-04 - val_root_mean_squared_error: 0.0270\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 919/1000\n", - "8/8 - 0s - loss: 1.2879e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.0761e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 4.7287e-04 - root_mean_squared_error: 0.0217 - val_loss: 6.9830e-04 - val_root_mean_squared_error: 0.0264\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 920/1000\n", - "8/8 - 0s - loss: 1.0259e-04 - root_mean_squared_error: 0.0101 - val_loss: 7.3706e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 3.9588e-04 - root_mean_squared_error: 0.0199 - val_loss: 9.9600e-04 - val_root_mean_squared_error: 0.0316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 921/1000\n", - "8/8 - 0s - loss: 8.9792e-05 - root_mean_squared_error: 0.0095 - val_loss: 9.7478e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 5.8781e-04 - root_mean_squared_error: 0.0242 - val_loss: 6.3652e-04 - val_root_mean_squared_error: 0.0252\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 922/1000\n", - "8/8 - 0s - loss: 9.7179e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1341e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 4.6281e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.0926e-04 - val_root_mean_squared_error: 0.0302\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 923/1000\n", - "8/8 - 0s - loss: 9.4046e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.3221e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 4.9443e-04 - root_mean_squared_error: 0.0222 - val_loss: 6.5486e-04 - val_root_mean_squared_error: 0.0256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 924/1000\n", - "8/8 - 0s - loss: 8.2226e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.2919e-05 - val_root_mean_squared_error: 0.0079\n", + "8/8 - 0s - loss: 4.8475e-04 - root_mean_squared_error: 0.0220 - val_loss: 7.5827e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 925/1000\n", - "8/8 - 0s - loss: 9.0594e-05 - root_mean_squared_error: 0.0095 - val_loss: 7.0370e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 3.6376e-04 - root_mean_squared_error: 0.0191 - val_loss: 8.7216e-04 - val_root_mean_squared_error: 0.0295\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 926/1000\n", - "8/8 - 0s - loss: 9.9493e-05 - root_mean_squared_error: 0.0100 - val_loss: 9.7639e-05 - val_root_mean_squared_error: 0.0099\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 5.5375e-04 - root_mean_squared_error: 0.0235 - val_loss: 7.0887e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 927/1000\n", - "8/8 - 0s - loss: 1.2080e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.0105e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 5.0677e-04 - root_mean_squared_error: 0.0225 - val_loss: 9.5029e-04 - val_root_mean_squared_error: 0.0308\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 928/1000\n", - "8/8 - 0s - loss: 1.3884e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3014e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 4.5818e-04 - root_mean_squared_error: 0.0214 - val_loss: 7.3219e-04 - val_root_mean_squared_error: 0.0271\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 929/1000\n", - "8/8 - 0s - loss: 1.3882e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3286e-04 - val_root_mean_squared_error: 0.0115\n", + "8/8 - 0s - loss: 4.8201e-04 - root_mean_squared_error: 0.0220 - val_loss: 7.6715e-04 - val_root_mean_squared_error: 0.0277\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 930/1000\n", - "8/8 - 0s - loss: 1.2523e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1835e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 4.4065e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.1801e-04 - val_root_mean_squared_error: 0.0303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 931/1000\n", - "8/8 - 0s - loss: 1.1752e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.0818e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.3190e-04 - root_mean_squared_error: 0.0251 - val_loss: 6.6803e-04 - val_root_mean_squared_error: 0.0258\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 932/1000\n", - "8/8 - 0s - loss: 7.8590e-05 - root_mean_squared_error: 0.0089 - val_loss: 8.5372e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 4.7593e-04 - root_mean_squared_error: 0.0218 - val_loss: 8.5854e-04 - val_root_mean_squared_error: 0.0293\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 933/1000\n", - "8/8 - 0s - loss: 6.8057e-05 - root_mean_squared_error: 0.0082 - val_loss: 7.9808e-05 - val_root_mean_squared_error: 0.0089\n", + "8/8 - 0s - loss: 4.8801e-04 - root_mean_squared_error: 0.0221 - val_loss: 6.5830e-04 - val_root_mean_squared_error: 0.0257\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 934/1000\n", - "8/8 - 0s - loss: 6.2886e-05 - root_mean_squared_error: 0.0079 - val_loss: 5.9097e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 4.1876e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.1569e-04 - val_root_mean_squared_error: 0.0286\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 935/1000\n", - "8/8 - 0s - loss: 5.6347e-05 - root_mean_squared_error: 0.0075 - val_loss: 5.3149e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 3.6698e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.5826e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 936/1000\n", - "8/8 - 0s - loss: 5.5876e-05 - root_mean_squared_error: 0.0075 - val_loss: 5.7601e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 4.7431e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.8053e-04 - val_root_mean_squared_error: 0.0261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 937/1000\n", - "8/8 - 0s - loss: 4.3466e-05 - root_mean_squared_error: 0.0066 - val_loss: 4.7957e-05 - val_root_mean_squared_error: 0.0069\n", + "8/8 - 0s - loss: 4.3758e-04 - root_mean_squared_error: 0.0209 - val_loss: 7.5901e-04 - val_root_mean_squared_error: 0.0276\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 938/1000\n", - "8/8 - 0s - loss: 3.8292e-05 - root_mean_squared_error: 0.0062 - val_loss: 4.9583e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 4.5056e-04 - root_mean_squared_error: 0.0212 - val_loss: 6.8690e-04 - val_root_mean_squared_error: 0.0262\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 939/1000\n", - "8/8 - 0s - loss: 3.1108e-05 - root_mean_squared_error: 0.0056 - val_loss: 4.2719e-05 - val_root_mean_squared_error: 0.0065\n", + "8/8 - 0s - loss: 4.1021e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.3496e-04 - val_root_mean_squared_error: 0.0289\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 940/1000\n", - "8/8 - 0s - loss: 2.7261e-05 - root_mean_squared_error: 0.0052 - val_loss: 3.8458e-05 - val_root_mean_squared_error: 0.0062\n", + "8/8 - 0s - loss: 4.6850e-04 - root_mean_squared_error: 0.0216 - val_loss: 7.5036e-04 - val_root_mean_squared_error: 0.0274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 941/1000\n", - "8/8 - 0s - loss: 2.2047e-05 - root_mean_squared_error: 0.0047 - val_loss: 3.0742e-05 - val_root_mean_squared_error: 0.0055\n", + "8/8 - 0s - loss: 5.7844e-04 - root_mean_squared_error: 0.0241 - val_loss: 8.1092e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 942/1000\n", - "8/8 - 0s - loss: 1.9378e-05 - root_mean_squared_error: 0.0044 - val_loss: 3.0574e-05 - val_root_mean_squared_error: 0.0055\n", + "8/8 - 0s - loss: 4.7935e-04 - root_mean_squared_error: 0.0219 - val_loss: 7.2757e-04 - val_root_mean_squared_error: 0.0270\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 943/1000\n", - "8/8 - 0s - loss: 1.9781e-05 - root_mean_squared_error: 0.0044 - val_loss: 2.8618e-05 - val_root_mean_squared_error: 0.0053\n", + "8/8 - 0s - loss: 4.6141e-04 - root_mean_squared_error: 0.0215 - val_loss: 7.1787e-04 - val_root_mean_squared_error: 0.0268\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 944/1000\n", - "8/8 - 0s - loss: 2.1801e-05 - root_mean_squared_error: 0.0047 - val_loss: 2.9400e-05 - val_root_mean_squared_error: 0.0054\n", + "8/8 - 0s - loss: 3.4088e-04 - root_mean_squared_error: 0.0185 - val_loss: 8.1507e-04 - val_root_mean_squared_error: 0.0285\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 945/1000\n", - "8/8 - 0s - loss: 2.2440e-05 - root_mean_squared_error: 0.0047 - val_loss: 2.9232e-05 - val_root_mean_squared_error: 0.0054\n", + "8/8 - 0s - loss: 4.3372e-04 - root_mean_squared_error: 0.0208 - val_loss: 5.7932e-04 - val_root_mean_squared_error: 0.0241\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 946/1000\n", - "8/8 - 0s - loss: 2.8331e-05 - root_mean_squared_error: 0.0053 - val_loss: 4.1730e-05 - val_root_mean_squared_error: 0.0065\n", + "8/8 - 0s - loss: 3.5032e-04 - root_mean_squared_error: 0.0187 - val_loss: 7.4793e-04 - val_root_mean_squared_error: 0.0273\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 947/1000\n", - "8/8 - 0s - loss: 4.0255e-05 - root_mean_squared_error: 0.0063 - val_loss: 6.1603e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 3.7756e-04 - root_mean_squared_error: 0.0194 - val_loss: 5.5401e-04 - val_root_mean_squared_error: 0.0235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 948/1000\n", - "8/8 - 0s - loss: 5.3308e-05 - root_mean_squared_error: 0.0073 - val_loss: 6.3179e-05 - val_root_mean_squared_error: 0.0079\n", + "8/8 - 0s - loss: 3.8742e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.5403e-04 - val_root_mean_squared_error: 0.0256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 949/1000\n", - "8/8 - 0s - loss: 5.1722e-05 - root_mean_squared_error: 0.0072 - val_loss: 4.5856e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 2.9782e-04 - root_mean_squared_error: 0.0173 - val_loss: 7.5386e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 950/1000\n", - "8/8 - 0s - loss: 4.1932e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.0316e-05 - val_root_mean_squared_error: 0.0063\n", + "8/8 - 0s - loss: 5.0036e-04 - root_mean_squared_error: 0.0224 - val_loss: 6.5988e-04 - val_root_mean_squared_error: 0.0257\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 951/1000\n", - "8/8 - 0s - loss: 7.0285e-05 - root_mean_squared_error: 0.0084 - val_loss: 8.0378e-05 - val_root_mean_squared_error: 0.0090\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 4.6097e-04 - root_mean_squared_error: 0.0215 - val_loss: 7.9197e-04 - val_root_mean_squared_error: 0.0281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 952/1000\n", - "8/8 - 0s - loss: 1.1564e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.2018e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 4.3037e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.2664e-04 - val_root_mean_squared_error: 0.0270\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 953/1000\n", - "8/8 - 0s - loss: 1.3298e-04 - root_mean_squared_error: 0.0115 - val_loss: 9.2302e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 3.4272e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.8429e-04 - val_root_mean_squared_error: 0.0262\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 954/1000\n", - "8/8 - 0s - loss: 1.5451e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4610e-04 - val_root_mean_squared_error: 0.0121\n", + "8/8 - 0s - loss: 3.5104e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.2732e-04 - val_root_mean_squared_error: 0.0250\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 955/1000\n", - "8/8 - 0s - loss: 1.3982e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3007e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 3.7668e-04 - root_mean_squared_error: 0.0194 - val_loss: 5.5137e-04 - val_root_mean_squared_error: 0.0235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 956/1000\n", - "8/8 - 0s - loss: 1.8497e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.6301e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 2.7234e-04 - root_mean_squared_error: 0.0165 - val_loss: 5.9712e-04 - val_root_mean_squared_error: 0.0244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 957/1000\n", - "8/8 - 0s - loss: 1.9531e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.6717e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 3.0763e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.7573e-04 - val_root_mean_squared_error: 0.0218\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 958/1000\n", - "8/8 - 0s - loss: 1.5251e-04 - root_mean_squared_error: 0.0123 - val_loss: 8.5285e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 2.7185e-04 - root_mean_squared_error: 0.0165 - val_loss: 6.6926e-04 - val_root_mean_squared_error: 0.0259\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 959/1000\n", - "8/8 - 0s - loss: 1.3727e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.0554e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 3.0236e-04 - root_mean_squared_error: 0.0174 - val_loss: 6.1363e-04 - val_root_mean_squared_error: 0.0248\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 960/1000\n", - "8/8 - 0s - loss: 1.3481e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.0125e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 3.9588e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.9664e-04 - val_root_mean_squared_error: 0.0244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 961/1000\n", - "8/8 - 0s - loss: 1.2541e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.0655e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 3.5421e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.9428e-04 - val_root_mean_squared_error: 0.0263\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 962/1000\n", - "8/8 - 0s - loss: 1.2616e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1832e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 5.1994e-04 - root_mean_squared_error: 0.0228 - val_loss: 5.7227e-04 - val_root_mean_squared_error: 0.0239\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 963/1000\n", - "8/8 - 0s - loss: 1.2871e-04 - root_mean_squared_error: 0.0113 - val_loss: 8.7537e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 4.6786e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 964/1000\n", - "8/8 - 0s - loss: 1.2592e-04 - root_mean_squared_error: 0.0112 - val_loss: 9.1618e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 6.2173e-04 - root_mean_squared_error: 0.0249 - val_loss: 6.5751e-04 - val_root_mean_squared_error: 0.0256\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 965/1000\n", - "8/8 - 0s - loss: 1.1988e-04 - root_mean_squared_error: 0.0109 - val_loss: 9.0373e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 7.0407e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0316\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 966/1000\n", - "8/8 - 0s - loss: 1.1389e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.9640e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 7.5009e-04 - root_mean_squared_error: 0.0274 - val_loss: 8.1880e-04 - val_root_mean_squared_error: 0.0286\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 967/1000\n", - "8/8 - 0s - loss: 9.6888e-05 - root_mean_squared_error: 0.0098 - val_loss: 7.4263e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 7.4476e-04 - root_mean_squared_error: 0.0273 - val_loss: 9.8662e-04 - val_root_mean_squared_error: 0.0314\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 968/1000\n", - "8/8 - 0s - loss: 9.1415e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.2326e-05 - val_root_mean_squared_error: 0.0079\n", + "8/8 - 0s - loss: 5.3592e-04 - root_mean_squared_error: 0.0231 - val_loss: 9.4878e-04 - val_root_mean_squared_error: 0.0308\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 969/1000\n", - "8/8 - 0s - loss: 7.2259e-05 - root_mean_squared_error: 0.0085 - val_loss: 5.7104e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 5.5794e-04 - root_mean_squared_error: 0.0236 - val_loss: 6.4221e-04 - val_root_mean_squared_error: 0.0253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 970/1000\n", - "8/8 - 0s - loss: 6.8295e-05 - root_mean_squared_error: 0.0083 - val_loss: 5.7322e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 4.6733e-04 - root_mean_squared_error: 0.0216 - val_loss: 8.2506e-04 - val_root_mean_squared_error: 0.0287\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 971/1000\n", - "8/8 - 0s - loss: 4.6255e-05 - root_mean_squared_error: 0.0068 - val_loss: 5.1797e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 4.8464e-04 - root_mean_squared_error: 0.0220 - val_loss: 6.1459e-04 - val_root_mean_squared_error: 0.0248\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 972/1000\n", - "8/8 - 0s - loss: 3.7687e-05 - root_mean_squared_error: 0.0061 - val_loss: 4.9673e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 5.3340e-04 - root_mean_squared_error: 0.0231 - val_loss: 7.2826e-04 - val_root_mean_squared_error: 0.0270\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 973/1000\n", - "8/8 - 0s - loss: 3.0702e-05 - root_mean_squared_error: 0.0055 - val_loss: 4.0822e-05 - val_root_mean_squared_error: 0.0064\n", + "8/8 - 0s - loss: 3.9421e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.8126e-04 - val_root_mean_squared_error: 0.0297\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 974/1000\n", - "8/8 - 0s - loss: 3.3565e-05 - root_mean_squared_error: 0.0058 - val_loss: 4.4309e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 6.9576e-04 - root_mean_squared_error: 0.0264 - val_loss: 8.6143e-04 - val_root_mean_squared_error: 0.0294\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 975/1000\n", - "8/8 - 0s - loss: 3.3553e-05 - root_mean_squared_error: 0.0058 - val_loss: 5.1481e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 5.9204e-04 - root_mean_squared_error: 0.0243 - val_loss: 7.6761e-04 - val_root_mean_squared_error: 0.0277\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 976/1000\n", - "8/8 - 0s - loss: 4.1456e-05 - root_mean_squared_error: 0.0064 - val_loss: 5.6749e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 5.4669e-04 - root_mean_squared_error: 0.0234 - val_loss: 8.6654e-04 - val_root_mean_squared_error: 0.0294\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 977/1000\n", - "8/8 - 0s - loss: 4.1719e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.9498e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 3.5836e-04 - root_mean_squared_error: 0.0189 - val_loss: 7.5830e-04 - val_root_mean_squared_error: 0.0275\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 978/1000\n", - "8/8 - 0s - loss: 3.4179e-05 - root_mean_squared_error: 0.0058 - val_loss: 3.2296e-05 - val_root_mean_squared_error: 0.0057\n", + "8/8 - 0s - loss: 4.6714e-04 - root_mean_squared_error: 0.0216 - val_loss: 6.0105e-04 - val_root_mean_squared_error: 0.0245\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 979/1000\n", - "8/8 - 0s - loss: 3.4550e-05 - root_mean_squared_error: 0.0059 - val_loss: 3.1676e-05 - val_root_mean_squared_error: 0.0056\n", + "8/8 - 0s - loss: 4.1262e-04 - root_mean_squared_error: 0.0203 - val_loss: 6.8306e-04 - val_root_mean_squared_error: 0.0261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 980/1000\n", - "8/8 - 0s - loss: 6.6102e-05 - root_mean_squared_error: 0.0081 - val_loss: 7.4812e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 4.7388e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.4694e-04 - val_root_mean_squared_error: 0.0254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 981/1000\n", - "8/8 - 0s - loss: 1.2834e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.0470e-04 - val_root_mean_squared_error: 0.0102\n", + "8/8 - 0s - loss: 3.7692e-04 - root_mean_squared_error: 0.0194 - val_loss: 6.3318e-04 - val_root_mean_squared_error: 0.0252\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 982/1000\n", - "8/8 - 0s - loss: 1.7333e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9454e-04 - val_root_mean_squared_error: 0.0139\n", + "8/8 - 0s - loss: 4.1830e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.0245e-04 - val_root_mean_squared_error: 0.0224\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 983/1000\n", - "8/8 - 0s - loss: 1.3208e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2640e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 3.9189e-04 - root_mean_squared_error: 0.0198 - val_loss: 5.9572e-04 - val_root_mean_squared_error: 0.0244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 984/1000\n", - "8/8 - 0s - loss: 1.7959e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.6001e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 4.8265e-04 - root_mean_squared_error: 0.0220 - val_loss: 5.0258e-04 - val_root_mean_squared_error: 0.0224\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 985/1000\n", - "8/8 - 0s - loss: 2.2595e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.1169e-04 - val_root_mean_squared_error: 0.0145\n", + "8/8 - 0s - loss: 4.5915e-04 - root_mean_squared_error: 0.0214 - val_loss: 5.5955e-04 - val_root_mean_squared_error: 0.0237\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 986/1000\n", - "8/8 - 0s - loss: 2.1086e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.6833e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 4.1379e-04 - root_mean_squared_error: 0.0203 - val_loss: 5.5322e-04 - val_root_mean_squared_error: 0.0235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 987/1000\n", - "8/8 - 0s - loss: 1.4464e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.1271e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 3.8322e-04 - root_mean_squared_error: 0.0196 - val_loss: 6.1142e-04 - val_root_mean_squared_error: 0.0247\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 988/1000\n", - "8/8 - 0s - loss: 1.1132e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.5204e-05 - val_root_mean_squared_error: 0.0087\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 3.9517e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.6110e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 989/1000\n", - "8/8 - 0s - loss: 7.1571e-05 - root_mean_squared_error: 0.0085 - val_loss: 8.1473e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 4.2032e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.4913e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 990/1000\n", - "8/8 - 0s - loss: 6.7915e-05 - root_mean_squared_error: 0.0082 - val_loss: 6.9879e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 3.6821e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.7610e-04 - val_root_mean_squared_error: 0.0240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 991/1000\n", - "8/8 - 0s - loss: 6.2029e-05 - root_mean_squared_error: 0.0079 - val_loss: 5.0120e-05 - val_root_mean_squared_error: 0.0071\n", + "8/8 - 0s - loss: 2.9640e-04 - root_mean_squared_error: 0.0172 - val_loss: 5.9535e-04 - val_root_mean_squared_error: 0.0244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 992/1000\n", - "8/8 - 0s - loss: 5.0002e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.4865e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 3.6939e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.1188e-04 - val_root_mean_squared_error: 0.0226\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 993/1000\n", - "8/8 - 0s - loss: 4.4919e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.6180e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 4.1019e-04 - root_mean_squared_error: 0.0203 - val_loss: 5.3067e-04 - val_root_mean_squared_error: 0.0230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 994/1000\n", - "8/8 - 0s - loss: 4.7784e-05 - root_mean_squared_error: 0.0069 - val_loss: 5.3897e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 4.0079e-04 - root_mean_squared_error: 0.0200 - val_loss: 5.0033e-04 - val_root_mean_squared_error: 0.0224\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 995/1000\n", - "8/8 - 0s - loss: 6.0128e-05 - root_mean_squared_error: 0.0078 - val_loss: 3.7373e-05 - val_root_mean_squared_error: 0.0061\n", + "8/8 - 0s - loss: 2.8907e-04 - root_mean_squared_error: 0.0170 - val_loss: 5.4961e-04 - val_root_mean_squared_error: 0.0234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 996/1000\n", - "8/8 - 0s - loss: 8.9006e-05 - root_mean_squared_error: 0.0094 - val_loss: 7.3439e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 3.1204e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.1957e-04 - val_root_mean_squared_error: 0.0205\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 997/1000\n", - "8/8 - 0s - loss: 1.1618e-04 - root_mean_squared_error: 0.0108 - val_loss: 8.3374e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 3.0608e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.1665e-04 - val_root_mean_squared_error: 0.0227\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 998/1000\n", - "8/8 - 0s - loss: 1.4546e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.7940e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 3.1387e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.4176e-04 - val_root_mean_squared_error: 0.0210\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 999/1000\n", - "8/8 - 0s - loss: 1.1602e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.3071e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 3.0872e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.7640e-04 - val_root_mean_squared_error: 0.0218\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 1000/1000\n", - "8/8 - 0s - loss: 1.4286e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3949e-04 - val_root_mean_squared_error: 0.0118\n", + "8/8 - 0s - loss: 3.5442e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.5259e-04 - val_root_mean_squared_error: 0.0255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -5642,7 +5487,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -5653,7 +5498,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -5670,7 +5515,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -5690,7 +5535,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -5724,12 +5569,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -5743,7 +5588,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 67926.15661142621.\n" + "The test root mean squared error is 77593.65912237932.\n" ] } ], @@ -5754,12 +5599,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5776,7 +5621,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -5784,10 +5629,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 636064\n", - "1 455702\n", - "2 452453\n", - "3 703108\n", + "0 491731\n", + "1 270690\n", + "2 235554\n", + "3 187220\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5805,7 +5650,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -5824,14 +5669,14 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_chris_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -5848,14 +5693,14 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 131301.65451832663.\n" + "The test root mean squared error is 191785.84849839678.\n" ] } ], @@ -5863,160 +5708,6 @@ "return_rmse(actual, preds)" ] }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "# def create_train_test(king_all):\n", - "# king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", - "# king_training = king_all[king_training_parse]\n", - "# king_training = king_training.reset_index()\n", - "# king_training = king_training.drop('index', axis=1)\n", - " \n", - "# king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", - "# king_test = king_all[king_test_parse]\n", - "# king_test = king_test.reset_index()\n", - "# king_test = king_test.drop('index', axis=1)\n", - "# print(king_test.shape)\n", - " \n", - "# # Normalizing Data\n", - "# king_training[king_training[\"king\"] < 0] = 0 \n", - "# # print('max val king_train:')\n", - "# print(max(king_training['king']))\n", - "# king_test[king_test[\"king\"] < 0] = 0\n", - "# # print('max val king_test:')\n", - "# print(max(king_test['king']))\n", - "# king_train_pre = king_training[\"king\"].to_frame()\n", - "# # print(king_train_norm)\n", - "# king_test_pre = king_test[\"king\"].to_frame()\n", - "# scaler = MinMaxScaler(feature_range=(0, 1))\n", - "# king_train_norm = scaler.fit_transform(king_train_pre)\n", - "# king_test_norm = scaler.fit_transform(king_test_pre)\n", - "# print('king_test_norm')\n", - "# print(king_test_norm.shape)\n", - "# print('king_train_norm')\n", - "# print(king_train_norm.shape)\n", - "# #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - "# #print(type(king_train_norm))\n", - "# #king_train_norm = king_train_norm.to_frame()\n", - "# x_train = []\n", - "# y_train = []\n", - "# x_test = []\n", - "# y_test = []\n", - "# y_test_not_norm = []\n", - "# y_train_not_norm = []\n", - " \n", - "# # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", - "# for i in range(6,924): # 30\n", - "# x_train.append(king_train_norm[i-6:i])\n", - "# y_train.append(king_train_norm[i])\n", - "# for i in range(6, 60):\n", - "# x_test.append(king_test_norm[i-6:i])\n", - "# y_test.append(king_test_norm[i])\n", - " \n", - "# # make y_test_not_norm\n", - "# for i in range(6, 60):\n", - "# y_test_not_norm.append(king_test['king'][i])\n", - "# for i in range(6,924): # 30\n", - "# y_train_not_norm.append(king_training['king'][i])\n", - " \n", - "# return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(60, 2)\n", - "717915\n", - "294611\n", - "king_test_norm\n", - "(60, 1)\n", - "king_train_norm\n", - "(924, 1)\n", - "(54, 1)\n", - "(54, 1)\n", - "(918, 1)\n", - "(918, 1)\n" - ] - } - ], - "source": [ - "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", - "x_train = np.array(x_train)\n", - "x_test = np.array(x_test)\n", - "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", - "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", - "y_train = np.array(y_train)\n", - "y_test = np.array(y_test)\n", - "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", - "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", - "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "# def load_pdo(pathname):\n", - "# pdo_data = pd.read_csv(pathname)\n", - "# # print(pdo_data.head())\n", - "# return pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_pdo(ismael_path_pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# data_copy = data_copy.join(pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "# print(data_copy)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -6048,7 +5739,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/Untitled1.ipynb b/Untitled1.ipynb new file mode 100644 index 0000000..bbe15a7 --- /dev/null +++ b/Untitled1.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Baselines

" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn import model_selection\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.linear_model import ElasticNet\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + "# print(salmon_copy)\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "max val king_train:\n", + "67521\n", + "max val king_test:\n", + "32446\n", + "(1824, 1)\n", + "(1644, 1)\n", + "(1644, 1)\n", + "(22365, 1)\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1644, 180, 1)\n", + "(22365, 180)\n" + ] + } + ], + "source": [ + "x_train_lr = x_train.reshape((x_train.shape[0], x_train.shape[1]))\n", + "x_test_lr = x_test.reshape((x_test.shape[0], x_test.shape[1]))\n", + "print(x_test.shape)\n", + "print(x_train_lr.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def create_linear_model(x_train, y_train, x_test, y_test, scaler):\n", + " lr = LinearRegression()\n", + " lr.fit(x_train, y_train)\n", + " train_preds_lr = lr.predict(x_train)\n", + " test_preds_lr = lr.predict(x_test)\n", + " \n", + " #Descale \n", + " \n", + " train_preds_lr = scaler.inverse_transform(train_preds_lr)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " test_preds_lr = scaler.inverse_transform(test_preds_lr)\n", + " test_preds_lr = test_preds_lr.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return train_preds_lr, test_preds_lr" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1659.7675369964575.\n", + "The root mean squared error is 3041.7349427380686.\n" + ] + } + ], + "source": [ + "lr_train, lr_test = create_linear_model(x_train_lr, y_train, x_test_lr, y_test, scaler)\n", + "\n", + "return_rmse(y_train, lr_train)\n", + "return_rmse(y_test, lr_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# List to maintain the different cross-validation scores\n", + "cross_val_scores_ridge = []\n", + " \n", + "# List to maintain the different values of alpha\n", + "alpha = []\n", + " \n", + "# Loop to compute the different values of cross-validation scores\n", + "for i in range(1, 9):\n", + " ridgeModel = Ridge(alpha = i * 0.25)\n", + " ridgeModel.fit(x_train, y_train)\n", + "# scores = cross_val_score(ridgeModel, X, y, cv = 10)\n", + "# avg_cross_val_score = mean(scores)*100\n", + "# cross_val_scores_ridge.append(avg_cross_val_score)\n", + " alpha.append(i * 0.25)\n", + " \n", + "# Loop to print the different values of cross-validation scores\n", + "for i in range(0, len(alpha)):\n", + " print(str(alpha[i])+' : '+str(cross_val_scores_ridge[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/daily_robust_gru.ipynb b/daily_robust_gru.ipynb new file mode 100644 index 0000000..46dafa9 --- /dev/null +++ b/daily_robust_gru.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data\n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(ismael_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " \n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Set up train and test (train is 180 day series, y val is 181st day etc.)\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "def create_GRU_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create GRU model trained on X_train and y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " # The GRU architecture\n", + " regressorGRU = Sequential()\n", + " # First GRU layer \n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=1, activation='tanh'))\n", + " regressorGRU.add(Dense(units=1))\n", + "\n", + " # Compiling the RNN\n", + " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + " # Fitting to the training set\n", + " history = regressorGRU.fit(x_train, y_train, epochs=5, batch_size=150)\n", + " \n", + " # Predictions \n", + " GRU_train_predict = regressorGRU.predict(x_train)\n", + " GRU_test_predict = regressorGRU.predict(x_test)\n", + "\n", + " # Descale \n", + " GRU_train_predict = scaler.inverse_transform(GRU_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " GRU_test_predict = scaler.inverse_transform(GRU_test_predict)\n", + " GRU_test_predict = GRU_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return regressorGRU, GRU_train_predict, GRU_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "150/150 [==============================] - 55s 328ms/step - loss: 0.0012\n", + "Epoch 2/5\n", + "150/150 [==============================] - 48s 317ms/step - loss: 4.8891e-04\n", + "Epoch 3/5\n", + "150/150 [==============================] - 52s 348ms/step - loss: 3.6427e-04\n", + "Epoch 4/5\n", + "150/150 [==============================] - 56s 371ms/step - loss: 3.1816e-04\n", + "Epoch 5/5\n", + "150/150 [==============================] - 48s 318ms/step - loss: 4.0049e-04\n" + ] + } + ], + "source": [ + "regressorGRU, GRU_train_day, GRU_test_day, history_GRU, y_train, y_test = create_GRU_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "y_test_year = y_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 558.6481625645915.\n" + ] + } + ], + "source": [ + "plot_predictions(y_train, GRU_train_day)\n", + "return_rmse(y_train, GRU_train_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1390.3482405235773.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, GRU_test_day)\n", + "return_rmse(y_test, GRU_test_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_GRU)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 40722.48405365272.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and GRU\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/daily_robust_lstm.ipynb b/daily_robust_lstm.ipynb new file mode 100644 index 0000000..9882c51 --- /dev/null +++ b/daily_robust_lstm.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + "\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): \n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "(1824, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(22365, 180, 1)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def create_LSTM_model(x_train, y_train, x_test, y_test): \n", + " '''\n", + " Create LSTM model trained on X_train and Y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " LSTM_model = Sequential()\n", + " LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(1))\n", + " #LSTM_model.add(Dense(1))\n", + " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=5, batch_size=150, verbose=2)\n", + " \n", + " train_preds = LSTM_model.predict(x_train)\n", + " test_preds = LSTM_model.predict(x_test)\n", + " train_preds = scaler.inverse_transform(train_preds)\n", + " test_preds = scaler.inverse_transform(test_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return LSTM_model, test_preds, train_preds, y_test, y_train, history_LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "150/150 - 25s - loss: 0.0019\n", + "Epoch 2/5\n", + "150/150 - 21s - loss: 0.0013\n", + "Epoch 3/5\n", + "150/150 - 22s - loss: 9.7316e-04\n", + "Epoch 4/5\n", + "150/150 - 21s - loss: 8.2923e-04\n", + "Epoch 5/5\n", + "150/150 - 19s - loss: 7.3949e-04\n" + ] + } + ], + "source": [ + "# running LSTM\n", + "LSTM_model, test_preds_LSTM, train_preds_LSTM, y_test, y_train, history_LSTM = create_LSTM_model(x_train, y_train, x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "y_test_year = y_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 857.0196027023247.\n" + ] + } + ], + "source": [ + "plot_predictions(y_train, train_preds_LSTM)\n", + "return_rmse(y_train, train_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XTwdmqWqBqq4H1gJDRaQj0FxVF6rTxezZMmV8db0GjPJ5K4ZhGEbtENU2EhFJFJGlwHZgnqouAtqraiaA+93Ozd4Z2BRQfLOb1tldLpteqoyqFgPZQJsgdkwSkcUisjgrKytCe2cY9Z/AYeTPOecc9u/fX+26AoeFv/zyy/nhhx8qzFvdEXC7du3Kjh07yqXv3buXP/zhD/73P0aMGMGiRYtKDQBZlttuu42PPvqoyjaEYurUqUybNq3SfM8++yx9+vShd+/e9OrVK6wyVeWuu+6KWF1RFRJV9ajqACADx7sI/o85BPMkNER6qDJl7XhcVYeo6hDfGDuGYVRO4DDyKSkpTJ8+vdR2j8dTrXqffPJJevXqVeH2SA+lfvnll9O6dWvWrFnDqlWrmDFjRlDBCeTvf/87J5xwQsRsCJe5c+fywAMP8OGHH7Jq1Sq+++47/1hikaTOCIkPVd0DfIrTtrHNDVfhfm93s20GugQUywC2uOkZQdJLlRGRJKAFsCsa+2AYDZ1jjjmGtWvX8umnn3L88cdzwQUX0LdvXzweDzfddBOHH344/fr143//+x/gDJ1y9dVX06tXL0455RS2b9/ur+u4447D92Lw+++/z6BBg+jfvz+jRo0KOpR6VlYWZ511FocffjiHH344X7ozj+7cuZPRo0czcOBA/vCHPwQd82rdunUsWrSIf/7znyQkOLe87t27c8oppwCOGF5xxRX07t2b0aNHk5eXB5T2oIIN+Q6wa9cuxo8fT79+/Rg2bBjLly8PmR7IE088wUknneT/PR93330306ZNo1OnTgCkpqZyxRVXAM5Ix8OGDaNfv36cccYZ7N69u9zx3LFjB127dgUqHmb/lltu8Q/IOXHixHD+/pBEbdBGEUkHilR1j4ikASfgNIa/BVwM3ON+v+kWeQt4UUTuBzrhNKp/o6oeEcl1G+oXARcBDwWUuRhYCJwNzNeG9qq+0SCI8SjyFBcXM3fuXMaOdfq5fPPNN6xcuZJu3brx+OOP06JFC7799lsKCgo4+uijGT16NN9//z0//fQTK1asYNu2bfTq1Yvf//73perNysriiiuuYMGCBXTr1o1du3bRunXrckOpX3DBBfzf//0fw4cPZ+PGjYwZM4bVq1dzxx13MHz4cG677TbeffddHn/88XK2r1q1igEDBpCYmFhuG8CaNWt46aWXeOKJJzj33HN5/fXXufDCC8vlCzbk++23387AgQOZM2cO8+fP56KLLmLp0qUVpvt4+OGH+fDDD5kzZ06pQSch9HD1F110EQ899BDHHnsst912G3fccQcPVPInBhtm/5577uHhhx8OOvBmdYjm6L8dgZluz6sE4BVVfUdEFgKviMhlwEbgHABVXSUirwA/AMXAZFX1+c1XATOANGCu+wF4CnhORNbieCITorg/htHg8D21guORXHbZZXz11VcMHTrUP0T8hx9+yPLly/1P79nZ2axZs4YFCxb4h1bv1KkTI0eOLFf/119/zYgRI/x1tW7dOqgdH330Uak2lZycHHJzc1mwYIF/+PVTTjmFVq1aBS0fim7duvn3cfDgwWzYsCFovmBDvn/xxRe8/vrrgDOo486dO8nOzq4wHeC5554jIyODOXPmkJycHLad2dnZ7Nmzxz/I5MUXX1xqKP+KCDbMfpcuXSopVTWiJiSquhwoN0KZqu4ERlVQ5k7gziDpi4Fy7Suqmo8rRIZRn4nRKPL+NpKyNGnSxL+sqjz00EOMGTOmVJ733nuPyjpRqmqlecAZFXjhwoWkpaWV21ZZ+d69e7Ns2TK8Xq8/tBVI2WHoy4aayuYLHPI9WABERCpMB+jTpw9Lly5l8+bNQedr8Q2tH0x4KyJwuPqKhqova3sksTfbDcOoEWPGjOGxxx6jqKgIgJ9//pl9+/YxYsQIZs2ahcfjITMzk08++aRc2SOPPJLPPvuM9evXA07bApQfSn306NE8/PDD/nWfuI0YMYIXXngBcBqpfW0GgfTo0YMhQ4Zw++23+2/wa9as8Y9YXBMCf//TTz+lbdu2NG/evMJ0cEZR/t///se4cePYsmVLuTqnTJnCn//8Z7Zu3Qo4k4Y9+OCDtGjRglatWvlHBH7uuef83knXrl1ZsmQJQNjz2ScnJ/v/s5piE1sZhlEjLr/8cjZs2MCgQYNQVdLT05kzZw5nnHEG8+fPp2/fvhx88MFBJ4tKT0/n8ccf58wzz8Tr9dKuXTvmzZvHaaedxtlnn82bb77JQw89xIMPPsjkyZPp168fxcXFjBgxgunTp3P77bdz/vnnM2jQII499lgOOOCAoDY++eST3HDDDRx00EE0btyYNm3a8K9//avG+z516lQuvfRS+vXrR+PGjf1zsFSU7mP48OFMmzaNU045hXnz5pUacv/kk09m27ZtnHDCCX6Pzde2NHPmTK688kr2799P9+7deeaZZwC48cYbOffcc3nuuefC9mQmTZpEv379GDRokF/0qosNIx9NbBh5owbYMPJGrLBh5A3DMIxaxYTEMAzDqBEmJIYRxzS00LMRe6pzzpmQGEackpqays6dO01MjFpDVdm5cyepqalVKme9tgwjTsnIyGDz5s3YQKNGbZKamkpGRkblGQMwITGMOCU5OTnoC2uGEW9YaMuoPYqLYd68WFthGEaEMSExao+77oLRo01MDKOeYUJi1B5r1jjfmZmxtcMwjIhiQmLUHjYLsmHUS0xIjNrHurMaRr3ChMSoPcwjMYx6iQmJUfuYR2IY9QoTEqP2sNGQDaNeYkJi1B4W2jKMeokJiVH7mEdiGPUKExKj9jCPxDDqJSYkhmEYRo2ImpCISBcR+UREVovIKhG51k2fKiK/ichS93NyQJkpIrJWRH4SkTEB6YNFZIW77UER59FWRBqJyMtu+iIR6Rqt/TEiiIW2DKNeEU2PpBi4QVUPA4YBk0Wkl7vtP6o6wP28B+BumwD0BsYCj4pIopv/MWAS0NP9jHXTLwN2q+pBwH+Ae6O4P0ZNsdCWYdRLoiYkqpqpqt+5y7nAaqBziCKnA7NUtUBV1wNrgaEi0hForqoL1Znh51lgfECZme7ya8Aon7dixCHW/dcw6iW10kbihpwGAovcpKtFZLmIPC0irdy0zsCmgGKb3bTO7nLZ9FJlVLUYyAbaRGMfjAhgQmIY9ZKoC4mINAVeB65T1RycMFUPYACQCfzblzVIcQ2RHqpMWRsmichiEVlss80ZhmFElqgKiYgk44jIC6o6G0BVt6mqR1W9wBPAUDf7ZqBLQPEMYIubnhEkvVQZEUkCWgC7ytqhqo+r6hBVHZKenh6p3TOqi3kkhlGviGavLQGeAlar6v0B6R0Dsp0BrHSX3wImuD2xuuE0qn+jqplArogMc+u8CHgzoMzF7vLZwHy3HcWIR6z5yjDqJdGcs/1o4HfAChFZ6qb9BThfRAbghKA2AH8AUNVVIvIK8ANOj6/Jqupxy10FzADSgLnuBxyhek5E1uJ4IhOiuD9GpDCtN4x6RdSERFW/IHgbxnshytwJ3BkkfTHQJ0h6PnBODcw0ahPzSAyjXmJvthu1j3kkhlGvMCExag/r/msY9RITEqP2sNCWYdRLTEiM2sc8EsOoV5iQGLWHeSSGUS8xITFqH/NIDKNeYUJiGIZh1AgTEqP2sNCWYdRLTEiM2sO6/xpGvcSExKg9TEgMo15iQmLUPiYkhlGvMCExDMMwaoQJiVF7WGO7YdRLKhUSEbk3nDTDCBsLbRlGvSIcj+TEIGknRdoQowFgHolh1EsqnI9ERK4C/gh0F5HlAZuaAV9G2zCjHmMeiWHUK0JNbPUizkyEdwO3BKTnqmq5edENo1Ks+69h1EsqFBJVzQaycabGTQTau/mbikhTVd1YSzbWTS66KNYWxB8W2jKMekmlU+2KyNXAVGAb4HWTFegXPbPqAc89F2sL4hfzSAyjXhHOnO3XAYeo6s4o22LUd8wjMYx6STi9tjbhhLgMIzKYR2IY9YpwPJJfgE9F5F2gwJeoqvdHzSqjfmIeiWHUS8LxSDYC84AUnK6/vk9IRKSLiHwiIqtFZJWIXOumtxaReSKyxv1uFVBmioisFZGfRGRMQPpgEVnhbntQxLkjiUgjEXnZTV8kIl2rtPeGYRhGjanUI1HVO6pZdzFwg6p+JyLNgCUiMg+4BPhYVe8RkVtwuhbfLCK9gAlAb6AT8JGIHKyqHuAxYBLwNfAeMBana/JlwG5VPUhEJgD3AudV016jtrDQlmHUK8LptfUJTi+tUqjqyFDlVDUTyHSXc0VkNdAZOB04zs02E/gUuNlNn6WqBcB6EVkLDBWRDUBzVV3o2vMsMB5HSE7H6VEG8BrwsIiIqt2p4hJ7j8Qw6iXhtJHcGLCcCpyF422EjRtyGggsAtq7IoOqZopIOzdbZxyPw8dmN63IXS6b7iuzya2rWESygTbAjjK/PwnHo+GAAw6oiulGJDEhMYx6STihrSVlkr4Ukc/C/QERaQq8DlynqjlScYNrsA0aIj1UmdIJqo8DjwMMGTLE7mKxwhrbDaNeEk5oq3XAagIwGOgQTuUikowjIi+o6mw3eZuIdHS9kY7Adjd9M9AloHgGsMVNzwiSHlhms4gkAS0AG77FMAyjFgmn19YSYLH7vRC4AaeROyRuz6qngNVlugq/BVzsLl8MvBmQPsHtidUN6Al844bBckVkmFvnRWXK+Oo6G5hv7SN1APuLDKNeEU5oq1s16z4a+B2wQkSWuml/Ae4BXhGRy3C6Fp/j/s4qEXkF+AGnDWay22ML4CpgBpCG08g+101/CnjObZjfhdPry4hXLLRlGPWScEJbyTg38hFu0qfA/1S1KFQ5Vf2C4G0YAKMqKHMncGeQ9MVAnyDp+bhCZNQhzCMxjHpFOL22HgOSgUfd9d+5aZdHyyijnmK9tgyjXhKOkByuqv0D1ueLyLJoGVQveeEFmDgx1lbEHgttGUa9JJzGdo+I9PCtiEh3wBMiv1GWCy+MtQXxhXkkhlGvCMcjuQn4RER+wWnzOBC4NKpWGfUTC20Zodi3D7xeaFbpUH5GnBFOr62PRaQncAiOkPzoDmNiGNXDhMQIRno65OXZ+VEHqVBIRORCQFT1OVc4lrvpV4jIPlV9sbaMNOoJPo/E6w2dz2iY5OXF2gKjmoRqI7kBmBMk/WV3m2FUD3viNIx6RSghSVTV3LKJqpqD0x3YMKqHCYlh1CtCCUmyiDQpm+jOLZISPZOMeo+FtgyjXhFKSJ4CXgucddBdnuVuM4yq4fNEzCMxjHpFhY3tqjpNRPYCn7lDwSuwD7hHVR+rLQONeoRPQMwjMYx6RcgXElV1uqoeiPPuSDdVPdBExKg25pEYRnj07QuX1p3X9cJ5sx1V3Rus4d0wqoQJiWGEx8qVMGNGrK0Im7CExIgAH3wQawtij4W2DKNyPHVvBKpqCYmINIq0IfWeU0+NtQWxxzwSwwjN//4HSeGMXBVfVCokIvJ0mfWmwHtRs6i+YjdP80gMozIeq5tN0OF4JL+JyGMAItIK+BB4PqpW1UdMSEqwoTAMIzgFdXMYw0qFRFX/BuSIyHQcEfm3qj4TdcvqGyYkJcdg06bY2mEYRkQJNWjjmQGr3wB/c79VRM5U1dnRNq5eYUJScgz27o2tHYYRr9TR+0SoVp3Tyqx/jzPG1mk4LyeakBhVwxrbDaNeEurN9rrzNoxRNzAhMYx6STi9tjJE5A0R2S4i20TkdRHJCKPc026ZlQFpU0XkNxFZ6n5ODtg2RUTWishPIjImIH2wiKxwtz0o4kxqISKNRORlN31R4JhgRpxiQmIY9ZJwem09A7wFdAI6A2+7aZUxAxgbJP0/qjrA/bwHICK9gAlAb7fMoyKS6OZ/DJgE9HQ/vjovA3ar6kHAf4B7w7DJiCUmJIZRLwlHSNJV9RlVLXY/M4D0ygqp6gJgV5h2nA7MUtUCVV0PrAWGikhHoLmqLlRVBZ4FxgeUmekuvwaM8nkrRpxiQmIYoamj10Y4QrJDRC4UkUT3cyGwswa/ebWILHdDX63ctM5AYJ/QzW5aZ3e5bHqpMqpaDGQDbYL9oIhMEpHFIrI4KyurBqYbNeVBrmHkigdibYZhGBEkHCH5PXAusNX9nO2mVYfHgB7AACAT+LebHsyT0BDpocqUT1R9XFWHqOqQ9PRKnSkjWqhyLQ/ySfbgWFtiGEYEqXRQF1XdCIyLxI+p6jbfsog8Abzjrm4GugRkzQC2uOkZQdIDy2wWkSSgBeGH0oxYUEfddsOoNeroNRK1XlsV1NUxYPUMwNej6y1ggtsTqxtOo/o3qpoJ5IrIMLf94yLgzYAyF7vLZwPz3XYUI14J+HvsnzKM+kM4w0w+A7wInOOuX+imnRiqkIi8BBwHtBWRzcDtwHEiMgAnBLUB+AOAqq4SkVeAH4BiYLKq+sZSvgqnB1gaMNf9gDPd73MishbHE5kQxr4YsSRAPQoLoZGNIW0Y9YJwhCS9zNhaM0TkusoKqer5QZIrnOtdVe8E7gySvhjoEyQ9nxJxM+oCAUJSUGBCYhj1hVj02qr/3HNPrC2ITwKEJD8/hnYYhhFRqtprK5Oa9dpqGEyZEmsL4p46Olq2YRhBqNVeW0YDJ8AjqYOziRpG9KmjvVAqFRIRSQeuALoG5ldV80qMqhFwkRQXx9AOwzAiSjiN7W8CnwMfAfYcaVSfACEpKoqhHYYRr9RXjwRorKo3R90So/5jHolhhKaODhcYTmP7O4HDvRtGtTEhMYx6SThCci2OmOSJSI6I5IpITrQNqw8UkcQfmM4vdIu1KfGBCYlhhCYlJdYWVItKhURVm6lqgqqmqWpzd715bRhX1/mMY3mcP3A1D8falPjAhMQwQnPggbG2oFpU2EYiIoeq6o8iMijYdlX9Lnpm1Q9yaQZAovVRKIcJiWEEoR42tl+PMzPhv4NsU2BkVCyqRxSRXOq7wWMeiWGEpr4JiapOcr+Prz1z6hcFOINJeUisJGcDwYTEMOol4XT/RUSOovwLic9GyaZ6g09IvGH1aWgAmJAYRmjqm0fiQ0Sew5nVcCklLyT65k83QuATEg06mWMDxF5INIzQ1FchAYYAvWzSqKpjQlIG80gMIzSqPMxkFjCCVzgv1taETThCshLogDPyr1EFTEjKYEJiGKFR5Rr3dYE8LiYtxuaES6juv2/jhLCaAT+IyDeAf/BvVbURgSshP6EJeK2x3Y8JiWGEJuAa2UwGPWNoSlUI5ZFMqzUr6ikFnbrCZiikbr6tGk1MSAwjCAFCsp129UJIfgPaq+qXgYkiMsLdZlRCgcd5fySvzjioUcY8EsMIm520ibUJYROqX+oDQG6Q9P3uNqMSCryOkOSTGmNL4gQTEiMcGnK/noB930cTeOSRGBoTPqGEpKuqLi+bqKqLcd4pMSrBJyTmkTio14TECIMGLiQJ7lsW+2gCV18dY4PCI5SQhHqMtjtjGDyddRoQ4JF4vTG0JvZ4vCW910xIjAppyNeJKikUAq6QAOTnx9Cg8AglJN+KyBVlE0XkMmBJZRWLyNMisl1EVgaktRaReSKyxv1uFbBtioisFZGfRGRMQPpgEVnhbntQxJn5RUQaicjLbvoiEeka5j7XOv4TYseO2BoSYwKFxF5INCqkgXskyTgXh/++8dlnMTQoPEIJyXXApSLyqYj82/18BlyOM0dJZcwAxpZJuwX4WFV7Ah+764hIL2AC0Nst86iI+PrMPoYzeGRP9+Or8zJgt6oeBPwHuDcMm2qNfPcdEoA8GuMhARIa9lApXjWPxAiDBi4kCeLsf10KiVd4Z1PVbap6FHAHsMH93KGqR6rq1soqVtUFwK4yyacDM93lmcD4gPRZqlqgquuBtcBQEekINFfVhe6b9c+WKeOr6zVglM9biQd8Q8h3a7UbgL00rbPTaEYKC20ZYfHrr7G2IHaoUqRl2lbrwH0jnImtPlHVh9zP/Br+XntVzXTrzQTauemdgU0B+Ta7aZ3d5bLppcqoajGQDcH7y4nIJBFZLCKLs7KyargL4bGfxgC0b54HuMJSB06IaOLRktOteFvDDvMZITj44FhbEFOK3Lcy/EKycyd89VUMLaqceIm1BLvDaoj0UGXKJ6o+rqpDVHVIenp6NU2sGn4haekMBuDzUBoygW2oxU/OiJkdhhGvqFcpcl9g9t1DuOACOProGFpVObUtJNvccBXu93Y3fTPQJSBfBrDFTc8Ikl6qjIgkAS0oH0qLGb6GsvaDHAdqL00bdm8UyoS28qy13XDJzYXTTou1FXFBsbfkllwv2kiixFvAxe7yxcCbAekT3J5Y3XAa1b9xw1+5IjLMbf+4qEwZX11nA/PjaYRi39NEhy7O00UuzRp2IyJlhCS8qXCMhsCLL8I778TairigyFsyLl85IYnj+0fUhEREXgIWAoeIyGa32/A9wIkisgY40V1HVVcBrwA/AO8Dk1XVN/fJVcCTOA3w64C5bvpTQBsRWYszLfAt0dqX6nAsCwBo395Zz6WZeSSBbSQmJIYPt+1QgQe4llyaxtaeGFJXhSRqV7Oqnl/BplEV5L8TuDNI+mKgT5D0fOCcmtgYLfb5YpuUEZI4PhFqg0AdtXnsDT+ukMxnJP/HAyxlADNia1HMqKtCEi+N7fWKHbT1L7dz+6XtpWlcnwi1gSewsd08EsOHKyQJOCfIhgY8ApMJieFnb4Br3qGD822hLfB4LbRlBMEVklScoUAacg/HIi25LvYHRDYAE5KGRmDYJj0dBK+FtijT/deExPDhConvumnIE8EVep3roim5bKVD6Y1x/CBqQhIFAoWkSRNoyl7zSJYuxfN5ydQ2JiSGH1dIfBPAeRvwbckX2jqEn9hNa/bQomRjHD+INtx/LIoECklyMuTSnAf4v7g+EaLOuHGlnjSL23QIkdloiBS449OZkDhCArCebiUb4/j+0XD/sShSUY+kgrwG6pE89RRs2lRaSLx26hku7mCmPo+kIYe2fG0kB/MzAL/QvWSjCUnDomzYZlTGjwBkbm+gF8jllwOlnzSLd+fEyprYc/HFMGtWrK2IHyy05cfnkXRjPQC/+YcWxISkoeHzSF494AYA/u9cZ1SXrdsb9uEu5ZGQBPv2xdCaGPLss3B+Ra9ZNVx8QqJBh9FrADz4IIVrNgCQjjO4bHZgG0kct7E27DtblPAJSbcUZ+DipqnOmOn798fMpLggUEjshUTDj3kkDv/9r/+6aHLlRTRmH3toWbLdPJKGhe9kSMYRkNRGzgmQX9BAn7RcSoW2SIrrJyzAuXBffRU8nsrzGtXHPb4NXkgSE0vuHRedT6tmxSYkDRnfyZCkzgi3qSnODTM/L35PhNqgXGgrji8MwAlBnXsuPPxwrC2p31x6KWC9tkoJSTK0bKEmJA0Z38mQIo6QNEp2hKSgsGF7JD4hEdG64ZFs2+Z8//ZbdOqfMwe2VjrZaIOhwffaKiskrRNNSBoyJaEt1yPxhbbyY2ZSXOC7QTRK8tQNj8RHtOw84wzo2DE6dddBSoW2Tj45xtbEgLJC0qVZaSGJ4wcvE5Io4LsgUigEILWRG9qyNhIgQEji+MIASqZGriuCVxcJOLa+66aYJJg7t6IS9ZekJP8xSE6Gli0xj6Qh43+qEF9ju5NeUBAri6rJSSfBvfdGrDqfR5KS2ICFJN73ubYJ6Mrou4n62koaHAEeSUqKIyS7aVWy3YSkAbF1a7nQlq+NpM55JO+/D7dEbr6wktCWt26EtiQK/5f1ACtNdjYABaT4R83OJzWWFsWOsqGtls57JF7i3zM2IYkkHg907FgS2vI1trsPWPmbd8TKsvBQhdtug1WrolK9L7SVllLsPHW+9x507x6/rpp5JNHH9Uh6sI5HuBqAAlJLbp4NhWXLYPv2ckLiJbFkWgoTkgZCsRPKKuuRJCQKKRRQMDvO56XOzoZ//AOOOy4qNzyfR9IyrdAZDXnyZFi/HjZujPhvRYRoCIl5JKUpLESB38goldzgwlsDBsAvv5QSklZuVGsnbZyFOH4IMSGJJGVerEpUR1gQoREF8e+yu4PnkZ8PeXkRr94vJI0LyaF5yYURjRBSJDGPJHoUuudCGfJJhc8+i4FBsSVQSAYNctJe5yxnwTySBkKAR5JCQcn9MSGBVPLLT50Zb/hucl5v5PoqB5z8PiFp1aSQfNIo8sT56WceSfQpKCg9MKFLPqmOZ9zA8IfFUxwnpSvreYMznI0mJA2EAI8kmaKSP16EVPLj31333eS8Xr8o1piAJ3Df01brpk6bSK63SWR+I1qIsJ80cgoi+L95POyiFScwjyUMily9dZXCwoqFpAHiHxUjyXmOGcn8kjlJHnoohpaFJiZCIiIbRGSFiCwVkcVuWmsRmScia9zvVgH5p4jIWhH5SUTGBKQPdutZKyIPisQ4RlLKIyksERLXI4n7iyNKQrKT1rRiFx8yGoA2TZ33a3I0zufmvv9++rKCFv+7L3J1er18xrF8zAlcxlORq7euUljIFjqVS4577z1K7KMJaez3O8Nt2cEuWqMAb70VS9NCEkuP5HhVHaCqQ9z1W4CPVbUn8LG7joj0AiYAvYGxwKMi4htD4TFgEtDT/YytRfvL4958K/JI6pSQRCoEo8oP9GIPrXiG3wPQppkrJPHukWzcyC/0iGydHo9/Lu5lDGAvcX4MoklhIZxwAttoX25T3F8rUSKH5rQg27/eml0UkMp+GsfQqsqJp9DW6cBMd3kmMD4gfZaqFqjqemAtMFREOgLNVXWhqirwbECZ2ODefItI9vfYAhq8RyKUju22dj2SXOLcIwkgYm3kHk+pxuXVHAa5uU6366KiEAVjzMsvw2mnRbZOdz6a7bQjjdJzLMT9tRIlsmlRSkjaNHPOiV20jpVJYRErIVHgQxFZIiKT3LT2qpoJ4H63c9M7A5sCym520zq7y2XTyyEik0RksYgszsrKiuBulKGi0FZd9EiKiykiib9wZ83GFfR6y+13m3xnEMRgvXXiirPO8i9GrBNbMCFp3tzpdj1zZoiCMWbCBHjnHfjhh8jV6V4f22lHu7RcAJLcB7CGGtrKpgXNKZk9tPW1vwPcLsBx3LsxVkJytKoOAk4CJovIiBB5gx09DZFePlH1cVUdoqpD0tPTq25tuFQU2kpKqhtCEvjY7fHwLqdwN3/h1ltrVmfZm0LrVOfp0++RxOsFEtBLJmKTku3bRw7NacJeAH7lwJJtUehyHXF6945cXe6Dy3ba0S41h1ya8nni8QBxH8qJFuU8kpbOMdpFa+u1VRZV3eJ+bwfeAIYC29xwFe73djf7ZqBLQPEMYIubnhEkPXZUFNqqK0IS2C5SXOyf5rNGL54HEZI2TZyuxf5pROP1Aiks9C/mHTUqMnXm5JBDc9LJojU7/e0lQMN7x8Td3yzSaZeaQ1P20SrBuYnWpbBnJCnXRpLiPHCYkJRBRJqISDPfMjAaWAm8BVzsZrsYeNNdfguYICKNRKQbTqP6N274K1dEhrm9tS4KKBMbiotRYDZnOaN2BngkaeTVOSHxDWlSowdl1fKhrVQnNu6/WcTrBRLQZrF/bYTmJPn6a3JoTnNy6EgmmQQMI9/Q3jEJ9EgaOzfMponOyeYfFqQhEHD+l/VIWmc4npn/7fY4JSkGv9keeMPtqZsEvKiq74vIt8ArInIZsBE4B0BVV4nIK8APQDEwWVV9V9xVwAwgDZjrfmJHcbE/VFFqyIc66pH4PImiwooiiWEQxCNpmVImtBWnQuIpLDkeEYvZ79lDDr1pRi6p5DdsIfF68SJsoz3tm6wATEjKtZGMORxwPZJ4DQETAyFR1V+A/kHSdwJB4weqeidwZ5D0xUCfSNtYbTwe9gV256xrbSSBNzKPx3/zLMz3QnVnrSsjJOmSRRLFNGFvSaNznIZ0CvID2khoDE8+6UwLm1iDGfyKisihOe2OPIjWC9/hc44p2dbQhMTjYTvtKCKFLi2cm2cTcR4yGqKQeEhgL81ocWpJk3FaYyGN/Y6QrF7ttMMmxeL5PzTx1P237lNc7J+I5iUmlKQHCkmcPn0DITySGtzoy/Taak4ueL00J6fEI4lTIckvLLk88kiDK66Ap2r4EmFhITnSguYZzenMb/xGZ3Li/DhEDa+Xza7nntHC7bWVBKnk1YkRbyOG+7/7rocWIweX2tyaXSWhre+/r1XTwsWEJJJ4PH4h6c4vJTfmww4rERK373xc4vGwhoPoynp+/CXFLyR5+2twMZfxSM5PeBk8HpqRW+KRxOnNInD+GH8vol27alap65E0b5XIyX8dTDHJzGeks62heSRer394lM4tnDYSLr+cpuwtEZKGIK7uPvo6n7RoUXpzG3baeyQNigCPpCV7Sro7JSWReuoJ5JOG3nlXzMyrFI+HdziVX+nKg6918gtAdnYl5UJRprE93ZNZZzySgqIyHgnUXPSKisjRZjRvDr0PcJ7CN9DV2RanxyFqeDz+G2SbNLd/dfv2tCCbHbR11hvCMXH38Vuc9pDmZV6vKuWRxGk7iQlJJAnoMtuSPaW6j6Y2ceKahff8OxaWhYfHQypO11yvR/03z5ycUIUqrzPQI2nLDvB6S3skcXqzCOqR1FBIigs87KcJzZtD62ZFpFBQ0uBeVzySSP1fXm/Jg1ejkq6BA45uwmLckZPi1FuNKF4vHhI4h9eA4B7JFxzD1digjQ2DgNBWC7JLvYCR6j6Ux3WDu8eDur2zNCAklbOtBv1/i4pKCUmLRvng8ZT2SOL0ZpFfVNKoHqleW7lzPwecp05JEDqSyRY6cRavcdU7p0TkN6JOpATP42E3rRC8NE8teegadUwR6+nO9wyI24eMiOL1st0/kAd07Bg82yNcbR5Jneedd5xhIkKd2G5oK439NKKwtJCkOSdAXA/94PX6e515iktszaVZ9a/n4uJS4tn//kvqjkcSICSR8kj2bHbaApo3B0T875LM5iymf39EjequNSIlJF4vu2lFS/aQcOsU+P3v4aqrGDfa8Yq/YHjcnhuAcy7Mmxf6nPjxx8qPVxkh6d699GZflABweg7GISYk4fLLL87AdTt3VpzHFRL/C0UBJ1hd8Uj8QuIpERIlgb17A/J9+CHcc094dboeycH8xEa6kPHHcTBsWJ1oI9lfWNLNMiJtJMXF/th/ejqQkEBHMvmRQ2tgZe2RRyoPM5mcXREa0NP1SFqyx5lX9qmnoEkTOh2YTFNyWctBceutsnatM6Po6NHw+uvB82zcCIcdBn/+c+i6Ah7g3mcMjcuMDvMP/gZAM3Jg+vSaWh4VTEjCpb071PW2bRXncUNbLdnjrDct6QufmuLcLOPaIwkQkn15CaVs9beT/PYbjBkDU6aE92R6wgnkkUYzcuniG2PzvPNoddX57KElHhLi9maxr7hkQqv9NOYm7uOqOaOrX+H+/X4hadsWv5AEvryalxuhm3QUuJFpXMPD3DOtBu/RBHLLLeymFa0ySg+lL2mpdGKLM3xMnD5k0LNnyXJFPTF9F01l84gECEljyg/q1o0N3MYd7KWpc73EIfFpVRyyUQ5kJhfB+vUVZ3Ib21uyB3r1giVL/JtaNnGG2/CPLxWPBAhJ7v7E4EKycSMAhSSjmzaXraE8W7aQR1pp9xzI6NuKYpKduSji9GaxvzilZJnGTOMmpi8ZWn1zCwvJwhk0tG1b/KGtQHb+JX47Y3yCM6DilwsjdNv44AP20JJWbcrUl5ZGB7bGt5AEUrablQ9faLuybo8BQtKE4KLUml0oCf422HjDhCRMXlzUnUuYya/jrq44kxvaaskeuPhiOPhg/6ZWTZzGxLgefC1QSPKSyCPNP6x3TrZr8wsv8COH0JwcHpxWWFFNpdhLU5qRWyqtywFOm9FGDojbm0WgR7IuYIKrHTuqWWFRUWmPRIQupWZIgF1LN1az8igj4n8I+mlp5EYp3k0rWjUr44WlptKebY6QxOu1EkhCkNvoCy/AEKfn2b6sffDBBxWX93r9bXAVCUk6zvQXWaTDihU1szcKmJCEybkXOHMpv8K5FWcqG9oKwCcku2nlxFfjkUAh2bib/TSmPU4ob09WkeOWPPIIjzCZAlJ5+tXwhrHYS1OasrdUWteuzvdaDopbIdnvcTyS5mTzXcD86tWenyUriyzSSUr0Ol08RTiYn0tl2ZUdobBRpFH1C8m2/Jbk51eSP0x20obWTctM6NWoUXx7JO50EY9zBR3IZPYX7crnuc+ZnvkFLqAp+3h/2sqK6wsMbZ0yMmiWdu5g6NtoDxdcUAPjo4MJSZh079uEw/mGlzkv+Ljq69fDxIkVCknrJk6Z3bQiYldhpAl45yO3IJk80jgA5wk567cC+O479pPGcziT7aze3oa8dZWP3B/MIznkEKfx8CuOguHDYdasCO9MDfF62afOU2I6WWQGzCue6YtGvfsu3HBD+MpyxBHsoC1tmxY4vTgTEjiEn0pl2VUYn2NMFZPIPppyEGsAf4SzRnhIYAdtadesjIcjQge2kkML8qbH4WRf7vX7CJPZRgce/7Br+Tw//YSHBO7EmcznmTXDK66vuLjEI2kc3APzPdBtp10EJ8eJHCYk4ZKSwnm8zBKGsHbanPLb3Ruhv9dWmf7eLRsHhLZq9Kp4FAkUEpqRSzN6DnBubFs3e2DrVt7hVLJpyQ1Mo4gUvhl8VcgqFccdb03poUWSkmAg3/Mkl7OBA50ecfHEvn3sowmCl1bsLrXJrxunngr3319xx/+y5OezjfakN3cfJDp1onWZuncWxuc8HL6u2v1ZBsCvvwbJVMWY3y5a4yWRdsO6ldvWyZ1aaNOtj8HKEE/zsSA/nyzastwde3bNqgL45BPHe3rkEefaLyjgb/yD1fQihQLm/9od3bgpeH3FxSUeSWKQh9QjjvALyTbaO1MzxxkmJFXgHF4F4JW/Liu/MT2ddXSnEMctL0tyopdm5Dgeyd13R9vU6hEgJHtoyR5aktGugMbsY+tdT8H557OAETRp7OVm7gXg8+y+Fdf3889spQP5pDljj6WklNp8ABspIoVe/AAtW0Zrr6pH167spzFN2OfvSXMcnwBux73TTwdgNy1ZTt/Q3cID+I3OZDR1HyQGDYLevXmeibzJOAB2FcWnkPjCWn1x4vPlhGThQqdPc0VdYYOQ1drp9tyuc0q5bT7BWsoA6BviHIsFOTksc0XkcL5hk6cT3pGjnFGhr3baUBV4ngs5uf1iHuRP7CCdXz6uoKNOURH7aUwCHholFJXf/vXXtGEnjdnnhIKzsmBubGfMKIsJSRU44Md5HMlXTnjr009Lb9y7l2e4FIDj+u6Cyy4rvf3QQ2nFbscjee+9GgTao0jA2+zFJOMlkRaNi+jAVudJCFjACI48EtKvOoc+rHBeGqtoHu9DDuEXnLeruk8+GZYuLbW5Lc4TbB6NYcGC6OxTddm1i300oTH7ScMJvfRhJU0S9rN1UxG89RZehKP4iv4s56lu/wyr2s1kkJGaVZJw5JFM5EVO420akc+u4vgWkl78gOBl0/oyDeR33okC+vkXYde5vbAl4L5TU4berCKZwlJtU3HDVVc5N3RgFB9TRIr/+gD4nOFM4W42cQDj/zGEYXwNwMJ3KnjYKCpiH01owj6k64FBsyTipT/LeIrL2E1LmBlfIT8TkqpwyCGc1/htltOfVcdPLv0eRW4uK+lDH1Yw4Ovp0LrMaJ2HHkrrw9qXDL4WbjikNikzLhbAgW33+xs+l9GPFfRj7EkJ8OijHMPnfMFw9i75qYIKKRGSP53qvJwVwI3XK+1cl33fL/EnrFmk0zZhFx53LpYMNtPem8m2J96kkGSu4jF+xNmnv+XeVOlrNYU9DmM77ck4IeA43HEHTJiA4IyptLMoPruHZ7dwbnBthh1MRzLZtLBMmObdd5nICxz6xPXk78kPa37mrKKWALQL0ladQhF9WMn3DCy/ceZM+Oabqu5C5CgsZC0HkUoeR7IQcHsfAl6EM5nNvdwCwNix0GfBY7RjG2/NDuJtABQVkUszmjX2wNSpFf7s5TzJXprxX66N6O5EAhOSKnL+q2eRxn7u4ZbSE8zkOm/i9mAdNGoUtGzPXim8y6n8lz/VkrVVxONhP41pGRC37zcggQ5sZRNdmMgLtE/IYuJEZ9tEXmAfTXlhXpBHSpdvGEpqQiEHBnnQ6vyns/wXhX8E3Dgik460lyz/CLUZsoX2bGNbQUue4jIe5w+kJhTyTLM/kUknPv88dH1bEpwXDzsfEtCg3qkTvPQS4HhomUVto7IvNSXb49jcYvQRdGETmz5ZWxLOy84mkw68xAX8vL8LH7Q6D4YOrbTO7cXOcQ3mkXDKKfRnGcvpV37bJZfAETEcTmbgQNbQkx6s40CcGN8mugDwAWPYQTopFPDPC1bRpQskHnMUZ/E673Aqe6Vp+RcYXSFp2ioZkpOD/2ZWFr8/K4fTmcO93MyugibB88UIE5Iq0u7kIVyb9CjP8zte50w46igoKKDoXw+wloPoyZoKZ9C77nrncN/NlPh8Q9X1SDIoedGw56Tj6TNxAD9zCKvow/0Pp9Chg7PtqFevpz9Lmf5cEydsVWauDi/Cq5zDKd63gmtrp050w4kbr6NHXL0zoMBK+nBYwk8lQtKjpFvqC0wklTyWPLqIs37XmCSK+PCi5/xdQ4OxMaelU09G8O3D+JrP9w4InCo+bsjOc9oxWgw7jAPY6DyB//YbbN8OLVvyOmf5877Dqby4vDc/fb49eGUeD+zYwXZPa0SUNsGmI+/enX4sZysd2U46zJgR+Z2qLnl5zrXeYru/V+NGDmAtPTiL1+mWtJHdtOLWJ0sGzTqPl8mjMe9wavlQcFGR07OxcQiXtm1baNuWG/g3+aTx5Y5DorFn1SYO72bxzx3rLmQoiziflzh/4TXk9j2KHzmEAlIZwNIKyx11FMziPLbRwWlbiLdhw10h6coGf1JSsnDkhc7LeG3awLlXlIRe5OyzuIQZLGUg6waeBcceW6q69XRjGx0YQwUvYyUn+4XkdN7inTfj5HgUF7OFTuTSnD6eZRzNlwB0feFOOrf3sIo+fMlw7uEWeg1KpVn3dIbxNR9uOgz++9/gde7Zw9ptzlP9QQcF2f7RR4zlfXK9TVk4o+JQYUwoLCTb4zwBtxjUg4NYy3q6sf/1udC+PYsYyh3cziCWcCav8yRXMJEXGXVKo+C6On48pKeznXa0bloYfObY++5jYKLTsH8Yq9l+6Z9h9uyo7WJV8O7PZx09OOjy42nJHpqSy0YO4D/8Hx4SWfCp0lj3Q1pJmHj4X4+nI1t4nEnsPnZ86fdjfB5J40remUlIYDBLSKKIr3f2DJ23ljEhqQYpB3Tg7anfcRlP8Qrncuaae3iIa0jAw7COoTvYn3JeM9LYz3F8xgePrqsli8NDCwrJI42+Z/SkR8d93H67kz56NDz2mDNWY9mLfjxzAJjD+NLdNFVZgdPbpl/zYH1FHXxv7AI88nCceCQ5OfyMMyrBwYe34EH+xIL/rebAoe0ZfM85/mxXzBgOhx8OEydyIvNYwhBevfFriouC7MeIEfzMwSQnFAcN8zFqFCNHeEiiiPcnuT2fvN748NL27i2Zva9VAsPHp1NECnf/vZC9NOFMZtO0sfLMTau5gX/TNeFXusgmfsttwdt/DzI17DvvsItWfMnR9DigAvcrNZWjvr7f6YBAG57kcqddJITHV1v8ui2VAlLpebAgQBc28V+u41Emc8H4PDKOLv8HJ155BZcwg08YSYe8X3j5zJfhs8+cjT6PpEnlQtKYPPqzjK93BnsaiSGq2qA+gwcP1oixZo0+xh/UudpVJ/NwWMXmdbpIkynQ0S0WRs6WCJA35Q4F1Tv/Uaxeb5iFvvtOB/OtduFX/Rt36LI316vu3686b57eyj80kSLd16x9xeVBF3KE9mG5tmKnFs9+MxK7UjN++UWnM0lBdeOzn6ju2OHftGWLarduqs88U7rI6i4nahNyFVQv4wnVwsLSGUAH8J0ecWBmxb87cqSO4FMdwHeqY8c6J9WFF6quWxexXasWK1fqjdynqclFqurs2pldF/vPe1D98gv3hPnmG9W8PC2afK1msFFP4EPVRYtKVedBtBcrFVTvv7ew7K+VYumfX9A2ZOlRfKF6yy2qe/eqgn7CsbpnT1T2NjTvvaevcaaCu1v//Kc+wWX+47D0O0+FRYuKVGc/m6vD+ErT2Kcr6K16wgmqoAfxs54/Oiv0b2dlqYJezYPalBwtLo7srlUGsFgruK/G/MZe0w8wFvgJWAvcUln+iAqJqnomX6NH8YW2TMvXzd/8Fl6h5ct1CndqIkW6jXTVyZNVN26MqF1VZudOXTfsAgXVp56qQrniYn352If9F1Jrdug7nKx5NNLhLNCBLFGdP7/i8uvWqd58s77EeQqqXzc/UXXJEtUpUzR8NYsw33+v1zNN0xLy1FMcvg1ZC9foZB5SUP0XN6jedZdz1/3qK13FYQqq/526q+IK1q/XfyVPUVBdRl9V0O8YoLtpobp7d413q9rMnq3n8ZL2PCDPn+QtKtbrD3lHQfWCC4KUefpp/Tt/VVAdzLd6Hzfqtxfcrx5EH+aPCqqPDX++8t9+9FGdym0Kqg/wJ/X26atfM1RB9bzzIreLYdOxo05iuqYm5Ov+/W7aXXfpHMbpf/5aiRC4ZPYepe3J1BTydSBL9H9coaB6xe+LKi88dKg+j3OdLpv2YfX3oxrUWyEBEoF1QHcgBVgG9ApVJtJCoh6PFixbrXl5lWf14/Xqcvo4Jw//07u4Rc9q/J5+9XktP2L4yM7WX+mio5jnPF1+WfUqvv0iXxcwXDuzSUFV8Cio3vX30E+cqqq6dKlm0cYvRp9xjN7IfTqy+3rdsDy76sbUlA8/1JN4V/t2y61y0eKPP9VzeFlBtTF7dRTz9Fku1JF8pI2SinTr1tDld77xmTZmr17EDH2OiQqqrdipd/d6VovCuM9EnGXLdD0Halu267iTS/+XXq9zrgQ99z0eLXh/vt57+Kt6OIv8/21j9iqotmu0W/P3V/z07uexx3Qr7fRoPldQPYxVpTyhso5fVCko0PXN+moK+TrptICHRo9HNTOEp1mW/ft1WcoQvZJH9SB+9u/LtGlhlB05UtfSXUH1Yf6oeuutWluuSSghEWd73UREjgSmquoYd30KgKpW+Or4kCFDdPHixbVkYQjmzeP00ft5C+cN6RQK8JLAJf2+49TTEmiXkQLNmvm7EvtHXHGvIRF1uha566qKep251lVLvtWr/lC71xuw7lW8a3/h12+3s2q18HzOOLwkMGXSLqZMP7B6M3p6vWQ98w6vX/4e1/JfmrdO4qc1ieVeqSlHYSE0asRt/J27mIKHkoaYZuRwRfu3Ofp33WnZuQkJaY3wpqTiTUzG66XqHxW8HnW+A9P35+PNK8Cbs5e8j7/kn1svZ+JE4fHnG4cwPDjFb8/lkevX8fNa4V1O4Ve6kir5TPtPIpOvraB7p4+tW7mu4yz+y3UA9GAt3VjPR5zI7wd8x5Tr8shNbMnehOZ4VZxzITEBQUEESQAB//khqPNfqiI454TH45wfHk/J/vuWPR7w7svDW+wlIVHI/s/T3LT9RgpoxNyFrRg2rMqHA7KzWf/UfD674U0WMIL+LGPMm5M5dNzBlZfNzYXZs1GFh69dwys5Y/iCY/ybPzj7CZL792J/47Y0a9+YtKQi1ON1poxOTfVLjnON4GxTwOv1r6OKeks+5da9Hva+u4CPPlKe43cUJzfmhzXJwdu6qoIqRQkpLKcf3/acyO++v54mlfXqXbcOPeggerOKIpK5hof4noG07Z/BgBPa0qFnMxLTUkhMhKRGiSQklr6Qu/ZtRvuDqvfSq4gsUdUhFexL7D2L6n6As4EnA9Z/BzwcqkzEPZIaUJBboF88v15/+36b7j77cr04+QV/nL02P03I1XGDNuovS0KEXarCggW6Ytr7umFDFcs995wuYaA2Ik9Htf1ev2k+Ss9lliZSVOvHpHeHLF27tuaHouDZWfrx/Ut11685YZfZuXyzZiRnanP26LpOR6uC/oV/1vox8H16NNqoP9w/t+YHQ9VxY3LCPxal8HjU27GTPnXwPfplq5O1Jbtq9Tg0Ik8ntnxbly7cX7mtVT0mnjC8swDmjX9Y25Dlv34bkRfWPjx2/mfVNpN67JGcA4xR1cvd9d8BQ1X1mjL5JgGTAA444IDBvwYdcS4OUCXvmxV8vzCf3L0Ce/eCx4Oqs83nJijiLAesS4KQkOAkJSSAJEjAMiQkiPN0GpBPBDr2bUuX43ogjcqPd1TrqEJmJrtSO9Gihfs6zs8/s3PNLjbvTGPPjmI0L5+EwnwSPEUkJECCaOnvytIq2o6XhJQkEjI6kdDtQJp1a1s9ryxC5ORAXp47MacqumIlc+cUsGN3Is0ae2havIekRPXfInyHT73qPz9UQUVw/RIU8e93YqKz34kJJccgMdHdliRIaiO8RR7o3Zt+x7ep8D25WsfjcXa0oID5T6/npXnpjO29iYyme8jdK+R7kpGkREQ9SKEzUKrvvPd7bAkJzn8r4nhzAiQIIs51VC6vemmUohx26TCaNo+fjq5523L4dlVjhvbcTfLq5azc0prsjdl4CorxqFBc4HpmAfQ+tRsHHN+jghpDE8ojqetCUndDW4ZhGHWIUEISP/JaPb4FeopINxFJASYAlUyQbBiGYUSSYO+U1hlUtVhErgY+wOnB9bSqroqxWYZhGA2KOi0kAKr6HvBerO0wDMNoqNT10JZhGIYRY0xIDMMwjBphQmIYhmHUCBMSwzAMo0aYkBiGYRg1ok6/kFgdRCQLqO6r7W2BHRE0pzapq7ab3bVPXbXd7I4uB6pq0Hm1G5yQ1AQRWVzRm53xTl213eyufeqq7WZ37LDQlmEYhlEjTEgMwzCMGmFCUjUej7UBNaCu2m521z511XazO0ZYG4lhGIZRI8wjMQzDMGqECYlhGIZRI0xIwkRExorITyKyVkRuibU9gYhIFxH5RERWi8gqEbnWTZ8qIr+JyFL3c3JAmSnuvvwkImNiaPsGEVnh2rfYTWstIvNEZI373Sqe7BaRQwKO6VIRyRGR6+L1eIvI0yKyXURWBqRV+RiLyGD3v1orIg+KRHcOyQrs/peI/Cgiy0XkDRFp6aZ3FZG8gGM/PVZ2h7C9yudHLGyvFhXNwWufUnPDJwLrgO5ACrAM6BVruwLs6wgMcpebAT8DvYCpwI1B8vdy96ER0M3dt8QY2b4BaFsm7T7gFnf5FuDeeLO7zLmxFTgwXo83MAIYBKysyTEGvgGOBASYC5wUA7tHA0nu8r0BdncNzFemnlq1O4TtVT4/YmF7dT7mkYTHUGCtqv6iqoXALOD0GNvkR1UzVfU7dzkXWA10DlHkdGCWqhao6npgLc4+xgunAzPd5ZnA+ID0eLN7FLBOVUONlhBTu1V1AbAriE1hH2MR6Qg0V9WF6tzhng0oU2t2q+qHqlrsrn4NZISqIxZ2Q4XHvCLi5phXFxOS8OgMbApY30zoG3XMEJGuwEBgkZt0tRsGeDogfBFP+6PAhyKyREQmuWntVTUTHJEE2rnp8WS3jwnASwHr8X68fVT1GHd2l8umx5Lf4zyl++gmIt+LyGcicoybFm92V+X8iDfbK8SEJDyCxSXjrt+0iDQFXgeuU9Uc4DGgBzAAyAT+7csapHis9udoVR0EnARMFpERIfLGk92ISAowDnjVTaoLx7syKrI1rvZBRG4FioEX3KRM4ABVHQhcD7woIs2JL7uren7Ek+0hMSEJj81Al4D1DGBLjGwJiogk44jIC6o6G0BVt6mqR1W9wBOUhFPiZn9UdYv7vR14A8fGba5b7wtNbHezx43dLicB36nqNqgbxzuAqh7jzZQOI8VsH0TkYuBUYKIb8sENC+10l5fgtDMcTBzZXY3zI25srwwTkvD4FugpIt3cp9AJwFsxtsmP25PjKWC1qt4fkN4xINsZgK8HyVvABBFpJCLdgJ44jXq1iog0EZFmvmWchtSVrn0Xu9kuBt50l+PC7gDOJyCsFe/HuwxVOsZu+CtXRIa559tFAWVqDREZC9wMjFPV/QHp6SKS6C53d+3+JV7sdu2q0vkRT7ZXSqxb++vKBzgZpzfUOuDWWNtTxrbhOC7vcmCp+zkZeA5Y4aa/BXQMKHOruy8/EaOeIDi94Ja5n1W+4wq0AT4G1rjfrePJbteOxsBOoEVAWlwebxyxywSKcJ5yL6vOMQaG4Nz81gEP446MUct2r8VpT/Cd59PdvGe559Ay4DvgtFjZHcL2Kp8fsbC9Oh8bIsUwDMOoERbaMgzDMGqECYlhGIZRI0xIDMMwjBphQmIYhmHUCBMSwzAMo0aYkBhGlBCRNgEjvW4NGPl1r4g8Gmv7DCNSWPdfw6gFRGQqsFdVp8XaFsOINOaRGEYtIyLHicg77vJUEZkpIh+KMzfLmSJynzsHxfvu0De+eSk+cwe3/KDMW9KGEVNMSAwj9vQATsEZTvx54BNV7QvkAae4YvIQcLaqDgaeBu6MlbGGUZakWBtgGAZzVbVIRFbgTJT1vpu+AmfCpkOAPsA8d4K8RJzhNwwjLjAhMYzYUwCgql4RKdKShksvzjUqwCpVPTJWBhpGKCy0ZRjxz09AuogcCc6UASLSO8Y2GYYfExLDiHPUmd75bOBeEVmGM+rtUTE1yjACsO6/hmEYRo0wj8QwDMOoESYkhmEYRo0wITEMwzBqhAmJYRiGUSNMSAzDMIwaYUJiGIZh1AgTEsMwDKNG/D8VM2yUCq4F5wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1575.7685555991309.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, test_preds_LSTM)\n", + "return_rmse(y_test, test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparing RMSE to curr Forecasting methods to LSTM\n", + "LSTM_test_year = day_to_year(test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 85883.11611329572.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and LSTM\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, LSTM_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/daily_robust_rnn.ipynb b/daily_robust_rnn.ipynb new file mode 100644 index 0000000..79ef2d4 --- /dev/null +++ b/daily_robust_rnn.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print(king_test_norm.shape)\n", + " \n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(180,22545): # 30\n", + " x_train.append(king_train_norm[i-180:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(180, 1824):\n", + " x_test.append(king_test_norm[i-180:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(180, 1824):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(180,22545): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1824, 2)\n", + "max val king_train:\n", + "67521\n", + "max val king_test:\n", + "32446\n", + "(1824, 1)\n", + "(1644, 1)\n", + "(1644, 1)\n", + "(22365, 1)\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create single layer rnn model trained on x_train and y_train\n", + " and make predictions on the x_test data\n", + " '''\n", + " # create a model\n", + " model = Sequential()\n", + " model.add(SimpleRNN(32))\n", + "# model.add(SimpleRNN(32, return_sequences=True))\n", + "# model.add(SimpleRNN(16))\n", + " model.add(Dense(1))\n", + "\n", + " model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + " # fit the RNN model\n", + " history = model.fit(x_train, y_train, epochs=3, batch_size=64)\n", + "\n", + " print(\"predicting\")\n", + " # Finalizing predictions\n", + " RNN_train_preds = model.predict(x_train)\n", + " RNN_test_preds = model.predict(x_test)\n", + " \n", + " #Descale\n", + " RNN_train_preds = scaler.inverse_transform(RNN_train_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " RNN_test_preds = scaler.inverse_transform(RNN_test_preds)\n", + " RNN_test_preds = RNN_test_preds.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + "# RNN_salmon_count = (RNN_preds * (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"])) + np.min(king_training[\"king\"])).astype(np.int64)\n", + "\n", + "# why are we normalizing the test and train set, then un-normalizing (maybe this can cause problems in the sense tht we are\n", + "# not comparing our preds to the proper y values)\n", + " return model, RNN_train_preds, RNN_test_preds, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/3\n", + "350/350 [==============================] - 8s 21ms/step - loss: 0.0030\n", + "Epoch 2/3\n", + "350/350 [==============================] - 7s 20ms/step - loss: 5.5065e-04\n", + "Epoch 3/3\n", + "350/350 [==============================] - 6s 18ms/step - loss: 3.9362e-04\n", + "predicting\n" + ] + } + ], + "source": [ + "# train single_layer_rnn_model\n", + "model, RNN_train_preds, RNN_test_preds, history_RNN, y_train, y_test = create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# global var for baseline\n", + "y_test_year = day_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_year = day_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "y_test_year = y_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 570.6108536758172.\n", + "(22365, 1)\n" + ] + } + ], + "source": [ + "# plot single_layer_rnn_model\n", + "plot_predictions(y_train, RNN_train_preds)\n", + "return_rmse(y_train, RNN_train_preds)\n", + "print(RNN_train_preds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 1367.212325791287.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, RNN_test_preds)\n", + "return_rmse(y_test, RNN_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_RNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Count
0479454
1343823
2384598
3524886
\n", + "
" + ], + "text/plain": [ + " Count\n", + "0 479454\n", + "1 343823\n", + "2 384598\n", + "3 524886" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RNN_test_year = day_to_year(RNN_test_preds)\n", + "RNN_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115830.72196205116.\n", + "The root mean squared error is 8328.45420831501.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, RNN_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/daily_simple_gru.ipynb b/daily_simple_gru.ipynb index 93fe44c..b7a1017 100644 --- a/daily_simple_gru.ipynb +++ b/daily_simple_gru.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -13,40 +13,30 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error\n", - "from sklearn import model_selection\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.linear_model import ElasticNet\n", "# plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy = salmon_data\n", " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", " inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", @@ -60,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -102,13 +92,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -122,24 +112,16 @@ " king_test = king_all[king_test_parse]\n", " king_test = king_test.reset_index()\n", " king_test = king_test.drop('index', axis=1)\n", - " print(king_test.shape)\n", " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - " print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - " print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", - " print(king_test_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + " \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -147,7 +129,7 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", + " # Set up train and test (train is 180 day series, y val is 181st day etc.)\n", " \n", " for i in range(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", @@ -167,23 +149,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 63, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1824, 2)\n", - "max val king_train:\n", - "67521\n", - "max val king_test:\n", - "32446\n", - "(1824, 1)\n", - "(22365, 180, 1)\n" - ] - } - ], + "outputs": [], "source": [ "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(king_all_copy)\n", "x_train = np.array(x_train)\n", @@ -193,19 +161,15 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "#print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "#print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "#print(y_train_not_norm.shape)\n", - "#print(y_train.shape)\n", - "print(x_train.shape)" + "\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -229,33 +193,19 @@ " rmse = math.sqrt(mean_squared_error(test, predicted))\n", " print(\"The root mean squared error is {}.\".format(rmse))\n", " \n", - "# def day_to_year(day_preds):\n", - "# day_preds = day_preds[183:]\n", - "# year_preds = []\n", - "# for i in range(365, len(day_preds), 365): \n", - "# salmon_count = np.sum(day_preds[i - 365:i])\n", - "# year_preds.append(salmon_count)\n", - "# year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", - "# return year_preds" + "def day_to_year(day_preds):\n", + " day_preds = day_preds[183:]\n", + " year_preds = []\n", + " for i in range(365, len(day_preds), 365): \n", + " salmon_count = np.sum(day_preds[i - 365:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 35, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -266,17 +216,15 @@ " '''\n", " # The GRU architecture\n", " regressorGRU = Sequential()\n", - " # First GRU layer \n", + " # First GRU layer with Dropout regularisation\n", " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=1, activation='tanh'))\n", - " #regressorGRU.add(Dense(units=1))\n", + " regressorGRU.add(GRU(units=50, activation='tanh'))\n", + " regressorGRU.add(Dense(units=1))\n", "\n", " # Compiling the RNN\n", " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", " # Fitting to the training set\n", - " history = regressorGRU.fit(x_train, y_train, epochs=1, batch_size=150)\n", + " history = regressorGRU.fit(x_train, y_train, epochs=3, batch_size=150)\n", " \n", " # Predictions \n", " GRU_train_predict = regressorGRU.predict(x_train)\n", @@ -294,14 +242,19 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "150/150 [==============================] - 76s 467ms/step - loss: 8.5667e-04\n" + "Epoch 1/3\n", + "150/150 [==============================] - 26s 157ms/step - loss: 8.1948e-04\n", + "Epoch 2/3\n", + "150/150 [==============================] - 26s 174ms/step - loss: 3.2887e-04\n", + "Epoch 3/3\n", + "150/150 [==============================] - 26s 171ms/step - loss: 3.1406e-04\n" ] } ], @@ -311,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -321,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -329,22 +282,19 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "# print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "# print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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GdQLlft6sKRJ12O5+rOG+FOgFhBY+DAF6u+97AUNVtVBVFwHzgS4i0hjYV1XHqTPF7O2YMqG6Pga6h0YrhmEYexrD6JU8UxbI6hhIRPJEZAqwFhihquOBA1V1FYD7/wA3e1NgWUTx5W5aU/d9bHpUGVUtBrYADX3kuEFE8kUkf926dRm6uswSGUb+oosuYufOnWWuKzIs/PXXX8+sWbPi5i1rBNzmzZuzfv16T/r27dv54x//GF7/0bVrV8aPHx8VADKWBx54gJEjR5ZahkQMHDiQQYMGJc339ttv07ZtW9q0aUPr1q1TKlNaHn300YzXaRgViawqElUNqGoHoBnO6MK/JXHwG0logvREZWLleFVVO6tq51Dsp4pGZBj5vfbai5dffjnqeCBQtuHq66+/TuvWreMez3Qo9euvv54GDRowb948Zs6cyeDBg30VTiQPP/wwp59+esZkSJVvvvmGZ555huHDhzNz5kwmT54cjiWWSUyRGFWdcvHKqOpmYAyOb2ONa67C/b/WzbYcODiiWDNgpZvezCc9qoyIVAfqARuzcQ3lySmnnML8+fMZM2YMv//977nssss4+uijCQQC3HXXXRx77LG0a9eOV155BXBCp9x66620bt2ac845h7Vr14brOvXUUwktwPz222/p2LEj7du3p3v37r6h1NetW8cf/vAHjj32WI499lh++uknADZs2ECPHj045phj+OMf/+gb82rBggWMHz+ef/zjH1Sr5jxahx12GOeccw7gKMP+/fvTpk0bevTowa5du4DoEZRfyHeAjRs30rt3b9q1a8fxxx/PtGnTEqZH8tprr3HWWWeFzxfiscceY9CgQTRp0gSAWrVq0b9/f8CJdHz88cfTrl07LrjgAjZt2uS5n+vXr6d58+ZA/DD79957bzgg5+WXX57K128YlY6sBW0UkUZAkapuFpHawOk4zvBhwNXA4+7/L9wiw4D/iMi/gCY4TvUJqhoQkW2uo348cBXwXESZq4FxwIXA95rmUv0cR5GnuLiYb775hp49nfkEEyZMYMaMGbRo0YJXX32VevXqMXHiRAoLCznppJPo0aMHv/76K3PnzmX69OmsWbOG1q1bc91110XVu27dOvr378/YsWNp0aIFGzdupEGDBp5Q6pdddhl/+ctfOPnkk1m6dClnnnkms2fP5qGHHuLkk0/mgQce4KuvvuLVV1/1yD5z5kw6dOhAXl6e5xjAvHnzeP/993nttde4+OKL+eSTT7jiiis8+fxCvj/44IMcc8wxfP7553z//fdcddVVTJkyJW56iOeff57hw4fz+eefRwWdhMTh6q+66iqee+45unXrxgMPPMBDDz3EM0m+RL8w+48//jjPP/+8b+BNw6gqZDP6b2NgiDvzqhrwoar+V0TGAR+KSD9gKXARgKrOFJEPgVlAMXCLqobsOTcBg4HawDfuC+AN4B0RmY8zEumbxevJKqFeKzgjkn79+vHzzz/TpUuXcIj44cOHM23atHDvfcuWLcybN4+xY8eGQ6s3adKE0047zVP/L7/8QteuXcN1NWjQwFeOkSNHRvlUtm7dyrZt2xg7dmw4/Po555xD/fr1fcsnokWLFuFr7NSpE4sXL/bN5xfy/ccff+STTz4BnKCOGzZsYMuWLXHTAd555x2aNWvG559/To0aNVKWc8uWLWzevDkcZPLqq6+OCuUfD78w+wcffHCSUoZR+cmaIlHVaYAncp6qbgC6xynzCPCIT3o+4PGvqGoBriLKFDmKIh/2kcSyzz77hN+rKs899xxnnnlmVJ6vv/6aZJPVVDVpHnCiAo8bN47atWt7jiUr36ZNG6ZOnUowGAybtiKJDUMfa2qKzRcZ8t1voCkicdMB2rZty5QpU1i+fLnvfi2h0Pp+ijcekeHq44Wqj5XdMKo6trK9EnHmmWfy0ksvUVRUBMBvv/3Gjh076Nq1K0OHDiUQCLBq1SpGjx7tKXvCCSfwww8/sGjRIsDxLYA3lHqPHj14/vnnw59Dyq1r16689957gOOkDvkMImnZsiWdO3fmwQcfDDfw8+bNC0csTofI848ZM4b999+ffffdN246OFGUX3nlFc4//3xWrlzpqXPAgAHcfffdrF69GnA2DXv22WepV68e9evXD0cEfuedd8Kjk+bNmzNp0iSAlPezr1GjRvg7M4yqiCmSSsT1119P69at6dixI23btuWPf/wjxcXFXHDBBbRq1Yqjjz6am266yXezqEaNGvHqq6/Sp08f2rdvzyWXXAJ4Q6k/++yz5Ofn065dO1q3bh2ePfbggw8yduxYOnbsyPDhwznkkEN8ZXz99ddZvXo1hx9+OEcffTT9+/cPO7PTYeDAgWG57r333vAeLPHSQ5x88skMGjSIc845xzN77Oyzz+aWW27h9NNPp02bNnTq1Ck8ihgyZAh33XUX7dq1Y8qUKTzwwAMA3Hnnnbz00kuceOKJSWejhbjhhhvC4e0NI1PczAuId5JqTrAw8lgobyN32LNnlBW/iPFZjCJvYeQNwzCM7GGKxDAMw0gLUyQue5qJz8g99swZVQVTJDgrmjds2GA/bKPcUFU2bNhArVq1ci2KYaRNNhckVhqaNWvG8uXLqagBHY2qSa1atWjWrFnyjIZRwTFFgjPP32/BmmEYhpEcM20ZhmEYaWGKxDAMw0gLUySGYRhGWpgiMQzDMNLCFIlhGIaRFqZIDMMwjLQwRWIYhmGkhSkSwzAMIy1MkRiGYRhpYYrEMAzDSAtTJIZhGEZamCIxDMMw0iJrikREDhaR0SIyW0Rmisif3fSBIrJCRKa4r7MjygwQkfkiMldEzoxI7yQi091jz4o4G0qKSE0R+cBNHy8izbN1PYZhGIY/2RyRFAN/VdWjgOOBW0SktXvsaVXt4L6+BnCP9QXaAD2BF0Ukz83/EnAD0Mp99XTT+wGbVPVw4GngiSxej2EYhuFD1hSJqq5S1cnu+23AbKBpgiK9gKGqWqiqi4D5QBcRaQzsq6rj1Nl56m2gd0SZIe77j4HuodGKYRiGUT6Ui4/ENTkdA4x3k24VkWki8qaI1HfTmgLLIootd9Oauu9j06PKqGoxsAVomI1rMAzDMPzJuiIRkTrAJ8DtqroVx0zVEugArAKeCmX1Ka4J0hOViZXhBhHJF5F82wXRMAwjs2RVkYhIDRwl8p6qfgqgqmtUNaCqQeA1oIubfTlwcETxZsBKN72ZT3pUGRGpDtQDNsbKoaqvqmpnVe3cqFGjTF2eYRiGQXZnbQnwBjBbVf8Vkd44ItsFwAz3/TCgrzsTqwWOU32Cqq4CtonI8W6dVwFfRJS52n1/IfC960cxDMMwyols7tl+EnAlMF1Eprhp9wGXikgHHBPUYuCPAKo6U0Q+BGbhzPi6RVUDbrmbgMFAbeAb9wWOonpHRObjjET6ZvF6DMMwDB9kT+vAd+7cWfPz83MthmEYRlqE5qdGNuF+aZk7n0xS1c5+x2xlu2EYhpEWpkgMwzCMtDBFYhiGYaSFKRLDMAwjLUyRGIZhGGlhisQwDMNIC1MkhmEYRlqYIjEMwzDSwhSJYRiGkRamSAzDMIy0MEViGIZhpIUpEsMw9jy2bcu1BFUKUySGYexZ/Pgj7LsvfPVVriWpMiRVJCLyRCpphmEYlYLx7o7fo0blVo4qRCojkjN80s7KtCCGYRjlgvjt0G2kQ9yNrUTkJuBm4DARmRZxqC7wU7YFq3IEgxAIQI0auZbEMAzIzqYdFZHCQti1C/bbL2unSDQi+Q9wHs52tudFvDqp6hVZk6iqctZZsNdeuZbCMIw9bUTSrRvUr5/VU8QdkajqFmALzta4ecCBbv46IlJHVZdmVbKqxvDhuZbAMIxIqsiIRDWJbgz5hLJIKs72W4E1wAjgK/f13yzLZRiGkRU2F9TibL5i1Y59cy1KlSHuiCSC24EjVHVDlmWp0ozgdMZzHPfnWhDD2MMZMuEovuFUHp+4D//OtTBVhFQUyTIcE5eRBj0YAWCKxDByzZ7mIykHUlEkC4ExIvIVUBhKVNV/ZU0qwzCMrFM1fCQVgVTWkSzF8Y/shTP1N/RKiIgcLCKjRWS2iMwUkT+76Q1EZISIzHP/148oM0BE5ovIXBE5MyK9k4hMd489K+J0KUSkpoh84KaPF5Hmpbp6wzAMI22SjkhU9aEy1l0M/FVVJ4tIXWCSiIwArgFGqerjInIvcC9wj4i0BvoCbYAmwEgR+Z2qBoCXgBuAX4CvgZ7AN0A/YJOqHi4ifYEngEvKKK9hGHsQqlXDxJV01lY5kFSRiMhofMaAqnpaonKqugpY5b7fJiKzgaZAL+BUN9sQYAxwj5s+VFULgUUiMh/oIiKLgX1VdZwrz9tAbxxF0gsY6Nb1MfC8iIhqFZnXZxhGxilpdK2ZyBSp+EjujHhfC/gDzmgjZVyT0zHAeOBAV8mgqqtE5AA3W1OcEUeI5W5akfs+Nj1UZplbV7GIbAEaAutjzn8DzoiGQw45pDSiG4ZR1agaA5EKRSqmrUkxST+JyA+pnkBE6gCfALer6laJPwbzO6AJ0hOViU5QfRV4FaBz587WDTEMwwYkGSQV01aDiI/VgE7AQalULiI1cJTIe6r6qZu8RkQau6ORxsBaN305cHBE8WbASje9mU96ZJnlIlIdqAdsTEU2wzD2VGxIkmlSmbU1Cch3/48D/orj5E6IO7PqDWB2zFThYcDV7vurgS8i0vu6M7FaAK2ACa4ZbJuIHO/WeVVMmVBdFwLfm3/EMIxUsIYic6Ri2mpRxrpPAq4EpovIFDftPuBx4EMR6Ycztfgi9zwzReRDYBaOD+YWd8YWwE3AYKA2jpP9Gzf9DeAd1zG/EWfWl2EYRnxyPcWpCpKKaasGTkPe1U0aA7yiqkWJyqnqj8QfQ3aPU+YR4BGf9HygrU96Aa4iMgwjg4QG9lW40ZUqMiZJZoMZwen8xEnh6a3ZIJVZWy8BNYAX3c9XumnXZ0sowzByTMOGzna0ixfnWhIjTULhmQZm8RypKJJjVbV9xOfvRWRqtgQyDKMCsGmT8zKMFEjF2R4QkZahDyJyGBBIkN8wjErOShqzKrXJmZWWqrKyvSKQyojkLmC0iCzE8XkcClybVakMw8gpTd0Z9lXDixCNrWzPPKnM2holIq2AI3AUyRw3jIlhGIZhxFckInIFIKr6jqs4prnp/UVkh6r+p7yENAzDyDg2IMkYiXwkfwU+90n/wD1mGIZR+ahiU5o1mHuNmEiR5KnqtthEVd2KMx3YMAyj0mIxMDJHIkVSQ0T2iU109xbZK3siGYZhZJGqNSCpECRSJG8AH0fuOui+H+oeMwzDMIz4znZVHSQi24Ef3FDwCuwAHlfVl8pLQMMwjEwSGpCYZStzJJz+q6ovAy+7ikT8fCaGYRiViSrma68QpLIgEVXdnm1BDMMwypOqsrK9IkwaSCVEilERKSiAnTtzLYVhVDqq3Mr2CqBJyqRIRKRmpgUxSkmLFrCPZ1KdYRjJcBVJVRmRVASSKhIReTPmcx3g66xJZKTG6tW5lsAwKiW+zvZ16+Doo2HBghxIVPlJZUSyQkReAhCR+sBw4N2sSlWZ6N3bvHeGUYkQP03y0UcwYwY89VQuRKr0JFUkqvp/wFYReRlHiTylqm9lXbLKwhdfeNMKCmwvB8OoqLiaJPeehapDXEUiIn1CL2ACcDzwK6BumhGPU06BBg1yLYVhGIkwTZIxEk3/PS/m8684MbbOw/kKPs2WUJWe/PzcnPfnn6FOHWjXLjfnN4zKgI8lOqjCi9xCv6Jq1C5/idKiAkzaSriy3TavqmycdJLzvyI8WYZRQSlxkZRolI+nHM5t3MTiCSMYlBuxyk4F+L2nMmurmYh8JiJrRWSNiHwiIs1SKPemW2ZGRNpAEVkhIlPc19kRxwaIyHwRmSsiZ0akdxKR6e6xZ0UcA6eI1BSRD9z08ZExwQzDMOLhNzdme6HTp95YuHc5S1M1SGXW1lvAMKAJ0BT40k1LxmCgp0/606rawX19DSAirYG+QBu3zIsikufmfwm4AWjlvkJ19gM2qerhwNPAEynIVGWYT0t+pUOuxTCMSotvRz73nftKSSqKpJGqvqWqxe5rMNAoWSFVHQtsTFGOXsBQVS1U1UXAfKCLiDQG9lXVcaqqwNtA74gyQ9z3HwPdQ6OV8mQAj9KamVFp/+Nknub2rJ63FfPpyK9ZPYdhVEVCrYRGOUtsCn86pKJI1ovIFSKS576uADakcc5bRWSaa/qq76Y1BZZF5FnupjV138emR5VR1WJgC9AwDbnKxOMMYDato9K68j/u4OnyFsUwjBQQCQ07NDIxNsUoBakokuuAi4HV7utCN60svAS0BDoAq4DQ6h+/7oAmSE9UxoOI3CAi+SKSv27dulIJbBhGVcNVGhEhUqQSq5AK4GtPHv1XVZcC52fiZKq6JvReRF4D/ut+XA4cHJG1GbDSTW/mkx5ZZrmIVAfqEceUpqqvAq8CdO7cuQLcdsMwckXCoI1VpXVQLdeIG1mbtRWnrsYRHy8AQjO6hgF93ZlYLXCc6hNUdRWwTUSOd/0fVwFfRJS52n1/IfC960cxDMNISlTQxioW5qi4uHzPl8p+JG8B/wEucj9f4aadkaiQiLwPnArsLyLLgQeBU0WkA47eXwz8EUBVZ4rIh8AsoBi4RVUDblU34cwAqw18477A2e73HRGZjzMS6ZvCtRiGsYdT4myPTPRJqyz49J/Lu0udiiJpFBNba7CI3J6skKpe6pMcd693VX0EeMQnPR9o65NeQIlyM4C+vE8j1vFcrgUxjIqMz+hDgkHnTVUxamg8F3N2yMWsLSNLfEBfnue2XIthGJWPbxxDhy5anFs5KimlnbW1ivRmbRmGYeSU0AytqFlbm91o3YGAX5GKTWFhriUo31lbhmEYucbXrx5eR1L5nO66ZSs0iNkttZxNdEkViYg0AvoDzSPzq6qNSgzDqHz4ONYr8zoSP6VR3pPQUnG2fwH8DxgJVMJxn5ES118PRxwBd92Va0kMI6skWkZSGdm4uRpNYtIq4qytvVX1nqxLYuSWN9wJdaZIjKqOXziUSryOZNcun8Ry1iSpONv/Gxnu3TAMo8LSsSM8/LCzIi80pTeGUKytqAWJLpXSRxLM/dAqFUXyZxxlsktEtorINhHZmm3BjPKlD59wF0/mWow9j+3b4bvvci1F1eHXX+HBB6FGDbjIf5lZSZBwjUgrB9nKkfI2bSVVJKpaV1WrqWptVd3X/bxveQhnlB+f0YdBmFmr3Ln2WujZExYuzLUkVYKjmMU9PO58+DTxbuCRI5LKOBIJ4b+vSgUxbYnIke7/jn6v8hPRMKowc+Y4/7dvz60cVYQ5HMWTlN6l+2rRNQB8wh8yLFH2iWPBK1cSOdvvwNmZ8CmfYwqclhWJjMxSvTp06gTjx+daEsOHAHks41Ca51qQKsgW9uXTpn/j2nl/g70Tb6H7W/BwAHZRNbbarTCmLVW9wf3/e5+XKZHKQiAAEybkWgojDvet/hMtWMyyValMoDRKw/W8znUrH2HSRwui0v2CNlbqdSQVgJSeXhE5Ee+CxLezJJNh7DGM3HE8AGvW50VtyGOkz0p3dcWuguj+sp9jPa4i2bQJ6tf3P1ZR8Bt+VBQfSQgReQcYBJwMHOu+OmdZLsPYI7CecPYIOdBl0cJwUMao48mc7e+/Dw0aQH5+1mTMBBVh+m8qI5LOQGvbNMowskkF/nmpwp/+BP36QYcOuZYmZcKK5InH4Ilx4V66VPMuSPRV6CNGOP+nToXOlavvHHcmV5bmOaeyjmQGcFBWzm4YezyVYNrp+vXw/PNwRsK97CoUW6nLbvYCvEoi/DlV3V3BF5n4XkY5m7vijkhE5EscGesCs0RkAhCOV6yqFhG4ErCR+tSgiLq5FsQoIRSqPC8vnKQVYApnVaIeJWumPaONHc5Ua002b7aSGGFSfnZyoUhw/CJGJachG9mH7dgqhQpE48bO5P/160vCdVQAO3dVxTMiGToU6AErVqRYQcUekaS81W6OFMkK4EBV/SkyUUS6useMSsIO6uRaBCOSdevCbyuVs72S9NBj+ZGTmUIH/hhKcEci0Q52H2VRSa8XiKtcsqUSE/lIngG2+aTvdI8ZRvrMnevY4PdwKnObVdG5k6e4kVfCn1NV3lt31+J+/k5xMBVXcu7QvWqmli+Lo95Ed6i5qk7zCKOaD7YQ18gQRx7p7IOyx1LBzSaRVHQTTxr4KZc78i/lEe7nvXGH5UCiUhDhawvh62vPkSKpleBY7UwLYuzBbNyYawlyTwUekqjCPTzOjOIjcy1KRokybfnoyDfmdQNg6rIGWRRC0/7ufUdYfqatHCmSiSLSPzZRRPoBk5JVLCJvishaEZkRkdZAREaIyDz3f/2IYwNEZL6IzBWRMyPSO4nIdPfYs+LGgBaRmiLygZs+XkSap3jNhlFhCDnbKzLrNwhPcg/dt3ySa1EyQqjhTTXi7+Zde2VPmD//GaqlZzprUN/nGVqzxpOUK0VyO3CtiIwRkafc1w/A9Th7lCRjMNAzJu1eYJSqtgJGuZ8RkdZAX6CNW+ZFEQmN117CCR7Zyn2F6uwHbFLVw4GngSdSkMkwck4vPqcH0XuQVOhZW65JK5jSsrOKz0p1lsVtpWQ3jER+E78NsDLGc8+lX0eKI5qcKBJVXaOqJwIPAYvd10OqeoKqrk5WsaqOBWJtFr2AIe77IUDviPShqlqoqouA+UAXEWkM7Kuq49yV9W/HlAnV9THQPTRaqTTs3Alb9+w9wl7jemZTtUwmyRhGL0bQA6gks7a0dD34is7fdzth5odzZpKclQM/BaF53gm52VQkSUOkqOpoYHSGznegqq5y610lIge46U2BXyLyLXfTitz3semhMsvcuopFZAvQEPBMARKRG3BGNRxyyCEZupQUCAR8HWFhDjkENmyo0PbxbHMDrwEVOkBIlnHDdVTgG1DJumdJKe3lVOCvJi5a17v3YK5MW+WJ33erCdITlfEmqr6qqp1VtXOjRo3KKGIZSLZydsOG1OoJBJw9qI0qR0m4jsrYXO0plIMmTeP791MQk6d4m/aqpEjWuOYq3P9r3fTlEBVFuxmw0k1v5pMeVUZEqgP18JrSqgYnneTsQZ0p1q6FzZszV59RdipDb19CQQ4rg7DJqYa3g5fYR5I9WT7lAk7ix4w38kU+/c6qpEiGAVe7768GvohI7+vOxGqB41Sf4JrBtonI8a7/46qYMqG6LgS+r2gRikMhleJxFl/TOKwXE5Dp3Q0PPBAOsjicZWLmTBgzJjqtuBh27Uqr2gr26EZR1UxbfiRUklm8/j/wKT9zUlqNvF/ZAp/HMac+krIiIu8DpwL7i8hy4EHgceBDdwrxUuAiAFWdKSIfArOAYuAWVQ01wzfhzACrDXzjvgDeAN4Rkfk4I5G+2bqWsrJzJ9RNsBrnW84qP2FiKSxMnsfw0rat8z+y4T/rLBg5skxd13AbVXH1yB5PVmdthc+R2foWL/WOEYKBSqhIVPXSOIe6x8n/CPCIT3o+0NYnvQBXERlGThk5Mu0qKvCAJExVMW35kejKyuOriRotBIPOKHev1Nav+I008vJSDOSYISqKs73S4ztszNA39ysd+JJzM1KXUbEIL0isDJqkiuCnEBMvDM2eAhXXXxPVflxxBdR042dt2wY9esCiRaWq94Aamz1pldK0tceh8SaZpU9HfnVOkaH6zuG/NGUFr2aovj2JFTRhE/WjhsizOZJZtOYPadRreqT8KO3anWx+N4KixJid3n+/5P3nnzs7NV53HfTpA7fdlpJ8gTXrKVkpET9fpjBFkiE0qEieN60i8jXnAJgiKQPN3B0UIr/Z1sz2pFUl9gRneyLKw6SXtJEfM8Z59e4NBx8cfcynsN/Kg6o0a6vKUlGVRkZYtw7efjvXUlRtbEjizzvvQO3aUFSUa0myStz2Q4QlHFLSSfFZT6ZBhTlz4NwS87ef8jNFUgnw+5Kq+zi8KiV9+sDVV8PSpbmWpPzZsQP++c/kc7mzwdNPOyaNCkJOnO133AEFBVlf95RwHUlWz+yeI04jP3lxA5qzhH9wP5/Qx3eooUGFW26Br74KpwVV4LffnPuX5ByZwBRJhshYh/L006FTpwxVlhm+nv87DmQ1u7ZW7V6hL3/7G9x9N3z4of/xL77wT88Ed9wBb72VvfpTpEKYtjI4YqtI8c1CskT6SLZRh7n8DoB5q+sC8AB/50I+YdkKnyZbla3Fe/MiN4WTArXrwHnnOZ2RUDZztld8fAOnleWLGzUqA9Jklr9uGMBaDmTRsgJaeyZiV3FCQTV37vQemzHDsVmn0TBViEbaSDja2rg9tR0I0zp/RFtxOiOZwHG+T5Xf8i9FuGXun3iXM8JpwTr7UhSoxmIO9z1HprERSabIUI9pJq2ZRMeM1JUpQj+xSmXG/8c/4Pjj068n1NL7Xfw2v52oXYqKYMmS1M9TCW5uLkxbQRW24A1ACMB338EnmdkjJdEo5btpjTNyjkREfv0TOC783tPRiGPaWl9ULzpbQPnL+vv4HfOi8mULUyQZIlMjkrbMpHPyfcOMZPzf/2UmtEyCIcOWHdXjN0B/+Qs0b57WfvTvcRkDeLTM5TNFLkdNd+8cyH5sYdt2HyF69oQLLyx/ocpKURFs2hSVFGXamjw56c0WjeMjiSkWVGH0ruiOlC1IrARU6VlbISpBrznTfLOsLYIybVl9z7H5KxLEv/nO3bgqpuGIi8+9vYL3eJwBqZXPIrn82t8vvACArdu8DewqDmIJpd8WolxGVgsWwLPPRqdddhk08N+2V4MavX4kDn4dFw2qJ90vHIqNSCoBqa5sr4xtsRQ5htnKJPtcfsd/3fUy6fD5UsfM+PP8AzzHEu6jFto+NdlWApWBCuDI8Xv2mrCK5pTCfJiAlK9Q1ZlFlozTTnO20Y3YuE4//jiuAU2DypaCmlzHG9FyxQjmNyJJeR2JjUgqPr6mrfJqeK+8Erp2zVr15bZnxoYN0K+fv2O7lBzJXM7jv2nXU80NneF36VItfvOjUo3t7JObacNlYfFiyM/3PVQB9Ehqz16Kz6dfrz5xiJQIHnnEWdeSbKS5ZYvzP+L7/wtPUy2OKtFp0/nnxFN5i8TTvX1HJH4r2wPevNkM2miKJEP4PsPlpUnefRf+97+sVT+L1kCal1NUlHwtwMMPs/bNL+GNNxLnK0ckkSJJ0MA+svEm6rKd9RsqQiucAi1awLHH+h+rLFvtprp3eTrXEVqYu3ZtwmwFWpM5HBH1kPyb2z35wj6S/37trP2IoLAQz1DJ10fic9nBoPc6zbRVGUjRjJUN3TKNo/mRkzJfsYu6j0nBriTCFxXFN+VccQXUj/YzxN6Lzxe240DWMnpO9mfJRLFtG0yf7nuoZMaaXy82fpUfbHW2CFi5JsFWyxFnSPpcbNrkrGnJwU6ZuRyRhBralH435dBxK563kEU0TzxjD7h610scxRy2bU1Rufk08itX+pjc4i1ITJ7NTFsVhnXrYPRo30PZjP6bjPZM4xR+zP6Jkl3PXnvBNdf4H4u3oC+Cn1YdBkD+Uq8/Iqucfz60a+d7faFGNBj0iRibwLSVJ45JI2VzQrJ7+5e/wKOPZm0B5JecyyPc53+wQti2UiBFf1Q6CxLv5kkOYxErPpuQMN/o4lMA2FWQ+N4V4YSKX72jbkrnjztrK4Zg0HudNiKpKJx6quNE8yFVH0llcljHktKD+M47vsl38FTSH3CoYc6mLdeX0I6HiUYdpbRthWzhseYKbxUp9rhDOzBmaURyPl9yv3c7oChS2uBpzRoYPjxDUpWScvhxjeR0ANbvqJ1S/lQb739PSc3HKUGvzy2eaSv2WdE5c1I6R1kwRVIaZs2KeyiXI5JyY+pUGDgw7uGfOJFV+G/h+zR3eNJib0+1am7jW5aJTosWwbXXphfcL4EiKa2PJDQiSdbuR1VRXOxU+sQTnnxfr+qAoMxdmVrPNZOUakDStSuceWbWZElIWX5vy5Y5F5iigp5OOwA27kpNkaRq8vbrcIiAxLTQvook6PX6BIN4fki6KDMz3PwwRVIK5nAEL3Cz77GMhUipwOiTT8JDD8U9fjI/0YEpietIcEtCM2bLdN+uvRYGD4affvI//txz8P33voe+5iy6M9J3JJTQ2Z7AtFWtNLZ93GsOjTr+/nfP8aFLTwTgl3kNU6swV/z2W3bqzeSsrcgZWqNH8yXnUkSNUokzYl7zxOco5UxH32w+8VA0UHYfyajfDvYmZgiLtVUKjmM8W6nHzUH1aUTSeGAqCcEU+h1rObDM9VcLmbbKMiJJdmP/9Ke4+f7AJxRQm8KCImrH7G5a1hGJp4s4eDCcfDIcfnhMtoiKRXiIB7gg8K3b7y3lObNMKrOdiqjONuriv+yu9JTKn5Fyo11yHaNnH8j5fFlasVL+HlLtFPmKvny5N1+RTxh5n+8lEIDimOZ9/fYEC2jTxEYkpWAr9QAIFPsNV32erMqsNXz4inO4msEZq89r2nL+l8VHsqVob57nFjTWFpCOQEQ6273ZU2lMwlVee23CqM6qUFQsDOQhjisYk7zibLF7tzdt9WqgZFtYJk+GE04oGUFF0I83aMjG8vdzQZl+b+u3lbFxTfLdh2ebpeM7FfEsei3NiGQuRyY/R4YwRVIKQj8kvzVme4Jp6xHu522uTquOVExbZRmR3DTvDm7jecZO28/3eHdG8g/+5i8T8bVFwoCVCTSJr2kjYpWz5wQR9ZXWzJJR/K5p5syoj/rn29nyyyyYONGT9T0uBzK/oD8T03/LOvnld028U32TdSKSmraGDImqJGUTqJ8i2VXgWVC5R0z/FZHFIjJdRKaISL6b1kBERojIPPd//Yj8A0RkvojMFZEzI9I7ufXMF5FnJWHMivTJw9EgRbtTUxo5Xe1eUUnQwoRWkSeb6eTHhmInQmxBkf+6je/pzv/xj8SVJHK2+2SvlpdcTlXnz+W8mzRkS6KnN6UZUxlgx3bvlW4vdBTbNjcK7zOrLmY/trB0pWs6mT3bWfQQSYYe9Eyatvw7dsnr37umt+eY/NuI3zlRBV5/PSZNUpLFrxc7ctCvnrQ9QpG4/F5VO6hqZ/fzvcAoVW0FjHI/IyKtgb5AG6An8KJIeHf0l4AbgFbuq2d5CJ6y0qhiI5JMoMd2iXssHUUS/pGk0Zfw+75CljL1dcTHJKxZA6tWOcdihPsPl/uGbJGIPKEKE/kikl7e6tXOmpMyThP2M0lNnB+9kPTz9c4aiUUrnX06fmh9IzObnuEpV2YmToy7XishSYZBZW1I/dYQpbo4Pl4bMGHrkbRgYUk+9XYWpJp4Rhp+I5JRK1t70nwVyWEtUxG5TFQk01YvYIj7fgjQOyJ9qKoWquoiYD7QRUQaA/uq6jh1lh2/HVEmq6Qz+qjwI5JDDoGjjspa9a9PKfETxN6LsD+iLL1vt65EM6mSVhH6Dk88MbweJjTITWnW1kEHQZMmPhUn/9IjsyR0avsd6tbNOTfAjTfCM8+URB8uJamMAEIKP9Q5PpUfaMvMqPJpdaK6dPGu10rlh5NMkfj+RuPf63rVdwBwwhEbPceSmrZCnSI/X5Eq/7f0ehbTIpwU/5mP8ZEUe0ckfmX9bkW16tkb1eZKkSgwXEQmicgNbtqBqroKwP0fWt7cFFgWUXa5m9bUfR+b7kFEbhCRfBHJX7duXfrC+46Qq8aIpGDZWgrmLIIHHnDWZqRKihryRl4p+RATZiLcCJUhFlL47GmMSEI/+m3jplN81bVARKPpt7I9JukYJnMoi6PlCmrCBi7c41RNeAsT3t2xY53REE5Ds56GZXZSxAtTHkneZmePFb9JJymHNJkzx9mP3sdU8wo38CADo2VI5Zkog2krUZEzDpgCQPd23jYjVSt6vHNKzInT8pH45PP7+nuetD21k5SBXCmSk1S1I3AWcIuIJFrW6feNaYJ0b6Lqq6raWVU7N2rUqPTSxtZXhU1btSmgEeuctQwXOHtBHFptWZJSlG2oFfO0J1xFnvT0El1HGQh9X/uyjYtxQrpUz3Nk9P0qY042hWNYyqHuoYgCKTTq0SMS788yfH0+Za/jDbrgbOL12rKeNGI9Mxbtk/iEP/3kyB+z7iOVxjbkKwys3xS3+qTP/iWXOPvRz5jhOXQjr/AwDyYu70eKI5JU/S4lHZv4x3zZsKEkGGOxj0zBoDd8iYLOjl557qc8/RSJ34jEJ1t4Mks2yIkiUdWV7v+1wGdAF2CNa67C/R8Kr7kciFxJ0wxY6aY380nPOumYsSq8aQvYTl0W0oKiQv8fZqYiHac1VddbG5Ah0xbwGX3cd65pq4zTf1FN2KhG6k7d4jOrK4VzvsV1TMTxPQ1fdwwAc5bX8eTb/PCz/HbU+c753n2PKbSHkSOjxU1hcdsPdANg44cjPXlLKkr8PMwtOJSrGZyCK6cU32eZnO3Jz+w7GqoZZx/3hQth//3DK9B9fRU+G1Ep8Nn206PT/NoPP9OWz2jZZwF81Yq1JSL7iEjd0HugBzADGAbhuaVXA6HodMOAviJSU0Ra4DjVJ7jmr20icrw7W+uqiDJZIfRApaw0KuGIJERLFnLHmnt8j+lv87zhYso0ioiTXpYw32k428Pfq5+zPdGCxBSUliL+vdJw/REV77VX3HwlBdz/u3b5tlLhCQs+pzzxwdM5Ys4wAF6ZdQrHMIXhs5pF5fF9jmM+F+Ksvfi/tbfGFTPZ43DFskd5m6v5dU7iUCMpm8q2boXHH0+YpfSKJMH3Nn2qb/r6X5chKCvcPq7fCAJVJFAUm8Q6oq0lGlTv4+y7v433F+N3v8a9OdtX5kyQixHJgcCPIjIVmAB8parfAo8DZ4jIPOAM9zOqOhP4EJgFfAvcoqqhu3kT8DqOA34B8E15XEBapq3KMCRxGbnjBADvzJEjj4Q2baIzR15X5EK1F1+EevWi817nbN4T64hMyywVepPG+N1XWSTw28SVd/jwqNlYqTRgqrBlcwrPhuJoib33hlu9DXl4woKPk3c2JbN7pm103Inz1+4bXb3vM+svSrCad71LRpztiVi5EnbsiE4LBPjt+ic59qlLEhYNy1SrpjctUbmQ6Sjiud6+2X/DstnLokeCviOSQBC2R/srgipsiokHoD4GfF8fSYqmre9n+cfBywTlrkhUdaGqtndfbVT1ETd9g6p2V9VW7v+NEWUeUdWWqnqEqn4TkZ6vqm3dY7eq36YR2biGNBRJJdIjcXeNu5GX6c1nJQk7dkQHS9x7bxg1CoDgLbdSuDV6a9Ir3nJm5MT/EZf9JqXtI/E4QTVuvXHt5GeeCTu2u+Xj2Ml9qFEt/m6KUWcKBHiV/vz26hhPvvCOjkkayNBgKrahSzVuE4DU2Ttu/aV6zlXhvvscB7zn5O49cSd+7GrakuJu3aOy3F/9Me776iTyibMxV+g0oWurEX/kd+Nxk73lENi2ja17l4T/GbSpn38FMR0Zv+9eA0G+jllT5DsS9FMaKfpI/L6z4PIV3sQMUZGm/1Ya0pq1VZk0SRxepz9fRM60rlMH+vYNf+zPq2z5dhwAt/MMtYgOPvceVwDxG7u4t+j66+Gxx/yPhcqkMiIJBh0zSMyOjcFAjCLp1w9d5vz4/Nw58UxbgjKdo6PrTYXIX79Hc0Xb2P7IqxwbGOepIpFpKypfnEjLpen8HHWQ19lephHJmjXMfuwzdp1+nudQaPLCtC+XALA3uzh30sCoPI9wP5/sPCvpaTSosHQpfz7VMUtd0GKKJ0/D2iWjjrDDPAg7V2+lHiU+rJriDajoFIqZruunmH2eBz8/h19U3/iKJLrOQMBHudSp50nLFKZISkEiW3qVWUdSGubPh59/RlCu//L8cPLr9OfRnxyH7EvcFLd4bG/N03Z+/DHccotjF969mwlvTGP2fe5Wp6rw5pthM0epbuu338KAASWBHF1iRyQfv7mFFSvij0jIi7/7YWgVOMSxk7tEhtIIFAW96aHPoZFGRFoo9lt0Pud/MkUirpdbd0WPFkvj6+txlDeooKeyBx+EpUu9hyKayB07oDWzuWL9M3Gr+mlRyfqc78q47jg47L98duifqb/ACe3SsNZ2z/VHTvMOTdHVXQXsLIz+rhvmbfY9R2znwldp+IxSgn4ztILqVUzxpv/GnMb3e9w7yUy+NDBFUgbSUhqVSJMICk2bxnHwwbZWx7DqpD8A8AbXx68jDsl6rdMuepi3X9zGjpPPZF3NphzHBFrjOgy//57x/V6h+PY73crc8+Ulf6TXrXGmX36/sLlXnojv5yI+ZgjXACWmoEj8dquLRbdsJbhxc/J8CoHd8U1b4RlEwcT3LTTSSHZvi2c6ZqTtw6PD7pdmGnsiZaVBRWfM5KGHYfF5tyWUZedO5/8Pu0+Imyf465S0fztvvrCLPnzGK3N/H06L3Qo3ar3QKmcSqL73H89AN+5zHdPw+5qY/EydcUxbsYopPGtrWcmU/KCKZ2JA0G8klMWmxxRJGUi111aZZ22FGLLy9KgVuJG0ZQZNWFXmuhO2C0OG0J5pXM3bdPrleQ4gelHYlGnVOJ7x3D8mZC9P/V5PmO84NZ+a3ytankAw7nfmGZE88AAyPIXV43fdSbDt0XEPRzZIfgv8Ss7vKghNbCpL1bT1NlcB8FDw/6LSfZ/jOLPoEk5OCCoLllRnIA/R67cn4+aDkuuurj6Rh11e23VF2pEgv5nsrHH+LXBYOC12a4Qo62INJ5ZYsF79lDoofoQ7J1+WhKr3+/7itSme5y4YJDjkHb49pH9EWfFMT/c1bWU4kGYkpkjKQDrTfyuXchGuCUet8RKyXyeuIf71xmsQVYWPrimJSxUbDhtg7TZn2uikzS3DZZwTxvTgUrjdkdO645mhYnuk3//9R+Y+/mnSurvxA2clmkwYsmwFNbURiSZ+hkqmKye+8F3iOMpDe4aHxfF7Zn/62beOZCOSkBN4ZzBxqPbQWpI1cXbXBNjCfmmPSEImsZ2UmHhiJ5REmbaaOuY07XEm1apHPwBRz/XWrc7uoeB1tgeUXZ9+w9Xnl4RZCcz2bv7l5zCftaCm74jkuVdrchbflpRF+O+mk6LrK/QqZd+YYRnCFEkpCDc4Ke4JULmUhpdSRV6NLSvA7NmlMm2V6ADlYj5KWH91N/Ds5m2hzXtcX0asjdrPirDSHUUVRf/YNBh/qm5sz7A739ODEQlldKSqxk+c7HtsvByHbnIc1joxn8CK1fEritAkidrTcGywJA7+o9RZB9SADdHy+lz/Z7OP8K0j2Ygk9F34TU8Nl83L82zAFJesmIWjZYtSJBH+poRrhs44Azp0cN5Pnx5dX7Hy0Vd7R22/MHxYtF8GoPrBXiU6fFwdTwdGA0EWbI1eb+KnhL773js1O5uTWk2RlIFUZ2NVdh9JWdYFRjKr7cXhxWt+eO5j6If724KkdYurBPILXbORO8c/NsyER5EMHMiW95zRTtHGbe5pXXNQIIEiycIv5XjGMwpnNXPPL2/h3r6LUiqXqIOS5/pIkq0Yn+HOKttI9Na9fnV/woW+dSQbkYQVSShxxAgYMwaAgDo3NK9GNd9tGQD46quksmWaUKNc8OKbBHc6Db6fryKSuyZciOAo+LH50VOitTjg+clv2+VVnFee7u1EaNDbgXE6sdEV+jUpB9TyRkkoUzDUFLGtdstAys72Ujjlc7mFalzS/N12DHo3Pook3vqKf2+8ImndGnPDqrnORl0b7UuJvd8HPXRjeAXxCHo4eSJm48Uzt2V5qxsAhnJp3GOpmrby8pxjifwtHn76CTgpXH+qJGyYNEKRBBWKipjQ428IyrE6Mfzd5xUVsLvQfz3K2HOfgIj1FsXzFwO/S13AMhAICkVrN1H7luvCackU2CDuct5s3cqKHftF11fsHUH6KeAaeUFqVSukIFgzKl9sB8Z3+m9BkSet1T6rmLTxsOh8ZtqqWFSale1LlqRVvEyhSiJINBoB760Qd1RRnMoOgdWip2NWx+mCFwerhbeGBa8fZg0HEaSk7M6RJfZ/3bET3endPhZyr+gjQ7Ukatiqu5dWXOTm+e03WJF4IZqcXGJfL/VCwhgKcRpCDQaR2U5o+UWBQ8mvfwbHMYEuOJ2LaYG2AMwZ/Au7C/1P2o2x4fd9Wk5l+ZL4PqSy4b8ivHBH9HBuyLjfeZ9Vn17W/PzNnir9FHq8Tae6/W51TD5FYmxbfookv6i9J6046G3abdZWBSPT60iypVtmN09hvv38+fDcc76H0hFr94rk4foj79muwR/wz7HxN72KZe910UoypEg2DhnGfxrfEU4PbtjkG2E2xD5nnBi20X916pOMb9ffN9/uDVuZef/7KcuXaSJnbSVWJE5DEzJt/feIO/ip2cUpnye27oKLroyfd+cu2Bi9V0cocvGE8TBjRUnIj2N3jPGt49ulreObtiJoUGtH1DqbTBF75mBQPCOtHxd695jxUyQF85fj8bn4yBzPJNj9yBWefJ71tcFgSj/MYr9Ajlm4fyHMtFUKQj+StMKhpOpLyQDhNRcJeLbjW/x52yO+x9IZkXy7ql3C4+uuvpP5b/8MOCOCBwfVYV14C5rETG/TlwdnXQvAgawGDgorkmsYzG5KzAMTT/4LY5ccArSNW1/oe72Jl4nxPYcZMPZsBoz1P1YqZs7E2eyzdKRq2gqPSNzOe2hXxlQfsdi6a3/8Tty8E0ZvZ1zDcwDvCvtPn19JnwuTf5951YIpKZJgUCjenfmGMNZkGVBJ6be8vng/T54BN27k0LbRscsCRUGvaSuOGbz+3tGr5auJwtdfA8eV5CsOpPS79BuR6C7/0XYmMEVSBuJF9ETVmQrorjhO1TlYbtauadNg0CCczSQd7t/mH+EX0lMkya7pgLcHRX3eFMdO7ke7WUPD72vj/DhCiiRSiQCcuiT+9OVcMKDtMEqtSIJBViwsCL9P5EgPzWYLlG2n3VI5tL+gd3SonAhWra1G4723JK2jmmiJGS4Bwbr1KN7t9QWkS+zIIhDwVySxRE4hDvEV53Kj/i+6Pl8fiY9Jrdi7ZuTiU9cx6M4To9ISRUmIpHifep602DUzmcRMW2XBb1SB8OON73LIfiU/nngjl63Pv83n0jtRdXHZ/tE3fF6rb/KMK71bs7x8+sfIO29HpSV6uNJRcKV9aAOFZbN/KwIDB4YVSUXncQaUuowWBxjOmQDs2Cnhhrca0fes6MPPyAs4s9mKizRuRAKAvxz7o2/6jhE/s2v0L6WWMZb8uXX5uf+bSfOpwr77JP/ue521m2D1FMLslwIRb2cpqELhkgTTsJMQmo0W/lysxI4HW53inerr50vJk2D4ew+hgWBKP8yTuniV7hoO9MmZGUyRlIF4CuKUV69kGYckzAfQb8ABXMDnSfP9evf79JHoRW839FcuKBzqmz+Sm5t+7kl7YpPX/p9o1JHOiKS0Uw1HLz+8TOdZQnPkoYF8W8b4S5WByF5ooFjDI5JqMWEx9rrkAp6b7PRgiwPiuwlSiNrV/Xv3R97bm4NOOypuuYJRP8U9FskqmjimwiSoQh2cSMn7EH8r2L2qFVN7vzibSZURxdsmB4LC12+t8eZNYZRy5D5LPVsyB4qVYGF0J2fOx16fXXGRemZoBX/ymgyHDktt5N6myWZP2m/4rwfKBKZIyoAGFV27jl3vfx6V5skXx0fy8fboRi/eQ3r5M8dG7NTn1De/8GDfvLG8xM2etNKqhYW7myXPFIfYnlkydmniDY6S4RfAsKoQLCpRCMEgESMSr5kjFCwyEIgfu8tvbUMk8e5lLXZR+/STfI8BJau7S4PC9886DesOvLs6hggGSagYy4Z4puMFVDxrRv56nP/orXjJCp5qVLKZVsMaW70jkqIga1ZHf0+/Lt/fW1eRevw1OuxLT74F2xql1MFLOeJ0hjBFUgY0EKT1gevZ+7LeJWnpzOSKY/eMjQiqChML4sdtSobfjm+JHspAGi60APEj4/pRXMr8exKRDeje9WtS7JpBYv1BkRQXx1ckz5wzgvXrSy9HAUl2M+zgnYaajPaH72Dp9vrhz9eJvzksUBTMuLM9tIgwkmBQqH9otMO8Xo2dvr/lN2+fxp3r7y0pq+JxcgeK1bPtrdTxKsxAwDvF3HeacJzfa4/6E6Lry+IMLT9MkZQBVZjDUZ40T76gooEg018fnzBj/H05kof7KA0Liw9Nr4JSUKylUwwb8PbSDIdgUYAeDfMBqF+3mOLC5A9CcYIRyR3De/La/N/7HisvTtjHGb0c2mE/jjmrcTj9La7zzR8IZL5xVMTHtAWHto52pMfr3a9eE/37HLelDQWB6DVQgYC3o/iH871xsPwmHBQEfdZT7eM/aqtdLXrGV6kWpGYAUyRlIOUQKQhDrhxJu/7HJcqGBoLs3lHEymnro/LFZs3G7K50Fx3Go7SKxIiPBoLUEMfOfum/j+fGHiUhZOItlFywpAZrZsWZy1yBCBQpB+wXP+pvOF+xUpxhRfLO7E6ezlkgIJ60n1ccyvzhCz3lI02OIT5ZeEx0fcUaHkGG8THR+c3a6s9rnnzHnbmfJw28ZutgirO7MoUpkjJQmqCNU2dGN6i/zfMJYBcI0q/teJq2L+mVFxYmDnFdWuIpoawpEptZnjFeuDafnUUlvdMRmxJvKQvw2arjaXX6IUnz5YrQcxcoVopSGGGN+C7I3InbMipDodb0TAoJ+oTJGb70SIoC3qayVo1ohVA/zzvdOVAcJK9W9Gyz3T7XW1zs7RT4jdL/PawFsV3M/djEgs3RMdPKOv27rJgiKQOlCSO/aXv0Q1RcpPRrOcaT793F0RFid24Penr12VAk2aJITZFkijuH92D0lk6lLhfp48p1iBcP7vOYqiJ5bcYJXPrv48OfV5d9hm60GDEdwMDSFR5F0nH/JTRs1YBYYhVJ29oLvWHpi5XftY1uA4p8QsL4jUj8WFXcyGPyvvn4X2lRZ21U2sqNicMTZRpTJGXgm/e8JoN4ymXIwlOi0vZev5S8ajE/HB8NsWVDcVQANyjFzDAf4uVLFg+rrERuNWsYkYz6aCOLdzt+keJiUlIksTRunDxPKmzaEd3If8tZnmCik9cf6utvqpEXne+gpnk0zotu0D8ZtR977R3dqVqxxtvJmrmkTsrKfvGW+lGfi6U6hzSNlmXAl9ELGbONKZIU+fCxErv0psVbOaF6dGTbjZu9t3J3gfcHMmF+A89W336msq2bAqzR6BATftFyU406nM3d0QyjNJx+cQNWFzsRmLdsz6NgZ+4ezhoBb9gQP+e634yxvfeJbvk/mtuOouLotNVLCj2O77s/7Oyp6/VJx6S8VcHq9dGKqKjIGx8M4Hy+SK3CDFDpFYmI9BSRuSIyX0TuTV6ibFxyX8vw+5N61qXePtFGyMBa7yhl9XKvoXLHlmKP/yB232iAxQu9PaAVi72LyPziFBXu9JatTNugGHsOtww+loufKd/ecyQHH+ENdeKnSPxGJNUO8PowQiF7QqzJa+L7G73syMlRn09uMIvdRV5lcKRPvLzLTlwc9bmgqBq7Y3w41asFytdPqaqV9gXkAQuAw4C9gKlA60RlOnXqpGWhJJhW5l+9T1nnSft969WetPNO2eRJ63POLk/akYfv9qTddXuhJ+3DD7N7XVXhtS+bcy6DvbL3atnI+/0+eOUCT9rtfVemVN9xjPOkNdtva0pl999npyet2wEzPWnVJFCqazyKkjoWLy5T8+e2geTHbYuTNdYV+QWcAHwX8XkAMCBRmbIqkuLho3L+0OfqdTv/yrkMyV5dGVPmsgezJOpzG6aH3xeMmxx17EW5Ofx+9nFXaycm+ta5P2t9049jnP6HvuHPwWf+7ZuvAeujPp/Ij1m7dy2ZF/V5PzZGfe7CLzn/fkvzmvfhZP2QC8OfP+GCqOO7f/hZD+e38OdDWKyX8H5UnjyK9By+9NTdn1eSnv9QFul3He/1pLdjiiftda7zpBU99s+ozweySif3+bsnnz74oCct8tkK55s2Lfz+pX4Ty9T+qdPAVllFciHwesTnK4HnffLdAOQD+YccckiZb6Sq6uKav9PxNU/RSfW76+KDjlOdMUM3UF8n1zxeF517qwYQ1dNP1y3U1Xw66gQ66yoO1MADA3V+rzt0eLNrdeLhfXUlB2kA0cUcovk1T9TfTrhKd1NdFXT1TQN10hn36IxjrtCCarVVFy7UVQP+rRNPu1und7tFC/94m+q8eTqPljqK3+uc8+7UYPMWqq+8ogtooZN/d4lO4hhdRlMN3He//kIX/aVBT51Bay0iT/W553QqR+uEU/6iIzlNN7OvFn/4ic7hd7q641mq06apDh2qMzlKl9JMdfduLXjrP/ol5zgy/vyzrmh6rM48so8G/3qnBqdO0xndbtbl/Qeqzp6tBX0u1el7d9FN1NNgr966rXErnd/mfF1HQw0uW64zaK3zaKn6xReqw4bpJI7RmSf0U123Tgvfek+ndPuTrmjbQwO/TNC17K/T9j1Ji2+4SXW5U3YabVW7dVMdNEhH0F3Xde2jumOHBu64U2c3OU3X00CLB9yvW874g67p0EP11VdVi4p0Ys2TdB0NVf/9bw28OVjHc6xupY7qvHm64dyrdD6Hqb79tur69broyJ66sV5zVVXdfU1//YUuWvzW26pFRbqu/Wm69qq/qu7erTpvni6kuQZPPEl1zhxdUO1wXXPrw6pbt+quD4fp0ivv0+JHn1CdMkWXX/M3LZo6UzUQ0DX1j9DN7wxTVdXAf4bqhsZtVLdtU122TDeynwYn5qsuXaqbajfWDdRXVdWiZ1/UubRSnThR9Z//1HEcp8HnX1B9+mldzCG69u5/qi5erMV/+ouuOLaXan6+6hVX6JrL/6K7vxmpumuXruJAXSWNVZcs0cAnn+nIvc7S3SN/UF22TLf94xldRlPVZctUVXUhzXVG0x7Og79hg0664O+69r6nHZn/8ahOpoMG//uV6htv6HTa6KZuvVTPOUe3s7dzL6+8UhV0Om1097h81Tvv1CUc7FzfE0/qttN762Q6aCE1NPDXu3TtKX00+MmnqjfdpEs4WLd1OU118mQNHne8rmt5nHPdQ4boChpr4LMvVIuKdE3rU3XjVX92ZPrdkTqbI1QLClQDAV1HQy3+8BNVVd10eGfNp6MW/vsl53pWrNBpzXrq+Mv/rVuef1t1wwbVuXN1yl7H6pSz7tXVf3pEtaBAgx9+pP/jJJ3Za4CuvfAm1W3bNPD2uzqRTjqr9wDd8vAzqqpa/ODDOpFOOve8v+rOZ19TVdXgpZfpZDrolBNv0vWdeqj+8osWntJdJ9NBp/f6mxa+78gWHDlK87vcpLOan6Ubfv8H57u+5c86mQ6af8a9uvahF51z3Ha7Tj6wp07peqsW3n2/k+/t/+j0lr10wcX36q7vf1ZV1W3X3Kpf01MXHnSCFr/+lqqq7nrkKf2c83XNB6PL3PYlUiTiHK+ciMhFwJmqer37+Uqgi6reFq9M586dNT8/v7xENAzDqBKIyCRV9c4UoPI725cDkVEMmwHe+OmGYRhG1qjsimQi0EpEWojIXkBfYFiOZTIMw9ijqNTLj1W1WERuBb7DmcH1pqrOzLFYhmEYexSVWpEAqOrXwNe5lsMwDGNPpbKbtgzDMIwcY4rEMAzDSAtTJIZhGEZamCIxDMMw0qJSL0gsCyKyDlhSxuL7A2XY7brKYvcjGrsf0dj9iKay349DVbWR34E9TpGkg4jkx1vZuSdi9yMaux/R2P2IpirfDzNtGYZhGGlhisQwDMNIC1MkpePVXAtQwbD7EY3dj2jsfkRTZe+H+UgMwzCMtLARiWEYhpEWpkgMwzCMtDBFkiIi0lNE5orIfBG5N9fyZAsRWSwi00Vkiojku2kNRGSEiMxz/9ePyD/AvSdzReTMiPRObj3zReRZEZFcXE9ZEJE3RWStiMyISMvYPRCRmiLygZs+XkSal+sFlpI492OgiKxwn5MpInJ2xLEqez9E5GARGS0is0Vkpoj82U3fY58PoHJvtVteL5wQ9QuAw4C9gKlA61zLlaVrXQzsH5P2JHCv+/5e4An3fWv3XtQEWrj3KM89NgE4ARDgG+CsXF9bKe5BV6AjMCMb9wC4GXjZfd8X+CDX11yG+zEQuNMnb5W+H0BjoKP7vi7wm3vNe+zzoao2IkmRLsB8VV2oqruBoUCvHMtUnvQChrjvhwC9I9KHqmqhqi4C5gNdRKQxsK+qjlPn1/B2RJkKj6qOBTbGJGfyHkTW9THQvSKP2OLcj3hU6fuhqqtUdbL7fhswG2jKHvx8gJm2UqUpsCzi83I3rSqiwHARmSQiN7hpB6rqKnB+SMABbnq8+9LUfR+bXpnJ5D0Il1HVYmAL0DBrkmePW0Vkmmv6Cply9pj74ZqcjgHGs4c/H6ZIUsOvN1BV502fpKodgbOAW0Ska4K88e7LnnS/ynIPqsL9eQloCXQAVgFPuel7xP0QkTrAJ8Dtqro1UVaftCp3P0yRpMZy4OCIz82AlTmSJauo6kr3/1rgMxyz3hp3KI77f62bPd59We6+j02vzGTyHoTLiEh1oB6pm44qBKq6RlUDqhoEXsN5TmAPuB8iUgNHibynqp+6yXv082GKJDUmAq1EpIWI7IXjABuWY5kyjojsIyJ1Q++BHsAMnGu92s12NfCF+34Y0NedZdICaAVMcIf220TkeNe2e1VEmcpKJu9BZF0XAt+7dvJKQ6jRdLkA5zmBKn4/XNnfAGar6r8iDu3Zz0euvf2V5QWcjTNDYwHwt1zLk6VrPAxnhslUYGboOnHss6OAee7/BhFl/ubek7lEzMwCOuM0LguA53GjKFSGF/A+jrmmCKd32C+T9wCoBXyE43idAByW62suw/14B5gOTMNp+BrvCfcDOBnHzDQNmOK+zt6Tnw9VtRAphmEYRnqYacswDMNIC1MkhmEYRlqYIjEMwzDSwhSJYRiGkRamSAzDMIy0MEViGFlCRBpGRMddHREtd7uIvJhr+QwjU9j0X8MoB0RkILBdVQflWhbDyDQ2IjGMckZEThWR/7rvB4rIEBEZLs5eMH1E5El3n4pv3XAcob0rfnCDaX4Xs7LcMHKKKRLDyD0tgXNwwoe/C4xW1aOBXcA5rjJ5DrhQVTsBbwKP5EpYw4ileq4FMAyDb1S1SESm42yi9q2bPh1oDhwBtAVGuNtS5OGELDGMCoEpEsPIPYUAqhoUkSItcVwGcX6jAsxU1RNyJaBhJMJMW4ZR8ZkLNBKRE8AJYy4ibXIsk2GEMUViGBUcdbZ3vhB4QkSm4kScPTGnQhlGBDb91zAMw0gLG5EYhmEYaWGKxDAMw0gLUySGYRhGWpgiMQzDMNLCFIlhGIaRFqZIDMMwjLQwRWIYhmGkxf8DM40pZ6TU7KQAAAAASUVORK5CYII=\n", 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" ] @@ -358,23 +308,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1482.919743387506.\n" + "The root mean squared error is 527.8020875820832.\n" ] } ], "source": [ - "plot_predictions(y_test, GRU_test_day)\n", - "return_rmse(y_test, GRU_test_day)" + "plot_predictions(y_train, GRU_train_day)\n", + "return_rmse(y_train, GRU_train_day)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -388,7 +338,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1482.919743387506.\n" + "The root mean squared error is 1391.4931524914728.\n" ] } ], @@ -399,12 +349,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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uoFNmGpee0SfRYYlIktHQZzlmGWkp3D9uJKMG5PGtp5fx53e2JDokEUkySjbSLDpkpDLrukJO7d2FG+Yt4Q0VXxORKEo20myys9KZc30R/fM6MuXREt5cvzPRIYlIklCykWaV1ymDeVOLyeucwaRHFvHuhyq+JiJKNhKCXl0ixdcy01IYP2sha7fvTXRIIpJgSjYSiv7dOjJvSjE1hw4zbuZCNu/an+iQRCSBlGwkNIN6ZTP3+mJ27jvI+JkL2b6nOtEhiUiCKNlIqIb27cqsSYVs2LGfiSq+JtJuhZpszGy0mb1rZmVmNqOe9WZm9wbrl5vZiKb6mlmemb1sZu8HP3PrbLO/me0xs+9ELXsl2Nay4NEzrH2WTys+vhsPTBjJe1sqmfJoiYqvibRDoSUbM0sF7gMuBAYD15rZ4DrNLgQGBY9pwP0x9J0BLHD3QcCC4HW0u4AX6wlpnLsPCx5bj3X/JD6fO7knd18znMVrd/C1eYuprlHCEWlPwjyzKQLK3H21ux8AngLG1GkzBpjrEW8AOWbWu4m+Y4A5wfM5wGW1GzOzy4DVwMpwdkmOxcWn9+bOy0/nr+9t4+anlqn4mkg7EmayyQfWR73eECyLpU1jfXu5+2aA4GdPADPrBNwK/KiBeB4JLqHdZg0UXzGzaWZWamal27Zta2r/5ChcPaoft10ymBff+pAZv13B4cOe6JBEpAWEmWzq+4Ve9zdLQ21i6VvXj4C73H1PPevGuftQ4JzgMaG+Dbj7Q+5e6O6FPXr0aOLt5GhNOXsgN39hEL9evIHbf/827ko4Im1dmHd93gD0i3rdF9gUY5uMRvpuMbPe7r45uORW+/1LMXClmf0MyAEOm1mVu//S3TcCuHulmT1B5DLd3GPdQTl6N50fKb4267U1dMlK45YvnpzokEQkRGGe2ZQAg8xsoJllAGOB+XXazAcmBqPSzgR2BZfGGus7H5gUPJ8EPAfg7ue4e4G7FwB3A3e4+y/NLM3MugOYWTpwCfBWOLsssTIz/u3iU7mmsB/3/rmMh/+6OtEhiUiIQjuzcfcaM7sReAlIBWa7+0ozmx6sfwB4AbgIKAP2AZMb6xts+k7gGTObAqwDrmoilEzgpSDRpAJ/Ah5uvj2Vo2Vm3HH5UPYcqOEnL6yic1Ya16r4mkibZLpeXr/CwkIvLS1NdBjtwoGaw0x7rJRX39vGPWOH8yUVXxNptcxssbsX1l2uOwhIwh0pvlaQxy0qvibSJinZSFLokJHKrEmFDO4TKb72jw9UfE2kLVGykaSRnZXOnMmR4mtT55SwTMXXRNoMJRtJKrlB8bVunTOZNFvF10TaCiUbSTq9umTx+NRistIjxdfKP1LxNZHWTslGklK/PBVfE2lLlGwkadUWX9u9X8XXRFo7JRtJakP7dmXWdaPYuDNSfG3XfhVfE2mNlGwk6RUNzOOB8R8XX9t3oCbRIYlInGJKNmZ2k5l1Ce5hNsvMlpjZF8MOTqTWeSf35J6xw1mybgdfe0zF10Ram1jPbK53993AF4EeRO5hdmdoUYnU46KhvbnzitP52/sfcdOTKr4m0prEmmxq68tcBDzi7m9Sf80ZkVBdXdiPH14ymD+s/JBbf6PiayKtRax3fV5sZn8EBgLfN7NsQH9WSkJcf/ZAKqtquOtP75Gdlca/XzqYBoqvikiSiDXZTAGGAavdfZ+Z5RGUAxBJhG+efyKVVQeZ+doasrPS+LaKr4kktViTzVnAMnffa2bjgRHAPeGFJdI4M+MHF5/KnuoafvHnMrKz0ph27gmJDktEGhDrdzb3A/vM7Azge8BaVFZZEszM+MmXh3LJ6b2544V3eGLhukSHJCINiPXMpsbd3czGAPe4+ywzm9RkL5GQpaYY/3P1MPZW1/CD362gU2YqY4blJzosEakj1jObSjP7PjABeN7MUoH08MISiV1GWgr3jx9JUUEe337mTRasUvE1kWQTa7K5BqgmMt/mQyAf+H+hRSUSp6z0VGZOKuS0Pl244fElvP7BR4kOSUSixJRsggTzONDVzC4Bqtxd39lIUsnOSufRyUUUdOvIV+eUsnTdjkSHJCKBWG9XczWwCLgKuBpYaGZXhhmYyNHI7ZTBvCmR4mvXPVLCOx/uTnRIIkLsl9F+AIxy90nuPhEoAm4LLyyRo9czKL7WIT2V8TMXqfiaSBKINdmkuPvWqNfb4+gr0uL65XVk3tQiDrszbuZCNu1U8TWRRIo1YfzBzF4ys+vM7DrgeeCF8MISOXYn9sxm7vVFkeJrsxbykYqviSRMrAMEvgs8BJwOnAE85O63hhmYSHMYkt+V2ZNHsWnnfibOUvE1kUSJ+VKYu//G3W9x92+5+7NhBiXSnEYV5PHghELe31rJ9Sq+JpIQjSYbM6s0s931PCrNrMlhPmY22szeNbMyM5tRz3ozs3uD9cvNbERTfc0sz8xeNrP3g5+5dbbZ38z2mNl3opaNNLMVwbbuNd0iuN35p5N6cO/Y4SxV8TWRhGg02bh7trt3qeeR7e5dGusb3GXgPuBCYDBwrZkNrtPsQmBQ8JhG5B5sTfWdASxw90HAguB1tLuAF+ssuz/Yfu17jW4sdmmbLhzam59deQZ/e/8jvvnkUhVfE2lBYY4oKwLK3H21ux8AngLG1GkzBpjrEW8AOWbWu4m+Y4A5wfM5wGW1GzOzy4DVwMqoZb2BLu7+D3d3IjcQPdJH2pcrR/blPy4dzEsrt/C9Xy9X8TWRFhJmsskH1ke93hAsi6VNY317uftmgOBnTwAz6wTcCvyonvfY0EQcBNuYZmalZla6bdu2RndOWq/rPjuQb19wEr9dupH/+L+VRP4GEZEwxXrX56NR3/cidf9XN9Qmlr51/Qi4y9331PlKJuZtuftDREbdUVhYqN9AbdiNnz+RyuoaHvrrarKz0vjuP5+S6JBE2rQwk80GoF/U677AphjbZDTSd4uZ9Xb3zcElstrJpsXAlWb2MyAHOGxmVcBvgv6NxSHtjJnx/QtPobKqhvv+8gHZWelM/ycVXxMJS5iX0UqAQWY20MwygLHA/Dpt5gMTg1FpZwK7gktjjfWdD9TW0pkEPAfg7ue4e4G7FwB3A3e4+y+D7VWa2ZnBKLSJtX2kfTMzfnzZEC49ow93vvgO895Ym+iQRNqs0M5s3L3GzG4EXgJSgdnuvtLMpgfrHyByF4KLgDJgHzC5sb7Bpu8EnjGzKcA6IjcHbcoNwKNAByIj1eqOVpN2KlJ87Qz2Vddw23NvkZ2VpuJrIiEwfTlav8LCQi8tLU10GNJCqg4eYvIjJSwqr+CB8SO5YHCvRIck0iqZ2WJ3L6y7XDfTFCFSfO3hSYUMye/KN55Ywt/LVHxNpDkp2YgEOmemMWfyKAZ268RX55ayRMXXRJqNko1IlJyOGTw2pYge2ZlcN3sRqzar+JpIc1CyEamjZ5cs5k0pplNmGhNmLWKNiq+JHDMlG5F69MvryGNTinF3xs9cyEYVXxM5Jko2Ig04sWdn5lxfxO6qg0yYuZBtlSq+JnK0lGxEGjEkvyuPXDeKzbuqmDh7Ebv2qfiayNFQshFpQmFBHg9OGMkHW/cw+dFF7K1W8TWReCnZiMTg3JN6cO+1w1i2fidfe2wxVQdVfE0kHko2IjEaPSRSfO21MhVfE4mXko1IHK4c2Zcffek0/vi2iq+JxCPMEgMibdKkzxRQWXWQn//xPTplpnH7mNOoU0NJROpQshE5Ct/43IlUVtXwYFB87XujVXxNpDFKNiJHwcyYceEpVFbX8KtXIsXXbjhPxddEGqJkI3KUzIz/HDOEvdU1/Ncf3qFzVhoTzhyQ6LBEkpKSjcgxSE0xfn7VGeytruGHz71F58xUvjy8b9MdRdoZjUYTOUbpqSn88isjOHNgN77zv8v548oPEx2SSNJRshFpBrXF14bmd+XGJ5aq+JpIHUo2Is2kc2Yaj04exfE9IsXXFq9V8TWRWko2Is0op2MGc6cU0TM7k8mPLOLtTSq+JgJKNiLNrmd2FvOmRoqvTZy9kNXb9iQ6JJGEU7IRCUHf3I7Mm1qMOyq+JoKSjUhoTujRmblTiqisrmG8iq9JO6dkIxKi0/p05dHJo/hwVxUTZi1U8TVpt5RsREI2ckAeD00cyepte7lOxdeknVKyEWkB5wzqwb3XDmf5hl1Me6xUxdek3VGyEWkho4ccx8+uOJ2/l23nX55cykEVX5N2JNRkY2ajzexdMyszsxn1rDczuzdYv9zMRjTV18zyzOxlM3s/+JkbLC8ys2XB400z+3JUn1eCbdWu7xnmfos05IqRfbl9zGm8rOJr0s6ElmzMLBW4D7gQGAxca2aD6zS7EBgUPKYB98fQdwawwN0HAQuC1wBvAYXuPgwYDTxoZtE3Gh3n7sOCx9Zm3VmROEw8q4Dv/vPJPLt0Iz+c/xbuSjjS9oV51+cioMzdVwOY2VPAGODtqDZjgLke+d/2hpnlmFlvoKCRvmOA84L+c4BXgFvdfV/UdrMA/Q+WpPX1805gd9VBHnx1NdlZ6dyq4mvSxoV5GS0fWB/1ekOwLJY2jfXt5e6bAYKfRy6JmVmxma0EVgDT3T162M8jwSW026yBGr5mNs3MSs2sdNu2bbHup0jczIwZo09hXHF/7n/lA371SlmiQxIJVZjJpr5f6HXPNhpqE0vfTzdwX+jupwGjgO+bWVawapy7DwXOCR4TGuj/kLsXunthjx49mno7kWNSW3xtzLA+/OwP7/LYP8oTHZJIaMJMNhuAflGv+wKbYmzTWN8twaU2gp+f+v7F3VcBe4EhweuNwc9K4Akil/hEEi4lKL72hVN7cdtzK3l26YZEhyQSijCTTQkwyMwGmlkGMBaYX6fNfGBiMCrtTGBXcGmssb7zgUnB80nAcwBB27Tg+QDgZKDczNLMrHuwPB24hMhgApGkECm+NpzPnBApvvaSiq9JGxRasgm+L7kReAlYBTzj7ivNbLqZTQ+avQCsBsqAh4GvN9Y36HMncIGZvQ9cELwGOBt408yWAc8CX3f3j4BM4CUzWw4sAzYG7yWSNLLSU3l4YqT42r88sZTX3lfxNWlbTMMu61dYWOilpaWJDkPamZ37DjD2oTdYu30f86YWM3JAbqJDEomLmS1298K6y3UHAZEkUlt8rVcXFV+TtkXJRiTJ1BZf6xwUX/tAxdekDVCyEUlCfXM78lhU8bUNO/Y13UkkiSnZiCSp2uJre4Lia1srqxIdkshRU7IRSWK1xde27K5m4qxF7Nx3INEhiRwVJRuRJDdyQB4PTyyMFF97pIQ9Kr4mrZCSjUgrcPag7vziK8NZsXEXX/rFa9z+f2/zh7c289Ge6kSHJhITzbNpgObZSDJ6aeWHzH5tDcvW76S6JlJ87fjunRhVkEdhQS5FA/Pon9eRBu41KxK6hubZKNk0QMlGktmBmsOs2LiLkvIKSssrKCnfwa79BwHomZ3JqII8RhXkMmpgHqcc14XUFCUfaRkNJZsw69mISEgy0lIYOSA3coeBfzqBw4edsm17WLSmIkhAO3h+xWYAsjPTGDEgN5J8CvI4o18OWempCd4DaW90ZtMAndlIa7dx535KguRTUl7Be1sik0MzUlMY2rfrkbOfwgF5dO2YnuBopa3QZbQ4KdlIW7Nz3wFKy3ccST4rNu7i4CHHDE7ulU1hcOYzqiCPPjkdEh2utFJKNnFSspG2bv+BQyxbv5PS8goWlVewZO0O9h44BEB+TgeKBgaDDgryOLFnZw06kJjoOxsR+YQOGamcdUI3zjqhGwA1hw7zzoeVLFpTQenaCv72/kc8u3QjALkd0xk5II+igbkUFuQxpE9XMtI0c0JipzObBujMRto7d2ft9n0sKq+gZE0FpWt3sOajvQBkpacwvF/ukRFvw/vn0jlTf7uKLqPFTclG5NO2VlaxuHxHJAGVV/D2pt0cdkhNMQb37vLxoIOCPHpkZyY6XEkAJZs4KdmING1PdQ1L1n486GDpuo8nmw7s3unIcOtRBXkM6KbJpu2Bkk2clGxE4neg5jBvbdoVNeT648mmPbIzKQrudDCqII9Te2uyaVukZBMnJRuRYxc92bT2Tgcbd+4HoHMw2bQouOw2TJNN2wQlmzgp2YiEY+PO/UHiqaBkzQ7e3VIJQHqqMTS/K6MG5lFUkMfIAbnkdMxIcLQSLyWbOCnZiLSMnfsOsHjtjiOj3monm0JksumogZps2poo2cRJyUYkMaoORk823cGStTuO1PDJz+lwZLj1qII8TuzRmRR975NUNKlTRFqFrPRUzjy+G2ce/8nJprUj3l4r287vlm0CIKdjOoUDgjOfgZpsmsx0ZtMAndmIJKfayaa1yaek/JOTTYf1yzly2W3EAE02bWk6sxGRNsHMKOjeiYLunbiqsB8A2yqrj4x2Kymv4L6/lHHYIcVgcJ/IZNPIsGtNNk0Undk0QGc2Iq3Xnuoalq7bEcz32cHS9TuoOvjxZNPCAblHRr1psmnz0gCBOCnZiLQdtZNNS8srWLRmB6VrK9i57+PJptF3OtBk02OTkGRjZqOBe4BUYKa731lnvQXrLwL2Ade5+5LG+ppZHvA0UACUA1e7+w4zKwIeqt008B/u/mzQZyTwKNABeAG4yZvYcSUbkbbr8GHng217WBRUNV20puITk02H98+hKBh0oMmm8WnxZGNmqcB7wAXABqAEuNbd345qcxHwL0SSTTFwj7sXN9bXzH4GVLj7nWY2A8h191vNrCNwwN1rzKw38CbQJ3i9CLgJeINIsrnX3V9sLH4lG5H2ZdPO/R8POqhvsmlw5lNYoMmmjUnEAIEioMzdVwcBPAWMAd6OajMGmBucZbxhZjlBoihopO8Y4Lyg/xzgFeBWd98Xtd0swIO+vYEu7v6P4PVc4DKg0WQjIu1Ln5wOjBmWz5hh+QDs2neQ0rUfDzqY/fc1PPjX1QCc1KvzkeQzamAe+Zps2qQwk00+sD7q9QYiZy9Ntclvom8vd98M4O6bzaxnbSMzKwZmAwOACcFZTX7Qv+57fIqZTQOmAfTv3z+GXRSRtqprx3TOP7UX55/aC4hMNn1z/c4jw62fW7aJxxeuAyKTTWtvMFo0UJNN6xNmsqnvk657za6hNrH0/XQD94XAaWZ2KjDHzF6MZ1vu/hDB9z6FhYUaOSEiR2Slp1J8fDeKg8mmhw47qzbvPjLk+vUPtvNcncmmhcHZz9B8TTYNM9lsAPpFve4LbIqxTUYjfbeYWe/grKY3sLXuG7v7KjPbCwwJ3qNvE3GIiMQlNcUYkt+VIfldue6zA3F31lXsC+5wHbn09qdVkV9PmWmRyaZFAyNzfUb0zyE7Kz3Be9Cywkw2JcAgMxsIbATGAl+p02Y+cGPwnUwxsCtIItsa6TsfmATcGfx8DiBouz64dDYAOBkod/ePzKzSzM4EFgITgV+Ettci0i6ZGQO6dWJAt09ONl289uPh1r965QMOHS47Mtm0cEBekIBy6ZmdleA9CFdoySb4pX8j8BKR4cuz3X2lmU0P1j9AZGTYRUAZkaHPkxvrG2z6TuAZM5sCrAOuCpafDcwws4PAYeDr7v5RsO4GPh76/CIaHCAiLaBHdiajh/Rm9JDeQNRk0/LIhNOnStbx6OvlABR06/iJQQcFbWyyqSZ1NkBDn0UkbAdqDrNy064jgw5KyyvYEUw27d657mTTbNJSk/97H91BIE5KNiLS0monm9YOty4pr2DDjshk004ZqYwY8HHyGdYvhw4ZyTfZVMkmTko2IpIMNu/a/4lBB+9uqcQ9Mtl0SH7XIzcYLRyQS26nxE82VbKJk5KNiCSjXfsOsnhdMOigvILlG3Zx4FDkJqODenY+coPRwoJc+uZ2bPH4lGzipGQjIq1B7WTT0rWRe7wtWbuDyqCyaZ+uWYwKhlsXFeQxqGf4k02VbOKkZCMirdGhw847H+6OlFdYGxn1trWyGoCuHdKPlFcYVZDL0PycZp9sqmQTJyUbEWkLaieb1g63LimvYHVQ2TQzLYUz+n18h+vmmGyqZBMnJRsRaas+2vPJyqYrN+3m0GEnxeDU3l2YN6X4qAcbqCy0iIgAkTk80ZNN91bXsHTdThaVV/DO5t3kdGz+W+ko2YiItHOdMtM4e1B3zh7UPbT3SP7pqCIi0uop2YiISOiUbEREJHRKNiIiEjolGxERCZ2SjYiIhE7JRkREQqdkIyIiodPtahpgZtuAtUfZvTvwUZOtWp7iio/iio/iik9bjWuAu/eou1DJJgRmVlrfvYESTXHFR3HFR3HFp73FpctoIiISOiUbEREJnZJNOB5KdAANUFzxUVzxUVzxaVdx6TsbEREJnc5sREQkdEo2IiISOiWbOJjZaDN718zKzGxGPevNzO4N1i83sxGx9g05rnFBPMvN7HUzOyNqXbmZrTCzZWbWrHWwY4jrPDPbFbz3MjP7Yax9Q47ru1ExvWVmh8wsL1gX5uc128y2mtlbDaxP1PHVVFyJOr6aiitRx1dTcSXq+OpnZn8xs1VmttLMbqqnTXjHmLvrEcMDSAU+AI4HMoA3gcF12lwEvAgYcCawMNa+Icf1GSA3eH5hbVzB63Kge4I+r/OA3x9N3zDjqtP+UuDPYX9ewbbPBUYAbzWwvsWPrxjjavHjK8a4Wvz4iiWuBB5fvYERwfNs4L2W/B2mM5vYFQFl7r7a3Q8ATwFj6rQZA8z1iDeAHDPrHWPf0OJy99fdfUfw8g2gbzO99zHFFVLf5t72tcCTzfTejXL3vwIVjTRJxPHVZFwJOr5i+bwaktDPq46WPL42u/uS4HklsArIr9MstGNMySZ2+cD6qNcb+PQ/VENtYukbZlzRphD5y6WWA380s8VmNq2ZYoonrrPM7E0ze9HMTouzb5hxYWYdgdHAb6IWh/V5xSIRx1e8Wur4ilVLH18xS+TxZWYFwHBgYZ1VoR1jaXFH2X5ZPcvqjhtvqE0sfY9WzNs2s88R+WVwdtTiz7r7JjPrCbxsZu8Ef5m1RFxLiNxHaY+ZXQT8DhgUY98w46p1KfB3d4/+KzWszysWiTi+YtbCx1csEnF8xSMhx5eZdSaS4G529911V9fTpVmOMZ3ZxG4D0C/qdV9gU4xtYukbZlyY2enATGCMu2+vXe7um4KfW4FniZwut0hc7r7b3fcEz18A0s2seyx9w4wryljqXOII8fOKRSKOr5gk4PhqUoKOr3i0+PFlZulEEs3j7v7bepqEd4yF8UVUW3wQOQtcDQzk4y/ITqvT5mI++eXaolj7hhxXf6AM+Eyd5Z2A7KjnrwOjWzCu4/h4YnERsC747BL6eQXtuhK57t6pJT6vqPcooOEvvFv8+IoxrhY/vmKMq8WPr1jiStTxFez7XODuRtqEdozpMlqM3L3GzG4EXiIyMmO2u680s+nB+geAF4iM5igD9gGTG+vbgnH9EOgG/MrMAGo8clfXXsCzwbI04Al3/0MLxnUlcIOZ1QD7gbEeObIT/XkBfBn4o7vvjeoe2ucFYGZPEhlB1d3MNgD/DqRHxdXix1eMcbX48RVjXC1+fMUYFyTg+AI+C0wAVpjZsmDZvxL5YyH0Y0y3qxERkdDpOxsREQmdko2IiIROyUZEREKnZCMiIqFTshERkdAp2Yi0McHdjn+f6DhEoinZiIhI6JRsRBLEzMab2aKgdsmDZpZqZnvM7L/NbImZLTCzHkHbYWb2RlBj5Fkzyw2Wn2hmfwpuNrnEzE4INt/ZzH5tZu+Y2eMWzBQUSRQlG5EEMLNTgWuI3HhxGHAIGEfkNiVL3H0E8CqR2ecQuc3Ire5+OrAiavnjwH3ufgaRujKbg+XDgZuBwURqkHw25F0SaZRuVyOSGOcDI4GS4KSjA7AVOAw8HbSZB/zWzLoCOe7+arB8DvC/ZpYN5Lv7swDuXgUQbG+Ru28IXi8jcq+u10LfK5EGKNmIJIYBc9z9+59YaHZbnXaN3U+qsUtj1VHPD6H/65JguowmkhgLgCuDuiWYWZ6ZDSDyf/LKoM1XgNfcfReww8zOCZZPAF71SC2SDWZ2WbCNzKAgl0jS0V87Igng7m+b2b8RqcqYAhwEvgHsBU4zs8XALiLf6wBMAh4IkslqgrvxEkk8D5rZ7cE2rmrB3RCJme76LJJEzGyPu3dOdBwizU2X0UREJHQ6sxERkdDpzEZEREKnZCMiIqFTshERkdAp2YiISOiUbEREJHT/Hw+hh2wIHrmkAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -421,43 +371,7 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# def day_to_year(day_preds):\n", - "# day_preds = day_preds[183:]\n", - "# year_preds = []\n", - "# for i in range(365, len(day_preds), 365): \n", - "# salmon_count = np.sum(day_preds[i - 365:i])\n", - "# year_preds.append(salmon_count)\n", - "# year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", - "# return year_preds" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# test = day_to_year(GRU_test_day)\n", - "# test" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# test_183 = day_to_year(GRU_test_day)\n", - "# test_183" - ] - }, - { - "cell_type": "code", - "execution_count": 45, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -487,19 +401,19 @@ " \n", " \n", " 0\n", - " 458768\n", + " 471249\n", " \n", " \n", " 1\n", - " 345403\n", + " 337436\n", " \n", " \n", " 2\n", - " 380981\n", + " 377191\n", " \n", " \n", " 3\n", - " 504885\n", + " 514875\n", " \n", " \n", "\n", @@ -507,13 +421,13 @@ ], "text/plain": [ " Count\n", - "0 458768\n", - "1 345403\n", - "2 380981\n", - "3 504885" + "0 471249\n", + "1 337436\n", + "2 377191\n", + "3 514875" ] }, - "execution_count": 45, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -533,7 +447,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 22101.086602246505.\n" + "The root mean squared error is 13902.218150352843.\n" ] } ], @@ -581,7 +495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/daily_simple_lstm.ipynb b/daily_simple_lstm.ipynb index 1be2450..73962ff 100644 --- a/daily_simple_lstm.ipynb +++ b/daily_simple_lstm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 45, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,37 +13,30 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error\n", - "plt.style.use('fivethirtyeight')" + "# plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -55,27 +48,13 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " date king\n", - "0 1938-05-01 201\n", - "1 1938-05-02 227\n", - "2 1938-05-03 78\n", - "3 1938-05-04 37\n", - "4 1938-05-05 29\n", - "... ... ...\n", - "24729 2021-04-28 2433\n", - "24730 2021-04-29 4782\n", - "24731 2021-04-30 4641\n", - "24732 2021-05-01 2087\n", - "24733 2021-05-02 2517\n", - "\n", - "[24734 rows x 2 columns]\n", " date king\n", "0 1939-01-01 0\n", "1 1939-01-02 0\n", @@ -94,16 +73,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -121,20 +100,14 @@ " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - " print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - " print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", " print(king_test_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + "\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -142,8 +115,6 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", " for i in range(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", " y_train.append(king_train_norm[i])\n", @@ -154,7 +125,7 @@ " # make y_test_not_norm\n", " for i in range(180, 1824):\n", " y_test_not_norm.append(king_test['king'][i])\n", - " for i in range(180,22545): # 30\n", + " for i in range(180,22545): \n", " y_train_not_norm.append(king_training['king'][i])\n", " \n", " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" @@ -162,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -170,15 +141,7 @@ "output_type": "stream", "text": [ "(1824, 2)\n", - "max val king_train:\n", - "67521\n", - "max val king_test:\n", - "32446\n", - "(1824, 1)\n", - "(1644, 1)\n", - "(1644, 1)\n", - "(22365, 1)\n", - "(22365, 1)\n" + "(1824, 1)\n" ] } ], @@ -191,18 +154,14 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -247,7 +206,7 @@ "(22365, 180, 1)" ] }, - "execution_count": 51, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -258,21 +217,40 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ + "# def create_LSTM_model(x_train, y_train, x_test, y_test): \n", + "# '''\n", + "# Create LSTM model trained on X_train and Y_train\n", + "# and make predictions on the X_test data\n", + "# '''\n", + "# LSTM_model = Sequential()\n", + "# LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", + "# LSTM_model.add(LSTM(5, return_sequences=True))\n", + "# LSTM_model.add(LSTM(5, return_sequences=True))\n", + "# LSTM_model.add(LSTM(1))\n", + "# #LSTM_model.add(Dense(1))\n", + "# LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", + "# history_LSTM = LSTM_model.fit(x_train, y_train, epochs=5, batch_size=150, verbose=2)\n", + " \n", + "# train_preds = LSTM_model.predict(x_train)\n", + "# test_preds = LSTM_model.predict(x_test)\n", + "# train_preds = scaler.inverse_transform(train_preds)\n", + "# test_preds = scaler.inverse_transform(test_preds)\n", + "# y_train = scaler.inverse_transform(y_train)\n", + "# y_test = scaler.inverse_transform(y_test)\n", + " \n", + "# return LSTM_model, test_preds, train_preds, y_test, y_train, history_LSTM\n", "def create_LSTM_model(x_train, y_train, x_test, y_test): \n", " '''\n", " Create LSTM model trained on X_train and Y_train\n", " and make predictions on the X_test data\n", " '''\n", " LSTM_model = Sequential()\n", - " LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", - " LSTM_model.add(LSTM(5, return_sequences=True))\n", - " LSTM_model.add(LSTM(5, return_sequences=True))\n", - " LSTM_model.add(LSTM(1))\n", - " #LSTM_model.add(Dense(1))\n", + " LSTM_model.add(LSTM(5, input_shape=(x_train.shape[1],1)))\n", + " LSTM_model.add(Dense(1))\n", " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=5, batch_size=150, verbose=2)\n", " \n", @@ -288,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -296,15 +274,15 @@ "output_type": "stream", "text": [ "Epoch 1/5\n", - "150/150 - 20s - loss: 0.0021\n", + "150/150 - 6s - loss: 9.8103e-04\n", "Epoch 2/5\n", - "150/150 - 15s - loss: 0.0014\n", + "150/150 - 5s - loss: 6.4020e-04\n", "Epoch 3/5\n", - "150/150 - 16s - loss: 0.0010\n", + "150/150 - 5s - loss: 5.2448e-04\n", "Epoch 4/5\n", - "150/150 - 16s - loss: 8.2973e-04\n", + "150/150 - 5s - loss: 4.7249e-04\n", "Epoch 5/5\n", - "150/150 - 16s - loss: 7.3109e-04\n" + "150/150 - 5s - loss: 4.3970e-04\n" ] } ], @@ -315,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -325,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -333,34 +311,33 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "# print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "# print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 892.0807246679778.\n" + "The root mean squared error is 664.9320006676919.\n" ] } ], @@ -371,24 +348,26 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1521.9290182538923.\n" + "The root mean squared error is 1375.1281041326058.\n" ] } ], @@ -399,17 +378,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -419,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -429,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -437,7 +418,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 70658.86738342504.\n" + "The root mean squared error is 35493.71476440035.\n" ] } ], @@ -471,7 +452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/daily_simple_rnn.ipynb b/daily_simple_rnn.ipynb index 7eaf943..79ef2d4 100644 --- a/daily_simple_rnn.ipynb +++ b/daily_simple_rnn.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,6 @@ "from tensorflow.keras.optimizers import SGD\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -27,21 +26,17 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy = salmon_data \n", " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", " inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", @@ -55,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -94,16 +89,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -132,9 +127,7 @@ " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", " print(king_test_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + " \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -142,8 +135,6 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", " for i in range(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", " y_train.append(king_train_norm[i])\n", @@ -162,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -202,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -250,8 +241,8 @@ " # create a model\n", " model = Sequential()\n", " model.add(SimpleRNN(32))\n", - " #model.add(SimpleRNN(32, return_sequences=True))\n", - " #model.add(SimpleRNN(16))\n", + "# model.add(SimpleRNN(32, return_sequences=True))\n", + "# model.add(SimpleRNN(16))\n", " model.add(Dense(1))\n", "\n", " model.compile(optimizer='adam', loss='mean_squared_error')\n", @@ -279,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -287,11 +278,11 @@ "output_type": "stream", "text": [ "Epoch 1/3\n", - "350/350 [==============================] - 5s 13ms/step - loss: 3.9202e-04\n", + "350/350 [==============================] - 8s 21ms/step - loss: 0.0030\n", "Epoch 2/3\n", - "350/350 [==============================] - 5s 13ms/step - loss: 2.8850e-04\n", + "350/350 [==============================] - 7s 20ms/step - loss: 5.5065e-04\n", "Epoch 3/3\n", - "350/350 [==============================] - 5s 13ms/step - loss: 2.8424e-04\n", + "350/350 [==============================] - 6s 18ms/step - loss: 3.9362e-04\n", "predicting\n" ] } @@ -303,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -313,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -321,22 +312,19 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "# print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "# print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -348,7 +336,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 527.0426809613447.\n", + "The root mean squared error is 570.6108536758172.\n", "(22365, 1)\n" ] } @@ -362,12 +350,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -379,7 +367,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1386.8344040416566.\n" + "The root mean squared error is 1367.212325791287.\n" ] } ], @@ -390,12 +378,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -410,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -440,19 +428,19 @@ " \n", " \n", " 0\n", - " 461289\n", + " 479454\n", " \n", " \n", " 1\n", - " 318556\n", + " 343823\n", " \n", " \n", " 2\n", - " 361129\n", + " 384598\n", " \n", " \n", " 3\n", - " 506459\n", + " 524886\n", " \n", " \n", "\n", @@ -460,13 +448,13 @@ ], "text/plain": [ " Count\n", - "0 461289\n", - "1 318556\n", - "2 361129\n", - "3 506459" + "0 479454\n", + "1 343823\n", + "2 384598\n", + "3 524886" ] }, - "execution_count": 38, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -478,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -486,7 +474,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 24270.60509134455.\n" + "The root mean squared error is 8328.45420831501.\n" ] } ], @@ -495,6 +483,20 @@ "return_rmse(y_test_year, traditional)\n", "return_rmse(y_test_year, RNN_test_year)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -513,7 +515,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/monthly_robust_gru.ipynb b/monthly_robust_gru.ipynb new file mode 100644 index 0000000..fc46cf8 --- /dev/null +++ b/monthly_robust_gru.ipynb @@ -0,0 +1,1598 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Robust GRU with Monthly Dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data\n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", + " inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " print(king_data)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(ismael_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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......
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + "# print('max val king_train:')\n", + " print(max(king_training['king']))\n", + " king_test[king_test[\"king\"] < 0] = 0\n", + "# print('max val king_test:')\n", + " print(max(king_test['king']))\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + "# print(king_train_norm)\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + " print('king_test_norm')\n", + " print(king_test_norm.shape)\n", + " print('king_train_norm')\n", + " print(king_train_norm.shape)\n", + " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", + " #print(type(king_train_norm))\n", + " #king_train_norm = king_train_norm.to_frame()\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " # Todo: Experiment with input size of input (ex. 30 days)\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "717915\n", + "294611\n", + "king_test_norm\n", + "(60, 1)\n", + "king_train_norm\n", + "(924, 1)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "def create_GRU_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create GRU model trained on X_train and y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " # The GRU architecture\n", + " regressorGRU = Sequential()\n", + " # First GRU layer \n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", + " regressorGRU.add(GRU(units=1, activation='tanh'))\n", + " regressorGRU.add(Dense(units=1))\n", + "\n", + " # Compiling the RNN\n", + " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", + " # Fitting to the training set\n", + " history = regressorGRU.fit(x_train, y_train, epochs=400, batch_size=150)\n", + " \n", + " # Predictions \n", + " GRU_train_predict = regressorGRU.predict(x_train)\n", + " GRU_test_predict = regressorGRU.predict(x_test)\n", + "\n", + " # Descale \n", + " GRU_train_predict = scaler.inverse_transform(GRU_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " GRU_test_predict = scaler.inverse_transform(GRU_test_predict)\n", + " GRU_test_predict = GRU_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return regressorGRU, GRU_train_predict, GRU_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/400\n", + "7/7 [==============================] - 5s 14ms/step - loss: 0.0130\n", + "Epoch 2/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0076\n", + "Epoch 3/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0079\n", + "Epoch 4/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0086\n", + "Epoch 5/400\n", + "7/7 [==============================] - 0s 20ms/step - loss: 0.0084\n", + "Epoch 6/400\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0098\n", + "Epoch 7/400\n", + "7/7 [==============================] - 0s 15ms/step - loss: 0.0087\n", + "Epoch 8/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0083\n", + "Epoch 9/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0074\n", + "Epoch 10/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0080\n", + "Epoch 11/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 12/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0094\n", + "Epoch 13/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0090\n", + "Epoch 14/400\n", + "7/7 [==============================] - 0s 18ms/step - loss: 0.0082\n", + "Epoch 15/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0097\n", + "Epoch 16/400\n", + "7/7 [==============================] - 0s 26ms/step - loss: 0.0101\n", + "Epoch 17/400\n", + "7/7 [==============================] - 0s 19ms/step - loss: 0.0082\n", + "Epoch 18/400\n", + "7/7 [==============================] - 0s 17ms/step - loss: 0.0079\n", + "Epoch 19/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0086\n", + "Epoch 20/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0081\n", + "Epoch 21/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0075\n", + "Epoch 22/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0098\n", + "Epoch 23/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0091\n", + "Epoch 24/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", + "Epoch 25/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", + "Epoch 26/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", + "Epoch 27/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0106\n", + "Epoch 28/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0091\n", + "Epoch 29/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", + "Epoch 30/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0091\n", + "Epoch 31/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", + "Epoch 32/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0098\n", + "Epoch 33/400\n", + "7/7 [==============================] - 0s 17ms/step - loss: 0.0083\n", + "Epoch 34/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0082\n", + "Epoch 35/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", + "Epoch 36/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0075\n", + "Epoch 37/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0109\n", + "Epoch 38/400\n", + "7/7 [==============================] - 0s 14ms/step - loss: 0.0089\n", + "Epoch 39/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 40/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0078\n", + "Epoch 41/400\n", + "7/7 [==============================] - 0s 15ms/step - loss: 0.0073\n", + "Epoch 42/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0083\n", + "Epoch 43/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0113\n", + "Epoch 44/400\n", + "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", + "Epoch 45/400\n", + "7/7 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loss: 0.0040\n", + "Epoch 296/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0035\n", + "Epoch 297/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0038\n", + "Epoch 298/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0043\n", + "Epoch 299/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0035\n", + "Epoch 300/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0042\n", + "Epoch 301/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0041\n", + "Epoch 302/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0062\n", + "Epoch 303/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0047\n", + "Epoch 304/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0048\n", + "Epoch 305/400\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7/7 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[==============================] - 0s 13ms/step - loss: 0.0021\n", + "Epoch 390/400\n", + "7/7 [==============================] - 0s 12ms/step - loss: 0.0029\n", + "Epoch 391/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0031\n", + "Epoch 392/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0029\n", + "Epoch 393/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0027\n", + "Epoch 394/400\n", + "7/7 [==============================] - 0s 10ms/step - loss: 0.0036\n", + "Epoch 395/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0027\n", + "Epoch 396/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0024\n", + "Epoch 397/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0029\n", + "Epoch 398/400\n", + "7/7 [==============================] - 0s 13ms/step - loss: 0.0030\n", + "Epoch 399/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0031\n", + "Epoch 400/400\n", + "7/7 [==============================] - 0s 11ms/step - loss: 0.0027\n" + ] + } + ], + "source": [ + "regressorGRU, GRU_train_day, GRU_test_day, history_GRU, y_train, y_test = create_GRU_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 15649.68598451201.\n" + ] + } + ], + "source": [ + "# plot training results\n", + "plot_predictions(y_train, GRU_train_day)\n", + "return_rmse(y_train, GRU_train_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 42762.87195427568.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, GRU_test_day)\n", + "return_rmse(y_test, GRU_test_day)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_GRU)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + }, + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# global var for baseline\n", + "y_test_year = month_to_year(y_test)\n", + "len(y_test)\n", + "len(y_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n", + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n", + " Count\n", + "0 488943\n", + "1 336031\n", + "2 381766\n", + "3 535809\n" + ] + } + ], + "source": [ + "y_test_year = month_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)\n", + "y_test_year = y_test_year.astype(np.int64)\n", + "print(y_test_year)\n", + "# print(GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count\n", + "0 185775\n", + "1 166868\n", + "2 188893\n", + "3 264217" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GRU_test_year = month_to_year(GRU_test_day)\n", + "GRU_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115854.5707848853.\n", + "The root mean squared error is 240566.6048031189.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "# after 200 epochs\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, GRU_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/monthly_robust_lstm.ipynb b/monthly_robust_lstm.ipynb new file mode 100644 index 0000000..bf60bc2 --- /dev/null +++ b/monthly_robust_lstm.ipynb @@ -0,0 +1,1512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Simple LSTM with Monthly Dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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......
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984 rows × 1 columns

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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + "\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "def create_LSTM_model(x_train, y_train, x_test, y_test): \n", + " '''\n", + " Create LSTM model trained on X_train and Y_train\n", + " and make predictions on the X_test data\n", + " '''\n", + " LSTM_model = Sequential()\n", + " LSTM_model.add(LSTM(5, return_sequences=True, input_shape=(x_train.shape[1],1)))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(5, return_sequences=True))\n", + " LSTM_model.add(LSTM(1))\n", + " #LSTM_model.add(Dense(1))\n", + " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=400, batch_size=150, verbose=2)\n", + " \n", + " train_preds = LSTM_model.predict(x_train)\n", + " test_preds = LSTM_model.predict(x_test)\n", + " train_preds = scaler.inverse_transform(train_preds)\n", + " test_preds = scaler.inverse_transform(test_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return LSTM_model, test_preds, train_preds, y_test, y_train, history_LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/400\n", + "7/7 - 5s - loss: 0.0108\n", + "Epoch 2/400\n", + "7/7 - 0s - loss: 0.0096\n", + "Epoch 3/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 4/400\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 5/400\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 6/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 7/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 8/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 9/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 10/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 11/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 12/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 13/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 14/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 15/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 16/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 17/400\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 18/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 19/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 20/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 21/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 22/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 23/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 24/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 25/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 26/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 27/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 28/400\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 29/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 30/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 31/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 32/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 33/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 34/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 35/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 36/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 37/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 38/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 39/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 40/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 41/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 42/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 43/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 44/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 45/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 46/400\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 47/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 48/400\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 49/400\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 50/400\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 51/400\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 52/400\n", 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+ "7/7 - 0s - loss: 0.0074\n", + "Epoch 283/400\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 284/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 285/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 286/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 287/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 288/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 289/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 290/400\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 291/400\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 292/400\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 293/400\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 294/400\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 295/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 296/400\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 297/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 298/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 299/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 300/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 301/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 302/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 303/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 304/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 305/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 306/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 307/400\n", + "7/7 - 0s - loss: 0.0082\n", + "Epoch 308/400\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 309/400\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 310/400\n", + "7/7 - 0s - loss: 0.0075\n", + "Epoch 311/400\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 312/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 313/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 314/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 315/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 316/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 317/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 318/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 319/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 320/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 321/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 322/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 323/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 324/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 325/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 326/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 327/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 328/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 329/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 330/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 331/400\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 332/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 333/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 334/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 335/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 336/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 337/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 338/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 339/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 340/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 341/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 342/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 343/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 344/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 345/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 346/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 347/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 348/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 349/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 350/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 351/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 352/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 353/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 354/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 355/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 356/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 357/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 358/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 359/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 360/400\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 361/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 362/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 363/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 364/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 365/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 366/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 367/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 368/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 369/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 370/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 371/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 372/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 373/400\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 374/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 375/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 376/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 377/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 378/400\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 379/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 380/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 381/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 382/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 383/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 384/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 385/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 386/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 387/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 388/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 389/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 390/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 391/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 392/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 393/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 394/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 395/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 396/400\n", + "7/7 - 0s - loss: 0.0067\n", + "Epoch 397/400\n", + "7/7 - 0s - loss: 0.0068\n", + "Epoch 398/400\n", + "7/7 - 0s - loss: 0.0066\n", + "Epoch 399/400\n", + "7/7 - 0s - loss: 0.0065\n", + "Epoch 400/400\n", + "7/7 - 0s - loss: 0.0067\n" + ] + } + ], + "source": [ + "# running LSTM\n", + "LSTM_model, test_preds_LSTM, train_preds_LSTM, y_test, y_train, history_LSTM = create_LSTM_model(x_train, y_train, x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 23958.18224178563.\n" + ] + } + ], + "source": [ + "plot_predictions(y_train, train_preds_LSTM)\n", + "return_rmse(y_train, train_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 56947.76536959329.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, test_preds_LSTM)\n", + "return_rmse(y_test, test_preds_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_LSTM)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + } + ], + "source": [ + "# global var for baseline\n", + "y_test_year = month_to_year(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + } + ], + "source": [ + "y_test_year = month_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "y_test_year = y_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + } + ], + "source": [ + "# Comparing RMSE to curr Forecasting methods to LSTM\n", + "LSTM_test_year = month_to_year(test_preds_LSTM)\n", + "LSTM_test_year = LSTM_test_year.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count\n", + "0 137251\n", + "1 178350\n", + "2 204074\n", + "3 166164" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LSTM_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115854.5707848853.\n", + "The root mean squared error is 281408.2592577908.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and LSTM\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, LSTM_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/monthly_robust_rnn.ipynb b/monthly_robust_rnn.ipynb new file mode 100644 index 0000000..bd9f074 --- /dev/null +++ b/monthly_robust_rnn.ipynb @@ -0,0 +1,1383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Robust RNN with Monthly dataset

" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", + "# salmon_data.head()\n", + "# salmon_copy = salmon_data # Create a copy for us to work with \n", + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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......
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "data_copy['date']\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def create_train_test(king_all):\n", + " king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", + " king_training = king_all[king_training_parse]\n", + " king_training = king_training.reset_index()\n", + " king_training = king_training.drop('index', axis=1)\n", + " \n", + " king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", + " king_test = king_all[king_test_parse]\n", + " king_test = king_test.reset_index()\n", + " king_test = king_test.drop('index', axis=1)\n", + " print(king_test.shape)\n", + " \n", + " # Normalizing Data\n", + " king_training[king_training[\"king\"] < 0] = 0 \n", + " king_test[king_test[\"king\"] < 0] = 0\n", + " king_train_pre = king_training[\"king\"].to_frame()\n", + " king_test_pre = king_test[\"king\"].to_frame()\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " king_train_norm = scaler.fit_transform(king_train_pre)\n", + " king_test_norm = scaler.fit_transform(king_test_pre)\n", + "\n", + " x_train = []\n", + " y_train = []\n", + " x_test = []\n", + " y_test = []\n", + " y_test_not_norm = []\n", + " y_train_not_norm = []\n", + " \n", + " for i in range(6,924): # 30\n", + " x_train.append(king_train_norm[i-6:i])\n", + " y_train.append(king_train_norm[i])\n", + " for i in range(6, 60):\n", + " x_test.append(king_test_norm[i-6:i])\n", + " y_test.append(king_test_norm[i])\n", + " \n", + " # make y_test_not_norm\n", + " for i in range(6, 60):\n", + " y_test_not_norm.append(king_test['king'][i])\n", + " for i in range(6,924): # 30\n", + " y_train_not_norm.append(king_training['king'][i])\n", + " \n", + " return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60, 2)\n", + "(54, 1)\n", + "(54, 1)\n", + "(918, 1)\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "x_train = np.array(x_train)\n", + "x_test = np.array(x_test)\n", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "y_test_not_norm = np.array(y_test_not_norm)\n", + "print(y_test.shape)\n", + "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", + "print(y_test_not_norm.shape)\n", + "y_train_not_norm = np.array(y_train_not_norm)\n", + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", + "print(y_train_not_norm.shape)\n", + "print(y_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The root mean squared error is {}.\".format(rmse))\n", + " \n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[5:]\n", + " print(len(month_preds))\n", + " year_preds = []\n", + " for i in range(12, len(month_preds), 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "def create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create single layer rnn model trained on x_train and y_train\n", + " and make predictions on the x_test data\n", + " '''\n", + " # create a model\n", + " model = Sequential()\n", + " model.add(SimpleRNN(32))\n", + " #model.add(SimpleRNN(32, return_sequences=True))\n", + " #model.add(SimpleRNN(32, return_sequences=True))\n", + " #model.add(SimpleRNN(1))\n", + " model.add(Dense(1))\n", + "\n", + " model.compile(optimizer='adam', loss='mean_squared_error')\n", + "\n", + " # fit the RNN model\n", + " history = model.fit(x_train, y_train, epochs=300, batch_size=64)\n", + "\n", + " print(\"predicting\")\n", + " # Finalizing predictions\n", + " RNN_train_preds = model.predict(x_train)\n", + " RNN_test_preds = model.predict(x_test)\n", + " \n", + " #Descale\n", + " RNN_train_preds = scaler.inverse_transform(RNN_train_preds)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " RNN_test_preds = scaler.inverse_transform(RNN_test_preds)\n", + " RNN_test_preds = RNN_test_preds.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + "\n", + " return model, RNN_train_preds, RNN_test_preds, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + "15/15 [==============================] - 1s 2ms/step - loss: 0.0102\n", + "Epoch 2/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "Epoch 3/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0097\n", + "Epoch 4/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "Epoch 5/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "Epoch 6/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0077\n", + "Epoch 7/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "Epoch 8/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0087\n", + "Epoch 9/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0113\n", + "Epoch 10/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0121\n", + "Epoch 11/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "Epoch 12/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "Epoch 13/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "Epoch 14/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "Epoch 15/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "Epoch 16/300\n", + "15/15 [==============================] - 0s 3ms/step - loss: 0.0068\n", + "Epoch 17/300\n", + "15/15 [==============================] - 0s 3ms/step - loss: 0.0100\n", + "Epoch 18/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0125\n", + "Epoch 19/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "Epoch 20/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0084\n", + "Epoch 21/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0107\n", + "Epoch 22/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0099\n", + "Epoch 23/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "Epoch 24/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "Epoch 25/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0075\n", + "Epoch 26/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0087\n", + "Epoch 27/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0093\n", + "Epoch 28/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0059\n", + "Epoch 29/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0068\n", + "Epoch 30/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0083\n", + "Epoch 31/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "Epoch 32/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0101\n", + "Epoch 33/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "Epoch 34/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0080\n", + "Epoch 35/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "Epoch 36/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "Epoch 37/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "Epoch 38/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0084\n", + "Epoch 39/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0091\n", + "Epoch 40/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "Epoch 41/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0074\n", + "Epoch 42/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0097\n", + "Epoch 43/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "Epoch 44/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "Epoch 45/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "Epoch 46/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "Epoch 47/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "Epoch 48/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0091\n", + "Epoch 49/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0094\n", + "Epoch 50/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "Epoch 51/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "Epoch 52/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0071\n", + "Epoch 53/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "Epoch 54/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "Epoch 55/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0075\n", + "Epoch 56/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0095\n", + "Epoch 57/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "Epoch 58/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0090\n", + "Epoch 59/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "Epoch 60/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "Epoch 61/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0053\n", + "Epoch 62/300\n", + "15/15 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2ms/step - loss: 0.0024\n", + "Epoch 258/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "Epoch 259/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 260/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "Epoch 261/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0031\n", + "Epoch 262/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0038\n", + "Epoch 263/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0032\n", + "Epoch 264/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "Epoch 265/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "Epoch 266/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "Epoch 267/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0029\n", + "Epoch 268/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "Epoch 269/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "Epoch 270/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0035\n", + "Epoch 271/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "Epoch 272/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "Epoch 273/300\n", + "15/15 [==============================] - 0s 3ms/step - loss: 0.0038\n", + "Epoch 274/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "Epoch 275/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 276/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0034\n", + "Epoch 277/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "Epoch 278/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0030\n", + "Epoch 279/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "Epoch 280/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0033\n", + "Epoch 281/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0040\n", + "Epoch 282/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0025\n", + "Epoch 283/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0033\n", + "Epoch 284/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "Epoch 285/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0027\n", + "Epoch 286/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "Epoch 287/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "Epoch 288/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "Epoch 289/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0034\n", + "Epoch 290/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "Epoch 291/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0031\n", + "Epoch 292/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "Epoch 293/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0043\n", + "Epoch 294/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0029\n", + "Epoch 295/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "Epoch 296/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0051\n", + "Epoch 297/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0036\n", + "Epoch 298/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "Epoch 299/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0025\n", + "Epoch 300/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0043\n", + "predicting\n" + ] + } + ], + "source": [ + "model, RNN_train_preds, RNN_test_preds, history_RNN, y_train, y_test = create_single_layer_rnn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 17289.742043500715.\n", + "(918, 1)\n" + ] + } + ], + "source": [ + "# plot single_layer_rnn_model\n", + "plot_predictions(y_train, RNN_train_preds)\n", + "return_rmse(y_train, RNN_train_preds)\n", + "print(RNN_train_preds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 50445.582783350335.\n" + ] + } + ], + "source": [ + "plot_predictions(y_test, RNN_test_preds)\n", + "return_rmse(y_test, RNN_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history_RNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + }, + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# global var for baseline\n", + "y_test_year = month_to_year(y_test)\n", + "len(y_test)\n", + "len(y_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n", + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n", + " Count\n", + "0 488943\n", + "1 336031\n", + "2 381766\n", + "3 535809\n" + ] + } + ], + "source": [ + "y_test_year = month_to_year(y_test)\n", + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)\n", + "y_test_year = y_test_year.astype(np.int64)\n", + "print(y_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count\n", + "0 440603\n", + "1 310447\n", + "2 271963\n", + "3 372677" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RNN_test_year = month_to_year(RNN_test_preds)\n", + "RNN_test_year" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 115854.5707848853.\n", + "The root mean squared error is 102053.96230548817.\n" + ] + } + ], + "source": [ + "# test RMSE with baseline and RNN\n", + "return_rmse(y_test_year, traditional)\n", + "return_rmse(y_test_year, RNN_test_year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/monthly_simple_gru.ipynb b/monthly_simple_gru.ipynb index 49ecdff..7a9cc4e 100644 --- a/monthly_simple_gru.ipynb +++ b/monthly_simple_gru.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Simple GRU with Monthly Dataset

" + "

Robust GRU with Monthly Dataset

" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -20,10 +20,9 @@ "import seaborn as sns\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", - "from tensorflow.keras.optimizers import SGD\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", + "from keras.optimizers import SGD\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -34,23 +33,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", + " salmon_copy = salmon_data\n", " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", " inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", + " print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -62,13 +57,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + " date king\n", + "0 1938-05-01 201\n", + "1 1938-05-02 227\n", + "2 1938-05-03 78\n", + "3 1938-05-04 37\n", + "4 1938-05-05 29\n", + "... ... ...\n", + "24729 2021-04-28 2433\n", + "24730 2021-04-29 4782\n", + "24731 2021-04-30 4641\n", + "24732 2021-05-01 2087\n", + "24733 2021-05-02 2517\n", + "\n", + "[24734 rows x 2 columns]\n", " date king\n", "0 1939-01-01 0\n", "1 1939-01-02 0\n", @@ -90,13 +99,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(chris_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -195,7 +204,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 6, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -234,33 +243,26 @@ "\n", "[984 rows x 1 columns]\n" ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "print(data_copy)\n", - "data_copy.shape\n", - "forecast_set = data_copy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " " + "data_copy.shape" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -270,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -300,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -404,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ @@ -452,17 +454,15 @@ " '''\n", " # The GRU architecture\n", " regressorGRU = Sequential()\n", - " # First GRU layer \n", + " # First GRU layer with Dropout regularisation\n", " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape= (x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(x_train.shape[1],1), activation='tanh'))\n", - " regressorGRU.add(GRU(units=1, activation='tanh'))\n", - " #regressorGRU.add(Dense(units=1))\n", + " regressorGRU.add(GRU(units=50, activation='tanh'))\n", + " regressorGRU.add(Dense(units=1))\n", "\n", " # Compiling the RNN\n", " regressorGRU.compile(optimizer = 'adam',loss = 'mean_squared_error')\n", " # Fitting to the training set\n", - " history = regressorGRU.fit(x_train, y_train, epochs=50000, batch_size=150)\n", + " history = regressorGRU.fit(x_train, y_train, epochs=400, batch_size=150)\n", " \n", " # Predictions \n", " GRU_train_predict = regressorGRU.predict(x_train)\n", @@ -480,101717 +480,831 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/50000\n", - "7/7 [==============================] - 5s 12ms/step - loss: 0.0100\n", - "Epoch 2/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0095\n", - "Epoch 3/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0094\n", - "Epoch 4/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 5/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0091\n", - "Epoch 6/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 7/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 8/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 9/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0091\n", - "Epoch 10/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0091\n", - "Epoch 11/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0090\n", - "Epoch 12/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0090\n", - "Epoch 13/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 14/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 15/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 16/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 17/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 18/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 19/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 20/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 21/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 22/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 23/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 24/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 25/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0092\n", - "Epoch 26/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 27/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 28/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 29/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 30/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 31/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0090\n", - "Epoch 32/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 33/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 34/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0090\n", - "Epoch 35/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 36/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0090\n", - "Epoch 37/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 38/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 39/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 40/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 41/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 42/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 43/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 44/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 45/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 46/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 47/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 48/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 49/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 50/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 51/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 52/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 53/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 54/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 55/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 56/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 57/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 58/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 59/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 60/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 61/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 62/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0089\n", - "Epoch 63/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 64/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 65/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 66/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 67/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 68/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 69/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 70/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 71/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0089\n", - "Epoch 72/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0087\n", - "Epoch 73/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0088\n", - "Epoch 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loss: 0.0086\n", - "Epoch 95/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0086\n", - "Epoch 96/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0086\n", - "Epoch 97/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0086\n", - "Epoch 98/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", - "Epoch 99/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", - "Epoch 100/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0086\n", - "Epoch 101/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0088\n", - "Epoch 102/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0086\n", - "Epoch 103/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0085\n", - "Epoch 104/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0087\n", - "Epoch 105/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0083\n", - "Epoch 106/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0085\n", - "Epoch 107/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", - "Epoch 108/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0084\n", - "Epoch 109/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0080\n", - "Epoch 110/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0080\n", - "Epoch 111/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0081\n", - "Epoch 112/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0078\n", - "Epoch 113/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0076\n", - "Epoch 114/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0077\n", - "Epoch 115/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0075\n", - "Epoch 116/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0076\n", - "Epoch 117/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0076\n", - "Epoch 118/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0074\n", - "Epoch 119/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 0.0073\n", - "Epoch 120/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 0.0074\n", - "Epoch 121/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0072\n", - "Epoch 122/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0070\n", - "Epoch 123/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0070\n", - "Epoch 124/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0073\n", - "Epoch 125/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0073\n", - "Epoch 126/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0073\n", - "Epoch 127/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 0.0074\n", - "Epoch 128/50000\n", + "Epoch 1/400\n", + "7/7 [==============================] - 4s 7ms/step - loss: 0.0101\n", + "Epoch 2/400\n", + "7/7 [==============================] - 0s 7ms/step - loss: 0.0089\n", + "Epoch 3/400\n", + "7/7 [==============================] - 0s 7ms/step - loss: 0.0103\n", + "Epoch 4/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0090\n", + "Epoch 5/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0081\n", + "Epoch 6/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0096\n", + "Epoch 7/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0091\n", + "Epoch 8/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0079\n", + "Epoch 9/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0095\n", + "Epoch 10/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0085\n", + "Epoch 11/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0092\n", + "Epoch 12/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0091\n", + "Epoch 13/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0084\n", + "Epoch 14/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0087\n", + "Epoch 15/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0096\n", + "Epoch 16/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0106\n", + "Epoch 17/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0080\n", + "Epoch 18/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0106\n", + "Epoch 19/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0086\n", + "Epoch 20/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0090\n", + "Epoch 21/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0081\n", + "Epoch 22/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0087\n", + "Epoch 23/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0083\n", + "Epoch 24/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0077\n", + "Epoch 25/400\n", + "7/7 [==============================] - 0s 7ms/step - loss: 0.0103\n", + "Epoch 26/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0088\n", + "Epoch 27/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0093\n", + "Epoch 28/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0098\n", + "Epoch 29/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0095\n", + "Epoch 30/400\n", + "7/7 [==============================] - 0s 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"text": [ - "7/7 [==============================] - 0s 10ms/step - loss: 5.1503e-04\n", - "Epoch 2005/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1942e-04\n", - "Epoch 2006/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3579e-04\n", - "Epoch 2007/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.9124e-04\n", - "Epoch 2008/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.7236e-04\n", - "Epoch 2009/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.5445e-04\n", - "Epoch 2010/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 5.3879e-04\n", - "Epoch 2011/50000\n", - "7/7 [==============================] - 0s 9ms/step - loss: 5.3033e-04\n", - "Epoch 2012/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3043e-04\n", - "Epoch 2013/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 4.6810e-05\n", - "Epoch 14419/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1051e-05\n", - "Epoch 14420/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6265e-05\n", - "Epoch 14421/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1998e-05\n", - "Epoch 14422/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2442e-05\n", - "Epoch 14423/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2149e-05\n", - "Epoch 14424/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4518e-05\n", - "Epoch 14425/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5227e-05\n", - "Epoch 14426/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.9346e-05\n", - "Epoch 14427/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 12ms/step - loss: 2.6709e-05\n", - "Epoch 17518/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7993e-05\n", - "Epoch 17519/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7036e-05\n", - "Epoch 17520/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.7235e-05\n", - "Epoch 17521/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5182e-05\n", - "Epoch 17522/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7018e-05\n", - "Epoch 17523/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3877e-05\n", - "Epoch 17524/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.2620e-05\n", - "Epoch 17525/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3546e-05\n", - "Epoch 17526/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4709e-05\n", - 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12ms/step - loss: 3.7644e-05\n", - "Epoch 19615/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9066e-05\n", - "Epoch 19616/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1441e-04\n", - "Epoch 19617/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.2794e-05\n", - "Epoch 19618/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.4153e-05\n", - "Epoch 19619/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.5082e-05\n", - "Epoch 19620/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3740e-05\n", - "Epoch 19621/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4310e-05\n", - "Epoch 19622/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0896e-05\n", - "Epoch 19623/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6757e-05\n", - "Epoch 19624/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3792e-05\n", - "Epoch 19625/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7456e-05\n", - "Epoch 19626/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3589e-05\n", - "Epoch 19627/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3124e-05\n", - "Epoch 19628/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1918e-05\n", - "Epoch 19629/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1129e-05\n", - "Epoch 19630/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.2920e-05\n", - "Epoch 19631/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.9227e-05\n", - "Epoch 19632/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6787e-04\n", - "Epoch 19633/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6417e-04\n", - 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11ms/step - loss: 9.0091e-05\n", - "Epoch 19644/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2268e-04\n", - "Epoch 19645/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0025e-04\n", - "Epoch 19646/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 9.2407e-05\n", - "Epoch 19647/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.2866e-05\n", - "Epoch 19648/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.1500e-05\n", - "Epoch 19649/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.1109e-05\n", - "Epoch 19650/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.7712e-05\n", - "Epoch 19651/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.6781e-05\n", - "Epoch 19652/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.7690e-05\n", - "Epoch 19653/50000\n", - "7/7 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[==============================] - 0s 12ms/step - loss: 1.1740e-04\n", - "Epoch 19663/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1840e-04\n", - "Epoch 19664/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.3169e-05\n", - "Epoch 19665/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.7434e-05\n", - "Epoch 19666/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.7789e-05\n", - "Epoch 19667/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.5670e-05\n", - "Epoch 19668/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.3678e-05\n", - "Epoch 19669/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1828e-05\n", - "Epoch 19670/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.9080e-05\n", - "Epoch 19671/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.2515e-05\n", - 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11ms/step - loss: 6.8488e-05\n", - "Epoch 19682/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9681e-05\n", - "Epoch 19683/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4222e-05\n", - "Epoch 19684/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9313e-05\n", - "Epoch 19685/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1658e-05\n", - "Epoch 19686/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4773e-05\n", - "Epoch 19687/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.8302e-04\n", - "Epoch 19688/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.0878e-04\n", - "Epoch 19689/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8714e-04\n", - "Epoch 19690/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0766e-04\n", - "Epoch 19691/50000\n", - "7/7 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"Epoch 19701/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.3273e-05\n", - "Epoch 19702/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.5212e-05\n", - "Epoch 19703/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6390e-05\n", - "Epoch 19704/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4507e-05\n", - "Epoch 19705/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0124e-05\n", - "Epoch 19706/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.0597e-05\n", - "Epoch 19707/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.5992e-05\n", - "Epoch 19708/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3712e-05\n", - "Epoch 19709/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4824e-05\n", - "Epoch 19710/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 5.8331e-05\n", - "Epoch 19721/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5712e-05\n", - "Epoch 19722/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6995e-05\n", - "Epoch 19723/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6741e-05\n", - "Epoch 19724/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.6178e-05\n", - "Epoch 19725/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2084e-05\n", - "Epoch 19726/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2548e-05\n", - "Epoch 19727/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1243e-05\n", - "Epoch 19728/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.7753e-05\n", - "Epoch 19729/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9061e-05\n", - 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12ms/step - loss: 9.1148e-05\n", - "Epoch 19740/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.8944e-05\n", - "Epoch 19741/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.6942e-05\n", - "Epoch 19742/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.0178e-05\n", - "Epoch 19743/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.4796e-05\n", - "Epoch 19744/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3559e-05\n", - "Epoch 19745/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.8222e-05\n", - "Epoch 19746/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.6119e-05\n", - "Epoch 19747/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4420e-05\n", - "Epoch 19748/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4153e-05\n", - "Epoch 19749/50000\n", - "7/7 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11ms/step - loss: 3.8953e-05\n", - "Epoch 19798/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7759e-05\n", - "Epoch 19799/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8308e-05\n", - "Epoch 19800/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7253e-05\n", - "Epoch 19801/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5372e-05\n", - "Epoch 19802/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3535e-05\n", - "Epoch 19803/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3665e-05\n", - "Epoch 19804/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4556e-05\n", - "Epoch 19805/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0452e-05\n", - "Epoch 19806/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1132e-05\n", - "Epoch 19807/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1322e-05\n", - "Epoch 19808/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7266e-05\n", - "Epoch 19809/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7346e-05\n", - "Epoch 19810/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7892e-05\n", - "Epoch 19811/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5241e-05\n", - "Epoch 19812/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3357e-05\n", - "Epoch 19813/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5763e-05\n", - "Epoch 19814/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1584e-05\n", - "Epoch 19815/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4088e-05\n", - "Epoch 19816/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1496e-05\n", - "Epoch 19817/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.0615e-05\n", - "Epoch 19818/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8272e-05\n", - "Epoch 19819/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.3576e-05\n", - "Epoch 19820/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6986e-05\n", - "Epoch 19821/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2957e-05\n", - "Epoch 19822/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2766e-05\n", - "Epoch 19823/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.4636e-05\n", - "Epoch 19824/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8781e-05\n", - "Epoch 19825/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2292e-05\n", - "Epoch 19826/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4951e-05\n", - "Epoch 19827/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3696e-05\n", - "Epoch 19828/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.2333e-05\n", - "Epoch 19829/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.1525e-05\n", - "Epoch 19830/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1870e-05\n", - "Epoch 19831/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0212e-05\n", - "Epoch 19832/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9785e-05\n", - "Epoch 19833/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1089e-05\n", - "Epoch 19834/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9214e-05\n", - "Epoch 19835/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.0178e-05\n", - "Epoch 19836/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8142e-05\n", - "Epoch 19837/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7970e-05\n", - "Epoch 19838/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7858e-05\n", - "Epoch 19839/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.5101e-05\n", - "Epoch 19840/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.8075e-05\n", - "Epoch 19841/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4313e-05\n", - "Epoch 19842/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3656e-05\n", - "Epoch 19843/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2711e-05\n", - "Epoch 19844/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0038e-05\n", - "Epoch 19845/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 2.8391e-05\n", - "Epoch 19846/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0548e-05\n", - "Epoch 19847/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7020e-05\n", - "Epoch 19848/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1362e-05\n", - "Epoch 19849/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5708e-05\n", - "Epoch 19850/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1243e-05\n", - "Epoch 19851/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4188e-05\n", - "Epoch 19852/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0943e-05\n", - "Epoch 19853/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8316e-05\n", - "Epoch 19854/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8953e-05\n", - "Epoch 19855/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9953e-05\n", - "Epoch 19856/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0953e-05\n", - "Epoch 19857/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8978e-05\n", - "Epoch 19858/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8310e-05\n", - "Epoch 19859/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0332e-05\n", - "Epoch 19860/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8098e-05\n", - "Epoch 19861/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5938e-05\n", - "Epoch 19862/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6720e-05\n", - "Epoch 19863/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5726e-05\n", - "Epoch 19864/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6639e-05\n", - "Epoch 19865/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8434e-05\n", - "Epoch 19866/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7275e-05\n", - "Epoch 19867/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5937e-05\n", - "Epoch 19868/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6671e-05\n", - "Epoch 19869/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7777e-05\n", - "Epoch 19870/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7721e-05\n", - "Epoch 19871/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0844e-05\n", - "Epoch 19872/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5492e-05\n", - "Epoch 19873/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4778e-05\n", - "Epoch 19874/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5549e-05\n", - "Epoch 19875/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6213e-05\n", - "Epoch 19876/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0020e-05\n", - "Epoch 19877/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3472e-05\n", - "Epoch 19878/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7020e-05\n", - "Epoch 19879/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7247e-05\n", - "Epoch 19880/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.9983e-05\n", - "Epoch 19881/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4895e-05\n", - "Epoch 19882/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6541e-05\n", - "Epoch 19883/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9694e-05\n", - "Epoch 19884/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2751e-05\n", - "Epoch 19885/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5745e-05\n", - "Epoch 19886/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5051e-05\n", - "Epoch 19887/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8104e-05\n", - "Epoch 19888/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7414e-05\n", - "Epoch 19889/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.0936e-05\n", - "Epoch 19890/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.4394e-05\n", - "Epoch 19891/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 8.1643e-05\n", - "Epoch 19892/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4778e-05\n", - "Epoch 19893/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.1243e-05\n", - "Epoch 19894/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3561e-05\n", - "Epoch 19895/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3074e-05\n", - "Epoch 19896/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3150e-05\n", - "Epoch 19897/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5327e-04\n", - "Epoch 19898/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4351e-04\n", - "Epoch 19899/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0699e-04\n", - "Epoch 19900/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.1196e-05\n", - "Epoch 19901/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.3795e-05\n", - "Epoch 19902/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8369e-05\n", - "Epoch 19903/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6336e-05\n", - "Epoch 19904/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1095e-05\n", - "Epoch 19905/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6060e-05\n", - "Epoch 19906/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4496e-05\n", - "Epoch 19907/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4652e-05\n", - "Epoch 19908/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4721e-05\n", - "Epoch 19909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1129e-05\n", - "Epoch 19910/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.0788e-05\n", - "Epoch 19911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9580e-05\n", - "Epoch 19912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8539e-05\n", - "Epoch 19913/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8825e-05\n", - "Epoch 19914/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8610e-05\n", - "Epoch 19915/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1507e-05\n", - "Epoch 19916/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9149e-05\n", - "Epoch 19917/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1521e-05\n", - "Epoch 19918/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.5481e-05\n", - "Epoch 19919/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4004e-05\n", - "Epoch 19920/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8972e-05\n", - "Epoch 19921/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6710e-05\n", - "Epoch 19922/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7500e-05\n", - "Epoch 19923/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.2198e-05\n", - "Epoch 19924/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.4236e-05\n", - "Epoch 19925/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1049e-05\n", - "Epoch 19926/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9835e-05\n", - "Epoch 19927/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.5283e-05\n", - "Epoch 19928/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.0171e-05\n", - "Epoch 19929/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.7182e-05\n", - "Epoch 19930/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7837e-05\n", - "Epoch 19931/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5037e-05\n", - "Epoch 19932/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5811e-05\n", - "Epoch 19933/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4857e-05\n", - "Epoch 19934/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4594e-05\n", - "Epoch 19935/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4452e-05\n", - "Epoch 19936/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3507e-05\n", - "Epoch 19937/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2983e-05\n", - "Epoch 19938/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2555e-05\n", - "Epoch 19939/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1412e-05\n", - "Epoch 19940/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2948e-05\n", - "Epoch 19941/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2265e-05\n", - "Epoch 19942/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6058e-05\n", - "Epoch 19943/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7408e-05\n", - "Epoch 19944/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5894e-05\n", - "Epoch 19945/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2402e-05\n", - "Epoch 19946/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4871e-05\n", - "Epoch 19947/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6699e-05\n", - "Epoch 19948/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6673e-05\n", - "Epoch 19949/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9339e-05\n", - "Epoch 19950/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6952e-05\n", - "Epoch 19951/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0897e-05\n", - "Epoch 19952/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3624e-05\n", - "Epoch 19953/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6300e-05\n", - "Epoch 19954/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3701e-05\n", - "Epoch 19955/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3905e-05\n", - "Epoch 19956/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0711e-05\n", - "Epoch 19957/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9610e-05\n", - "Epoch 19958/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1955e-05\n", - "Epoch 19959/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3909e-05\n", - "Epoch 19960/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1153e-05\n", - "Epoch 19961/50000\n", - "7/7 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[==============================] - 0s 14ms/step - loss: 8.0999e-05\n", - "Epoch 21500/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 15ms/step - loss: 5.6058e-05\n", - "Epoch 21501/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 4.4914e-05\n", - "Epoch 21502/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 4.0218e-05\n", - "Epoch 21503/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 3.3074e-05\n", - "Epoch 21504/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.1414e-05\n", - "Epoch 21505/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.7450e-05\n", - "Epoch 21506/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.0277e-05\n", - "Epoch 21507/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.7433e-05\n", - "Epoch 21508/50000\n", - "7/7 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[==============================] - 0s 11ms/step - loss: 2.8087e-05\n", - "Epoch 22904/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4897e-05\n", - "Epoch 22905/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5691e-05\n", - "Epoch 22906/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3407e-05\n", - "Epoch 22907/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.8705e-05\n", - "Epoch 22908/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2274e-05\n", - "Epoch 22909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1918e-05\n", - "Epoch 22910/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2531e-05\n", - "Epoch 22911/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.2124e-05\n", - "Epoch 22912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1156e-05\n", - 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11ms/step - loss: 2.7724e-05\n", - "Epoch 22923/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5164e-05\n", - "Epoch 22924/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.3992e-05\n", - "Epoch 22925/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3496e-05\n", - "Epoch 22926/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.4048e-05\n", - "Epoch 22927/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4499e-05\n", - "Epoch 22928/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.3431e-05\n", - "Epoch 22929/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.4352e-05\n", - "Epoch 22930/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.2198e-05\n", - "Epoch 22931/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.1045e-05\n", - "Epoch 22932/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9884e-05\n", - "Epoch 22933/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9170e-05\n", - "Epoch 22934/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8703e-05\n", - "Epoch 22935/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.0165e-05\n", - "Epoch 22936/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.2132e-05\n", - "Epoch 22937/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.2734e-05\n", - "Epoch 22938/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.3266e-05\n", - "Epoch 22939/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.1883e-05\n", - "Epoch 22940/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0865e-05\n", - "Epoch 22941/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8733e-05\n", - 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[==============================] - 0s 11ms/step - loss: 1.4783e-04\n", - "Epoch 22962/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.2728e-04\n", - "Epoch 22963/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1563e-04\n", - "Epoch 22964/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0632e-04\n", - "Epoch 22965/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.0879e-05\n", - "Epoch 22966/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6701e-05\n", - "Epoch 22967/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.0145e-05\n", - "Epoch 22968/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.7726e-05\n", - "Epoch 22969/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6526e-05\n", - "Epoch 22970/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5221e-05\n", - "Epoch 22971/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.0845e-05\n", - "Epoch 22972/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5755e-05\n", - "Epoch 22973/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.2032e-05\n", - "Epoch 22974/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 9.8464e-05\n", - "Epoch 22975/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.9626e-05\n", - "Epoch 22976/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8416e-05\n", - "Epoch 22977/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.3086e-05\n", - "Epoch 22978/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.3389e-05\n", - "Epoch 22979/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4483e-05\n", - "Epoch 22980/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1890e-05\n", - "Epoch 22981/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.0016e-05\n", - "Epoch 22982/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.7137e-05\n", - "Epoch 22983/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.6234e-05\n", - "Epoch 22984/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.4104e-05\n", - "Epoch 22985/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.2918e-05\n", - "Epoch 22986/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.3188e-05\n", - "Epoch 22987/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.9922e-05\n", - "Epoch 22988/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9529e-05\n", - "Epoch 22989/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3057e-05\n", - "Epoch 22990/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.0813e-05\n", - "Epoch 22991/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 3.9765e-05\n", - "Epoch 22992/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.0317e-04\n", - "Epoch 22993/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.1908e-05\n", - "Epoch 22994/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.0743e-04\n", - "Epoch 22995/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.9747e-05\n", - "Epoch 22996/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.1016e-04\n", - "Epoch 22997/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.0461e-04\n", - "Epoch 22998/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.6702e-05\n", - "Epoch 22999/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.3898e-05\n", - 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[==============================] - 0s 10ms/step - loss: 9.5531e-05\n", - "Epoch 23020/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 9.1969e-05\n", - "Epoch 23021/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 9.6018e-05\n", - "Epoch 23022/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.2586e-04\n", - "Epoch 23023/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1264e-04\n", - "Epoch 23024/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0072e-04\n", - "Epoch 23025/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.8126e-05\n", - "Epoch 23026/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.9426e-05\n", - "Epoch 23027/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.4441e-05\n", - "Epoch 23028/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6082e-05\n", - "Epoch 23029/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.8215e-05\n", - "Epoch 23030/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.2410e-05\n", - "Epoch 23031/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.3021e-05\n", - "Epoch 23032/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.0341e-05\n", - "Epoch 23033/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.8636e-05\n", - "Epoch 23034/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.1564e-05\n", - "Epoch 23035/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.9048e-05\n", - "Epoch 23036/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.2665e-05\n", - "Epoch 23037/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.5109e-05\n", - "Epoch 23038/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.8562e-05\n", - "Epoch 23039/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.1356e-05\n", - "Epoch 23040/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.8538e-05\n", - "Epoch 23041/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.5233e-05\n", - "Epoch 23042/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.9215e-05\n", - "Epoch 23043/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.6604e-05\n", - "Epoch 23044/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.7693e-05\n", - "Epoch 23045/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.4061e-05\n", - "Epoch 23046/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.2096e-05\n", - "Epoch 23047/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.4128e-05\n", - "Epoch 23048/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1662e-05\n", - "Epoch 23049/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0105e-05\n", - "Epoch 23050/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.3369e-05\n", - "Epoch 23051/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.4897e-05\n", - "Epoch 23052/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 9.0992e-05\n", - "Epoch 23053/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.3338e-04\n", - "Epoch 23054/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.1902e-04\n", - "Epoch 23055/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.2856e-04\n", - "Epoch 23056/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.0281e-04\n", - "Epoch 23057/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 9.4304e-05\n", - "Epoch 23058/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 8.2251e-05\n", - "Epoch 23059/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.3031e-05\n", - "Epoch 23060/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.7500e-05\n", - "Epoch 23061/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.5403e-05\n", - "Epoch 23062/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2750e-05\n", - "Epoch 23063/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.1738e-05\n", - "Epoch 23064/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 5.8701e-05\n", - "Epoch 23065/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.5722e-05\n", - "Epoch 23066/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 5.1705e-05\n", - 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[==============================] - 0s 12ms/step - loss: 4.3498e-05\n", - "Epoch 23357/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6625e-05\n", - "Epoch 23358/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7320e-05\n", - "Epoch 23359/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8995e-05\n", - "Epoch 23360/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.0361e-05\n", - "Epoch 23361/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8342e-05\n", - "Epoch 23362/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8765e-05\n", - "Epoch 23363/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4881e-04\n", - "Epoch 23364/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4515e-04\n", - "Epoch 23365/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6694e-04\n", - 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"Epoch 23433/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.6978e-05\n", - "Epoch 23434/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3599e-05\n", - "Epoch 23435/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1078e-05\n", - "Epoch 23436/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2430e-05\n", - "Epoch 23437/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0820e-05\n", - "Epoch 23438/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0635e-05\n", - "Epoch 23439/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9129e-05\n", - "Epoch 23440/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9187e-05\n", - "Epoch 23441/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.1008e-05\n", - "Epoch 23442/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 5.0709e-05\n", - "Epoch 23453/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7598e-05\n", - "Epoch 23454/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3560e-05\n", - "Epoch 23455/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2733e-05\n", - "Epoch 23456/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2517e-05\n", - "Epoch 23457/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9840e-05\n", - "Epoch 23458/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8508e-05\n", - "Epoch 23459/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 4.3410e-05\n", - "Epoch 23460/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4771e-05\n", - "Epoch 23461/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2814e-05\n", - 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13ms/step - loss: 4.2620e-05\n", - "Epoch 23472/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8842e-05\n", - "Epoch 23473/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.7303e-05\n", - "Epoch 23474/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8750e-05\n", - "Epoch 23475/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.3939e-05\n", - "Epoch 23476/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.9546e-05\n", - "Epoch 23477/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.5309e-05\n", - "Epoch 23478/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1923e-05\n", - "Epoch 23479/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8150e-05\n", - "Epoch 23480/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.9208e-05\n", - "Epoch 23481/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4498e-05\n", - "Epoch 23482/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2063e-05\n", - "Epoch 23483/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7811e-05\n", - "Epoch 23484/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.7583e-05\n", - "Epoch 23485/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5951e-05\n", - "Epoch 23486/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5151e-05\n", - "Epoch 23487/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6067e-05\n", - "Epoch 23488/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1394e-05\n", - "Epoch 23489/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7233e-05\n", - "Epoch 23490/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4651e-05\n", - 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12ms/step - loss: 4.6259e-05\n", - "Epoch 23501/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7480e-05\n", - "Epoch 23502/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.3655e-05\n", - "Epoch 23503/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1550e-05\n", - "Epoch 23504/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 4.4370e-05\n", - "Epoch 23505/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9314e-05\n", - "Epoch 23506/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6549e-05\n", - "Epoch 23507/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4755e-05\n", - "Epoch 23508/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3518e-05\n", - "Epoch 23509/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4187e-05\n", - "Epoch 23510/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3798e-05\n", - "Epoch 23511/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5226e-05\n", - "Epoch 23512/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5348e-05\n", - "Epoch 23513/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.9022e-05\n", - "Epoch 23514/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0759e-05\n", - "Epoch 23515/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.0604e-05\n", - "Epoch 23516/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7109e-05\n", - "Epoch 23517/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0146e-05\n", - "Epoch 23518/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7829e-05\n", - "Epoch 23519/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.0784e-05\n", - 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13ms/step - loss: 3.2637e-05\n", - "Epoch 23530/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2154e-05\n", - "Epoch 23531/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1768e-05\n", - "Epoch 23532/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3506e-05\n", - "Epoch 23533/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0778e-05\n", - "Epoch 23534/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5901e-05\n", - "Epoch 23535/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0218e-05\n", - "Epoch 23536/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0550e-05\n", - "Epoch 23537/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3878e-05\n", - "Epoch 23538/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5844e-05\n", - "Epoch 23539/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.4375e-05\n", - "Epoch 23540/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.7345e-05\n", - "Epoch 23541/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9480e-05\n", - "Epoch 23542/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1917e-05\n", - "Epoch 23543/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.1697e-05\n", - "Epoch 23544/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7104e-05\n", - "Epoch 23545/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9606e-05\n", - "Epoch 23546/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9767e-05\n", - "Epoch 23547/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6691e-05\n", - "Epoch 23548/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3007e-05\n", - "Epoch 23549/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4046e-05\n", - "Epoch 23550/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5667e-05\n", - "Epoch 23551/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1777e-05\n", - "Epoch 23552/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8845e-05\n", - "Epoch 23553/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1837e-05\n", - "Epoch 23554/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8923e-05\n", - "Epoch 23555/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9526e-05\n", - "Epoch 23556/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8342e-05\n", - "Epoch 23557/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8063e-05\n", - "Epoch 23558/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.6172e-05\n", - "Epoch 23559/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.2864e-05\n", - "Epoch 23560/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8676e-05\n", - "Epoch 23561/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.3519e-05\n", - "Epoch 23562/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8222e-05\n", - "Epoch 23563/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.4155e-05\n", - "Epoch 23564/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9131e-05\n", - "Epoch 23565/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3653e-05\n", - "Epoch 23566/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6954e-05\n", - "Epoch 23567/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8022e-05\n", - "Epoch 23568/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6864e-05\n", - "Epoch 23569/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2261e-05\n", - "Epoch 23570/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.6646e-05\n", - "Epoch 23571/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7534e-05\n", - "Epoch 23572/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8265e-05\n", - "Epoch 23573/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.8859e-05\n", - "Epoch 23574/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2807e-05\n", - "Epoch 23575/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.2236e-05\n", - "Epoch 23576/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4960e-05\n", - "Epoch 23577/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0156e-05\n", - "Epoch 23578/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6483e-05\n", - "Epoch 23579/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9604e-05\n", - "Epoch 23580/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5583e-05\n", - "Epoch 23581/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4188e-05\n", - "Epoch 23582/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2398e-05\n", - "Epoch 23583/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8665e-05\n", - "Epoch 23584/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8440e-05\n", - "Epoch 23585/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.6623e-05\n", - "Epoch 23586/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9043e-05\n", - "Epoch 23587/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8429e-05\n", - "Epoch 23588/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6642e-05\n", - "Epoch 23589/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9073e-05\n", - "Epoch 23590/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9482e-05\n", - "Epoch 23591/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4810e-05\n", - "Epoch 23592/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1569e-05\n", - "Epoch 23593/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 2.9189e-05\n", - "Epoch 23594/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6146e-05\n", - "Epoch 23595/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.5590e-05\n", - "Epoch 23596/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3512e-05\n", - "Epoch 23597/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8793e-05\n", - "Epoch 23598/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.6993e-05\n", - "Epoch 23599/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3477e-05\n", - "Epoch 23600/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4538e-05\n", - "Epoch 23601/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3575e-05\n", - "Epoch 23602/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8831e-05\n", - "Epoch 23603/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6663e-05\n", - "Epoch 23604/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7512e-05\n", - "Epoch 23605/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7444e-05\n", - "Epoch 23606/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5704e-05\n", - "Epoch 23607/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4365e-05\n", - "Epoch 23608/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4373e-05\n", - "Epoch 23609/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4142e-05\n", - "Epoch 23610/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2904e-05\n", - "Epoch 23611/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6730e-05\n", - "Epoch 23612/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5792e-05\n", - "Epoch 23613/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.4164e-05\n", - "Epoch 23614/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9055e-05\n", - "Epoch 23615/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.6864e-05\n", - "Epoch 23616/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 3.4038e-05\n", - "Epoch 23617/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.1968e-05\n", - "Epoch 23618/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7091e-05\n", - "Epoch 23619/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7266e-05\n", - "Epoch 23620/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3614e-05\n", - "Epoch 23621/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7237e-05\n", - "Epoch 23622/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7758e-05\n", - "Epoch 23623/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5257e-05\n", - "Epoch 23624/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0767e-05\n", - "Epoch 23625/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7683e-05\n", - "Epoch 23626/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0619e-05\n", - "Epoch 23627/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0319e-05\n", - "Epoch 23628/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7991e-05\n", - "Epoch 23629/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8876e-05\n", - "Epoch 23630/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8809e-05\n", - "Epoch 23631/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.2010e-05\n", - "Epoch 23632/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.7894e-05\n", - "Epoch 23633/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3718e-05\n", - "Epoch 23634/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5204e-05\n", - "Epoch 23635/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.2014e-05\n", - "Epoch 23636/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2880e-05\n", - "Epoch 23637/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.5564e-05\n", - "Epoch 23638/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.9026e-05\n", - "Epoch 23639/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5445e-05\n", - "Epoch 23640/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5169e-05\n", - "Epoch 23641/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8014e-05\n", - "Epoch 23642/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5679e-05\n", - "Epoch 23643/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0406e-05\n", - "Epoch 23644/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3643e-05\n", - 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12ms/step - loss: 2.4639e-05\n", - "Epoch 23655/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4955e-05\n", - "Epoch 23656/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0767e-05\n", - "Epoch 23657/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8016e-05\n", - "Epoch 23658/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8854e-05\n", - "Epoch 23659/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5960e-05\n", - "Epoch 23660/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4098e-05\n", - "Epoch 23661/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4636e-05\n", - "Epoch 23662/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2445e-05\n", - "Epoch 23663/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3344e-05\n", - "Epoch 23664/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3481e-05\n", - "Epoch 23665/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1670e-05\n", - "Epoch 23666/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4447e-05\n", - "Epoch 23667/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.1847e-05\n", - "Epoch 23668/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.1552e-05\n", - "Epoch 23669/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.5797e-05\n", - "Epoch 23670/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.1826e-05\n", - "Epoch 23671/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.8771e-05\n", - "Epoch 23672/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.4872e-05\n", - "Epoch 23673/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.3653e-05\n", - 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11ms/step - loss: 3.6932e-05\n", - "Epoch 23684/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5854e-05\n", - "Epoch 23685/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4326e-05\n", - "Epoch 23686/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5780e-05\n", - "Epoch 23687/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1066e-05\n", - "Epoch 23688/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.5521e-05\n", - "Epoch 23689/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3718e-05\n", - "Epoch 23690/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1832e-05\n", - "Epoch 23691/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.1163e-05\n", - "Epoch 23692/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8843e-05\n", - "Epoch 23693/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9241e-05\n", - "Epoch 23694/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9073e-05\n", - "Epoch 23695/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8197e-05\n", - "Epoch 23696/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9026e-05\n", - "Epoch 23697/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8002e-05\n", - "Epoch 23698/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8679e-05\n", - "Epoch 23699/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2048e-05\n", - "Epoch 23700/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2018e-05\n", - "Epoch 23701/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6756e-05\n", - "Epoch 23702/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9201e-05\n", - 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12ms/step - loss: 2.2409e-05\n", - "Epoch 23713/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7743e-05\n", - "Epoch 23714/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6637e-05\n", - "Epoch 23715/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2084e-05\n", - "Epoch 23716/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1979e-05\n", - "Epoch 23717/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3245e-05\n", - "Epoch 23718/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2369e-05\n", - "Epoch 23719/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7115e-05\n", - "Epoch 23720/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3830e-05\n", - "Epoch 23721/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.1199e-05\n", - "Epoch 23722/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.0127e-05\n", - "Epoch 23723/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2311e-05\n", - "Epoch 23724/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3387e-05\n", - "Epoch 23725/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1512e-05\n", - "Epoch 23726/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0352e-05\n", - "Epoch 23727/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8119e-05\n", - "Epoch 23728/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2063e-05\n", - "Epoch 23729/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4763e-05\n", - "Epoch 23730/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4621e-05\n", - "Epoch 23731/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7095e-05\n", - "Epoch 23732/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5056e-05\n", - "Epoch 23733/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6370e-05\n", - "Epoch 23734/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3280e-05\n", - "Epoch 23735/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1391e-05\n", - "Epoch 23736/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0699e-05\n", - "Epoch 23737/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0009e-05\n", - "Epoch 23738/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0589e-05\n", - "Epoch 23739/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2473e-05\n", - "Epoch 23740/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2581e-05\n", - "Epoch 23741/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1752e-05\n", - "Epoch 23742/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5551e-05\n", - "Epoch 23743/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3815e-05\n", - "Epoch 23744/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2509e-05\n", - "Epoch 23745/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3618e-05\n", - "Epoch 23746/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2252e-05\n", - "Epoch 23747/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4351e-05\n", - "Epoch 23748/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.0722e-05\n", - "Epoch 23749/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7665e-05\n", - "Epoch 23750/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6781e-05\n", - "Epoch 23751/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6616e-05\n", - "Epoch 23752/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.9325e-05\n", - "Epoch 23753/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.2813e-05\n", - "Epoch 23754/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3721e-05\n", - "Epoch 23755/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5558e-05\n", - "Epoch 23756/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.2096e-05\n", - "Epoch 23757/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2963e-05\n", - "Epoch 23758/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8401e-05\n", - "Epoch 23759/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2599e-05\n", - "Epoch 23760/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.2255e-05\n", - "Epoch 23761/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0272e-05\n", - "Epoch 23762/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8055e-05\n", - "Epoch 23763/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5438e-05\n", - "Epoch 23764/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4125e-05\n", - "Epoch 23765/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.0397e-05\n", - "Epoch 23766/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4767e-05\n", - "Epoch 23767/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.5200e-05\n", - "Epoch 23768/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.7804e-05\n", - "Epoch 23769/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3146e-05\n", - "Epoch 23770/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0784e-05\n", - "Epoch 23771/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0197e-05\n", - "Epoch 23772/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.6247e-05\n", - "Epoch 23773/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.6731e-05\n", - "Epoch 23774/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4810e-05\n", - "Epoch 23775/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.9114e-05\n", - "Epoch 23776/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7849e-05\n", - "Epoch 23777/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6189e-05\n", - "Epoch 23778/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0369e-05\n", - "Epoch 23779/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0686e-05\n", - "Epoch 23780/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6870e-05\n", - "Epoch 23781/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6105e-05\n", - "Epoch 23782/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4775e-05\n", - "Epoch 23783/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.7131e-05\n", - "Epoch 23784/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7772e-05\n", - "Epoch 23785/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4854e-05\n", - "Epoch 23786/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4601e-05\n", - "Epoch 23787/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2468e-05\n", - "Epoch 23788/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1642e-05\n", - "Epoch 23789/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9379e-05\n", - "Epoch 23790/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1691e-05\n", - "Epoch 23791/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3722e-05\n", - "Epoch 23792/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2429e-05\n", - "Epoch 23793/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1399e-05\n", - "Epoch 23794/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2518e-05\n", - "Epoch 23795/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.4716e-05\n", - "Epoch 23796/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.9540e-05\n", - "Epoch 23797/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.7829e-05\n", - "Epoch 23798/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0593e-05\n", - "Epoch 23799/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 3.2804e-05\n", - "Epoch 23819/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.5313e-05\n", - "Epoch 23820/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1655e-05\n", - "Epoch 23821/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3518e-05\n", - "Epoch 23822/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4233e-05\n", - "Epoch 23823/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.3891e-05\n", - "Epoch 23824/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.3367e-05\n", - "Epoch 23825/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.9486e-05\n", - "Epoch 23826/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.8639e-05\n", - "Epoch 23827/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0562e-05\n", - 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12ms/step - loss: 3.2115e-05\n", - "Epoch 23838/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1604e-05\n", - "Epoch 23839/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.8024e-05\n", - "Epoch 23840/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7015e-05\n", - "Epoch 23841/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.8219e-05\n", - "Epoch 23842/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0857e-05\n", - "Epoch 23843/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0822e-05\n", - "Epoch 23844/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0770e-05\n", - "Epoch 23845/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8265e-05\n", - "Epoch 23846/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7338e-05\n", - "Epoch 23847/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.5964e-05\n", - "Epoch 23848/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6698e-05\n", - "Epoch 23849/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9312e-05\n", - "Epoch 23850/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7258e-05\n", - "Epoch 23851/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7102e-05\n", - "Epoch 23852/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.9390e-05\n", - "Epoch 23853/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6908e-05\n", - "Epoch 23854/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6553e-05\n", - "Epoch 23855/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6067e-05\n", - "Epoch 23856/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.5755e-05\n", - 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[==============================] - 0s 11ms/step - loss: 4.2784e-05\n", - "Epoch 23877/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.1111e-05\n", - "Epoch 23878/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2117e-05\n", - "Epoch 23879/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7067e-05\n", - "Epoch 23880/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.8795e-05\n", - "Epoch 23881/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4407e-05\n", - "Epoch 23882/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.8703e-05\n", - "Epoch 23883/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.3880e-05\n", - "Epoch 23884/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7448e-05\n", - "Epoch 23885/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.3471e-05\n", - 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12ms/step - loss: 2.8914e-05\n", - "Epoch 23896/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3483e-05\n", - "Epoch 23897/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2336e-05\n", - "Epoch 23898/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0174e-05\n", - "Epoch 23899/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0452e-05\n", - "Epoch 23900/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1412e-05\n", - "Epoch 23901/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9840e-05\n", - "Epoch 23902/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.8522e-05\n", - "Epoch 23903/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0964e-05\n", - "Epoch 23904/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8856e-05\n", - "Epoch 23905/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.9022e-05\n", - "Epoch 23906/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3402e-05\n", - "Epoch 23907/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1087e-05\n", - "Epoch 23908/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0539e-05\n", - "Epoch 23909/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2833e-05\n", - "Epoch 23910/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1388e-05\n", - "Epoch 23911/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6788e-05\n", - "Epoch 23912/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1430e-05\n", - "Epoch 23913/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2329e-05\n", - "Epoch 23914/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2421e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.4222e-05\n", - "Epoch 26930/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3310e-05\n", - "Epoch 26931/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3239e-05\n", - "Epoch 26932/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2884e-05\n", - "Epoch 26933/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2390e-05\n", - "Epoch 26934/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3655e-05\n", - "Epoch 26935/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.2763e-05\n", - "Epoch 26936/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4072e-05\n", - "Epoch 26937/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3450e-05\n", - "Epoch 26938/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4546e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.4216e-05\n", - "Epoch 26959/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6558e-05\n", - "Epoch 26960/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 2.7741e-05\n", - "Epoch 26961/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.8454e-05\n", - "Epoch 26962/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7167e-05\n", - "Epoch 26963/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6216e-05\n", - "Epoch 26964/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.8900e-05\n", - "Epoch 26965/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1199e-04\n", - "Epoch 26966/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7643e-04\n", - "Epoch 26967/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4175e-04\n", - "Epoch 26968/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.8195e-05\n", - "Epoch 26969/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.9880e-05\n", - "Epoch 26970/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.4876e-05\n", - "Epoch 26971/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.4510e-05\n", - "Epoch 26972/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.4896e-05\n", - "Epoch 26973/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.2310e-05\n", - "Epoch 26974/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.4270e-05\n", - "Epoch 26975/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.2708e-05\n", - "Epoch 26976/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1721e-05\n", - "Epoch 26977/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 2.2136e-05\n", - "Epoch 26988/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2402e-05\n", - "Epoch 26989/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0936e-05\n", - "Epoch 26990/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1420e-05\n", - "Epoch 26991/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2483e-05\n", - "Epoch 26992/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1144e-05\n", - "Epoch 26993/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1456e-05\n", - "Epoch 26994/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0421e-05\n", - "Epoch 26995/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1660e-05\n", - "Epoch 26996/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0939e-05\n", - 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[==============================] - 0s 11ms/step - loss: 2.0203e-05\n", - "Epoch 27017/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0583e-05\n", - "Epoch 27018/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2282e-05\n", - "Epoch 27019/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2098e-05\n", - "Epoch 27020/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0471e-05\n", - "Epoch 27021/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1555e-05\n", - "Epoch 27022/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8258e-05\n", - "Epoch 27023/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9451e-05\n", - "Epoch 27024/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0709e-05\n", - "Epoch 27025/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8411e-05\n", - 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"stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 1.1347e-05\n", - "Epoch 28953/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1102e-05\n", - "Epoch 28954/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1389e-05\n", - "Epoch 28955/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1125e-05\n", - "Epoch 28956/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1661e-05\n", - "Epoch 28957/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1700e-05\n", - "Epoch 28958/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3256e-05\n", - "Epoch 28959/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2147e-05\n", - "Epoch 28960/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1268e-05\n", - "Epoch 28961/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 13ms/step - loss: 1.4788e-05\n", - "Epoch 29030/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.3361e-05\n", - "Epoch 29031/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.5716e-05\n", - "Epoch 29032/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.4491e-05\n", - "Epoch 29033/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.0193e-05\n", - "Epoch 29034/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.9272e-05\n", - "Epoch 29035/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.4714e-05\n", - "Epoch 29036/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.1858e-05\n", - "Epoch 29037/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.0451e-05\n", - "Epoch 29038/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.2258e-05\n", - 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11ms/step - loss: 1.5985e-05\n", - "Epoch 29049/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.6282e-05\n", - "Epoch 29050/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4928e-05\n", - "Epoch 29051/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3506e-05\n", - "Epoch 29052/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2638e-05\n", - "Epoch 29053/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1376e-05\n", - "Epoch 29054/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1761e-05\n", - "Epoch 29055/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1228e-05\n", - "Epoch 29056/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2898e-05\n", - "Epoch 29057/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1820e-05\n", - "Epoch 29058/50000\n", - "7/7 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"Epoch 30416/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5149e-04\n", - "Epoch 30417/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.9145e-05\n", - "Epoch 30418/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.6298e-05\n", - "Epoch 30419/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1113e-04\n", - "Epoch 30420/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0255e-04\n", - "Epoch 30421/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.6734e-05\n", - "Epoch 30422/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 9.3542e-05\n", - "Epoch 30423/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.3275e-05\n", - "Epoch 30424/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 13ms/step - loss: 7.3849e-05\n", - "Epoch 30425/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.4921e-05\n", - "Epoch 30426/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.4640e-05\n", - "Epoch 30427/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.1544e-05\n", - "Epoch 30428/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9716e-05\n", - "Epoch 30429/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3887e-04\n", - "Epoch 30430/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9610e-05\n", - "Epoch 30431/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.9877e-05\n", - "Epoch 30432/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.5305e-05\n", - "Epoch 30433/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.5198e-05\n", - "Epoch 30434/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 7.2354e-07\n", - "Epoch 30609/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.5926e-07\n", - "Epoch 30610/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.7251e-07\n", - "Epoch 30611/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.9550e-07\n", - "Epoch 30612/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.1488e-07\n", - "Epoch 30613/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7931e-07\n", - "Epoch 30614/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3074e-06\n", - "Epoch 30615/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0787e-06\n", - "Epoch 30616/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3515e-06\n", - "Epoch 30617/50000\n", - "7/7 [==============================] - 0s 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"stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 3.4796e-05\n", - "Epoch 33093/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1403e-05\n", - "Epoch 33094/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.1091e-05\n", - "Epoch 33095/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.0811e-05\n", - "Epoch 33096/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3025e-05\n", - "Epoch 33097/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.1240e-05\n", - "Epoch 33098/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.3728e-05\n", - "Epoch 33099/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.6165e-05\n", - "Epoch 33100/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.1283e-05\n", - "Epoch 33101/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 7.0067e-06\n", - "Epoch 34846/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6719e-06\n", - "Epoch 34847/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0048e-05\n", - "Epoch 34848/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.1147e-06\n", - "Epoch 34849/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.9373e-06\n", - "Epoch 34850/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.5595e-06\n", - "Epoch 34851/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7333e-06\n", - "Epoch 34852/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.2702e-05\n", - "Epoch 34853/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5646e-05\n", - "Epoch 34854/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 8.3583e-06\n", - 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"stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 6.3847e-06\n", - "Epoch 37417/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9692e-06\n", - "Epoch 37418/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.3123e-06\n", - "Epoch 37419/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1583e-05\n", - "Epoch 37420/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6687e-05\n", - "Epoch 37421/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.3051e-05\n", - "Epoch 37422/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.0317e-06\n", - "Epoch 37423/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 9.0442e-06\n", - "Epoch 37424/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0759e-05\n", - "Epoch 37425/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 12ms/step - loss: 2.0061e-06\n", - "Epoch 37735/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.0950e-06\n", - "Epoch 37736/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.1839e-06\n", - "Epoch 37737/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6224e-06\n", - "Epoch 37738/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3901e-06\n", - "Epoch 37739/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1574e-06\n", - "Epoch 37740/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5711e-06\n", - "Epoch 37741/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4687e-06\n", - "Epoch 37742/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.5395e-06\n", - "Epoch 37743/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.9643e-06\n", - 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[==============================] - 0s 11ms/step - loss: 4.0520e-06\n", - "Epoch 37764/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.6428e-06\n", - "Epoch 37765/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.7166e-06\n", - "Epoch 37766/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.3465e-06\n", - "Epoch 37767/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7502e-06\n", - "Epoch 37768/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4421e-06\n", - "Epoch 37769/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5173e-06\n", - "Epoch 37770/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4224e-06\n", - "Epoch 37771/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7219e-06\n", - "Epoch 37772/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1571e-06\n", - 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[==============================] - 0s 11ms/step - loss: 4.7616e-06\n", - "Epoch 37802/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 5.0812e-06\n", - "Epoch 37803/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.7419e-06\n", - "Epoch 37804/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 4.0494e-06\n", - "Epoch 37805/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.3112e-06\n", - "Epoch 37806/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 6.2114e-06\n", - "Epoch 37807/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.5206e-06\n", - "Epoch 37808/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7640e-06\n", - "Epoch 37809/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4818e-06\n", - "Epoch 37810/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.0308e-06\n", - 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[==============================] - 0s 12ms/step - loss: 1.0834e-04\n", - "Epoch 37831/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 5.9072e-05\n", - "Epoch 37832/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.7451e-05\n", - "Epoch 37833/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 4.0281e-05\n", - "Epoch 37834/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.1374e-05\n", - "Epoch 37835/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 3.4982e-05\n", - "Epoch 37836/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.3362e-05\n", - "Epoch 37837/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.9914e-05\n", - "Epoch 37838/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.4116e-05\n", - "Epoch 37839/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.3624e-05\n", - 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[==============================] - 0s 13ms/step - loss: 3.8309e-05\n", - "Epoch 39411/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.4411e-05\n", - "Epoch 39412/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.9916e-05\n", - "Epoch 39413/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.7863e-05\n", - "Epoch 39414/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5408e-05\n", - "Epoch 39415/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5524e-05\n", - "Epoch 39416/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.4642e-05\n", - "Epoch 39417/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2116e-05\n", - "Epoch 39418/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0530e-05\n", - "Epoch 39419/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0116e-05\n", - 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[==============================] - 0s 12ms/step - loss: 9.5087e-06\n", - "Epoch 39440/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 9.3842e-06\n", - "Epoch 39441/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7309e-06\n", - "Epoch 39442/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.6794e-06\n", - "Epoch 39443/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0357e-05\n", - "Epoch 39444/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.8119e-05\n", - "Epoch 39445/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1915e-05\n", - "Epoch 39446/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0549e-05\n", - "Epoch 39447/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0620e-05\n", - "Epoch 39448/50000\n", - "7/7 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[==============================] - 0s 13ms/step - loss: 2.1267e-06\n", - "Epoch 42854/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.8368e-06\n", - "Epoch 42855/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.2964e-06\n", - "Epoch 42856/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.2213e-06\n", - "Epoch 42857/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 1.0944e-06\n", - "Epoch 42858/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.5814e-06\n", - "Epoch 42859/50000\n", - "7/7 [==============================] - 0s 17ms/step - loss: 1.1158e-06\n", - "Epoch 42860/50000\n", - "7/7 [==============================] - 0s 16ms/step - loss: 7.9675e-07\n", - "Epoch 42861/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.0887e-06\n", - "Epoch 42862/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 9.7343e-07\n", - 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"Epoch 43027/50000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7/7 [==============================] - 0s 11ms/step - loss: 1.9751e-05\n", - "Epoch 43028/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 2.1007e-05\n", - "Epoch 43029/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7124e-05\n", - "Epoch 43030/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6615e-05\n", - "Epoch 43031/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4610e-05\n", - "Epoch 43032/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4102e-05\n", - "Epoch 43033/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.3383e-05\n", - "Epoch 43034/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6234e-05\n", - "Epoch 43035/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.8382e-05\n", - 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[==============================] - 0s 13ms/step - loss: 1.4949e-05\n", - "Epoch 43056/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.7834e-05\n", - "Epoch 43057/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5944e-05\n", - "Epoch 43058/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.3280e-05\n", - "Epoch 43059/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.3367e-05\n", - "Epoch 43060/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.3094e-05\n", - "Epoch 43061/50000\n", - "7/7 [==============================] - 0s 16ms/step - loss: 1.0960e-05\n", - "Epoch 43062/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 8.8424e-06\n", - "Epoch 43063/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 8.7432e-06\n", - "Epoch 43064/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.0358e-05\n", - 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[==============================] - 0s 11ms/step - loss: 1.4165e-05\n", - "Epoch 43229/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.0005e-05\n", - "Epoch 43230/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5544e-05\n", - "Epoch 43231/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.4702e-05\n", - "Epoch 43232/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.1398e-05\n", - "Epoch 43233/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2091e-05\n", - "Epoch 43234/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.6230e-05\n", - "Epoch 43235/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 6.6845e-06\n", - "Epoch 43236/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2940e-05\n", - "Epoch 43237/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.6532e-05\n", - 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[==============================] - 0s 14ms/step - loss: 2.7320e-05\n", - "Epoch 43258/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7625e-05\n", - "Epoch 43259/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.0003e-05\n", - "Epoch 43260/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 3.4829e-05\n", - "Epoch 43261/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 2.7798e-05\n", - "Epoch 43262/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 2.2164e-05\n", - "Epoch 43263/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.9165e-05\n", - "Epoch 43264/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.7024e-05\n", - "Epoch 43265/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.4928e-05\n", - "Epoch 43266/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.2713e-05\n", - 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[==============================] - 0s 14ms/step - loss: 1.6810e-05\n", - "Epoch 43277/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.7967e-05\n", - "Epoch 43278/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.3057e-05\n", - "Epoch 43279/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.3917e-05\n", - "Epoch 43280/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1796e-05\n", - "Epoch 43281/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.5818e-05\n", - "Epoch 43282/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.0157e-05\n", - "Epoch 43283/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 6.1765e-05\n", - "Epoch 43284/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 3.3659e-05\n", - "Epoch 43285/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 2.6422e-05\n", - 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[==============================] - 0s 12ms/step - loss: 1.3376e-06\n", - "Epoch 43402/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1438e-06\n", - "Epoch 43403/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0462e-06\n", - "Epoch 43404/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 8.7771e-07\n", - "Epoch 43405/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.0987e-06\n", - "Epoch 43406/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.6762e-07\n", - "Epoch 43407/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.1265e-06\n", - "Epoch 43408/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5209e-06\n", - "Epoch 43409/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2177e-06\n", - "Epoch 43410/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.0026e-06\n", - 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[==============================] - 0s 13ms/step - loss: 4.5550e-07\n", - "Epoch 43460/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.7984e-07\n", - "Epoch 43461/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 5.0551e-07\n", - "Epoch 43462/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 4.9062e-07\n", - "Epoch 43463/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 5.0418e-07\n", - "Epoch 43464/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 3.8128e-07\n", - "Epoch 43465/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 5.7372e-07\n", - "Epoch 43466/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 7.2306e-07\n", - "Epoch 43467/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 7.7589e-07\n", - "Epoch 43468/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 8.3435e-07\n", - 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[==============================] - 0s 12ms/step - loss: 1.7561e-05\n", - "Epoch 43518/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.2382e-05\n", - "Epoch 43519/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5336e-05\n", - "Epoch 43520/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.7958e-05\n", - "Epoch 43521/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5001e-05\n", - "Epoch 43522/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.0526e-05\n", - "Epoch 43523/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 9.0907e-06\n", - "Epoch 43524/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.9229e-06\n", - "Epoch 43525/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.1174e-06\n", - "Epoch 43526/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 7.0494e-06\n", - 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"stream", - "text": [ - "7/7 [==============================] - 0s 12ms/step - loss: 1.5880e-05\n", - "Epoch 45511/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 1.5010e-05\n", - "Epoch 45512/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.8003e-05\n", - "Epoch 45513/50000\n", - "7/7 [==============================] - 0s 13ms/step - loss: 1.4155e-05\n", - "Epoch 45514/50000\n", - "7/7 [==============================] - 0s 15ms/step - loss: 1.1731e-05\n", - "Epoch 45515/50000\n", - "7/7 [==============================] - 0s 14ms/step - loss: 1.1871e-05\n", - "Epoch 45516/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.5284e-05\n", - "Epoch 45517/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.2316e-05\n", - "Epoch 45518/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.1150e-05\n", - "Epoch 45519/50000\n", - "7/7 [==============================] - 0s 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[==============================] - 0s 11ms/step - loss: 6.7405e-06\n", - "Epoch 49450/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.1168e-06\n", - "Epoch 49451/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.7316e-06\n", - "Epoch 49452/50000\n", - "7/7 [==============================] - 0s 11ms/step - loss: 7.4038e-06\n", - "Epoch 49453/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.4025e-06\n", - "Epoch 49454/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 6.3590e-06\n", - "Epoch 49455/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.6321e-05\n", - "Epoch 49456/50000\n", - "7/7 [==============================] - 0s 12ms/step - loss: 1.3289e-05\n", - "Epoch 49457/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 1.0026e-05\n", - "Epoch 49458/50000\n", - "7/7 [==============================] - 0s 10ms/step - loss: 7.8640e-06\n", - 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6ms/step - loss: 0.0029\n", + "Epoch 326/400\n", + "7/7 [==============================] - 0s 7ms/step - loss: 0.0028\n", + "Epoch 327/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0026\n", + "Epoch 328/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 329/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 330/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0032\n", + "Epoch 331/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0024\n", + "Epoch 332/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0030\n", + "Epoch 333/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 334/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 335/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0031\n", + "Epoch 336/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 337/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 338/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0024\n", + "Epoch 339/400\n", + "7/7 [==============================] - 0s 21ms/step - loss: 0.0032\n", + "Epoch 340/400\n", + "7/7 [==============================] - 0s 7ms/step - loss: 0.0027\n", + "Epoch 341/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0028\n", + "Epoch 342/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0033\n", + "Epoch 343/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0025\n", + "Epoch 344/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 345/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 346/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 347/400\n", + "7/7 [==============================] - 0s 6ms/step - loss: 0.0035\n", + "Epoch 348/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 349/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 350/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0031\n", + "Epoch 351/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 352/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 353/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 354/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0023\n", + "Epoch 355/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 356/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0031\n", + "Epoch 357/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 358/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 359/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0032\n", + "Epoch 360/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 361/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 362/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0034\n", + "Epoch 363/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0032\n", + "Epoch 364/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 365/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0034\n", + "Epoch 366/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 367/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 368/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 369/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 370/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 371/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 372/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 373/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 374/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 375/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0031\n", + "Epoch 376/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 377/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 378/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 379/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 380/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0029\n", + "Epoch 381/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 382/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0025\n", + "Epoch 383/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0032\n", + "Epoch 384/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0030\n", + "Epoch 385/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0024\n", + "Epoch 386/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 387/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0031\n", + "Epoch 388/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0023\n", + "Epoch 389/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0024\n", + "Epoch 390/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 391/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 392/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 393/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0025\n", + "Epoch 394/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 395/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0028\n", + "Epoch 396/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0032\n", + "Epoch 397/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0026\n", + "Epoch 398/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 399/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n", + "Epoch 400/400\n", + "7/7 [==============================] - 0s 5ms/step - loss: 0.0027\n" ] } ], @@ -102200,12 +1314,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 145, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -102219,7 +1333,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1440.9631330881728.\n" + "The root mean squared error is 15174.32081730476.\n" ] } ], @@ -102231,12 +1345,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 146, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -102250,7 +1364,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 48966.743530875305.\n" + "The root mean squared error is 33819.83961019418.\n" ] } ], @@ -102261,12 +1375,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 147, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -102283,7 +1397,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -102299,7 +1413,7 @@ "4" ] }, - "execution_count": 16, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -102313,7 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -102339,7 +1453,7 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)\n", "y_test_year = y_test_year.astype(np.int64)\n", @@ -102349,7 +1463,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -102386,19 +1500,19 @@ " \n", " \n", " 0\n", - " 237144\n", + " 258548\n", " \n", " \n", " 1\n", - " 223614\n", + " 305917\n", " \n", " \n", " 2\n", - " 187817\n", + " 303982\n", " \n", " \n", " 3\n", - " 265150\n", + " 445359\n", " \n", " \n", "\n", @@ -102406,13 +1520,13 @@ ], "text/plain": [ " Count\n", - "0 237144\n", - "1 223614\n", - "2 187817\n", - "3 265150" + "0 258548\n", + "1 305917\n", + "2 303982\n", + "3 445359" ] }, - "execution_count": 18, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -102424,7 +1538,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -102432,7 +1546,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 216167.08073386198.\n" + "The root mean squared error is 130595.04410294443.\n" ] } ], @@ -102474,7 +1588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/monthly_simple_lstm.ipynb b/monthly_simple_lstm.ipynb index 698dd60..d2f3e09 100644 --- a/monthly_simple_lstm.ipynb +++ b/monthly_simple_lstm.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,6 @@ "from tensorflow.keras.optimizers import SGD\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -34,23 +33,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "# salmon_data = pd.read_csv(r\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\")\n", - "# salmon_data.head()\n", - "# salmon_copy = salmon_data # Create a copy for us to work with \n", "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -62,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -87,16 +80,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -195,7 +188,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 37, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -241,7 +234,7 @@ "(984, 1)" ] }, - "execution_count": 38, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -293,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -311,24 +304,13 @@ " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - "# print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - "# print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", - "# print(king_train_norm)\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", - " print('king_test_norm')\n", - " print(king_test_norm.shape)\n", - " print('king_train_norm')\n", - " print(king_train_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + "\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -336,8 +318,6 @@ " y_test_not_norm = []\n", " y_train_not_norm = []\n", " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", " for i in range(6,924): # 30\n", " x_train.append(king_train_norm[i-6:i])\n", " y_train.append(king_train_norm[i])\n", @@ -356,24 +336,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(60, 2)\n", - "717915\n", - "294611\n", - "king_test_norm\n", - "(60, 1)\n", - "king_train_norm\n", - "(924, 1)\n", - "(54, 1)\n", - "(54, 1)\n", - "(918, 1)\n", - "(918, 1)\n" + "(60, 2)\n" ] } ], @@ -386,18 +356,14 @@ "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -434,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -445,12 +411,9 @@ " '''\n", " LSTM_model = Sequential()\n", " LSTM_model.add(LSTM(5, input_shape=(x_train.shape[1],1)))\n", - " #LSTM_model.add(LSTM(5, return_sequences=True))\n", - " #LSTM_model.add(LSTM(5, return_sequences=True))\n", - " #LSTM_model.add(LSTM(1))\n", " LSTM_model.add(Dense(1))\n", " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", - " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=3000, batch_size=300, verbose=2)\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=1000, batch_size=150, verbose=2)\n", " \n", " train_preds = LSTM_model.predict(x_train)\n", " test_preds = LSTM_model.predict(x_test)\n", @@ -464,6097 +427,2037 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/3000\n", - "4/4 - 2s - loss: 0.0119\n", - "Epoch 2/3000\n", - "4/4 - 0s - loss: 0.0107\n", - "Epoch 3/3000\n", - "4/4 - 0s - loss: 0.0098\n", - "Epoch 4/3000\n", - "4/4 - 0s - loss: 0.0093\n", - "Epoch 5/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 6/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 7/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 8/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 9/3000\n", - "4/4 - 0s - loss: 0.0093\n", - "Epoch 10/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 11/3000\n", - "4/4 - 0s - loss: 0.0092\n", - "Epoch 12/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 13/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 14/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 15/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 16/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 17/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 18/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 19/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 20/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 21/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 22/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 23/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 24/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 25/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 26/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 27/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 28/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 29/3000\n", - "4/4 - 0s - loss: 0.0091\n", - "Epoch 30/3000\n", - "4/4 - 0s - loss: 0.0090\n", - "Epoch 31/3000\n", - "4/4 - 0s - loss: 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969/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 970/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 971/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 972/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 973/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 974/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 975/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 976/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 977/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 978/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 979/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 980/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 981/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 982/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 983/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 984/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 985/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 986/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 987/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 988/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 989/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 990/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 991/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 992/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 993/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 994/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 995/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 996/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 997/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 998/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 999/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1000/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1001/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1002/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1003/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1004/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1005/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1006/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1007/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1008/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1009/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1010/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1011/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1012/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1013/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1014/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1015/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1016/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1017/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1018/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1019/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1020/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1021/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1022/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1023/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1024/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1025/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1026/3000\n", - "4/4 - 0s - loss: 0.0083\n", - "Epoch 1027/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1028/3000\n", - "4/4 - 0s - loss: 0.0083\n", - "Epoch 1029/3000\n", - "4/4 - 0s - loss: 0.0084\n", - "Epoch 1030/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1031/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1032/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1033/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1034/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1035/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1036/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1037/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1038/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1039/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1040/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1041/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1042/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1043/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1044/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1045/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1046/3000\n", - "4/4 - 0s - loss: 0.0082\n", - "Epoch 1047/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1048/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1049/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1050/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1051/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1052/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1053/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1054/3000\n", - "4/4 - 0s - loss: 0.0081\n", - "Epoch 1055/3000\n", - "4/4 - 0s - loss: 0.0081\n" + "Epoch 1/1000\n", + "7/7 - 1s - loss: 0.0110\n", + "Epoch 2/1000\n", + "7/7 - 0s - loss: 0.0098\n", + "Epoch 3/1000\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 4/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 5/1000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 6/1000\n", + "7/7 - 0s - loss: 0.0091\n", + "Epoch 7/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 8/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 9/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 10/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 11/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 12/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 13/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 14/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 15/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 16/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 17/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 18/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 19/1000\n", + "7/7 - 0s - loss: 0.0090\n", + "Epoch 20/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 21/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 22/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 23/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 24/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 25/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 26/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 27/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 28/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 29/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 30/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 31/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 32/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 33/1000\n", + "7/7 - 0s - loss: 0.0089\n", + "Epoch 34/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 35/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 36/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 37/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 38/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 39/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 40/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 41/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 42/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 43/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 44/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 45/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 46/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 47/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 48/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 49/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 50/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 51/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 52/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 53/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 54/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 55/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 56/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 57/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 58/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 59/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 60/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 61/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 62/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 63/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 64/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 65/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 66/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 67/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 68/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 69/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 70/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 71/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 72/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 73/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 74/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 75/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 76/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 77/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 78/1000\n", + "7/7 - 0s - loss: 0.0088\n", + "Epoch 79/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 80/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 81/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 82/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 83/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 84/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 85/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 86/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 87/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 88/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 89/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 90/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 91/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 92/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 93/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 94/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 95/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 96/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 97/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 98/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 99/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 100/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 101/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 102/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 103/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 104/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 105/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 106/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 107/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 108/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 109/1000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 110/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 111/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 112/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 113/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 114/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 115/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 116/1000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 117/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 118/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 119/1000\n", + "7/7 - 0s - loss: 0.0087\n", + "Epoch 120/1000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 121/1000\n", + "7/7 - 0s - loss: 0.0086\n", + "Epoch 122/1000\n", + 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1547/3000\n", - "4/4 - 0s - loss: 0.0078\n", - "Epoch 1548/3000\n", - "4/4 - 0s - loss: 0.0079\n", - "Epoch 1549/3000\n", - "4/4 - 0s - loss: 0.0078\n", - "Epoch 1550/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1551/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1552/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1553/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1554/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1555/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1556/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1557/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1558/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1559/3000\n", - "4/4 - 0s - loss: 0.0078\n", - "Epoch 1560/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1561/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1562/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1563/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1564/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1565/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1566/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1567/3000\n", - "4/4 - 0s - loss: 0.0079\n", - "Epoch 1568/3000\n", - "4/4 - 0s - loss: 0.0080\n", - "Epoch 1569/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1570/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1571/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1572/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1573/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1574/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1575/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1576/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1577/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1578/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1579/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1580/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1581/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1582/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1583/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1584/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1585/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1586/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1587/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1588/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1589/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1590/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1591/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1592/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1593/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1594/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1595/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1596/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1597/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1598/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1599/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1600/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1601/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1602/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1603/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1604/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1605/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1606/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1607/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1608/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1609/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1610/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1611/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1612/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1613/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1614/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1615/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1616/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1617/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1618/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1619/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1620/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1621/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1622/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1623/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1624/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1625/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1626/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1627/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1628/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1629/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1630/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1631/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1632/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1633/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1634/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1635/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1636/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1637/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1638/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1639/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1640/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1641/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1642/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1643/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1644/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1645/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1646/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1647/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1648/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1649/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1650/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1651/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1652/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1653/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1654/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1655/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1656/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1657/3000\n", - "4/4 - 0s - loss: 0.0077\n", - "Epoch 1658/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1659/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1660/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1661/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1662/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1663/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1664/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1665/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1666/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1667/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1668/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1669/3000\n", - "4/4 - 0s - loss: 0.0076\n", - "Epoch 1670/3000\n", - "4/4 - 0s - loss: 0.0076\n" + "7/7 - 0s - loss: 0.0079\n", + "Epoch 636/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 637/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 638/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 639/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 640/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 641/1000\n", + "7/7 - 0s - loss: 0.0081\n", + "Epoch 642/1000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 643/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 644/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 645/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 646/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 647/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 648/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 649/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 650/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 651/1000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 652/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 653/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 654/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 655/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 656/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 657/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 658/1000\n", + "7/7 - 0s - loss: 0.0079\n", + "Epoch 659/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 660/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 661/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 662/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 663/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 664/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 665/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 666/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 667/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 668/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 669/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 670/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 671/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 672/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 673/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 674/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 675/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 676/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 677/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 678/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 679/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 680/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 681/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 682/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 683/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 684/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 685/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 686/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 687/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 688/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 689/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 690/1000\n", + "7/7 - 0s - loss: 0.0078\n", + "Epoch 691/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 692/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 693/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 694/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 695/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 696/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 697/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 698/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 699/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 700/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 701/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 702/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 703/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 704/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 705/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 706/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 707/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 708/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 709/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 710/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 711/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 712/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 713/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 714/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 715/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 716/1000\n", + "7/7 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loss: 0.0036\n", - "Epoch 2977/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2978/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2979/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2980/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2981/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2982/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2983/3000\n", - "4/4 - 0s - loss: 0.0037\n", - "Epoch 2984/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2985/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2986/3000\n", - "4/4 - 0s - loss: 0.0037\n", - "Epoch 2987/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2988/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2989/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2990/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2991/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2992/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2993/3000\n", - "4/4 - 0s - loss: 0.0037\n", - "Epoch 2994/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2995/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2996/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2997/3000\n", - "4/4 - 0s - loss: 0.0037\n", - "Epoch 2998/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 2999/3000\n", - "4/4 - 0s - loss: 0.0036\n", - "Epoch 3000/3000\n", - "4/4 - 0s - loss: 0.0035\n" + "Epoch 846/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 847/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 848/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 849/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 850/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 851/1000\n", + "7/7 - 0s - loss: 0.0076\n", + "Epoch 852/1000\n", + "7/7 - 0s - loss: 0.0075\n", + "Epoch 853/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 854/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 855/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 856/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 857/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 858/1000\n", + "7/7 - 0s - loss: 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"Epoch 953/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 954/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 955/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 956/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 957/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 958/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 959/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 960/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 961/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 962/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 963/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 964/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 965/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 966/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 967/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 968/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 969/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 970/1000\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 971/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 972/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 973/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 974/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 975/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 976/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 977/1000\n", + "7/7 - 0s - loss: 0.0080\n", + "Epoch 978/1000\n", + "7/7 - 0s - loss: 0.0072\n", + "Epoch 979/1000\n", + "7/7 - 0s - loss: 0.0071\n", + "Epoch 980/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 981/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 982/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 983/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 984/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 985/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 986/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 987/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 988/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 989/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 990/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 991/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 992/1000\n", + "7/7 - 0s - loss: 0.0069\n", + "Epoch 993/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 994/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 995/1000\n", + "7/7 - 0s - loss: 0.0070\n", + "Epoch 996/1000\n", + "7/7 - 0s - loss: 0.0077\n", + "Epoch 997/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 998/1000\n", + "7/7 - 0s - loss: 0.0074\n", + "Epoch 999/1000\n", + "7/7 - 0s - loss: 0.0073\n", + "Epoch 1000/1000\n", + "7/7 - 0s - loss: 0.0070\n" ] } ], @@ -6565,12 +2468,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -6584,7 +2487,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 17528.772884745023.\n" + "The root mean squared error is 24708.75384775695.\n" ] } ], @@ -6595,12 +2498,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -6614,7 +2517,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 54148.83127675472.\n" + "The root mean squared error is 55689.75246047483.\n" ] } ], @@ -6625,12 +2528,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -6647,7 +2550,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -6656,45 +2559,23 @@ "text": [ "49\n" ] - }, - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "# global var for baseline\n", - "y_test_year = month_to_year(y_test)\n", - "len(y_test)\n", - "len(y_test_year)" + "y_test_year = month_to_year(y_test)" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "49\n", - " Count\n", - "0 498710\n", - "1 439060\n", - "2 294840\n", - "3 347600\n", - " Count\n", - "0 488943\n", - "1 336031\n", - "2 381766\n", - "3 535809\n" + "49\n" ] } ], @@ -6703,17 +2584,14 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", - "print(traditional)\n", - "y_test_year = y_test_year.astype(np.int64)\n", - "print(y_test_year)\n", - "# print(GRU_test_year)" + "y_test_year = y_test_year.astype(np.int64)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -6732,7 +2610,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -6762,19 +2640,19 @@ " \n", " \n", " 0\n", - " 567283\n", + " 225430\n", " \n", " \n", " 1\n", - " 499230\n", + " 248594\n", " \n", " \n", " 2\n", - " 390270\n", + " 230231\n", " \n", " \n", " 3\n", - " 534137\n", + " 243977\n", " \n", " \n", "\n", @@ -6782,13 +2660,13 @@ ], "text/plain": [ " Count\n", - "0 567283\n", - "1 499230\n", - "2 390270\n", - "3 534137" + "0 225430\n", + "1 248594\n", + "2 230231\n", + "3 243977" ] }, - "execution_count": 52, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -6799,7 +2677,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -6807,7 +2685,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 90617.57942171044.\n" + "The root mean squared error is 215181.95938960588.\n" ] } ], @@ -6816,6 +2694,13 @@ "return_rmse(y_test_year, traditional)\n", "return_rmse(y_test_year, LSTM_test_year)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -6834,7 +2719,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/monthly_simple_rnn.ipynb b/monthly_simple_rnn.ipynb index 099e144..53d4681 100644 --- a/monthly_simple_rnn.ipynb +++ b/monthly_simple_rnn.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,6 @@ "from tensorflow.keras.optimizers import SGD\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -34,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -44,13 +43,10 @@ "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -62,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -87,16 +83,16 @@ } ], "source": [ - " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", - " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", - " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", - " print(king_all_copy)" + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -195,7 +191,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 43, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -241,7 +237,7 @@ "(984, 1)" ] }, - "execution_count": 44, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -321,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -339,33 +335,20 @@ " \n", " # Normalizing Data\n", " king_training[king_training[\"king\"] < 0] = 0 \n", - "# print('max val king_train:')\n", - " print(max(king_training['king']))\n", " king_test[king_test[\"king\"] < 0] = 0\n", - "# print('max val king_test:')\n", - " print(max(king_test['king']))\n", " king_train_pre = king_training[\"king\"].to_frame()\n", - "# print(king_train_norm)\n", " king_test_pre = king_test[\"king\"].to_frame()\n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " king_train_norm = scaler.fit_transform(king_train_pre)\n", " king_test_norm = scaler.fit_transform(king_test_pre)\n", - " print('king_test_norm')\n", - " print(king_test_norm.shape)\n", - " print('king_train_norm')\n", - " print(king_train_norm.shape)\n", - " #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - " #print(type(king_train_norm))\n", - " #king_train_norm = king_train_norm.to_frame()\n", + "\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", " y_test = []\n", " y_test_not_norm = []\n", " y_train_not_norm = []\n", - " \n", - " # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", + " \n", " for i in range(6,924): # 30\n", " x_train.append(king_train_norm[i-6:i])\n", " y_train.append(king_train_norm[i])\n", @@ -384,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -392,12 +375,6 @@ "output_type": "stream", "text": [ "(60, 2)\n", - "717915\n", - "294611\n", - "king_test_norm\n", - "(60, 1)\n", - "king_train_norm\n", - "(924, 1)\n", "(54, 1)\n", "(54, 1)\n", "(918, 1)\n", @@ -425,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -462,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -482,7 +459,7 @@ " model.compile(optimizer='adam', loss='mean_squared_error')\n", "\n", " # fit the RNN model\n", - " history = model.fit(x_train, y_train, epochs=200, batch_size=64)\n", + " history = model.fit(x_train, y_train, epochs=300, batch_size=64)\n", "\n", " print(\"predicting\")\n", " # Finalizing predictions\n", @@ -495,434 +472,631 @@ " RNN_test_preds = scaler.inverse_transform(RNN_test_preds)\n", " RNN_test_preds = RNN_test_preds.astype(np.int64)\n", " y_test = scaler.inverse_transform(y_test)\n", - "# RNN_salmon_count = (RNN_preds * (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"])) + np.min(king_training[\"king\"])).astype(np.int64)\n", "\n", - "# why are we normalizing the test and train set, then un-normalizing (maybe this can cause problems in the sense tht we are\n", - "# not comparing our preds to the proper y values)\n", " return model, RNN_train_preds, RNN_test_preds, history, y_train, y_test" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/200\n", - "15/15 [==============================] - 1s 1ms/step - loss: 0.0129\n", - "Epoch 2/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0097\n", - "Epoch 3/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0092\n", - "Epoch 4/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0087\n", - "Epoch 5/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0086\n", - "Epoch 6/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 7/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 8/200\n", - "15/15 [==============================] - 0s 858us/step - loss: 0.0084\n", - "Epoch 9/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 10/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 11/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 12/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 13/200\n", - "15/15 [==============================] - 0s 858us/step - loss: 0.0084\n", - "Epoch 14/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 15/200\n", - "15/15 [==============================] - 0s 858us/step - loss: 0.0084\n", - "Epoch 16/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 17/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 18/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 19/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 20/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 21/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0085\n", - "Epoch 22/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0083\n", - "Epoch 23/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0083\n", - "Epoch 24/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0084\n", - "Epoch 25/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0084\n", - "Epoch 26/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0083\n", - "Epoch 27/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0082\n", - "Epoch 28/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0083\n", - "Epoch 29/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0083\n", - "Epoch 30/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0083\n", - "Epoch 31/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0082\n", - "Epoch 32/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0081\n", - "Epoch 33/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0081\n", - "Epoch 34/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0080\n", - "Epoch 35/200\n", - "15/15 [==============================] - 0s 858us/step - loss: 0.0080\n", - "Epoch 36/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0080\n", - "Epoch 37/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0079\n", - "Epoch 38/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0079\n", - "Epoch 39/200\n", - "15/15 [==============================] - 0s 987us/step - loss: 0.0079\n", - "Epoch 40/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0078\n", - "Epoch 41/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0079\n", - "Epoch 42/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0075\n", - "Epoch 43/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0076\n", - "Epoch 44/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0074\n", - "Epoch 45/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0074\n", - "Epoch 46/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0071\n", - "Epoch 47/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0070\n", - "Epoch 48/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0075\n", - "Epoch 49/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0069\n", - "Epoch 50/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0069\n", - "Epoch 51/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0068\n", - "Epoch 52/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0067\n", - "Epoch 53/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0067\n", - "Epoch 54/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0066\n", - "Epoch 55/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0065\n", - "Epoch 56/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0065\n", - "Epoch 57/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0064\n", - "Epoch 58/200\n", - "15/15 [==============================] - 0s 924us/step - loss: 0.0066\n", - "Epoch 59/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0063\n", - "Epoch 60/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0063\n", - "Epoch 61/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0063\n", - "Epoch 62/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0062\n", - "Epoch 63/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0062\n", - "Epoch 64/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0062\n", - "Epoch 65/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 66/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 67/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 68/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0062\n", - "Epoch 69/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0063\n", - "Epoch 70/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 71/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0062\n", - "Epoch 72/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0061\n", - "Epoch 73/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 74/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 75/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 76/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0059\n", - "Epoch 77/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 78/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 79/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0059\n", - "Epoch 80/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0059\n", - "Epoch 81/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 82/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 83/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 84/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0060\n", - "Epoch 85/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 86/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 87/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0059\n", - "Epoch 88/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0061\n", - "Epoch 89/200\n", - "15/15 [==============================] - 0s 929us/step - loss: 0.0058\n", - "Epoch 90/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 91/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 92/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 93/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 94/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0058\n", - "Epoch 95/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0060\n", - "Epoch 96/200\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0057\n", - "Epoch 97/200\n", - "15/15 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2ms/step - loss: 0.0041\n", + "Epoch 292/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0040\n", + "Epoch 293/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", + "Epoch 294/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "Epoch 295/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0040\n", + "Epoch 296/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0033\n", + "Epoch 297/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "Epoch 298/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "Epoch 299/300\n", + "15/15 [==============================] - 0s 1ms/step - loss: 0.0028\n", + "Epoch 300/300\n", + "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", "predicting\n" ] } @@ -933,12 +1107,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -950,7 +1124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 18461.73462511329.\n", + "The root mean squared error is 17280.786003235695.\n", "(918, 1)\n" ] } @@ -964,12 +1138,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -981,7 +1155,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 61001.100830079136.\n" + "The root mean squared error is 53707.14979473472.\n" ] } ], @@ -992,12 +1166,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1012,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1028,7 +1202,7 @@ "4" ] }, - "execution_count": 55, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1042,7 +1216,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1068,7 +1242,7 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)\n", "y_test_year = y_test_year.astype(np.int64)\n", @@ -1078,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1115,19 +1289,19 @@ " \n", " \n", " 0\n", - " 522309\n", + " 431033\n", " \n", " \n", " 1\n", - " 345410\n", + " 305159\n", " \n", " \n", " 2\n", - " 325419\n", + " 288853\n", " \n", " \n", " 3\n", - " 399043\n", + " 376991\n", " \n", " \n", "\n", @@ -1135,13 +1309,13 @@ ], "text/plain": [ " Count\n", - "0 522309\n", - "1 345410\n", - "2 325419\n", - "3 399043" + "0 431033\n", + "1 305159\n", + "2 288853\n", + "3 376991" ] }, - "execution_count": 57, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1153,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1161,7 +1335,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 75962.45579560997.\n" + "The root mean squared error is 97676.29085018534.\n" ] } ], @@ -1202,7 +1376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/multivar_lstm.ipynb b/multivar_robust_gru.ipynb similarity index 62% rename from multivar_lstm.ipynb rename to multivar_robust_gru.ipynb index 2ef57c7..3eb6a51 100644 --- a/multivar_lstm.ipynb +++ b/multivar_robust_gru.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 62, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,6 @@ "import tensorflow.keras\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import LabelEncoder\n", @@ -41,20 +40,17 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -66,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -94,13 +90,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -217,7 +213,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 65, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -263,7 +259,7 @@ "(984, 1)" ] }, - "execution_count": 66, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -391,7 +387,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 68, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -508,7 +504,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 69, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -635,7 +631,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 71, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -669,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -697,16 +693,6 @@ "print(master_data)" ] }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "# type(data_copy['date'])\n", - "# # data_copy['date'].astype(p)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -716,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -893,7 +879,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 77, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -902,13 +888,13 @@ "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", - "cov_data = load_cov_set(abdul_path_cov)\n", + "cov_data = load_cov_set(ismael_path_cov)\n", "cov_data" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -1026,7 +1012,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 78, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -1169,7 +1155,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 79, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -1182,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -1324,7 +1310,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 80, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -1337,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -1491,7 +1477,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 81, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -1504,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -1670,7 +1656,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 82, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -1850,7 +1836,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 83, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -1860,80 +1846,6 @@ "master_data" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_cov_set(ismael_path_pdo)\n", - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = data_copy" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# pdo = pdo[:984]\n", - "# pdo\n", - "# master_data = master_data.join(pdo)\n", - "# # master_data\n", - "# # master_data = master_data[:984]\n", - "# # master_data = master_data.reindex(columns=[\"Date\", \"Month\", \"king\", \"PDO\"])\n", - "# # master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "# # master_data.columns = ['year', 'month', 'king', 'pdo']\n", - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data['year']=pd.to_datetime(master_data[['year','month']])\n", - "# master_data.set_index('date', inplace=True)\n", - "# master_data.index = pd.to_datetime(master_data.index)\n", - "# master_data" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1943,71 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_noi = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/noi.csv'\n", - "# noi_data = load_cov_set(ismael_path_noi)\n", - "# noi_data = noi_data[:877]\n", - "# noi_data = noi_data.drop(labels=0, axis=0)\n", - "# noi_data.reset_index()\n", - "# print(noi_data)\n", - "# print(noi_data['noix'])\n", - "# # noi_data = noi_data.drop(columns=\"index\")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "# noi = noi_data[\"noix\"]\n", - "# # noi\n", - "# print(master_data)\n", - "# master_data = master_data[120:]\n", - "# print(master_data)\n", - "# master_data.reset_index()\n", - "# master_data = master_data.join(noi)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = master_data.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data\n", - "# master_data = master_data.drop(labels=\"index\", axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data.head(700)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 95, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -2171,7 +2019,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 95, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -2184,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -2200,7 +2048,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 150, "metadata": {}, "outputs": [], "source": [ @@ -2208,7 +2056,7 @@ "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=abdul_checkpoint_path,\n", + " filepath=checkpoint_filepath,\n", " save_weights_only=True,\n", " monitor='val_accuracy',\n", " mode='max',\n", @@ -2252,12 +2100,12 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 151, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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GPgP0wA+iKL57Np9nVDmplh/K4bFFBwBJ+ljz1GjNZJUn9sn8Kl776ygvTenFffP3EerrTnJBFc9d3oPD2eW8vzqR/13eg4u7BDOoYyB1jUau/3onCXmVHH39cm79TkrhsT+jjOp6A89M7M57qxMctrG63oBe58r2pCL+PpjDG9f0YVdyMS56HXfN2cuUfmF8/Z/BAAy32s//sYX7ySmvI7OkhoLKejLMkpLeSnU/lFXOFxuT+GyDhVhU1BlYcSiXHLPaqxPgSHa5JrHMnpRijTOurKaBp36JJ7lAIpiVdQbKaxqVMupE87nldeSW1/GfYG1ekao6A8vis5m1JVlz3l7UiIydyUV8uPaEclxnMPHSn4eVY10TnOLOOXvJLa/jzotjCPFxJ9f8vpklUl/ZY9S/xWWxK7mYj24eoBwD7EstoUuoj9JHrnqBFYdy+WaLhemIosiWE4UK8atrNBKXXqKETbqZJV/5+SARo9j0UqVtNQ1GvN1dNG1LL65GFGHsh5u5bVhHbh/WkcX7MjicXU5SQRXH3pC2mJYZhGzWkKXnjQkFPDWhu0a7NJpE9qRaJP3ymkb8PFwVpi1L6WriWGqOQHpzuaSVvndDP03fWZuwDCaRxHytL+6vgzlsO1mkHMumpwbF9CSw5mgeD/1ky7wnfLwFgNSZU7hr7l4OmIWimgYDby4/rpic/DxcCPFxV/pD3ScyrJmarFGUqKKs4tJLNZJ+sZWJtrreoGE2/p6u1DUamTZ7j6VMg4FX/zrCH+awV7k+OQpSHr95dnxArY3znlGY06h+BUxE2nZ8nyAIf4miaGvQbgWsPpKnEMZGo6gxT9QbTDYOsuxSSVLPq6hj3s40JvRqrxnML/x+mD4RfgB8sEaKzEl7dypTP9+mmFUS8rQTorSmgR1JxSzep81nqx68VfUG3ll5nF/NxOjiLsE8s+Sgcn1zYiF/H8zh8Z8PKOeMJpE/9mcpRL60pkFhUAB6nY649FKFwAGctHKcJ+ZVaoitCFz5xXaH7QRYczSPzSo7fGVdI1d8tlXDbKxhTcPLaxt5cnG85tyy+GzNuXXH8nlkYZxybO0/sdbqLKYKW2YjT+yiqnpyymqV1b8uegGjSbTrXH7uV6n/37uhHy56ncWGrROYuyOVPWYfiauLjqd/iVcIHMCqI3k8otJOahqM3PDNLstxvZH5O9MUQgvw/bZUjTBRUt1ASmG1YnrRCQJjPtisXP95bwaxaSWcNDNsNWRCXVot9VFuuTSuPV0lSbegwjLulx/K0fR7eW0jUVjMgLJ/oVBlJlWbzsDWTFNao/029QYTLjpBo8FYjwmDUSSjuEaZVyZRJKPENqxY/X3Laxs187Oq3qgIMHId1qac3DItIbbWhgQB0oqqeXeV5VscytJqNcVWdKOwsp6oQC/NueTCKvallSrHtQ1GlsRa5mJJdQP7M0qZ/ucRQPq+dY1GbpplGScGowmXVtzvSsZ5zyiQcl8kmTcnRBCExUipU88Ko3joJwuhqWkw2EgP1ozCemDmlGt9FT7uLpRZmQsq6xoVJgFw2Gx+kuGm17M3VUuIDmeVc9WXFoKcXFilMAmwqKlKHS46DVEB2JRQwP9+s5jIjuVqmYBeB88siVekSbBMeqUdVmq9PZNaSpF2KwU1kQFpkuSoHMf2fEGCVQxLQp6W+RhNog3j2JlcpImSySrVfgvrY0EQWBafrXFqpxZV89yvBxUin1FcwwM/WsaEXqdjxtLDiq0ZpG/x5GILQ04rruGbzcksPyT5L8pqGnhnpYWIeLu54OmqVzQBkEwjTbW1qt7Aq6p2AmSUaPs5qaBKMWWAfUe/rJnIqGkwkFNWp0TWyVKxLLx4uumJTSvhRhUxyrEinGU1jXyy7oRiciqvbeR4bgUv/CGNNV93FzxctKaVbCufXqHVvKo3mOgY7EWKap5YE+yaBgO3fmdpV4PBZCO570kp5haVMGTN4KvrDXiozD6VdQYaDVpneEqR9ttU1TcS6OWqMLd6g4lf47RC3S6r58ja0dvX9WX5wVwKKus1psC6RpPN+5VYvUtRVT1fb07SOMA3J2qjtqrqDQR4udHaEE4njvhcQhCEG4HJoijebz6eBgwXRfExe+WHDBkixsbG2rvUJGRHnPUH9nDVYTJZ1NsQH3e7YXfDOgWxN7UENxdJkrx6QAR/HczB201PdYORyX3CSCmq4kR+FTHBXqQV13DdoEj+VK2mfHBMZ77dkkKorzsl1Q2agRQV5ElpdSN6ndBiJ6Wvh4vGfunn4UJFnQEfdxeHDjBXvUCglxu9I/w0WoAMaylPJ1ici+rn9g73Y3yvdhoCObV/OCsOWZy/Y7qHsuVEod161BMRJMlWTfj0OsEmSivI2w1RFBkaE8RalSbYFKzrbe/nTr6KsXm46myYsCBoY/ejgjwVhzlAiI8bRSqzizwmWhvWY9HdRWc3AEINeQw4qsPf0xV/T1dFALJ+V4BgbzcNQbYeT/ba4evuQqXVmNMJ8Na1/TQa6he3DWLD8XyWmv1ID4/twpDoQO6bL81pD1cdz03qwUdrTyjfbXB0IAm5FVQ3GJXvOTAqgPjMMvw9XTXzxfpbyGYydXu93PSE+rrj5eai0Y4/umkA761OoKbBaCbIroovw9dssrrhokjF3BkZ4Mnsu4dw23e7lbG85X9jmbsjjXk703DVC/xneDSebnq+2Zys9JtMS6z7zF4fyvPkir5hrDqSx6XdQvjxvuGcDgRBiBNFcYi9axeCRtFsOtTWypltMJmY0KsdL03pxe6UEpbFZ6MTBF6a0gtPNz1vrzhGTYOR+0Z14tahUbz611EKKuvoGebHq1f1ZuaqBA5nlePppuelKb2Y0Ls9S/ZlYjSJ3Da8Ix0CPXlnxXEq6hoZ0z2Ul6b2wk2vI7W4mgBPV54c340wPw/WHctHJwg8PbE7OgE+XX+S2gYjd1/ciRsHd2D6n4cpqKwnMsCTD28awOcbTrIntRh3Fz1vXtOXtOJqZm9PxWgSueviGPpE+PG2+bkXdwnh4bFd+HzDSXYkFRHq687M6/uxObGQxfsycNXreHxcV8L9PXn1r6OU1zZy9YAIbh/WkTeWHyO5sAoPVz2vXNmb3SnF/HkgGy83F16a0pOiqga+2pSEToBbhnZkTPdQjudWkl1Wy0UdA3nhip50DklkX1oJPu6uvHtDP9KLq/lsQxIGo4lHL+tKZIAnb604Tr3BSKcQb/5vck++2ZxMbHoJwd7uPDmhG1mlNcw2S6/PTeqBr4cLby4/ToPBxHWDIrlxcAfeXZ1AfGYZg6MDef7yHiw/lMvifRkEebvzznV9ySyp5cO1iTQYTNwxIppBHQN4c/kxiqsauHZgJFcPjGDl4VwOZpYT4uPGlH7hpBZVs+VEIaIIU/qF4e6iZ+2xPCrqDEzuE0anEB+SC6s4mlPO0Bg3bhrSgeyyOtYcycPf05UbB0sJHhfuSae63sgtQ6MI9/dgaXwOyYVVXNo1hCsHRBCXXsqvsZmE+rrz3KQenMivZP6uNIwmkVuHdqRHmC+frT9JRV0j946KoW+EP2uO5pFcWEWApxsTerfnWE4FifkVBHi6MSQmkNLqBg5kltFgMDG+Vzv8PV3ZnlRMYWUdPu567hgRTUWdgT1mQWl8r3Z0DvFm7bF8GszEK9zfkx3JRRRV1nNH52B6R/ixM6mIxPxKPFz1XDcokvLaRlYdzkMQ4MbBHegc6sOszcnUNBq5qn84/TsE8P22FNKLq7myfwS3D+9Iez935u5Iw9fDheGdgujfwZ+aBiN1BhN3XxxDqI87j13Wldj0EobFBHH/pZ3x83Rl6YFsPF31fHLrQPLK63h3VQLV9QbuuaQT43q24/W/j3Iyv4pJfdpzx4hoZm9PZUdSEe39PPjwpgFsSihg7s5UBAReu7oPLjqBt1cep7rewLQR0UzuG8Yry45yIr+S3hF+3DC4A4Ig+Z5c9To+unkAJdUNvLsqgbpGI49e1pXhnYLILqslpbCaCb3a0zPMj8fHdWP98Xw6BHrSIdCLaSOjSSuuxmAUuXVYFKE+7mSX1pJbXkv/DgHMmNqLrzcnsyWxkDB/D16/ug+7U4pZtDcDUYQnJ3QjMsCT1/8+SllNI3eOjGFSn/Y8/9shPO04xVsDF4JGMRJ4TRTFy83HLwKIojjTQflCIN3etRYiBChqttS/E86+aRrO/nEMZ980jfOhf6JFUQy1d+FCYBQuwAmkJEnZSNnubhdF8WiTN57+82IdqV//djj7pmk4+8cxnH3TNM73/jnvTU+iKBoEQXgMKVe2HphztpiEE0444YQTtjjvGQWAKIorgQtz32gnnHDCiQsczpXZtviurRtwHsPZN03D2T+O4eybpnFe989576NwwgknnHCibeHUKJxwwgknznMIgnC3IAjbVceiIAhdzb9nCYLw8ll9/j9NowgJCRFjYmLauhlOOOHEvww1DUaq6w2E+rq3et1FRUUUFRXRs2dPAOLi4ujTpw8eHh6t9oy4uLiiCzY89lRxuiuznXDCCSfOBDEvrAAsm2+2JubNm8cPP/zA9u2SUiEIAidPnqRr166t9oymVmY7TU9OOOFEs0gqqFK2Ff83QhAEkpIsu/3efffdzJgxA4DNmzfToUMHynctIfPz24mJiWHhwoUApKamEhAQgMmcgOn++++nXbt2Sj133HEHn376KQDl5eXcd999hIeHExkZyYwZMzAam8+tYa8tH330Ee3atSM8PJy5c+cqZYuLi7nqqqvw8/Nj6NChzJgxg1GjRjX7DCejOI9RXtuo2Vb6fMHx3Aou+3Azq4/kNV/4X4y6RqMm8c+FjBu+2cnLS4/YZKY7ExiMJm7/fje7ki/MNK9q5OXlYawpp8Mj8/n2hzk88MADJCYm0qlTJ/z8/DhwQNo0cuvWbfj4+HD8+HHz8VbGjBkDwF133YWLiwtJSUkcOHCAtWvX8sMPP5xWW8rLy8nOzmb27Nk8+uijlJZKu9I++uijeHt7k5eXx/z585k/f36L6nQyihYgs6Sm2ZSPZwPXfbWDS9/fpDl3qpvL1TYYiUsvbb6gCtX1Bpt80muO5im5unclF5NaVM3ifRmae9SbrzUaTRyxk0DmVKFOH3kuUW617XViXqVm2+zmYDCa6Pnyat5eefZTltrbwfdU7495YUWTjF/+tvI25K2BvIo6diYX84wq93Ndo/GM51pBRR2PLdpvs7X3qeDX2ExiXlhxSn0bcOk0BBdXhoy4hKlTp7JkyRIAxowZw5YtW5i/IZ7U4mrGT7maLVu2kJqaSkVFBQMGDCA/P59Vq1bx/ocfk1pmoF27djz99NMsXrz4lNvu6urKK6+8gqurK1OmTMHHx4fExESMRiO///47r7/+Ol5eXvTu3Zu77rqrRXU6GUUzMBhNXPr+Jnq+vPqMBl5lXSM3frNTIZ5Gk8js7alNJt6Rt+uWpbi49BK6z1jFjqSWbwnzv98OcsM3O5tMNK+GwWhi0idbuf7rnYCUUCfmhRU8+GMc15nPyTuLqrdBvuzDzQx43ZI+cubKBK78YjvpxdqtsE8VvV9ZwwML7PucRFHkvnn7TikdKUiMTp06tKymQfMdMktqGPDGWt5X5Xq4/NOtjPtwc4ufIe8WuqCJtKcgEUa1n/Dxnw/w8tIjLX7OtpOF9HttrbKZ3+lA3lb+0/Unmilpu82+DKNJ5OZZuxjzgUWwKa9tVPJa2EOtKm+GjNu/382A19cqfZJUUMU3m5ObzIOdkFehSS/7/O+HWH4ol+2nME+sIecot5ffQkZceil/HZR2uQ0MDETnJjmWaxuNREdHk5CcTs+XV9G1/zA2b97MnN9W4tGhDxE9B7Nlyxa2bNnCJaNGsepIPmlpaTQ2NhIeHk7/LpH4+vnz4IMPUlBQwJ8HsqhpIs2qNYKDg3Fxsayl9vLyoqqqisLCQgwGA1FRUco19e+m4GQUdhCfWaZIj2opeZN52+0dSVKKyzxVPoHS6oYmzQx/HsgmNr1USbKyPamIN5cf4y1VHuq6RqPdROn55fXmdklMRp3isrnE6vvN2oT8HoWV9crW3nbLZ5SRXVbL4exyNicWaLb6lrcsl81h6pwJ1rm8t52UnqHesnvV4VxizQTaZBJtiKQ1ZCIj93tKYRW9X1nNsnhpa/aKOgMbEgqaTEdaUdfInO2pmgx8t32/W5PsZeAb65j82VblWM7pPW9nmqYueYvnjOIa7vhhT5PMt6ymwebc7d/v5sovLClic8tr6fnyan5RJaj6+2COxhdQYE636wiy2WZvqqVf75yzV8M8Z648zqOLtH2k3hZcFkTkT1HTYCC1yD6Dt86RIOO3uEz2ppWQXlyjaGPXfrWDkTM3Kul+QcrdITNleUyqU57szyij3mBSxtbt3+/mvdUJHMxyrJ1O/nQbLy87qryTzPhakjtbxot/HNYk+ZJ3YZWz9Hl5eVFTY2EaeXl57Ewu5omfDyCKIqWlpZgapDFW12gkIyODahdf6hpNnNBFsW3bNrKPx+LesR8hXfuzY8cONm3aTJZ7Jx5dtJ8SwRd3d3f+99N2Oj71C2/8vo+KigpWb9vH078cZN7OVKXuN/7W5pmpaTDa5DOxh9DQUFxcXMjKsuSxyczMbOIOC5yMwgrV9Qau/WoHt3wrERLrnAhgkRJlyb68ppFBb65zKJGlFlXzyjJpe6pgbzcq6hqVbGgy8RdFkZ4vr7ZJxgOWZEhyBjFZAttyopCu01dpCElBRZ1GipN5l0y4Hvopjrvm7FUm6YJdafwel6UQPfW9d8/dp8m1LYf9FZmJRVmNxBzVzEom/PL+/vnmNI0n8yt5eOF+njBPxlf/OkrPl1drMuytPZrHW8uPKW1LMCdWkjPgzd2RRk2DkScXxyOKol1CvSu5mI0JllwUv8dl8cbyY8q25NaQ26vOJ7HdnAFNb36wtSlkWXw225OK+GitYwncHkHdmVzMkewK5Zly9kSZcdtjmiNmbuCGb3YqWdoyS2qY8tk2TubLfSO1UU7fuye1hK0nCnl44X4q6xrZmJDPt1tTWHEoVyH+647l0/fVNco4lnMqyHU88fMBLvtwsyZPhIw8Je2qgVeWHVGYgjrHtJwVUX7e1V/uUN5z/EdbeMdsjpPvsZeSVhY85P9yIi+jSWT1kVy7vpKkgirNuJAtALuSi20Yd1FVvWbc/rw3g7/N2gGAh3muy+/bf8AArn36PV5deojVq1ezZcsWpaysnZZvX4hobGT7tu0sX76cbiMmAuDTrgOenp6k71mDR1QfUspF2rdvz5LffqfAp7PUB95BTJo0idWz38dUX0NxZR3Jyck8/rHkFJetC8sP5TJnh3Ys70gqYtWRPJuMjtbQ6/Vcf/31vPbaa9TU1JCQkMCCBQuavEfGBcEoBEGYLAhCoiAISYIgvHA2n3X5p5JkKX+Yw9llyjVLontXANLNkrWcAesv1UDbcqKQ77emIIoiaSrprLy2kdi0EkVjkQmALEGtMEuCBao8uPJg3WwmKLLkdCBDYhDqdJgj393IyJkb2ZFURE2DQcmnW2K2Lctt2XKiEJNJ5JVlR3n214MMeWs9dY1GmyTw6ixsXoqUJbXdJEoS+4iZG5Qyct5keSLLjGfdcYkoypntZKl5j1kSrm0w8sCPcfywPZXpfx4mvbhaydYmZ+xSZ2crr21kp8oJKhPb277fzb3zLKYquf2J5oxttVZSpjoBj5wI6Vuz2cHVnFLSOlGULG3+vDdDmZybEwv4+2COwlhL7RAmGfK3PplfpXm/P/Zna+4RRVFh9HJWuf8uiOVYbgVLzVqVzERlYUFNRH7Zl8kxVe7yVPM4lcvKPglZGJLzv29IkLKmLdqTYdP24+ZMg0v2ZbJgVzqztkp5n9XJnXLK6+wmxvpmc5K5HdIYfMRKy1Ez5MLKOiU3NEhZAwHmbE/loZ/2K+l61d8mvbia3PI6RfP9clMS1fUGbvt+N1M/367Uv+ZoHkPeWq9kgFQzaHkMyAmN5PHz4PNvkHVwO2/eMoKFCxdy1dXXKPdkldQSFhaGzsOHrK/u4oXH/8usWbOo8QxT2jhmzBhcPP1w8WvHnpRiRo8ejUk04da+CyAJVAsWLKCiupacHx7m/Tsu5sYbb2THIUu+erDNhAgWehBvlSnTHr788kvKy8sJCwtj2rRp3Hbbbbi7N7/u47xnFKqc2VcAvYHbBEHofbaepzanmEwiT/9iyUNdWtOAKIok5kuT5ajZ3yCnNU0rrsFk9j3cNWcvb688zon8KsXs0Tvcj+yyWk32MznV6qojFlNBTYOBTaoUhznltSTkVSgTXJaO5Axfe1NLyC6rpbbBqAz0uTtSOZJtIRKFlfVkl9XSIdATgEV70m3sr6uP5FFUVY9eJxDg5UrPMF9Kqi1EQiYkxVUNhPhIg6ukukHzPol5FZhMokJ8UgqrKaqq55tNEkHRCbbmsnqDkdh0i89g+aFcftxlMb+46gWl/2VkldZqbPmrDudqMt7J5g6ZyR7JLkcURU3KykajSTPxlh7I1hArOXBA7cSurGvUMJcNCQWUVDdw99x9PP7zAS7/RBI0ss2EvdEoYjKJHFUR7OO5FdQbjIpGKjPe77elaNqm7lcpGU6VkqI0rUj6djIh23ayyOZ96hqNioAAlvzucpljuZJ2I5uvsktrMRhNBKpSaeaV12nSkcoZ3xSBwDx+1aa94qp6XvjdknJ3QFQAYDEJyeNI7t/04hpyympZqsr2mFlSS4YqJa8s4Gwz99kWszlSHRVYXNVAfGaZ6v1NZJbWKP13xWeS2e+bzdJYnL8rnSPZ5Ur2PEARrGQGlFch9ZkhuDMR939Nx6d/5ccff2TSY+8QOHoaALUGI0ZRxP/iW4h6YhGfLN3JtGnTFBNmcVUDP//8M92emIcgSIz0qelv8uLivfh4uOGiEyTN282L2mH30uHR+Ux+fzU/rdiMd28pIsqn3wQ2bdmqMPuuL60gNCKa4qp6Aq94isDR00jMq2Ts2LEa0xJAWloaEyZMACTz04oVK6ioqGDfPkkQ69ChA83hQtg99pzmzFan4NxpFbZXWt3A15uTFQK8L62E7SeL+HqzJb56m9n3IONARimfrpekggFRASw/mKMxSxRW1VNdb9CkDN2cWMiu5GI8XHW46nRkltSy5ogkkXcO8eZwdjm1DUaKKi31nMyv5H7VgK+oM2hsw9ZmhBP5Vdz+vWT2eeGKnry3OoHD2eWKiWZCr/bsTCrSmBTyy+vJLa9VUk0WVdVTWtNAuL8H/p6uJORVsuJQHiXVjQoRSMyvZFNCAZX1Bm4f3pFFezLItMoHvWhPhk00l6yttfdzp6CynrpGI/vSSpQ0sutUqU67tfOhtEZKPC/j7rn7mDG1l6LlFVU1kFFSw2frLRLaHT/sITrYkuB+d0ox7q6S7OTtpqeq3kBdo5Hpqr47lFVOWlE1IT7uGEwmYtNKCPGxENbKegMFFXUkqFJo7s8o1dTxzZZkdqeUKFprVmkt1fUGUgqrlTSj208W8dt+y4SPSy9V6hwYFcDBrDLWH8vX5E3fkVREalE1E3q1Y/3xAlKLavjdXIenq55juZWIoshB87goq2lkf0YZfx/MUdKZbksqItTHXRmjq4/kcsTM5AZEBXA8t5JdycWsPSppI+W1jRRX1bPicC6XdgthZ3IxxVUNisA1pnsoyYVVZJXWKEwtpbBa0fBkbE4sZPpSqY8CvVxZdjCbrWY/V4/2vqQVVVNQUaeYoNYey+fRRfuJzyhT6kgrrubLTUm46XU8Nq4rH687wSwzUwCUDIXqefHLvkw2JliEsl3JxfSN9FNyw+eU1dFoNPFrrKWfp/95mIV7LBF/KYXVFFbUI5PbF/84TK6KwRaZ53hFnYGrBkTw98EcticVUVzdQKivO2W1jVTWGRTG6KoXOJ5bwdTPpcV18phfeTiXNUfzlZSpQ99er6RoBnh3VQJ3jYxpMstdQkICDQ0N9OvXj3379jF79uwWheCe9xoFEAmoPS5Z5nMKBEF4QBCEWEEQYgsLHTtqm0J5TSP3zdtHaU0jtw2TIgFkKffVq3ozICqAH7anKs7oD28aQEWdgTtm7yGlsJrB0YEAmugLkByieRV1dGvnQ6cQLyrrDby7SmIKix8YgdEk0ufVNQBcMzCC6GAv3l5xnKXxOfQM82Nkl2DWHM1j2cFserT35bnLe2AS4ZVlR/h9fxb+npIZLKesTsllfdWACPanl/LWCskWfNfIaE2booI8KaluUFT0Gwd3oL2vh+Ikv21YFH4eUq7hstpGXPUCs+8aQoPRxMiZGwEY20Na6f+/Xw9RXtvIqK4hdGvnQ3ZZDf81Ryld0jWYQ1nl7EwuxttNz8NjJDV7ptlGPbxTEP0i/Xn972N8vTmZDoGefH+ntDD0QEYpvcL9eP3qvoii5MDPLKnlP8Oj6RPhxxozoXr96j4EebtRWtPIrM3J+Lq78NXtF1FS3aB8q1ev6o0gwDsrj1Nc3UCfCD9AMnstic1iVNcQpvYL59e4LB5bJPlQbhkqpdRdFp+tcaT+tDudDccLiAn2onOINxklNQpR/OK2QQDsTi3hr4M5xJiZ0I2zdpFVWsvTE7rj5qLjQEYZv8ZKQ3pq/3CO5Vbw/bYUGowm3r6uHwD3zNun5BfvGebL5xtOMnNVApEBntwwuANZpbWKg3rWHYMBiTkCPDWhO/0i/YlTaWkTerdn3bE88irqKKysZ9oIaUzc8M1OGowmFj8wAjcXHS/8fojE/EqmjYimQ6AnfxzIVsw8Y7uHUl7byG3f71b6ZGl8Nt9tTaGsppFnJ/UgyNuNoqp6Kmobmdo/nM6h3pRWN/DxuhN4uup5dmJ3CirruddsVvz5vyNwd9GxLD4bUYTpU3oxtkc7jmRXsM3sKxrdPYSc8jo+XneCukYTfzxyMWF+Hqw4lEt2Wa3Szwt2pVNS3UCD0aTMCzn39pPju0lzYc5eTCIsf3wU7i46xQT66GVdiAryZN7OVBbuzsDDRc+EXu3YcqKQbtNXkZhfqdAFNZOIDvbiaI6to/3zDSepbTQyvmc7SmsaFS3m7otj6BDoyQ/bUiiuqifYxx0vVz0LdqVz7deSL+fhMV1oNFq04wdGd8HH3UXxXz52WVd83F00TEJGU1FaAJWVlVx//fV4e3tz88038+yzz3LNNdc0eQ9cGIyi2ZzZoih+J4riEFEUh4SG2t2qpEWQbbPDOwUDKAO1X6Q/F3UMUMo9P7kHA6P8Nfcu+u9wvNz0ihTx5yMX0zHISzEV/PbQxXRt56O5Z0TnYML9LXu13D6sI/8Z3pFss537qgER3DosipLqBlIKq7lhcCRT+oXTo72vIkleOzACnSD5AgK9XJk2Ipouod4K03j1qt4MVLUd4PnLpf1iBAGGxgQS4uNOx2AvxQH9f5N74u/pSnWDkW82J9NoFLmka4hiDweJ8IDky6lpMOLv6UpEgKdiSwe4/9LOGE0iGxMKaOfnQVSQF0Hebqw1awOjuobQN1Ii2uW1jYzsHKxM8KKqBjqHejOhVztcdILC9HqE+RIT7K30a6ivO4FebhzJLmfziULuGBnNZT1DEQTJLOPn4cI9l3Siva8HcellALx1bV82PzcWF/MLDYwKYHwvy2pZqe2dAPhpt0QUtv7vMoZEB7LqSB4NRhPXXRRJh0AvskprFVt6pNms98TPB6g3mHj/xgGaOp8Y35W3ru0LSJrHxV2Cef+G/gCK1jkkJlCxjwOsf2YMfuY+ASiurueWIVG093On3pxT2rrtvcP9aO/nrtj17xwZzYAO/hRVNTDpY8k0Nq5XO2XsdQ7xpm+kP93b+yhRasE+bgyLCdIQnpuHakMp1z8zGqNJVMJQIwIkzbKirpHy2kb8PV3x9ZDGUXxmGaO7h3DTEKmO7LJaruwfzsguwfh6uLIntQRBgOsvisTHXWvoGNlFmo+/78+iR3tfLuoYyPOTeyjX59w9FGvIgScAkQGePD7OstXFHSM60jfSXxnvAI9e1pVLu4WSV17HgcxSLooO4PFx3TR1XjtQI59y69AoOgZ54dGxP+Pe+J2Ud6aQOnOKhlbIffblpiRGdw9lcHQgVw2IIKVIMskGe7spGoBsOu3W3le5P9jbjesvilT6AODOkTHMu8fyzpueG8vvD18MQFZp04xi6NChJCUlUVNTQ1paGi+++CKCnWACa1wIjCILUI/QDkCOg7KnDbW61rWdD34eLsSllyIIEnHy87BM1s4h3rTzsxD4/wzviLuLnpoGo2JO8PN0JTJAIhzB3m74e7nSJ8LCXB69TJKu5UlxZf9whsYEEeBpMWP4urvQI8xPOe5p/t2lnbf52JcZV/YmzM+D7NJaKuoM+Hu6KhEbE3u3555LOhHkrXVWxQRL91fUGfA2P1/WiAD8PFzx99ROVg9XvUIIbx0apWFwAP5eroT7eygq+z2XxDCgQwAgMQHZPBMRYLnP3VXHiM6WCRAd7KUwCoBOwd646HWab9M7wk/xs4DUt4Herkoo5K1Do/Byc1F8KBVmram9v4filA30ciMmxFth3H6eLprvCxDkLbX3eG4FOgHCAzwI9LZ8m8gAT7qE+pBZWsPR7Apc9QIBnto6+nfQChOCINAv0nKuR5gv3u4u+HlY+jrEx50AL6meYTFBdG3noyFodY0m3Fx0yve6om8YrnqdZiM6nU7QjM+JvdvT1/xcOcQ33N9DiUjTmRmmPC6kesMJ8nbTmB4jrL55h0BJkpf9Lx6uejxcdaw8nEdxdQMBnq74uEvfLqWwmnB/T9r7ueNrHnPy/b7m9w/z8yDYx105lnFxlxBA8vfIfSPf297Pnc6hPrxxTR+lvKte0IyZjkFeuOh1SjDErWZt8Ynx3fB1dyH5nSl4uUljoKJWMgF1CfXRfCuQaIR63MuCD0DfCH90OgFBEJhxpcWFqq7jpsGSccrPwxVRlKwAwT5uyhyUof4O/xkRjYernvZ+0vcd1ikIfy9X2vmq5pGLjijznMgu05p1WwsXAqPYB3QTBKGTIAhuwK3AX639EHVETTtfafCBJJ35erhqpBw/D1d83V0UqUV9rwx/T1dl8sqDqZ1qMof5Sx/28j5SZMSLU3qh0wmaSeLhptcQn3bmwSIPpIu7hOCq1xER4MnJgiqMJhE/Txcm9m5P13Y+TJ/SC8CGgEUFWQitt5uL8p4ydDoBfy/LPRPN2kOQ2ckZ6O2mkdjk9w1SEVJ3Fz1B3m5Kv8mEWz72dNVz+/Borh4QwRV9w5Q6Ar0tzw03MxVZwh7QwZ8QH3c6h1omUrCPG+HmvryyfzjR5r757NaBmvaFqwin7KyViYm/pys+HraM0c1Fh8EkEubngatep2Fi/p6uXDMwAlGUAhF8PVzxcrOtwxrqcTSqa4im3FMTJAlWZnqyv0Tt/L/RTGzeuKYvb17bl2GdggDYN32C5jkXqyRQb3cXhncK4t5LOqn6w1OJqLrTbJqUx17nEG96hPkS5KP+njqN5PnXY5fYvJ+nq568covj38/TVYkQBEn7EwSBEPM8kImft5mZyPPD+lu4u+iUUGWZUYSZv6fs21ILYYFebkqEHsCn5rEwupvU37KA8MzE7hx6bZJSt6+HZM6pqDPQ3s9DYaAyvNz0bP7fWMv7ebgQbBbC1EJE5xBpDE7o1Z6IAMtck/1hctuq6g0Ee7vjptfSD/X7y2NfFiBlgUYtGHi46gnxkerJLv2XMgpRFA2AnDP7OLDkbOfM9vFwYXR3yYQlawVy5A1IE0AQBOWD2mMUvh4uCmGRywmCQMcgabDIktbTE7uz5X9jleeozQxernrNgJeJrTyh5Kin8ABPRZL383ClS6gP658ZQ4x5wPpbMYoALzem9gsHwGDerEwtxVjf88jYLkr7QXI2CoKgceJaMwrZHCMP9I7KJJH64v0b++Pj7oIgCMrg1+kE2vl6KEwo1Py+8nOfnyyZzGQJEyDU10OJqrmkq+X80JggzfuomYtMEIPN7fVw1duYO8AyKWWCqe6T7u196RjkhZuLDpMo1WnNPNVY+uglmmcDiiQp21F9zc+TfUeyGdNgtlf/dN9wZl4v+TBCfNyZNiLaodlgUEeLhij38ytX9Wb/yxP5dtpgjSBws9kcJAsNclhwsOp7zrtnmKZ+a2lbvk8eTyARRJkJgIVAdzP/lxm83PeyFuRr9S0EQcDDimC295fGxn9HS+sQ5LECsOC+Ycq3iPD3oL253s9uHcSGZ8doGJy6/9RzvL2fVnsC8HRzwd1Fz0tTpHE4JCZIMceqv2uAlxt/PHIxn982ELAwNXmOq7Ud9ZwBWPXkpZo+k0N3ZQYpm0vVdbi76NDpBCIDPcn6F2sUiKK4UhTF7qIodhFF8e2z/TxPV71CBOVJrHaKyARElgTc9bbd6O6iVz6ut0rS9DObdGSCqdcJihQM2gHn5abXDGR54soTys08sHuFq2yaPlozE1gGmRq3D5fUbzm0NyZEIuSTzNqDr8oUI/+W91yS4/5jZ0xUyvh7umoIqawlyKGkQ6Ilwi0TBZMqdl1+Z5kpyqY1+TnyZJTrjwryIvmdKRx4eSL+nq6M6R7KiidGKao9WIidDDVhkyVF2RZc32iyMXcAeCgRUNI1F3N/v3BFT7zdXdDpLIzfz8MVDzfH06mH+VlqM4PcRpkYyG24ZmAEYFlj84jZTDkgyt/mvdTY8OwYNj47RlOX9TODvN0ULbaXWYuUCadcTu4fdZisbCO/a2Q0U/uFO2RQBpUT1k1v0UIGRgUoY+uVq3rz+tV9lIAIWeuQBSD12PvrsUs0bZTHsruLnrR3p/LIWMn3EOJraWvPMD9czP2klry93V3oEqr1E6pRUWtZ+6EWgmTIzOeB0V1InTmFXuF+jOoWgq+HC1cNiNCUvahjoDLHf35gBNOn9FLmplr469be0p6xPULpFe6nEVqs/dVediKaZGEsMsDzrGkUF0J47DmHIAgKUbK3w4RM7GWV1d1Kkpxrdq5ZS/JgYRCOpE/1JPGwGhTypJvUuz3PTuzOnSNjABjXsx3vr5YifDqFaDUD6zpl9DPbz2X139fDlf0vTyTQPBHVWrccVSIvVrMnbQV4uSl99fDYLlzaTRtU0MnMiF6a0guTKDKhV3vl2lMTu+Pn6cqV/aXJ9vCYrjy6aL/yLvLKXTXx0+sEjbqvNj3IuLRbiMIgrAMJAB4f1xV3Fx1XD4ywu+eWHM0ka253jowhvaiG8T0tzmNZIvT1cLExIaghMx01oZcnuGwCkn0Vb1zdl2XxOYpmcc3ASK6xcqTag5oIqoUTbwfhkkseHKE8AyxMXBYIgs3EUk00X7+mr6aOmdf348U/LKG/ao3CVa9TdhPo1s5HGb8dAr246+IYpZxsYpPt7mpCKb+TbI4L87cde4CN2U9m6hepfG/Noa9KmLCuTzpnq4mM7dGOQ69OatIh3CnEW9F8rOsZGBWAfKssIKhpgyxQyUKNdUABoDDFZyZ1R98Cx/TpwMkoHMCinkofSk305YEsfxNrAnGZmZDIErE6jE0eJI52gVVPErnswvuHa8676HU8Pt4SkeHlarkmS7hq6HUCh16bxIbj+fQOlyaDn4crP/93BN1VEo1aDR4UFcjj47oybWS0MhCrzRJumB1G4e/pytUDI8ivqOP+SzvbXJd9MmH+Hnx5+0U27/yE6n2m9g9nan9L8pfHx3XjpT8Pn3LmsB/vG678jrLTL15uLjw1oTtgUenVuGVIFL/EZipEKjLAk1nTBmvKyNqlr4eLhlioTV2AXUIimyzlaBeZoft5unDT4A5cd1HzzMER9Kr3sWdWk5+nFiJkjUIem3IQRFM7FndXRegAmkWPri46JvZpz9pj+Tw9sbvDOmSNXPZP2bPRy8w6UmXzbwoXdQzk+zuHMKZ7y6MgJ5u1YGhacrdGS6KG1FAzIR93S+CFLGip65MZxZjuoRx8dZJd4VPGRR1bzhRPFU5G4QCyFCt/vGsGRlJYWc+wTkEK4ZQ/pzzhbxkSpdlwT5ZiUwotq2Wv6BvG5sRCzUIvNdQDVJYs1LZ3e1CbPOz5S0AiaNcN0q7AVIfcWUOnE3h2Ug/NOVkaVjvlP7ixP++vScTf0xW9TtAwMDUcEauW4PbhHRVT2enCw1XPuJ7tGKfSBtRwUTF72dl/7aBIfonNbHKbc1m7tNba1jw1GoCNz45RViRbQxYwrhsUybydaQqTFwSBD24aYPee04FLE5qOGrJtXGEUZkHH0MRml9YmO09XPY1GSaBw0+vw83C1G76qhuwvkdcOqOu0brsc7dQSyEEYpwN7gQinyhAcwVoz6d8hgFVH8jRmu+cmdefDtSc0JtqmmMTZhpNRqDCoY4CydF8m8vJ6Ab1O4EHzgjEZ8sCRifN7N/bXXO8X6U9MsJdGmrplaEcm9Q7TmE3UUKud1mFzjtCUE7U1Mffuoaw5mqfxedw0JEqJjbeH1U9dqtnrqi3RHMH6/eGLWXssj/vM0UHyezY2IVHLGoX8391Fx4Re7RWm2jnUR4mgs4aredy8cmVv/m9yzyZX1J4uHEnB9iAzc1kD9vN04Y4RHW0EDDWsx+ivD12s7Jfm5tIywvroZV3Jr6jjBrMG5evumCBGBjrWKH55YARN8LRTgvW3+MpKCz4TWPfZfy/tRICXq0aDnDYihqM5Fdw3ylY7bwucM0YhCMIHwFVAA5AM3COKYpkgCDFI0UyJ5qK7RVF8yHzPYGAe4AmsBJ4Uz2KS7z8fuUT53SnEm4OvTtLEuFtD0SgcSGx6ncDm/11mc94RkwA0IXn2HKz2YE/6ORvo1t5XsxioJegZ5qes/zjfMTg6ULOeRJbgGoyOh5xMXOUotsS3rmjx8+Rxo9MJZ4VJ7H1pPO4uLa/X2vQkCAJvXduvyXuCvd0I8HLljuFSiG2PMF86h3iTUlTdpONdjRAfd77+j8Wk19S4b0qqHt7ZsYZ8qpCFrz0vjae8ttHGxHYmsH4HF72O24ZpNWZ/L1e+uUNr5rRGdLCXQ221tXEuNYp1wIuiKBoEQXgPeBH4P/O1ZFEUB9q55xvgAWA3EqOYDKw6B20FWqDqmWm67IxrbbR0krd0QjpxapA1ivEOzFXqMtY7zLYEjsyErYV2dnxJTcHHilG0BB6uevbPmGiz5gBOf1za06Q7h3prNic825AZRXs/D7vBG2eC1jIhrXlqtN1tPM4GzhmjEEVxrepwN3BjU+UFQQgH/ERR3GU+XgBcyzlkFM2hOY3CiQsbXm4u7HpxnLKoyh5uH96R+MwyZW+oU8GpmIXOBbytTE8thT0mAafPCN1cdAR6uTK5b7hybuUTlzbpK2ltnM1v01qCnbQS/txYE9rKR3Ev8IvquJMgCAeACmCGKIrbkDb+U++Xa7MZYFuj3Bx3bb1oxol/DuRFYY7g5eZiE8XVUpxvmqB6oVdr4EwEqLgZE1H7js8VQZThiPn9W9GqjEIQhPVAmJ1L00VRXGYuMx0wAAvN13KBjqIoFpt9EksFQehDCzYDVD33ASQTFR07nll0zKlA3jvoVO32ZwP/vbSTsoDKiQsD+vOMGJ1JZJo9nAkjbCtCfffFMTYpcM8W2jKK6VTRqiNDFMUJTV0XBOEu4EpgvOyUFkWxHqg3/44TBCEZ6I6kQajDLRxuBiiK4nfAdwBDhgw5Z/rp+zf255d9ma3+wV+7qvcpR29Mn3rWcjk58S9Ba0fPnW+MsCV47eo+vHZ1n+YLniHiZkxQot4uBJzLqKfJSM7rMaIo1qjOhwIloigaBUHoDHQDUkRRLBEEoVIQhBHAHuBO4Itz1d6W4OYhUco+Oa2Ju1Wbtznxz4O3m17JEHc+QRAEOgR6cpd5xf/p4tx5Ei5c2Ntq53zGufRRfAm4A+vM6w/kMNjRwBuCIBgAI/CQKIpyxpWHsYTHruI8cmQ74cTpYv2zY87anjxniu3/N66tm+DEeYhzGfXU1cH534HfHVyLBfrau+aEExcqwv09m3WSX8h4cHRnXvjjsMN9mZy48OBcme2EE060Km4d1pFbh527oBInzj6Es7jQuU0gCEIhkH4GVYQARa3UnH8anH3TNJz94xjOvmka50P/RIuiaHcXxX8cozhTCIIQK4rikLZux/kIZ980DWf/OIazb5rG+d4/F058lhNOOOGEE20CJ6NwwgknnHCiSTgZhS2+a+sGnMdw9k3TcPaPYzj7pmmc1/3j9FE44YQTTjjRJP5x4bEhISFiTExMWzfDCSdaFQ0GEyZRPOeb4znxz8aBAwfo3bs37u7uxMXFFTmKevrHMYqYmBhiY2PbuhlOONGqiHlhBQBH3p3aTMm2hckk8umGk9w5MlrJBd1WyCuvY/3xfO4YEa2cSy+u5s8D2Tw5vttppTY1GE0czCrXJLiqbTDy98EcbhrSodXSpbYFBEFwuKzA6aM4Q0z9fBsv/XnY4fXiqnoe+jGOspqGc9iq8xsHM8v4LS6r+YL/YhiMJv48kIXpHOZgaA3sTSvh8w0nefEPx3PiXOHeefuYsfQIBZV1ADQaTdzwzU4+XX+S/Ir606rzq03J3PDNTuLSS5Vz05ce5vnfD3Egs6w1mn1ewskozhBHcypYtCfD4fW5O9JYfTSPBbvsM+uS6gZ+3JXGmfqKPliToEidAHWNRkqqz0/mdM1XO3ju14Nt3YzzGt9tS+HpXw7y9yG7Gya3KX6LyyKzxH4KTp1Zoi5Vjb0eM1Zx55y956RtahRUSsyg0ZzK9tklBymqktpVVS9lJHxn5XGWxWe3uM6kwioAMkos2fZ2Jxe3SnutkV1W22TmxENZZXSfsYq88jpiYmL48MMP6d+/P/7+/txyyy3U1UkM8vvvv6dr164EBQVx9dVXk5NjGVOCIJCUlNRsW5yMohWRW17Ljd/spLjKIq14mNOk1jjYLfTRhft5edlR0prJfVtvMFJvcLzj6FebkgFJEgW49bvdXPTmuibrjE0rOaVJAhCXXsKBjNLmC7YAhlPIpLbqcC5/HnCshRzOKueDNQlnzHDPBn7Zl8GIdzacUttkQlxVbzhbzTotNBpNPPfrQW6atcvudXln8TrVWK03mNh6ovBcNM8KUn/XNkh9+NdBC4Esq5EI8HdbU3hycXyLa/Q25zavrre8X6m5rtpW3hH4knc3MuWzbQ6vf7M5mQaDib1p0h6qS5YsYfXq1aSmpnLo0CHmzZvHxo0befHFF1myZAm5ublER0dz6623nnJbnIyiFTFneyqx6aUas4qnm+QGqmu0DKIl+zKZb06OciS7HGg+T/HwdzYw6I2mCT9Anbme+BaowTfO2mUzSdTttIcbvtnFdV/vbLbulqDsFPJMP7xwP0//YtFCGo0mDeG96svtfLUpmZMFVa3SttbE9D+PkFdRpxCnlqCuUfqO1nnTjW1sipLHaV5Fnd3rcrvrG1s/l3NWaY3G5NMc5OGx5mi+zbWymsZmxzpIycmeXHxASVLmaWYUaqYgM0dZGEwqqCKlsHXGYXaZ412G883fIMCcD2fQFbexPKmWoKAgrrrqKuLj41m4cCH33nsvF110Ee7u7sycOZNdu3aRlpZ2Su1wMoozgDVxl7NyGVUETLYxywNLFEWe//0Qr/51FIBKs8TYnORYVtPoUCtRoyWD3xpx6ZJE8sf+LHq+vJr04tZPYp9XXocoihriLvttNhzPZ+Gelm/PZTKJDHpjnV1JcNInW5XfjyyM47FF+0+/0aeJtUfzNGNDTmzV1KS3hqw9erjqNMyhuqFtNYzmBJpa8/jLKKlxaJ46llOhEN5Twej3N3HDN5KQsvJwLsutzHLlNY0aX6Dcax+sSbSpq6y2kbxyC7M7mlNu95mbEgpYFp/DS2afi5wHvFDVftmBXWP+NhM+3sK4j7acyqvZoCXzWDYty98ksVzP+mMFAHh5eVFVVUVOTg7R0RZnvo+PD8HBwWRnn5olwckozgDWqqZsn1U7IGUG8EtsJifzKxV7qTWqW8nEYN0mR85Q9fkbvtnF8HfW88oyiXmN+2gL5ack/RqbHNjHcysYMXMDC/dkKIQEoKS6kU2JBdw3P5bpfx7R3NNgMLEsPtvGXFPXaCS5sIqqegN/HcwhMa/SIfFaeTiP5YdyT4t5ni6OZJfzwI9xvPqX5X38vSRGkXMKjEKWzAUEDfErr2mkos7225RUN5BV2rT5sjVQ3wyjkPu63mDi0vc32b025fNtXPXF9lN+tnooP7JwP48tOqAcF1TWMeCNtQxUad1NmfrKaxs13+P/fj+kub4psYC6RiP/+006v/ZYPnWNRkVT2m9Hs7EW5Ox9J3t46c/DbFGZ5uoajby/2pa5WUN+O9nMV1HXSLCPm6ZMREQE6ekWIay6upri4mIiIyNb1DYZTkZhhiiK/LAthamfb1OIe1JBFWM/2MR+Bzb5mkYLcTeaRPQyo1CNTzUD+GzDSZ7+JV45njZ7j91yzWH1kTzFZGWNeoOWaM/ZkUp1vYEX/zjE3tQS5XyVlWSaX1GvvLfRJLJor62DvsaBNDvs7fUMe3u9w/amFUkaysrDuVTUWupIKqjinrn77N7zzsrjPLk4nl1WjsL758eSq5IEL/90K5e+v9HhswFKVYR23bF8Jn685ZSk+1OByUyc1pklO7CYBtTtbg6yRtFoNGn8V3fP3Uv/19YiiqJGC73xm52Mem+Tw2/UWmipRiFDTaxHzNygjHPrvvhjfxbfb01pURusfVvJhVUMe3uDTTn1PBzxjvZ6eU0DyUUWzTld1cepRdXcM3cft32/W3PP15uTlf49nltBVmkNv8dlKeesfXelLQgmMRhNLNqTwV1z9rIxIZ/YtBI+XneCOTtSbcpaWx3kQFxZqKiotWUUt99+O3PnziU+Pp76+npeeuklhg8fzqmuNbsgGIUgCJMFQUgUBCFJEIQXzsYz0otreGvFcY7mVPDIQslcMeHjLaQV1/DuqgRAksK/2pREgdk2qHZoVdUZFFulSRQxGE1kltRoTAXLD+Wy4nCucrztpGVX4ZY6LePSS3nopziu/GI7+RV17Eou1kQ7vbMyQWN+eWvFcfq8uoaf92Zy87e7lElWWSc9z1Fe40Y7jubiKsvA35QgEUJJwjVQUee4/UXmCZNUUMWMpRZJe9tJrYNTrcVsNV978Mc4Vqn6bHtSkU0Ejb1QR3X71b6BnclFnCyoYu52y0T8LS5LcbZW1RuU77s5sUAj6bUEMiFVm1ZkP4Nagv3zQBYj3tnA+mOS2S21SGvuqzKPrQajSUNAkwulct9tTaHvq2sUO3WK+f7lB3NpMJhoMJhaRKhOBRnFNXy56WSTZU7mV2qOi1VtKKtp1DCIpxYfUNr/zJKDvL3yOJUtkMKt08jmW/lLZCarjhiy9qmU1TZqHOyVqvErt+FARpnmnqKqekVrqG4wMuq9TTz760GFIS2JzdIwRnWdfx/MUeZ4ZkkNq4/kKu2Qce+8WG6ctUtjEpOx8nAufV9dw7GcCuWcbPKSBcPKeiPB3tq1K+PHj+fNN9/khhtuIDw8nOTkZBYvXmxTf3M47xmFIAh64CvgCqA3cJsgCL1b+zkxId6M69kOgK0nCjWmmZ5hvoAkuXywJpHrvt6JySSSpHKcDnhjLZ9vlMLMKmoN/LA9lUvf32TXkWYP1fUGymsbOZhZplFZCyvrGaqS1mUbLUgO7o/XaVXUjQkFZDiwDQNc8dk2ymssane/SH+75ewxio0JFin5nnn7SC2qZsAba5VzX2+W3v+RhXG8veKYcj6vXHpWQWU9649L/dGtnQ+rjuRp6r9vvkW78DI7DSvrDTy8sHk/w7CYIABczIyvSjVJUwqrFTu/PI9/3pvBAwti2ZlUxHO/HlSYz43f7GTYOxvYlVzM3XP3cdecvaw/lk9KYRVztqeyYFcav+xzHA6tNs3c/v1u7p8fy64USSvKVJmGnv7lIHkVddy/QDK73fadVnqVifyf+7NZfVTbTwA/mX06+9NLNcT1+d8P8dBPcTz8UxyDmol6SyqopPv0Vco4TsyrZE+KRYNLyKvgQEYp9QYjv+zLYPQHm1gSaz/yrK7RyD1z9/L9Nq0knGwVXHCnSoteGp/D8Hc2aMba8Vwto7GHv6wi9azNrblldZhMokMhCGDBrnRlLMpYtCeDtKJqh0KbKKKZ8/aQomL4P2xLIaO4hsS8Sh7/+YCytmTG0iM89NN+TuRXaoQ65f1U0VlB3m5sSizg19hMQPom1ubkOdtTmbt6L54xA+kY5AXAa6+9xk8//QTAQw89RHJyMiUlJSxfvpwOHTqo3kmka1e7yUc1uBBWZg8DkkRRTAEQBGExcA1wrMm7TgNz7h7KZ+tP8sn6ExxRObd83KVukp1H2WW1dH5ppeN6VGpjSXUDwzoFUdtgJKesViNhqVHdYGTMB5sU6ffQa5MoqKhnwsdNO8X2pZ1aqOrJgioNce8Z5ms3QqrBaMJoEnlz+TFuHNyBnLJaxQEv47IPN2uO31+dyMNjurDysETYurXz5aYhHTREW8a4Xu2UCKWu7XxIKqgiVmX39XV3PaX3WvLQSF5eeoTlh3IorW7gSpUN/NFF+3lwdGceGN1ZMX1UNxhZeyzfRtJMyJMIldrscP8C25X+twzVZnCrbTByNKdcY/bbaWU2W3k4j2mz99ApxBudoDWNqM1jyYVVCrPflaKtQ0ZmicR8l8XnEGy1AlrN0I0mEZ0gjZMZSw8z+66hRJmJycI9GTQYTdw7b59GuEgzr/6e8tk2Whpktf54PpsSbbUvWQOSkWNHWlaH2i7ak86AKH9NtJfRJGq0hpeXWcbhoawy7puv/T5ZpbUczanAaBJ545o+HMupYPG+TJvnWrsw5IWz06f0sveKbEzIJ7+ini6h3jbvdUXfMNYczWOiar4ujc+hqKqB/13eA5C0imcndldMVfaYhDVKqhs05tlnlhzk+d8O8cDozooWmlJUzT3z9uGiE7hyQHizdZ4OznuNAogE1F85y3zurGBy3zAArv5yh3KupLqBj9cmcouV1NdS3Dwkir8fH0XcyxMdltmbWqIxkVzy7sZmmURLIEvmjjCxd3u759OLath6spB5O9O48ovtPPBjXIue99Nui+Ps+d8PsT+jzMZUAHBJlxDl99MTuuPlpqd7ex9A8mk0F3nlqdrz6JKuwQAIghTTfs+8fTY+iG+3pjD4rfXUNBjp2s6Hz24dCMChLPu+nuaQbBX+ePfcvdw4a5cS0/7aVVql189DEja2nSyyu/iy3mDieG4FsWkljD+FiJnVR/O4+Vv7axpA8hlsO1nEzd/u4kR+FXN2pFJvMLIjqUjxHdnTQE/kVzbLJNTreuoatVFeNw+RpFZH2tfSRy9RfqsFlaXxOUz+dBu/xmbyzeZklsRm0uWllVz8rn0/lHqeypi/K41HzdFu0cHedA71BqBziDdp707F16Np+Xj2dlv/AFhMnLfZSfPar4M/twyNsumzfWklNKg0piWxmS1mvo5gMIl8vTnZ5ryvh4tNOHVr4UJgFPb0R01XC4LwgCAIsYIgxBYWntnCnq7tfGzOLd6XqZiVTgWyyWqIal8YGQvvH86vD43k6gERADa28MombP4tfS5Al1AfZt1xkcOyXUJ9SHt3KiFWTrDVR/McOprtYfqUXrTzdddIeyCZyuw5WPtF+nNxF4nAXxQdwLQR0aQV1fDxuhOM/XAzOeV1+Hm4sP/liaS9O5VLu4Vo7o8I8ADg+ck9WHj/CAAKzBNZJjxje9jub1ZVb8DbTc81A21ljZbYx2WM/2gLF8/cQFFVPY1GE3vMgQLfbpH8CZ1CtePIOoe0PWKxMaHA4QrmhfcPtzl3zyUxym9HZpaaeoOGqe1NLaHHjNX854c9djUAgKUHslsk7a49mq/Y5DcnWrSY6y+K5L0b+tOjvS8HHTDigVEBNudkhp9aVM3/fjvEe6sTeP63QzblmsO6YxaTUnSQF95mi4C7Wbj485GLm7xf1jJjgr2Uc4+M7QLAuJ7tGGU1FkFijn0ibM249QaTZk1FTYPxlNfCBHq1TLuODvY+pXpPBRcCo8gColTHHQBNALUoit+JojhEFMUhoaF2Nz9sMfQ6geWPj2q23HWDtITm8j62kvmvD41k3/QJxITYfkAPVz1DY4L4/LZBTT6nZ5gvz0/uoTn3xPhuAHz9n4t47arepLwzRbmWOnMKfz02isl9JM3I18OFyX3Def+G/jZ16wSICPAEYO9LE0h6+4om29IUooI8ee9G22eAZHIZ0MGfAyqNys/TlW+nDebn/44g3N+TQR0DaDCa+HyDxVk6ICqAIG83c1slQjj37qGsf2YMn906iGsGRvCfYZYYcevY/Ll3D7VpS3W9QSEc1nji5wN0syMoAIzqaksccsrrmLU52W7EWlSgp+Z4Qi9pfHg2sfvrB2sSHa6V6WBV3/s39qdDoIWQxQR7YW8/uuoGo8bZf1TlDHWEp1SReWo8dpnWlv34zweYuSqBukYjyw9ZAg5EUXK0XuXADPLJLQMAmHXHYB4fZ6nz/yb3bLZtvz000u75dr72NyCMDPRUTMfyLglqh2//DvZ9dADLn7hU+f3IZV35dtpgvv7PRXQOsR0jEf6eyiaItwyRyJXMaH7fb/GpzNuZdkrh2p1CvImbMdGm760R5ufB7LvOXibVC4FR7AO6CYLQSRAEN+BW4K+z+cA+EX4254ZEB3JRxwDl+PVr+ii/j78xmW+nDSHt3amcfPsKHhjdmbl3D8XXw5VQBwNYHrTN4dpBkZqB+ecjF/PMxO6kvTuVKf3CufuSTspCvx7tfREEATcXHdcMlDQVeZLcONjiwJKl8w6BXsoCIp1OwEVv26Z+kf50CfXmvRv6Kec2PjuGW4dGacr5e7oxpluoQ0Lo5eZCoLdFa9HrBHw9XBlp1irG9mjH0Bit5qUOxZQJdcdgL7q286FvpD+f3TpIWaMAMKJzsOZ+QRDY+cI4zbmqJhjFpsRCThZUMSQ6kL0vjVeCGwDuHBlNhL+HzT2HssoV09pdIy1My9/TlV9VRK1fpD9/PzaKtU+PZlinIOX8iifsCyVqaRYgTPXs7u19uHlIFJP7hhEVJDGQe0d1wtu8C8A3/7mIcHP5faklzNpia6aQ8ezE7ux8YRwrVUTREa4dZKuFfbc1hZ4vr1aOPV313DpMGhuXdrMvtF03SBqLk/uG8ewkixAUGeBpt7yM/h38GRITxDvX9bO59ttDkpZwy5Ao0t6dyoGXJ/L3Y6Nw1evo1s4Xbzc9118kPdff05WLOgZwzyUx/HjvcD68SWJcPlbjwks1ln3cXbi8TxgernrcXHS8eW1fPr1loHI92MeN8b3a8b/LezDjyl4kvDmZjc+OJdTXXROWDhY/GEDvcD/NLruXdA3m14dGsvTRS4idMYFNz41FpxN4emL3Jvvm41sG2PiqWhPnvTNbFEWDIAiPAWsAPTBHFMWjzdx2RpDDzqKCPBWn4We3DbIZyDOm9uKP/dnKsn4AV72Olxw4w9QI87MlOvbg6apXTEmPjO3CoI62ZiyAAy9P1OQqkE1Xvh4SIZWZSaivO8Fmgi1LufYw8/p+vPjHYdxddPz9+FgA/u93ydnXOdSH+0Z10jgIA71d0ekEgn3cyCqV+uy/l3YC4Pttqc36Sjxc9fzywEgW7klXzFfq8Mb7L+3EVQMiNATTGk9N6MYdI6Lx9XBRpLaIAE/evq6vsqCvuLqB3laCwP2jOvGDyi7tqtfRzs+DO0Z0VBzDMSHeds1FSYVV1Jg1in4dAgDJ/+Bu1hjH9ghlc2Ihnm56+pmlV5mIX39RpF1zBWhX969/ZgzuLnq+nTaYYG83epjHQ2SAJ1v/dxkZJTVEB3vz6fqTVNUbiAry4oMbB3DH7D0sbiJCCyQGFBHgSUSAJ7PuGMxDP0m+qOcmdeeagZEs3JPBrC3JPDuxO13b+dAxyKvJqLrjb05Wfg+ICuDI65dz95y9xKaXEh3sxRdNaNBB3m78dN9w7lBFRgG8fV1f/jPcwoRvGRpFWnE136nChjsGe/HrQyOVKL5AbzdFMOkd4cfRNyzt0ukE/njE4iOZ3DeM5349yP9N7qGMvWkjopU5Yw/TzFuXu+p1PLpoP51CvKXfVpJ/mJ8HhZWOV6HPuXsoRlHk9b+OsvZYPn0i/BkaE2RTztq0+N20wTzwYxwhPu7sfWl8k21tDVwIGgWiKK4URbG7KIpdRFF8+1w8c+9L4zVSlj1p8v5LO7PyyeYlMXtoKfd31euICfFm/TOjm5QqAr3dNAzLz1OSAXqFW/wVyx69hJVPXKpIwE1FSMgaj9qc0b+Dv+Kk7Nbelyn9wpRr8jYVMrOaPqUX06f2VjQqmew9PaE7N1xk0W7U0OkEpo2MYdYdgwFtiK4gCE0yCQAXvY4wfw+83V00/fuf4dG8fKXkXC6srCfQy01p47ie7ZhxZW/m3mMxU8laVoC5nIerju7tffnmjos0WiVIgQ5y5FmwSmPyMNfx3bQhHHptkuYeWUiQy49XaS4yXp4qtXdYpyDFb3Z5nzCGxAQpzF/uF9k2LTNjD1cdXu7S7/0ZZQzo4M+6p0fbPMPX3YXR3S1Sv1rL9XF3ISrIS9lQTx5bX94+yK4fD2DO3bamDx93Fx4aI9n3O4d4079DgE2Z92/oz83mXA6juoUoAgbAX49domESIBFNtTB29PXLARgaE3RaiZ183F1Ie3cq00bGKGuhZJPs7LuGMGOqY8Fvav9ws4Pcvh9BDqpw0Qlse/4ym+th/h5EBnhyiVljbsnGgkdev1zRdv97aaezziTgAtAo2grtzJN5WEwQacXVrZaQZOH9wzWhkCBFUfxsXgnt5qJDJ1iiSOTJ37WdL6eCy/uEMfuuIVzWw0KEBpgdiK9d3YeLOgYy0M6k3fzcWIqq6hV7v6CKJfjrMa2Z5PNbB1HbEMumxEICPCWiJ5ueZKIjn5ednk9O6NZs2+V3NrTiBnhqIiivlP7v6M78d3RnAC7r0Y4p/cJYeTgPV7MJTmYoskliUMdAvr9zCIPfkta1yFqXHFbp5abHw1VHXaNJMeO5uegUxiNDrk+e4LPvHqosmtzxwjhFcz346qQWmygB+kb4k15cg6teR2SApxKCG+LjTrf2vlzep72yrqd3uJ+NkKNup7yAclKfMObvSlfMSP07BLD6yUvpOn2VzfO7t7c/RvXKHmj2233z0ChuVpkybx8erazHsMdYrOHIlHg6cNHpaDCalDE4vgmtuyV4akI3PlidSOzLE3CzY9qVIc+blvgv5PGTdg6TWDkZRTNY8tDIVk0ec4kdp+jM6/txy9AoUgqruP6iDjy6cL+ygntKv9OLixYEweEgjwzw5GFzFIc1YkK8iQnxZpMcxdIEf3TR6/jmjsFkldYqEqc84N2V/9LkGGJHnXYEb7M0bHBEWU4Dat9JgLeb3TL9OwSw8nCeEv0kh1Gq1f4ALzf6Rvrx2GVd0eu0E9/b3YV90yc0aZoBFCZi7/3UTllZS2sp3r+xP9dfFKloGP0i/TmYVa4QUjVBtSf3qEMrrzCHiV/SNcSGIMntH9M9VBOt50ial8eGXzNhqTJO9b1bEzodYESjnZ8J7hwZw50jY5Tj9c+MprreyDVfacN6ZXNik871x0dxsqD5BYlnA05G0QKcC9VuYFSAEjIo7xf00pSeNtLouYJ7E9KPGh6ueo0pQp5gsvR0Zf8IRBGu7N9yhufpKg1Le6vDTxcaRuGAEMkRT3LCm2BvNx4c3VnjxJWi4iRJfGdykeZ+b3cXfD3sh0mqITvw1Waf3uF+HMutULSZ04G3u4tGOPCXNSIzgW4uxt5dNda6OdAOZMjMQ719jCNGMbxTENOn9OKmIfZNjtZoKUPpGearcQy3Blx0OsDUrE/tdOHIMjAgKoDNz40l2iqIQY2+kf70dbCTwtmGk1Gch5BNLl5ubfh5BM2/FuOlKb2orGukb6TkMNbrBLvRMk1BnqStySjURGywnXUtYPFJyOGugiDwYhOBCSM7B+Ppqlc2wvNuIXEZGBVAwpuTNW365cERlFa3fB1HSyATXNlUoWYE9jSKMxVKPBzcLwiCYuJrCVz0Oh4Y3VkTdWYPfz8+ymZ19ZlC7hbvNph79sLozxc4GcV5CHlBjnW43jmFeQKeqmumR5ivJqLkdOB1FkxP4QEWR3iEgzDMAHOobUvyfoBEAO8Y0VGxp3udwveylr59PVwdOkRPFzKxk/+rF1XaM++4nyGjsBdefbpoSeTgmWhfjiDnh4kMbDpUtzUQ7MAEej6izaOeBEG4SRCEo4IgmARBGGJ17UXzjrGJgiBc3lZtPNewaBRnR/1tCWTpukczJoizAVmTajS1nkbR1bxS2noFuhqy8/pUkgOpCa7XaUTcnE3IJlPZ5yNLrG56HZ+o1gDIaCsz5/mIpkxArYGDr05iq50oqPMV54NGcQS4HvhWfdK8Q+ytQB8gAlgvCEJ3URTPXRaaNoLRTCBPJ9SvtdA7wo9F9w9ncIx9M83ZhOxPkFegtwZc9DqWPDiySUlRNtWcijnDT8UozoUv61TQxbzHkaxBTekbzjvXGbhuUKRdZ21TUTn/Fiz673D2pZaetT2TZLSlw/500OaMQhTF44C98NNrgMWiKNYDqYIgJCHtJOt4B7R/CGSTi4u+bQnPxXYitM4F9DrhrIT+qVdE24OLXsdDY7owvlfTtnE1/FrZXNSauPviGCb1DqOjWTrW6QRuH267oZ2M09Eolj56CddaRfBcyLi4SwgXd2mbcX8+o80ZRROIBNTbtZ7VXWPPJ8g+ChedU8I713jhiub3G1LjfJYMXfQ6hUm0BKcjRdvb3M+Jfx7OCSUSBGG9IAhH7Pxd09Rtds7ZNQq05u6x5wNkW3JAC3eNdKLtIK+A/yfAtY01WCfOX5yTUS6K4oTTuK3ZXWNV9X8HfAcwZMiQVg6YO/d445o+TO0f7nClqxPnD85n09Op4nR3H5h3z1BNPgon/nk4n8Whv4BFgiB8jOTM7gbY36z/HwYvNxfN1htOnL+Qt0GX84pc6HhyfDe7uwc0hbHOsfqPR5szCkEQrgO+AEKBFYIgxIuieLkoikcFQViClPLUADz6b4h4cuLCQrCPOyufuFTJzneho7ntrJ34d0IQW3tpYxtDEIRC5L2eTw8hQFGzpf6dcPZN03D2j2M4+6ZpnA/9Ey2Kot0kIv84RnGmEAQhVhTFs5cq6gKGs2+ahrN/HMPZN03jfO8fZ/ylE0444YQTTcLJKJxwwgknnGgSTkZhi+/augHnMZx90zSc/eMYzr5pGud1/zh9FE444YQTTjSJNguPFQQhClgAhAEm4DtRFD+zKjMWWAakmk/9IYriG03VGxISIsbExLR2c51wwolWhEkUEQThlPOdOHH2EBcXV+Qo6qkt11EYgGdFUdwvCIIvECcIwjpRFI9ZldsmiuKVLa00JiaG2NjYVm2oE0440XqoqGuk/2truWVIFO/d2L+tm+OEGYIgOFxW0GY+ClEUc0VR3G/+XQkc51+y6Z8TTvybcTirHIBfYjMdlnn976M2qWadaDucF85sQRBigEHAHjuXRwqCcFAQhFWCIPQ5ty1z4kLCruRixn20mQkfb+GZJfFt3Zx/LeoajRzNKdecSy+u5qO1iTQaTWSX1irnF+6xFWLrGo3M3ZHG7d/bIweth/PZPxsTE8OHH35I//798ff355ZbbqGuro7NmzfToUMH3nnnHUJCQoiJiWHhwoXKfcXFxVx11VX4+fkxdOhQZsyYwahRo5TrO3fuZOjQofj7+zN06FB27tzZova0OaMQBMEH+B14ShTFCqvL+5FWCw5A2uZjqYM6zovdYyvrGimoqGuz5//b8eWmk6QUVpNUUMUf+7Pbujn/Wny0NpGpn28npbAKkLbNv2fuPr7YmMTcHalklNQoZaf/eYSM4hqWHsjm/347hCiKFFc3nPU2/nkgi/6vrSVT1RaQzGJpRdVn/fktwZIlS1i9ejWpqakcOnSIefPmAZCXl0dRURHZ2dnMnz+fBx54gMTERAAeffRRvL29ycvLY/78+cyfP1+pr6SkhKlTp/LEE09QXFzMM888w9SpUykuLm62LW3KKARBcEViEgtFUfzD+rooihWiKFaZf68EXAVBsNmxTBTF70RRHCKK4pDQULu+mHOCa7/awbB3NrTZ8//tCPPTZq/7aG0ifx+0u+HwBQGTSaSu8fzf3mz7ySLWH8tXjjNLJI3h7rn7OJZTQVJBFSlm4rtgVzp/Hsimd7ifUn70B5t46pd4fonNJLmwmsLK+rPe5o/WnqCy3sCl72/SnH/ml4OM/XAzC3alUdNEStzX/z5Kv1fXMHdHqmJKa2088cQTREREEBQUxFVXXUV8fLxy7c0338Td3Z0xY8YwdepUlixZgtFo5Pfff+f111/Hy8uL3r17c9dddyn3rFixgm7dujFt2jRcXFy47bbb6NmzJ3///XezbWkzRiFIexrPBo6LovixgzJh5nIIgjAMqb3Ns782QnKhNBkaDOd+y+Xc8lq6T19FbFrJKd+bXlxNUdXZn5xnGwWVdfSL9OfDmwYA8MXGJB7/+cB5bWJoCu+vSaTPq2v4afeZbF129nHH7D3cv8ASQNLezx2AjJIaHvopjpwyiXFM6RdGVmkt2WW13DkymgdHd7apa/b2FI1p6mzgjb+PkeXgGSfyKwF4ZdlRXl1m308iiiJzd6RRWW/g9b+PcdWX289KO8PCwpTfXl5eVFVJGlpgYCDe3t7KtejoaHJycigsLMRgMBAVZcnOoP6dk5NDdHS05hnR0dFkZzevfbelRnEJMA0YJwhCvPlviiAIDwmC8JC5zI3AEUEQDgKfA7eKF8CslyfGucS6Y/k0GE3c/sOeUyaMYz7YzGUfbD47DTuHSCuupmOwFyO7BGvOnyyoYmNCPrO3pzq48/zEb3GZGE0iM5Ye4ZN1J5TMh+c7ymsbld8ZJTXsMwsvV/a3bMU+qlsI913ayeben/dm8uii/cqxPR/G6eC7rcnEZ5ZR22Bkzg7tOGg0WgS7iAAP5fevcVnc/v0e4jPLiM8sI7VI0nZKaxqxhukcfpvS0lKqqy3msYyMDCIiIggNDcXFxYWsrCzlWmamJWAgIiKC9HRtf2ZkZBAZ2XwMUVtGPW0XRVEQRbG/KIoDzX8rRVGcJYriLHOZL0VR7COK4gBRFEeIotgyz8s5RFx6KZM/3cpbyy1RvV9tSjqnbRBFUSGCDQYTmxILWnyvwTxJKusdq9lngrKahrNu880rr+PimRvILKmleztfIgM8mdi7vXJ90idbuXdeLG8uP8Yry45Q02Ag5oUV/B6X1UStZ4ZVh3OpqLMlKC1FQl4FRVUNRAVJ5rTPNpxk6NvrWXogm/vn72utZrYqZAGlvLaRvpF+3DJEkma/3pwMwGU92uHtpicqyJMOgV608/Xg6QlNb2s+/c8jTPlsGzEvrKDyNPvzx93pvLMygWu/2sFzvx5UzndrJ20NX20e+zlltexOsdXI1x3L49qvdnDZh5sZ+vZ6NiZI8+t/l/dQytw5Z68yl04VJpPI3wdzTkkQePXVV2loaGDbtm0sX76cm266Cb1ez/XXX89rr71GTU0NCQkJLFiwQLlnypQpnDhxgkWLFmEwGPjll184duwYV17Z/OqDNndmX8iobTBy87e7SMir5AeVtPrrWSRAIE3EG7/ZyXdbkzmaU86y+BzSiy1OuXvnxZLfjFPdaBKJeWEFL/5xWDl3Nuzhkz/dxtgPN9ucr2s0nvbEAkgqqOLtFcdIKqhixMwN5JRL7zu8cxAAH944gNl3DSHEx01z34Jd6cRnlgHwzZbk035+U0grqubhhft5YEEsyw/lkJBnHaPRNOoajUz+dBsA06f05q6RkrmgpLqBp36JZ/3xAoW4nU+oqJXaVFhVT6CXGzOv76chpp5uejY+N5ZVT45Wzj0xvitAkwzjWK7Uf3nlpxco8le8xbSy4nAufSP9eOvavtw3StJoNidKATA3f7sLAA9XLVn8apN2nMwyj5tLuoaw84VxhPt7sD2piB3JLbeK704pViLDFu5J5/GfD/BrE+HCaoSFhREYGEhERAT/+c9/mDVrFj17Srnev/zyS8rLywkLC2PatGncdtttuLtLpsDg4GCWL1/ORx99RHBwMO+//z7Lly8nJKT5RFVORnEG+GLjSYdSQG3D2XNC7k8vJTa9lHdWJjD18+0s2JUGwE2DOxAT7AXApoSmtYr4zFJAy9Q+XX+y1dua54Bh9Xx5NV2nr+LN5cdO2VRmNIlM+HgL329LZcLHW5Tzix8YwYjOktnJ38uV8b3a8/zknjb3rzmSB0CYn4fNtaZwJLuc6X8etvnm1sdHcyTCtjulhMcWHeDqL3Y0WW9do5EX/zikaIKJeZXKtf4d/PnPiGibew5nl1Na3dDmUXbqb5ddVktSQSVHsisYFBWATifw6GVd+em+4ax+6lIA2vt54ONuWecrCAJp707lyQndCPe3fI8AL1f6RfprnrU7tYQ/D2Qx/qPNJBVU0hKIoqjpT4CRnYO5Y0Q0/p5SGtunfolnd0oxWaW1dA7xZt3TY0idOYVXruxtt86kgioCvVzpG+FHRIAnP/93BAB3zdnLsviWRdvd+t1upn4u+TZkR3+VivmnpaUxYYIlg/Rrr73GTz/9pBxPnz6doqIiMjIymDZtmnI+NDSUFStWUFFRwb59kubZoUMH5fqoUaOIi4ujvLycuLg4TehsU2jrqKfJgiAkCoKQJAjCC3auC4IgfG6+fkgQhIvaop2OIEs4n94ykBAfiWtP6CWZPEpqpBC/nLJa6g22TCOtqJpHF+1n8d6MU35uWrHWlLM/o4yJvdvzwU0D2PTcWAK9XFl3LF+jIWxOLGDbSUlyMppEbvhml029+07DEd5SrD6Sy/JDOaQVaaNaZm9PZVeKfUlsZ3IRX21K0hDiukYjx3PtS+gyk1Dj5iFR7HpxHNNGRPPcJElqnb9LstO6uZza8J+56jgL92RoHJzL4rPp8tJKpU0P/RinsbEDNBhNTZpNdiYX8fPeTO6Zu491x/JZFi9Fak2f0ouIAE+6hPoQ6KXNzf3JuhNM/GRLk1F2a4/mnVZww6mgWiUQbT5RwE+7pfF80xCLE3VUtxB6hvnZ3GuNTc+NZdeL43hqQje+vv0i/n58FG9e21e5/vLSIzz9y0GSC6uZ8PHWFrUvv6KeijoDr1/dhwBzH8aESI5gF73l+9/63W5cdAJ/PT6KqCAvBEHg3lGd+K/KjzJjai+Czalv/zM8Wrm/Q6Al2u7JxfE2a0j2pZU41J7LahqoqpMYhJqBni4SEhI4dEgKM967dy+zZ8/muuuuO+N62zLqSQ98BVwB9AZuEwTBmoVfgZQruxvwAPDNOW1kMygz22KvHRRJ51Bp8Mk25X2pJWxOLGDUexu5e84+3l+dwIt/HFYc3Z9tOMmKQ7m88MdhG0eYySQ2SVhOFlTh6+HClf3DlXPyb0EQ6BTizYaEAsUeO2PpYe6eu4+750oShrWEBXDVgAhyW9kJn62q76Gf9vPYogOM/XAzP1sxx++3pti9/+45+/hgTSJdXlpJzAsrKK1uYPxHW7jt+90AfHHbIIK93QjydmPt06Pt1gEQ7u/Jm9f25ZGxXdGpNhdqaaRXUVU9iXmVhJqFAVljANhwXNIC3jT7qFYflbSVAR200nC/19by5vJjdkM/i6ss6wb+uyCWOTtSGd09lP+ao4L0OoH9L0/kwMsTueeSGGKCvdiTWkKR+T6181iNB36M48ZZtgLBqWD7ySJKmljXsFtlbnl/dSLzdqbRvb0PUUFep/wsD1c94f6ePDWhOxeb83ZPGxGtaCPWaCp8VUaiOYqpR5gvL17Rk+hgL0WYk30UMib3DbMh1u3NWmfPMF/uuaQTBvNcHRgVoJRx0et4VpVC9huzT2ZTQgFbThRy06xdPLPE4htR47MNJxVNwt31zMlxZWUl119/Pd7e3tx88808++yzXHPNNWdcb1vu9TQMSBJFMQVAEITFwDVIObJlXAMsMEc67RYEIUAQhHBRFHPPduOmfLaN/h38efcGx3vRlNU0EOApSRgf3TSAT9afYHzP9szdkcZTv8Qr5XalFCtS8x/7s6i3Cp9NzK+klyqu/N75+9h+soi4GRPx93Jl0Z4MXvrzMIlvTWbN0XwW7ZEI7YypvQn39+C5y3vg7qJX7n9sXFfunRfL8kO5fHyzSZHyZMk8LkMyO02f0ou3Vx7nyv7hdAr2YsWhHBqNJlz1zQ9YURT5fX82Azr40629r8312gajxsGvxsfrTqDXCax9ejQrDuXy8boTnMyvVOoxmkS2JxXRYCWFTfp0q4bQjuoaQtzLE5ttqwydTuDk21OITSthwe50djmwKeeV1xHm70FxVT0BXm489GMcsemlynV1+Kbcp+nFNfyx32LGGxgVwAc3DSCnrFZh0LO3pzJ7eypp707VPK9MFUXj5aanpsHIIBUhAkkACPR249Wr+tC/gz9P/2IhPJsTC7hmoBS5Ul7bSIPBZKOBnA7Kaxu5Y/YeRnYO5ucHRtgt8+3WZEJ83BnXM5QlsdL7+3mc+bPVcFTfvrRSxnRvet1Uuln77hzizYjOwdw8JApzxD0xId4kvX0F325NIdDLjasGhNvcf8vQKEprGnj0sq7odQLje7bjjwPZhAdozZaPj+/G4+O7MW32HtKKqzEYTdwzzxJ08PehHD6/bZBN/aIIlWaNoiVh9WPHjtVENVlj6NChJCW1fjBNW5qeIgG19yYL272eWlKm1Vdmi6LIsdwKFu9z7FwymUT2Z5Th6SYR6KggLz6+eSDd2vs4vAfQMIn3zRuirT2arymzObEQg0kkv7JOYRIAuWV1SqTOFX3DCPP3YPrU3homATCuZ3s+vWUggCJ9yyivaWTt0Txigr24/9JOpL07lS9vv4jIQE9MotZhKIqiQ1/L3tQSnvv1IBM/2aqUaTSa+Gl3OttOFtLrldWsMvsCZLx5jWUHFh93F7qE+nDHiGj0OoG/DuZgMolU1xuYtSWZu+bsBeD7Oy3ZIdVMwsfdRTElnAr0OoHhnYPpF+lPSXUDX29O0oRHxmeWMWLmBj5Zd4LBb61n8FvrNEwCtJqS/Du7rFYjNUYFedG9vS9je7Tj+kHaIas2RZpMInHppbjoBFJnTiH+lUl8fPMA7r3ENnRURri/dmHhk4vj6ffaGvamljDg9bWM+3AzS+NbvtCwvKZRYx+XcdIsjSebV1jbQ1pxDeN7tuONa/ryf5N7Mrp7qF2/0JlA9iV0b+/Dd9MGEzdDst0fybZd6HY4q5x+r61h4Z503l2VwCvLjgIopmGZSchw0et49LKu3D68I752GJKvhyv/u7wnXm6STP3O9f2YdcdgzYJBNTqHeJNeVMOWE1o6pHbDqf061fUGxXowY+kRYl5YQUaxdrX4+YC2ZBT2dhi29mq2pEyrr8wuVJkk/vPDbk2ESXJhFTNXHefvQ9JE9PXQKmXtfN01xw+M7oyrXuDRy7pozv/ywAhuHhLFsJggNiRoGYWM4qoGhUkAjP1wM1tOFBId7MUnZkbgCIOjAwEpfFeNAW+sZdvJIib2bq+ZNBEBEvHJLqulrtHIh2sS+XjdCXq9sloxPRiMJsVck6IKeZWjQObvTGPG0iPcq5Kkbh/eUfndM9yP/zMTkXeu6wdAkLcbJlHki41JfLhWWmD2u0oyn9i7vSZy5snx3bh5SAc2PDvGZtKfCjqaTSPvr07kT/N2H1tPFLIvVbLpf7ZBcuyX2YmZzy6tpbbByKwtyUoElTVkUyRIxGXnC+MURplaVM2u5GJ+3J3Ogl1prD6ah8Ekbbvt5qLj+os64N8EE4wJ9rY5V1lnUKJ2KusNGvNec8ECA99cy8iZkq/jWE4FpebvLb9bQWU9S1QROUaTSFx6CVmlNRRW1hMe4IGHq56Hx3Zhwb3DGNYpqMnnnSq83V344c4hLHlwJJP6hBHs406EvwdJBbYMbEdyEZV1Bqb/eUQZlyBpk60BD1c9k/uGORx7HYO9qaw3cN982x2sZWZc12gRTOIySjloXtndaJS+0+gPNpFVen4xi7Y0PWUBUarjDoC1GNSSMq2KH3el8bJZCgHYkVTMnXP28vvDFwPw/G+HiEsvVcIup0/ppblfEAS+nTaYYG83+nXwx91Fz4tX9EQQBNxd9Hy87gQAw82O1x5hviyNz0YURfIq6tCpBuBDP8XZbeNNgzvg4aq3e02G2sEGcOfIaBbssiy2GdujneZ6pJlRPLpwPw+O6cyXqrUgN83ayV+PjeLZJQdZfTSP24d3VJx6IBHVib3bK6td5QEPMKVvOJf3CWNnchFDogMZGhPEw2O1TLNrqA8nC6qUePsU8wr3R8zlRnUN4YM10l42T09sOu6+pVAz9P0ZpXQI9OROsxbTFCb0asf64wXcPXcve8xMxcfdxUYiVztvPVz1RAR40jtC8lukFVXz0E9ah7d63UdzCPP3YN3Town2ccff05W49FKFSchQCwjZZbV0CJQYY4PBRH5FncaHIJs/8ivqmPL5NvpE+LHiiUtZq9qW4/nfDnFRx0C6hHrzxt9HlYAAQDMWzhYmWPVP1/a+nCyoxGgSMZhMuLvo+WN/Fu+uSjjrbWkKctShPfywLYWJvdtrNEJ5rFvjoZ/iWP64fd9MW6AtNYp9QDdBEDoJguAG3Ar8ZVXmL+BOc/TTCKD8bPonqusNvG8mSAAHX51El1BvjuaUYzSJFFXVK5y+qKqBsT1CCfZxt6nn8j5hDIkJUkxCsvTx2GVd2fTcWA6+Okkp2ynEm8o6A8XVDYycuZHhqigWtZOyfwd/Drw8keNvTObRy7o2+y6CICiE9vPbBtE5RCuFDokJ1BzLhKO4uoF3VmonW3JhNW/8fUxx1C7ak2Gzh9Ky+Gy70nXvCD/GdA/lxSt6OZTCfnv4YoVRqfHcJEmTkLW20zE1OUI7X4uNefG+TG7/oemdSi/uEsyADv4EeElEUWYSAJd2kxyvr1zZmy9uG8SwmCBNqKcMmXlbM4nOod58e8fgU2p/t/a+BHm7odcJNhL8rUOjNMdx6aVsOVHIsZwK3ludwKXvb7LryH9qcTwgOetrGgwctPqeEz7ewtebk23WC1wzyMYafNbRNdSHpIIqnvj5AD1mrOaXfRmK6e/xcV25qGMAAPeP6sT2/7vsnLUrWsUoHh7bRRnXgiCFn0/9fLvihB/fs53dOib0akeqFQMRRZFnlxzU7Kl1LtFmGoUoigZBEB4D1gB6YI4oikfl7TvMq7NXAlOAJKAGuOdstumvgzlU1hn4dtpgxvdsh4tex4NjuvD8b4fo8tJKm/IXdQy0U4tj6HRSRJIa8vGVn9vfL+aRsV24ZmAk3dv7nLKp5fnJPXlwTBf8PFwwmkRe+1tyLse/MtHGr+Gq17HnpfEKo3LRCUqEB8CvcVp/TVpxDRN7t+eNa/owcuZGvt+Wirp5793Qj4u7hBDUAmnT39OVHS+M42R+JRM/kcIe/3rsEsVcIBPnawZEOKzjVNHOz5bBWyPAy1UxPS28fziCIJBRXMNvZj9RuL8HueV1PD+5J29f148AT1d0OoGrHLQz1McdX3cXm1XwV/YLP2PTiE4AkyhpjjcNjmLxvkwiAzzJKa8lPrOMuTvSAJS1CbFppUzuG6YJoVaHKfd+ZQ0Atw3rqDFjfbr+hKIxBni5svzxUa3uvG4Jurbzoa7RxIrDktz4f79bTLSjuoZQVNXA/owyRnQOVrSpcwH1s/5vck8eGduFvPI6/vPDHgrMPjZZAJzYuz0bEgq4++IY5u1MAyS/ZWl1A+uPF1BZ16j4TQ5llfP7/iy2JxVqtKvUomou+3Az917SiSn9JAH1bKAtTU/yjrArrc7NUv0WgUfPVXuO51bg7aZnksp+3yfC1mklEwhHDq1TgRzTbb0wrb2fO69f3YfJfW0jMU4FsiPQRS/w60MjiUsvVQivNdr7SSaNB3+KY8G9w/jPD3uUFd8mEXa8MI7KukZl1fCIzsGE+3tyabcQtp0s4vNbB/H4zwcA6NrO95RDJLu192X1U5fye1wWfSMs4aVB3m5s+d9Yu1rH6cLDVc+9l3RiSEwge1KKmb8rXWEMnUK8eXhMFyIDPfH3dKWitlEZDx2DvfjrsUvIKatlVLdQ8sprbZi/I+h0AmN7tlO0sbsvjuGagRH0Ub3r6UKvEzAZRe4YEU339r68elVv+kb68+TPBxQmASj+psS8Sib3DVOI1v2jOml2F5AxY2ovhVFcNyiSPw9I/pxXr+rNPU043M82OjoYW51CvBkaE0RMiDcXdwlmnAOp/WzB2iTs6+GKr4crkYGeCqOQF9q19/cg6e0r0OsEhVHcPCRKWbSXWlRN/w4BgCUkO9rKP7U3VWLuc3akMmeHbURda6FNGcX5hqSCKrq299VI7t3aWUI/h8YE0i8ygHtHxfD15mRGdWt+6XtzUPsSVj91KftSS3hj+TG2PT/ulBeENYehMUEMbUbi6Nbel43PjgVg07Nj2Z1arCSQkQi1JynvTOFwdjn9zWsFvps2hON5FVzUMVBhFN7uTftQHKFnmB/Tp9quiLWeIK2BV66SnjOlXzhXD4xkd0oxH6xJJNTHnZutzDdq9O8QoEzgru1sQ4ObQtdQS1TcJV1DGHSKWqnDetv5cjy3Qon7l4m4Xq/VVOQorU/Wn6BDoKcSpTckJsguo/B2dyHlnSmsO57P+J7t2JVcTF5FXYuZ49mCtQ9Oxvs39kenE2jv5+FQszvbcNPrbIJcHhnbled/O6jZUDDQy02z6E+G7MO4+ssdzLl7CH8eyKHCzNDV2ltdo5GSam2wRVpRtSJ8tibahFEIgvABcBXQACQD94iiWGanXBpQCRgBgyiKQ6zLtCZyy+vobaVBuLnoOPbG5Xi46DXmATlq50yhXrPQvZ0vPcP8mDYyplXqPlPodIJiXpP3G5LPD1DF+Xu66W3McF6uF5YMMjg6kAPm9SUhvmfPORtpJnBje4SekgO7Ocy7Zyi7U4oVDVJGuJ8nmSW1/HDnEB78KU6zyv37bSmKE75PhB+dQ70V56qLTlAEBp1O4PI+0pbX71zflyd/jmeAmVG2FSIcaJetoeWfKeJfnYhgFbA5sXd7DrwyiUve3agw6wDVt/p22mCizGYrtX/rkYX7NVFSalPhf37YowQtrHryUq74bBt/HMjmmVYK+FCjrWbzOuBFs5/iPeBF4P8clL1MFMWznjxXFEVyymqZ0MtWVZVjqM82WiuErzXh4arn0GuT8GomysoaXqepUbQlZBNQU2sYzhQTerXjwdGdeWRs8wEJp4L2fh7Kojs1Prp5AAt2pTG2RyiBXm4UVdVz98UxrDuWT4JqhX6HQE+WPz6KRXsyeGvFca7oF05HOxE843q25/Drl7dq208Hbi46kt6+grLaRnw9XHB30WMyiefFHGqKXoT4uCmMIlBlApYZMVhWg4M2lBZge1IRMS+ssKm3V7gfl/dpz46kIp6e0O2MQsftoU0YhSiKa1WHu5HyTrQpSmsaqTeYbBYznQvsenHcWd1E8ExxOs5K73PEXFsTI7sEk/jWZBtHf2siwMuNF61Cqs8mooK8FFNemXn/sWGdghAEFN/FtucvQxAEvNxcuGlwFEeyy3npHLbxdOGi1ykL6eD8FLSs8fo1fbn2K2mTSGvzlAxrk7N1YIk15CjK928YgI+HS6szCTg/do+9F1jl4JoIrBUEIU4QhAfOZiM8XHV8efsgxvY496lUw/096Rza9IruCw3WWzVfKDibTKKtIRObjkFejDSv43HRCZqgA38vVz69dRDtTnFnXSdahoFRAax/ZjTv39C/RYxt5ROXsui/IzSh4ep9pa4eEKGYG/29XNGfJWZ51sQ+QRDWA2F2Lk0XRXGZucx0wAAsdFDNJaIo5giC0A5YJwhCgiiKNttGmpnIAwAdO3a0vtwieLm5aDJwOXF6ePPavvy0K/2sSDVOtA46BHoqUmtTkqoTZwdd2/m2OAhC9pnGvzKJrzYl8XtcFg+O6cKC3ekUVta36tqipnDWGIUoihOaui4Iwl3AlcB4R+lNRVHMMf8vEAThT6SNBG0YhSiK3wHfAQwZMsQ58tsQ00ZEM81O/gQn2h5PjO/G5xtO4u/p2uoRdU60Lrb+7zKbbTwevayrsth2aEwgKw/n2QQvnC20VdTTZCTn9RhRFO1uaiIIgjegE0Wx0vx7EvDGOWymE078o/DMxO5KRIyXmwt3joxmfK/Wi7xyovXQMdjLbjCBDJPZx32uTIRt5XH8EnBHMicB7BZF8SFBECKAH0RRnAK0B/40X3cBFomiuLqN2uuEE/84vHFN3+YLOXFeQt6CP9TOFkJnA20V9WQ3NtBsappi/p0CDDiX7XLCCSecuBCgOLD/yaYnJ5xwwgknTh+vXtWbPhF+DG/lLd0dQTjVxPbnOwRBKATSmy3oGCHAWV/gd4HC2TdNw9k/juHsm6ZxPvRPtCiKdtcH/OMYxZlCEITYs71VyIUKZ980DWf/OIazb5rG+d4/zhg5J5xwwgknmoSTUTjhhBNOONEk2oxRCIIQJQjCJkEQjguCcFQQhCftlBkrCEK5IAjx5r9XzkHTvjsHz7hQ4eybpuHsH8dw9k3TOK/7p818FIIghAPhoijuFwTBF4gDrhVF8ZiqzFjgOVEUr2yTRjrhhBNOONGmqVBzgVzz70pBEI4DkcCxJm9sBiEhIWJMTMyZN9AJJ5xw4h+AuLg4+vTpg4dH06u44+LiihxFPZ0X6ygEQYgBBgH2MtyPFAThIJCDpF0cbaqumJgYYmNjW7+RTjjRBOoajSw9kM3NQ6IuiO2uzxRLYjPZlFDAN3cMbuumONEMBEFg6dKldO3adA4UQRAcLitoc0YhCIIP8DvwlCiKFVaX9yPF9lYJgjAFWAp0s1PHGe8e64QTZ4KP1iby/bZUQn3d/xX7Jz3/2yEA9meU2mQ3dOKfhzaNehIEwRWJSSwURfEP6+uiKFaIolhl/r0ScBUEwSZRtSiK34miOEQUxSGhoec+n4QTTiTmVwFoUo2er9icWMCy+OwWtzWtqJpOL64gUZURT8b1X+9s7eY50QLExMQwc+ZMevfuTWBgIPfccw91dXUAfPDBB4SHhxMREcGcOXM095WXl3PnnXcSGhpKdHQ0b731FiaTyd4jNGjLqCcBmA0cF0XxYwdlwszlEARhGFJ7i89dK88tGgwm3l2VQF659MH/PphDalF1G7fKiQaDiTnbU2kw2J9QjUYTBRXSN2swmjAYm594jlBdb6CmwXDa9zcHURS5e+4+nlwcz7urjrfonuWHchBF+PNAtt3rFwJzbEsk5lXy4ZpErAOHGo0mGs9grCxcuJA1a9aQnJzMiRMneOutt1i9ejUffvgh69at4+TJk6xfv15zz+OPP055eTkpKSls2bKFBQsWMHfu3Gaf1ZYaxSXANGCcKvx1iiAIDwmC8JC5zI3AEbOP4nPgVke5K/4J2JhQwKwtyVz5xTYq6xp5/OcD3D13b1s364JFXaPxjIi2jLk7Unlj+TF+ic20e33SJ1uV/NNv/H2MrtNXsTe1pMk6n1p8gDnbUxFFkamfb+OGbyTJ/JL3NjLpE5uUK2cEk0lUiJScrxngr4M5LbpfZpCOcljM35lGdX3rMrddycUUVta3ap1thUcX7efLTUkczi5XzomiyG3f7ebGb3ZiOk1G+9hjjxEVFUVQUBDTp0/n559/ZsmSJdxzzz307dsXb29vXnvtNaW80Wjkl19+YebMmfj6+hITE8Ozzz7Ljz/+2Oyz2oxRiKK4XRRFQRTF/qIoDjT/rRRFcZYoirPMZb4URbGPKIoDRFEcIYriP1bP3ZRYwEM/xQFQVNXAvjSJ0NTYyaW97lg+x3Ks3TlOqJFUUEnPl1fz4I9xZ1xXRomUMqXRgUah1voKzMTt5m93UVLdYLd8SmEVS+NzeGP5MUqqGziaU0FceinXfLmdsppGskprqTe0Xg71277fzegPNpFeXE2+WfMBWkyI683MVvbRW8tqbyw/xoDX17ZOY4F6g5Hbvt/NyJkbmi2bWVKjzJXzEdX1BpIKJLPkY4sO8PXmJOIzy0gurCY2vZSDWeVkltpNydMsoqKilN/R0dHk5OSQk5Njc15GUVERDQ0NmnPR0dFkZ9vXFNVwrsw+T2BN+A9nScdBXm7szyjleK50bDKJ/HdBLFM+30ZWaQ0zlh4+ZanZZBIpr2lsnYY3g4KKOr7ZnMwHaxLOyfNAmpwTPpak8g0JBcp5URRZezSPukZbImw0iSQXVmnOiaLI9D8Ps3BPhlLmVJBWbN9sGJ9ZpvzOKrVI+AezLBJnblkdrYFdycXsSS0hs6SWMR9sZv1xS3+YROm6PTQaTTy6cD9HsssprJAYSkWtQfN/cHQgE8yOe4NJVEymZ4q0ohqlTutvYo2pn2/jplm7bJjX+YDuM1bx6KL9ynFGSQ3vr07k2q92KPMZIPc0+y0z06LhZmRkEBERQXh4uM15GSEhIbi6upKenq65HhkZ2eyznIziLKPWjkZgD+5Wav0n608AkJhfyfVf7+SKz7bRYDBppI9nfjnIT7szOJhVzvHcCjYnFtASfLExiQFvrHUo8bYWKusaGfbOBt5bncBXm5LPyB57Kpg22xJlHe5viR3fk1rCAz/G8eySgzb3vPbXUcZ/tEWRuBuNJu6bH6swCYBiO/3V1Pc9YTZHlVQ3aPwOaqfwNV/tAGDxAyM097614thpmyTUiLWStuXjGy7qAMBvcVk292QU19Bt+ipWHM7lyi+284fZN1FYJTGMomrp/7QR0XQO9VbuK6pqHVORWkP7K75p81hFnUHTtvMFNQ0GGgwmNicWAvDeDf0013enWBh0enE1H65JPGXz3VdffUVWVhYlJSW888473HLLLdx8883MmzePY8eOUVNTw+uvv66U1+v13HzzzUyfPp3KykrS09P5+OOPueOOO5p9lpNRnEWsPpJLr1dW240WsUZLchhnldaQrZJAc8ql3656gSs+28bdc/dRVtM88V97LA+Q1HZHGPPBJp5afKDZuhxh3Eeb6fea1hzRGozJZBJZEpvpkOlU1xvYn1EGwNT+4RoH9D6z32Dd8XwbCfTH3ZKUtTmxgDvn7GX7ySI2mrWR/17aCQ9XHcV2iFF2mdSHPcN8lXOvX92HqCBPlpmJ3EVvruPW73Yr13PsSJAxwd6a4/XHCxS/x5kgt0L7rIS8Slz1Ah/e1B+A3/dnaTQcgIV77IfTZ5g1pOIq6TsG+7hx85AoupiZRUVt62ip8hj2cXfheG4FGcU1fLnxpM03U3/blMLzK+ijqFI71i/tpo3GXLgng8gATwC+3pzMl5uS+NQsHLYUt99+O5MmTaJz58507tyZGTNmcMUVV/DUU08xbtw4unbtyrhx4zT3fPHFF3h7e9O5c2dGjRrF7bffzr333tvss9o6PHayIAiJgiAkCYLwgp3rgiAIn5uvHxIE4aK2aOfpYFl8NmuP5QMoBKcptETzyCipoVIldchmC7UfY19aabP1+HlIWbF2OjA7GE0i6cU1LI3PsWumsYfymkaNzdvexG0N5+SqI3k8/9shuk1fZZdZ5JZbGGmXUB9KahoU09yKw7mARGBkp255TSO3f28h4v/3+2G2nijkgzWJyrnLerajS6gPv8ZlMeHjLYpNfPmhHP4+KNX5zvUWibFXuB8DOgSQX1HHL/skjeRQVjmbEgtYfSSXwso6Bkdr1x4EettmKntvdYKNr2JjQj6vLjvSXDex5mgeL/x+iLzyOo0QUllnoJ2vB4IgMMyc9Obmb3ex1Kw1iKKoMdeBxASv7B9OSlE1dY1G1h+XxnWHQC+6tvPhy9ulaVlR1zJGIYoiKw7lOmT2cj09w3zZlVLM6A828eHaExzNqWDgG2uZ/udhAL7flqLck1Vay+9xWSzck44oijZMRT0uzgVkrQsgzM+DCDNTAIgx58IeEhNIj/a+pBdLwsb321KZNnsP5WaG68icNndHKjUNRoYOHcqxY8coKytj/vz5eHlJ9b7wwgvk5eWRk5PDvffeiyiKymK7wMBAfvrpJwoLC8nMzOSVV15Bp2ueDbRleKwe+Aq4AugN3CYIQm+rYlcgLbDrhrSg7ptz2sjTxPHcCp5cHM8f+6XJl1/RvA3SWu3s3t6Hpyd015yLzyxTzBlqqOvfm9p89LCfp7TO8r3VCSzem2FzXa21fKgimNZYEpvJXXOkqKwxH25i6NvrlTBRNZ6bJL2HPdPNqULNuBJybfsix2zbv/viGEJ93BBFFGZxsqCKkZ2DAThuvnfryUK7DDNDpW35e7oqduSkgio2mO38jy06wGcbTgKSRnDvJZ0AyXYf5O1GXkUd//f7YaWee+bu46Gf9lNYWU87X3devcoy3N1d9MrvRf8dDsCWE4Vc8u5GjQ/q3nmxzN+V3qRZKruslgd/jGPxvkyO51ZwWQ+tNNvOT8qzvODeYYDEOF//W9rwYE9qieJ8lTGlXzi9wv2orDPQ8+XVfLc1xfzOEmHyM6fjlH0XzWHtsXweXbSfWZuT7V6vrDOgE6Bbe18q6yx1VtcbKKtpZOGeDHLLaxXNBiRt+9lfDzL9zyM88GMcV3y2Tbn298EcRs7cyJ4Ux3NDFEVe++soceklbEosOOOQ3/3pFoGtT4Sf5lqor9T/PcP86BXuq7m27WQR7646zsHMMjq9uJK4dK3pMLusltf/PnbWzcbWaEuNYhiQJIpiiiiKDcBi4BqrMtcAC0QJu4EA82aC5xVEUeTX2EwKKiViUm8VHdMSAllVLxHA/13eg6OvX86ap0bz5IRuvH9DfwK8pIn46fqTfLTOVj2VJRKAY7n2o6Fyy2uZ/OlWFu/NwNfDIr1+YqXu1jYYSciz1HEwq4yNCfl8uCbRhjg9/9shtpwo5KO1iZSZnePD3tlgU25M93YAlLbC4D6YVab8Pp5n+66ynfzui2MIMSee/2N/NrnldRhNIiPMjOLvgzksi8/WECI1qsyMO9zfQ9JMVG1PKqhSNAWQJN8gbzdevrIXSW9fgV4nEOjlpmh6E3q109SdXFhNqK87Q2Psp7HsE+Gvep8GtpwotCnTlPR+WO0UL6+jb4Q/B1+dRKcQyUQkmzw8XPUKc6uqN2AyiRxRhXDK6BLqg5+HdhOHmdf3w7zESbnWUo0ix6zN2XNUJxVU8sXGJDxc9Yzrqe039die/Ok2TCqJWx0UsO5YvsZsF2cm2o40aIDKegPzdqZxwze7uGfuPubuSG3RuzjCJpW/cGJvyeH/1e0X8frVfZQ818M6BdnNeb0/vYwNZq1t/fECcstrFe3iclXo9O6UolaNjmsKbckoIgF1YHqW+dyplkEQhAcEQYgVBCG2sNB2Up0pahuM/LIvg2eWxGvUQaNJZMGuNPamlvC/3w7x+l/HWHU4V1EdZeS3IKqhqr6R9n7uPHpZV7zdXZRJePPQKOJfmUTXdj6a8vumT+Dr/0gq/0mzBOjr7uLQvLPqcB4JeZX8EpuJeieiKitCec1X23nAHFLaIdCT8tpG3lmZwJebktiQUEBsWomNOeqLjUma45VHcjXH7f0lgv3iH4c1/SeKIr/HZSkMpK7RyC3f7iLmhRV248sNRhMLdlns52qJUnkfM4H38XAhxCy5vbsqgYcXSu8k9+NfB3N4cnE825Ms42VAB39CfNzppurrlU9cioernh7tLZLfgYxSjaYgO3QFQcBFL02pIG83AFx0guI4ViPUx92GSMyY2guQCO+Blydy9PXLcdEJfLbhJJd9uJm1R/OUsv/f3nmHR1XlDfg9M5Nkksyk90YIJITeQpcOioANy2LXVdf6WXZd1111XV3L6rq61tV17S66u1YUlA4CgkgTCEkgQAjpvffM+f64Mzczk8kkgRSQ+z4PD5mZO3fOnLn3/Prv/HVVBi+tO8w/Nh6hqqHZQQN21jYvGReNv7eH6t6cYecv/+MFw3j8ouE0t0rK6po4UlxDsK+nKkxAWdD8nMZ65cS2Vjm+ngb0OqEqC51hW9Sd7xNQfitQ3KkT4h3dc1/YFfxV1jezJbOE5Agz4wcEcqiwvXVpi2HYYh6ltR27Pp3vmydWpLmMSXUV+0ymeVZBsWhUJNdPjefRC4bzylVjGT8g0KWgyCis5gdrPK24upEpT6/n0eWp7MkuV6/vmNvf5l9HzTz8eeduyJ6gP3s9ueqc5mzvdeUYpJT/xNrPPSUlpcfz5Ba9tJmj1kyMW6YnMDRSMSXXpRXyxy/behSu2J/Piv35XDwmyuH9BR24npb9kM2flqdy8PHzKK5uVDVgVywYHsErRW0LcojJk2mDlW4mX1kLp2Ynh7H5sGtBuS5d0VD2ZFc4uLlqm1ppaG7F6KFHSsmhwjYtb3CYSc3aALjl/Z1tY7lqbIdjdQ7eB/ooi2Z9cyvFNY2EmZVMpPXpRfzmfz+xaGQkr149jsyiGvUG2Xm8nL05FQ59hOzH5u2hd3kjq4LCy0CYuW0+D+Qq2uiYuACH41fuL2BAsA+bfjtbfW76s+vVv2038vs3TeT51YdotlhUl6I7bAIpzOxFpN2iG+jjQXldM/4+HurvfddsxX988/QEbp6eoBxnFTSDw0zss1oId/y7LdXSPhvrmW+VxTXrL4sAKLNbEJ9eMpKYQMVFZHOHzXbS1AOsv095bZPiFvMz8uLSMXy84wQPLxqKTidUQRFi8uST26Y6vF+nEwT5enY568kmyOx/z8yiGoJ8PdVrZXSMf7tF1JY+HGr2ori6kcyiGh5YMISMgmqXxYM/HCsl0t+bNdZYoTvXWFFV+7GvTSvkFxO61juu1SJZlVrAguERCOGY3hxs/S1txAb5EBvk6LYDeOWqsWQW1fD3tYfV+2ClNa72/rbjqpI0e0goG6z35fYuuJp7gv60KHKAWLvHMSgdYrt7TK9z1C5dzz51766PXGcFFTlpJwVVDS4DUw9/sZ+mVgtVDS0UVTcS7tdxG+D7zxvikEIphMDXs82vHRPozeAwE+V1ze0C48dKatmaWapqyvY3KCiusRNldTz2lWOHd6Od39ye1PxKjrvImPrvrVNIjjC3y6Lx0LddZvZWgM09kGHVBp3dQGlObrQX1ylusqeXjCTY5OnSpVfb2IJeJ/Ay6Iiz3ow2zF4GogO8MTh1d40J9HZ4XGhdNNbcN0PtBBvuZ+SZy0bx1CUj8fZwPS/2TE4IZtGoSP56+Wg1KwhwUAa8PfUceuJ87j9vSIfnibBL723pxG9us/TKapvx9dTz40PzHDT/N64dz+vXjFd95DaCrItzWW0TxTVNhJg8SQo388cLhqnfX2+1cJPCzcSHOGZogWIhuUtWKKpq4H87T3C8tFYVFLkV9eq1Ou/5TVz86lYaWywYPXQsu2WyalWPjG5zxb1x7XhevrJNSRkW6UdMoDeu4r7XvrWDec9votb6Ga4sGBv2FetzksPQ64RDnKq4upFqF661VovkRFkdr286wh3/3s3qgwX8lFNJfXMrV0+K483rUtTv4Qp7Ybh4VBQDgh2vWVcFt79fOFT920OnY/qzSvylq+nxJ0N/CoofgUQhxEAhhCewFFjudMxy4Dpr9tNkoNK6j0W/YTOvqxqaO+z9k+20iDa1WFya5bb7vrCqgdS8KgcN2BWTE4J554YJPHHxCAAMeh0LR0YA8KsZCarP++9rDyGlZF9OBSU1jcx+biMA5490DO/cNnMQAN8eKOCuZbt59/ssh9eHOQXhbOSW13OkqL1/Odjkydi4ALftKwqrGtQbzrZI2Cwc2/Nf3XUO/t4eqr/cYpGsTy9kVaqiGS6dEEu4n5HssjpOlNWxzy5uUdvYiq+nHiEEQgjW/2am6tKxLbQ6pxvX3s0CEG4N9sY53bSg+PXHWq2SSQOD0AlUP789ep3g1avGMW1wiENM6OKxiufUFofoLC06wkl5mJwQ5FAbAvDIYiUobtOKy2obCTJ5thMIsUE+LBgR0e4zbBlXx0pqKenAsrV9pnPcwIa3p5516UUczKvi4x3ZfLnX0eq68d0f+e0n+5j5140OKdxF1Q3qdZBdVkdhlRJT8fVSnB3pf17AZ3dM5Z65iYwfEMg5g0Mc5iQhxNROIXDFpIFBFFY1kOWid1pDcyuPLW/zDMQGehMVYOREWZvwmPDkWrXmxZ5nV6Uz/dkNqlVf09jKpoxihIDfnZ+sxic6wmbt2eqoutJ5OMFOUB8tqeVEWT2/+Od2bnjnx1Nyl7mjPzcuahFC3AWsAvTA21LKVFufJ2sbj5XAQiATqANu7MPxsSq1gBlJjhkjNq0kzU0LDfvAmo2CqgbVneDMF9abKinc7PJ1e5zdBrZFzmw0MMma7vjGd0exSMmbmx0Dcvaas9nLwEVjonh90xH+/LWjJXHHrEEE+Xpy3ZR4nrcGz5ffNY3axla2HSnhpfWZbD+qCIPnrxiNycvAsh3ZxAR6kxRubhfMt+eGd35ECNh4/yzqrRpwaW0TUkrVbWQ2GhgZ7c+B3CoKqxp4+IsDqvtgybhohBBMTgji9U1Hmf7sBgD2/nE+/9h0hKMltZi82i7rhFATUQHePLkyjd9Ys6+arFlEkxOC2H60TF2UbCy7eTLpBdUOmUj2DI304/sjpSSGm/jPrVM6/K723HTOQN7acozrpgzgFxNi3boZ7XG+ZkJMXrz3y4lc8cZ2frJabjaL5UBeJU2tFkprmwjy7dr5oS0L58HPlLiLs6sElAykzQ/Mbmd92UgI8WXX8XIWvtSWbXTh6ChVm7YPXJfXNRNi8qSkponjpXUO8ZWjJbXMtLvnjFbr7b75Sdw3X/n97IVrVICRAXY1KOcOC+e84RH85n+ORZUhZi9+OFbGrOc2kvnk+WosCZQK+urGFvy9PaisbyYpwszB/Co1OcVmKdmnfH+w/TjDo/z49oASN7JZxy+vP8zx0jpig7zVNHR3TBoYxLnDwrlojKJA+Bk9OPzk+ew9UUGAtwd3LtvN7xcOJT7Yl59OVODnbcCg12HQCZcW5i/f/ZEv7zqn08/tLv26H4W1dfhKp+det/tbAnf29bhAybC47cPdpDjlu9suGufsokvHxfDp7vZVrrdMH8ibm4/xY1aZGttw5o1NSrrhFRNiXb7ujnvmJWE2erB4VJTDhjm2AjJ77M3c2clh7czccwaHcPP0gcwa0iaMnr1sFBV1TYyKCQAUwfTS+kzetmaFzB8WjtnowbnDFU01IdQx6D4iWvnOG+6fpVo2Uir7GNjM6qYWCzWNLarryWQ0MDzaj3e2ZPHLd38k1U4oXzclHoDLxsfy6oa29MpXN2SqgjHKSeM2eug5+tTCdi6ACfGKoHB2Jdn7kF1hc/nZXDZd4aGFQ7l91iDMRg86VwfauH3WIKobmtmQXkxuRT0hJi+8DHpeXjqWGX9VhKQt5mOLYUT6Gx0KADsjzGzEbDSo8+8cuLbhbk6euGQE/3Oq8l6fXsTcoeHUNbXQ0GxBCCXoWFjVwKSEYL47VMx1b+/g2ctGqe8prm4kKdyEO+xdmQa9jij/NuF15+zBDpaUXieQUjos2q9syORea+r5wbwqtdvBW9enkFFYzdIJcWxILyLXGmfYmlmivre51YJOCB75QgkiO1ujtgzE8V3co0OnE/zzupR238/mHVh930z1+YF2lsQPf5jLlW9ub+dGdn7cU2iV2U7YYgm2PPuddvnQAG9vPcZ/fszmpxMVhJg8VTfQzdPb3A+jYvx58Pxksv6yiIcWDSPcz4ufTjimHbpKFTV5dV9um7wM3D03Ub15fmv1dzc0t9fqzUYD4wcEMjkhiOevGI2Pp+PnXTgmykFIAFyREsuvZgxSH8cGOd4YzmMeFxfAzKRQ/rJkJJlPns8Xd0wDHC9ygBX7Cqi3a2uxNbNUDfrbLIqmVouDkADURSQ+2AcfuxjN5sNtN/MIO5+2DVd+Ylu17Nyhrt0pHXHVpAEsGRvNzTMSuvwenU502Yqwx8/owRMXj2SQNb5kc7OE+3sR7ufFZeNj1LoIG/mVDd2yKMAxDnAy16GXQe/gZjHohHrv2DKARsUEYJFK+vjc5DA1k+yDbY5KzbnD2rvHnHn8ouE8duFwwLFY0ddL7+DC3f3wfHY/Mt9BSfr72sMUVDbw7YECFr60mTc2KQpHQqiJqycNQG8N3lfVN9PY0urQXr2q3rFA09vTtdW5dGLXguAnS7DJq909Bcr37w36fYe704WCygYWvbSZ352fzBUpsWS4yNG38cy3GTS3WDhvRATXTB7ANZMHYLFIQkye3H/ukHYXSYjJyyETBeDS13unEe6dswc7VBXbY/by4NPbp7p8DWDywOBOzy+EINLfqN78zguw2ejBe9ZCro6YPyxcdSXZsHXO9TMa8DLomTbIcX+qW2cmMDMpVBVuQgjC/YxqckF6QTUT44O4PCWGy8a3T0d1xcSBQRx+8nwHDbUrRPgbef4XY7r1nlPlDwuTsVgkS8YpLgovg54f/jAPUGI4QuAQ0A02dd3aAbhkbLRaZ2A2ntyycNWkOPV3jQ3yIduqXdsSGMbGBqjusqgAbx5YMISb3tvp0H57zyPzO3TR2mOzLMFRsHl7GlS30pBwM/7WGiTnDKpffbBTjRPtzq7AoBME+rQd4+/tQW5FPUMe/hZQYkUFVQ1UNbSoGUmg1IREB3jjZdCRGG5S42iuFvGeZsGICPXz/vOryfzin9sZ20u7DfaLoBBC/BW4AGgCjgA3SikrXByXBVQDrUCLlDLF+ZieIsSaRfPAJ/tYPCqS9IJq9eJwxpa1YWuBAIrGuPPh+S7P7etpYENGMWW1TWp+vXOLizX3zeipr0KAjwcVdc1cP2UA79lpawE+HftMH71gmMvgrSu+vXcGZbVNeOi7tze0La3xhV+M4Z6P9rAuvQijh87B+hljvdADfT15+4YUBIIR0f7tArPQZv35eOqpa2rl3vmJTHUSMK5458YJahZPd4VEf5Ec4ceHN09y+ZpOJ9pl/TgHwTvjsvExPLo8lbqmVofge3ew/8y4IB9W7M8n55UthFpdY0vGRbM/t5KWVgszkkIcrMXLx8fw5CUju9TzzBl7ZcXH6kb88aF5Dhanyappz0wKZdOhYvblVKppx4BD7RLQLr4wf1g4H2w/3s76qWtq5fwRwfztitGA0l4jq6S208SUnmDxqCju+89PLB4VyaSEYN69cQIpHRRxnir9ZVGsAX5vDWg/A/we+F0Hx86WUpZ08FqPYR/ceu/742QUVDM9MZS/XjaK42V1NLa0suDvmx3e05XgM8AOa2+g59dk8LsFyS6rgRO7eK6usOa+mZTXNZEYZuLm6QmsOVjI3KFhbn3MV3bDVPb39nBZKNQZ//nVZFotEpOXgaeXjGTiU+vaucjC7W6wOcnuM0CiArzJKq3jzetSGBcX2KEbwJnZQ7rnajqT8DMaqGpoUYsNu4oQimssu6zupFxPgEPMKz7Yh03Yah+UBTnCz+hg0drHeMYNCDwpIWHD20NPfXOrGl9xVixsAfPYIG+evGQED39xACmVDLfCqsZ2qbPOFtm5wxVB8baLim17d8+NLjLgegsPvY7dj8xXP9/ZbdyT9Is6JaVcLaW0rZbbUeoj+p1x1rTHw0XVFFU3khxhRqcTDAzxJTnCTzX7zx0WzryhYYzoIH3UmVnWXjt5FQ08+Nl+pv5FKej69fwkd287aULNXiSFmxFCEBvkwy/PGeiQGeIK5zbnvUFCqEkViGF22ucji4cxKkZxA1ye0vWA/l8vH801k+NIie+6kPi5YtsXYqE1BTrgJAS5LXX3ZP3cNregt4eeOBfXm7NLKci6GI8fENgtRcUVK++Zzrbfz0Gvc23lNrcqgsJTr+fqSQPY9+i5HHt6Id89oBRaOlvb9t1e37lhQrsap0Wj2lLN+/PaC/L17DA7ryc5HWIUvwT+08FrElgthJDAG9YK7F7jv7dOYfBD36iVt4lO2RfPXDqKhxYOJbibQclXrhrHkte2tusia8sI6k8uHB3F8p/y3BYF9Raf3TEVX08DQyLM3HTOQKSU3RpHdIA3T1w8svMDzwJeXDqGYyW1DA4zMSkhiOmJnbvgnLl3biLTE0NOyc+96t4Z+HkbqGloYVikn5od+OntU9u5+fyMHqz99Qy3lm5X6SwmsGRcNN+mFnCTNenE5l7zMuhZc9+Mdout/flmJ4e163R737xEDhdWc6iwBh+P02EZ7V1Eb+0MJYRYC7hKX3hISvml9ZiHgBRgiau9sIUQUVLKPCFEGIq76v+klO02FBZC/AqluyxxcXHj7Xdw6i7xD65Q/95w/6weC0rd/7+f2m0Ss+reGZiMBgQ4tCHuSywWSauUZ4yvXuPMYs3BQjYfLubxi0b091C6zfHSWppbLQwOM6uPZ/51IwAZTyzg2W8zeGvLMe6aPdhtdf2ZghBiV0dx4F4ThVLKee5eF0JcDywG5roSEtZz5Fn/LxJCfI7ScbadoOitXk9RAd0LCLrjyolx7QTFoFBfh9hIf6DTCXQuW2ppaJw684eFd1qdfLri7K61f+xl0DPMWhfV0Za3Pyf6K+tpAUrweqaU0uU2a0IIX0Anpay2/n0u8HgfDrNHfX/jBwQyMtqf/bmVPLJ4GBF+xn4XEhoaGt3j7RtS1OaC542I4NPdOdw1Z3A/j6r36a+V6hXADKwRQuwVQrwOiqtJCGGr1A4HtgghfgJ2ACuklN/29sD+alcl2tPYiqVmJIY4BMM0NDTODOYkh6tBf5OXgWW3TCY5ov9jjb1Nv1gUUkqXItjqalpo/fsoMLovxwVKPvlvP9nXK+d+aslIzhsR0aOpsBoaGhq9zc8/XN9NhBBcMjZaTWntSfy9PbhwdFTnB2poaGicRmiCwgUv9HF7Bg0NDY3TmV5Lj+0vhBDFwMnnx0II0OuV4Gco2ty4R5ufjtHmxj2nw/wMkFK6dKX87ATFqSKE2NmbPaXOZLS5cY82Px2jzY17Tvf56bf8TCFErBBigxAiTQiRKoS4x8Uxs4QQldbMqL1CiD/2x1g1NDQ0zmb6M0bRAvxGSrlbCGEGdgkh1kgpDzodt1lKubgfxqehoaGhQT9aFFLKfCnlbuvf1UAaEN1f47GjV/tJneFoc+MebX46Rpsb95zW83NaxCiEEPEorTlGSCmr7J6fBXwK5AB5wP1SylQXp9DQ0NDQ6CX6PT1WCGFCEQb32gsJK7tRIvE1QoiFwBdAootzqE0BfX19xycnJ/fuoDU0NACoamimvqm1XRtujY6REkprGwnw8cTQQVv0vmTPnj0MGzaMAwcOlJyWWU9CCA/ga2CVlPL5LhyfBaS428goJSVF7ty5s+cGqaHhhgO5ldZtMM++avvSmkbGP7EWgBV3n6NuLarhnn9tPsoTK9JYMja6z7fUdYe77rH9mfUkgLeAtI6EhBAiwnocQoiJKOMt7btRavQ1Fovkn98dIbeivr+H0ilp+VUsfnkL81/4rt0OaWcDmw+36Wvbj5a5OVLDnrVpyj7X246Wcjq4/rtCf7YvnQZcC8yxS39dKIS4TQhxm/WYy4AD1saALwFLO2pJrtHznCirU7eQ7CsO5FXy1Mp0rn5ze59+7snw3aFi9e+Mgup+HEn/sDu7HF9PPUG+nqTnO3uNNVzRapH8dELZGja/soGy2qYeO3daWhqzZs0iICCA4cOHs3z5cgBuuOEG7rzzThYtWoTZbGbSpEkcOXJEfZ8QgszMTLfn7s+spy1SSiGlHCWlHGP9t1JK+bqU8nXrMa9IKYdLKUdLKSdLKb/vr/H2JZsOFbP5cHHnB/Yi3x8pYfqzG/hk14k+/dwfrJppVmkdTS2WTo7uX44Wt+1DkF5w9i2U6QXVJEf6MTTSzKGimv4ezhlBdlkd9c2t6ta1eRUNPXLe5uZmLrjgAs4991yKiop4+eWXufrqq8nIyADgo48+4tFHH6W8vJzBgwfz0EMPdev82oYIpxm1jS1c//YOrn1rR7+O49UNioax90RFn35ump1merpvCJNXWc/o2AD8vT1I7yGL4rGvUrnwlS00NLee8rke+eIAL6493AOjcs3hwmqSwk3EBfmQW+5yWxkNJzKsCsXcoWEAPeZi3b59OzU1NTz44IN4enoyZ84cFi9ezEcffQTAkiVLmDhxIgaDgauvvpq9e/d26/yaoDjNOGi3UOb1k5++1SLZmVUOwJGivl2sU/OqMHool2VxdWOffnZ3yS2vJybAmyER5h5xPTW2tPLO1iz25VSy49ip+fxrG1v4YPtxXlh7iKySnv8Ny2ubKK9rZlCoiegAb0pqmqhvOnXh9nMnvaAaIWBmkpJc1FOCIi8vj9jYWHS6tiV9wIAB5ObmAhAR0bYrtY+PDzU13bMANUFxGtHUYmFjRpH6uDfdGYVVDdQ1tbh87XhpLY0tFoJ9PdlzorzPFoCi6gYyCqu5YFSU+vh0RUpJbkU9UQFGkq2C4lTCZ5X1zRzMa/u9t2aeWn+4H4615Xx8vif3lM7liqMlykIzMMSXmEBlQ66eTECoqOs53313uPWDndy5bHevxeb2ZFcwONREpL8Rbw99jymDUVFRnDhxAoulzV2bnZ1NdHTP1DBrguI04vGvU3l1wxFCTF4APebOcCazqJrpz2zgwle20tzaPg5g+9yrJsXR3CrJKOybQO33mcridsk45eIuqjp9LYqy2iYaWyxEB3gzNNKPmsYWskpPzv2yP6eS0Y+t5pLXlBBcpL+Rbw4U0OLit+kqO46V46EXJIWb+P5IzzclPWKNzySEmogO9AZ6TlB8sP04Yx5fw7YjfZvgeKKsjlWphazYl3/KgtoVjS2t7DhWxrTBIQghiAow9pigmDRpEr6+vjz77LM0NzezceNGvvrqK5YuXdoj59cERRdparH0qpYjpeQ/PyqB49euHkewrycnynrH7/viukyaWi1kFtXw5d68dq+nF1SjE7DYqtnvz6no8rkLqxq48Z0dDtpxV9l1vByTl4FJA4MxeuhOa9eTbVGMDPBm4sAggJNa2BpbWrn937scnntgwRCyy+rYn1t50uPLKqklLsiHuUPD2ZNdQU2ja+vRnvSCKm54Zwf5lZ0vXsdKajHoBLGB3sRYBUVOD8UpPtym7BLwv52nlkhxoqyuQ6vZFfYCNfUkrt/O2JNdQX1zK9MGhwAQFeDdZeHa1GJxa9l7enqyfPlyvvnmG0JCQrjjjjt4//336aniY01QdJEb393BmMfXdGiSVjc089rGTDYdOrlspe1Hy2hulTx76SgmDgwiNsiH7F4SFNuOlHDpuBgCfDzYk13e7vWMgiriQ3xJCjeRFG7ik91dd108v/oQGzKKefqbNPblVPD8mkNdXkAyCqoZEmFGrxOEmY0UnYKgWHuwkFWpBerjg3lVvL3lGI0tPeNGs8VwhkX6kRDiS4CPx0kt7N/sLyCnvJ6XrxwLwMhof8bFBQKnlnJ7vKyOuCAfzhkcQotFssPqirJYJB/vyHaZlvncqkNszCjmn98d7fT8J8rqiAn0xqDXEWY2YtAJcstPXTtelVqgWrBbj5SctDsvs6iG6c9u4I5/7+7ye7YdKSXU7EWkv5FDvWBFbzlcgl4nmJSgKBZR/t5dznq65+M9jHpsldt02uHDh7Np0yYqKys5ePAgl1xyCQDvvvsuTzzxhHrcrFmzyMnJUR9LKRk82OXu1CqaoOgChwur2Wp1izhnAW3IKOK8F75j1GOrefbbDK5/ewef7c5xcRb3rE8vxKATLBwVCcDwKD92ZpVTUNlAUVXP5VuX1DRSUtPE0EgzcR0Io/SCapIjzAghmDUkjLT8qi65QRqaW/liryJU9mZX8PhXB3lp3WHuWran0xu+qcXCgbxKhkcpG9WHmb1OOkZxpLiGm9/fya0f7CI1T1m8H/86lce/Psh732d1+L6WVgtFVZ1/ZqtF8tGObJLCTcQG+SCEYEi4+aRiSt8dLibY15NFIyP57rezeffGCcQG+uDjqT8p12NLq4XbP9xFWn4Vw6P8GT8gEINOqIJt5YF8HvxsPy+sOdTuvba4g80F6I68inqiAhRLQq8T3dKO3fHUyjQA7puXRGFVI0eK2wddt2aWEP/gCuIfXNFhGrkt1rcxo9ile9UVafnVjIz2Jy7Ip0eteSklR4tr+GRXDhPjg/AzegAQGWCkpKaxU+WltrGFbw4U0Nwqmfu3jWw53Pf7G2mCogusPlio/v3ahrbClMaWVh7+/AC1TS0sGhnJoxcMA+DrffndOn+rRfLxjyeYNzQck5fSfuu2mYOwSMn1b+9g6l/WM/2Z9T1y8dq01OQIP5c3RF1TC9lldSRHKAv2kHAzTS2WTv3vqXmVvLI+k8YWC7fOSKC6sYWdx5XFae+Jik4rd/fnVlDX1MqUhGAAQs1eJ21RrLH7vd7YdJTi6kZ2Z1cAHbuHymqbmP/Cd0z9y3qeX3OIr35q75Kz8fmeXA4X1XDvvCT1ucRwE5lFNd3WgPfnVDI2LgCdThAX7EOwyQudTpB0koJnbVoh3xwoYPaQUG6ZnoDRQ09CqK/6u9vmxjnuVNPYQlZJLUYPHRmF1Z0KzNyKeqKtggIgOsCbnFO0KE6U1XG8tI4/XTCMhSOVLB1nK6251cIfPt+vPr71g10cc5HVZR9jcPW6Mz9mlZFRWM3QSDMxgT49Gph/Ye1h5vxtEwVVDdw7r61VXZS/Mn+Fle6vc5sb7NaZCfh4Gvjz1wcpqm7g+rd38K/NR7H0QVFsvwoKIcQCIUSGECJTCPGgi9eFEOIl6+v7hBDj+nqMFovkvztPMDrGn98tSGZdehHnv7iZH7PK2HyohNyKeh6/aDivXDWOG6cN5KIxUd12GWSX1VHd0MIca241QGyQD1dPGkBGYTUtFkltUysvrTv1nHhbnUKy1aLIKa93cKcdKqxBShgSofQusv3f2aJ16T++5xWrEP2/uYkMi1QEzcOLhqIT8OBn+9xmkhzMV+ZsVGwAACEmL0prTs6K2ppZQlK4iSXjoln+Ux7nPLOeVotkdIx/u99GSkl+ZT3vb8viWEktLRbJS+sO838f7aGyznVbji/35jIo1JfzR7SlHCaEmKhuaKGkG2NuaG7laEktQ61zZU9SuInMLqQmWyySD7ZlEf/gCi54eQsvr8/E7GXgzetS8PdRNNfkCD/SC6qxWKS6gNpnaT23KoNFL23GIuHX8xXht9VNALypxUJRdaNqUYDiby+oPLUsNdvYzkkMIT7EF0+9jnTrdZFTXkdWSS3bjpRyvLSOP188gpeuHEudi/uiqcXCD8fKmBCvuPA6s8waW1q5/cNdGHSCX6TEER3oTWFVQ6cFnxaL5FhJrdual9KaRt7YpFRBXzouhklWRQgUiwKUehxQlJgLX9nSzh1sE5Y3TRvIXXMGk1FYzd0f7WHToWKeWJHGxz/2flFsf/Z60gOvAucDw4ArhRDDnA47H6VbbCJKd9h/9OkggS/25nK8tI6rJw/ghqnx3DA1nuOltTy3KoNnvk3H20OvBqcABoWayK2o71bBlK0IJznCsbHcPXMTuWpSHB/dMpnLxsew+mBhu8W2obmVFfvyaWm1IKXk2wMFHfYdam618M7WLKIDvAkxeREX5EOLRToEL22tGGxjGRxmwkMv+Pf27A5vHCklDc3Ka+MHBGLyMvD6NeO5a/Zgrp0ygGsmD+B4aZ2Dpu/MoYJqzF4GovyVmyfE5EVlfXO3q7MbmtsySy4ao2RPNbZYeObSUSwYEUleZYPD/Pzxy1SmPL2ev689zKgYf8ZYBRXgEOOwJy2/mrFxgVjbkAGQEOoLdE17tbE7u9wqwALavRYb6ENJTWOH15GUkq/35bHwpc088qXSeX9/biWpeVVMSgjGoG+7tYdEmMmtqGdDRhElNU2Mjg2gsr6Zl9Zl8mNWGa9syCTI15OrJsVx/dR4An08WLnf9XcHJWFBShwsihCzJ8U1jaeUIrwls4RwPy8GhZrw0OsYEOzDkeJaHv5iP+c8s4FZz23kurd34KnXcem4aC4cHcUFo6PYmFFErV2wfu8JxTq9bko8Bp3otL3IrqxySmqa+Mc144kL9iEm0BuLpFPB968tR5n93Ea3cZA92RU0tlj4321T+NsVox1eswlaW+bTxz9msy+nks+cYoL7cyoIM3sR5mfkwtFRmLwMbD9axrTBwSSE+LL6YMe/VU/RnxbFRCBTSnlUStkEfAxc5HTMRcD7UmE7ECCEiOzLQdqk9awhoXh76vnThcOZOiiYH46VcbiohjtnD8LLoFePjwtScsq7Y4an5StFOIlhjoIi0NeTpy4ZyZRBwcwbGkZlfTOr7RavhuZWnl6Zxp3LdvP6piN8sTeX2z7cxT0f73H5Oct+yCa3op4FVk3YNlb7OEV6QTU+nnpirbnxRg89N0yNZ9vRUt79/ph6XGFVg2ry2rToKyfG8eFNk5RzB/tw/3lD8DLoeWTxMExeBr5z05bkSHENCWEmdfENMXsCdDs2s/t4OY0tFqYnhjAzKZT/3TaFHQ/N5bLxMarws1kVB3Ir+WD7cfW9N0yN51/Xp7Dq3hlEB3jzwKf7eNMpsLvmYCElNY2MjQtweH5QqAmAoy586h2xNVMJbk4eFNzutZgg9ymn246UcteyPaQXVHPpuBg+umUyr1w1Fi+DjsWjHG8RW9znoc8P4Gc08I+rxxHhZ+SFtYe4/PVthJi8+PCmSTx1yUi8DHqunBjH2rTCDufedm3b0mIBQny9aGqxUN2F7CpXFFY1sDatkDnJYeo1EBfkw9q0Qj7cnk1yhJl5Vov7krHR+HgqLtqrJsZRXtfskL331U956ATMSAxlUKipU4til9VFOtkaZI6xLuA5Fe7drevSlDjIlswSlwJdSskuq3XgrARCm+sp3yqQdluPtblsQbnHNx4qZrLVEvH1MnDBaCUb8eIx0UyID2JPdkWvNxfsz/0oogF7mykHmNSFY6KB7gUBToHs0jqWjI0mzNzWb39CfBBr04p4cekYVWu1EWtdfE+U1TE4zNSlz8goqGZgsC/envoOj5k/LAKz0cCyHdkcL6ujsKqBd7Zmqa8/t7otOLnjWBktrRYMeh2V9c3szCpjTnIYWzJLiA/24ZHFw9qNlUFt7x0V44/Ork/+Q4uGkV5QzTPfZjA5IZji6kZuem8n98xN5L75SerieN7wcJffwUOvY2S0v0N7DmeOFtcy1W7BtNWSFFU3EOHf9b0OtmSWYNAJJg5UzjUhPkh9LTnSJiiqSAwzceU/t+PjqeeX0wbSbLFw0Zho9DpBiMmL6YkhfPzjCZ5cmcaFY6II9zOyLq2QW97fSaS/kUvHxTh8blSAN14GncvgqytaLZJVqYWMjQ1Q41L22IrYcsrrVSFkz8oD+Xh76Nn1yDx10QQ4f0Qkeqc9DibEByEEFFQ1sGhkJFEB3nz/4BxeXHeYg/lVXD8lHl+7McwbFs5rG4+w7Ugpi0a118ts33FAsI/6nE2wl1Q3qsHazqhuaOZ/O3NIiQ9k5f4CWlolt80cpL4eZz2/v7cHK+6eTmV9M69vOsKtMxLUY2ypyX/4fD8R/l546HV8sP04M5JC8ffxYExsAN8cyKfVItvNi430gmoGBPtgto47Wk337VjZq2tqYXd2uZoQcqiwmlFOluFdy/awYn8+Y+MC1HPb4+2pJ8DHg7yKemoaWzhRpnzekaIamlsteOh1fLY7l4q6Zq6ZPEB930OLhnLusHBmDQnFIiX/2XmCoyW1Lq+TnqI/BYWrX81ZLHblGIeNi+Li4k59ZFYamlspqGpgQLCvw/M3ThvIqJgAVQOxx5WW3hnpBVUu/dT26HVKZs3mwyUO7Z0BXr1qHMfLannv+yymDQrhsz25jH5sNYPCTOzLUfyb108ZQHpBFaOiA9T3RforaY3HrYHq0ppGDuZX8dvzhrT7/D8sHMr5L27mqZVpJFgvyFWpBdw7L5GjVneLuws1IdSXr/flI6V0cNmAktVRUNWgum8Au9z8+nY3oDu2ZpYwNs714hvhZ8TPaGBHVjkSqG5s4e0bUpiTHN7u2IcXD2NkjD8PfX6Ahz7fj0GnY0NGEXqd4J0bJ2D0cBSIep1gcJiivTY0t9JikaxOLSCrpJZfn9s2n8dLa3l+zSGGR/mRWVSjpsU601FtgpSS46V1fL0vnxlJIQ5CwjYOZ3y9DNwxaxDvf3+cX54zEACdTnDf/KR2xwKMivbH7GVgS2YJi0ZFUlLTyF3LdhNqNvL8FaNJL6jCbDQ4up6sgr20tokEl1vftOdPyw/y6e4cTF4GvD31TIgPcrjXxsYF8s7WLIaEKynTQb6e/GHh0Hbf93cLknnm23SW780jwEcRWC9a93mYnRzGf3aeYOX+fFUTt81jVX0L/j4epBdUMcRuP5FIf2+EoF26b1ltE0G+yvl3HFPS2W+ePpA/fpnKc6sPUVXfTHVDM0snxDE5IZgV+xV99o+LnT3qbUT6e5Nf2aBaueePiOCbAwVkl9UxKNTED8dKifAzqgIRwORlYHayYl2NH6DEYXZllf9sBUUOEGv3OAZlu9PuHoOU8p9Y95xNSUnpMRvMplHEBXs7PO9p0DHFhbsAIMTkibeHXl18O0LJxCknLb+KrNI6Lhkb4/Z4gDlDw9h5vJzLxscwMtqfmsYW8irqmTcsDC+DnttnDqKstonP9uRS29RKpl1Hz/esRUzX2mkmBr2O6EBvVah9b80Iso+52Bga6cfN5wzkPWvQFxRN7L3vs8itqMfToHMIbjqTEGqisr6Zstomgq2Lig3b+RLsLvTYDgTuibI6Nh4q5oqUGAeXHyiL6r7cSu6Z224TREBppzwnOYwv9ubx1U95xAR6uxQSoNyMV06I46kVaaxNa2ursvq+GSR1sEnRkAgzWw6XcN1bO9iR1ZbldcHoKBLDzaTmVXLzezvJr2zgy715DI/yY9FI157UMLMRD71op9U+/U26Wufwf3Ncf09X/Pa8ZO6bl+QQu+gIg17HmLgA9lkLLVenFqpZa+n5VdQ1tTI6JsBB4Af7Kr9pSRcz1TIKqvlsTw7TE0PYfLiEmsYW7pvnKLjmDQ3jyomx3DnbfY7/7bMGsTWzhCPFtdQ3VzEzKZRA64I+f1g4yRFm/rY6gwUjIvCwfv/7/rOXlQcKeOeGCRwrqXX4HTwNOsLNRge33wtrDvHiusO8eV0KM5NCeX3TETwNOi4dF8MTK9IcWs4/aU3x1QnY9fB8dSyuiA4wcqKsXk0WOW+4Iij+tfkoTy8ZxZ7sinZuTnsGhZoI9vVkfXoRV0yI7fC4U6U/BcWPQKIQYiCQCywFrnI6ZjlwlxDiYxS3VKWUstfdTifK6nh+zSHGWX8gm5XQFYQQJEeaeXvrMVosFrLL6rh28gBmJIVS3dBCkK8nR4trmPO3TQ7vWzKu854st84YREKIiZT4QFWDc/7sYJMXy26eRJifF5H+3mw+XMKsIaHc+e/dbD5cwtyhjgtjXJCPqs1szSzBbDQwMtr1TmXXTB7AO99nUVjVyPVTBrAuvYg/fXWQ+GAfBgb7dmjaQ1uw92hJbTtBYRNo9hqRn9GDQB+PdoLiT8tTWZdexEc/ZDMw1JdnLx2luk1W7MtHSrgipeMb5qklIzlWUsv+3MpOFyCdTnDx2GiW783jVzMSmJEU2qGQABga4cdnu3PbpfXuy6kkMdzM7z7dR35lA2ajgVaL5KFFQx1cfPbYahPsBUVDcyvvfZ/FqBh/Hr1gOCM6+J06oitCwkZyhJn3th2nudXC1swSIvyMXDo+mlc3KBk8N06LdzhedT3VdCwoWi2Sx75KZcHwCN7bloXJ08BLS8fy6gYlqH55iqOy5ONp4Oklo7o03iERZt7aosTQfmH3++t1gnvnJXLbh7vZmlnCrCFh/HSigi+sMY3ffboPi4RhTrvzRQd6qxZFSU0jr21UMvp2HS/ncFE124+WcfusQfh6GbhkTDTfHMjnnRsnklNex6sbMjlUWMPYuEC3QgJgUJiJTYeKOZBbhcnLoCqgH+04wW/OVSr0r57UsZdECMFl42N4c7OSBh5qbr8u9AT9JiiklC1CiLuAVYAeeFtKmWrbtMi6J8VKYCGQCdQBN/bBuFj88hYq65vVZmqx3RAUAH+6YDgXvbqV961a/MaMNm3jjWvH85v//gRAoI8HI6L9uXB0VJc+Q68TaiDaHVPtLALb8W/dMIHGltZ2Wnh8sC+bD5fw1pZj/JRTybi4wA4X/PgQX568eATfphZweUosd8wezKSn1pFVWtepoBtsFQKHCquZEB+ExSL5NrUAb2thmYdeOLiegHZ1HhaLVAN+B/OrOJhfRXZpHV/eOQ2dTnCspJYQk6dby8bH08AXd06jodniNiZk44mLR/DoBcPxNHS+yNpiIADv3jiByQnBjHpsNZsPF5NdVseB3CrumDWIBxYk09Ri6fScMYHeDq4nWwbN3XMSVZdDbzE6NoCmzcfYfrSUzYeLmT8sgt+el0x8sC8/ZpVx5UTHxSvIxxMhcJse/NTKNN7fdly9L246ZyCBvp48vHgYFovsUGh2dbw2nC3imUlheOp1bD9axqwhYaw8kI+HXjAzKVS1Fp3dyBF+RjWmtiG9iOZWxVGRmldJRV0zo2MD+N0CpT3GXy4dyZ8vHoGnQcf4AYEMjfTjb6szumTxDY3wo7lVsmJfHkMizISZvTB7GahubGHZD9kApMS7/60vHBPFG98d5btDxVw6vnPPxMnQnxYFUsqVKMLA/rnX7f6WwJ19Oaai6kaH9MkQk6dDILsrjI4N4OUrx3L3x3u4a/ZgPtmVo2Y2fLM/n5rGFm6ZPpCHFnXsu+wNnIUEwJ2zB7Nifz5//vogADOSEtodY8/SiXEstVskAnw8qKhrZtaQMDfvUha9MLMXD31+gKGRfhwtruX+//2kvj46NkB1C9iIDfJRYyygFB6V1zWTHGGmprGFeUPDeff7LL5NLWDhyEiOltQS7xRPcoUQoktCwnasp6FrC9jYuLYbemS0P0YPPYlhJlV7DTV7cd2UeIAuCZ6YAB/W23UTtrWgt18Ue4upg0IQAh74ZB9VDS1cNUnR0i9PieVyFxabQa8jxOSlpnpKKalvblVjKMdLa1WN38bc5LZr5lSEBMA5g0MweRmsC7Wj1eftqScx3KTOnxLHCuTO2YPZdqSUJeNi1NiGjQh/IxsyipBSabkf6OPB4lFRapbcr+3iO87XSFK4mTeudbn1dDsmWGMPVQ0tqjvvi7umMfdvm3h+zSH8jAaX6dP2DAk342XQuU0WOVX6VVCcjtia2b11fQor9uWraWnd5YLRUcwbqmQB3TM3kcYWC/Of36Sm1C3swDfd10T4G9lw/yz++OUBymqb1BbfXeXdGyfy5d5cFnZi6QghuHtuIg9/cYDHvzpIVYNjrccdswa1e8/QSD++3pdPSU0jISYvXt90BB9PPctumYy/t5JF8u2BAlbsz2fhyEiySmqZkdTFSGovYPIycPfcRMrt4jDJEX6k5lUxIymUN68b71JYd0RMoDfF1UothdFDT1p+FcG+nr3mXrAnyNeTKH+lLUeIyUvtP+WOIeFmteL7zmW72ZBezG/OTeKX0wayxVpMt/bXM/hybx5NrZYO43wnO96dD8/DU69rlywByu+w6VARZbVNpOZVcd+8JMbGBbLrkfntEhNASfSoa2qlqr5FTTa5YHSboHAVxzsZogO8uWFqPIVVDdw2U1HSEkLalJ2LxkR36jI06HUkhpt6tcuzJijsOFJcw43v/ggoGpWzP7+72LRWg16HQa8jNsiHH6wb0nTXndWb+Ht78OJS19k3nTEmNsChSM0d10wewInyOt7YpARjH7twOL+YEMvu7HK1dYc95wwO4a+rMvj+SCnTBilZJLfNHKRmnoBS3/Ll3jxOlNVRVN3IwJDOLYre5NdOmUS2FOmkMFO3hAS01VLc/uEu/nX9BLYdKXVI9+1tHlgwhIe/OMATF49wufg6MyzKj39+d5RDhdVqwd4TK9Lw8tCz7UgJkf5GBoWa+M257bPqegJXC76NCfGBfLo7hw+2HUfKtoW+o/fY3KDpBVUcKqxh6cRYJg4MUrLAqhsZ24NW3Z8uHO7wWAjBs5eNYl1aIQ+e37Xur8kRfnyyK4fy2qZO4yIng9bryY7ff6b0kBkTG9Bl10R3sAXFPfSCIJ+e/zHPBIZGtKUBTxscgtFDb3VztF+IRkT742c0sOVwsZp1c95wR+F9zeQB1De3qjUlXXE99SVXTozlwtFR/Gqme5eeK2zXy4aMYpbtUIolO/NX9yQXjYlmzyPzuxQXA1QL4dwXvgPgm3umMzLan1fXZ7I3u4KU+KAuCZzewCYYXlh7CJOXgdEx7hMBbL3Ovk0toL65Vb1uX7lyLMtumXzKrrLOuCIlljeuTXGob3GHrahy1nMbe6X3kyYorDS3WjiQW0lSuIn3fjmxVz7DduP7e3v0+oV2ujJ3aBiR/kYSQnwZFOp+UdfrBFMHhbA1s5SD+ZUYdILhTtkpwyL9iPAz8vZWxf8dH3L6WGoAAT6evHTl2G7HuQDGxgby7GVK1s/rG5Vso87qbXqa7mRKzUgMVeMOfkYDyRFmrpsygIKqBvIqG1xWJ/cVsUE+xFottFEx/p1+r0h/I5H+RlUBGWkVLEIIt9l9/cWScTFMSQjm+StG98raogkKKyU1jYyI8ue35yWr/u+eJtl6k9c2nr17C5uNHmy4fxYr75neJe0yJT6Q3Ip6vjtUQkKob7sgsE4nuGN2W3zjdLMoTgWdTnBFSixzksPIrahHCBgR1b2U2L5ErxO8sHQMC0dGcP95QxBCcE5imy+/s6Bsb/PpbVO5ZGw0j180otNjhRDqvtaXj4/pcwHdXfy9PfjoV5NP2V3eEVqMwkqkvzf/vW1Kr36GbcOSuzsoCDtbcOdLdsZ2g+7Prewwn3zphDje33acYZF+XTbVzySSI8ysTy9iSLhZ7Qh7uuJn9OC1q8erjyP9vUmOMJNT3rduM1eE+Rl5wVqx3RX+dOFwzh0ezqwk9xl9ZwM/v7vqNMbP6EH6nxfg1YXUSA0Few36nA4yTTwNOlbePR0P/ennEugJzhkcwmsbj7Rzu50pfHHnNJpbLd1SEE4HjB76Div3zzY0QdHHnGk3S3/j7+PBuLgA9uVUuk2n7EpdwpnKlEHBvHb1uJNO1e5vjB567bo/w9EEhcZpz7JbJlPf1NquKOpsQQhx2tTdaJydiN7uY97XCCGKgeOdHtgxIUDfb0p7ZqDNjXu0+ekYbW7cczrMzwAppcuK1Z+doDhVhBA7pZRdq78/y9Dmxj3a/HSMNjfuOd3n5+fr2NXQ0NDQ6BE0QaGhoaGh4RZNULTnn/09gNMYbW7co81Px2hz457Ten60GIWGhoaGhls0i0JDQ0NDwy2aoLAihFgghMgQQmQKIR7s7/H0NUKIWCHEBiFEmhAiVQhxj/X5ICHEGiHEYev/gXbv+b11vjKEEOf13+j7DiGEXgixRwjxtfWxNj+AECJACPGJECLdeg1N0eamDSHEfdb76oAQ4iMhhPFMmh9NUKDc/MCrwPnAMOBKIUTfbj/X/7QAv5FSDgUmA3da5+BBYJ2UMhFYZ32M9bWlwHBgAfCadR5/7twDpNk91uZH4UXgWyllMjAaZY60uQGEENHA3UCKlHIEytbPSzmD5kcTFAoTgUwp5VEpZRPwMXBRP4+pT5FS5kspd1v/rka50aNR5uE962HvARdb/74I+FhK2SilPIayr3nv9Gc/TRBCxACLgH/ZPX3Wz48Qwg+YAbwFIKVsklJWoM2NPQbAWwhhAHyAPM6g+dEEhUI0cMLucY71ubMSIUQ8MBb4AQiXUuaDIkwAWyvNs3HO/g48AFjsntPmBxKAYuAdq1vuX0IIX7S5AUBKmQs8B2QD+UCllHI1Z9D8aIJCwVXb0bMyHUwIYQI+Be6VUrrbrf2smjMhxGKgSEq5q6tvcfHcz3V+DMA44B9SyrFALVY3SgecTXODNfZwETAQiAJ8hRDXuHuLi+f6dX40QaGQA8TaPY5BMQ3PKoQQHihC4t9Sys+sTxcKISKtr0cCRdbnz7Y5mwZcKITIQnFNzhFCfIg2P6B81xwp5Q/Wx5+gCA5tbhTmAceklMVSymbgM2AqZ9D8aIJC4UcgUQgxUAjhiRJIWt7PY+pThLLd3FtAmpTyebuXlgPXW/++HvjS7vmlQggvIcRAIBHY0Vfj7WuklL+XUsZIKeNRro/1Uspr0OYHKWUBcEIIMcT61FzgINrc2MgGJgshfKz32VyUGOAZMz9am3FAStkihLgLWIWSkfC2lDK1n4fV10wDrgX2CyH2Wp/7A/AX4L9CiJtQLvjLAaSUqUKI/6IsCC3AnVLKs3GPV21+FP4P+LdV0ToK3IiiiJ71cyOl/EEI8QmwG+X77kGpxDZxhsyPVpmtoaGhoeEWzfWkoaGhoeEWTVBoaGhoaLhFExQaGhoaGm7RBIWGhoaGhls0QaGhoaGh4RZNUGhoaGhouEUTFBoaGhoabtEEhYaGhoaGW/4f+j9FyTibe8UAAAAASUVORK5CYII=\n", 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" ] @@ -2293,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -2334,6 +2182,7 @@ ], "source": [ "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", " n_vars = 1 if type(data) is list else data.shape[1]\n", " df = DataFrame(data)\n", @@ -2379,7 +2228,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -2400,8 +2249,6 @@ "n_obs = n_months * n_features\n", "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", - "# print(train_X.shape, len(train_X), train_y.shape)\n", - "# reshape input to be 3D [samples, timesteps, features]\n", "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" @@ -2409,7 +2256,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -2418,7 +2265,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -2445,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -2453,121 +2300,121 @@ "output_type": "stream", "text": [ "Epoch 1/1000\n", - "1/1 - 11s - loss: 0.0119 - root_mean_squared_error: 0.1092 - val_loss: 0.0479 - val_root_mean_squared_error: 0.2189\n", + "1/1 - 14s - loss: 0.0114 - root_mean_squared_error: 0.1067 - val_loss: 0.0476 - val_root_mean_squared_error: 0.2181\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 2/1000\n", - "1/1 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1051 - val_loss: 0.0462 - val_root_mean_squared_error: 0.2149\n", + "1/1 - 0s - loss: 0.0109 - root_mean_squared_error: 0.1044 - val_loss: 0.0465 - val_root_mean_squared_error: 0.2155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 3/1000\n", - "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1017 - val_loss: 0.0446 - val_root_mean_squared_error: 0.2112\n", + "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1022 - val_loss: 0.0454 - val_root_mean_squared_error: 0.2130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 4/1000\n", - "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0990 - val_loss: 0.0432 - val_root_mean_squared_error: 0.2079\n", + "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1003 - val_loss: 0.0444 - val_root_mean_squared_error: 0.2107\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 5/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0973 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0987 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2087\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 6/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0976 - val_loss: 0.0428 - val_root_mean_squared_error: 0.2069\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 7/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0967 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2014\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0422 - val_root_mean_squared_error: 0.2055\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 8/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 9/1000\n", - "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0980 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2035\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 10/1000\n", - "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0982 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 11/1000\n", - "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0980 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 12/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0975 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 13/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0970 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0967 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 14/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 15/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 16/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2048\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 17/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0422 - val_root_mean_squared_error: 0.2055\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 18/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0425 - val_root_mean_squared_error: 0.2061\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 19/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2064\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 20/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 21/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2067\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0967 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 22/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 23/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2063\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 24/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2060\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 25/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0423 - val_root_mean_squared_error: 0.2056\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 26/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2036\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 27/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2047\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 28/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 29/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2038\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 30/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2046\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 31/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2048\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 32/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2049\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 33/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2051\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 34/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2052\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 35/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2052\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 36/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2053\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 37/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2053\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 38/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2052\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 39/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2035\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2052\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2576,121 +2423,121 @@ "output_type": "stream", "text": [ "Epoch 40/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2038\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 41/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2050\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 42/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2049\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 43/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2048\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 44/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2047\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 45/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2046\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 46/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 47/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 48/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 49/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2036\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 50/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 51/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0951 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 52/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 53/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 54/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2026\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 55/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 56/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 57/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 58/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 59/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 60/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 61/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 62/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2027\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 63/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2024\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 64/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 65/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 66/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0962 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 67/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0962 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 68/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 69/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 70/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 71/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 72/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 73/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 74/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2014\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 75/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 76/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 77/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 78/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2699,121 +2546,121 @@ "output_type": "stream", "text": [ "Epoch 79/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 80/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2009\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 81/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 82/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 83/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2011\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 84/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 85/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 86/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 87/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0930 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 88/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 89/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 90/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 91/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2004\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 92/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2001\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 93/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0400 - val_root_mean_squared_error: 0.1999\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 94/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 95/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 96/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0951 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 97/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0925 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2038\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 98/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 99/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 100/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2036\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 101/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 102/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 103/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1990\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 104/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 105/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0919 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 106/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 107/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1986\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 108/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 109/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 110/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1982\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1999\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 111/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1982\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 112/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1986\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 113/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 114/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 115/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1976\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 116/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 117/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1971\n" + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n" ] }, { @@ -2822,121 +2669,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 118/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 119/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 120/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 121/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 122/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 123/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 124/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 125/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 126/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 127/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 128/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 129/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1917\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 130/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 131/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 132/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 133/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1885\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 134/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 135/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 136/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 137/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 138/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 139/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 140/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 141/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 142/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 143/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 144/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 145/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 146/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 147/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 148/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 149/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 150/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 151/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 152/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 153/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 154/1000\n", - "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 155/1000\n", - "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 156/1000\n", - "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0796 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n" + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n" ] }, { @@ -2945,121 +2792,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 157/1000\n", - "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1723\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 158/1000\n", - "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1712\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 159/1000\n", - "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1682\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 160/1000\n", - "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1677\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 161/1000\n", - "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1671\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 162/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1646\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 163/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0768 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1644\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 164/1000\n", - "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1623\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 165/1000\n", - "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0762 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1600\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 166/1000\n", - "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1608\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 167/1000\n", - "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1582\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 168/1000\n", - "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0755 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1590\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 169/1000\n", - "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1573\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 170/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 171/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1562\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 172/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1538\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 173/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0746 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1557\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 174/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1534\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 175/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 176/1000\n", - "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1527\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 177/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0738 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1517\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 178/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 179/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1511\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 180/1000\n", - "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1512\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 181/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1506\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 182/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1505\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 183/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1509\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 184/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 185/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0722 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1499\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 186/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1488\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 187/1000\n", - "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0718 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1496\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 188/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1476\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 189/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0714 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1484\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 190/1000\n", - "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1473\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 191/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1475\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 192/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1459\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 193/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1466\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 194/1000\n", - "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1455\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 195/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1452\n" + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0803 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n" ] }, { @@ -3068,121 +2915,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 196/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1444\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 197/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1446\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1818\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 198/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1434\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 199/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1433\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 200/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1428\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 201/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 202/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1416\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 203/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 204/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1399\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0778 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 205/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0775 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 206/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0772 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 207/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1472\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 208/1000\n", - "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1383\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 209/1000\n", - "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1377\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0765 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 210/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1441\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 211/1000\n", - "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1377\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0761 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1765\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 212/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1366\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 213/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1409\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 214/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1375\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 215/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1358\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1749\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 216/1000\n", - "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1373\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 217/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1384\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0746 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 218/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 219/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0667 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1735\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 220/1000\n", - "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1362\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0740 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1730\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 221/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0667 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1351\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0738 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 222/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1348\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 223/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 224/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1716\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 225/1000\n", - "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 226/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1317\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1709\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 227/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1331\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 228/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1345\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 229/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1310\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 230/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0721 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1696\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 231/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1341\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 232/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1313\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0718 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 233/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0652 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1279\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 234/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0652 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1323\n" + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0714 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1682\n" ] }, { @@ -3191,121 +3038,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 235/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1312\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 236/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 237/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1293\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1672\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 238/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1294\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 239/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1668\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 240/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0644 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1257\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1660\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 241/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1659\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 242/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1655\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 243/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 244/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1647\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 245/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1343\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1638\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 246/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1632\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 247/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1631\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 248/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1245\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1619\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 249/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0635 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0689 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1624\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 250/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1295\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 251/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1227\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 252/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1209\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1589\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 253/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1611\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 254/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0682 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 255/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1206\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1580\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 256/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1235\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1602\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 257/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 258/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 259/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1593\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 260/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1169\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 261/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 262/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1200\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 263/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 264/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0667 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1560\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 265/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 266/1000\n", - "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 267/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1171\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1556\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 268/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1147\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1564\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 269/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0238 - val_root_mean_squared_error: 0.1544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 270/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1167\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0661 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1553\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 271/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1186\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1550\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 272/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1537\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 273/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n" + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1550\n" ] }, { @@ -3314,121 +3161,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 274/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1163\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1535\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 275/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1156\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 276/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1541\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 277/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1525\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 278/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1539\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 279/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0652 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1524\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 280/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 281/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1526\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 282/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1520\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 283/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1529\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 284/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 285/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1095\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1541\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 286/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 287/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 288/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1122\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1509\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 289/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1079\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1501\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 290/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1087\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 291/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 292/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1506\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 293/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1525\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 294/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1085\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1480\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 295/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1145\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1500\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 296/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1074\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 297/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 298/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1487\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 299/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1104\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 300/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1123\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1484\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 301/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1005\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1476\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 302/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0638 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1517\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 303/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1487\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 304/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 305/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 306/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1088\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0635 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1484\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 307/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1140\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1474\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 308/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1002\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1485\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 309/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1488\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 310/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1059\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 311/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0977\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1475\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 312/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1140\n" + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1494\n" ] }, { @@ -3437,121 +3284,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 313/1000\n", - "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1461\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 314/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1005\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 315/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 316/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 317/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1022\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 318/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0985\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1474\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 319/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1073\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1466\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 320/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1047\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1446\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 321/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 322/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 323/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0970\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1444\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 324/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1450\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 325/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1450\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 326/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1017\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1442\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 327/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1443\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 328/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 329/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 330/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0983\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1434\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 331/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0963\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0612 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1434\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 332/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0927\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 333/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0552 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 334/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1433\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 335/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0951\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0610 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 336/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1421\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 337/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 338/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0929\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1416\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 339/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0923\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 340/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 341/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 342/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0914\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 343/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0929\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 344/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1400\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 345/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1415\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 346/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 347/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0880\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 348/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0895\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 349/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1415\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 350/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 351/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n" + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1414\n" ] }, { @@ -3560,121 +3407,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 352/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 353/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0855\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 354/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 355/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 356/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0869\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 357/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0858\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1374\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 358/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 359/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1371\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 360/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0846\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 361/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1372\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 362/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1374\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 363/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 364/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 365/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 366/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 367/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 368/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 369/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0515 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 370/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1348\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 371/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0859\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 372/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1349\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 373/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 374/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1152\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 375/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1152\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1348\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 376/1000\n", - "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 377/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1347\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 378/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 379/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1109\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 380/1000\n", - "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 381/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 382/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1009\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1325\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 383/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1366\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 384/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1331\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 385/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0921\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 386/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 387/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1328\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 388/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 389/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 390/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n" + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1327\n" ] }, { @@ -3683,121 +3530,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 391/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 392/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1320\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 393/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 394/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1335\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 395/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 396/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1311\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 397/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 398/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 399/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 400/1000\n", - "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1411\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1321\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 401/1000\n", - "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1312\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 402/1000\n", - "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1683\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 403/1000\n", - "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0750 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 404/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1304\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 405/1000\n", - "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0172 - val_root_mean_squared_error: 0.1311\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 406/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0859\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1305\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 407/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1105\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1295\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 408/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1107\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1308\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 409/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1301\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 410/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1291\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 411/1000\n", - "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 412/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0570 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1295\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 413/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0570 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1293\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 414/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1020\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1293\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 415/1000\n", - "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0552 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0932\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1287\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 416/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1293\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 417/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1288\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 418/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 419/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1290\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 420/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 421/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 422/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 423/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 424/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1277\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 425/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 426/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 427/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 428/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0784\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1271\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 429/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n" + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1268\n" ] }, { @@ -3806,121 +3653,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 430/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 431/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1266\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 432/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1264\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 433/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1267\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 434/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1262\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 435/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1262\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 436/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 437/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1258\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 438/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1259\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 439/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 440/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0741\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1255\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 441/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1254\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 442/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 443/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 444/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 445/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 446/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1247\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 447/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1244\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 448/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1243\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 449/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1242\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 450/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1240\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 451/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0552 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1239\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 452/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1237\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 453/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1236\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 454/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0745\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1235\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 455/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 456/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 457/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1229\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 458/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 459/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1227\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 460/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1225\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 461/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 462/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 463/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 464/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 465/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 466/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 467/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1214\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 468/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n" + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n" ] }, { @@ -3929,121 +3776,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 469/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 470/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1210\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 471/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1208\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 472/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1206\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 473/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 474/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1203\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 475/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 476/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1200\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 477/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1197\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 478/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 479/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 480/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1193\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 481/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1191\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 482/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 483/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 484/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 485/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0474 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 486/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1198\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 487/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 488/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 489/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1259\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 490/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1215\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 491/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1208\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 492/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 493/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1172\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 494/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1206\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 495/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1171\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 496/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1183\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 497/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1168\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 498/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 499/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0683\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 500/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1178\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 501/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1164\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 502/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1185\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 503/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1160\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 504/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1165\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 505/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1156\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 506/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1169\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 507/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n" + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1152\n" ] }, { @@ -4052,121 +3899,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 508/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 509/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1144\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 510/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1145\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 511/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 512/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1141\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 513/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 514/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 515/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 516/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1135\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 517/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1135\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 518/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1130\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 519/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 520/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0941\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 521/1000\n", - "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 522/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 523/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 524/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1120\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 525/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1119\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 526/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1024\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 527/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 528/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1113\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 529/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 530/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 531/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0828\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 532/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 533/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0864\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1104\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 534/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0683\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 535/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 536/1000\n", - "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0505 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 537/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1041\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1098\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 538/1000\n", - "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1097\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 539/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1095\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 540/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 541/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 542/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0745\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 543/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 544/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 545/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1082\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 546/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n" + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1081\n" ] }, { @@ -4175,121 +4022,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 547/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0632\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1080\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 548/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1077\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 549/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1075\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 550/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1074\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 551/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 552/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0635\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1069\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 553/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0651\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 554/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 555/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 556/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 557/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1061\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 558/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1059\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 559/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0612\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 560/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1056\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 561/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 562/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 563/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 564/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 565/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1046\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 566/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1044\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 567/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 568/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1041\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 569/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 570/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 571/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1036\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 572/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0611\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 573/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1032\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 574/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 575/1000\n", - "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0671\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1029\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 576/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0891\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1027\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 577/1000\n", - "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1154\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1027\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 578/1000\n", - "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 579/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1046\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 580/1000\n", - "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1088\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 581/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 582/1000\n", - "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 583/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 584/1000\n", - "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 585/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n" + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1180\n" ] }, { @@ -4298,121 +4145,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 586/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 587/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1106\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 588/1000\n", - "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0649\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1012\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 589/1000\n", - "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 590/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1039\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 591/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1067\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 592/1000\n", - "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 593/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1016\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 594/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1010\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 595/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0659\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1027\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 596/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1015\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 597/1000\n", - "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1038\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 598/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 599/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 600/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 601/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1018\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 602/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1000\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 603/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 604/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0997\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 605/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1000\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 606/1000\n", - "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 607/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1000\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 608/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0997\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 609/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0994\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 610/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0993\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 611/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 612/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 613/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0474 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0982\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 614/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0982\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 615/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 616/1000\n", - "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 617/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0980\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 618/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0600\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0979\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 619/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0977\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 620/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0588\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 621/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0588\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 622/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 623/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 624/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n" + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0971\n" ] }, { @@ -4421,121 +4268,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 625/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 626/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0967\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 627/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0967\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 628/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 629/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 630/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 631/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0960\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 632/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0958\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 633/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0956\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 634/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0579\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0956\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 635/1000\n", - "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 636/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0953\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 637/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 638/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 639/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 640/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0948\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 641/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0946\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 642/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 643/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 644/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0943\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 645/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0941\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 646/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0940\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 647/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 648/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 649/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0936\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 650/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 651/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 652/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0932\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 653/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0930\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 654/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0929\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 655/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 656/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 657/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0925\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 658/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 659/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 660/1000\n", - "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 661/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 662/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0919\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 663/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n" + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n" ] }, { @@ -4544,121 +4391,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 664/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 665/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 666/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0914\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 667/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 668/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0561\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0911\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 669/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 670/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 671/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0561\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0908\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 672/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0906\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 673/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0905\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 674/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 675/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 676/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 677/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0900\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 678/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 679/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 680/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 681/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0895\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 682/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 683/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 684/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0892\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 685/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0891\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 686/1000\n", - "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 687/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 688/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 689/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0886\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 690/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 691/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0884\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 692/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 693/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0882\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 694/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 695/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0879\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 696/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 697/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 698/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 699/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 700/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 701/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 702/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n" + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n" ] }, { @@ -4667,121 +4514,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 703/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 704/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 705/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0879\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 706/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 707/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 708/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 709/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 710/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 711/1000\n", - "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 712/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 713/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 714/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 715/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0442 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 716/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 717/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 718/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 719/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0552\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 720/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0552\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 721/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 722/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 723/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0550\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 724/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0550\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0854\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 725/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 726/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 727/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 728/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 729/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0547\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 730/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0547\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0846\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 731/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 732/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 733/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 734/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 735/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 736/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0543\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 737/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0430 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 738/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 739/1000\n", - "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 740/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0541\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 741/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0541\n" + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n" ] }, { @@ -4790,121 +4637,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 742/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 743/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 744/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 745/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 746/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 747/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 748/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 749/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 750/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 751/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0817\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 752/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0816\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 753/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 754/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0534\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 755/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0534\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 756/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0812\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 757/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 758/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 759/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 760/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 761/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 762/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 763/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 764/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 765/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 766/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 767/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0802\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 768/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 769/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0800\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 770/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 771/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 772/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 773/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 774/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 775/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0795\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 776/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0367 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 777/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 778/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 779/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 780/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n" + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n" ] }, { @@ -4913,121 +4760,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 781/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 782/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 783/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 784/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 785/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 786/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 787/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 788/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 789/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0795\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 790/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 791/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 792/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 793/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 794/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 795/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 796/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 797/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 798/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 799/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 800/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 801/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 802/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0515\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 803/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0418 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 804/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 805/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 806/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 807/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 808/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 809/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 810/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 811/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 812/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0765\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 813/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 814/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0418 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 815/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 816/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 817/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 818/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 819/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n" + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n" ] }, { @@ -5036,121 +4883,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 820/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0761\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 821/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0761\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 822/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0418 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 823/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 824/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 825/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 826/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 827/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 828/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 829/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 830/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 831/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 832/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 833/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 834/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0505\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 835/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 836/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 837/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 838/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 839/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 840/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 841/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0745\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 842/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 843/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 844/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 845/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 846/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 847/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 848/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 849/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0745\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 850/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 851/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 852/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 853/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 854/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 855/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 856/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 857/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 858/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n" + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0735\n" ] }, { @@ -5159,121 +5006,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 859/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 860/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 861/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 862/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 863/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0741\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 864/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 865/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 866/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 867/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 868/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 869/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 870/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 871/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 872/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 873/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 874/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0726\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 875/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 876/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 877/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 878/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 879/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 880/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0730\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 881/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 882/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 883/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 884/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 885/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0726\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 886/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 887/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 888/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 889/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 890/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 891/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 892/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 893/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 894/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 895/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0716\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 896/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 897/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n" + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n" ] }, { @@ -5282,121 +5129,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 898/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 899/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 900/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 901/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 902/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 903/1000\n", - "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0367 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 904/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0484\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 905/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 906/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 907/1000\n", - "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 908/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 909/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 910/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 911/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 912/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 913/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 914/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 915/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 916/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 917/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 918/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0488\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 919/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 920/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 921/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 922/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 923/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 924/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 925/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 926/1000\n", - "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 927/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 928/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 929/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 930/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 931/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 932/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 933/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 934/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 935/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 936/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0485\n" + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n" ] }, { @@ -5405,208 +5252,208 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 937/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 938/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 939/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 940/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 941/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 942/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 943/1000\n", - "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 944/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 945/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 946/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 947/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0484\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 948/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0691\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 949/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 950/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 951/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 952/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 953/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 954/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0689\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 955/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 956/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 957/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 958/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 959/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 960/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 961/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0688\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 962/1000\n", - "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 963/1000\n", - "1/1 - 0s - loss: 9.9714e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0689\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 964/1000\n", - "1/1 - 0s - loss: 9.9839e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 965/1000\n", - "1/1 - 0s - loss: 9.9519e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 966/1000\n", - "1/1 - 0s - loss: 9.9262e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 967/1000\n", - "1/1 - 0s - loss: 9.9173e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 968/1000\n", - "1/1 - 0s - loss: 9.8882e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 969/1000\n", - "1/1 - 0s - loss: 9.8820e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0474\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 970/1000\n", - "1/1 - 0s - loss: 9.8525e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 971/1000\n", - "1/1 - 0s - loss: 9.8353e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 972/1000\n", - "1/1 - 0s - loss: 9.8230e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 973/1000\n", - "1/1 - 0s - loss: 9.8036e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 974/1000\n", - "1/1 - 0s - loss: 9.7855e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 975/1000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 975/1000\n", - "1/1 - 0s - loss: 9.7732e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 976/1000\n", - "1/1 - 0s - loss: 9.7487e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 977/1000\n", - "1/1 - 0s - loss: 9.7390e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 978/1000\n", - "1/1 - 0s - loss: 9.7169e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 979/1000\n", - "1/1 - 0s - loss: 9.7033e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 980/1000\n", - "1/1 - 0s - loss: 9.6909e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 981/1000\n", - "1/1 - 0s - loss: 9.6702e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 982/1000\n", - "1/1 - 0s - loss: 9.6580e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 983/1000\n", - "1/1 - 0s - loss: 9.6393e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 984/1000\n", - "1/1 - 0s - loss: 9.6250e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 985/1000\n", - "1/1 - 0s - loss: 9.6117e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0475\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 986/1000\n", - "1/1 - 0s - loss: 9.5945e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 987/1000\n", - "1/1 - 0s - loss: 9.5783e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 988/1000\n", - "1/1 - 0s - loss: 9.5663e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 989/1000\n", - "1/1 - 0s - loss: 9.5472e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 990/1000\n", - "1/1 - 0s - loss: 9.5358e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 991/1000\n", - "1/1 - 0s - loss: 9.5189e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 992/1000\n", - "1/1 - 0s - loss: 9.5037e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 993/1000\n", - "1/1 - 0s - loss: 9.4909e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 994/1000\n", - "1/1 - 0s - loss: 9.4751e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 995/1000\n", - "1/1 - 0s - loss: 9.4612e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 996/1000\n", - "1/1 - 0s - loss: 9.4470e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 997/1000\n", - "1/1 - 0s - loss: 9.4317e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 998/1000\n", - "1/1 - 0s - loss: 9.4178e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 999/1000\n", - "1/1 - 0s - loss: 9.4041e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 1000/1000\n", - "1/1 - 0s - loss: 9.3894e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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\n", 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gHRFZhf3CeNMY82qMt0EdzvGXw3Gfgzd+oCdmlerFxHTDe44WFBSYwsJCp8twh4piuOcEGHIKXPsCeLxOV6SU6gQistQYU9DWMr0y1u3S8+whnM2L4JPnnK5GKeUADfreoOB66DcBXr4Zyre3314p5Soa9L2BxwtXPAahRvj3HdAND9cppTqPBn1vkZUPZ/wvrH0JivX8h1K9iQZ9b1JwHfiS4Y3bna5EKdWFNOh7k9R+dniE4iVQ9JbT1SiluogGfW8zarZ9/vhhZ+tQSnUZDfreJmckTLwGtn2gd6JSqpfQoO+NCq6D+gr49RD49w+drkYp1ck06HujQVObXn9wr3N1KKW6hAZ9bzXu4qbXjTXO1aGU6nQa9L3VyTc1vf7r6c7VoZTqdBr0vVXumKbXpUWw+gXnalFKdSoN+t7Knwz+1Kbp567ToYyVcikN+t7sc3ceOv3H4yHY4EgpSqnOo0Hfm534NRh13qHzXvm2I6UopTqPBn1vd8U/IL3Z7X5XPgk1pc7Vo5SKOQ363s7rg5uWQFxC07zfDnOuHqVUzGnQK/Alwg92wYATmuYteRhCQedqUkrFjAa9sjwe+OqbTdPzvwdv3+lYOUqp2NGgV028Ppi7qGn6g3vh/lPh44fgznTYu96x0pRSR0+DXh1qwAlwzbNN03tWw4Jb7eu/nAQ7V9i9/TvTYf18R0pUSnWMBr1qbeTn4Y7dbS978Ax7/B5g1TNdV5NS6qhp0Ku2+RLhyieO3Gbti1C6qUvKUUodPQ16dXhjzoeflB+5zcqnuqQUpdTR06BXRyYCp95y+OX7P+u6WpRSR0WDXrXv+MsPv+yTZ2Dp3+G3x9lnpVS3o0Gv2tfvePjiv2DYmW0vf+UWqCmxz5++0aWlKaXap0GvojN8Bnzh+fbb/fceMKbz61FKRU2DXkXPGwff2wCf+zmMvwx8ya3bfPY+bHm362tTSh1WnNMFqB4mtR+c+q2maWPgpxmHtnlsDnxnLaQP7NLSlFJt0z16dWxE4KZCGHvRofOf/bIOiqZUN6FBr45dnxF2XPuRM5vmFS+BJy6DQL1zdSmlAA16FUvXPG3Hyekz0k5vfgfu6gvrXnW2LqV6uaiCXkRmisgGESkSkdvaWC4ick9k+SoRmRyZP0hE3hGRdSKyRkSOcOWNcoWRn7c3MvnBLhCvnff0F2DFk87WpVQv1m7Qi4gXuA+YBYwFrhaRsS2azQJGRB5zgfsj84PA94wxY4BpwDfbWFe5kT/p0IHRXvwGzPsClG2BcFi7YCrVhaLZo58KFBljNhtjGoF5wJwWbeYAjxnrIyBDRPobY3YZY5YBGGOqgHWAdsXoLeL88O1PIKmPnV7/KtwzCX6WaXvq6L1pleoS0QT9QGB7s+liWod1u21EZChwArC4rQ8RkbkiUigihSUlJVGUpXqEjMHw/4qg4PrWyza93fX1KNULRRP00sa8lr+7j9hGRFKA54FvG2Mq2/oQY8yDxpgCY0xBTk5OFGWpHkMEzv8DpOUdOv+Fr9sbmGx5z5m6lOologn6YmBQs+k8YGe0bUTEhw35J4wxLxx9qarHu2Ul9BnVev4/zrfH7Rtru76mI7mrP/zrG05XodQxiybolwAjRCRfRPzAVcDLLdq8DHwp0vtmGlBhjNklIgI8Aqwzxvw+ppWrnscbB99cDHEJrZf9LBN+2d/en7a7CNTCSu0tpHq+doPeGBMEbgLewJ5MfcYYs0ZEbhCRGyLNFgCbgSLgIeDGyPxTgS8CM0RkReQxO9YboXoQEdsb538Oc7hmwa0QCtjXjbWwYynsXt119SnlQmK6YTe3goICU1hY6HQZqrPVlcOLN8KGFjcZHzAZ4lOhZh/sXWPn3VnR5eVxZ7pzn61UB4nIUmNMQVvLdFAz5ZzEDLj6SajbD78e2jR/5zKnKlLKlXQIBOW8xEy4vp0blhTrLzyljpYGveoeBk+D27bBtc/D5X9vvfzhs+HNH0NDdZeXplRPp0Gvuo+EdDjuHBh3MQyc0nr5f/8Edw+Eveu6vjalejANetU9fX0hbV+HB/xlGlTv7dzP74adFJQ6WnoyVnVfd5bD0r9DsBHi4uGVZne2+t0Ie9PySx6G3NGx/+xwKPbvqZRDNOhV9zblK02vU/rCU1c2Te/+BP5yEnzjA+g7Lraf++G9sX0/pRykh25UzzFqJty8zF5sNeikpvn3n2L7vG94LXaf9dadsXsvpRymQa96luzh0H8CXPcazPgRJGY1LXvqKnjmy3bcHKXUQRr0qmfyeOH0W+F/t8C3loPXb+evfdGOm/PwOdBQ5WiJSnUXGvSq58saBj8qgRv+C9O/Z+cVL4G782D543piVfV6GvTKPfqNh7N/DLduhMGn2HkvfdPe1eo/vz36Qzq7VsasRKWcoEGv3CclF65/DW7fAdNvtYOjLfwF/H4MbH63433kt7V5UzSlegwNeuVe8Slw9o/g26thwpVQvRseuxD+Ngs2LYz+feLiO69GpbqABr1yv+RsuORB+MEumPUb2LcR/nkx/ONC2L6k/fXDwc6vUalOpEGveg9/Epz0P7aXzhm3wc4V8Mg58NxXYf/WpnahFsFeX96FRSoVexr0qvdJSIOzbofvrrHH8NfPhz+fCG/cAfWVEKw7tH3h32DncmdqVSoG9A5TSlXsgHd+CSuesMMsnPg1eOcXrdv5kuGOnV1fn1JRONIdpnSPXqn0gXDRffD1tyG1X1PIx6fByTc1tQvUwLpXnalRqWOgQa/UAQOn2OGRL/sbXPUU3L4dhp91aJunvwCbF3VeDeEw/OMCKHq78z5D9Toa9Eo15/HC+Etg9Gw7nX1c6zaPzYHSTZ3z+fXlsOU/8Nx1nfP+qlfSoFfqSDKHwrl3t54//7ud83kHh2s4zE1XlDoKGvRKtefkG+HOCju0woHB0zYvgh3LYj+OTqjBPosGvYodDXqlopWSawdPu+AeO/3QWfDgGbBnbWzePxyCFU9GJjToVexo0CvVUVO+DNc+b4dV2LMW7j/ZjoNfXXJs77vqaXjnLvta9+hVDGnQK3U0jjvHDqvwjQ9g8Ml2HPy/TofFf4VAXburt6mxptmEBr2KHQ16pY5F7mi4/nX44r8gtT+89n1705NjPZxTuy829SmFBr1SsTF8Bsx9B655Bqp2w19Ph3lfgL3ro3+Puv2HTt9/WmxrVL2WBr1SsTTyXLjxIzt42tb34YFT4e2ftTgscxgtg37PJ51To+p1XBX0xhgCIb0xtHJYSg6cexfcvBSOvxze+z+4dwos++eRu2PWlnVdjapXcU3QB0NhpvziLe59e6PTpShlJfeBix+A69+AtIHw8k3wwGmw4bW273LVco8eYtd1U/Vqrgn6OK+HtIQ4ikqqnS5FqUMNngZfewsu/wcE6+Gpq+CRz9mhDpqra2OPfvk/u6ZG5WquCXqA4TkpbNobxbFQpbqaCIy7CL75MVzwJzs08j8usOPmFC+1bWpLW6/nS4ruLlhKHYGrgv643BS27KshqMfpVXfl9cGUr9i7XJ17N+xeDQ/PgH9eAmWbW7d/73f2LljbP+7yUpV7RBX0IjJTRDaISJGI3NbGchGReyLLV4nI5GbLHhWRvSKyOpaFt2V4TgqNoTDF+4/yghWluoovwY6hc8tKmPHD1odxWnrkc/aWh0odhXaDXkS8wH3ALGAscLWIjG3RbBYwIvKYC9zfbNnfgZmxKLY9w3NTACjaq8fpVQ8RnwKn/z/4/ma44X24bTt88cW2265+DhqqurQ85Q7R7NFPBYqMMZuNMY3APGBOizZzgMeM9RGQISL9AYwx/wG6pN/YcZGgX7+7sis+TqnYSUiDfsfb5+Fn2RO3w88G8dohFg64Ow+WP+5cnapHiiboBwLbm00XR+Z1tM0RichcESkUkcKSkqMbHCo90cfofql8uLmNk1pK9STjLoIvvgA/KYOvzIfz/wAJGZCYCS99E+7MgEdnQYP+elXtiybo2xpdqWUn4GjaHJEx5kFjTIExpiAnJ6cjqx7itOP6sGTrfipqAyzZWsZzS4t5e90eivZW0xCM8djhSnUFjxcKrofbPoPvrofR5wMGtn0Avx8Db9xhT9a21TdfKSAuijbFwKBm03nAzqNo0yUuOmEgD7+/hUk//3erf/ci0D8tgQSfF8R+O4kIHgGf10N8nAd/nAd/nBe/10O8z0O898C8puXpiT4GZSYxODuJUX1TifO6qvOS6s58CXDVE3ZPfucyePPH8NH98OGfIWc0TP06TLjKHvtXKkJMO3sBIhIHfAqcDewAlgDXGGPWNGtzHnATMBs4CbjHGDO12fKhwKvGmPHRFFVQUGAKCws7tiXNzF+1i8VbSjlxaBbHD0ynrLaRz0pr2Lqvlu37a2kMhu3PDQMGQzgMgVCYxlCYhkCYhlCYxmCYhmCIxqB9fWBZYyhMKNz0N0uJj+Pk4dlcPXUQZ47MxePR4WVVF6veC+tegWWPwa4V9i5Y4y6BCVdA/hngjWZ/TvV0IrLUGFPQ5rL2gj7yBrOBPwJe4FFjzF0icgOAMeYBERHgz9jeNbXAdcaYwsi6TwFnAn2APcBPjDGPHOnzjjXoO1tFXYDtZbVsKqnm4y1lvLl2D3urGjh+YDq/uvR4xg1Id7pE1RsZA8VL7F2qVj8PDZWQ1Mce7x9/GQw6CTz669Otjjnou1p3D/qWAqEwr6zcyS8XrKeqPsDvr5jEeRP6O12W6s0C9VD0FnzyLHz6uh16IaUfjDkfxlwIQ07VPX2X0aDvImU1jXz9sUKWbdvPXRcdzzUnDXa6JKVs3/sNr8O6l2HjmxCsg8QsGD0bxsyBYWdAXHzb69aVw6+HwCUPw4TLu7Rs1TEa9F2oPhDixieWsXD9Xu6YPYavTc9H9P6fqrtorLV7+utesXv6DZUQnwYjPm8fx51tR9084NM34MkrIGs4fGuZc3WrdmnQd7HGYJhvP72cBZ/sZlTfVKYNy+KkYdmcPjKHlHj9uay6iWCDHXph7Ut26OTafYDAgBNgxOfsBVsfP2ivyAU73PKgk/TG5d2UBr0DQmHDkx9v4/XVu1i+rZzaxhA+rzB5cCanj8xhdL9UhvZJZlBmEv44PUGmHBYO2x47RW/Zwzs7CsEcZnDAMRfaC7ia7/krx2nQOywQCrPss/28s6GE/3xawtpdTUM0eD1CXmYi+X2SGZqdzLAc+5zfJ5kBGYl4tbumckJtmd3bDzbAcefAwp/D0r8d2ubEr0PBddBnlJ7Y7QY06LuZ8tpGNu+rYUtJDVtLa9i8r4at+2rYsq+G2samq3f9Xg+Ds5OYkJfO5VMGMW1Ylh7vV84xBnattIdzdq1quqetPxVO/CpMnQvpHRr5RMWQBn0PYYyhpKrhkODfvK+GjzaXUlUfZHS/VP531mjOHJmjga+cV/IpvPgNe5gH7ABseSdC/nQYepo9nu9LdLbGXkSDvoerD4R4ddUu7l24kc9KazljZA4/vmAsw3P0MnfVDYSCsP5V2LEUtr5vj/WbsL1CN+9EG/pDp0NegQZ/J9Kgd4nGYJjHPtzKn97aSH0wxPWn5nPz2SO0J4/qXuorYduHsPU92PIe7F5lg98TB33HwYDJMHCyfc4ZbY/vB+ph9yfQfyLE+Z3egh5Jg95lSqoa+M3r63l2aTG5qfHcPns0F00aqIdzVPdUVw6ffQDFH8OOZbBzBTRU2GVxCZA24NDbKJ72HdvFc/QFOmRDB2jQu9Tybfu58+U1rCyuYMqQTH564TjGD9RxdlQ3Fw7bYN+5zJ7cLdsCJmQv4GoubaC96cqAE+yef8l6WDkP4lPhjO/DsDO1T38zGvQuFg4bnltazK9fX09ZbSNXTx3Mt2aMoF96gtOlKdUxxkDVLijdBOvnQ8k62FcElcVtt0/pZ0N/zAX2XMDIc+3Y/b2UBn0vUFEX4E9vbeQfH24F4OzRuXxh2hCmH9dHh05WPZcxUP6ZHYo52ACp/W1Pn+KPW7eNS4Tc0Tb8y7bC0FMhZxSk5dlzBqn97Q1ckrO7fDO6ggZ9L7K9rJYnFm/j2cLtlNY0MjgriWtOGswlkweSm6p7+cpFjLGPfZ/Cyiehbj+UbLAjdVbvtb8O2uJPseP0VxZDcg4Mngb9J9lnrx/EA15fU/tQEOrLu/2VwBr0vVBDMMTrq3fzxOJtfLylDBE4KT+L8yYMYOa4fuSkHma0QqXc4kBPntpS6Dce3v65Hcmz6E3wJ9tQr97Tej1PHOSOsYeGUvrCqqchHLBdRPuOg4lXwf6tkDEEModCUlZXb1mbNOh7uaK9Vby8chfzV+1kU0kNHoGT8rOZOb4fJwzOYPyAdD28o3qnih320NDuT+xInrVl9uTwvg1QX2F7DPkSofEIN2HPzLcnjMNBSEiD7OMiXwDZ9gshpZ/9oolPs7d4LNsCW961vYriU2PWnVSDXgH2yttP91Qzf9VO5n+yi00lNQAMzEjknDG5TB+Rw4S8dHJS47WrplLNBRtt2G9eZH8FJKRDcSGsegay8qGmxA4B3VhtexAdTmr/1oeUhs+wvxz2roWRs+DM246qN5EGvWrTln01vL1uD2+s2c3K7RU0huxohX1S/Izpn8bYAWmM7Z/GuAFpgJCWEEduWu85zl9a3UB2ih7iUh0QCkL1bqjcaQ8ThQK291DVHgjU2F8JSdng8cHHf21azxsPoQZIHwTfWX1UH61Br9pVHwixYns563ZVsnZnJWt3VbJxT/XB8AfITPKRnRJP0V77M3ZwVhJfOnkIEwdlcMKgDOK87rm4ZeX2cubc91/uvfoELpg4wOlylJuFgnYP3uO15xV8R7czdaSg12vnFQAJPi/ThmUzbVhT17NAKMymkmrW7qxk8eYyNu6tYltZ7cHl28pq+cX8dQB4BEbkppLfJ5kJg9IZmJFIXmYiAzOSyE2N73HnAFYVlwOwaEOJBr3qXM2HeD7KkG+PBr06LJ/Xw+h+aYzul8Ylk/MOzg+GwnhEaAiGmf/JLt7bWEJWsp81OytZum0/r6/Z3eJ9hNzUBHLT4slNjadPSuSRGk9Oiv+Q6WS/l8ZQmPmrdnHmqFzSE32OjMnfGLK/dJ9fVsz/XTGxSz97Z3kdobBhUFZSl36uci8NetVhBw7RJPq9XDYlj8um5B2yvLohyM7yOor317KjvJ6d5XXsqaxnb2UDW/bVsGTrfspqGtt87wSfh/g4LxV1gVbLTj0um4l5GQzMTCQ/O5mhfZLJSvaT4Iv91ZDBZoesdlXU0T+960ZdPOVXCwHYcvdsPSmuYkKDXsVcSnwcI/umMrJv6mHbBENhymoaKaluYF91I/uqGthXbR/7awM0BsO8vHLnIev8t6iU/xaVtnqvjCQf6Yk+0hJ8ZCT5yEjykxl5zkj0kZnsIzPJT1ay/+Bzkt97xBBtfgOYk+9eyOZfzu7yw08vrtjBxSfktd9QqXZo0CtHxHk95KYlHLEXzz1Xn3DIdDhsWLOzkq2lNQRCYRqC9stiV0UdlXVBKusDlNcG2F5Wy/7aAJX1AQ7X18Af5yEryU9msp+sZB9ZyfFkJfnITPaTnexnwSeHdoEb9oMFpMbH8dvLJ3DmqNxO+RVhjOHqhz46OD1/1S4NehUT2utGuVYobKisC7C/tpH9tQH21zRSVtt46HPksb82QFlNY5uHjNpyXG4KI/umkJuaQN+0BHJT4+mblkDftHhyUuNJT/R1+LDLln01nPW7RYfM++pp+fxg9hi9d7Bql/a6Ub2S1yNkJtu99mgFQ2H21waoaQgS7/OQEh9HaoKPFdvL+fPCIt5at4ckv5eMRB/rd1fx3qf7qGoItnqfuMhnZyf7yU7xk50cT1aynz4pfvvrIdl/yCMj0cfiza0PSz3y/hYeeX8LAzMS+eF5Yzh3XL8e14NJOU/36JU6RjUNQfZWNbCnsp49lfWUVDUc/KVQWtNIaXUDpTWNlFU3tvmlALZ7arjZ/4oFQzIp/Gx/m20n5qWTl5VEXmYiOSn2F0Rago/UBPulZJ/jSPbHxeRLoXBrGf44DxPyMo75vVTn0T16pTpRcnwc+fFx5PdJbrdtfSDU7HBR06GjsppGquqDXDBxAHsr65k5vh9gw/+lFTt4ccVO/lu0jwsm9OfTPdW892kJlfVtf2kcIAIp/rhWXwCpCT5SEuJI9HlJ8HlIiPOS4PMSH3kd7/OQ4LPzymoa+M7TKwGYmp9FeqKPPil+8jKTGDsgjX5pCWRFfrm46YI5t9E9eqV6IGMMwbChtiFESXUDVfUBquqDkUfg4HNlfZDqhmCr5dUNQeoDYeoCIULhY88AEfCIkOjz4o/zkOjzEh/nAQEBRCTyDILY5+bzBOoaQ2Qnx5OR5CPR7yXJb79s/F4PIkIgFGb1jgoyknyM6Z+GJ7J+ZX2A7BR7XsQTef+axiDJ/jgyk/00BsMU77cX+g3MTDz4ZVbTECIYDpOTEk+Cz4snck6lIRgiLdGHRwSvR9hVUUdpdSNDspNIiY/D5/UQChtqGoMMykrCK4JHBI8HvJF12js/U9sYxOf14Ivhl6Pu0SvlMiKCzyukJ3lIT/K1v8IRBEJh6gMh6gP2uSHY9NrjEcb2T6OmIUhlfdB2f42ctK4PhEDk4HmNYChMfSBMQzBEXSBEYzCMATBgMAeHjz/wOhxZaOfZ8xpFJdWU1zXSEAxT19jsfYy9fWx+nxTW7qzkjTVNwwvHx3loCIbb3jgHSeRLziOCP86DP86DAA3BMLWNIfxxHpL8XpL9cfi8Ql0gREp8HG9/78yY16JBr1Qvd2DP8kj3pUnweclOiY/q8FRnM8YQChtCxhAO2wv3ahrsr5WwMYSNIckfR3ltI9UNds95QHoitYEgpdWNBMOGhkCI5Pg4PCLsr22kIRjiwDVy/jgPlXUBwpHPyU1NINHvYUd5PY3BMKFwGK/Hg88rlFQ1EAobwoaD7cPGEA4bDE1fbA0B2x1YxP69s5L97K2sJxC2yxpDYZJ8XrJTYjNkcUsa9EqpHkVEiPPKIeGVHB9HcvyhcZbVordVOr5jusJ5ypCjXtVxevZEKaVcToNeKaVcLqqgF5GZIrJBRIpE5LY2louI3BNZvkpEJke7rlJKqc7VbtCLiBe4D5gFjAWuFpGxLZrNAkZEHnOB+zuwrlJKqU4UzR79VKDIGLPZGNMIzAPmtGgzB3jMWB8BGSLSP8p1lVJKdaJogn4gsL3ZdHFkXjRtolkXABGZKyKFIlJYUlISRVlKKaWiEU3Qt3WJV8tL6Q7XJpp17UxjHjTGFBhjCnJycqIoSymlVDSi6UdfDAxqNp0H7IyyjT+KdZVSSnWiaIJ+CTBCRPKBHcBVwDUt2rwM3CQi84CTgApjzC4RKYli3VaWLl26T0Q+68B2NNcH2HeU6/ZUus29g26z+x3L9h72kq52g94YExSRm4A3AC/wqDFmjYjcEFn+ALAAmA0UAbXAdUdaN4rPPOpjNyJSeLiBfdxKt7l30G12v87a3qiGQDDGLMCGefN5DzR7bYBvRruuUkqprqNXxiqllMu5MegfdLoAB+g29w66ze7XKdvbLW88opRSKnbcuEevlFKqGQ16pZRyOdcEvVtHyRSRQSLyjoisE5E1InJLZH6WiLwpIhsjz5nN1rk98nfYICLnOlf9sRERr4gsF5FXI9Ou3mYRyRCR50RkfeS/98m9YJu/E/l3vVpEnhKRBLdts4g8KiJ7RWR1s3kd3kYRmSIin0SW3SPt3Zi2OWNMj39g++hvAoZhr8ZdCYx1uq4YbVt/YHLkdSrwKXYk0N8At0Xm3wb8OvJ6bGT744H8yN/F6/R2HOW2fxd4Eng1Mu3qbQb+AXwt8toPZLh5m7HjXm0BEiPTzwBfcds2A6cDk4HVzeZ1eBuBj4GTsUPLvAbMirYGt+zRu3aUTGPMLmPMssjrKmAd9n+QOdhgIPJ8UeT1HGCeMabBGLMFexHb1C4tOgZEJA84D3i42WzXbrOIpGED4REAY0yjMaYcF29zRByQKCJxQBJ2iBRXbbMx5j9AWYvZHdrGyGjAacaYD41N/ceardMutwR91KNk9mQiMhQ4AVgM9DXG7AL7ZQDkRpq55W/xR+D7QLjZPDdv8zCgBPhb5HDVwyKSjIu32RizA/gdsA3YhR065d+4eJub6eg2Doy8bjk/Km4J+qhHyeypRCQFeB74tjGm8khN25jXo/4WInI+sNcYszTaVdqY16O2GbtnOxm43xhzAlCD/Ul/OD1+myPHpedgD1EMAJJF5NojrdLGvB61zVE45pGA2+KWoI9mhM0eS0R82JB/whjzQmT2nsjPOSLPeyPz3fC3OBW4UES2Yg/DzRCRx3H3NhcDxcaYxZHp57DB7+ZtPgfYYowpMcYEgBeAU3D3Nh/Q0W0sjrxuOT8qbgn6gyNsiogfO0rmyw7XFBORM+uPAOuMMb9vtuhl4MuR118GXmo2/yoRiY+MGjoCexKnxzDG3G6MyTPGDMX+t1xojLkWd2/zbmC7iIyKzDobWIuLtxl7yGaaiCRF/p2fjT0H5eZtPqBD2xg5vFMlItMif6svNVunfU6fkY7hme3Z2B4pm4A7nK4nhtt1GvYn2ipgReQxG8gG3gY2Rp6zmq1zR+TvsIEOnJnvjg/gTJp63bh6m4FJQGHkv/WLQGYv2OafAuuB1cA/sb1NXLXNwFPYcxAB7J75V49mG4GCyN9pE/BnIiMbRPPQIRCUUsrl3HLoRiml1GFo0CullMtp0CullMtp0CullMtp0CullMtp0CullMtp0CullMv9fxBlWmOWN7RPAAAAAElFTkSuQmCC\n", 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" ] @@ -5620,10 +5467,10 @@ "source": [ "# design network\n", "model = Sequential()\n", - "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(LSTM(1))\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(1))\n", "model.add(Dense(1))\n", "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", "# fit network\n", @@ -5638,38 +5485,52 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ "# make a prediction\n", "yhat = model.predict(test_X)\n", - "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))" + "train_yhat = model.predict(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ "# invert scaling for forecast\n", - "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", - "inv_yhat = scaler.inverse_transform(inv_yhat)\n", - "inv_yhat = inv_yhat[:,0]\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", "# invert scaling for actual\n", - "test_y = test_y.reshape((len(test_y), 1))\n", - "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", - "inv_y = scaler.inverse_transform(inv_y)\n", - "inv_y = inv_y[:,0]" + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 160, "metadata": {}, "outputs": [], "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", "# invert scaling for actual\n", "test_y = test_y.reshape((len(test_y), 1))\n", "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", @@ -5679,14 +5540,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 107, + "execution_count": 161, "metadata": {}, "outputs": [], "source": [ @@ -5720,12 +5574,12 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 162, "metadata": {}, "outputs": [ { "data": { - "image/png": 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8+OCDfP311yW2oShGjx7NU089Vexxb775Ju3bt6ddu3a0bds2rDwl5bHHHiu3siIqMCJSR0TeF5EVIrJcRHqLSD0RmSIiq7zXugHH3y8iq0VkpYgMDEjvJiI/e/ueE29pNRGpJiLveunzRKRlQJ5h3jlWiciwSF6nUf4Eei7WTBZ7BE7XX7VqVV555ZV8+3NyckpV7muvvUbbtm1D7i/vKetvvPFG6tWrx6pVq1i6dCnjxo0LKkSBPPzww5x77rnlZkO4TJo0iWeffZbJkyezdOlSFi1alDfXWnlSaQQG+AfwH1U9BegELAdGAlNVtQ0w1fuMiLQFhgDtgEHASyKS5JXzMnAz0MbbBnnpw4E9qnoi8AzwN6+sesAo4DSgJzAqUMiM2McEpvJw1llnsXr1ambMmME555zDb37zGzp06EBOTg733HMPPXr0oGPHjvzzn/8E3BQyI0aMoG3btlx44YVs3749r6y+ffviD3j+z3/+Q9euXenUqRP9+/cPOmX9jh07+J//+R969OhBjx49mD17NgC7du1iwIABdOnShVtuuSXonGC//PIL8+bN469//StVqrhHYevWrbnwwgsBJ5I33XQT7dq1Y8CAARw+fBjI73EFm1ofYPfu3Vx66aV07NiRXr168dNPPxWZHsirr77K+eefn3c+n8cff5ynnnqKpk2bApCamspNN90EuJmje/XqRceOHbnsssvYs2dPofu5c+dOWrZsCYRezmDkyJF5E5lec8014Xz9RRKxyS5FpBbQB7geQFWPAEdE5BKgr3fYeGAGcB9wCTBRVTOBtSKyGugpIuuAWqo61yv3TeBSYJKXZ7RX1vvAC553MxCYoqq7vTxTcKI0IVLXa5QvgaJicZjQRHm2frKzs5k0aRKDBrk63/fff8+SJUto1aoVY8aMoXbt2syfP5/MzEzOOOMMBgwYwA8//MDKlSv5+eef2bZtG23btuWGG27IV+6OHTu46aabmDlzJq1atWL37t3Uq1ev0JT1v/nNb/jf//1fzjzzTDZs2MDAgQNZvnw5Dz30EGeeeSYPPvggX3zxBWPGjClk+9KlS+ncuTNJSUmF9gGsWrWKCRMm8Oqrr3LVVVfxwQcfcO211xY6LtjU+qNGjaJLly58/PHHTJs2jeuuu47FixeHTPd54YUXmDx5Mh9//HG+yTqh6GUBrrvuOp5//nnOPvtsHnzwQR566CGeLeZLDLacwRNPPMELL7wQdMLS0hDJ2ZRbAzuAN0SkE7AQuANorKpbAFR1i4g08o5vBnwXkH+jl5blvS+Y7udJ98rKFpG9QP3A9CB58hCRm3GeES1atCj1hRrlj3kwsY1fywXnwQwfPpw5c+bQs2fPvKn4J0+ezE8//ZRX29+7dy+rVq1i5syZeVPYN23alH79+hUq/7vvvqNPnz55ZdWrVy+oHV9//XW+mM2+ffvYv38/M2fOzJvm/sILL6Ru3ZI3YLRq1SrvGrt168a6deuCHhdsav1Zs2bxwQcfAG4yzF27drF3796Q6QBvvfUWaWlpfPzxx6SkpIRt5969e/n111/zJuccNmxYviUTQhFsOYPmzZuHfd5wiKTAJANdgdtVdZ6I/AOvOSwEEiRNi0gvbZ6jCapjgDHg5iIrwjajggkUGPNgQhOl2frzYjAFOfbYY/PeqyrPP/88AwcOzHfMl19+iRdGDYmqFnsMuFmW586dS/Xq1QvtKy5/u3bt+PHHH8nNzc1rIguk4HT/BZusCh4XOLV+sCY5EQmZDtC+fXsWL17Mxo0bg66X4y9hEEyQQxG4LECoJQEK2l6eRDIGsxHYqKrzvM/v4wRnm4g0AfBetwccHyifacBmLz0tSHq+PCKSDNQGdhdRllFJMA+m8jNw4EBefvllsrKyAPjvf//LwYMH6dOnDxMnTiQnJ4ctW7Ywffr0Qnl79+7NN998w9q1awEXu4DCU9YPGDCAF154Ie+zL3p9+vTh7bffBlxw3I9JBHLCCSfQvXt3Ro0alffgX7VqVd4M0GUh8PwzZsygQYMG1KpVK2Q6uFmp//nPfzJ48GA2by78uLr//vu599572bp1K+AWc3vuueeoXbs2devWzZth+a233srzZlq2bMlCb7bYUGvWFCQlJSXvOysrERMYVd0KpIvIyV5Sf2AZ8Cng9+oaBvjf5qfAEK9nWCtcMP97rzltv4j08uIr1xXI45d1BTBN3S/lK2CAiNT1gvsDvDSjkmAeTOXnxhtvpG3btnTt2pX27dtzyy23kJ2dzWWXXUabNm3o0KEDt956a9BFvBo2bMiYMWO4/PLL6dSpE1dffTVQeMr65557jgULFtCxY0fatm2b15tt1KhRzJw5k65duzJ58uSQTeCvvfYaW7du5cQTT6RDhw7cdNNNeUH0sjB69Og8u0aOHJm3Bk6odJ8zzzyTp556igsvvLBQb7YLLriA2267jXPPPZd27drRrVu3PK9j/Pjx3HPPPXTs2JHFixfz4IMPAnD33Xfz8ssvc/rppxfbO87n5ptvzltGoKxEdLp+EekMvAZUBdYAv8WJ2ntAC2ADcGVAMP7PwA1ANnCnqk7y0rsD44DquOD+7aqqIpIKvAV0wXkuQ1R1jZfnBuBPnimPquobRdlq0/XHFldfDe+9597PnAlnnRVde2IJm67fiBYlna4/oksmq+piINiJ+4c4/lHg0SDpC4BCo55UNQMIGs1S1bHA2BKYa8QQ5sEYRuXHRvIbMUlGBvi9Ry0GYxiVExMYIybJzAR/kLJ5MIWxlWiNiqY0vzkTGCMmycg4KjDmweQnNTWVXbt2mcgYFYaqsmvXLlJTU0uUL6IxGMMoLRkZUKfO0ffGUdLS0ti4cSM7duyItilGApGamkpaWlrxBwZgAmPEJBkZ0KSJe28eTH5SUlKCDsQzjFjDmsiMmMQ8GMOo/JjAGDGJxWAMo/JjAmPEJIECYx6MYVROTGCMmCQzE1JToWpV82AMo7JiAmPEHKrOa0lNdZt5MIZROTGBMWKOI0fca2oqVKtmHoxhVFZMYIyYw/dYzIMxjMqNCYwRcwQKjHkwhlF5MYExYg7zYAwjPjCBMWIO82AMIz4wgTFiDvNgDCM+MIExYg7fY6lWzTwYw6jMmMAYMYd5MIYRH5jAGDGHxWAMIz4wgTFiDvNgDCM+iKjAiMg6EflZRBaLyAIvrZ6ITBGRVd5r3YDj7xeR1SKyUkQGBqR388pZLSLPiYh46dVE5F0vfZ6ItAzIM8w7xyoRGRbJ6zTKF/NgDCM+qAgP5hxV7ayq3b3PI4GpqtoGmOp9RkTaAkOAdsAg4CURSfLyvAzcDLTxtkFe+nBgj6qeCDwD/M0rqx4wCjgN6AmMChQyI7YxD8Yw4oNoNJFdAoz33o8HLg1In6iqmaq6FlgN9BSRJkAtVZ2rbhHyNwvk8ct6H+jveTcDgSmqultV9wBTOCpKRoxjHoxhxAeRFhgFJovIQhG52UtrrKpbALzXRl56MyA9IO9GL62Z975ger48qpoN7AXqF1FWPkTkZhFZICILbH3z2MEXmGrVzIMxjMpMcoTLP0NVN4tII2CKiKwo4lgJkqZFpJc2z9EE1THAGIDu3bsX2m9EB99j8T2YI0fcFP4S7Fs1DCNmiagHo6qbvdftwEe4eMg2r9kL73W7d/hGoHlA9jRgs5eeFiQ9Xx4RSQZqA7uLKMuoBBT0YMCayQyjMhIxgRGRY0Wkpv8eGAAsAT4F/F5dw4BPvPefAkO8nmGtcMH8771mtP0i0suLr1xXII9f1hXANC9O8xUwQETqesH9AV6aUQnIyIDkZLdVq+bSTGAMo/IRySayxsBHXo/iZOAdVf2PiMwH3hOR4cAG4EoAVV0qIu8By4Bs4DZVzfHKuhUYB1QHJnkbwOvAWyKyGue5DPHK2i0ijwDzveMeVtXdEbxWoxzxV7OEo68ZGVC7dvRsMgyj5ERMYFR1DdApSPouoH+IPI8CjwZJXwC0D5KegSdQQfaNBcaWzGojFggUGPNgDKPyYiP5jZgjlAdjGEblwgTGiDnMgzGM+MAExog5MjOPCot5MIZReTGBMWIO82AMIz4wgTFiDovBGEZ8YAJjxBzmwRhGfGACY8Qc5sEYRnxgAmPEHObBGEZ8YAJjxBzmwRhGfGACY8Qc5sEYRnxQrMCIyN/CSTOM8sLGwRhGfBCOB3NekLTzy9sQw/AxD8Yw4oOQk12KyK3A74HWIvJTwK6awOxIG2YkLsEExjwYw6h8FDWb8ju4afEfB0YGpO+3qe+NSJGd7TZfYKpUgZQU82AMozISUmBUdS9ujfuhIpKEW98lGaghIjVUdUMF2WgkEIHLJfukppoHYxiVkWLXgxGREcBoYBuQ6yUr0DFyZhmJii8kgQJTrZp5MIZRGQlnwbE7gZO9hcIMI6IEExjzYAyjchJOL7J0XFOZYUQcX0j84L7/3jwYw6h8hOPBrAFmiMgXQN7fXFWfjphVRsJiMRjDiB/CEZgN3lbV2wwjYlgMxjDih2IFRlUfqghDDAMsBmMY8UQ4U8VMF5FpBbdwTyAiSSLyg4h87n2uJyJTRGSV91o34Nj7RWS1iKwUkYEB6d1E5Gdv33MiIl56NRF510ufJyItA/IM886xSkSGhWuvEV3MgzGM+CGcIP/dwD3e9gCwGFhQgnPcASwP+DwSmKqqbYCp3mdEpC0wBGgHDAJe8sbfALwM3Ay08bZBXvpwYI+qngg8A/zNK6seMAo4DegJjAoUMiN2MQ/GMOKHYgVGVRcGbLNV9S7cg7tYRCQNuBB4LSD5EmC89348cGlA+kRVzVTVtcBqoKeINAFqqepcVVXgzQJ5/LLeB/p73s1AYIqq7lbVPcAUjoqSEcOYB2MY8UM4Ay3rBXysAnQDjguz/GeBe3Hzl/k0VtUtAKq6RUQaeenNgO8CjtvopWV57wum+3nSvbKyRWQvUD8wPUiewGu7GecZ0aJFizAvyYgk5sEYRvwQTi+yhbiR+wJkA2txTVNFIiIXAdtVdaGI9A3jPBIkTYtIL22eowmqY4AxAN27dy+036h4bByMYcQP4fQia1XKss8ABovIBUAqUEtE/gVsE5EmnvfSBNjuHb8RaB6QPw3Y7KWnBUkPzLNRRJKB2sBuL71vgTwzSnkdRgVi42AMI34IpxdZioj8QUTe97YRIpJSXD5VvV9V01S1JS54P01VrwU+BfxeXcOAT7z3nwJDvJ5hrXDB/O+95rT9ItLLi69cVyCPX9YV3jkU+AoYICJ1veD+AC/NiHEsBmMY8UM4TWQvAynAS97n/+el3VjKcz4BvCciw3EDOK8EUNWlIvIesAzXFHebquZ4eW4FxgHVcUsITPLSXwfeEpHVOM9liFfWbhF5BJjvHfewLTFQObAYjGHED+EITA9V7RTweZqI/FiSk6jqDLwmKm/SzP4hjnsUeDRI+gKgfZD0DDyBCrJvLDC2JHYa0ccXkqoBc0b4HowqSLDommEYMUk442ByROQE/4OItAZyijjeMEqNv5ploJD43syRI9GxyTCM0hGOB3MPMF1E1uB6Zx0P/DaiVhkJS+ByyT5+j7LMzPy9ywzDiG3C6UU2VUTaACfjBGaFqlrI1YgIGRmFRcQXnIwMqFWr4m0yDKN0hBQYEbkWEFV9yxOUn7z0m0TkoKq+U1FGGolDZmbRHoxhGJWHomIwfwQ+DpL+rrfPMMqdYE1kgR6MYRiVh6IEJklV9xdMVNV9uG7LhlHuFBeDMQyj8lCUwKSIyLEFE0WkJrbwmBEhzIMxjPihKIF5HXi/wBorLYGJ3j7DKHfMgzGM+CFkkF9VnxKRA8A3IlIDN1nkQeAJVX25ogw0EouMDKhTJ3+aeTCGUTkpspuyqr4CvOIJjASLyRhGeWIejGHED+EMtERVD0TaEMOA4sfBGIZReQhnqhjDqDBsHIxhxA+lEhgRsQk7jIhgvcgMI34IZz2YsQU+1wC+jJhFRkJjMRjDiB/C8WA2icjLAN7iXZOBf0XUKiNhMQ/GMOKHYgVGVR8A9onIKzhx+buqvhFxy4yEQ9U8GMOIJ4qa7PLygI/fAw94ryoil6vqh5E2zkgs/PVeQgmMeTCGUbkoqpvyxQU+/4Cbg+xi3KBLExijXAm2XDJAUhIkJ5sHYxiVjaJG8tuiYkaF4gtMsEXFUlPNgzGMykY4vcjSROQjEdkuIttE5AMRSasI44zEwvdQCnow4ETHPBjDqFyE04vsDeBToCnQDPjMSzOMciVUE5mfZh6MYVQuwhGYhqr6hqpme9s4oGFxmUQkVUS+F5EfRWSpiDzkpdcTkSkissp7rRuQ534RWS0iK0VkYEB6NxH52dv3nIiIl15NRN710ucVmPl5mHeOVSIyLPxbYkSLogTGPBjDqHyEIzA7ReRaEUnytmuBXWHkywT6qWonoDMwSER6ASOBqaraBpjqfUZE2gJDgHbAIOAlEUnyynoZuBlo422DvPThwB5VPRF4BvibV1Y9YBRwGtATGBUoZEZsYh6MYcQX4QjMDcBVwFZvu8JLKxJ1+JNkpnibApcA47308cCl3vtLgImqmqmqa4HVQE8RaQLUUtW5qqrAmwXy+GW9D/T3vJuBwBRV3a2qe4ApHBUlI0YxD8Yw4otiZ1NW1Q3A4NIU7nkgC4ETgRdVdZ6INFbVLV7ZW0SkkXd4M+C7gOwbvbQs733BdD9PuldWtojsBeoHpgfJE2jfzTjPiBYtWpTmEkvOwYOwfz8cd1zFnK8SYR6MYcQXEe1Fpqo5qtoZSMN5I+2LOlWwIopIL22eQPvGqGp3Ve3esGGxYaXy4YEHoE+fijlXJaOobsrmwRhG5aNCepGp6q/ADFwz1Tav2Qvvdbt32EageUC2NGCzl54WJD1fHhFJBmoDu4soK/osWwbr17t5UYx8FNVN2TwYw6h8RLIXWUMRqeO9rw6cC6zAiZXfq2sY8In3/lNgiNczrBUumP+915y2X0R6efGV6wrk8cu6ApjmxWm+AgaISF0vuD/AS4s+6eluTpRDh6JtScxhMRjDiC/CWdFyp9dzbIL3eSjh9SJrAoz34jBVgPdU9XMRmQu8JyLDgQ3AlQCqulRE3gOWAdnAbaqa45V1KzAOqA5M8jaA14G3RGQ1znMZ4pW1W0QeAeZ7xz2sqrvDsDmyqMKGDe797t1w7LHRtSfGsBiMYcQX4QjMDcALuG7ACswhvF5kPwFdgqTvAvqHyPMo8GiQ9AVAofiNqmbgCVSQfWOBscH2RY29e+GA17Fu925o3rzo4xMM82AMI76IaC8yowDpAR3bdkffoYo1zIMxjPiiWIERkYbATUDLwONVtVgvxiiA3zwGJjBBsF5khhFfhNNE9gnwLfA1kFPMsUZRmAdTJBkZblr+5CC/SvNgDKPyEY7AHKOq90XckkQgPR1EXLDfBKYQGRnBvRc46sGoultoGEbsE0435c9F5IKIW5IIbNgALVq4p6UJTCEyM4PHX8Clq0JWVsXaZBhG6QlHYO7AicxhEdknIvtFZF+kDYtL0tNdz7F69UxggpCREVpgfM/G4jCGUXkoVmBUtaaqVlHV6qpay/tcqyKMizt8D8YEJihFCYyfbnEYwwhg+nTYtCnaVoQkpMCIyCnea9dgW8WZGCfk5sLGjebBFIF5MIZRAnJy4IIL4P/+L9qWhKSoIP9duJmG/x5knwL9ImJRvLJ9uwsgtGgBy5fDunXRtijmMA/GMErA5s3uD7FlS7QtCUlIgVHVm73XcyrOnDjGHwPjezCLFkXXnhjEPBjDKAFr17rX7duLPi6KhNNNGRE5ncIDLd+MkE3xiT8GxprIQlJUN2XzYAyjAPEgMCLyFnACsJijAy39lSWNcPE9GD/If+iQq46HeqImIBkZULt28H3mwRhGAeJBYIDuQFtvGnyjtKSnwzHHQN26TmAA9uyxlS0BZsyA3bvJzLzcYjCGES6+wOzaBdnZwafAiDLhjINZAthTsKykpzvvReSowFgzmePRR+G++ywGYxglwRcYVScyMUhIyRORz3BNYTWBZSLyPZD391ZVm2G5JGzYcHR6fhOY/GzaBNu2kVHbepEZRtisXQs1arglQLZvh8aNo21RIYryqZ6qMCsSgfR0aO8taWMCk59Nm2D/fjKqKqmpwScaMw/GMALIzHT/m7593WDLGI3DFNVEtgnIVtVvAjecV7OxYsyLE44cga1bXRMZmMAEcuAA7HMzD2VkqHkwhhEOGza4prHTTnOfK6HAPAvsD5J+yNtnhMumTe7HYE1khdm8Oe+txWAMI0zWrHGvlVhgWnrLHufDW764ZcQsikcCx8AA1KoFSUkmMJA3j1IOVcjOqWLjYAwjHPwAf9eurvfYtm3RtScERQlMiLokANXL25C4JnAMDLieZHXrmsBAnsBk4pTFPBjDCIO1ayElBZo1g4YNK6UHM19EbiqYKCLDgYXFFSwizUVkuogsF5GlInKHl15PRKaIyCrvtW5AnvtFZLWIrBSRgQHp3UTkZ2/fcyJuySkRqSYi73rp80SkZUCeYd45VonIsLDuRqQo6MGAjeb38QQmw6vPFCcw5sEYBk5gjj/etYQ0ahSzAlNUL7I7gY9E5BqOCkp3oCpwWRhlZwN/VNVFIlITWCgiU4Drgamq+oSIjARGAveJSFtgCNAOaAp8LSInqWoO8DJu4s3vgC+BQcAkYDiwR1VPFJEhwN+Aq0WkHjDKs1e9c3+qqnvCuivlTXo61K/vBlr6mMA4Nm+GmjXJqNYcdoYWmORk918yD8YwcALTqpV737hxzApMSA9GVbep6unAQ8A6b3tIVXur6tbiClbVLaq6yHu/H1gONAMuAcZ7h40HLvXeXwJMVNVMVV0LrAZ6ikgToJaqzvVmE3izQB6/rPeB/p53MxCYoqq7PVGZghOl6BA4BsbHBMaxaRM0a0ZG/WZAaIHx95kHYxjkF5hK6sEAoKrTgellOYnXdNUFmAc0VtUtXtlbRKSRd1gznIfis9FLyyJ/t2g/3c+T7pWVLSJ7gfqB6UHyVDzp6c6dDaRePTdtf6LjC8zh4gWmWjXzYBKazz93MYcuXaJtSXTZv9+N3K8EAhPOVDFlQkRqAB8Ad6pqUUstBxthp0WklzZPoG03i8gCEVmwY8eOIkwrI/5KloGYB+PwBaZuE8A8GKMIbrwRHnkk2lZEH78HWaDAHDzothgjogIjIik4cXlbVT/0krd5zV54r770bgQC25HSgM1eelqQ9Hx5RCQZqA3sLqKsfKjqGFXtrqrdGzZsWNrLLJoDB+DXXws3kdWtC3v3uknqEpXcXLdYUrNmZNRx092lVgs9p6p5MAnMwYOuK+5GG+OdJzCtWzN8OHyU3t19jmQluZRETGC8WMjrwHJVfTpg16eA36trGPBJQPoQr2dYK6AN8L3XnLZfRHp5ZV5XII9f1hXANC9O8xUwQETqer3UBnhpFU+wHmRwdLDlr79WqDkxxY4dTmCbNiWjlmsprZZ9IOTh5sEkMP5D1QQm714catyKsWPhjfneFFQx2EwWyfmdzwD+H/CziCz20v4EPAG853V33gBcCaCqS0XkPWAZrgfabV4PMoBbgXG48TeTvA2cgL0lIqtxnssQr6zdIvIIMN877mFVjU57VMExMD6Bo/kbNKhYm2IFr4syzZqRme56q6ce2IWbX7Uw5sEkML7AbN3qpl6qWjW69kQTb5LLdfvrA7BgjTfSI5EERlVnETwWAtA/RJ5HgUeDpC8A2gdJz8ATqCD7xgJjw7U3YhTnwSRyHCZAYDKOdWN3U/fvINREEebBJDD+1Ciqrlm1YKeZRMLrQbZ2nXu8btlZlU00pVkMCkzEg/wJT3o6VKkCTZvmTzeByS8w1T0PZl/oP4l5MAmM78GANZN5AuNrLsB8esSkB2MCE2k2bIAmTdy0DoEErmqZqGza5MS3cWMyUusAkPpr6CFW5sEkMGvWHO1imJ5e9LHxjOpRD2atuyXJyTA/5YyYnI/MBCbSpKcXbh4D82DAjeJv3BiSk8lIOhaA1D1bQh5uHkwCs3Yt9Orl3ieyB7Nzp+tR5wlM69Zuman5yb3Mg0lIgo2BAahTx70mssB4Y2AAMjJde3Lqrk0hDzcPJkFRdR5M585Qs2ZiC0zAGBh/MH+PHrDgSCd0mwlMYqEa2oNJTobatU1gfIHxhKPaztACYx5MgrJ9Oxw65KrraWkmMIC2dDEYX2D25NTil40h1rqIIiYwkWTXLvfkDObBgI3mDyYw20O3r5sHk6AEDCwkLS2xYzDevdhduxX797tb0qOH2zV/a5CKbJQxgYkk/hiYYB4MJLbAHD7sOjh4ApOZCdWSspBtoYP85sEkKH53qVatzINZuxYaNGDtjhqAuyXt2kFqchYL9p3kZseIIUxgIkmoMTA+iSww/lLJXvftjAxITc52zSE5OUGzmAeToPgeTMuW7r+0ZQtkZUXVpKgR0IMMnMCkpEDntJ3M124x1yvVBCaShBrF75PIAhMwBgY8gamqrga2c2fQLL4Ho6GnKzPikTVr4Ljj3HpKaWlHB1smIgXGwPjzXfY4eT+L6ErOltgK9JvARJL0dPdUDDWRpglMfoFJ9ZRja/BmstRUpz+JPD9oQuL3xwUnMJCYzWQ5ObB+fZ4HU68e1KrldvXoks1BarB8fui5/KKBCUwkSU93fwgJMWOOLzAx1m5aIQQTmOrefQohMP6yyRaHSTDWrDkqMH5zcyIKzKZNrmkwYAyMT4/ebtav+fND5I0SJjCRJNQYGJ969Zy47N9fcTbFCps2wbHH5lXBMjIg9dgkt68ID8Y/1kgQsrJcRc1vC/I9mETsSRbQmy5wQUuAk3rWoRZ7mb+kiAWVooAJTCQJNQbGJ5FH82/a5AL8nneXkQHVjvXmXg0x5YV5MAnIhg2uEuZX12vXdhWTRPRgPIHJadHKbynLo0rD+nRjIfNX14uSccExgYkU2dnuIVqUB1PXm2Y7EQVm8+a85jFwopF6TBLUqGEejHGUgtFskcTtqrx2LYiwObkFR47kFxiSkuhRfSk/bmscUxUwE5hIsWWLq3mZBxOcgEGW4Af5cb2FLAZj+AQOsvRp3jxxm8jS0li7ya2FE3hLAHo0WEtWbjI//xwF20JgAhMpihsDA4krMKqFPJg8gWnc2DwY4yhr1rjFxQKXu0hkD6bAGJhAeqS5rtuxFOg3gYkUv/ziXotaGClRBWbnTrcqoXkwRnGsXev+Q0lJR9PS0lwLQaL1Vw8QGJHCre8tWkCDpN0mMAnB3Llu5teTTw59TKLGYPwuygG10nAEJqE8mFmz4Pvvo21F9AnsouzTvLlrfk6kwZaZmc7r9wZZNmt2tMLlI40b0UMWmMAkBHPmuPUrAmteBUlNdaOTE01g/GliQnkwe/YEdVMSxoNRhWuvhREjom1J9PGnDA4kEQdbrl/vfhdBxsDk0agRPbLnsmyZcvBghVsYFBOYSLBvH/z8M5xxRvHH1qsXc/MHRZwCgyzB66ZcDScwEHTxpITxYFatcg+UJUtCzsuWEOzd6ypfBZ+miSgwQdaBKUSjRvRgPrm5wqJFFWpdSExgIsF33zkXPlyBSTQPZtMm14jcpEleUj4PBoI2kyWMBzN5sns9fNiJTaISKpqdiKP5vXuR0aSV31JWGE9gIHYC/RETGBEZKyLbRWRJQFo9EZkiIqu817oB++4XkdUislJEBgakdxORn719z4m4kXkiUk1E3vXS54lIy4A8w7xzrBKRYZG6xpDMnu3Wmj/ttOKPTVSBadTITQOL8/wzM8MXmLj3YCZPPuqu/fhjdG2JJsG6KINbDfaYYxKrq/LatVC1KuuzmvotZYVp1IjGbKd5w8PxLzDAOGBQgbSRwFRVbQNM9T4jIm2BIUA7L89LIuIHL14GbgbaeJtf5nBgj6qeCDwD/M0rqx4wCjgN6AmMChSyCmH2bOjY0QX5iyNRBSYgwJ+V5USmOIHxn7lx7cEcOQLTp8NvfuNWPU1kgfEHWRYUmEQcbPnLL3D88axd7x7ZoQQGoMfx2+NfYFR1JlDwyXkJMN57Px64NCB9oqpmqupaYDXQU0SaALVUda6qKvBmgTx+We8D/T3vZiAwRVV3q+oeYAqFhS5yZGe7JrJwmscgMQUmyBgY8ATE+5MkrAczdy4cOACDB8Mpp8BPP0Xbouixdq3zVurUAWDIEHjsMW9fIgmMqutV2KNHSKcOOCowDdfzyy+xEdqt6BhMY1XdAuC9ek8TmgGB/u5GL62Z975ger48qpoN7AXqF1FWIUTkZhFZICILduzYUYbLCuDnn+HgwZILTCItchJkFD94AlO1qrsnierBTJ7seh6ecw506mQejPck3bwZ3n0Xnn3WG/6SSKP5V6xw8/Odcw5r17qKVkD48ig1akBqKj1qLAdgwYKKNTMYsRLkDzafvRaRXto8+RNVx6hqd1Xt3jDUmi0lZfZs91oSgcnMdAHdRCAz0w20DCUwEHIsTEJ4MJMnQ+/ebpbpTp1cLT3RPFyfgC7Kkya5pB07YNo0Emuw5bRp7rVfv7xxp1WCPblFoHFjulZZDMDChRVmYUgqWmC2ec1eeK9+X9SNQOCcKmnAZi89LUh6vjwikgzUxjXJhSqrYpg92/34i5rkMpBEG80fYgwMFC8wycnujxW3HszOne6pMGCA+9ypk3tNRC8mNxfWrcvzYL74wv1katWCCRNw/7GcnJAzb8cV06Y5VfEGWQaNv/g0akTdves44YTE9GA+BfxeXcOATwLSh3g9w1rhgvnfe81o+0Wklxdfua5AHr+sK4BpXpzmK2CAiNT1gvsDvLSKYfZsOP308I9PNIEJMYofAkYmH3dc0AeHiDsmbj2YqVNdU6kJjPNOMjOhdWuOHIEpU+Cii+Cyy+DDDyGjsTcFU7w3k+XmwowZrslUJPQgS59GjWD7drp1i3MPRkQmAHOBk0Vko4gMB54AzhORVcB53mdUdSnwHrAM+A9wm6r6I8xuBV7DBf5/ATxnmdeB+iKyGrgLr0eaqu4GHgHme9vDXlrkSU93W7jNY5B4AlMGD8Y/Jm49mMmTXUC7e3f3uXFjtyWiwASMgfn2W9fv4cILYehQN4550rpT3P54D/T/9JN7NvTrx969LnBfnAfjC8y6dbBrV0UZGpzkSBWsqkND7Oof4vhHgUeDpC8A2gdJzwCuDFHWWGBs2MaWFyWNv0DlEZhFi9yf/n/+p2zlBBnF7wtGPoE5eNA9VWrUyJc9pjyY2293o83ffLPsZak6gTn33PzTCyVqoD+gi/IXL7vvvV8/99qwIUz4pimXQfwLzPTp7tUL8EOYAtPVhaMXLYLzzou0kaGJlSB/fDBnjlttz2/aCIfKIjB33AFXXAEvvFC2cjZtckriT/RJEA+mcWP3GqInWUx4MNnZTljeeutoxaIsrFjhHpZ+85hPp06wdKkbLJRIBEwZ/OWX0Lev+2slJ8OVV8JnX1Vlf7UG8d9ENm0atGkDaWmF1l4LSqNGcOQIXdvsA6LfTGYCU57Mnu1G7yeXwDGsDDMqHzoE8+Y5b+L228tWY/e7KMvRzn5Bm8ggZE+ymPBgFi1ybTUADzxQ9vK+8sKEBaubnTq5wZcrV5b9HJWJNWsgLY1fNlZj5UrXPOYzdChkZAif1BkW3x5MdjZ8841z3Qg9sUE+vLEwdY9sp3VrE5j44cAB15RRkuYxcNWylJTYFpi5c10N+s033Y/9hhvgk0+KzxeMAqP4oWQCEzMejN908ac/ufdTp5atvMmT4aSToGXL/OmJGuj3xsB88YX7eMEFR3edfrobBjMh64r4FphFi2D/fhfgxwlM7dr5nP/C+AOVt2+ne/fo9yQzgSkv5s1z3SZL0oMMXE0+1kfzz5jh4gL9+8PHH0O3bnDVVaV7qBYYxQ+V1IOZNg3atXPeS1oa/OUvpR8sm5np7vHAvCn4WL4cVq/GrSdUtWriCYw3ZfCXX7pbcMIJR3dVqeJG9U/e051d6w9Ez8ZI449/6dsXIPQsyoEECEwsBPpNYMqL2bOdWPTuXfK8sS4w06c7UalVy82vNmmSq21fcokT1nBRLTSKH4IITP36TtBi1YM5csRN3dGvnzPowQfd9EB+dbukzJ7tBtp68ZcDB1yl9ZJLQJNTnJAlksBkZMCmTRxMO5kZM/I3j/kMHQrZmswHm3vH75IG06ZB+/Z5Mclix8BAIYEBojp1vwlMeTF7tvsx1K5d8ryxLDAHD7qVFT03HXD2Tp7sfvjnn+/WLQmHPXvcwyOEwOSNg0lKcn+UIGNhYsKD+f57F5fy78n117sq9gMPuHELJWXyZNdM6tVUn33WXfqyZZ5+d+yYWAKzfj0AUw/2IjMzf/OYT+fOcHLjPUzQqyvvYMuiPF6/EuP9xlTzjTsNjT8jybZtdO3q3kYzDmMCUx7k5JRsgsuCxPKiY378xXv45dGkCXz9tXvi/+Y34fVyCtJFGYJ0U4aQY2FiwoOZNs15q2ef7T6npMDo0bB4MXzwQcnLmzzZNa3WqMHOnfDkky7Wf8wx8PrruDjMtm2V90FaUrzuUl+uOYWaNeGsswofIgJD+23nG85m06JKdl+ys92snbVrw/vvBz9m3jzn1XoB/q1bXcWqWA8mJcU9T7Zvp25doh7oN4EpD5YudT2KyiIwserB+PGXYNfWqhW89JKb4PMf/yi+rCCj+CFIExkUOR9Z1D2YadOgS5ejXczBtdm0beuay0rSZLN9O/zwQ17z2KOPOqfx2WddmGviRDh4Uhd3bKLMrLxmDQp8Mb8h553nQlDBGHJlDkoV3nu/Ej3GVqxw/6U//9n9r265Jfig4unT81ViwhoD4+ONhQGiPqK/En0zMUxpBlgGEssCM3069OgRem2bSy91c3iMGgUbNhRdlr86Y5AmsqSkAr27GzeOTQ/m8GHn1QU2GYK7gIcfdg+Qt98Ov7wpU9zrgAGsX+/0+vrrnVYNH+7iMf9e643srwzNZJ9/7jzbsrBqFT9X68HGzUlBm8d8Tj6rEV1YxITpjct2vnJizBi49dYQ82/m5sIzz7iKyerVrubw3XeuqfWmmwo3l/mVGK/LWGkFpnt3lzdqgX5VtU2Vbt26aam55hrV445Tzc0tXf5HHlEF1czM0tsQCQ4cUE1OVh05sujj1q1TPeYY1cGDQx+zeLFqzZqqnTurZmfn2/XHP6oee2yB40eOVE1JUc3JyZc8fLhqkyYluIby5uuv3Xf1xReF9+XkqHbpotqqleqRI8WXlZ3tjm/RQjU7W6+7TrVaNdUNG9zu3FzVk05SPfNMVW3WTPXaa8v1UsqdXbtUq1d392fUqELfXVhs365au7Y+fso4BdXNm4s4NjdXn0waqaC6alVpjS4fMjJU69d3l/6HPxTYuWaNap8+budFF6lu2XJ03zPPuPQ33jiaduiQatWqqnffnZf08MPusEOHwjDmiitUTz1VVY/+XCdPLu2VFQ+wQEM8V82DKQ9mz3beS8DgwRLhN7XEWhxmzhxXHSsYfynI8cc7D+bTT4OPj9m0yXUFqlULPvss/1QoOA8mX/MYuCayrKxC9yTqHsz06c7+YIGBKlXgr391VcZXXim+rLFjXfPYk0/y87Ik3nrLjWP1l5wXcUOOZs2C/55wfux7MGPHOg/vwgvhoYfczJR795asjNGj4cABvjjmSrp2DbHuiY8IQ9JmUUVyeeONshhedj791HkJffrAc885TxRw19+7t4vPvfGGO9Dvhg/whz+4ZrA77jjaAjBnjgvye/GXnBzXzNWkCVSvHoYxAR5M1AP9oZQn0bZSezCbNrkqwtNPh3X44cOqO3cWSJwwwZWxbFnpbIgU99/vPJj9+4s/9sgR1fbtVZs3z3/8vn2qnTqp1qjhvJggDB/uKuj5mDjR3ZMlS/Il33WXc5aiRu/eqr16hd6fm6s6YICrya9YEfq4PXtUGzRQPess1dxcvegi1dq1nRMQyJYtqklJqvf1muG+i4yM8riKMvPll6rXXaf6zjuqv/6qzhtr2VL17LPdPXjuOWf4ySerLl8eXqFLl6omJemu4fdolSqqf/lLGHn69tVL632j9euHWbuPEAMHup9+ZqbqhRe6S//qK3X/IVD9/vvQmdescS78uee6e/enP7kC9u3T775T7drVFXHLLWEaM3q0y5CVpaqqrVs7pyZSUIQHE/UHe6xspRaYjAzV6dOPtmsUwdy57stu1Eh19+6AHV995b6KWbNKZ0M58vXXzru+5BLV0Wmv6scn36vr14fZ+jdrlruOP/7Rfc7KUh00yP1ZJk0Kme2aa9x9yceMGa6sqVPzJfv/vYpk9WrVJ55Q/XHOAc2tkuSMCML+/apvvql60bmH9czkOdq35nw9t3+ODhrkWkauukp17FjvgXznnaoiqosW6cyZ7lIffzz4+QcPVj2uziHNIkn1hx8idp3hkpnpWvVc4MC1ZA7sslVf5hbdNObzowdOn+5EtGZN1U8/Lb7gQYNUa9fWt1/Zp+D+L8Vy7bU6vfHVCqqvvVbaKyobGza4r/KBB9znfftUO3RQrVUzR5dW7ex+4MXxyivuZr70kmqvXrq9+/k6fLhLatrU1UHDboF/6SWX0WuKu/JK12obKUxgIikwYZCV5dpQk5JU09Lcj9F/Bquq6vz57qsI508YQXJy3B+jYUPVk9vkqJCT9xCpV0+1Rw/Vvn1dDe2qq1R/+1vVESOcV3H33ar33qt6f6cv9M/yqI66ZYuOPXucfssZuu2pNwv9ObZscZrz2GPux9+2bQFjVqxwJ3777XzJDz2kgZWziJOb6yrl/n04kf/qyCFrdMECty8724nydde5SiioHn+86jnttulZfKO909Zrz56uFpqW5vZXq5qjl8sH+u9zX9FDh5xT1LSp6sGDwW34+GPv58FFquPGVcyFF8GYMc6ezz5zdYq771Y9ofrGvHt0/PGqp5/uHmx3Dt+r/9fsGZ3A1bri3tdDPiRzPv9Sv+I8vbLTCk1JcfejQKguOCNHam5SsnbsmKsdOpQ+DFoW/PjImjVH09atU22cukdbsUa3Lyy+8ul7voeq19MX5Tatk3pIk5Pdvd23r4QGvf++M+jHH1XVVY6gsHdcXpjARFBgsrJcbf/JJ13lsmBcc+1a1TPOcHf6mmtc7fW3v3UxvF9+8Q765Rd3QJQfHn5L3TvvqOpXX+kBjtE5z87Tl15Svekm1wxw5pkuLn3SSa5Zq04d1/p1zDEuQJ2SkqtJZOU9bPytVi3V7t1VzzvP9YcI3NeqlfsT5OPXX93Ov/89X/Lf/uaSDxyomHvy5ZfufI88ovrP8/6tA2SyJiXl5tnti0bt2u4ezZwZ8BsYNky1ShXVOXNU1T1Dvpubq39o8aE2lq0KR2PiY8aEtuHIEdXGjXP1kiqfqv7v/0b6kovkyBHXEta9e8DDfOlSzQVdcscYffRR9zs/5xz3G/FF198aVN+vgwfn6pNPqs6e7R7Kj4zO1pYp6V5FJlfvvNN5jWHxwguqoK//fY+C6rRpkbry4OTkuPvRv3+BHQsX6necptWSjuiZZ7rKQzDxS09Xffdd1TvuUO3RKVOTOaKg2q/LLl26tJRG+S7xRx+pauQD/SYwERSY9etd7TvvD9RA9eqrVV991W21arntX/86mmfTJvdAvvJKL2HPHi1JHCcSZGW5B0L79t4DcuRI1+Zfmif5+PGaRZKuuvAO/fLzHH32WdXbbnPi0q2be+4+84xrBduzJ0QZubnu6TtkSL7kZ591t6pQHCsC5OSoduzomu8yM9W5IX366M6drqnrootc89W774Zo/9+71z19Wrc+Wg395BNV0Oyn/6FTprjKxhVXFO+R3XuvahJZuuWM/ynvyywRr7+ued5LHr/7natd7NhR6PjcXFdX+HFRtr7aZ7xez1g9sdbWQhWQfnytE+76vuQhJu9+Hv52vjZo4Cp7FYn/8H7nnYDE3FynsPXr68SxB/OuUUQ1NdVVRho1ci0F/r7UVNfRbOSly3XyySM092AZAkoHD7qAUNu2qhkZunu3FtkEW1ZMYCIoMD6bNrn29+uuc91o/R/OGWc4L6Ygfhxu1ix1T7Kwo5qRwX9weJUeF8g+/fTSFZabq/rdd2Xvdn3vvc6o8ePzkvym6k2bylZ0OLz1VsDDY/du94QYPbpkhXz7rftuhw938boTTnB//HC6MQfgtxg+ecyo6LQDqTO5dWtXScgzYc8eV1v67W+LL8APYINuvfhG/fDdI/r3Rw7q6jrdXNtraa5r4UJ3YyZO1D//2X1FeS0DFcDQoap167rOO3l8/rmz6fnnVdX1aH/kEff3vuce1435lltUb7zRVZi+/77EP4fi8W3wfq+RDPSbwFSAwASSm+s6xHzxReia6YEDrp35tNO8/1X9+qq//3252VASMjJc0LZHD8+W/ftdwOjPf46KPXlkZbmaYGpqXnD7jTfcrzbSD5GMDOd8dOnieXQffeROPHNmyQvzHqo6aJCWpa3ijNab9GSWa276xlLlLyv+vf/kk4DEp592iYsWhV/Q3//u8px3nvN+RErfeeHAAVeja9xYN05bqcnJFdeKuHu3c9xGjAhIzMpyvWTatImAapSQoUNdD4wlSyIa6DeBqWCBCZexY903MGGCuuaX444L2ZU3knjN2K5bpaqLvoPqlCkVbkshtm1zwZ5WrVR37aqwHt3/+EeBe/KHP7gmu9J0E87MPNrXtAxtOGNHrlRQnf54ON2rypesLOd8dekS4GhkZ7uq8RlnlLzAN95wlRhQveGGshm3bJlq48aqjRrpkPN/1Vq1ShEYLwXPPx9EW30XO68pIIps3+4qrr166ROP5UQs0G8CE6MCk53tBrYff7zq4QVL3IO0Vq0KjVQePOh0rU+fgAfHffe5mk+obk0Vzdy5zp7zz9cP388pcYW5pOzd62Jp/foF3JP27V2Nu7SsWKF6+eXB20vDZP/GX7UxW7RWlf36wfMV0EYYwPjxQZ6bn33mEt99t3SFfvKJu6eBI9tLy/Llqscdp3Prnh/YOhVRunRxWx779rngije2KSZ4801V0Cm3fRSxOqMJTIwKjKob5gFeL6oNG1TbtXNdzCZOrJDzP/mkFm75Oe00b36SGOLll1VBv7zmX+GPkSglDz7o7kne2Lht2zSiUdISsP6fk7Rn0gIF1bv6L9YjmZF/kGVluRafTp0KPDcHDHCVomg3BfmsWKHapIn2TF6gbY7PKNVMNeGyaJH7Sbzwgpewdavr3QOq8+ZF7sQlJTdXdeBA3X1Ms4j9hBNWYIBBwEpgNTCyqGOjJTCqrjdSrVruOaa7d7saELiuVhFk717nQQ8cGJC4b59ruohih4Og5OaqDhumU+nnmommR+Y0W7e6rrX5evg98ID7Pr77LjInLSEZazbpiOYfuU4kdZfoxh+2R/R8fmeHDz4ISPR7HTzySETPXWJWrtS36/xeQfWLF9dG7DS33ebiL7vX7XW/j2OPjc3/jarzmo89VltX36xXXFH+FZKiBEbc/vhDRJKA/wLnARuB+cBQVV0W7Pju3bvrgigtYL1ixdGF6zp0gJNaZ9NmznhO+vE9Thh+DlVuvIHDqXU5nJ3C4cNu7q6MDDddUcGtalU3G37Tpm7S4gYN3BRZwXj4YTeF2PzpB+he9Se3cNg338A777gZcfv3r9gbURyHDzOn062csWoct3WeTYeOQnLz40hu3pTkGqmkpLg1VKpXz/+aknJ0mrjA6eKqV4caNdyrnz5iBLzyirJ89Hu0mfWGWxY6O9tN6jRvXoEpn6NIbi4Tb/iKG8efxTFymPH3r6TzkFPIqVmHbJLJyXFzWOXmHn31N381AZHCW5Uq+TcRGDwYqqbksvj5WVSZNRO+/dbNKJ2ZCenpR1dRjBGOLF1Fy441OVVW8vLZE0nq2I7kTu1I6tKR5Mb1qV07vDm9VN3aZ6puDrzq1d2rKjRtqlzQegVvrz/LTUJ21VXwyCNupddY5B//4Ko7mzC33oW88uaxVK3qlr6oWtVttWrBiSeWrmgRWaiq3YPuDKU8lX0DegNfBXy+H7g/1PHR9GBU3eDboUNdF9CaNbXQOIHSbilyRFukbtW2NdZp2xrrtF2Ntdq+xhrtUGONplbJ0EurT8qfwZ8TKV+/y9hh/bfrtapkltv9AVUhR2smHdAm1XZqEln6O1xznLZu7fqVzp1bupmBK4Bln/xX21ZbXa73I9j27+Srj35o31711ltL16OugvjrXTtDXkuSZGvXmiv11rRPdHy7v+nKM36ruQMG6uHzLtZvT/ujPnHS63pxw7laP+XXIu/JVM5xzYQLFkT7cosnO1v/cfzfQ17LacetLXXRJKgHcwUwSFVv9D7/P+A0VR0RcMzNwM0ALVq06LbeW6o12qi6yVD/u1JZ89lS2L6d6hl7qH54N9UP7SL1wE5SD+6imhyhqmRRVY5QVbKpKllk5KSwOashm440ZFNWIzYfacCmrEYczq2GIt4GipAsuTx+5hec2Luhc6Hat4cWLUK7PDHCoUNwcF8O2b+sJ3v5KrJXrCZ75S8c2bSDw9kpHMqpxqGcahzOqcqh3FSy1M3erHrUfVGEDK3Ggdzq7M85lgO51TmQcwy5VZJ44tqlNLzufOdOlnaG7Ark4O5M3nvgJzJ3HiDp8AGSD+0j6aDbqmQcIokcksihCrne5lwYVQn4TYh71EgVchFySfKOFmqmZnPxoCykz1lu1vDAhdZilIwM+OIL95pz4DDZazaQs2Y92WvT2bgthXn7T+X7A23Zn3MsAHWS9nEoN5Uj6lY3Oyl1PWfU+InTjl1CapUjHNZqZORWI0OrcVirUbdmDn947kSqnNsvmpdZInLXp7Ps4fc5/GsmRw5mceRQNpkHszlyOIdaLevR98t7S1VuUR5MPAvMlcDAAgLTU1VvD3Z8NJvIDMOoeHJyXPP0d9/B/PmumeiMM9zq1f7S9kbxFCUwMdKgHBE2As0DPqcBm6Nki2EYMUZSErRr57bhw6NtTXwS220hZWM+0EZEWolIVWAI8GmUbTIMw0gY4taDUdVsERkBfAUkAWNVdWmUzTIMw0gY4lZgAFT1S+DLaNthGIaRiMRzE5lhGIYRRUxgDMMwjIhgAmMYhmFEBBMYwzAMIyKYwBiGYRgRIW5H8pcUEdkBlGWumAbAznIyJ1ZJhGuExLhOu8b4IdrXebyqBp37wASmnBCRBaGmS4gXEuEaITGu064xfojl67QmMsMwDCMimMAYhmEYEcEEpvwYE20DKoBEuEZIjOu0a4wfYvY6LQZjGIZhRATzYAzDMIyIYAJjGIZhRAQTmDIiIoNEZKWIrBaRkdG2p7wQkbEisl1ElgSk1RORKSKyynutG00by4qINBeR6SKyXESWisgdXnrcXKeIpIrI9yLyo3eND3npcXONPiKSJCI/iMjn3ud4vMZ1IvKziCwWkQVeWsxepwlMGRCRJOBF4HygLTBURNpG16pyYxwwqEDaSGCqqrYBpnqfKzPZwB9V9VSgF3Cb9/3F03VmAv1UtRPQGRgkIr2Ir2v0uQNYHvA5Hq8R4BxV7Rww9iVmr9MEpmz0BFar6hpVPQJMBC6Jsk3lgqrOBHYXSL4EGO+9Hw9cWpE2lTequkVVF3nv9+MeTs2Io+tUxwHvY4q3KXF0jQAikgZcCLwWkBxX11gEMXudJjBloxmQHvB5o5cWrzRW1S3gHs5AoyjbU26ISEugCzCPOLtOr+loMbAdmKKqcXeNwLPAvUBuQFq8XSO4ysFkEVkoIjd7aTF7nXG9omUFIEHSrN93JUNEagAfAHeq6j6RYF9r5UVVc4DOIlIH+EhE2kfZpHJFRC4CtqvqQhHpG2VzIs0ZqrpZRBoBU0RkRbQNKgrzYMrGRqB5wOc0YHOUbKkItolIEwDvdXuU7SkzIpKCE5e3VfVDLznurhNAVX8FZuBia/F0jWcAg0VkHa6Zup+I/Iv4ukYAVHWz97od+AjXTB+z12kCUzbmA21EpJWIVAWGAJ9G2aZI8ikwzHs/DPgkiraUGXGuyuvAclV9OmBX3FyniDT0PBdEpDpwLrCCOLpGVb1fVdNUtSXuPzhNVa8ljq4RQESOFZGa/ntgALCEGL5OG8lfRkTkAlz7bxIwVlUfja5F5YOITAD64qYC3waMAj4G3gNaABuAK1W1YEeASoOInAl8C/zM0bb7P+HiMHFxnSLSERf4TcJVKN9T1YdFpD5xco2BeE1kd6vqRfF2jSLSGue1gAtvvKOqj8bydZrAGIZhGBHBmsgMwzCMiGACYxiGYUQEExjDMAwjIpjAGIZhGBHBBMYwDMOICCYwhhEFRKS+NyPuYhHZKiKbvPcHROSlaNtnGOWBdVM2jCgjIqOBA6r6VLRtMYzyxDwYw4ghRKRvwHomo0VkvIhM9tYBuVxEnvTWA/mPN80NItJNRL7xJkD8yp82xDCijQmMYcQ2J+Cmob8E+BcwXVU7AIeBCz2ReR64QlW7AWOBuJhNwqj82GzKhhHbTFLVLBH5GTfdy3+89J+BlsDJQHvczLp4x2yJgp2GUQgTGMOIbTIBVDVXRLL0aNA0F/f/FWCpqvaOloGGEQprIjOMys1KoKGI9Aa3/ICItIuyTYYBmMAYRqXGW6r7CuBvIvIjsBg4PapGGYaHdVM2DMMwIoJ5MIZhGEZEMIExDMMwIoIJjGEYhhERTGAMwzCMiGACYxiGYUQEExjDMAwjIpjAGIZhGBHh/wPP3P7fAYXPhwAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -5739,7 +5593,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 101646.89563385594.\n" + "The test root mean squared error is 46821.8670281312.\n" ] } ], @@ -5750,12 +5604,42 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 28662.783116787527.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, "metadata": {}, "outputs": [ { "data": { - "image/png": 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5N75+ulaOt1NmxuWn9KOopILbXlgR6+qISAeiwNGOfWvSEDJSElm1vTjWVRGRDkSBox1LTfLxpVP6sWVvCcu37o91dUSkg1DgaOfOO74HAH+bvzm2FRGRDkOBo507a3gO55/Qg9krdpBXcDDW1RGRDkCBIw5cfop/reTzuVoQKCLRp8ARBy4a1ROARz/cRJX2rxKRKFPgiANmxm0XjQDg421Fsa2MiMQ9BY44ceXEgST5jJeXa7hKRKJLgSNOZKUlceHIXsxdWxDrqohInFPgiCNj+2exo7iUvIJDsa6KiMQxBY44Mu0k/1lXb36yM8Y1EZF4psARR3pmpjKwWzor87UFiYhEjwJHnDlrWA4fbCiktKIq1lURkTilwBFnzhjajbLKatbuPBDrqohInFLgiDNj+mUDaLhKRKImrMBhZt83s0zviNe/mtkyM7sw2pWTyPXOSiUnI4UVWggoIlESbo/jG865A8CFQA5wHXB/1Golx8zMGNsvixX5RbGuiojEqXADh3lfpwB/c86tCEiTNmZsv2w+LTzMgdKKWFdFROJQuIFjqZm9jT9wvGVmGUB19KolzTGmfzYAn2ieQ0SiINzA8U3gDmCCc64ESMI/XCVt0Ji+WQB8rOEqEYmCcAPH6cB651yRmV0J3A3oz9k2qkunZP9CwG36JxKRlhdu4PgzUGJmY4HbgS3AU1GrlTTbmH7ZmiAXkagIN3BUOuccMA34g3PuD0BG9KolzTW2XxY7i0spOFAa66qISJwJN3AcNLM7gauA18zMh3+eQ9qoUwZ2AWDJ5v0xromIxJtwA8dXgTL86zl2AX2B30StVtJsJ/bNIj3Zx6LP9sa6KiISZ8IKHF6w+BeQZWafB0qdc5rjaMOSfAmcMrALizbti3VVRCTOhLvlyFeAxcCXga8Ai8zs8jDKXWxm680sz8zuCHLdzGymd32lmY0Lp6yZfc+7ttrMfh1OGzqi0wZ3Zf3ug+w7XB7rqohIHEkMM99d+NdwFACYWQ7wDvBCYwW8eZCHgQuAfGCJmc12zq0JyHYJMMx7nIb/7q3TQpU1s3PxT9KPcc6VmVmP8JvbsZw2pBsASzbv46JRvWJcGxGJF+HOcSTUBA3P3jDKngrkOec2OefKgWfw/8IPNA14yvktBLLNrHcTZf8XuN85VwZQr14SYEy/LFISE1i4SfMcItJywg0cb5rZW2Z2rZldC7wGvN5Emb7AtoDX+V5aOHlClR0OTDKzRWb2gZlNCPbhZna9meWaWW5hYWETVY1PKYk+zhjanTc/2UVVtYt1dUQkToQ7OX4b8BgwBhgLPOac+2ETxYJtglj/t1djeUKVTQS6ABOB24DnzKxBfufcY8658c658Tk5OU1UNX59aVw/dhaXMj9vT6yrIiJxItw5DpxzLwIvRvDe+UD/gNf9gB1h5kkOUTYfeMlbkLjYzKqB7kDH7FY04fyRPchOT+L5pfmcNbzjBlARaTkhexxmdtDMDgR5HDSzps4mXQIMM7PBZpYMXAHMrpdnNnC1d3fVRKDYObezibL/Bs7z6jccf5DRn9ONSEn0MW1sH95avYuiEt1dJSLNFzJwOOcynHOZQR4ZzrnMJspWAjcCbwFrgeecc6vNbIaZzfCyvQ5sAvKAx4HvhirrlXkCGGJmn+CfNL/G631II7522kAqqqp59MNNsa6KiMQB6wi/c8ePH+9yc3NjXY2YuvmZ5bzxyS4+uO1cemWlxro6bU55ZTVJPiPIdJlIh2VmS51z4+unh3tXlbRzt144gmrneODt9bGuSptTcLCU4Xe/wd/mb451VUTaBQWODqJ/13S+eeYQXliaz391h1Ud2/YdAWD2ivr3bohIMAocHcjN5w9jQNd07pm9mooqnfx7lH+4NkGjVCJhUeDoQFKTfNzz+ZHkFRziyf9ujnV12oyatZGa3xAJjwJHBzP5hB6cOyKHB9/ZSMFBHfIEUHN/iHocIuFR4OhgzIx7LhtFeWU197+xLtbVaRPW7/IvSbKgGxaISH0KHB3Q4O6d+Nakwby0bDtLt+i8jh+/4i0RUtwQCYsCRwd143lD6Z2Vyj2vrNYGiB7FDZHwKHB0UOnJifxoygms3nGAWYu3xro6bUKCJsdFwqLA0YF9fkxvJg7pygNvr2e/TglEcUMkPAocHZiZce/UEzlYWqkV5ajHIRIuBY4ObkSvDK4+fSBPL97KqvziWFcnphQ3RMKjwCHcfP5wundO4fYXV1Je2XFXlGsBoEh4FDiErLQk7vvCiazdeYA/v/8pAIUHyzrcqYEKGyLhUeAQAC4c1YupY/vw0HsbWbfrAFc8toCv/2VRh+qBaOW4SHgUOKTWT6eOIistidueX8mnhYcBeGjuxhjXqvVoqEokPAocUqtrp2R+Nu1EVm0/Okk+c25eDGvUutTjEAmPAofUMWV0b6aM7lUnrbikIka1aW2KHCLhUOCQBmZecTLPzzi99vXYn73N4bLKGNaodajHIRIeBQ5pINGXwIRBXeukjfrJW8T7+fSa4hAJjwKHNGrtzy5m0rDuta83FhyKYW2iT9uqi4RHgUMalZbs4+fTTqx9feHvPyR3s7ZhF+noFDgkpEHdO/HhbeeSkZoIwOWPLGBXcXyeHFgd50NxIi1FgUOaNKBbOh/fc2Ht67N+8x5b9h6uk2dlfhFz1+1u7aqFpeBgKZ8WNj3MFm+B486XVuqUR4kKBQ4Jiy/B2PCLS/jJZSOpqKrm7N+8z9/mf1Z7fepD8/nG33NjWMPGnXn/e0z+7QdN5ou386xmLd7GIx98GutqSBxS4JCwJScmcN0Zg3n6WxMBuPfVNTy7ZGubv1W3vCq8bVN0EqJIeBQ4JGKnH9eN+Xecx5h+WfzwxVXc/sLK2mt3vrQyRMm2LcmnHweRcET1J8XMLjaz9WaWZ2Z3BLluZjbTu77SzMZFUPYHZubMrHv9axJ9fbPTeH7G6Zx/Qk9eW7WzNn3W4m3c/e9VMaxZ45pah5JXcLCVaiLSvkUtcJiZD3gYuAQYCUw3s5H1sl0CDPMe1wN/DqesmfUHLgB0WHYMpST6eOTKcVx/1hAyUhJr0/+5cCtf/NN8ikra1nG0pRXBh6w6e3XfvLdEw1UiYYhmj+NUIM85t8k5Vw48A0yrl2ca8JTzWwhkm1nvMMr+Hrgd0E95jCX6EvjRlBNY8KPJddKXby3ipJ/NYUkbWvdRUh58LibwbqrK6o6zjbzIsYpm4OgLbAt4ne+lhZOn0bJmNhXY7pxbEerDzex6M8s1s9zCwsJja4GErXNKIrl3n98g/cuPLOCWZz9uE9uVlJRXBU0P7GWoxyHStGgGjmD7N9T/qWwsT9B0M0sH7gLuaerDnXOPOefGO+fG5+TkNFlZab7unVPY9MspXDGhf530l5dvZ+hdb8R84WBpRfDAEdjjqKhS4BBpSjQDRz4Q+BukH7AjzDyNpR8HDAZWmNlmL32ZmdXdB1xiJiHBuP9LY7j1guF10quqHec88B4/eH4Fs1fsqLMGJJBzjofmbmywwLAlVDbSmwhMjsceR2MBU+RYRTNwLAGGmdlgM0sGrgBm18szG7jau7tqIlDsnNvZWFnn3CrnXA/n3CDn3CD8AWacc25XFNshx+B7k4fxyJWn1EkrrajmhaX53DRrOfe+uoZBd7zGO2t2U1pRVTv/sPtAGQ+8vYFvPdnyiwkbCwqB6ZVhrvloT3795vpYV0HiTGLTWY6Nc67SzG4E3gJ8wBPOudVmNsO7/gjwOjAFyANKgOtClY1WXSU6Lj6xF0vuOp+bZi1nwaa9QfM8+O4Gfv3WOjbsPsS3zhzM2P7ZgH8+ovhIBVlpSS1Wn2CBo/7cS2O9kvZsfxu7u03av6gFDgDn3Ov4g0Ng2iMBzx1wQ7hlg+QZ1PxaSjTlZKQw6/qJXPPEYj7Y0PAmhX2HytnhzX38Zd7R4avtRUcYe+/bbL7/0harS2W1Y1V+MSf2zaw9X7x+MKnUHIdIk7RUVlrFo1edwns/OKdBelPbgbTknMMHGwq57KF5/GPhltq0+j2MCt2OK9IkBQ5pFalJPgZ378T3Jw/j+F4ZtemHmtjnan9JOVP+8NExnwMSOBRVszJ8w+6jK8TrB454nBwXaWkKHNKqbrlgOM9ef/Q888ZWc9dYtGkfa3Ye4FfHuD34fz89OrdSXun/LF/AGbEVlXU//+f/WaPgIdIEBQ5pdVnpSbx205ncfekJTea94ellAHTvnHxMnxV4K+o7awsA/y3DNSrqDZV9tHEPH28rOqbPaik7io4w6I7XeCNgDzCRtkSBQ2JiVJ8spp86gB4ZKWHlz05L5uH38li2dX9En2NBlpImBgaOIL2LpobPoq3mbPdZS7Y1kVMkNqJ6V5VIKJ1SEnnj+5PYc6icix78MGTeZ3OP/hKN5E6r8sqGgaFOj6Oy4VDZA2+tZ9LQ7nXytabURP/fc6WNbJESjmkPz2+p6og0oB6HxFS3zimM6JXBO/93NkN7dG7x9w+2aaHPjKkPzeOLf5rfYKgKYNX2Yubl7WnxuoQryQscR5qx4ntFwHDbkWYEIJFg1OOQNmFoj868ftMkPttzuMneRySCBQZfgrEyv9i7HnwivPhIRYvVIVI1k/MttVXIm6u1sYK0LPU4pM1ITkxgRK8M/jj9ZHpnpTaarzzI8FJjggWGBGt8crxGLI/DralTc3ocItGkwCFtzmVj+7DgzsksuavhNu0Aw+9+gzxvArkpwQJDYCj56avBd7KJ5QR5zer1SAKkSGtS4JA2Kycjhc33X8qSu85n2Y8vYGC39Npr5//ug7DWWwTbQmTmuxtrny/fWhS03C9eW8viz2JzCFXNvExjvSGRWFPgkDYvJyOFrp2SefP7Z3Hv1FG16aN/+hYFB0Of8dGcX75feXTBMZdtjprhtcPlVbz5yc7a1e8/enkV849x0r4tHKQl8UOBQ9qNtGQf13xuEJ/9agqnDu5KSXkVp973LvsON777a3s8mClwqGrGP5cxP28vzjmeXrSVr/9l0TG9Z3v8PkjbpcAh7Y6Z8dx3TueysX0AGPfzOWzdWxI0b3N6HMN6dI7J+Rz161x8pKLZ26DoLHVpSQoc0m79cfrJ/GjK8QBMmfkRv5+zoeH5Gs34xb+x4BB3//uTZtXxWNQPHAnW/HNC4vGcEYkdBQ5p164/6zheueEM+man8Yd3NzJl5jxWeWs0AMrrDdG8efOkBu/xu6+MrX3ep95twM8s2dbqmx7W/yVvZk1uP9/ke2qoSlqQAoe0e2P7Z/Pidz/HzecPY/Oew3zhT/O5+9/+ieSS8so6e1N1ST+6WWL3zilkpiYypl92bdq7t57Dr780ps773/zsx9FuQh31e0lmwbdGac57ijSHVo5LXOicksjN5w/nqokDue+1tTy7ZBv/WrQV52Bk70zW7DxAcmICPTNTmffDc8nff4Sx/bJJTUrAzJhzy1mkJvlIS/bxlQn9WZFfxL8WbQXg1RU7yEhN5JdfHN0qbak/kV1Z5Xh3XUGz3vPTwsP0yGx8UaVIJNTjkLjSrXMKv/vqSaz66UVcc/ogOqckcsmJvXj7lrNYcc+FAPTrks7EId1IS/bVHiE7rGcG/bseXSfy82knsu7nF/Ods4YA8PSirZz9m/da5S/3+hPZ768v4PYXVjbrPac/vrBZ5UUCKXBIXEpN8vHTqaP45N6L+N7kYQzvmUFasi/s8gkJRmqSj29NGsKEQV0A2LK3hAn3vcPGgBMEo+Fgad1V688vza/zWgdNSawpcIiEkJORwp+vPIUhOZ0A2F9SwQW//5Bbnv2YzXsOR+Uz95eU0zml8VHkRZv2NnotlMKDZcdaJZE6FDhEmtC9cwpzbz2H1fdexDWnD6RnZgovL9/OOQ+8z7V/W8ysxVtbdG+r/SUV9MhIYcroXkGvHyitDNnraOzahPveoaQ8todUSXxQ4BAJU6eURO6ddiIL7pjMqzeeyfRT+/PxtiLufGkV4342h6ufWMw/Fmwmf3/wxYjhKiopJzs9iYe/Ni7o9Rn/XMr0xxqfswi1Hfv2/UeaVTcR0F1VIhFLSDBG98viV/3G8MsvjiZ3y37eXr2Ld9YW8ONXVsMrqxmS04lJQ7tzxtDuTDyuG5mpSWG///7DFfTOSsXMeP2mSUyZ+VGDPIs37+NQWWXQIa1Q27HvKC5lWM+MsOsiEowCh0gzmBkTBnVlwqCu3HXpSD4tPMTctQXMy9vDs7nbeHLBFnwJxth+WZw5LIczh3bn5AHZJPka7+wXlZRzQu9MAEb2yWw03xPzPuOiUb0YktOpzvsFO/Fv8Y8mM/FX7/Kn9/I4c2h3fDE6FlfigwKHSAs6Lqczx+V05ttnDaGssoplW4qYn7eHeXl7eGjuRma+u5FOyT5OG9KNM4d258xh3RnWo3PtbcEARUcq6JJ+tIfy4W3nct3fF/NpYd3J+N/N2cDv5mzgO2cP4c5LTqhN3xZkqKxHZiqTT+jJnDW7mfKHj3jrlrOi0HrpKBQ4RKIkJdHH6cd14/TjuvGDi0ZQXFLBgk17mZdXyPy8vcz1FvX1yEhh0rAczhmRU7vrb3ZA4BjQLZ23bj6L11btZOrYPlz04Ids2H30IKtHP9jE7uJSHrziZAC+9vjRHXR/+cXRnHd8DwDOOK4bc9bsZv3ugzwx7zO+cebg1vg2SByyaO7Tb2YXA38AfMBfnHP317tu3vUpQAlwrXNuWaiyZvYb4DKgHPgUuM45VxSqHuPHj3e5ubkt2DKR5svfX8L8vD18tNH/CDzn/PGrx3PByJ5Byx0pr+KeVz5psL4DYMVPLmTsvW/Xvp5zy1m1cxo7io7wufvn1l4bktOJl//3DLLSw59/kY7FzJY658Y3SI9W4DAzH7ABuADIB5YA051zawLyTAG+hz9wnAb8wTl3WqiyZnYhMNc5V2lm/w/AOffDUHVR4JC2rqra8fG2Ij7YUEh6so/rJw0hoYl5iB1FR1jw6V5mLd5K7pb9Da5PHduHmdNPrpNWeLCMCfe9Uyft+RmnM2FQ1+Y3QuJOY4EjmrfjngrkOec2OefKgWeAafXyTAOecn4LgWwz6x2qrHPubedczc3oC4F+UWyDSKvwJRinDOzC/10wnBlnH9dk0ADok53Gl07pxwv/+zleueGMBtevPn1gg7ScjBQ+uv1cRvY+Oun+5UcWMP2xhby/vkAnBUpYojnH0RfYFvA6H3+voqk8fcMsC/AN4NlgH25m1wPXAwwYMCCSeou0O2P7Z5N33yXMWrKNfyzYzC3nD2d8I72I/l3Tef37kyivrObpRVv47ZwNLNi0lwWb9tI7K5WvTujP1LF9GJLTuZVbIe1FNANHsD+Z6v8501ieJsua2V1AJfCvYB/unHsMeAz8Q1VNVVakvUv0JXDVxIFcNbFhTyOY5MQErj1jMNeeMZite0v4+38385+VO3jwnY08+M5GRvTMYNKw7pwzogfjB3UhNSn8vb4kvkUzcOQD/QNe9wN2hJknOVRZM7sG+Dww2alvLdJsA7qlc89lI7nnspHkFRxi7rrdfLhhD08t2MJf5n1Gsi+Bk/pnM3FIV04b0o2T+mfTKcR+WhLfojk5noh/gnsysB3/BPfXnHOrA/JcCtzI0cnxmc65U0OV9e62+h1wtnOuMJy6aHJc5NgcLqtk0Wd7WbRpHws37WXV9mKqnf842xN6Z3LKwC61j77ZaXXWo0j719jkeNT+ZPDueroReAv/LbVPeL/4Z3jXHwFexx808vDfjntdqLLeWz8EpABzvP+kC51zM6LVDpGOrFNKIucd35PzjvffGnywtIKlW/azbGsRS7fs44Wl+Ty1YAsAPTNTOGVgF8YN8AeSUX2ySE7UdnjxKKrrONoK9ThEoqOyqpr1uw+ybMt+crfsZ+mW/eR7GykmJyZwYp9Mxg3owskDujBuYDa9s9JiXGOJRKuv42hLFDhEWs/uA6Us27KfZVv3s3xrESu3F1PunZneOyuVkwdke8Ekm1F9sjTp3oa1+lCViHRMPTNTuWR0by4Z3RuA8spq1uw8wPKt/iGuZVv28/qqXQAk+YxRfbLqBBPNlbR96nGISKsrOFDKsq1FLN+2n+Vbili5vYjSCn+vpEdGSm0QGTewC6P7qlcSK+pxiEib0SMzlYtP7MXFJ/pPOayoqmbdzoPe8Ja/Z/Lman+vJDHBGNknk9F9sxjWozNDe2QwtEdnemamqGcSI+pxiEibtOdQGcu3FtUGk9XbD3Aw4IjejJREhvTozNCczgzomk7/rmn06+L/2jMjNaxtW1pLcUkFlkBEB3q1BepxiEi70r1zCheM7Fm7S7BzjsKDZeQVHCKv8JD/a8Eh5uUVsvtAWZ2yST6jb3Ya/bum0zsrlV6ZqfSs+ZqZSq+sVLqmJ7dKcKmudoz92dsc3yuDN2+Oj3NQFDhEpF0wM3pkptIjM5XPDe1e51ppRRU7io6wbf8Rtu0rYdv+EvL3HyF/Xwnrdh1kz6Ey6g+uJPmMHhmp9MxMoVeWF1C8oFLztWdmarPnV/IK/WenrNt1kPfWFXCudz5Ke6bAISLtXmqSjyE5nRvdmLGyqprCQ2XsKi5l94FSdhWXsutAGQUHStl1oJR1uw7ywfpCDgc5drdLehK9stLolZnifU2ld5a/B9Pbe2SEGIL6eFtR7fPr/r6E9b+4GJ8Zh8oq+bTwMP27pNEjM7XZ34PWpMAhInEv0ZdA76y0JhcgHiytYPeBUnYWe8Gl2B9Yar6uzC9m7+HyBuW6pCcxoFsnBnZNZ2C3dAZ09T8GduvE8q1FdfKOuPvNBuXPHp7D/V8a3W4WSGpyXEQkAmWVVRQcKPMHlwOlbN9/hK37Sti67zBb9pawo+gI1fV+raYmJdTebhzKb788lvOO70Fqko+05NjfgqzJcRGRFpCS6KN/13T6d00Per28sprtRV4w2XuY4iMVTD91APM/3ctnhYd5deUOBnfvxJLN+5hx9nEkJhi/eG0tALc+vwLwH+w1tl8Ww3tmMKBbOiN6ZtC1UzIn9M5sE2ta1OMQEWkDdh8oZdFn/l2I9x8uZ0fREVbkFzfI1yMjhd7ZafTxJu9zMlLI6ZxC94xkcjqn0j0jmW6dUlpkg0ntVaXAISLtTEl5Jdv2HWF7UQkFB8ooOFjG5j2HKTxUVjsPcyhgbUug7PQkunVK5r4vjmbikG7H9PkaqhIRaWfSkxMZ0SuDEb0yGs1TWlFF4cEy9hwq876W1z7fd7icrLSWX3SowCEi0o6lJoWec4kGnbIiIiIRUeAQEZGIKHCIiEhEFDhERCQiChwiIhIRBQ4REYmIAoeIiEREgUNERCLSIbYcMbNCYMsxFu8O7GnB6rQHanPHoDZ3DM1p80DnXE79xA4ROJrDzHKD7dUSz9TmjkFt7hii0WYNVYmISEQUOEREJCIKHE17LNYViAG1uWNQmzuGFm+z5jhERCQi6nGIiEhEFDhERCQiChwhmNnFZrbezPLM7I5Y16clmFl/M3vPzNaa2Woz+76X3tXM5pjZRu9rl4Ayd3rfg/VmdlHsat88ZuYzs+Vm9h/vdVy32cyyzewFM1vn/Xuf3gHafIv3//oTM5tlZqnx1mYze8LMCszsk4C0iNtoZqeY2Srv2kwzs7Ar4ZzTI8gD8AGfAkOAZGAFMDLW9WqBdvUGxnnPM4ANwEjg18AdXvodwP/zno/02p4CDPa+J75Yt+MY2/5/wNPAf7zXcd1m4EngW97zZCA7ntsM9AU+A9K8188B18Zbm4GzgHHAJwFpEbcRWAycDhjwBnBJuHVQj6NxpwJ5zrlNzrly4BlgWozr1GzOuZ3OuWXe84PAWvw/cNPw/6LB+/oF7/k04BnnXJlz7jMgD//3pl0xs37ApcBfApLjts1mlon/F8xfAZxz5c65IuK4zZ5EIM3MEoF0YAdx1mbn3IfAvnrJEbXRzHoDmc65Bc4fRZ4KKNMkBY7G9QW2BbzO99LihpkNAk4GFgE9nXM7wR9cgB5etnj5PjwI3A5UB6TFc5uHAIXA37zhub+YWSfiuM3Oue3AA8BWYCdQ7Jx7mzhuc4BI29jXe14/PSwKHI0LNt4XN/cum1ln4EXgZufcgVBZg6S1q++DmX0eKHDOLQ23SJC0dtVm/H95jwP+7Jw7GTiMfwijMe2+zd64/jT8QzJ9gE5mdmWoIkHS2lWbw9BYG5vVdgWOxuUD/QNe98Pf7W33zCwJf9D4l3PuJS95t9d9xfta4KXHw/fhDGCqmW3GP+R4npn9k/hucz6Q75xb5L1+AX8giec2nw985pwrdM5VAC8BnyO+21wj0jbme8/rp4dFgaNxS4BhZjbYzJKBK4DZMa5Ts3l3TvwVWOuc+13ApdnANd7za4BXAtKvMLMUMxsMDMM/qdZuOOfudM71c84Nwv/vONc5dyXx3eZdwDYzG+ElTQbWEMdtxj9ENdHM0r3/55Pxz+HFc5trRNRGbzjroJlN9L5XVweUaVqs7xBoyw9gCv67jj4F7op1fVqoTWfi75KuBD72HlOAbsC7wEbva9eAMnd534P1RHDnRVt8AOdw9K6quG4zcBKQ6/1b/xvo0gHafC+wDvgE+Af+u4niqs3ALPxzOBX4ew7fPJY2AuO979OnwEN4O4mE89CWIyIiEhENVYmISEQUOEREJCIKHCIiEhEFDhERiYgCh4iIRESBQ6SNM7Nzanb0FWkLFDhERCQiChwiLcTMrjSzxWb2sZk96p3/ccjMfmtmy8zsXTPL8fKeZGYLzWylmb1cc36CmQ01s3fMbIVX5jjv7TsHnK3xr4jOThBpYQocIi3AzE4Avgqc4Zw7CagCvg50ApY558YBHwA/8Yo8BfzQOTcGWBWQ/i/gYefcWPz7LO300k8GbsZ/vsIQ/PtvicREYqwrIBInJgOnAEu8zkAa/o3mqoFnvTz/BF4ysywg2zn3gZf+JPC8mWUAfZ1zLwM450oBvPdb7JzL915/DAwC5kW9VSJBKHCItAwDnnTO3Vkn0ezH9fKF2uMn1PBTWcDzKvSzKzGkoSqRlvEucLmZ9YDaM6AH4v8Zu9zL8zVgnnOuGNhvZpO89KuAD5z/XJR8M/uC9x4pZpbemo0QCYf+ahFpAc65NWZ2N/C2mSXg37n0BvwHKI0ys6VAMf55EPBvff2IFxg2Add56VcBj5rZz7z3+HIrNkMkLNodVySKzOyQc65zrOsh0pI0VCUiIhFRj0NERCKiHoeIiEREgUNERCKiwCEiIhFR4BARkYgocIiISET+P+YUl26p/E9kAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -5772,7 +5656,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -5780,10 +5664,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 871577\n", - "1 259078\n", - "2 325744\n", - "3 475728\n", + "0 431686\n", + "1 313752\n", + "2 294203\n", + "3 355503\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5801,7 +5685,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -5820,14 +5704,14 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)" ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -5844,14 +5728,14 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 199400.80158126247.\n" + "The test root mean squared error is 104803.47114719055.\n" ] } ], @@ -5859,153 +5743,12 @@ "return_rmse(actual, preds)" ] }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "# def create_train_test(king_all):\n", - "# king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", - "# king_training = king_all[king_training_parse]\n", - "# king_training = king_training.reset_index()\n", - "# king_training = king_training.drop('index', axis=1)\n", - " \n", - "# king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", - "# king_test = king_all[king_test_parse]\n", - "# king_test = king_test.reset_index()\n", - "# king_test = king_test.drop('index', axis=1)\n", - "# print(king_test.shape)\n", - " \n", - "# # Normalizing Data\n", - "# king_training[king_training[\"king\"] < 0] = 0 \n", - "# # print('max val king_train:')\n", - "# print(max(king_training['king']))\n", - "# king_test[king_test[\"king\"] < 0] = 0\n", - "# # print('max val king_test:')\n", - "# print(max(king_test['king']))\n", - "# king_train_pre = king_training[\"king\"].to_frame()\n", - "# # print(king_train_norm)\n", - "# king_test_pre = king_test[\"king\"].to_frame()\n", - "# scaler = MinMaxScaler(feature_range=(0, 1))\n", - "# king_train_norm = scaler.fit_transform(king_train_pre)\n", - "# king_test_norm = scaler.fit_transform(king_test_pre)\n", - "# print('king_test_norm')\n", - "# print(king_test_norm.shape)\n", - "# print('king_train_norm')\n", - "# print(king_train_norm.shape)\n", - "# #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - "# #print(type(king_train_norm))\n", - "# #king_train_norm = king_train_norm.to_frame()\n", - "# x_train = []\n", - "# y_train = []\n", - "# x_test = []\n", - "# y_test = []\n", - "# y_test_not_norm = []\n", - "# y_train_not_norm = []\n", - " \n", - "# # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", - "# for i in range(6,924): # 30\n", - "# x_train.append(king_train_norm[i-6:i])\n", - "# y_train.append(king_train_norm[i])\n", - "# for i in range(6, 60):\n", - "# x_test.append(king_test_norm[i-6:i])\n", - "# y_test.append(king_test_norm[i])\n", - " \n", - "# # make y_test_not_norm\n", - "# for i in range(6, 60):\n", - "# y_test_not_norm.append(king_test['king'][i])\n", - "# for i in range(6,924): # 30\n", - "# y_train_not_norm.append(king_training['king'][i])\n", - " \n", - "# return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - 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"\u001b[1;31mNameError\u001b[0m: name 'create_train_test' is not defined" - ] - } - ], - "source": [ - "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", - "x_train = np.array(x_train)\n", - "x_test = np.array(x_test)\n", - "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", - "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", - "y_train = np.array(y_train)\n", - "y_test = np.array(y_test)\n", - "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", - "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", - "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# def load_pdo(pathname):\n", - "# pdo_data = pd.read_csv(pathname)\n", - "# # print(pdo_data.head())\n", - "# return pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_pdo(ismael_path_pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# data_copy = data_copy.join(pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(data_copy)" - ] + "source": [] } ], "metadata": { @@ -6024,7 +5767,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/multivar_robust_lstm.ipynb b/multivar_robust_lstm.ipynb new file mode 100644 index 0000000..3f64bf4 --- /dev/null +++ b/multivar_robust_lstm.ipynb @@ -0,0 +1,3906 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1939-01-01\n", + "1 1939-01-02\n", + "2 1939-01-03\n", + "3 1939-01-04\n", + "4 1939-01-05\n", + " ... \n", + "24364 2020-12-25\n", + "24365 2020-12-26\n", + "24366 2020-12-27\n", + "24367 2020-12-28\n", + "24368 2020-12-29\n", + "Name: date, Length: 24369, dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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dateking
1321950-01-310
1331950-02-280
1341950-03-3121
1351950-04-306630
1361950-05-3150638
.........
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852 rows × 2 columns

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Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearmonthupwellingnoinpgopdooni
019501-162.644-2.190-1.61-1.40
119502-1662.077-1.450-2.17-1.20
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319504-41.923-0.860-1.99-1.20
419505492.211-0.630-3.19-1.10
........................
8472020843-0.463-1.422-1.32-0.57
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849202010101.612-1.476-0.62-1.17
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851202012-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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datekingupwelling
01950-01-310-16
11950-02-280-166
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............
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852 rows × 3 columns

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datekingupwellingnoi
01950-01-310-162.644
11950-02-280-1662.077
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31950-04-306630-41.923
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...............
8472020-08-3110526943-0.463
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852 rows × 4 columns

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" + ], + "text/plain": [ + " date king upwelling noi\n", + "0 1950-01-31 0 -16 2.644\n", + "1 1950-02-28 0 -166 2.077\n", + "2 1950-03-31 21 -49 3.091\n", + "3 1950-04-30 6630 -4 1.923\n", + "4 1950-05-31 50638 49 2.211\n", + ".. ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463\n", + "848 2020-09-30 254930 -1 -0.276\n", + "849 2020-10-31 30917 10 1.612\n", + "850 2020-11-30 843 -43 1.998\n", + "851 2020-12-31 9 -97 5.098\n", + "\n", + "[852 rows x 4 columns]" + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noi = cov_data[\"noi\"]\n", + "master_data = master_data.join(noi)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgo
01950-01-310-162.644-2.190
11950-02-280-1662.077-1.450
21950-03-3121-493.091-0.970
31950-04-306630-41.923-0.860
41950-05-3150638492.211-0.630
..................
8472020-08-3110526943-0.463-1.422
8482020-09-30254930-1-0.276-1.161
8492020-10-3130917101.612-1.476
8502020-11-30843-431.998-1.710
8512020-12-319-975.098-1.870
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852 rows × 5 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo\n", + "0 1950-01-31 0 -16 2.644 -2.190\n", + "1 1950-02-28 0 -166 2.077 -1.450\n", + "2 1950-03-31 21 -49 3.091 -0.970\n", + "3 1950-04-30 6630 -4 1.923 -0.860\n", + "4 1950-05-31 50638 49 2.211 -0.630\n", + ".. ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422\n", + "848 2020-09-30 254930 -1 -0.276 -1.161\n", + "849 2020-10-31 30917 10 1.612 -1.476\n", + "850 2020-11-30 843 -43 1.998 -1.710\n", + "851 2020-12-31 9 -97 5.098 -1.870\n", + "\n", + "[852 rows x 5 columns]" + ] + }, + "execution_count": 314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npgo = cov_data[\"npgo\"]\n", + "master_data = master_data.join(npgo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdo
01950-01-310-162.644-2.190-1.61
11950-02-280-1662.077-1.450-2.17
21950-03-3121-493.091-0.970-1.89
31950-04-306630-41.923-0.860-1.99
41950-05-3150638492.211-0.630-3.19
.....................
8472020-08-3110526943-0.463-1.422-1.32
8482020-09-30254930-1-0.276-1.161-1.03
8492020-10-3130917101.612-1.476-0.62
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852 rows × 6 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19\n", + ".. ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni \n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 316, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oni = cov_data[\"oni \"]\n", + "master_data = master_data.join(oni)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = master_data.rename(columns={\"oni \": \"oni\"})\n", + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kingupwellingnoinpgopdooni
date
1950-01-310-162.644-2.190-1.61-1.40
1950-02-280-1662.077-1.450-2.17-1.20
1950-03-3121-493.091-0.970-1.89-1.10
1950-04-306630-41.923-0.860-1.99-1.20
1950-05-3150638492.211-0.630-3.19-1.10
.....................
2020-08-3110526943-0.463-1.422-1.32-0.57
2020-09-30254930-1-0.276-1.161-1.03-0.89
2020-10-3130917101.612-1.476-0.62-1.17
2020-11-30843-431.998-1.710-1.58-1.27
2020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " king upwelling noi npgo pdo oni\n", + "date \n", + "1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + "... ... ... ... ... ... ...\n", + "2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 318, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " var1(t-6) var2(t-6) var3(t-6) var4(t-6) var5(t-6) var6(t-6) \\\n", + "6 0.000006 0.520913 0.710488 0.220877 0.329032 0.119048 \n", + "7 0.000006 0.079848 0.683284 0.332829 0.238710 0.166667 \n", + "8 0.000035 0.399240 0.731936 0.405446 0.283871 0.190476 \n", + "9 0.009241 0.566540 0.675895 0.422088 0.267742 0.166667 \n", + "10 0.070540 0.764259 0.689713 0.456883 0.074194 0.190476 \n", + "\n", + " var1(t-5) var2(t-5) var3(t-5) var4(t-5) ... var3(t-1) var4(t-1) \\\n", + "6 0.000006 0.079848 0.683284 0.332829 ... 0.632281 0.464448 \n", + "7 0.000035 0.399240 0.731936 0.405446 ... 0.567508 0.440242 \n", + "8 0.009241 0.566540 0.675895 0.422088 ... 0.572306 0.468986 \n", + "9 0.070540 0.764259 0.689713 0.456883 ... 0.591786 0.461422 \n", + "10 0.023221 0.703422 0.632281 0.464448 ... 0.461760 0.570348 \n", + "\n", + " var5(t-1) var6(t-1) var1(t) var2(t) var3(t) var4(t) var5(t) \\\n", + "6 0.182258 0.238095 0.045884 0.847909 0.567508 0.440242 0.000000 \n", + "7 0.000000 0.309524 0.056366 0.638783 0.572306 0.468986 0.108065 \n", + "8 0.108065 0.309524 0.286279 0.634981 0.591786 0.461422 0.201613 \n", + "9 0.201613 0.333333 0.006073 0.380228 0.461760 0.570348 0.279032 \n", + "10 0.279032 0.309524 0.000205 0.311787 0.606804 0.512859 0.354839 \n", + "\n", + " var6(t) \n", + "6 0.309524 \n", + "7 0.309524 \n", + "8 0.333333 \n", + "9 0.309524 \n", + "10 0.285714 \n", + "\n", + "[5 rows x 42 columns]\n" + ] + } + ], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [], + "source": [ + "#create train, test, dev split\n", + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 6)\n", + "(80,)\n", + "(712, 6, 6)\n", + "(712,)\n", + "(54, 6, 6)\n", + "(54,)\n" + ] + } + ], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/400\n", + "1/1 - 7s - loss: 0.0132 - root_mean_squared_error: 0.1150 - val_loss: 0.0473 - val_root_mean_squared_error: 0.2175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2/400\n", + "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1037 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3/400\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0975 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 5/400\n", + "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0988 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 6/400\n", + "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1006 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 7/400\n", + "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1007 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 8/400\n", + "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0995 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 9/400\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0980 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 10/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 11/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2050\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 12/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 13/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0431 - val_root_mean_squared_error: 0.2076\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 14/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0434 - val_root_mean_squared_error: 0.2083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 15/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0435 - val_root_mean_squared_error: 0.2086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 16/400\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0972 - val_loss: 0.0435 - val_root_mean_squared_error: 0.2085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 17/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0433 - val_root_mean_squared_error: 0.2081\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 18/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0430 - val_root_mean_squared_error: 0.2074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 19/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 20/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0423 - val_root_mean_squared_error: 0.2056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 21/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 22/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2038\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 23/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 24/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 25/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 26/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 27/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 28/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 29/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 30/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 31/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 32/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2038\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 33/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 34/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2047\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 35/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2050\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 36/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 37/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 38/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0420 - val_root_mean_squared_error: 0.2049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 39/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0419 - val_root_mean_squared_error: 0.2046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 40/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 41/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 42/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 43/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 44/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 45/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 46/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 47/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 48/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 49/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 50/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 51/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 52/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 53/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2036\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 54/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 55/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 56/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 57/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 58/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 59/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 60/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 61/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 62/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 63/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 64/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 65/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 66/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 67/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 68/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 69/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2012\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 70/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 71/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 72/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 73/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 74/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 75/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2011\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 76/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 77/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 78/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 80/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 81/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 82/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 83/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 84/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 85/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 86/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 87/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 88/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 89/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 90/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 91/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 92/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0930 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 93/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 94/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 95/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 96/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 97/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 98/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 99/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 100/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 101/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 102/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 103/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 104/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1994\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 105/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 106/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 107/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 108/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 109/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 110/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1989\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 111/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 112/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 113/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 114/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 115/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 116/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 117/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 118/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1982\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 119/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1981\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 120/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 121/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 122/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 123/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 124/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1975\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 125/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1973\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 126/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1971\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 127/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 128/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1969\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 129/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 130/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 131/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 132/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 133/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 134/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 135/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 136/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 137/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 138/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 139/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 140/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 141/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 142/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 143/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 144/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 145/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 146/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 147/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 148/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 149/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 150/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 151/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 152/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 153/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 154/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 155/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 156/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 157/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 158/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 159/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 160/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 161/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 162/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 163/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 164/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 165/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 166/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 167/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 168/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 169/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 170/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 171/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 172/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 173/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 174/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 175/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 176/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 177/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 178/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 179/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 180/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 181/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 182/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 183/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 184/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 185/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 186/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 187/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 188/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 189/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 190/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 191/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 192/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 193/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 194/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 195/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 196/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 197/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 198/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 199/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 200/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 201/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 202/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 203/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 204/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 205/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 206/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 207/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 208/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 209/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 210/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 211/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 212/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 213/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 214/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 215/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 216/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 217/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 218/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 219/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 220/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 221/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 222/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 223/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 224/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 225/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 226/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 227/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 228/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 229/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 230/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 231/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 232/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 233/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 234/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1736\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 235/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1728\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 236/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0296 - val_root_mean_squared_error: 0.1721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 237/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1713\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 238/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1704\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 239/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1696\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 240/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1685\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 241/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1675\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 242/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 243/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1652\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 244/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0761 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1641\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 245/400\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0756 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 246/400\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1616\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 247/400\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1603\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 248/400\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0743 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1589\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 249/400\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 250/400\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1562\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 251/400\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1552\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 252/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 253/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 254/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1480\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 255/400\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 256/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1431\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 257/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1547\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 258/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1434\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 259/400\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 260/400\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 261/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 262/400\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1366\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 263/400\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 264/400\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 265/400\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 266/400\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 267/400\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1446\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 268/400\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 269/400\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 270/400\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 271/400\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 272/400\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 273/400\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 274/400\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0672 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 275/400\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 276/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0667 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 277/400\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 278/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 279/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 280/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 281/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 282/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 283/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 284/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0652 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 285/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 286/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 287/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 288/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 289/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 290/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 291/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 292/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 293/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 294/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 295/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 296/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 297/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 298/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 299/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 300/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 301/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 302/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 303/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 304/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1099\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 305/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 306/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 307/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 308/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0648 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 309/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 310/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 311/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 313/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 314/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 315/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1070\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 316/400\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0636 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 317/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 318/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 319/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 320/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 321/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 322/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 323/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 324/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1040\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 325/400\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 326/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 327/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 328/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 329/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 330/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 331/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 332/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1105\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 333/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 334/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 335/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 336/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 337/400\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 338/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 339/400\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 340/400\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 341/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 342/400\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 343/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 344/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 345/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 346/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0652 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 347/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 348/400\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 349/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/400\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 352/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 353/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 354/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 355/400\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 356/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 357/400\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1044\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 358/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 359/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0604 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 360/400\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0948\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 361/400\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 362/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0985\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 363/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 364/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 365/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 366/400\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 367/400\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 368/400\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0587 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 369/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 370/400\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 371/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 372/400\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 373/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 374/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 375/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 376/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 377/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 378/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 379/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 380/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 381/400\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 382/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 383/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 384/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 385/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 386/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 387/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/400\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 391/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 392/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 393/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 394/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 395/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 396/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 397/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 398/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 399/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 400/400\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(1))\n", + "# model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=400, batch_size=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "train_yhat = model.predict(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", + "# invert scaling for actual\n", + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 93912.36221073347.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DDzzAXXfdxfjx4xkzZgxPPvkkAJdffjknnngi++yzD/3792+2zHHjxnHDDTdwyimnsOOOOzJ27FiWLVvWZlmHDBnC1VdfzQEHHMD48ePZZZddOPbYY5tM9wiHwzz00EO8+uqr3HLLLVlligiPP/44U6ZMYeutt2bMmDFcfvnlDB06lOOPP55x48Yxfvx4DjzwQK699loGDx7MiBEjOOmkkxg3bhzf+c532HnnnZuV/eijj+bxxx93QX6Ho62oalE/wEPAGRn//w+42P6+GLjW/h4DTAfKgFHAV0DYbvsQ2ANjmTwPHGHTzwdus79PBh6xv/sC84E+9jMf6JtPzokTJ2ous2bNapTmcHQE7tlztBtgPh733mv+f/e77VQ8U7WJerWoMRgR6QYcAvwwI/ka4BERORNYCJwIoKozReQRYBaQAM5X04MM4DzgHqAco2Cet+l3AffbDgFrMUoGVV0rIn/EKCaAK9QG/B0Oh8PRMRRVwahqLSbonpm2BtOrLGj/K4ErA9KnAmMD0uuxCipg293A3S2X2uFwODZSXn0VfvITmDoVMjrfZNGBc925kfwOh8OxqfCjH8GMGfDVV03vk6cnZ3vjFIzD4XA4ioJTMA6Hw7Gp0IHWSSE4BeNwOBybGoXEWTogFuMUTBcnc7r+E088kdra2laXlTn9/llnncWsWbOa3Le1MwqPHDmS1atXN0qvrq7mhz/8oT9+Zd999+X999/Pmjgzl0svvZT//e9/LZYhH5dffjnXXXdds/vdd999jB07ljFjxjB69OiC8rSUq666qt3LdDiAwiyZDrB2nILp4mRO119aWsptt92WtT2ZTDaRMz933nkno0ePbnJ7e09Zf9ZZZ9G3b1/mzp3LzJkzueeeewIVUSZXXHEFBx98cLvJUCjPP/88N9xwAy+99BIzZ87k448/9udaa0+cgnFs6jgFsxGxzz77MG/ePF577TUOOOAATj31VHbaaSeSySS//OUv2XXXXRk3bhy33347YAbR/vjHP2b06NEceeSRrMxYA2L//fdn6tSpALzwwgvssssujB8/noMOOihwyvpVq1bxrW99i1133ZVdd92Vt99+G4A1a9YwefJkdt55Z374wx8Gzgn25Zdf8v777/OnP/2JUMg8cltttRVHHnkkYJTk2WefzZgxY5g8eTJ1dXVAtsUVNLU+wNq1aznuuOMYN24ce+yxB5/alfuaSs/kH//4B4cffrh/PI+rr76a6667jqFDhwIQjUY5++yzATNz9B577MG4ceM4/vjjWbduXaPruXr1akaOHAk0vZzBxRdf7E9k+p3vfKeQ2+9wFE4XcZF11GSXGz2dPFs/iUSC559/nsMOOwyADz74gBkzZjBq1CjuuOMOevXqxYcffkgsFuMb3/gGkydP5pNPPmHOnDl89tlnrFixgtGjR/ODH/wgq9xVq1Zx9tln88YbbzBq1CjWrl1L3759G01Zf+qpp/Kzn/2Mvffem4ULF3LooYfy+eef84c//IG9996bSy+9lGeffZY77rijkewzZ85kwoQJhMPhwHObO3cuDz74IP/4xz846aSTeOyxx/jud7/baL+gqfUvu+wydt55Z5544gleeeUVTjvtNKZNm9ZkusfNN9/MSy+9xBNPPJE1WSfkXxbgtNNO46abbmK//fbj0ksv5Q9/+AM3NHMTg5YzuOaaa7j55psDJyx1OFpNFwvyOwXTxfFauWAsmDPPPJN33nmH3XbbzZ+K/6WXXuLTTz/1W/sbNmxg7ty5vPHGG/4U9kOHDuXAAw9sVP57773Hvvvu65fVt2/fQDn+97//ZcVsKisrqaqq4o033vCnuT/yyCPp06dPYP58jBo1yj/HiRMnsmDBgsD9gqbWf+utt3jssccAMxnmmjVr2LBhQ5PpAPfffz/Dhw/niSeeoKSkpGA5N2zYwPr16/3JOU8//fSsJROaImg5gxEjRjSTy+FoA3msk0VrurEFypsr/8TeRRbDKZgC6aTZ+v0YTC7du3f3f6sqN910E4ceemjWPs899xzSjBmsqs3uA2aW5XfffZfy8vJG25rLP2bMGKZPn04qlfJdZJnkTvef67LK3S9zav0gl5yINJkOMHbsWKZNm8bixYsD18vxljAIUshNkbksQFNLAuTK7nAUjTyWzGuzBwNw+xcHFF3BuBjMJsChhx7KrbfeSjweB+CLL76gpqaGfffdl4ceeohkMsmyZct49dVXG+Xdc889ef3115k/fz5gYhfQeMr6yZMnc/PNN/v/PaW377778sADDwAmOO7FJDLZeuutmTRpEpdddplf8c+dO9efAbotZB7/tddeo3///vTs2bPJdDCzUt9+++0cc8wxLF3aeJLtSy65hF/96lcsX74cMIu53XjjjfTq1Ys+ffr4Myzff//9vjUzcuRIPvroI4Am16zJpaSkxL9nDsemiLNgNgHOOussFixYwC677IKqMmDAAJ544gmOP/54XnnlFXbaaSe22267wEW8BgwYwB133ME3v/lNUqkUAwcOZMqUKRx99NGccMIJPPnkk9x0003ceOONnH/++YwbN45EIsG+++7LbbfdxmWXXcYpp5zCLrvswn777dfkwm133nknv/jFL9hmm23o1q0b/fr14//+7//afO6XX345Z5xxBuPGjaNbt27+GjhNpXvsvffeXHfddRx55JFMmTIla2mDI444ghUrVnDwwQf7Fp4Xu7r33ns599xzqa2tZauttuKfdl3ziy66iJNOOon777+/YMvnnHPOYdy4ceyyyy6+MnQ42oU8XoWODNNIkCthc2TSpEnq9QLy+Pzzz9lxxx07SSLH5ox79hytYrvtYO5cmD0btt/epHnKxtb1953zFqf/Y2++O+pt7v/qG20+pIh8pKqTgrY5F5nD4XA4ioJTMA6Hw7GpUNDYFjebcpfBuRAdHY175hytpos9O07B5CEajbJmzRr3wjs6DFVlzZo1RKPRzhbFsTGT15LpuAXHXC+yPAwfPpzFixezatWqzhbFsRkRjUYZPnx4Z4vh2GQxDWaR4jeci6pgRKQ3cCdmuWMFfgDMAR4GRgILgJNUdZ3d/xLgTCAJ/ERVX7TpE4F7gHLgOeCnqqoiUgbcB0wE1gDfVtUFNs/pwO+sKH9S1ex+qgVQUlISOBDP4XA4HM1TbBfZ34AXVHUHYDzwOXAx8LKqbgu8bP8jIqOBk4ExwGHALSLiTV51K3AOsK39HGbTzwTWqeo2wF+BP9uy+gKXAbsDuwGXiUjL5zBxOBwOR6spmoIRkZ7AvsBdAKraoKrrgWMBz5q4FzjO/j4WeEhVY6o6H5gH7CYiQ4CeqvqummDIfTl5vLIeBQ4SMx/IocAUVV1rraMppJWSw+FwbJq0IF6sHRCLKaYFsxWwCviniHwiIneKSHdgkKouA7DfA+3+w4BFGfkX27Rh9nduelYeVU0AG4B+ecrKQkTOEZGpIjLVxVkcDscmQyHdlTug71IxFUwE2AW4VVV3Bmqw7rAmCLoimie9tXnSCap3qOokVZ00YMCAPKI5HA7HxskXbMvXNJ7CqSOC/MVUMIuBxar6vv3/KEbhrLBuL+z3yoz9M+cwHw4stenDA9Kz8ohIBOgFrM1TlsPhcGxWbM8XjOTrgC0bsYtMVZcDi0TETojDQcAs4CngdJt2OuBNqfsUcLKIlInIKEww/wPrRqsSkT1sfOW0nDxeWScAr9g4zYvAZBHpY4P7k22aw+FwODqIYo+DuQB4QERKga+AMzBK7RERORNYCJwIoKozReQRjBJKAOerqrfg/Hmkuyk/bz9gOhDcLyLzMJbLybastSLyR+BDu98Vqrq2mCfqcDgcnU4BQX7VTWSgpapOA4Jm2Tyoif2vBK4MSJ+KGUuTm16PVVAB2+4G7m6BuA6Hw+FoR9xUMQ6Hw7EZ0RHBfQ+nYBwOh2NTI4+rrCNdZE7BOBwOx6ZGF5mg1ykYh8Ph2FToIorFwykYh8Ph2NQoQNFIBwzldwrG4XA4HEXBKRiHw+HY1OgirjKnYBwOh2NTI5+C6UDl4xSMw+FwOIqCUzAOh8OxqZHPSilkKv92wikYh8Ph2FQoaC6yDpDD4hSMw+FwbGq4IL/D4XA4ioJTMA6Hw+HoLDoiFOMUjMPhcGxqdJFYjFMwDofDsanQRVxjHk7BOBwOx6ZGIYrGucgcDofD0WK6iCVTVAUjIgtE5DMRmSYiU21aXxGZIiJz7XefjP0vEZF5IjJHRA7NSJ9oy5knIjeKmPCUiJSJyMM2/X0RGZmR53R7jLkicnoxz9PhcDg2NjpiuGVHWDAHqOoEVZ1k/18MvKyq2wIv2/+IyGjgZGAMcBhwi4iEbZ5bgXOAbe3nMJt+JrBOVbcB/gr82ZbVF7gM2B3YDbgsU5E5HA7HJs1mPBfZscC99ve9wHEZ6Q+pakxV5wPzgN1EZAjQU1XfVVUF7svJ45X1KHCQtW4OBaao6lpVXQdMIa2UHA6HIz+JBNTWdrYULaeLuMY8iq1gFHhJRD4SkXNs2iBVXQZgvwfa9GHAooy8i23aMPs7Nz0rj6omgA1AvzxlZSEi54jIVBGZumrVqlafpMPh2MQ49FDo3r2zpWg9XWQuskiRy/+Gqi4VkYHAFBGZnWffoLPWPOmtzZNOUL0DuANg0qRJXUv1OxyOzuOVVzpbgrbRRSyZolowqrrUfq8EHsfEQ1ZYtxf2e6XdfTEwIiP7cGCpTR8ekJ6VR0QiQC9gbZ6yHA6Hw9FBFE3BiEh3EanwfgOTgRnAU4DXq+t04En7+yngZNszbBQmmP+BdaNVicgeNr5yWk4er6wTgFdsnOZFYLKI9LHB/ck2zeFwOJrlGY7kN1zZ2WK0njwWjKY6zroppotsEPC47VEcAf6tqi+IyIfAIyJyJrAQOBFAVWeKyCPALCABnK+qSVvWecA9QDnwvP0A3AXcLyLzMJbLybastSLyR+BDu98Vqrq2iOfqcDg2IY7mGQCu6mQ5WkwXcY15NKtgROTPqvrr5tJyUdWvgPEB6WuAg5rIcyU0bjao6lRgbEB6PVZBBWy7G7g7n4wOh8OxSdJFgvyFuMgOCUg7vL0FcTgcDkcHYJWPNO731O40acGIyHnAj4CtROTTjE0VwNvFFszhcDgcraSQ2ZQ7YCx/PhfZvzGxjquxo+0tVS6e4XA4HF2YLhKLaVLBqOoGzMDFU+yULYPs/j1EpIeqLuwgGR0Oh8PRzoh0oossLYT8GLgcWAGkbLIC44onlsPhcDhaTSEWTAcYOYV0U74Q2N72/nI4HA5HV6WLuMY8CulFtgjjKnM4HA7HxkC+gZZecL8DeisXYsF8BbwmIs8CMS9RVa8vmlQOh8PhaD1dxJIpxIJZiJnuvhTTRdn7OBwOx+aDCPzxj50tRZvpiPEvHs1aMKr6h44QxOFwOLo8l14Kv/99Z0vRPIW4yDqAQnqRvUrwVPcHFkUih8PhcLSOLuIa8ygkBnNRxu8o8C3MZJQOh8Ph6Ip0EUVTiIvso5ykt0Xk9SLJ43A4HI62kk/BdKDyKcRF1jfjbwiYCAwumkQOh8Ph2CQoxEX2EelliBPAfODMYgrlcDgcjjZQwHT9HRHqL8RFNqoD5HA4HI6uSxeJaTRLC+TsiDMqxEVWgllRcl+b9Bpwu6rGiyiXw+FwdB02FgXjsbHEYIBbgRLgFvv/ezbtrGIJ5XA4HF2KjUXBeKtV5pXXW3Cs+BQykn9XVT1dVV+xnzOAXQs9gIiEReQTEXnG/u8rIlNEZK797pOx7yUiMk9E5ojIoRnpE0XkM7vtRhFzFUWkTEQetunvi8jIjDyn22PMFZHTC5XX4XA4GrGRKJiv40O5jMu7jLiFKJikiGzt/RGRrYBkC47xU+DzjP8XAy+r6rbAy/Y/IjIaOBkYAxwG3GLXoQFjMZ0DbGs/h9n0M4F1qroN8Ffgz7asvsBlwO7AbsBlmYrM4XA4WkRXqbGb4bi1d3EFlzFnYXnTO3XgqRSiYH4JvCoir9nxL68AvyikcBEZDhwJ3JmRfCxwr/19L3BcRvpDqhpT1fnAPGA3ERkC9FTVd1VVgfty8nhlPQocZK2bQ4EpqrpWVddh5lLzlJLD4XC0jFSq+X26AHUaBbpMCKagXmQvi8i2wPYYt91sVY01k83jBuBXZE+OOUhVl9myl4nIQJs+DHgvY7/FNi1uf+eme3kW2bISIrIB6JeZHpDHR0TOwVhGbLHFFgWeksPh2OzYSCwYj46c0DIfTVowIvJdEfkegLUqPlXV6cBpInJqcwWLyFHAyoCZAJrMEpCmedJbmyedoHqHqk5S1UkDBgwoUEyHw7HZsZEpmHzydqTyyeci+wXwRED6wxTmIvsGcIyILAAeAg4UkX8BK6zbC/u90u6/GBiRkX84sNSmDw9Iz8ojIhGgF7A2T1kOh8PRcjY2BZMH9c6lA7qR5VMwYVWtyk1U1UpMt+W8qOolqjpcVUdigvevqOp3gacAr1fX6cCT9vdTwMm2Z9goTDD/A+tOqxKRPWx85bScPF5ZJ9hjKPAiMFlE+tjg/mSb5nA4HC1nY1MwhcjbAaeULwZTIiLdVbUmM1FEKjCLj7WWa4BHRORMzGJmJwKo6kwReQSYhZmS5nxV9XqrnQfcA5QDz9sPwF3A/SIyD2O5nGzLWisifwQ+tPtdoapr2yCzw+HYHFE1Y0s2NgXjoQERgy4S5L8LeFREzlPVBQB2nMnf7baCUdXXMDMAoKprgIOa2O9K4MqA9KnA2ID0eqyCCth2N3B3S+R0OByOQDY2BePJG6hg7EDLDnCRNalgVPU6EakGXheRHhi9VwNco6q3Fl80h8Ph6GQ2VgumAHk7fS4yVb0NuM0qGAmKyTgcDscmS5YlsBGQK2Yny13IXGSoanWxBXE4HI6uhqYUCdPpFXW74rnIOuBQhYzkdzgcjs0STW1kFkwunSx3qxSMiJS1tyAOh8PR1fDr541NweR17XWNgZYAiMjdOf97AM8VTSKHw+HoKmxsMRiPPPKqdoRzzFCIBbNERG4FsIMWXwL+VVSpHA6HowvQmS6yxYub3yeXgPmwAnbqQhaMqv4eqBSR2zDK5S+q+s+iS+ZwOBydjK9ggmZTnj0bDj4Yamvb/biP3VfDiBEw5am6VuXPN99Yl5iLTES+6X2AD4A9gE8AtWkOh8OxSZM3BvOzn8HLL8Prr7f7cd+9aSoA025/v20FBcitHdJ/zJCvm/LROf8/wcxBdjTGEvtvsYRyOByOLkEnx2BaPdq+iywIk28k/xkdJoXD4XB0QTotBtPW4+VRjB15KoX0IhsuIo+LyEoRWSEij9mVKh0Oh2OTprMUTFvdWF2le3Uhvcj+iZkWfyhmVcinbZrD4XBs0uStqItaebdtQsou4iErSMEMUNV/qmrCfu4B3PKPDodj06eQGExHTEvcQvIpRm/BMZHia5pCFMxqu3xy2H6+C6wptmAOh8PR2WysU8UUtN5YBwy4LETB/AA4CVhuPyfYNIfD4dg0aGIsS0EKpgsqn3xya8CQnmJRyEDLhap6jKoOsJ/jVPXrjhDO4XA4is7UqdC9Ozz1VKNNeRVMEV1jbbUu8nn2vLQu4SJzvcgcDscmzXvvme8XX2y0qUsF+R95BK66qrDcnoJJda51VbReZCISFZEPRGS6iMwUkT/Y9L4iMkVE5trvPhl5LhGReSIyR0QOzUifKCKf2W03ipjLLiJlIvKwTX/fLuns5TndHmOuiJxe4PVwOBybG15tHAqoDjspyO9bMJlFf/vb8NvfFpg/90fAtg6gmL3IYsCBqjoemAAcJiJ7ABcDL6vqtsDL9j8iMho4GRgDHAbcIiJhW9atwDnAtvZzmE0/E1inqtsAfwX+bMvqC1wG7A7sBlyWqcgcDofDJ+0zarypk4P8LVVd3viZQlxkHUHRepGpwVsJs8R+FDgWuNem3wscZ38fCzykqjFVnQ/MA3YTkSFAT1V9V03/uvty8nhlPQocZK2bQ4EpqrpWVdcBU0grJYfDsTly4IHmk0veqe2b36eotHYcjFWMG4OLLLMX2TJa0IvMKqRpwEpMhf8+MEhVlwHY74F292HAoozsi23aMPs7Nz0rj6omgA1Avzxl5cp3johMFZGpq1atKuSUHA7Hxsqrr5pPLvksGK9+DppNuYikFVsr8+dYMoFldwD5JrsETC8y4JjWFK6qSWCCiPQGHheRsXl2D9LVmie9tXky5bsDuANg0qRJXa+vocPhaH/eegt69YKddjL/8yiYzp/ssnXHzdtNuSspGBEZAJwNjMzcX1ULHgujqutF5DWMm2qFiAxR1WXW/bXS7rYYGJGRbTiw1KYPD0jPzLNYRCJAL2CtTd8/J89rhcrrcDg2YfbZx3znKo8uFINJz0XWOh/ZxtSL7ElMxf0/4NmMT15EZIC1XBCRcuBgYDamR5rXq+t0Wz42/WTbM2wUJpj/gXWjVYnIHja+clpOHq+sE4BXbJzmRWCyiPSxwf3JNs3hcDiCaUbBxCgl1WFrqbRNMXSVIH+zFgzQTVV/3YqyhwD32p5gIeARVX1GRN4FHhGRM4GFwIkAqjpTRB4BZgEJ4HzrYgM4D7gHKAeetx+Au4D7RWQexnI52Za1VkT+CHxo97tCVde24hwcDsemToFB/igxTuNev1dRMfHVWCu7QGseDeMbbK0quWUUomCeEZEjVPW5lhSsqp8COwekrwEOaiLPlcCVAelTgUbxG1WtxyqogG13A3e3RGaHw7EZ0oJxMPdxeocomLYvB9N0kL8jKcRF9lOMkqkTkUoRqRKRymIL5nA4HB2C10OspTGYDqi9W2rA+L3H8nRT1g7UOoX0IqvoCEEcDoejU2hrkL8YI/n9Hy1UBl1kFmWPJhWMiOygqrNFZJeg7ar6cfHEcjgcjg4mzziYzuqN1dYFx4Ll7hoWzM8x07P8JWCbAgFDYh2OTZAFC+Cgg+D112G4m+d1k6OAIH9eBdOZgY6vvoJhw6CszPwXT6Q8LrKuMF2/qp5jvw8I+Djl4th8uP128yLff39nS+IoBgUMtAxUMEWdrr+AnWpqYOut4YwzMjJ6+fOM5O+wrtaF9SJDRPai8UDL+4okk8PhcHQcBcRgUslOCvLn64ZVV2e+M5YZyA3yd/ZAmEJG8t8PbA1MA7xxKd6kkw7Hpk9n9/V0dAx5FEzeR6CY0/W3Nr+naAKsla42m/Ik4Buq+iNVvcB+flJswRyO9qa+Hg45BKZNa2UBRXSJdBi33GLOIx7vbEm6DgXEYAItmK5CPsW4EUwVMwMYXGxBHI5iM3Uq/O9/cP75nS1JJ3Lxxea7iTXoN0sKGGjZ0RW1FNbfuDWbusZUMSLyNMYVVgHMEpEPMIuIAaCqrZph2eHobDYFQ6TNuIuQJt9Ay06yYFoUiM+SO9ulFzzQsg2CtZB8MZjrOkyKzZQlS+Cll7I7gTiKR8Ev1oYNpttnNNrCjI6Nkg4O8qtCMgmRArpYSb6GQFu7V3cA+VxkS4CEqr6e+cGoyMV58jkKZPJk+MEPYN26zpZk86LZxnvv3rD77q3IuBHglGXT5Bto2Y6X7YILoKQk/z4FHa+AhdLydyIr/rOQT8HcAFQFpNfabY42smyZ+e7gxfI2W1pUSXz6adHk6BI4RZOmgIBFoAXjVewtfIH//vfmD5t7iEDyHDffUs8dOZtyPgUz0s6InIWd2Xhk0STaRJg5Ez74IP8++cZ3OdqfVl/vTaky9oMKrlXjU4CLrBgj+dv8WBUQO8p/jOI/1/m8gNE828rbW5BNjbF2cYE2t1Ic7YZT6BlsSkqzrRTgasqrj1t5LVOp4I5rBRdZgAWTN8jfAY9APgvmQxE5OzfRLhT2UfFE2nwo6ju+aBH85CcmmujIYrNWMM6CaUxbg/yZL/KZZ8L3vteiw+Yjb3flAiyY1h63vchnwVwIPC4i3yGtUCYBpcDxRZZrs6IoN/yMM+Dll+Fb34L99ivCATY+2nydu7hmOu440zPxww/z7OQUTGMKMG3zPjuZG++2axwWMG9dvltQUDflQiyYTjZUm1QwqroC2EtEDiC9muSzqvpKh0i2GVDUd91ZLk2yqcZgnnyyBTtvJOfUIbTWgsnN30IKee/zzkXWVhdZB9DsSH5VfVVVb7KfgpWLiIwQkVdF5HMRmSkiP7XpfUVkiojMtd99MvJcIiLzRGSOiByakT5RRD6z224U2zlcRMpE5GGb/r6IjMzIc7o9xlwROb1QuVtFLGZaLC28c64x2bG4OhX30AVRSJC/UAumFYfNtzHvnGT5xsHk6ZSg6Tn984nXLhQyVUxrSQC/UNUdgT2A80VkNHAx8LKqbgu8bP9jt50MjAEOA24RkbAt61bM2jTb2s9hNv1MYJ2qbgP8FfizLasvcBmwO7AbcFmmImt3Lr8cTjsNnnqqVdmLcp9dbdokXdzT1W7EYrDHHvDWWwEbN5Pn47zz4LrmhowXEuTPZ8G0UlkXlK2F3ZRzJ7kMVDQdeO+LpmBUdZm36qWqVgGfA8OAY4F77W73AsfZ38cCD6lqTFXnA/OA3URkCNBTVd9Vs4rOfTl5vLIeBQ6y1s2hwBRVXauq64AppJVS+7N0qflev75V2YvamNxcatMAFi6E+fPT/9vci2wju5ZffAHvv28qWZ/NzIK57Tb45S+b2akTe5E1R94nLl+QP4/l1aVcZO2BdV3tDLwPDFLVZWCUEDDQ7jYMWJSRbbFNG0b2zAFeelYeVU0AG4B+ecrKlescEZkqIlNXrVrV+hNsZc3VIe/6ZtJSDWLLLWGrrdL/N7duynnPN+O5ePFFmD69Y2Tq0rR0wbGcfVpKq4P8+SoOL/aSx4LxthU0oWYbKbqCEZEewGPAhapamW/XgDTNk97aPOkE1TtUdZKqThowYEAe0ZqhjQpmM9YBHcrmdp3zPpYZldNhh8GECR0iUtekgFhGMSyYwuYJC9jHEybQgslRiJmy5VQ4HfE6FFXBiEgJRrk8oKr/tckrrNsL+73Spi8GRmRkHw4stenDA9Kz8ohIBOgFrM1TVpfEucia4aOP0t0/24FNtRdZLoFib2YusoIoZE6vZD5zo5UWTJ64TroXWJ6NhfQiy2xr+x0HvP8FCtoGiqZgbCzkLuBzVb0+Y9NTgNer63TgyYz0k23PsFGYYP4H1o1WJSJ72DJPy8njlXUC8IqN07wITBaRPja4P9mmdSncu14gkyaZAWxtZCPVE63HnrAkAhYX2+wuRh5aG4NpowsilWj+xS+okRAkt/fdyS6yAiaMbjXfAL4HfCYi02zab4BrgEfsjAALgRMBVHWmiDwCzML0QDtfVb3BHOcB92CmqHnefsAosPtFZB7GcjnZlrVWRP4IeEPOrlDVtUU6zzbjepF1LJuCUVcI+tV8YCv4egGmvYZr1QRRyGSXxQjy51UwebpHFxKDKYJCbA1FUzCq+hZNd4I4qIk8VwJXBqRPJT3YMzO9HqugArbdDbSfXyUfbbxhXf1df/NN2GILEzTfmNnUR/I3IpEAQJKJxttcAyRNngq7qJNd5nG7eeNfAovOG4Ox+fMF+TtQz3RIL7JNnq7ci6wdKsV994WRI9suSnvy2GPw1Vcty5O+TS18s4LexA0b0ustdFHy9kTq6q2ajiRPjZv3HW3ldP0ehayS2fI4WrZCzMrvB/ezx8oUE6dg2pOu1IssqPBZs+Dzz4twsI7nhBNa3vMptXI1APL1gqZ3KvRmbLstDB3aMgE6GF+hZvraN2UXWSwGAwfCf//b/L6Z5FMwRbRg8rnIfCXQ2hiM7yrLNxBmE+im7GieDnvXx4yB0aM76GDFpypzObw334RwGPKMZ0otWwGArFjedKGF3oyg4/zxj0aJdxHSCibPxk2JZcvMffnZz1qU7aUF23Elv8l7TYoR5M/bM83bJ0hB5LrIgvJp7g8yLBjvr7NgNmmK2pj0WjUbW9ygtfz5z+ZCvvdek7ukCnmhAgOnBbhAq6vh0kuNP7GLkeUS3JQtGO+cmlpkpQkOfeI8fseVXcqC8azOvIqtpd2U/W0dVyc4BdMetLE16HqRtYzW1o2BHoXaWpg3r/0Kj8Val78IeJVioIss3/NRW2sWjq8KWjF9I6C1jaqgIH8h+rg9xsGMHAkXXpguMl/RBdzDwCB/jmvMBfk3NrpikL+r869/wQsvtChLsysRPPNMeuHzDAKv8wknmHhKewXEupDFGHQq9alS5rJN/ofu5pvN59priydcMWjrXEB5LJiid1P++mv4298yysxTdJ5eZHknufT26cC2p1Mw7UFX7kXWRor+MH7ve3D44VlJr72W/5rkVTAicPTR8OMfN9rkuciyWvTPP59daL4D57u/XfAmJlPe+ab5HvexHXOpq7MJQTfYs8I2Nis4p8Jt8S3JZxEUMFampeSfUj9P0S1xkQVaMNnHKCZOwXQBivoet7Hi6+h687nn4IAD4K9/bXqfZKJ1F8zTIYFqopnA6Qp/TtYm6IIKJkikF+yk4vEG2zIPigN4FyocbrxtY8AqmGYt3RUrshsN+SyYfGVtLCP5/ali8vRQa2ecgukCFKVuaifzqOCFMSsr8/bgKpSFC8333LkBG+05JWIBQhXwtvjvY1DbLY8F88S8sQxmBa/OG9FoW6P8mVRXG43ZSaQVavp8U94rb88zGQsYhNkVFEws1vJVWXMq3Gazf/FFYPbMR6Agz2lbx8EU0hss6Hj58uXpq+JcZBsbrTWRC9AB8Thccw3U17fqEG1WMImA+ieQLbYwYxDaSF5vo1cpNuSpOfK4sfL2Isvz0j43f0cAZq9Mr1mXIEwDJemdgmqzM86AI4+EL79s+rhFxKvAMnuReQrGO81EXcA8Zd7GzlQw0ahxdbYEX6MWqGDKy7P+xuPZxUBxYzB+N+WGhoAiC1gwrJCR/LkDLauq/Hyum/LGRnMxmIcegrXpKdEKaVHccQdcckkr4q2eLG1srhSsYDZsaNNxPApSMEGt7gJIv48ts2BW13UHoH/3tJbfi3coI10xLF4Mf+K32Zd79mzzXVPTKnnbiudKzLRgvIrHc8/ktWBa2N233fHiY2A6gojAp5/6SZWVsHp1xv4FKJinOYpfmYVvjRLLIJFsnM+LvTQ16n4NfWmIt66i9susr2cWO6bdsBkPUZMuslWr4OOPmyxbfXdYRtrSZdCzJ/rJJ62StzU4BdOe5FMwX30Fp5wCp57qJxViwXg9RWtrWyhLB7vIGiihjmjzOzZDQQom3kqXhJ8toPA8Fsya+m4A9CpPd0H+kN2y9jnhnD78nj/xue7QuOxO6lkWVCl6Fox3XwOtwWSSL9i2a8VgvNH5777rJ40YAVnLOHmtIXu9gxpHx/A0/8evqKes0bZ4MvvaZNJUQL4/azjx9sCpFZvFj8HU1zOGWWzPHNMoCYXQhWa9RFVMJXDUURkZUzBuHPzmN+Z/4IqWAXIvWWLS8s0S0M44BdMeFHKnqqvN95IlZv+Mple+7IWOHauqakIZdJCLbCwz6EZd/p3mzIGLLmqmt475zmx1/4zrqaAyr4tsXvVgjuYp6mIBF0oVfvELUl+blzavBRMg2+q6HgCkUk0risqqgPmdWttt9skn4ayzWpYngLQFk8Z3kVnlk6xv7CJ74ovRbM8XPD5zu/wHeP31HBOiiATEhSpzly8swIIpx7TUZrNDox08BZP5zGvT7Q7//j41fWSz4gfhNwBsr70N9AbPuqivSx/iP/+BZ5/NPu7y5aQQTuZB3q6fmN6UZ54xDXV8g8EpmPagEAXjPbWRiPF3DRgA+UbrWgpVMD17NlEn5ZGtuhrGjoUPPmhe7OaYSzOVEZh4xF/+AgsWNLmLzjJzpclnaVfIDfyMairyWjAXfv5DnuFoXv40YGXStWvh+utJPfhQ07LlcZHVJSJZuwTKHaAY8/nI83LccXDXXS3LE0BeC8YqnyBl/dpCs870V+vTMacFC3ImSUilYP/94eCD2yxnQRTS8SDHtRd0v4ZhWvGLGd7oXscTAS6yPGvbFzQzRB78GExmgLWiInsfpfE5W2HW0I+HOZljV97RuGzPRZZhwXgKJq2Eio9TMO1JPkXjPbWRCDz9dNbuhYz5yKdgPCVwzz0BsuQpfPp0mDkTfvKT5stuF3JcGEGkVpsYlaxbE7CxaQsmqeYChYKsE69Fi3nBAnuR5XGReWXnmy3EbwgQcL2bUzALFhTFX+FbMBnXRAtQMKtrjUtwQEXaJThqFOy5Z2bhNt/06S0TShWuvx7Wr8+7TxU9mJPZaClEwWQ+X08/TWLI8CZ3TRJupIESSYF4nMTqtGz5XqO4tm21E68BkKhuWsGkVBq//FYYr7EQ+MxZsh6rcI6CcUH+jYx8msJ7+MNhv8IppD960Hs1dy688kr6v9cJJVAJ5ZGph/H8sHJlk7u0uKdoXgqoRPNO0JjHgvFak+FQ08fwXsjAsvNYMIlU0y1iD8+VEjgtSz6mTjW19y23NL9vC8k3zsKv3OobtyBW2U4Nvbs37t3kk+OOKpgpU+AXv8jfqkmlOIQp7MCcxscrxIIBuPFGv0GRVbR9BhJEGlswyRCcdhrJ7XcMEqkR8VTbXE7ePajdkOGmzHmJNaWNX2yv4wFNN6oCuyl3wPT8uTgF054UomAikUYvZUtdZNttBwdlxBW9gdeZ715DKsK77JG3kvPyrVjRvNit4qmnsnrN+eSplDzTvqXdlFP25cmnZH0FE2Tl5LFgPOWVTOaxvLxsmYJ7ifkUjTcW4623mt6nlQSNg8ndFnQt11gFkwqooH0KsEYD8XqrZPY6fO217BmQEwneZ49ggQt1kYVCgQrGq2QTRBrHYBICDz1ktnn755nsMtHG9Rq9BkCWgvG6EGcG4ptwkcVtN3nfggnofZblIosnGpddZJyCaU+CRgIrxsTwXqhIpFEtWIiCyfdeeRZM5j6XfH0ue/EuM+Z3bzZfvh5qrVYwq1bBscfC8cen0wqyYOz0JnmUQJAFk9dFZiuSVL7HPY8F45UdaMHkdAcNPEZBwZuAijrf9VqzxlgC8YBxLBZ/HEyAgvGD/AEKJpY0D1JS8lSgrTVtg873gAPghhvS/4POqUAXWYxSU4GGw80rmJx77VuqGfnyusjaaMF4lX9tVca1tOeZV8FYYRooBTIUTMY9UYD169Hbbm+y7I6gaApGRO4WkZUiMiMjra+ITBGRufa7T8a2S0RknojMEZFDM9InishndtuNIubJFJEyEXnYpr8vIiMz8pxujzFXRE4v1jn65HkKVYH99ksPGguwYNraiyy2yPi4Mp/D6TXbALBiXWnT+QqY+DdQwahmz0AchBe4DFp2soCRywKmojnzzHQ22+JLNARZGYUrmNbGYJJBvcg868hWFpmV05zYSH7IbfkHhuZTMPlaHhddZGIZjz3W5C5Bvcj8bZ4FEzAOxj9fLUAhtxTvfAsJKpJxCQpQMFVVECXGFSt+CKFQtoWR41YKisH4Qf5MBZNSWLcOjTV2F7ZXDKa2OuM+BymYJlxkaQWTrn/Sk11irMJnn0lnS2Sf78Ye5L8H7MRHaS4GXlbVbYGX7X9EZDRwMjDG5rlFRLy7fCtwDrCt/XhlngmsU9VtgL+CGT0lIn2By4Ddgd2AyzIVWVFoTsFkdr8Jh1tlwTT5Ps6eTcOuewFGd3lExDxM+SwQT8Hk83IE1iP3329mIM4MBOWSrwdVnsopq759+224+25/m6dYAmMwnotMg8d1mH28i5hHpgBt7ymWwPuUSsGSJWmLIKNyOmHZjdzBD5k5L2PMRU0N6Zkmya9g8lXingLPN8V8niV581kw6ZhTniqoQBdZVZWZMDt94DzPhSd4xkPrX4ICFMzqtUbue9Yd09iCyXE9NRmDIcD11bcvqev/2uh4jSyYFvqcfAWT6UFogYLxXGRhaWx9K2IUY8az7ikYP00x1/rUU01vnyJQNAWjqm8AuQ74Y4F77e97geMy0h9S1ZiqzgfmAbuJyBCgp6q+q8Y5f19OHq+sR4GDrHVzKDBFVdeq6jpgCo0VXXEI8t/nJrW3gpk7l5gdNJb57oWxCiZPHRUUu8klUEG9/775zrd6Y0BLdX2ygmc5onAFk+vCyKdgvNZ20MVsLxdZSszKmddck3HgFAwfjq4w87BlVmqeTN69AEzPiuFN924KlCmIHMWUNWLb/g7qReYXbbcFWYO+gsnnHi3QgjnrLDNhtj94PJ8F45WZ4SJrpGDyVOKehRtCG8dgghRM0DgYkcYWDMHPTkKDXVeNqK0Ndp/bbsqeghFSjeKFgWfbyEXWuIGrSpZFAzRWXgh89hk8+CB85zvBsreRjo7BDFLVZQD225u8ahiwKGO/xTZtmP2dm56VR1UTwAagX56yGiEi54jIVBGZuqodJmpMz/HTKClNe7vIVNMPWiLt8wqLrYzzVBJBsZu5c+G++9L/m3SRQWGmT8Y+J625laN4luV5VizOGk+Sc2EScdvqztOLzK+HAmRpazflZBKzYuUllzTK51lQWQqmKasqs+NDKy2YuniEOzgblVBWMVmnkicGk89FVkivuUIVjDfkyXfH5rNgvIctyILx8uU5rq9gJNXYgrEDQpuNwYTD2UF+L1geYPV6LrKQBDw7XsZFi6B798Begr4FYw3aMmKNlYBnZWSS6yILOL6qeX+CLJisYrzpcuqaGSTdSrpKkD+optI86a3Nk52oeoeqTlLVSQMGBAzQaymenzfgOfNpYS+yZsfB3HWXb8FEKtMVV0tcZJkKZpedU5yeEbVqtYLxWqGhEDzwAIgwK2YG8HlTxTfZKaKJovMpGF8JJLJbgOZPbi+yAHnzWTCpDAumiXwaqGDs8YL6jHq0UsFc8tkp/JA7ePrjYY12LaAuTrvI8l3LfDqkQBdZZufJLOHynW8+F1mel8U7F8+CyXJ15TwDTVowOZZPPgvGc5H5cT9vKvBMOb1pwR99tFH+VFLhlluoveZGAEqJN7K2NUXzCoYgBSONr1VukD/z/WvxXFSF0dEKZoV1e2G/vREYi4ERGfsNB5ba9OEB6Vl5RCQC9MK45Joqq3jkxGBSH33ibwq0YAp1ka1eTbLG+NqbVDBPPeU/aJmumIiYhzKRp2ttkIKprsluEefrOVWQghHxTSLvhQ9rosnCs4rO6U1USAzGs9iyWq9tDvJ7MZgAhZjILjvzuF5FndtyzC6gdQpmeV1vAKrrI43EbmzBNF103hhMvnV3gmSrrfUHEXvEa8xDVuJNPJ3PReZVpEEuskSCdfTOe0388WDWgollzjdmy8wbg0lIYwVTgAUjAjz+OGyzTXpjrpYP8EOnkgo33UQtZmBrmcQaK5ggC8aWne6m3ISLLNeCSeZ0gU5p+lpvIhbMU4DXPj4deDIj/WTbM2wUJpj/gXWjVYnIHja+clpOHq+sE4BXbJzmRWCyiPSxwf3JNq14ZCqYJ58kuXt6yLP/siP8jOv5omZYsIts+nSYMSOdWF0NAwaQemlK1iGC8GMwGQrGc5HVN7RMwXh4Vka7WDCS8VKToSDyKRi00UOf30WWPY9UVoszNwbTUgvG+tqDLBjPxeTPUpxxXL+1HM9z81qpYPx6Otx413Td1rSLLH+QP9zk4f3LE/RgnHceHHOM8et7u8350sjgnV4hMZgAC+Y/K/ahL+v48ItejfNZGmKm7LCkIBTyK24gWMHkjuQPcJF55IvBhET9INOxPMGRPNNYwYRCpmNG5vLGKYWSEl/OUon7+eowSwmo0vhGFOQio1EMplFDR4uvYNrWzy4PIvIgsD/QX0QWY3p2XQM8IiJnAguBEwFUdaaIPALMAhLA+aq+4/o8TI+0cuB5+wG4C7hfROZhLJeTbVlrReSPwId2vytUNbezQXFIpWDu3KyH0Xuf5rENN/AzXnp7MTO/8cNG2Zgwwfy59FLTEppoJrBLLTJzJzX1socg0IIJW7M5cPJHS1AMxiMRS1JaFuk8BSMEK5gFC0iuN1NMCym8NlLagmncm0sTSYRmLJhCXDAB1mCyroFIRtnZQf5smQIpVMHcc495JnbayWSzyV49neWWTaagWw9Sw/8ETMof5A9Qfok8LrJkPEWoLBQYY/MHjXoTu5JuZTeKpbQwBvPCml0B+PTrXuzaOCfQCgUT1IusCRdZ/hiM+lO8PMWx9jzr/PP9ilFsGSoh7E2NbkkljYKpwYxVK6XBP2FPwaRU/OvxNnuxjCGcUJCLjMatUltO2v2m6UpgY1MwqnpKE5sC57ZW1SuBKwPSpwJjA9LrsQoqYNvdwN1B24pKKgWlpdmt2Jz6qiYRze8iu+IK823neMqdXj2TRAJKSVswEdItwEjCuNbqYvZlUeVN9iZEim/Y/LE6U0Gbbo7ZWiZel4CewQqmtiHCjfyaizRMhJznOGVe7iwXWc5cYH6lFtTFlnS2RgqmIQWjtiLJIcBLWdtyg/xZ9yBujpzlNlOFn/yEuWzDFA7hR1aWd6eVsxfKNMYzPke2ZEpYziAWskU6zc5GHKhg2tOCOeOMrP298/UepSwLJhaHujqSc79s8rDe/g2xlsVgkg1JSjIVTCaZyuOmm2DpUhKck12WKjMZTbhyKI0WNshjwTQkTVVVGm78QHqPnO8iszEYr5IGIB7nbs5gJYPMIfLEYIKC/IExGM9FhprZZgOuxdxFUbbjKy7/6gEui8WymjaegsmyYKxMXlqmi2xv3rZppktxS3uReS4y/7pkWjBFGtZfNAWzWaLapILxWilxDTd2kdUGtB5yWhuBL7v38nkWjNcf/nvfI7zezHJb1xDyZduXN81Pmz82ez6wNaGqDTBvLfz738ClRs46c/ya6sb9Jq6adgRXchQD3n2DM8/NDpVoIomUhvJaMImGFHz4YeCEh+n6No+LLKA3mO/GChiPkkqYSU/SFY7AsmVw881MYj2V9OKc2DtEgIdfMC6Y/3FwIwWTSsEkprIkIyyYrDO1WnAMJkehAifxMANYxd8bn3Cja5G3x5Q/40HjMTqphuxnJ5+LrDZgLbSENu0iMy61kuAgf+a52LnGEvzIfHt6IZlkLDPh6YCeN3m6KTfY2QVKpLFQiQSUlubEYHItmIYGzsxocwZaMKlwo95nXqUc6CJLZQzu7Z4zY4a9Fl8vN42/N9aNhfp6vx4AY0F8c96feZz9AHufciyYQmIwweNggGQy0EWWLjtDwWT27GlHnIJpDzJjMCUlgUFC78FqSAX0ImsIMBPiOS3j2XMxoak0npsjHYOxD9hDDxHiQCDDgkkmyQ25eTGYlIpZ0GjOHDwF402CuGFtEohkuaPWx8wDWt1QCq+8QuztT4ELTVmJFOFSAi2YLBfZbtkLdlFXB4cdRnLDhX62phSMV072WvPZQf4sN6V9sbx7kEL8xUQqMQolHjcvQ129Kac8YG2bZEqylAukLZjgXmT2vDPGmvyHkwBapmACWpe+grE91LIsGPs8pWURU/t+9hkw0T8Xpkyh7uN5GE92miwFk0wai8guJufHbDIPqGq64wb0qW/kIssztU2TLrJUigZrLZRK4/zJeApKQxkKRiEcDnSR+XkIQyrFArZMHz4pjXuf5ZleJctFlmuNe0rAxjJLQkmoq2MV6d6qqaTy+Jr9GskE2Qpm/sruzCNjWQRVfs8VviXtvweZU8WkzDkHjYPxraMUaa187rmNzq896CrdlDduMhVMJBJowVRjpi6OJwMGWiZzHk7wXzJPeSS/XGDSMwKoXoB5Pb0B6CHp5qj3YnvTXzRqjq5dS+XzZoLFpIYaVebxerP/hnXmu4K0/zirRXzQQdRdelVaJlsBPflmX4ax2MjvKxgjU6DLaOpUeOMNktO985PCLZhPP00vARxkwcRz3Q7SaBLOhnr7YucomMzLFhjkr2tgNtuznj6Nj5vH+vTJ55oIaNGns1nLxZ5vVgwmlm1VCWqmlpk0KS1bUmHyZGpfzVzkxR42U8Gcdhobug/xtyViAQrm3nthyy3ho48aleU3KgpRMEEuss9mQThMfL15tiMBFoz3zHlLF4dQFtQM4KfcmN4pt0ciEb5cWs4oFqR3CYrB5PQSzCSrm3ITVoZ3vSIhZfniBFfy2/QuOTMlZLrtMhXMuGu/w2SmpHdU5U/8nvtsH6dQyFQLO+w30Kxzg1WI8XiWks21YNBU+rr43fzaF6dg2pNUCpJJ6jOWDs51kTWkAlxk8YAayN74tfQFSE88OG6cv0vCdgFdQz8AKqRxcLUhYYPguVO3//73rK+2LittrPS8F2PDOvMSZLbo/YrdnoenPCH9sv/41tEsZRjLkwMana/Xoq+njAu4kTX09Xsa+BWShhpNlhaoYNatg/HjbSsbEomA3lz23NMWTMhMFpmB35kmZvKXYY6dGZcNagck6+PsyOz0/5bGYAoZBOW1MjOzWQXjtZA3rM+w5uobGsnC1KmBRWe18nMPnxL497/9ZxAyLJhMF9nbb2dnzKhsfbdoovG2RgR1U55mGhyeG9iz2lMZ74yvYBrSQf6fv39SVtHa0FjBrKnMrlTj9t0MUjDNdlOOx2kgozzPgrGu5pJwku9fviV3kV4VsFAFUx3Lnk9Qcx7EbuEYV18Nc74sIWmvt6aM0vPqhsxz8cvOtGBKm56zsC04BdMOqMJctjEPVSyWfVNzXGSCgmR7xb2XJUmIu/gBcSL+y+aVlZJwo9ZustZUgqvpb46VUZHnWjDG1ZVBfT0brHsooeFGXcni9UlYsYINyxu7ijzpvR42WQrG9hDzWvtxafzgehXuY3yLm7mAS7jaH4nnvdwplcatziAF86HpLOhd36BeZP606J4FA42W+m2oT4EqdbUZ+ZcuZX6fndPnlhJ/yV0/LWckfFAvsrwKJt+AxQwFU013EpkVn/32LtGq5en7m6ozz0XW8gSRbG+4V7l5lU0JpqLJnGUhlYJPmMBl/CEtUpCLLFf2jPuW6yKrr21eoXrWM0Cy3DxbnoLxLlfmUs+eTDHbOAiJUlGa3TjxlK5HgghloeznK54M8c81x3A02RNEfsQuvmUA5jX89NO08vQsmMz4iqdg6us8F1mq0VjG3CUAkoRJxlNcw699V1qQgVtbl329y0IJevXKKTuRhHg8qy5KJVL8mmuYw/bpE3EWTNfn1gWHsx1zef/rwdDQ4Ff4YJ+zkhK/Eo6ETAAycxBYKp6kkgr+zK85i7u4k7P8G+89IEnCsGIF60k/SZ6CydrHkqtgsupTNcFEz7XWlIK5Y/Dvue9+GpXtVW/eBLOBCsbWEdXavVEF5CmBUluprWCQb0FlWTA5LfdcBWOEMNdgGUOyjhtkwXgKJqUhqKrKanE2xBSuuYa690zvvRhlMG8eX7FVupyU0J3sqHiiLruSSmW0dP1ec7ZSzG15ZgmcT8HEYlRQzcmkl3zOtWBWrUiX7SkYX3lIwriZMmILuRZMGTFYsICPhhyZtc+RPMsDfDedZu9vQ32KN9nbJOaOacmwUrzn0EvaUJWnyrE7rVuXcQmi1vK3CsarDz3LIFcmI47SP5p9n1INCfphXoJ+rCZBJD2jhHf4VIgfJHKWH04mmcRHXMRfANM1/qGHYPx4eHD5AYCNr8TjVFGRcUBrNVsxSsJJ+veozyq6kQWjYZ77bASXcI2/8qhq455zldU575OGGnVi06SRKbMu+mxRb67l16xgsC07Q8E4C6br8r+Vxm21cF1FswqmRBIoYipViyaSDGEZv8XEMlKE/JfNKyspER57qIGt+MrPl6htyN4nowLxX0g7Q+zyz9NvbawuRcUDt/K2rSCSGqJOujGDMemyY0l+yB1+bCGzwvask6o6c7xcF9lbb8HKStsRQLs1dpHZCsFTsuvp7T/ovgVjA9OZr2C8LkEdUf/a9WIDxGKsYGB6KeAkXMzVjMiYwi7XRaYIxGJZrdKGBrjgNxW8YnvRxyijKlbKCTyWdd692JB1LrHabMswSYS6OnjiCWjQ7JhTQ22AeyjDgqmuNi3jw3iehznJ1wL1leY+P8YJfjYvBhOPw/cnzeC4k9IVhNdaX2fvHQCRSPZ9SsLzHMYzHAXYMVSffcZHTLQdOsw+ueebjKdIpeD8G7ZhX97kS90KQiHqA0bNA8Q915a9TGur81Rkdqd1azU3qVGDyXv2wTxzH38MqzZ4Aw+VfqU5Y04aEpRTxxncTQlxo2BywkHxZIg+4cqstNzBiaU0+POrPbnadPgfWlFlZxrIuN5WwVTXGHlLwin6d8v2BqSSSo+ISTuYKSQJ0y2cbXkp0KssWzFV5ijphIbo1TNbWWki1chFFtGcE1blrZl9OI170YizYLosNUkTc+kWaeDqKZP4No/42xZ8leKixDV8ydaAae3c8/UBjORrf59UPElthnmdJAyJBLWU+72ckoS5+9FerMv0h9fGuJzL/Eox04ebG4NZftvjfr71a1NUJ9JjBBIa4berf8ZOpGcSyHRTeMdfs8aMB/37F5MBqK4Ls4jhvMTk9LkkUuyzT8a1CbBgvArXs6AaKIV4nCc41nfbJVPGgslUAom6ON2o42fcAEA/1vLmp70YTHpJzmQS/mxWgciS6WbOZxajzX+rvDKtk4YGuJkfp/9Tyqy56ZcuTIJkCrYme2xJXU22VZIkzFNPmXXW6qx14FlsNesbB7ifm7EFP+UGEOG880zL+EUO43vcD8kk778Pb70bMM1IhoK596PsYWJekN+7vilCNISivEv2DBNH8Ly/cmSKEGtj3bmMKyilwT/fSeTEbhqS9OoFdz5v7ssShvLq0u0pp57/cjwrGeArzSCLacna9HM3bx5cdRU0UMIq+vv51q7LsAKt+8trVMTjplW/fHFGnKchxcSJ8NvXzXNYIglCdkzYMXbij1RDgjgllBAnQoIEEX9gpkc8FaZPJEfB5Fidg8KrGTQoK8nYrLnxjmSKK6+ETxaZxl8kpPQuzfaReS6yn3E9o5llPAk5MTlV2KZPdrywsjpXwYQbufs0kaQ2FuYa0hOzZlp95vhw5I2TuZ/TWF3pLJguS03CtN5i8RC/+d+BWduuuQb+0nABd2BG78eSEd5YPTprn8rsZ9o8qPE4d3Gmn5YkzJYDcvz/dQ38gcvT/yVMqi7GdMZltfjuuw/Ofzft5jADLA1bWEW3IJ494XQ8liJqA/un8gApQkx5cLU3/hOA6roIp/Agf+GidNk5LfrqVDcUMfNIWRJxuISruICbAdOBYNGSEMfzBPdgBhSmUrBgbU9+YV0TkNGDyaIiLFmV/WIkk3AoLwCYihsTqL+Am6nC+BEUoaE2wSH8z8+XG0ePUea/kDdyASFSJFPpHkYn2kaEp2B2wfSgShJqNCg6kTAx9hdfyla079w3jyPvOZEb+SmxVIk/EB5gT96FZJK994ZDzjCVeS/Ws2gRDB4M06tGGbnj6TLD2PEv1kXmKxgNcdX8UziS57KuUyYpQixaZyyco3iGMEmSSSFEiiEs5W57X5INycyB+izW4cxaPxSAb/FftuRr34JZwMisawCweF26IfXLX8Jvf2t6KA5kFSSTvP02vPhxuiuvF+NaZLvkJpLCb38L2+8z0N8n83kGKJcYcduw2pN3TTn1cV/BhEkaBWPv+Rl2fEwiFWDBxLMr5TDJ3HAWSRV++/zeHMQrftq6tcrvfgf3f2pGUyVT4ndu2ZfXAUillHgqTCkNREiQJEzMTu10NnfQj9WoCj1LjQVzEf8HpN2Mt3MOh/ICSQ35c75Nx3hTNJHkmap0F2iA+pqc90eVft1N2UvWRCkGTsG0kfp6mFZpXvbqWAlb984OHm8xPPumxlIlbN8ze+7Nl941L/a53Epv1rGWvsxZWM5PuMnfJ5mCWL0ynEU8wKkmrTbbnE4S5tknE0xgOq9h/MPxZIjTT4dVSdvTjEo/8HgMT3IWdwKwZdmyrLISDUm6Ucv53MwQlpEkTJ8vs1uz1fWRRvM21azLrqmrtTtPLt+dvqRddCaQmW5ZJQlTVZkT8EzBRR9+2x83AumxOR6pUAmhZE5HgKSQIMI3eIvtMDV21oqBmAr3g68HZqU1xNRXqGAUjFdxjWc6YZKkUsYy3I/XOI4ngLSCOdgqq6SG/bXAxobNejmJBOy6K5x6VrrH1vKpi/nG6enJERfV9GX86PS59KAaksmsTle9Wc9rr8GKFbCiwViy8Xhaod7hjZqvj/Mj/s7THG3OF2F+3eDsa5ATYE4Rot4Oyj2Tu4yCSRlLroIqv1edF++IYGRdwjB6RdMunHrKIZFAgRfxF6b1Fdoiq2C6RWJsaYegNFj3msYT7L03/OHpXfx8S1eXMjDDQj3vycO4+uos0bPu7/bMJqEhGhIhhBQlVs5krLEF48Vgzt3hdSLEiafCjCwxUzP1Yj3QuIOGIsTqc65dSrjqjb2z0nJmhSGWCNEQU/qyhtttYzOVNOPifKWnaQXzI25BUFIp44XYmzf5hh3J71kwxtI0+RqqzHs30na71mSS6qSxFk+zy2blKhhU6d/NNFoXr3Ausi7J8uVQY29kTSzC9r3Ny3CB7YOfsu6R07mH87iFWKqElL3PXiv4jU8qKKeWm7iAfqxhLX394CBY90zC9EgpI+bPORavNS/Pt3mII3iWJGFqNmRXwvGkEC1Lv4AJItSvq7My3UsUUzlUa/ZI5HgsRYwy/3gpQtSVGvfVn/kVu/AR1bES02LNoHZdttKrDvfiy5rsyi2RwPfzA9QTbdS7KJWEASXr/eMBxOqyX5BUKOwry5ftwNJkUqknShkxf46mzBdrW75AEaYtMQrmPr5nzjcOPankHG4nQtwoGGuNRak3Fa4KcUr8Fxvwe53542ZIK5h/irFAg3qR1azKtkYXbOhD6brllBJjFz6ikp6QTNI/Hc5jLDPo3Tu7nHjCKMN9ed0/37qqBLfyIz8ulSLENhUrsvJlrhP0Hf5lFExNzvkmhZiU+y1sSM/39Tv+RAWVLNZhxJLpyilCHBIJfsFffAsV0hbMqwuMWzIsKcozZnKBdI8rgInWNffpggpWkd0Y8DjWKnkz2wT8beJ99GUtiVSYeDJEKQ3+8x2r1ywFkyTsW3+lt9xAiSSIp0KENMn2PZbw9O8+MPkyHudSYqQ0RGyteTmP43HGMZ2kCv3KszsVZHolhBQNiTANMaWUBr8XZiIBSihL6dXHjYVcRgxBUYWGhLFyvGcsU8FESBgFU2Pcmt5zqYkUdQlzX7ZgoTmXHO+CpmBID6MJp7zuXGRdkpF9NrDQrg5Q01BCXUOEfXiDXe1cm14FdCdnMYgVJDVMTdzczOusa6m6LkwFVURI0osNVNLT74p4B2fTjzUkE0p9vX35tzAuk7oq89aOZzqlNJAIlfkB3gl8Qj9W05AIs8uWxoe7O+/RQCn1K83T71UkAJWpHgxhKXfxAwCmzY76CiZEiiRhauPmgT2WJ+lBNdWxCPVE2YovuRUzEtizYP4mF5pzS5YTDRtF+BjfBEyFuyOfA9aiylAwz3MYQ1hKMiWEU3H6hddx/L3HGxkrzTU5nXv4LvebStFW5p6iSyYlS25IWxl3cwb9WEMKobrevMiDbOu4ocFU1FHqKSNmrpO1YDwlm0xKlpsFoK4uR8FIWsFUJNZamdJjHL/HfUSI+/t4rKjpQf2GGANYxQgW+QomFlMu/NYiduc94pT4+T7b3YyniMeFeqJEqffPt7Yqx9LTkD+OapJ9LpMpYSAr+CG3sQULTQOiOkfBpIQGKc1q1NTWqL/PcBYbBZMyz8X+vGo6ocTjzGI0O/A577G7fw2uvx5eXWZmIGtIhonFoEcP5U928GF9bYqB/ZJMjEzjT/zOpNmBr9/nn1nnNGZUDd/m4SyZyrTOVLipEA1JU3F7vf5qatONgxLiNFDqu8hKykJGwSTDNKQilIUThIYPtcc3+9zAT/kOD6BAbLmxxu/jNLZnDsmUMLpftgKvrDJyX8zV7MwnxBJhGhqMgvHuk6e80gqmxLdgotQbBYNpJJbS4J/LuspwVr6khmmojmcpL00kqbOKv9u3DgfSddF5p9UwbsBSUKW77VSwdEW4KNOROQXTVlIphv3JzLdUHSuhPhH2Hw4wNzVEkghJ381Q2RAlSl3GssYhutnxFRVUUUlP34+/41/PIUSKVFKJxezLv6eZZqWm0lQkUerNgxaK+D2VHud4tmcO8aRQt76Bo3iaw3jB9HJaV5+VD4yC6U4NY22g/xc3jSJBCWXE6EYtcUqp3GDOqRu1VFBFdayEGGUMYJUvv6dgev/gm4RIUp0oI2Z7U3mVeTJhrIzvcj9H8Bz1RH0l0J0a3x0VS5jgZbdyc9w1682LNZYZ9sUK+SPvvZcvmaRRheu9WJ7SURVqY2GEFD0xytZTMGXGdiEW6kasLl2ZGgsm1EjBeHN5eQompSHqPpqZJVMiAbElqzh+yLtszZckKKF2g1G6/7AD79bXlVG/3hy7J5W+gmmoTVD22ANGJsr8Ci+aqqWEBuLxxudbU2Xk3ps32ZUPSCHUaxll1PMgZg7aVFJ9hRoiZZS1VaiZCiYm0SyLzeuoUEaMYSxhiQ7xFUwf1pmu0YkEdZQziBX+PV+3VrnoItir7+dcyF+Ja8Q0mMqUAZjVZOvqoH5dLfskXk1b1nWmijqPW8mkNJxMy2QtmGiy1rqMQr4F492DqirTw69k2CB6UE0VFb6LrDQaSismq2AkZJ4rb7kLz6JIqVC/psZPM9cpRH08zOE8x+3WTVm50si/Kx+ae5c0MZ9MBeO5JEtp8N+f9bWlWcdThYZ4iNLyCBV3/Q1Id4JIu8hCNNQmTNkTjXsxmVDqkqas8sG9zfHs83zu9+sREeamtuLBGTvRnWr+86g0t3Zcq3AKpq306UPot5dQTi01DSXUx8PZlVsd/sviK5h4eZYSAlNpU1rqVy6eBdNtp21s61mpj5kyIt3Mg1Nt4xbRH36f8OABJO2DBvgttXgyRF1tinLq/HEnVWvMd6aCqUp2oxu1vs/aw6vwAFasifiy9qCa6oZS3x3lv+zWRRftFqJHuI7qeJR6+6BnVrhepRil3lSc1oKJDuxlA+pCfSJCNBynezdznqs3mONHJ+9HaPBAUpq2YLpXpKc3iRGlrEdp+kW2ZXstvJRVMN2o9a9JrCFt+ZTSYGSyL2TZA/8kJOrHYLItGHP8LBfZl0spo96/lokE1C1bT3TZfP94lavN92DMyMb1sSj1dUqUenpSyQZ6oYkkDUnjHvEVzCcmrhP98A1zfxNCLFSeZbF5Fe7Z/IMeVKMSoi5VlmWxJhuSWYopRYj62hyFmoIGyb6/ngs0Sr1VMEOJJcy17816M64mHqeOcsozGlFr15jK8vuDX6QP64wiroOyUvWvXV2tUp8qzZKz2naFz5yqCKB01RL/2a31GhDJ2rTLKBHOsmC8WStKdp1Ar+0GsYFeaRdZ1FowqTCxVAml4WRawdgGTNmPzkTGjUMVYvYdK/nhmb7rtC5eQpR6Sgf0Nvd3RZ1/ncqIUZ+M0BCXLCvDs1ZKiNPHxiiXVxlXta9gkkpDMkRpJEWPfiZW5c1y5D0X9Wo6pJTSQPTfd9MnvIFFNX2oS5YRCSWJlIXtuZh80XJBQjDLDkuoyei+3t44BdNO9KSSylhZWsHY1kCdjZtAWsFsiJcTjSQJvZReB60btfD739OTSqqo8Fvm3SrChCVlYjAxMS9fd9Pjw2+5Rc04yWQq5PtZvYoyngxRn4hQPqCCEquYKtdmWz4AldqTbtT6/z3KhvT1x0IsW2NbRPNm0CNUS3Ws1G8F+wqm0rpZuofpHo5RkyjzW7heKy0RS2YpmEwXWfRfdxKOCKkUaQVjw0Pe1B7RUUMIV3Q3LXM7ervbcjM+KJkSYpQSHdonS8l718RYMFAbi2QpmNpa4w8vI0Z3aqjW7n4wNzphB3MPNMBFZtfb8RVMaZSPKrchkuEPz1So/jOwyhy3Fxsop5b1sXLfBTqQlayjry9TloKxreco9fSgmg11pdRLdoPFey7KqTPKQ4W6eDirwk82JI0i9q4Joax4kh+DKemebcFYC7WMGH1Zy3rt5VeUvVmfZcFkHq+60tyL8nilr3irqyFakvIbYLW1JuCf9TxZV2a3nBkUyjasbGRFls2Yai2RMPFUtgXjK5iI0ivaQCU9G7vIUmEaUiWURZJQZirz+mrzPpSVhwmVmXkGP5xtBlTKb39DOITfGCovU6KnfxuAdYvT96knlVTGy2lokCwLxlNwmQpmWZWp7MvuuBkRQeMJGhIhSkqUioEmaLXGs2D2nES/QRHWJHvTUJc0z3LPnmxbvpjbFh7J1Xox0UgCKTHn7tUpZeUhglfda3+cgmkn+sh61tVFTUtmUC/kPBOTqK+HMhpgr70o62UekA0N3YiGGgiVpntglVMHFRVUjN+KSnpR60262CNMmBTJpBJrCDWtYEJmwFWDDYT7vuZEmLpECeVRpeRwMyNr1XqrhCRdUVYmyunWq4Taa/15fk05++xBz0PMWInl68xLF+3bjR6hOpZU9+I99kQRwseYEeCe/z/aPUyPSB3ViSixRJhSYukWfSzpV0BlA3pluciiPSKE0CwLpqRUKKGBNVVWwZWb0dpJDVEfE0qJEe5WRpgEiaSJSZT1KEXsFOReyy3TzVHTUJKlYLygbJR6BrOc5Toona9HCWFSfLZ2GHPZLlvBeA0BWwF+xda8/OUoaujhK+tEhtvOO543iWiUenqz3iiYBtOAGGpX+F64vNS/l2XEmM8oFq7u7ucbxXwWrO9FvebEYGrTz5SnYIIsa6+c3HyZnRoatDRLwdRmuGV7s55qKqiJRYhYa6GOcrQhbcF416C6Kq1gfEu6CspKkr5yXl+ZVta+YrJLQufObu0935COd5QRsxZMiIZkExZMROlV3sAGevmDNj0LJpEKEUtFKI0kkZ5GicQqbeOwDKSkhOUM4YVZtvtb9+6ES8Mkk1CXiBANN9B3iHlHln4d969TP9YwvXobnlk0Lqvjwfoa02AqGdCHPluaDjTLq60F8/1TkJIw2mC8EKUlUDHAvPeei6xkpx0YMDBEVaoHVVVGUdGzJ2N6LPSvU12ihN59zf6XfHW2kalbCOmgmt8pmHaij2xg6orhLIgNIVIaIhQxl7auTiijHvbdl7Lhpn//hkQ3ouEGkqF075sqKqCkhJ7ROJVUUFdv8nfrGUlbMHGjYCLdzUPs9TQr7waRsFoXmXmwS998Je0iS5URLRdKS4xCqrQKJvrYA5QOMz101sV70C3cwPBx6YGcAGVRoVc3U+byDVHKqSXUvZyKULrXzMxuuxLe3iwl4Pn/oz0i9IjUU500CqaMGOEnzWDPLAumxPz23FHRHhHCYmMwyQhl4SSEQnSnhjXVVsGVCyGBuJZQZ5Uu4LsSY5RRVpLy74GnKEppINSrp7Fg4tkKproqHacZOijFUob4Qdhoz1LCkuSdleYclzOY8I9spwYbIyg/xIwuXRpPj+GIlNjZBawFU06dbw2uWGm2RamnD+tYH+tGvT0XT8EsWBzxZSojxnr6cN1zo/18W/EVX63rQ72WZsdgMuJCaQumJKvivv298X453gj0P780wU/zLZhUJMtF9tBbw3yZetuuvCtruvmxOiVErC7VtAWzYJavGKqqIBpJ+PdvrQ1eZ7nIYk0rmIGsBGDh1+l7FyFBfaqUuHUt9sAM2vEq89KSFL26GQXzxgIztqY0GqJey3h/zTY0JMKURUO+gpm3yoydKisDKc0ZANOjB+GyiFEwyVLKQw30G24akUuX2WB9jxLfalVClHaLMGDee4RIsmiNXWhs3A70PdjETpZX9zAx2wiohHhq1R40pCKUlirR/mbbWhuLLO1VTv/eRoE/NmM78yyXl3Pa8PR4nGQqxLBhOXOXRYsTbwlik1YwInKYiMwRkXkicnHzOVpPn9B65m8wY01m1o7yK7faOtMqJWKChwAb4t2JhhoYODhED+tbXsYQiEToWR4nRtSfUqNbzwgloQSxRIj6BltRl5ttL3xuXpBoVOgXrWFlsh8z5thW74hBxkWWMgqmvBuUWAWzwc7+Ee1VRr8y8wJWJnvQq7SWYUOVv/ET/7zKykP0LDcVwvuLh5sXvaSEgeH06OJQCMIR88R+vMAoKKNgYlQny40lEkkQ2dq0/OrqzMsWpZ5o/+7EiKZjGRURQqLEUyGmVO9JWSThK5jpy80Q6mi5sGPfFazVPnyyYihRMUoiTgl/fm9/NtCbaEmSUFj844GpgL6ODeKNygmsrK+gG7X0vtAMIFyy1LaCv3MiQ7YsYRlDmL2it0mrKKVnJO2i+aJiIoMnmZ58j882vaKi3zIW3Mp4erqQyBLTsy2eSPf0Gmi7Xi9cYZWltZi+ru1PzFoZnoKZvyAdzPUqKTDd1iMk2Yqv+HpDb6q1e1YMxuuM4SuYpBoXjqTjQjNWm67j0cMPZNaA/QFYtN5UptHvnkg4pMzaMJRZDdtmuXVemjXCl9tTMCtqKygj5iuBqkptZMFULa/1Zcp0kZWF4mkLZn26B1VawZT6+TIpIc4wzJiVr75K39+d+YTZsVF8UTOUEkmke17VmutdElZ69iuljm78Z+4EkxYNsyrZly9qhjGd8ZSWh5FyYy3842szlqcsKuw/fn2WDJSWUhINsybWg7WxHpSHG+i3pXFxLVlj7+8/bqJm6/HpLOEU4T496c9q/v2+nd2jIkrf3ub6frpqKCnCiEDvsjpWJfqyLD6A0lJBenSngio+XWXuXWnvbkR6GiWVSIUpDSVBhL0GzssSc+gW2Yox2j3caPq4YrHJKhgRCWPWdTocGA2cIiKj8+dqPZnLs66J90QiNrDmxWCiUQZGjR/mq/qhlIUShKMlfGQXgFrBYCgpYev+pva/9FUzULK8R5gtI0uZX9nPuFBKlXCpKfvZL7YDTIV79BbTadBS/v2haWXL0CGUEOfzVQOIU0pJNMIW/cxLfvvbZmqRaO+0TAA7954PkUjWfGdl5UKf7unBkxNCn4EIAyPp9VRqE6Vm8THgX9NM2WU9SuhRYhRMLGl6g3nBxrkrTUVW/o2JREcYC6qyxrZeK0pYn+rJY4uNW+61VWN9BbO+3k7eWBbiyJGmp9Yby7YjGkq3ED0SGqZ7uXlpn/7CzB5b+uwTzK833U/fWD+ebr1K6P3Xy+jDOj5fbFqsZVFhm77rqKQX90wzMymHwsJJI971y15Y3ZedDujPRKby2RrTou9RYSrH12N7pK9dRSndqWbKF1uQoITo/nsy8Bdm3M1tngXxf39iQve5fLZhC6rjpVkK5r/vGVlLaciaC05Q+M9/GBVZTFLDxCklMWpbRh9k9n9trvkuP+u7hHpWsDg2gBWxXkQrSuivq/l1xS1pGYf0ZX1Z9twnZf+8jUgYPlyztb2XpVlz54GpzL24wQtLx7OWfr4SuOGNXRpbMOuMUslUMIvmx4l+/olvwaxbmg6MZ1owEeJEBqctQzADPAeykjAJvlpU4l/L079nlOu0qm0o7VZCz/mfAnD5u0ZRlJRHGL5N9qj10jLh2QOv9//HQuWM2iq7iV8bL+Hbk9eRy/FbfkyDmmc/GknQb5R5tpdUWmXdO0pNOD0TZUkkBX36+Es3AzREezJip96Nyr5z/3/5v19ePQ4iEfrJWuIpozDCvXpwyK7r/X1mpEz1VhoNcW3FH/30oSOypxoqLQ8zpFe6wfSHbe5vdOz2YpNVMMBuwDxV/UpVG4CHgGOLdbDMF7B/tzrKR5gRcvOXmUAqo0ez94A5/j7vV42GkhJ/5C0AkQh7jkyPqC+hgVAIti1byCdrR7K4pi/lfcvp3zd7UGK0W4jdBi7ISqOkJKtH2NQ1I5m80zJ24HNW13anjHoqBkQZEE33ztlnwBwoKWFyxnr3izf0ZOv+G/yR2+d2Nw/joAFpGWripWwxPFum0u4l9O2dYnFdPz5etQVhSRHuafzLN31sRj1HRw4m2su09FbPMy9vtKKE5YnsyoRBg/yurGCWPx85uN6XqXc4Z9g08OGSoRyx+xp2ZBZTvjaKuKx7hOEV6/19upcYxbll2TJeXGVcFKXlYSZPWNmovJ/u+SEH2dH6R241G4YPp3tG4Ln3oDLO33+m//+4ipeRaBlblSzi1flmpodYuBsDtjcWnqcMoyccxX6D5lCfKmN23Uiiw/vTf9E0AF7+cqSR+1tHExq/k192ghI44QS22jLdIWP8TsoO//kjQ1nCCytNo6X81OM5enKMZTqED6t2pDxirtc12/+To8RMSR+Nwn3nv++XU0Y9Eglz+tZv+Wlf9hjPMW/+iuEs8tOi1/yBQftnt9eO5mkA5i8MUU850YljCB96CABVy4ylXH7K8XzjSGPlrasqYREjKN/NTG/y+VtmFozyQ/YhfIyZhHPO+sHGClqWPdNEdZ8RhL+axzCW8Oky87yUDe7Dljv3ZWc+AeCjmh3oNaInh4bTi3VN2L83hx9XllVWOCJM3m09kzGdbt7+cjADch7BbbZoQCp6UEkFh4dfZJvIfAAO2i59TeJ9BtBjQDll1PNZvWnoRft246LD089FXEpBhLN6pucrDPXoRmSrLTiCZwH4fdm1AEzaKq3QTt/BDPz8R8Uv/Od+8JZlbLF9OVMyV7sEKCvjl1WXcgCv8L3d5vi9zwBq6EYoEmLLQaZR9hP+xqXjn6RYbMoKZhhkvBGw2KYVhQePfpC/8HP+wwn897QnOPCMLf3uvfPYBnbZhfAhB3INvwbgjxOfgL59KR02kHO4nSc4Fvr2ZcvdB/MXfs4glvPv4Wbfw8amZwbed+xadjx4GK+VH+anbbtjhOjYbTjTTvviMWBg+vb+4vDPkdE7cgTPA7ArUyntV8GAPdPTlew6chWMGkXpjtswl20YxmKOPaEEOXQyB/EyAMMnGfO854Hp1RFjyRL2OXcMlb234DkO53eRa9h+r35MPizEMoYyvWFHvo4NoSSa3ZLqPmog448zle8Ny05mi8gSIqUhrj1kStZ+jBjBhD4mcHl8+Cn2OG4woWOO4hieAuBPW/4DgEcPv4tv8Bajmcmfjv2Q0kMP4PqStGe0rH8FM2/4H9+0MyTvM8RMXNmvR9pC6ze4hO1OS1shJ3czL1/vscP5H4ewjME8/LflEIlw4DCz0Nh9Ay9i2wNHcNMf1jKb7bmNH/L4gWaan2hFOs42c/1QBh4+MevUon27cdRBaffPSTt85g/y8yjZaQf+eGNvruD3WeljD0iPcP/+WRGkT28mls/y08qH9+OHp9fzNEexP69y4eF2srMxY7hdz+FX8n8c+oNhbH/qRBYznOP5L9eVXwrAb05b7B/vwG0W0n3PcTzT/wy/7NjgLdn1/N34DVf6aUNr5rFrn7m8t9i4D6MjBhK520x//+FaYw2V/+oCdjwmvfT3PLZl8MNmfMcd600PrPpd9iJ83jn+PvXWO/DnvZ7w02pT5TByJNuWpGeSKBs+AHbckYu5Jn2RwmGu3dPk+37oXiYcuyWDdxnKgiF78hKH8OAW5h1j1115jG/xY27i/j8uAODyPcwUPMsZxJhdymC77ajoAc8lD2Pu6OMACE3cmRv4KQB77q6IwIFD0gvQ9RhSwZ5H9aOGblzM1dy8m2mg/ePQR0khvMnenHpsDWy/PQ+VfZ9qunPFINPRpnT0NvyNn/BnfsXFh5i57g4cu5I4pcQoZdAOfWDHHTmYl/mAXXlnFztZ60Fm8ttXOIj7LvwYundn6tBjeIqj6bbtcIhE2HIv84z1oBr6pSfpbHdUdZP8ACcCd2b8/x5wU84+5wBTgalbbLGFtom1a1X/8x/VBx9UXb9eVVW/c+Q6BdWxW9WYfVIp1Zde0tTDj6jW1pq05ctVH31U9aWXzPZUSvXNN1UfeshsU1WtqdG19z6l9573rsaXrzZpc+fqR1e/qHUvvm7yJJOqL76oZv5VK9KXa/Ub26/US0+Y6R/vq6c+03FbrNWp//zU7BSP6zmHLdAXL3lVdd06k7Zqleojj6h+8IF/estfnqGXfvNTTawwx0/FGvSeH3+ooPqrX6bMTrNmqT7wgOrnn6uqam11UrcYWOvLlEqp/3tIv5iuXasaj6uOHlWjpZGEvvvUSlN2bZ0eteuyrHP58OllesTOS3TBlC98mWJTXtf5NzyhumyZSaisVH38cfNpaDBpH33kl7NggTnfVQ9O0dcv/Z+mVplz+eLtlXrSngv1vb+8pclYXFVVn7h6lv77R2+qfvWVPVjMlPvUU+ZEVDW+dKWuvOU//vlqKqX6xhvmGVizRlVVn3lgvX/8559Nqqrq/Vd85afF46paX6+De9eZa7Ta5DvqwGp/n3vuNvn0nXf050fO1lOPqTL/16/X/ces0B8cvsS/Ji/ea67bN3at12RSzXPxv/+Z56m+3uy0YYOR8ZNP/Hz6/vuq//pX+p7X1ak+/bRWP/CEJmvqVFW1ftrnOnbEOt11xyqNxez5Pv981n06/oh6//+lv4lrMpm+56C6cKG5lodPXKGgesrx5rnM3GfWZwldvjz9/9tHVPr397O/TlFQ7d/f3IN5U77S/Ucv16P3WqV1dfZ8X3pJp17zkn78jpFbFy825/v22+nzXbRI9f77VVeuTKe98orqs8/69zcVT+ja//xP9Z130vvMmWPKmjnT/E8mVZ95xlzfSiPnzDfX6PG7LtKHLpuVzvfaa+adWrrU/F+3TvXhh1Wfe84+BKo6bZqRad68dNnPP2/qB1un6OrV5vgvvJAu+913VR97zJyTqmoiYf4/+6wpQ9Uc94EH/Hdl2TLV352xWL+68WlTZhsApmpT9XBTGzb2D7An8GLG/0uAS5raf+LEiW26yEGsW6c6ZUr2M1xsMl/2rkBtbbZMCxeqzphh3gGPdevS70Ymf/2r6ssvt12G3r3N8YOO0RHY+sonmTR1xIwZ6bQVK0zd5dHQoHr22Ubuv/2tY+RsLZ99Zuo4VVMXHnKI6qGHpttH3/1u+hlYtcqk1debNpnHd76jeuKJ2eXOn686fXp22qJFppxu3YpyKo5WkE/BRHItmk2ID4FtRWQUsAQ4Gew0xB1E795w8MHN7rZJU14Od9+dXvp1xIjG+/TuTaNJHAEuvLB9ZHjwQTM1/MDgOROLTm6X0FAITj45O23gwGz5Skpg1Kjiy9YejM1YjuZb3zKfTO43S9vw4IPQww4aLyvzxzIC8K9/0YiRIxunec9J7pIIjq6JqPfmb4KIyBHADUAYuFtVr2xq30mTJunUqVOb2rzR4FVmm/Bt3WyIxcwEkT//eXZlvDGSSMCSJfhT9LcWVaOgzz4b7rij+f0dxUdEPlLVSUHbNmULBlV9DjJWWdoMePFFWLOm+f0cXZ+yMrjkkub32xiIRNquXMA0oGpqNn6Fu7mwSSuYzZHJk5vfx+HYmOnWrfl9HF2DTbmbssPhcDg6EadgHA6Hw1EUnIJxOBwOR1FwCsbhcDgcRcEpGIfD4XAUBadgHA6Hw1EUnIJxOBwOR1FwCsbhcDgcRWGTniqmJYjIKuDrZndsmv7A6nYSpz3pqnJB15Wtq8oFXVe2rioXdF3ZNhW5tlTVAUEbnIJpJ0RkalPz8XQmXVUu6LqydVW5oOvK1lXlgq4r2+Ygl3ORORwOh6MoOAXjcDgcjqLgFEz70VUnD++qckHXla2rygVdV7auKhd0Xdk2eblcDMbhcDgcRcFZMA6Hw+EoCk7BOBwOh6MoOAXTRkTkMBGZIyLzROTiTjj+3SKyUkRmZKT1FZEpIjLXfvfJ2HaJlXWOiBxaRLlGiMirIvK5iMwUkZ92BdlEJCoiH4jIdCvXH7qCXBnHCovIJyLyTBeTa4GIfCYi00RkaheTrbeIPCois+3ztmdnyyYi29tr5X0qReTCzpYr41g/s8//DBF50L4X7S+bqrpPKz9AGPgS2AooBaYDoztYhn2BXYAZGWnXAhfb3xcDf7a/R1sZy4BRVvZwkeQaAuxif1cAX9jjd6psgAA97O8S4H1gj86WK0O+nwP/Bp7pKvfSHm8B0D8nravIdi9wlv1dCvTuKrLZY4aB5cCWXUEuYBgwHyi3/x8Bvl8M2Yp2UTeHD7An8GLG/0uASzpBjpFkK5g5wBD7ewgwJ0g+4EVgzw6S8UngkK4kG9AN+BjYvSvIBQwHXgYOJK1gOl0uW/4CGiuYTpcN6GkrS+lqsmUcYzLwdleRC6NgFgF9gQjwjJWx3WVzLrK24d0oj8U2rbMZpKrLAOz3QJveKfKKyEhgZ4y10OmyWTfUNGAlMEVVu4RcwA3Ar4BURlpXkAtAgZdE5CMROacLybYVsAr4p3Ut3iki3buIbB4nAw/a350ul6ouAa4DFgLLgA2q+lIxZHMKpm1IQFpX7vfd4fKKSA/gMeBCVa3Mt2tAWlFkU9Wkqk7AWAy7icjYzpZLRI4CVqrqR4VmCUgr5r38hqruAhwOnC8i++bZtyNli2BcxLeq6s5ADca90xQdet1EpBQ4BvhPc7sGpBVFLhtbORbj7hoKdBeR7xZDNqdg2sZiYETG/+HA0k6SJZMVIjIEwH6vtOkdKq+IlGCUywOq+t+uJBuAqq4HXgMO6wJyfQM4RkQWAA8BB4rIv7qAXACo6lL7vRJ4HNiti8i2GFhsrVCARzEKpyvIBkYhf6yqK+z/riDXwcB8VV2lqnHgv8BexZDNKZi28SGwrYiMsi2Vk4GnOlkmMDKcbn+fjol/eOkni0iZiIwCtgU+KIYAIiLAXcDnqnp9V5FNRAaISG/7uxzzss3ubLlU9RJVHa6qIzHP0Suq+t3OlgtARLqLSIX3G+Ovn9EVZFPV5cAiEdneJh0EzOoKsllOIe0e847f2XItBPYQkW72PT0I+LwoshUzuLU5fIAjMD2kvgR+2wnHfxDjR41jWhpnAv0wweK59rtvxv6/tbLOAQ4volx7Y8zoT4Fp9nNEZ8sGjAM+sXLNAC616Z1+zTKOtz/pIH+ny4WJc0y3n5nec94VZLPHmgBMtff0CaBPV5AN04lkDdArI63T5bLH+gOmYTUDuB/TQ6zdZXNTxTgcDoejKDgXmcPhcDiKglMwDofD4SgKTsE4HA6Hoyg4BeNwOByOouAUjMPhcDiKglMwDkcnICL9MmbaXS4iS+zvahG5pbPlczjaA9dN2eHoZETkcqBaVa/rbFkcjvbEWTAORxdCRPaX9Fowl4vIvSLykpj1WL4pIteKWZflBTsVDyIyUURetxNRvuhN9+FwdDZOwTgcXZutgSMxkxP+C3hVVXcC6oAjrZK5CThBVScCdwNXdpawDkcmkc4WwOFw5OV5VY2LyGeYhatesOmfYdYB2h4YC0wx00oRxkwd5HB0Ok7BOBxdmxiAqqZEJK7poGkK8/4KMFNV9+wsAR2OpnAuModj42YOMEBE9gSzRIKIjOlkmRwOwCkYh2OjRlUbgBOAP4vIdMys1Xt1qlAOh8V1U3Y4HA5HUXAWjMPhcDiKglMwDofD4SgKTsE4HA6Hoyg4BeNwOByOouAUjMPhcDiKglMwDofD4SgKTsE4HA6Hoyj8P9dZGcT1V7S6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 39630.128336910544.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 820598\n", + "1 288891\n", + "2 299017\n", + "3 499303\n", + " Count\n", + "0 488981\n", + "1 336030\n", + "2 381773\n", + "3 535746\n" + ] + } + ], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n" + ] + } + ], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 115829.72216361394.\n" + ] + } + ], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 173470.8677235172.\n" + ] + } + ], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/multivar_robust_rnn.ipynb b/multivar_robust_rnn.ipynb new file mode 100644 index 0000000..72ba4df --- /dev/null +++ b/multivar_robust_rnn.ipynb @@ -0,0 +1,5797 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(ismael_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1939-01-01\n", + "1 1939-01-02\n", + "2 1939-01-03\n", + "3 1939-01-04\n", + "4 1939-01-05\n", + " ... \n", + "24364 2020-12-25\n", + "24365 2020-12-26\n", + "24366 2020-12-27\n", + "24367 2020-12-28\n", + "24368 2020-12-29\n", + "Name: date, Length: 24369, dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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dateking
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" + ], + "text/plain": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = data_copy\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data[132:]" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwelling
01950-01-310-16
11950-02-280-166
21950-03-3121-49
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852 rows × 3 columns

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datekingupwellingnoi
01950-01-310-162.644
11950-02-280-1662.077
21950-03-3121-493.091
31950-04-306630-41.923
41950-05-3150638492.211
...............
8472020-08-3110526943-0.463
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852 rows × 4 columns

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datekingupwellingnoinpgo
01950-01-310-162.644-2.190
11950-02-280-1662.077-1.450
21950-03-3121-493.091-0.970
31950-04-306630-41.923-0.860
41950-05-3150638492.211-0.630
..................
8472020-08-3110526943-0.463-1.422
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8492020-10-3130917101.612-1.476
8502020-11-30843-431.998-1.710
8512020-12-319-975.098-1.870
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852 rows × 5 columns

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datekingupwellingnoinpgopdo
01950-01-310-162.644-2.190-1.61
11950-02-280-1662.077-1.450-2.17
21950-03-3121-493.091-0.970-1.89
31950-04-306630-41.923-0.860-1.99
41950-05-3150638492.211-0.630-3.19
.....................
8472020-08-3110526943-0.463-1.422-1.32
8482020-09-30254930-1-0.276-1.161-1.03
8492020-10-3130917101.612-1.476-0.62
8502020-11-30843-431.998-1.710-1.58
8512020-12-319-975.098-1.870-0.98
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852 rows × 6 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19\n", + ".. ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni \n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oni = cov_data[\"oni \"]\n", + "master_data = master_data.join(oni)\n", + "master_data\n", + "# cov_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = master_data.rename(columns={\"oni \": \"oni\"})\n", + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kingupwellingnoinpgopdooni
date
1950-01-310-162.644-2.190-1.61-1.40
1950-02-280-1662.077-1.450-2.17-1.20
1950-03-3121-493.091-0.970-1.89-1.10
1950-04-306630-41.923-0.860-1.99-1.20
1950-05-3150638492.211-0.630-3.19-1.10
.....................
2020-08-3110526943-0.463-1.422-1.32-0.57
2020-09-30254930-1-0.276-1.161-1.03-0.89
2020-10-3130917101.612-1.476-0.62-1.17
2020-11-30843-431.998-1.710-1.58-1.27
2020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 6 columns

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" + ], + "text/plain": [ + " king upwelling noi npgo pdo oni\n", + "date \n", + "1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + "... ... ... ... ... ... ...\n", + "2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " var1(t-6) var2(t-6) var3(t-6) var4(t-6) var5(t-6) var6(t-6) \\\n", + "6 0.000006 0.520913 0.710488 0.220877 0.329032 0.119048 \n", + "7 0.000006 0.079848 0.683284 0.332829 0.238710 0.166667 \n", + "8 0.000035 0.399240 0.731936 0.405446 0.283871 0.190476 \n", + "9 0.009241 0.566540 0.675895 0.422088 0.267742 0.166667 \n", + "10 0.070540 0.764259 0.689713 0.456883 0.074194 0.190476 \n", + "\n", + " var1(t-5) var2(t-5) var3(t-5) var4(t-5) ... var3(t-1) var4(t-1) \\\n", + "6 0.000006 0.079848 0.683284 0.332829 ... 0.632281 0.464448 \n", + "7 0.000035 0.399240 0.731936 0.405446 ... 0.567508 0.440242 \n", + "8 0.009241 0.566540 0.675895 0.422088 ... 0.572306 0.468986 \n", + "9 0.070540 0.764259 0.689713 0.456883 ... 0.591786 0.461422 \n", + "10 0.023221 0.703422 0.632281 0.464448 ... 0.461760 0.570348 \n", + "\n", + " var5(t-1) var6(t-1) var1(t) var2(t) var3(t) var4(t) var5(t) \\\n", + "6 0.182258 0.238095 0.045884 0.847909 0.567508 0.440242 0.000000 \n", + "7 0.000000 0.309524 0.056366 0.638783 0.572306 0.468986 0.108065 \n", + "8 0.108065 0.309524 0.286279 0.634981 0.591786 0.461422 0.201613 \n", + "9 0.201613 0.333333 0.006073 0.380228 0.461760 0.570348 0.279032 \n", + "10 0.279032 0.309524 0.000205 0.311787 0.606804 0.512859 0.354839 \n", + "\n", + " var6(t) \n", + "6 0.309524 \n", + "7 0.309524 \n", + "8 0.333333 \n", + "9 0.309524 \n", + "10 0.285714 \n", + "\n", + "[5 rows x 42 columns]\n" + ] + } + ], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "# print(train_X.shape, len(train_X), train_y.shape)\n", + "# reshape input to be 3D [samples, timesteps, features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 6)\n", + "(80,)\n", + "(712, 6, 6)\n", + "(712,)\n", + "(54, 6, 6)\n", + "(54,)\n" + ] + } + ], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "8/8 - 3s - loss: 0.0123 - root_mean_squared_error: 0.1110 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2/1000\n", + "8/8 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3/1000\n", + "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4/1000\n", + "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 5/1000\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 6/1000\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 7/1000\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 8/1000\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 9/1000\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 10/1000\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 11/1000\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 12/1000\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 13/1000\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 14/1000\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 15/1000\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 16/1000\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 17/1000\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 18/1000\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 19/1000\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 20/1000\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 21/1000\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 22/1000\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0794 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 23/1000\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1738\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 24/1000\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1728\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 25/1000\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1718\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 26/1000\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1709\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 27/1000\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 28/1000\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1696\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 29/1000\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 30/1000\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 31/1000\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0743 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1663\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 32/1000\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0740 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1654\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 33/1000\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0737 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1647\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 34/1000\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1639\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 35/1000\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0719 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1627\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 36/1000\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1614\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 37/1000\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1602\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 38/1000\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1593\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 39/1000\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1585\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 40/1000\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 41/1000\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1569\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 42/1000\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1558\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 43/1000\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1545\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 44/1000\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 45/1000\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 46/1000\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1512\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 47/1000\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1506\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 48/1000\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1506\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 49/1000\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1505\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 50/1000\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1494\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 51/1000\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0644 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1473\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 52/1000\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 53/1000\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 54/1000\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1465\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 55/1000\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 56/1000\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1437\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 57/1000\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 58/1000\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0640 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 59/1000\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 60/1000\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1409\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 61/1000\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 62/1000\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1404\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 63/1000\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 64/1000\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 65/1000\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 66/1000\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0635 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1384\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 67/1000\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1380\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 68/1000\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 69/1000\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 70/1000\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1357\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 71/1000\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 72/1000\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1323\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 73/1000\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 74/1000\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 75/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 76/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 77/1000\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 78/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/1000\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 80/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 81/1000\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 82/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 83/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 84/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 85/1000\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 86/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1229\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 87/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 88/1000\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 89/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 90/1000\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 91/1000\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 92/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 93/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 94/1000\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 95/1000\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 96/1000\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 97/1000\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 98/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 99/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 100/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 101/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 102/1000\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 103/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 104/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 105/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 106/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 107/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 108/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 109/1000\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 110/1000\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1096\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 111/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 112/1000\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 113/1000\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 114/1000\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1092\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 115/1000\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 116/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 117/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1061\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 118/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 119/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 120/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 121/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1040\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 122/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 123/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 124/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 125/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 126/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 127/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0994\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 128/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 129/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 130/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 131/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 132/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 133/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0977\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 134/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 135/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0453 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 136/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 137/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 138/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 139/1000\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 140/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 141/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0950\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 142/1000\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 143/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 144/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0919\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 145/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 146/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 147/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 148/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 149/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 150/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 151/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 152/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 153/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 154/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 155/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 156/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 157/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 158/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 159/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 160/1000\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 161/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 162/1000\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 163/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 164/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 165/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 166/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 167/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 168/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 169/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 170/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 171/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 172/1000\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 173/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 174/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 175/1000\n", + "8/8 - 0s - loss: 9.9866e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 176/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 177/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 178/1000\n", + "8/8 - 0s - loss: 9.4696e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 179/1000\n", + "8/8 - 0s - loss: 9.6126e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 180/1000\n", + "8/8 - 0s - loss: 9.9797e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 181/1000\n", + "8/8 - 0s - loss: 9.6271e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 182/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 183/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 184/1000\n", + "8/8 - 0s - loss: 9.9936e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 185/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0749\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 186/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0761\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 187/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 188/1000\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 189/1000\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 190/1000\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 191/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 192/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 193/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 194/1000\n", + "8/8 - 0s - loss: 9.4607e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 195/1000\n", + "8/8 - 0s - loss: 9.5571e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 196/1000\n", + "8/8 - 0s - loss: 9.6568e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 197/1000\n", + "8/8 - 0s - loss: 8.8447e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0698\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 198/1000\n", + "8/8 - 0s - loss: 8.1300e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 199/1000\n", + "8/8 - 0s - loss: 7.6942e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 200/1000\n", + "8/8 - 0s - loss: 7.5727e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 201/1000\n", + "8/8 - 0s - loss: 7.8614e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0679\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 202/1000\n", + "8/8 - 0s - loss: 7.7747e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 203/1000\n", + "8/8 - 0s - loss: 7.6934e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 204/1000\n", + "8/8 - 0s - loss: 7.8984e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 205/1000\n", + "8/8 - 0s - loss: 9.2235e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0673\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 206/1000\n", + "8/8 - 0s - loss: 8.8747e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 207/1000\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 208/1000\n", + "8/8 - 0s - loss: 9.4116e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 209/1000\n", + "8/8 - 0s - loss: 9.4043e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 210/1000\n", + "8/8 - 0s - loss: 9.3007e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 211/1000\n", + "8/8 - 0s - loss: 8.9690e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 212/1000\n", + "8/8 - 0s - loss: 8.2528e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0662\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 213/1000\n", + "8/8 - 0s - loss: 7.7245e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 214/1000\n", + "8/8 - 0s - loss: 6.7134e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 215/1000\n", + "8/8 - 0s - loss: 6.9207e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 216/1000\n", + "8/8 - 0s - loss: 6.3785e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 217/1000\n", + "8/8 - 0s - loss: 6.1269e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 218/1000\n", + "8/8 - 0s - loss: 6.4166e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0611\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 219/1000\n", + "8/8 - 0s - loss: 6.1906e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 220/1000\n", + "8/8 - 0s - loss: 6.7147e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0609\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 221/1000\n", + "8/8 - 0s - loss: 6.5222e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 222/1000\n", + "8/8 - 0s - loss: 6.7911e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 223/1000\n", + "8/8 - 0s - loss: 7.2988e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 224/1000\n", + "8/8 - 0s - loss: 6.9174e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0612\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 225/1000\n", + "8/8 - 0s - loss: 8.0977e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 226/1000\n", + "8/8 - 0s - loss: 7.5138e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 227/1000\n", + "8/8 - 0s - loss: 7.5123e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 228/1000\n", + "8/8 - 0s - loss: 7.7082e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 229/1000\n", + "8/8 - 0s - loss: 7.2479e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 230/1000\n", + "8/8 - 0s - loss: 7.3139e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 231/1000\n", + "8/8 - 0s - loss: 7.1083e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 232/1000\n", + "8/8 - 0s - loss: 6.6073e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 233/1000\n", + "8/8 - 0s - loss: 6.6962e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 234/1000\n", + "8/8 - 0s - loss: 8.8168e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 235/1000\n", + "8/8 - 0s - loss: 8.6639e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 236/1000\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 237/1000\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 238/1000\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 239/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 240/1000\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 241/1000\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0613\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 242/1000\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 243/1000\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 244/1000\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0634\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 245/1000\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 246/1000\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 247/1000\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 248/1000\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 249/1000\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 250/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 251/1000\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 252/1000\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0650\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 253/1000\n", + "8/8 - 0s - loss: 9.3112e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0600\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 254/1000\n", + "8/8 - 0s - loss: 8.0208e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 255/1000\n", + "8/8 - 0s - loss: 7.0911e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 256/1000\n", + "8/8 - 0s - loss: 6.7416e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 257/1000\n", + "8/8 - 0s - loss: 6.5133e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 258/1000\n", + "8/8 - 0s - loss: 5.9806e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0541\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 259/1000\n", + "8/8 - 0s - loss: 5.5737e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0541\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 260/1000\n", + "8/8 - 0s - loss: 5.2261e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 261/1000\n", + "8/8 - 0s - loss: 5.1290e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 262/1000\n", + "8/8 - 0s - loss: 5.0185e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 263/1000\n", + "8/8 - 0s - loss: 4.9620e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 264/1000\n", + "8/8 - 0s - loss: 4.7932e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 265/1000\n", + "8/8 - 0s - loss: 4.5475e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 266/1000\n", + "8/8 - 0s - loss: 4.1959e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 267/1000\n", + "8/8 - 0s - loss: 4.0883e-04 - root_mean_squared_error: 0.0202 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 268/1000\n", + "8/8 - 0s - loss: 4.1235e-04 - root_mean_squared_error: 0.0203 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 269/1000\n", + "8/8 - 0s - loss: 4.0644e-04 - root_mean_squared_error: 0.0202 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 270/1000\n", + "8/8 - 0s - loss: 4.0539e-04 - root_mean_squared_error: 0.0201 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 271/1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 - 0s - loss: 3.9208e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 272/1000\n", + "8/8 - 0s - loss: 3.7445e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 273/1000\n", + "8/8 - 0s - loss: 3.6639e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 274/1000\n", + "8/8 - 0s - loss: 3.6617e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 275/1000\n", + "8/8 - 0s - loss: 3.6556e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 276/1000\n", + "8/8 - 0s - loss: 3.7019e-04 - root_mean_squared_error: 0.0192 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 277/1000\n", + "8/8 - 0s - loss: 3.6301e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 278/1000\n", + "8/8 - 0s - loss: 3.6487e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 279/1000\n", + "8/8 - 0s - loss: 3.6552e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 280/1000\n", + "8/8 - 0s - loss: 3.7990e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 281/1000\n", + "8/8 - 0s - loss: 3.8249e-04 - root_mean_squared_error: 0.0196 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 282/1000\n", + "8/8 - 0s - loss: 3.8216e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 283/1000\n", + "8/8 - 0s - loss: 3.8034e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 284/1000\n", + "8/8 - 0s - loss: 3.8925e-04 - root_mean_squared_error: 0.0197 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 285/1000\n", + "8/8 - 0s - loss: 3.9608e-04 - root_mean_squared_error: 0.0199 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 286/1000\n", + "8/8 - 0s - loss: 4.3072e-04 - root_mean_squared_error: 0.0208 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 287/1000\n", + "8/8 - 0s - loss: 4.5969e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 288/1000\n", + "8/8 - 0s - loss: 4.8185e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 289/1000\n", + "8/8 - 0s - loss: 5.0901e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 290/1000\n", + "8/8 - 0s - loss: 5.5908e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 291/1000\n", + "8/8 - 0s - loss: 5.9563e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 292/1000\n", + "8/8 - 0s - loss: 5.6431e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0446\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 293/1000\n", + "8/8 - 0s - loss: 5.3776e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 294/1000\n", + "8/8 - 0s - loss: 4.4007e-04 - root_mean_squared_error: 0.0210 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0434\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 295/1000\n", + "8/8 - 0s - loss: 3.6585e-04 - root_mean_squared_error: 0.0191 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 296/1000\n", + "8/8 - 0s - loss: 3.2278e-04 - root_mean_squared_error: 0.0180 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 297/1000\n", + "8/8 - 0s - loss: 3.2182e-04 - root_mean_squared_error: 0.0179 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 298/1000\n", + "8/8 - 0s - loss: 2.9864e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 299/1000\n", + "8/8 - 0s - loss: 2.7896e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 300/1000\n", + "8/8 - 0s - loss: 2.6392e-04 - root_mean_squared_error: 0.0162 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 301/1000\n", + "8/8 - 0s - loss: 2.7571e-04 - root_mean_squared_error: 0.0166 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 302/1000\n", + "8/8 - 0s - loss: 2.6808e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 303/1000\n", + "8/8 - 0s - loss: 2.7594e-04 - root_mean_squared_error: 0.0166 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 304/1000\n", + "8/8 - 0s - loss: 2.4823e-04 - root_mean_squared_error: 0.0158 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 305/1000\n", + "8/8 - 0s - loss: 2.5750e-04 - root_mean_squared_error: 0.0160 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 306/1000\n", + "8/8 - 0s - loss: 2.5739e-04 - root_mean_squared_error: 0.0160 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 307/1000\n", + "8/8 - 0s - loss: 2.8684e-04 - root_mean_squared_error: 0.0169 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 308/1000\n", + "8/8 - 0s - loss: 2.7196e-04 - root_mean_squared_error: 0.0165 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 309/1000\n", + "8/8 - 0s - loss: 2.6220e-04 - root_mean_squared_error: 0.0162 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 310/1000\n", + "8/8 - 0s - loss: 2.7073e-04 - root_mean_squared_error: 0.0165 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 311/1000\n", + "8/8 - 0s - loss: 2.9599e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/1000\n", + "8/8 - 0s - loss: 2.9600e-04 - root_mean_squared_error: 0.0172 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 313/1000\n", + "8/8 - 0s - loss: 2.7291e-04 - root_mean_squared_error: 0.0165 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 314/1000\n", + "8/8 - 0s - loss: 2.8079e-04 - root_mean_squared_error: 0.0168 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 315/1000\n", + "8/8 - 0s - loss: 3.3179e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 316/1000\n", + "8/8 - 0s - loss: 3.2377e-04 - root_mean_squared_error: 0.0180 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 317/1000\n", + "8/8 - 0s - loss: 3.5995e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 318/1000\n", + "8/8 - 0s - loss: 2.8341e-04 - root_mean_squared_error: 0.0168 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 319/1000\n", + "8/8 - 0s - loss: 3.3303e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 320/1000\n", + "8/8 - 0s - loss: 3.1967e-04 - root_mean_squared_error: 0.0179 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 321/1000\n", + "8/8 - 0s - loss: 3.8497e-04 - root_mean_squared_error: 0.0196 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 322/1000\n", + "8/8 - 0s - loss: 4.2054e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 323/1000\n", + "8/8 - 0s - loss: 3.8067e-04 - root_mean_squared_error: 0.0195 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 324/1000\n", + "8/8 - 0s - loss: 4.7234e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 325/1000\n", + "8/8 - 0s - loss: 4.5903e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 326/1000\n", + "8/8 - 0s - loss: 4.5416e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 327/1000\n", + "8/8 - 0s - loss: 4.5089e-04 - root_mean_squared_error: 0.0212 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 328/1000\n", + "8/8 - 0s - loss: 3.5682e-04 - root_mean_squared_error: 0.0189 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 329/1000\n", + "8/8 - 0s - loss: 3.5882e-04 - root_mean_squared_error: 0.0189 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 330/1000\n", + "8/8 - 0s - loss: 3.3906e-04 - root_mean_squared_error: 0.0184 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 331/1000\n", + "8/8 - 0s - loss: 3.4385e-04 - root_mean_squared_error: 0.0185 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 332/1000\n", + "8/8 - 0s - loss: 4.1088e-04 - root_mean_squared_error: 0.0203 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 333/1000\n", + "8/8 - 0s - loss: 3.9067e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 334/1000\n", + "8/8 - 0s - loss: 3.7405e-04 - root_mean_squared_error: 0.0193 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 335/1000\n", + "8/8 - 0s - loss: 4.1864e-04 - root_mean_squared_error: 0.0205 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 336/1000\n", + "8/8 - 0s - loss: 4.6296e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 337/1000\n", + "8/8 - 0s - loss: 5.0534e-04 - root_mean_squared_error: 0.0225 - val_loss: 9.8185e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 338/1000\n", + "8/8 - 0s - loss: 5.4065e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 339/1000\n", + "8/8 - 0s - loss: 4.6143e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 340/1000\n", + "8/8 - 0s - loss: 3.7565e-04 - root_mean_squared_error: 0.0194 - val_loss: 9.9802e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 341/1000\n", + "8/8 - 0s - loss: 2.9607e-04 - root_mean_squared_error: 0.0172 - val_loss: 9.7345e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 342/1000\n", + "8/8 - 0s - loss: 2.9148e-04 - root_mean_squared_error: 0.0171 - val_loss: 9.9748e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 343/1000\n", + "8/8 - 0s - loss: 3.0591e-04 - root_mean_squared_error: 0.0175 - val_loss: 8.7304e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 344/1000\n", + "8/8 - 0s - loss: 3.1212e-04 - root_mean_squared_error: 0.0177 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 345/1000\n", + "8/8 - 0s - loss: 3.1526e-04 - root_mean_squared_error: 0.0178 - val_loss: 9.3995e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 346/1000\n", + "8/8 - 0s - loss: 2.5499e-04 - root_mean_squared_error: 0.0160 - val_loss: 8.9239e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 347/1000\n", + "8/8 - 0s - loss: 2.4672e-04 - root_mean_squared_error: 0.0157 - val_loss: 8.1254e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 348/1000\n", + "8/8 - 0s - loss: 3.3231e-04 - root_mean_squared_error: 0.0182 - val_loss: 9.4248e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 349/1000\n", + "8/8 - 0s - loss: 3.4854e-04 - root_mean_squared_error: 0.0187 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/1000\n", + "8/8 - 0s - loss: 3.7812e-04 - root_mean_squared_error: 0.0194 - val_loss: 9.4820e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "8/8 - 0s - loss: 2.9125e-04 - root_mean_squared_error: 0.0171 - val_loss: 8.8562e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 352/1000\n", + "8/8 - 0s - loss: 2.8584e-04 - root_mean_squared_error: 0.0169 - val_loss: 8.4053e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 353/1000\n", + "8/8 - 0s - loss: 3.4327e-04 - root_mean_squared_error: 0.0185 - val_loss: 8.9247e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 354/1000\n", + "8/8 - 0s - loss: 3.2188e-04 - root_mean_squared_error: 0.0179 - val_loss: 9.5819e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 355/1000\n", + "8/8 - 0s - loss: 2.9155e-04 - root_mean_squared_error: 0.0171 - val_loss: 8.6005e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 356/1000\n", + "8/8 - 0s - loss: 2.2387e-04 - root_mean_squared_error: 0.0150 - val_loss: 7.9329e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 357/1000\n", + "8/8 - 0s - loss: 2.0239e-04 - root_mean_squared_error: 0.0142 - val_loss: 7.1392e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 358/1000\n", + "8/8 - 0s - loss: 2.0832e-04 - root_mean_squared_error: 0.0144 - val_loss: 7.0163e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 359/1000\n", + "8/8 - 0s - loss: 2.1363e-04 - root_mean_squared_error: 0.0146 - val_loss: 7.4264e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 360/1000\n", + "8/8 - 0s - loss: 3.3831e-04 - root_mean_squared_error: 0.0184 - val_loss: 7.4326e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 361/1000\n", + "8/8 - 0s - loss: 3.7024e-04 - root_mean_squared_error: 0.0192 - val_loss: 8.8741e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 362/1000\n", + "8/8 - 0s - loss: 3.9782e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.9750e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 363/1000\n", + "8/8 - 0s - loss: 4.5324e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.0095e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 364/1000\n", + "8/8 - 0s - loss: 3.9210e-04 - root_mean_squared_error: 0.0198 - val_loss: 8.7269e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 365/1000\n", + "8/8 - 0s - loss: 3.5137e-04 - root_mean_squared_error: 0.0187 - val_loss: 9.0240e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 366/1000\n", + "8/8 - 0s - loss: 3.4581e-04 - root_mean_squared_error: 0.0186 - val_loss: 8.2063e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 367/1000\n", + "8/8 - 0s - loss: 3.2791e-04 - root_mean_squared_error: 0.0181 - val_loss: 7.8211e-04 - val_root_mean_squared_error: 0.0280\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 368/1000\n", + "8/8 - 0s - loss: 3.1952e-04 - root_mean_squared_error: 0.0179 - val_loss: 7.9833e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 369/1000\n", + "8/8 - 0s - loss: 3.4628e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.7482e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 370/1000\n", + "8/8 - 0s - loss: 3.1505e-04 - root_mean_squared_error: 0.0177 - val_loss: 8.4241e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 371/1000\n", + "8/8 - 0s - loss: 3.0310e-04 - root_mean_squared_error: 0.0174 - val_loss: 7.6543e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 372/1000\n", + "8/8 - 0s - loss: 3.6347e-04 - root_mean_squared_error: 0.0191 - val_loss: 7.0970e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 373/1000\n", + "8/8 - 0s - loss: 3.7001e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.7984e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 374/1000\n", + "8/8 - 0s - loss: 4.0342e-04 - root_mean_squared_error: 0.0201 - val_loss: 7.6471e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 375/1000\n", + "8/8 - 0s - loss: 3.6853e-04 - root_mean_squared_error: 0.0192 - val_loss: 8.4686e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 376/1000\n", + "8/8 - 0s - loss: 2.7142e-04 - root_mean_squared_error: 0.0165 - val_loss: 7.0361e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 377/1000\n", + "8/8 - 0s - loss: 2.3347e-04 - root_mean_squared_error: 0.0153 - val_loss: 6.7371e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 378/1000\n", + "8/8 - 0s - loss: 1.9692e-04 - root_mean_squared_error: 0.0140 - val_loss: 5.8998e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 379/1000\n", + "8/8 - 0s - loss: 1.8062e-04 - root_mean_squared_error: 0.0134 - val_loss: 5.7943e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 380/1000\n", + "8/8 - 0s - loss: 1.6522e-04 - root_mean_squared_error: 0.0129 - val_loss: 5.8993e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 381/1000\n", + "8/8 - 0s - loss: 1.6909e-04 - root_mean_squared_error: 0.0130 - val_loss: 5.3696e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 382/1000\n", + "8/8 - 0s - loss: 1.5946e-04 - root_mean_squared_error: 0.0126 - val_loss: 5.4218e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 383/1000\n", + "8/8 - 0s - loss: 1.5963e-04 - root_mean_squared_error: 0.0126 - val_loss: 5.1927e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 384/1000\n", + "8/8 - 0s - loss: 1.9076e-04 - root_mean_squared_error: 0.0138 - val_loss: 5.6585e-04 - val_root_mean_squared_error: 0.0238\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 385/1000\n", + "8/8 - 0s - loss: 1.7567e-04 - root_mean_squared_error: 0.0133 - val_loss: 5.4621e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 386/1000\n", + "8/8 - 0s - loss: 1.6423e-04 - root_mean_squared_error: 0.0128 - val_loss: 5.0314e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 387/1000\n", + "8/8 - 0s - loss: 1.3911e-04 - root_mean_squared_error: 0.0118 - val_loss: 4.9026e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/1000\n", + "8/8 - 0s - loss: 1.3503e-04 - root_mean_squared_error: 0.0116 - val_loss: 4.6437e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "8/8 - 0s - loss: 1.2562e-04 - root_mean_squared_error: 0.0112 - val_loss: 4.6352e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "8/8 - 0s - loss: 1.3101e-04 - root_mean_squared_error: 0.0114 - val_loss: 4.5292e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 391/1000\n", + "8/8 - 0s - loss: 1.6638e-04 - root_mean_squared_error: 0.0129 - val_loss: 4.8454e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 392/1000\n", + "8/8 - 0s - loss: 1.6841e-04 - root_mean_squared_error: 0.0130 - val_loss: 5.2222e-04 - val_root_mean_squared_error: 0.0229\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 393/1000\n", + "8/8 - 0s - loss: 1.6889e-04 - root_mean_squared_error: 0.0130 - val_loss: 4.8362e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 394/1000\n", + "8/8 - 0s - loss: 1.6841e-04 - root_mean_squared_error: 0.0130 - val_loss: 4.5617e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 395/1000\n", + "8/8 - 0s - loss: 1.7095e-04 - root_mean_squared_error: 0.0131 - val_loss: 4.5653e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 396/1000\n", + "8/8 - 0s - loss: 2.0250e-04 - root_mean_squared_error: 0.0142 - val_loss: 4.4386e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 397/1000\n", + "8/8 - 0s - loss: 2.0394e-04 - root_mean_squared_error: 0.0143 - val_loss: 5.2898e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 398/1000\n", + "8/8 - 0s - loss: 2.7855e-04 - root_mean_squared_error: 0.0167 - val_loss: 5.7866e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 399/1000\n", + "8/8 - 0s - loss: 2.8902e-04 - root_mean_squared_error: 0.0170 - val_loss: 6.3443e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 400/1000\n", + "8/8 - 0s - loss: 2.5326e-04 - root_mean_squared_error: 0.0159 - val_loss: 4.9216e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 401/1000\n", + "8/8 - 0s - loss: 2.9930e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.9982e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 402/1000\n", + "8/8 - 0s - loss: 2.8291e-04 - root_mean_squared_error: 0.0168 - val_loss: 5.6848e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 403/1000\n", + "8/8 - 0s - loss: 2.6662e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.7301e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 404/1000\n", + "8/8 - 0s - loss: 2.8694e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4767e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 405/1000\n", + "8/8 - 0s - loss: 3.0356e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.1804e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 406/1000\n", + "8/8 - 0s - loss: 3.1154e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.4386e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 407/1000\n", + "8/8 - 0s - loss: 2.9483e-04 - root_mean_squared_error: 0.0172 - val_loss: 5.3699e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 408/1000\n", + "8/8 - 0s - loss: 3.0310e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.5030e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 409/1000\n", + "8/8 - 0s - loss: 2.7902e-04 - root_mean_squared_error: 0.0167 - val_loss: 5.9367e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 410/1000\n", + "8/8 - 0s - loss: 2.5167e-04 - root_mean_squared_error: 0.0159 - val_loss: 4.2295e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 411/1000\n", + "8/8 - 0s - loss: 2.2468e-04 - root_mean_squared_error: 0.0150 - val_loss: 4.4917e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 412/1000\n", + "8/8 - 0s - loss: 2.5449e-04 - root_mean_squared_error: 0.0160 - val_loss: 4.6997e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 413/1000\n", + "8/8 - 0s - loss: 2.7708e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.3705e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 414/1000\n", + "8/8 - 0s - loss: 2.8035e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.7603e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 415/1000\n", + "8/8 - 0s - loss: 3.0782e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8424e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 416/1000\n", + "8/8 - 0s - loss: 3.1276e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.8906e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 417/1000\n", + "8/8 - 0s - loss: 2.4744e-04 - root_mean_squared_error: 0.0157 - val_loss: 4.5465e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 418/1000\n", + "8/8 - 0s - loss: 1.8707e-04 - root_mean_squared_error: 0.0137 - val_loss: 3.4440e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 419/1000\n", + "8/8 - 0s - loss: 1.4368e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.3133e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 420/1000\n", + "8/8 - 0s - loss: 1.5376e-04 - root_mean_squared_error: 0.0124 - val_loss: 3.4275e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 421/1000\n", + "8/8 - 0s - loss: 1.5187e-04 - root_mean_squared_error: 0.0123 - val_loss: 3.5454e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 422/1000\n", + "8/8 - 0s - loss: 1.7437e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.6994e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 423/1000\n", + "8/8 - 0s - loss: 1.9246e-04 - root_mean_squared_error: 0.0139 - val_loss: 4.0662e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 424/1000\n", + "8/8 - 0s - loss: 2.0656e-04 - root_mean_squared_error: 0.0144 - val_loss: 3.5873e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 425/1000\n", + "8/8 - 0s - loss: 2.3355e-04 - root_mean_squared_error: 0.0153 - val_loss: 5.2895e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 426/1000\n", + "8/8 - 0s - loss: 2.1027e-04 - root_mean_squared_error: 0.0145 - val_loss: 3.9575e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 427/1000\n", + "8/8 - 0s - loss: 2.0541e-04 - root_mean_squared_error: 0.0143 - val_loss: 3.1340e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 428/1000\n", + "8/8 - 0s - loss: 2.0533e-04 - root_mean_squared_error: 0.0143 - val_loss: 3.3227e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 429/1000\n", + "8/8 - 0s - loss: 2.0091e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.4073e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 430/1000\n", + "8/8 - 0s - loss: 1.7643e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.4492e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 431/1000\n", + "8/8 - 0s - loss: 1.9437e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.4438e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 432/1000\n", + "8/8 - 0s - loss: 1.7336e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.0247e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 433/1000\n", + "8/8 - 0s - loss: 1.6849e-04 - root_mean_squared_error: 0.0130 - val_loss: 3.0111e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 434/1000\n", + "8/8 - 0s - loss: 1.7610e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.3223e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 435/1000\n", + "8/8 - 0s - loss: 1.6393e-04 - root_mean_squared_error: 0.0128 - val_loss: 2.8549e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 436/1000\n", + "8/8 - 0s - loss: 1.6222e-04 - root_mean_squared_error: 0.0127 - val_loss: 2.6771e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 437/1000\n", + "8/8 - 0s - loss: 1.8371e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.9920e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 438/1000\n", + "8/8 - 0s - loss: 1.9730e-04 - root_mean_squared_error: 0.0140 - val_loss: 3.2730e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 439/1000\n", + "8/8 - 0s - loss: 1.6517e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.9630e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 440/1000\n", + "8/8 - 0s - loss: 1.6638e-04 - root_mean_squared_error: 0.0129 - val_loss: 3.0073e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 441/1000\n", + "8/8 - 0s - loss: 2.1650e-04 - root_mean_squared_error: 0.0147 - val_loss: 3.0038e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 442/1000\n", + "8/8 - 0s - loss: 2.5842e-04 - root_mean_squared_error: 0.0161 - val_loss: 4.0346e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 443/1000\n", + "8/8 - 0s - loss: 2.8185e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0899e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 444/1000\n", + "8/8 - 0s - loss: 2.7942e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.7594e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 445/1000\n", + "8/8 - 0s - loss: 2.7344e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.4911e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 446/1000\n", + "8/8 - 0s - loss: 2.7626e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.0377e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 447/1000\n", + "8/8 - 0s - loss: 2.7307e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.7073e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 448/1000\n", + "8/8 - 0s - loss: 2.8247e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3286e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 449/1000\n", + "8/8 - 0s - loss: 1.9334e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.3722e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 450/1000\n", + "8/8 - 0s - loss: 1.6911e-04 - root_mean_squared_error: 0.0130 - val_loss: 2.6445e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 451/1000\n", + "8/8 - 0s - loss: 1.4431e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.8749e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 452/1000\n", + "8/8 - 0s - loss: 1.3938e-04 - root_mean_squared_error: 0.0118 - val_loss: 2.3897e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 453/1000\n", + "8/8 - 0s - loss: 1.4487e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.3409e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 454/1000\n", + "8/8 - 0s - loss: 1.4374e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.8231e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 455/1000\n", + "8/8 - 0s - loss: 1.7977e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.6352e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 456/1000\n", + "8/8 - 0s - loss: 1.8730e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.8393e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 457/1000\n", + "8/8 - 0s - loss: 2.1194e-04 - root_mean_squared_error: 0.0146 - val_loss: 3.4468e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 458/1000\n", + "8/8 - 0s - loss: 2.5368e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.2450e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 459/1000\n", + "8/8 - 0s - loss: 2.6441e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.4322e-04 - val_root_mean_squared_error: 0.0185\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 460/1000\n", + "8/8 - 0s - loss: 2.4059e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.3127e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 461/1000\n", + "8/8 - 0s - loss: 2.6388e-04 - root_mean_squared_error: 0.0162 - val_loss: 4.3444e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 462/1000\n", + "8/8 - 0s - loss: 2.5659e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.5771e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 463/1000\n", + "8/8 - 0s - loss: 2.1928e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.2687e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 464/1000\n", + "8/8 - 0s - loss: 2.3282e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.2835e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 465/1000\n", + "8/8 - 0s - loss: 2.2256e-04 - root_mean_squared_error: 0.0149 - val_loss: 3.8465e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 466/1000\n", + "8/8 - 0s - loss: 2.8230e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.0125e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 467/1000\n", + "8/8 - 0s - loss: 3.1939e-04 - root_mean_squared_error: 0.0179 - val_loss: 4.6603e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 468/1000\n", + "8/8 - 0s - loss: 2.0695e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.5775e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 469/1000\n", + "8/8 - 0s - loss: 2.4613e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.2609e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 470/1000\n", + "8/8 - 0s - loss: 2.8712e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2419e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 471/1000\n", + "8/8 - 0s - loss: 2.5158e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.5902e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 472/1000\n", + "8/8 - 0s - loss: 2.7710e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.8076e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 473/1000\n", + "8/8 - 0s - loss: 3.2868e-04 - root_mean_squared_error: 0.0181 - val_loss: 4.6352e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 474/1000\n", + "8/8 - 0s - loss: 4.2695e-04 - root_mean_squared_error: 0.0207 - val_loss: 4.9033e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 475/1000\n", + "8/8 - 0s - loss: 3.7588e-04 - root_mean_squared_error: 0.0194 - val_loss: 4.7812e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 476/1000\n", + "8/8 - 0s - loss: 3.0220e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.5152e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 477/1000\n", + "8/8 - 0s - loss: 3.1090e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.6430e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 478/1000\n", + "8/8 - 0s - loss: 2.8737e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.4850e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 479/1000\n", + "8/8 - 0s - loss: 2.1893e-04 - root_mean_squared_error: 0.0148 - val_loss: 3.0336e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 480/1000\n", + "8/8 - 0s - loss: 2.1027e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.8054e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 481/1000\n", + "8/8 - 0s - loss: 2.2051e-04 - root_mean_squared_error: 0.0148 - val_loss: 3.2889e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 482/1000\n", + "8/8 - 0s - loss: 2.5913e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.8655e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 483/1000\n", + "8/8 - 0s - loss: 2.7624e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.9257e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 484/1000\n", + "8/8 - 0s - loss: 2.6310e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.0897e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 485/1000\n", + "8/8 - 0s - loss: 2.7427e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0015e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 486/1000\n", + "8/8 - 0s - loss: 2.0991e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.8247e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 487/1000\n", + "8/8 - 0s - loss: 1.7955e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.1399e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 488/1000\n", + "8/8 - 0s - loss: 1.9336e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.0536e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 489/1000\n", + "8/8 - 0s - loss: 2.0380e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.9290e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 490/1000\n", + "8/8 - 0s - loss: 2.2575e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.9347e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 491/1000\n", + "8/8 - 0s - loss: 1.9588e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.9161e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 492/1000\n", + "8/8 - 0s - loss: 1.7693e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.2919e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 493/1000\n", + "8/8 - 0s - loss: 1.5024e-04 - root_mean_squared_error: 0.0123 - val_loss: 2.3565e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 494/1000\n", + "8/8 - 0s - loss: 1.2440e-04 - root_mean_squared_error: 0.0112 - val_loss: 2.2037e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 495/1000\n", + "8/8 - 0s - loss: 1.0782e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.8443e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 496/1000\n", + "8/8 - 0s - loss: 9.1686e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.6398e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 497/1000\n", + "8/8 - 0s - loss: 7.9665e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.6007e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 498/1000\n", + "8/8 - 0s - loss: 7.7504e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.5143e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 499/1000\n", + "8/8 - 0s - loss: 7.7057e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.5898e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 500/1000\n", + "8/8 - 0s - loss: 7.0320e-05 - root_mean_squared_error: 0.0084 - val_loss: 1.4674e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 501/1000\n", + "8/8 - 0s - loss: 6.8068e-05 - root_mean_squared_error: 0.0083 - val_loss: 1.4634e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 502/1000\n", + "8/8 - 0s - loss: 6.2563e-05 - root_mean_squared_error: 0.0079 - val_loss: 1.3579e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 503/1000\n", + "8/8 - 0s - loss: 5.7546e-05 - root_mean_squared_error: 0.0076 - val_loss: 1.2040e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 504/1000\n", + "8/8 - 0s - loss: 5.2447e-05 - root_mean_squared_error: 0.0072 - val_loss: 1.1784e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 505/1000\n", + "8/8 - 0s - loss: 4.5600e-05 - root_mean_squared_error: 0.0068 - val_loss: 1.0684e-04 - val_root_mean_squared_error: 0.0103\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 506/1000\n", + "8/8 - 0s - loss: 4.3977e-05 - root_mean_squared_error: 0.0066 - val_loss: 9.9984e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 507/1000\n", + "8/8 - 0s - loss: 4.4062e-05 - root_mean_squared_error: 0.0066 - val_loss: 1.0312e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 508/1000\n", + "8/8 - 0s - loss: 4.1562e-05 - root_mean_squared_error: 0.0064 - val_loss: 1.0168e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 509/1000\n", + "8/8 - 0s - loss: 4.5618e-05 - root_mean_squared_error: 0.0068 - val_loss: 9.5918e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 510/1000\n", + "8/8 - 0s - loss: 6.6449e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.1036e-04 - val_root_mean_squared_error: 0.0105\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 511/1000\n", + "8/8 - 0s - loss: 8.5710e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.3664e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 512/1000\n", + "8/8 - 0s - loss: 1.3040e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.7137e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 513/1000\n", + "8/8 - 0s - loss: 1.2953e-04 - root_mean_squared_error: 0.0114 - val_loss: 2.1174e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 514/1000\n", + "8/8 - 0s - loss: 1.1546e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.4742e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 515/1000\n", + "8/8 - 0s - loss: 1.2781e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.6082e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 516/1000\n", + "8/8 - 0s - loss: 1.5410e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.9261e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 517/1000\n", + "8/8 - 0s - loss: 1.8437e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.7883e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 518/1000\n", + "8/8 - 0s - loss: 1.8247e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.3875e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 519/1000\n", + "8/8 - 0s - loss: 1.8961e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2524e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 520/1000\n", + "8/8 - 0s - loss: 2.3919e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.5988e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 521/1000\n", + "8/8 - 0s - loss: 2.4549e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.9455e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 522/1000\n", + "8/8 - 0s - loss: 1.9320e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.8001e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 523/1000\n", + "8/8 - 0s - loss: 1.8193e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.1741e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 524/1000\n", + "8/8 - 0s - loss: 1.9505e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.4617e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 525/1000\n", + "8/8 - 0s - loss: 1.7444e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8546e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 526/1000\n", + "8/8 - 0s - loss: 1.9278e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.6846e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 527/1000\n", + "8/8 - 0s - loss: 1.3878e-04 - root_mean_squared_error: 0.0118 - val_loss: 2.2324e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 528/1000\n", + "8/8 - 0s - loss: 1.4487e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.5364e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 529/1000\n", + "8/8 - 0s - loss: 1.5240e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.6430e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 530/1000\n", + "8/8 - 0s - loss: 1.6625e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.3547e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 531/1000\n", + "8/8 - 0s - loss: 2.3072e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.3760e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 532/1000\n", + "8/8 - 0s - loss: 2.3202e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.7243e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 533/1000\n", + "8/8 - 0s - loss: 2.3263e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.0930e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 534/1000\n", + "8/8 - 0s - loss: 3.1752e-04 - root_mean_squared_error: 0.0178 - val_loss: 3.5418e-04 - val_root_mean_squared_error: 0.0188\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 535/1000\n", + "8/8 - 0s - loss: 3.0592e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.2390e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 536/1000\n", + "8/8 - 0s - loss: 2.7187e-04 - root_mean_squared_error: 0.0165 - val_loss: 1.7844e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 537/1000\n", + "8/8 - 0s - loss: 2.1983e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.2824e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 538/1000\n", + "8/8 - 0s - loss: 1.9850e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1682e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 539/1000\n", + "8/8 - 0s - loss: 1.8954e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2123e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 540/1000\n", + "8/8 - 0s - loss: 1.6051e-04 - root_mean_squared_error: 0.0127 - val_loss: 2.5392e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 541/1000\n", + "8/8 - 0s - loss: 1.0042e-04 - root_mean_squared_error: 0.0100 - val_loss: 1.6708e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 542/1000\n", + "8/8 - 0s - loss: 9.6591e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.1924e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 543/1000\n", + "8/8 - 0s - loss: 9.1886e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.1478e-04 - val_root_mean_squared_error: 0.0107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 544/1000\n", + "8/8 - 0s - loss: 9.4656e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.1531e-04 - val_root_mean_squared_error: 0.0107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 545/1000\n", + "8/8 - 0s - loss: 1.0414e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.1350e-04 - val_root_mean_squared_error: 0.0107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 546/1000\n", + "8/8 - 0s - loss: 1.0662e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.3999e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 547/1000\n", + "8/8 - 0s - loss: 1.0799e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.4174e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 548/1000\n", + "8/8 - 0s - loss: 9.0041e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.3268e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 549/1000\n", + "8/8 - 0s - loss: 7.8117e-05 - root_mean_squared_error: 0.0088 - val_loss: 9.9817e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 550/1000\n", + "8/8 - 0s - loss: 9.8991e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1798e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 551/1000\n", + "8/8 - 0s - loss: 1.1038e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.2945e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 552/1000\n", + "8/8 - 0s - loss: 1.3602e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5082e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 553/1000\n", + "8/8 - 0s - loss: 1.3527e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.6805e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 554/1000\n", + "8/8 - 0s - loss: 1.2692e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.4944e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 555/1000\n", + "8/8 - 0s - loss: 1.0514e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.5138e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 556/1000\n", + "8/8 - 0s - loss: 8.9949e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.2847e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 557/1000\n", + "8/8 - 0s - loss: 6.8599e-05 - root_mean_squared_error: 0.0083 - val_loss: 9.0850e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 558/1000\n", + "8/8 - 0s - loss: 7.1690e-05 - root_mean_squared_error: 0.0085 - val_loss: 8.8339e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 559/1000\n", + "8/8 - 0s - loss: 7.9605e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.0265e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 560/1000\n", + "8/8 - 0s - loss: 1.0057e-04 - root_mean_squared_error: 0.0100 - val_loss: 1.2898e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 561/1000\n", + "8/8 - 0s - loss: 9.8640e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1727e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 562/1000\n", + "8/8 - 0s - loss: 7.9338e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.1798e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 563/1000\n", + "8/8 - 0s - loss: 6.9573e-05 - root_mean_squared_error: 0.0083 - val_loss: 9.2316e-05 - val_root_mean_squared_error: 0.0096\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 564/1000\n", + "8/8 - 0s - loss: 7.8939e-05 - root_mean_squared_error: 0.0089 - val_loss: 9.9446e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 565/1000\n", + "8/8 - 0s - loss: 8.0164e-05 - root_mean_squared_error: 0.0090 - val_loss: 8.7531e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 566/1000\n", + "8/8 - 0s - loss: 1.1105e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.1238e-04 - val_root_mean_squared_error: 0.0106\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 567/1000\n", + "8/8 - 0s - loss: 1.1287e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.4663e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 568/1000\n", + "8/8 - 0s - loss: 1.1568e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.2607e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 569/1000\n", + "8/8 - 0s - loss: 8.7391e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.1950e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 570/1000\n", + "8/8 - 0s - loss: 7.9434e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.0442e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 571/1000\n", + "8/8 - 0s - loss: 7.7488e-05 - root_mean_squared_error: 0.0088 - val_loss: 9.0511e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 572/1000\n", + "8/8 - 0s - loss: 7.9047e-05 - root_mean_squared_error: 0.0089 - val_loss: 8.9366e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 573/1000\n", + "8/8 - 0s - loss: 7.3826e-05 - root_mean_squared_error: 0.0086 - val_loss: 9.2226e-05 - val_root_mean_squared_error: 0.0096\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 574/1000\n", + "8/8 - 0s - loss: 7.6655e-05 - root_mean_squared_error: 0.0088 - val_loss: 9.9456e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 575/1000\n", + "8/8 - 0s - loss: 8.9108e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.5983e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 576/1000\n", + "8/8 - 0s - loss: 1.1819e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.3780e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 577/1000\n", + "8/8 - 0s - loss: 1.0916e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0865e-04 - val_root_mean_squared_error: 0.0104\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 578/1000\n", + "8/8 - 0s - loss: 1.0711e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.1689e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 579/1000\n", + "8/8 - 0s - loss: 1.0316e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.2619e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 580/1000\n", + "8/8 - 0s - loss: 1.1040e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0281e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 581/1000\n", + "8/8 - 0s - loss: 7.7554e-05 - root_mean_squared_error: 0.0088 - val_loss: 8.7146e-05 - val_root_mean_squared_error: 0.0093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 582/1000\n", + "8/8 - 0s - loss: 8.8976e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.4210e-05 - val_root_mean_squared_error: 0.0097\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 583/1000\n", + "8/8 - 0s - loss: 8.3188e-05 - root_mean_squared_error: 0.0091 - val_loss: 9.7366e-05 - val_root_mean_squared_error: 0.0099\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 584/1000\n", + "8/8 - 0s - loss: 9.4476e-05 - root_mean_squared_error: 0.0097 - val_loss: 9.6165e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 585/1000\n", + "8/8 - 0s - loss: 8.8189e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.9936e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 586/1000\n", + "8/8 - 0s - loss: 8.2158e-05 - root_mean_squared_error: 0.0091 - val_loss: 8.9903e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 587/1000\n", + "8/8 - 0s - loss: 9.9446e-05 - root_mean_squared_error: 0.0100 - val_loss: 8.9662e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 588/1000\n", + "8/8 - 0s - loss: 1.1881e-04 - root_mean_squared_error: 0.0109 - val_loss: 9.0051e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 589/1000\n", + "8/8 - 0s - loss: 1.0863e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0758e-04 - val_root_mean_squared_error: 0.0104\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 590/1000\n", + "8/8 - 0s - loss: 1.0443e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.2193e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 591/1000\n", + "8/8 - 0s - loss: 1.2233e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.0974e-04 - val_root_mean_squared_error: 0.0105\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 592/1000\n", + "8/8 - 0s - loss: 1.3869e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4947e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 593/1000\n", + "8/8 - 0s - loss: 1.3156e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.5021e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 594/1000\n", + "8/8 - 0s - loss: 1.4937e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.2868e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 595/1000\n", + "8/8 - 0s - loss: 1.7960e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.5647e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 596/1000\n", + "8/8 - 0s - loss: 2.3772e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.4728e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 597/1000\n", + "8/8 - 0s - loss: 2.4107e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.4505e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 598/1000\n", + "8/8 - 0s - loss: 2.8595e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.2547e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 599/1000\n", + "8/8 - 0s - loss: 3.6235e-04 - root_mean_squared_error: 0.0190 - val_loss: 3.8119e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 600/1000\n", + "8/8 - 0s - loss: 3.2820e-04 - root_mean_squared_error: 0.0181 - val_loss: 2.8752e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 601/1000\n", + "8/8 - 0s - loss: 3.2677e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.3310e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 602/1000\n", + "8/8 - 0s - loss: 3.3059e-04 - root_mean_squared_error: 0.0182 - val_loss: 1.8297e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 603/1000\n", + "8/8 - 0s - loss: 3.9149e-04 - root_mean_squared_error: 0.0198 - val_loss: 2.4162e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 604/1000\n", + "8/8 - 0s - loss: 2.8439e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.0729e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 605/1000\n", + "8/8 - 0s - loss: 2.5991e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.2921e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 606/1000\n", + "8/8 - 0s - loss: 1.9179e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.4240e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 607/1000\n", + "8/8 - 0s - loss: 2.2425e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.8450e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 608/1000\n", + "8/8 - 0s - loss: 2.5932e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.1582e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 609/1000\n", + "8/8 - 0s - loss: 3.8945e-04 - root_mean_squared_error: 0.0197 - val_loss: 2.2850e-04 - val_root_mean_squared_error: 0.0151\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 610/1000\n", + "8/8 - 0s - loss: 3.0556e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.8136e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 611/1000\n", + "8/8 - 0s - loss: 3.5153e-04 - root_mean_squared_error: 0.0187 - val_loss: 3.7615e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 612/1000\n", + "8/8 - 0s - loss: 2.3703e-04 - root_mean_squared_error: 0.0154 - val_loss: 1.9225e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 613/1000\n", + "8/8 - 0s - loss: 1.9151e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.5818e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 614/1000\n", + "8/8 - 0s - loss: 1.6096e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.2744e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 615/1000\n", + "8/8 - 0s - loss: 1.3329e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.6300e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 616/1000\n", + "8/8 - 0s - loss: 1.0784e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0967e-04 - val_root_mean_squared_error: 0.0105\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 617/1000\n", + "8/8 - 0s - loss: 1.1941e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.3003e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 618/1000\n", + "8/8 - 0s - loss: 1.1364e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.4227e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 619/1000\n", + "8/8 - 0s - loss: 1.2439e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.5534e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 620/1000\n", + "8/8 - 0s - loss: 9.8275e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1671e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 621/1000\n", + "8/8 - 0s - loss: 8.3052e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.2806e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 622/1000\n", + "8/8 - 0s - loss: 6.5043e-05 - root_mean_squared_error: 0.0081 - val_loss: 8.7729e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 623/1000\n", + "8/8 - 0s - loss: 5.3668e-05 - root_mean_squared_error: 0.0073 - val_loss: 8.5592e-05 - val_root_mean_squared_error: 0.0093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 624/1000\n", + "8/8 - 0s - loss: 4.7275e-05 - root_mean_squared_error: 0.0069 - val_loss: 7.9123e-05 - val_root_mean_squared_error: 0.0089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 625/1000\n", + "8/8 - 0s - loss: 4.4813e-05 - root_mean_squared_error: 0.0067 - val_loss: 7.4799e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 626/1000\n", + "8/8 - 0s - loss: 4.3142e-05 - root_mean_squared_error: 0.0066 - val_loss: 7.3069e-05 - val_root_mean_squared_error: 0.0085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 627/1000\n", + "8/8 - 0s - loss: 3.8240e-05 - root_mean_squared_error: 0.0062 - val_loss: 6.6798e-05 - val_root_mean_squared_error: 0.0082\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 628/1000\n", + "8/8 - 0s - loss: 3.1072e-05 - root_mean_squared_error: 0.0056 - val_loss: 5.0094e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 629/1000\n", + "8/8 - 0s - loss: 2.5773e-05 - root_mean_squared_error: 0.0051 - val_loss: 4.9812e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 630/1000\n", + "8/8 - 0s - loss: 2.3521e-05 - root_mean_squared_error: 0.0048 - val_loss: 4.1113e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 631/1000\n", + "8/8 - 0s - loss: 2.3375e-05 - root_mean_squared_error: 0.0048 - val_loss: 3.8833e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 632/1000\n", + "8/8 - 0s - loss: 2.3597e-05 - root_mean_squared_error: 0.0049 - val_loss: 3.8680e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 633/1000\n", + "8/8 - 0s - loss: 2.9184e-05 - root_mean_squared_error: 0.0054 - val_loss: 4.0115e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 634/1000\n", + "8/8 - 0s - loss: 3.6901e-05 - root_mean_squared_error: 0.0061 - val_loss: 4.7405e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 635/1000\n", + "8/8 - 0s - loss: 3.5485e-05 - root_mean_squared_error: 0.0060 - val_loss: 4.3732e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 636/1000\n", + "8/8 - 0s - loss: 2.7861e-05 - root_mean_squared_error: 0.0053 - val_loss: 3.2758e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 637/1000\n", + "8/8 - 0s - loss: 3.1004e-05 - root_mean_squared_error: 0.0056 - val_loss: 3.9401e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 638/1000\n", + "8/8 - 0s - loss: 3.5001e-05 - root_mean_squared_error: 0.0059 - val_loss: 4.3152e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 639/1000\n", + "8/8 - 0s - loss: 3.9151e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.9040e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 640/1000\n", + "8/8 - 0s - loss: 3.7297e-05 - root_mean_squared_error: 0.0061 - val_loss: 3.6910e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 641/1000\n", + "8/8 - 0s - loss: 4.3764e-05 - root_mean_squared_error: 0.0066 - val_loss: 3.3639e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 642/1000\n", + "8/8 - 0s - loss: 5.7379e-05 - root_mean_squared_error: 0.0076 - val_loss: 5.0747e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 643/1000\n", + "8/8 - 0s - loss: 6.0025e-05 - root_mean_squared_error: 0.0077 - val_loss: 5.7656e-05 - val_root_mean_squared_error: 0.0076\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 644/1000\n", + "8/8 - 0s - loss: 5.0328e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.9403e-05 - val_root_mean_squared_error: 0.0070\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 645/1000\n", + "8/8 - 0s - loss: 4.5479e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.5130e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 646/1000\n", + "8/8 - 0s - loss: 4.8451e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.2644e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 647/1000\n", + "8/8 - 0s - loss: 5.0999e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.0218e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 648/1000\n", + "8/8 - 0s - loss: 5.5625e-05 - root_mean_squared_error: 0.0075 - val_loss: 6.6786e-05 - val_root_mean_squared_error: 0.0082\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 649/1000\n", + "8/8 - 0s - loss: 4.2477e-05 - root_mean_squared_error: 0.0065 - val_loss: 3.0559e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 650/1000\n", + "8/8 - 0s - loss: 4.2423e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.3539e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 651/1000\n", + "8/8 - 0s - loss: 3.8124e-05 - root_mean_squared_error: 0.0062 - val_loss: 4.0051e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 652/1000\n", + "8/8 - 0s - loss: 3.9212e-05 - root_mean_squared_error: 0.0063 - val_loss: 4.2770e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 653/1000\n", + "8/8 - 0s - loss: 4.0653e-05 - root_mean_squared_error: 0.0064 - val_loss: 5.1009e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 654/1000\n", + "8/8 - 0s - loss: 5.0084e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.6525e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 655/1000\n", + "8/8 - 0s - loss: 4.9415e-05 - root_mean_squared_error: 0.0070 - val_loss: 4.9742e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 656/1000\n", + "8/8 - 0s - loss: 4.8455e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.5623e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 657/1000\n", + "8/8 - 0s - loss: 4.0524e-05 - root_mean_squared_error: 0.0064 - val_loss: 3.4294e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 658/1000\n", + "8/8 - 0s - loss: 4.8927e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.1970e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 659/1000\n", + "8/8 - 0s - loss: 5.0458e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.5723e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 660/1000\n", + "8/8 - 0s - loss: 5.0260e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.9752e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 661/1000\n", + "8/8 - 0s - loss: 5.1947e-05 - root_mean_squared_error: 0.0072 - val_loss: 5.6034e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 662/1000\n", + "8/8 - 0s - loss: 5.4956e-05 - root_mean_squared_error: 0.0074 - val_loss: 5.5517e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 663/1000\n", + "8/8 - 0s - loss: 4.4530e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.4570e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 664/1000\n", + "8/8 - 0s - loss: 4.2559e-05 - root_mean_squared_error: 0.0065 - val_loss: 5.3831e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 665/1000\n", + "8/8 - 0s - loss: 4.5559e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.2794e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 666/1000\n", + "8/8 - 0s - loss: 6.8008e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.2813e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 667/1000\n", + "8/8 - 0s - loss: 7.5790e-05 - root_mean_squared_error: 0.0087 - val_loss: 6.3113e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 668/1000\n", + "8/8 - 0s - loss: 6.6210e-05 - root_mean_squared_error: 0.0081 - val_loss: 4.7730e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 669/1000\n", + "8/8 - 0s - loss: 6.3502e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.1702e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 670/1000\n", + "8/8 - 0s - loss: 5.5082e-05 - root_mean_squared_error: 0.0074 - val_loss: 5.2062e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 671/1000\n", + "8/8 - 0s - loss: 4.3727e-05 - root_mean_squared_error: 0.0066 - val_loss: 5.0438e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 672/1000\n", + "8/8 - 0s - loss: 4.3490e-05 - root_mean_squared_error: 0.0066 - val_loss: 4.7066e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 673/1000\n", + "8/8 - 0s - loss: 4.2886e-05 - root_mean_squared_error: 0.0065 - val_loss: 4.8645e-05 - val_root_mean_squared_error: 0.0070\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 674/1000\n", + "8/8 - 0s - loss: 5.0917e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.0362e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 675/1000\n", + "8/8 - 0s - loss: 5.3816e-05 - root_mean_squared_error: 0.0073 - val_loss: 5.1131e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 676/1000\n", + "8/8 - 0s - loss: 4.5090e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.6630e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 677/1000\n", + "8/8 - 0s - loss: 3.9097e-05 - root_mean_squared_error: 0.0063 - val_loss: 4.0864e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 678/1000\n", + "8/8 - 0s - loss: 3.8115e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.5374e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 679/1000\n", + "8/8 - 0s - loss: 4.0709e-05 - root_mean_squared_error: 0.0064 - val_loss: 4.3017e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 680/1000\n", + "8/8 - 0s - loss: 6.0336e-05 - root_mean_squared_error: 0.0078 - val_loss: 5.6544e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 681/1000\n", + "8/8 - 0s - loss: 6.7729e-05 - root_mean_squared_error: 0.0082 - val_loss: 7.0570e-05 - val_root_mean_squared_error: 0.0084\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 682/1000\n", + "8/8 - 0s - loss: 8.2957e-05 - root_mean_squared_error: 0.0091 - val_loss: 8.7589e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 683/1000\n", + "8/8 - 0s - loss: 6.7540e-05 - root_mean_squared_error: 0.0082 - val_loss: 8.6958e-05 - val_root_mean_squared_error: 0.0093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 684/1000\n", + "8/8 - 0s - loss: 7.4349e-05 - root_mean_squared_error: 0.0086 - val_loss: 7.7205e-05 - val_root_mean_squared_error: 0.0088\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 685/1000\n", + "8/8 - 0s - loss: 6.2038e-05 - root_mean_squared_error: 0.0079 - val_loss: 5.6101e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 686/1000\n", + "8/8 - 0s - loss: 7.7672e-05 - root_mean_squared_error: 0.0088 - val_loss: 8.2291e-05 - val_root_mean_squared_error: 0.0091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 687/1000\n", + "8/8 - 0s - loss: 8.0998e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.9232e-05 - val_root_mean_squared_error: 0.0083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 688/1000\n", + "8/8 - 0s - loss: 6.9354e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.0514e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 689/1000\n", + "8/8 - 0s - loss: 6.4015e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.0173e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 690/1000\n", + "8/8 - 0s - loss: 6.8907e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.3200e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 691/1000\n", + "8/8 - 0s - loss: 8.1165e-05 - root_mean_squared_error: 0.0090 - val_loss: 8.1322e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 692/1000\n", + "8/8 - 0s - loss: 1.0921e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0270e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 693/1000\n", + "8/8 - 0s - loss: 1.6026e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6079e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 694/1000\n", + "8/8 - 0s - loss: 2.0718e-04 - root_mean_squared_error: 0.0144 - val_loss: 8.8847e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 695/1000\n", + "8/8 - 0s - loss: 2.0605e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.8644e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 696/1000\n", + "8/8 - 0s - loss: 1.4565e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4425e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 697/1000\n", + "8/8 - 0s - loss: 1.1341e-04 - root_mean_squared_error: 0.0106 - val_loss: 8.7898e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 698/1000\n", + "8/8 - 0s - loss: 1.2436e-04 - root_mean_squared_error: 0.0112 - val_loss: 8.4190e-05 - val_root_mean_squared_error: 0.0092\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 699/1000\n", + "8/8 - 0s - loss: 1.6031e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.4725e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 700/1000\n", + "8/8 - 0s - loss: 2.6331e-04 - root_mean_squared_error: 0.0162 - val_loss: 1.5528e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 701/1000\n", + "8/8 - 0s - loss: 2.8133e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.5492e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 702/1000\n", + "8/8 - 0s - loss: 2.9225e-04 - root_mean_squared_error: 0.0171 - val_loss: 2.0734e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 703/1000\n", + "8/8 - 0s - loss: 2.3942e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.0338e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 704/1000\n", + "8/8 - 0s - loss: 3.3850e-04 - root_mean_squared_error: 0.0184 - val_loss: 2.3985e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 705/1000\n", + "8/8 - 0s - loss: 2.7298e-04 - root_mean_squared_error: 0.0165 - val_loss: 1.3462e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 706/1000\n", + "8/8 - 0s - loss: 2.8075e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.2510e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 707/1000\n", + "8/8 - 0s - loss: 1.9859e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.0828e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 708/1000\n", + "8/8 - 0s - loss: 1.9748e-04 - root_mean_squared_error: 0.0141 - val_loss: 3.0130e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 709/1000\n", + "8/8 - 0s - loss: 1.2305e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.7951e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 710/1000\n", + "8/8 - 0s - loss: 1.0695e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.0378e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 711/1000\n", + "8/8 - 0s - loss: 7.4042e-05 - root_mean_squared_error: 0.0086 - val_loss: 8.2323e-05 - val_root_mean_squared_error: 0.0091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 712/1000\n", + "8/8 - 0s - loss: 7.1533e-05 - root_mean_squared_error: 0.0085 - val_loss: 5.0552e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 713/1000\n", + "8/8 - 0s - loss: 7.4822e-05 - root_mean_squared_error: 0.0086 - val_loss: 7.2841e-05 - val_root_mean_squared_error: 0.0085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 714/1000\n", + "8/8 - 0s - loss: 8.3328e-05 - root_mean_squared_error: 0.0091 - val_loss: 8.5569e-05 - val_root_mean_squared_error: 0.0093\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 715/1000\n", + "8/8 - 0s - loss: 8.5707e-05 - root_mean_squared_error: 0.0093 - val_loss: 8.0163e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 716/1000\n", + "8/8 - 0s - loss: 6.6695e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.9143e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 717/1000\n", + "8/8 - 0s - loss: 4.8496e-05 - root_mean_squared_error: 0.0070 - val_loss: 3.9574e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 718/1000\n", + "8/8 - 0s - loss: 3.8121e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.2116e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 719/1000\n", + "8/8 - 0s - loss: 2.9362e-05 - root_mean_squared_error: 0.0054 - val_loss: 2.7826e-05 - val_root_mean_squared_error: 0.0053\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 720/1000\n", + "8/8 - 0s - loss: 3.2501e-05 - root_mean_squared_error: 0.0057 - val_loss: 3.6286e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 721/1000\n", + "8/8 - 0s - loss: 4.0003e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.5280e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 722/1000\n", + "8/8 - 0s - loss: 3.6744e-05 - root_mean_squared_error: 0.0061 - val_loss: 3.2876e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 723/1000\n", + "8/8 - 0s - loss: 3.8455e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.6622e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 724/1000\n", + "8/8 - 0s - loss: 3.4448e-05 - root_mean_squared_error: 0.0059 - val_loss: 3.1808e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 725/1000\n", + "8/8 - 0s - loss: 3.1155e-05 - root_mean_squared_error: 0.0056 - val_loss: 3.4094e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 726/1000\n", + "8/8 - 0s - loss: 3.9915e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.6207e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 727/1000\n", + "8/8 - 0s - loss: 4.1309e-05 - root_mean_squared_error: 0.0064 - val_loss: 2.8889e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 728/1000\n", + "8/8 - 0s - loss: 2.8091e-05 - root_mean_squared_error: 0.0053 - val_loss: 2.7071e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 729/1000\n", + "8/8 - 0s - loss: 2.4275e-05 - root_mean_squared_error: 0.0049 - val_loss: 2.6666e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 730/1000\n", + "8/8 - 0s - loss: 2.6219e-05 - root_mean_squared_error: 0.0051 - val_loss: 1.9605e-05 - val_root_mean_squared_error: 0.0044\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 731/1000\n", + "8/8 - 0s - loss: 2.5563e-05 - root_mean_squared_error: 0.0051 - val_loss: 2.6297e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 732/1000\n", + "8/8 - 0s - loss: 3.5839e-05 - root_mean_squared_error: 0.0060 - val_loss: 3.5628e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 733/1000\n", + "8/8 - 0s - loss: 4.0729e-05 - root_mean_squared_error: 0.0064 - val_loss: 2.8781e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 734/1000\n", + "8/8 - 0s - loss: 3.9203e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.6647e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 735/1000\n", + "8/8 - 0s - loss: 5.5617e-05 - root_mean_squared_error: 0.0075 - val_loss: 4.1005e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 736/1000\n", + "8/8 - 0s - loss: 5.5102e-05 - root_mean_squared_error: 0.0074 - val_loss: 2.7104e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 737/1000\n", + "8/8 - 0s - loss: 4.2423e-05 - root_mean_squared_error: 0.0065 - val_loss: 3.6141e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 738/1000\n", + "8/8 - 0s - loss: 3.9078e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.2983e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 739/1000\n", + "8/8 - 0s - loss: 4.0338e-05 - root_mean_squared_error: 0.0064 - val_loss: 1.7623e-05 - val_root_mean_squared_error: 0.0042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 740/1000\n", + "8/8 - 0s - loss: 3.1028e-05 - root_mean_squared_error: 0.0056 - val_loss: 2.7512e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 741/1000\n", + "8/8 - 0s - loss: 4.4237e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.6411e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 742/1000\n", + "8/8 - 0s - loss: 5.9928e-05 - root_mean_squared_error: 0.0077 - val_loss: 3.5265e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 743/1000\n", + "8/8 - 0s - loss: 6.6157e-05 - root_mean_squared_error: 0.0081 - val_loss: 5.4170e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 744/1000\n", + "8/8 - 0s - loss: 8.3001e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.7920e-05 - val_root_mean_squared_error: 0.0082\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 745/1000\n", + "8/8 - 0s - loss: 8.4915e-05 - root_mean_squared_error: 0.0092 - val_loss: 7.3382e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 746/1000\n", + "8/8 - 0s - loss: 8.0459e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.1800e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 747/1000\n", + "8/8 - 0s - loss: 1.0631e-04 - root_mean_squared_error: 0.0103 - val_loss: 6.0377e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 748/1000\n", + "8/8 - 0s - loss: 1.0277e-04 - root_mean_squared_error: 0.0101 - val_loss: 5.4096e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 749/1000\n", + "8/8 - 0s - loss: 8.0007e-05 - root_mean_squared_error: 0.0089 - val_loss: 6.1967e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 750/1000\n", + "8/8 - 0s - loss: 6.6465e-05 - root_mean_squared_error: 0.0082 - val_loss: 6.4653e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 751/1000\n", + "8/8 - 0s - loss: 6.4652e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.5532e-05 - val_root_mean_squared_error: 0.0075\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 752/1000\n", + "8/8 - 0s - loss: 5.1056e-05 - root_mean_squared_error: 0.0071 - val_loss: 2.6102e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 753/1000\n", + "8/8 - 0s - loss: 3.5726e-05 - root_mean_squared_error: 0.0060 - val_loss: 3.2256e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 754/1000\n", + "8/8 - 0s - loss: 4.7234e-05 - root_mean_squared_error: 0.0069 - val_loss: 3.3256e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 755/1000\n", + "8/8 - 0s - loss: 6.3060e-05 - root_mean_squared_error: 0.0079 - val_loss: 2.3117e-05 - val_root_mean_squared_error: 0.0048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 756/1000\n", + "8/8 - 0s - loss: 1.0388e-04 - root_mean_squared_error: 0.0102 - val_loss: 6.2919e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 757/1000\n", + "8/8 - 0s - loss: 1.2588e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.2707e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 758/1000\n", + "8/8 - 0s - loss: 1.4220e-04 - root_mean_squared_error: 0.0119 - val_loss: 7.3461e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 759/1000\n", + "8/8 - 0s - loss: 1.5063e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.3222e-04 - val_root_mean_squared_error: 0.0115\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 760/1000\n", + "8/8 - 0s - loss: 1.8496e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.4512e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 761/1000\n", + "8/8 - 0s - loss: 1.7964e-04 - root_mean_squared_error: 0.0134 - val_loss: 9.4805e-05 - val_root_mean_squared_error: 0.0097\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 762/1000\n", + "8/8 - 0s - loss: 1.8515e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.2634e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 763/1000\n", + "8/8 - 0s - loss: 1.6666e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.2928e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 764/1000\n", + "8/8 - 0s - loss: 2.4350e-04 - root_mean_squared_error: 0.0156 - val_loss: 1.6024e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 765/1000\n", + "8/8 - 0s - loss: 2.5257e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.2904e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 766/1000\n", + "8/8 - 0s - loss: 2.4894e-04 - root_mean_squared_error: 0.0158 - val_loss: 1.9579e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 767/1000\n", + "8/8 - 0s - loss: 2.0505e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.7899e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 768/1000\n", + "8/8 - 0s - loss: 1.8431e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.6510e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 769/1000\n", + "8/8 - 0s - loss: 2.0407e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.5482e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 770/1000\n", + "8/8 - 0s - loss: 2.2979e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.2899e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 771/1000\n", + "8/8 - 0s - loss: 2.3799e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.5924e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 772/1000\n", + "8/8 - 0s - loss: 2.7106e-04 - root_mean_squared_error: 0.0165 - val_loss: 1.8655e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 773/1000\n", + "8/8 - 0s - loss: 2.0997e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.6326e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 774/1000\n", + "8/8 - 0s - loss: 1.5116e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.3096e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 775/1000\n", + "8/8 - 0s - loss: 1.2023e-04 - root_mean_squared_error: 0.0110 - val_loss: 7.5696e-05 - val_root_mean_squared_error: 0.0087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 776/1000\n", + "8/8 - 0s - loss: 9.9383e-05 - root_mean_squared_error: 0.0100 - val_loss: 1.3087e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 777/1000\n", + "8/8 - 0s - loss: 7.7551e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.8774e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 778/1000\n", + "8/8 - 0s - loss: 5.6817e-05 - root_mean_squared_error: 0.0075 - val_loss: 5.4706e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 779/1000\n", + "8/8 - 0s - loss: 4.8672e-05 - root_mean_squared_error: 0.0070 - val_loss: 5.0353e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 780/1000\n", + "8/8 - 0s - loss: 6.1385e-05 - root_mean_squared_error: 0.0078 - val_loss: 6.2722e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 781/1000\n", + "8/8 - 0s - loss: 7.1944e-05 - root_mean_squared_error: 0.0085 - val_loss: 9.0607e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 782/1000\n", + "8/8 - 0s - loss: 6.6464e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.4220e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 783/1000\n", + "8/8 - 0s - loss: 4.9789e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.2778e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 784/1000\n", + "8/8 - 0s - loss: 3.8505e-05 - root_mean_squared_error: 0.0062 - val_loss: 2.8868e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 785/1000\n", + "8/8 - 0s - loss: 4.3631e-05 - root_mean_squared_error: 0.0066 - val_loss: 3.9317e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 786/1000\n", + "8/8 - 0s - loss: 4.6756e-05 - root_mean_squared_error: 0.0068 - val_loss: 5.5077e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 787/1000\n", + "8/8 - 0s - loss: 4.1276e-05 - root_mean_squared_error: 0.0064 - val_loss: 3.5775e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 788/1000\n", + "8/8 - 0s - loss: 2.9358e-05 - root_mean_squared_error: 0.0054 - val_loss: 2.3470e-05 - val_root_mean_squared_error: 0.0048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 789/1000\n", + "8/8 - 0s - loss: 2.5064e-05 - root_mean_squared_error: 0.0050 - val_loss: 2.1134e-05 - val_root_mean_squared_error: 0.0046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 790/1000\n", + "8/8 - 0s - loss: 3.2770e-05 - root_mean_squared_error: 0.0057 - val_loss: 2.9707e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 791/1000\n", + "8/8 - 0s - loss: 3.9000e-05 - root_mean_squared_error: 0.0062 - val_loss: 4.1083e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 792/1000\n", + "8/8 - 0s - loss: 3.7457e-05 - root_mean_squared_error: 0.0061 - val_loss: 3.0078e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 793/1000\n", + "8/8 - 0s - loss: 2.9228e-05 - root_mean_squared_error: 0.0054 - val_loss: 2.3878e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 794/1000\n", + "8/8 - 0s - loss: 2.3598e-05 - root_mean_squared_error: 0.0049 - val_loss: 2.0947e-05 - val_root_mean_squared_error: 0.0046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 795/1000\n", + "8/8 - 0s - loss: 2.5250e-05 - root_mean_squared_error: 0.0050 - val_loss: 2.4123e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 796/1000\n", + "8/8 - 0s - loss: 2.6494e-05 - root_mean_squared_error: 0.0051 - val_loss: 2.6482e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 797/1000\n", + "8/8 - 0s - loss: 2.4446e-05 - root_mean_squared_error: 0.0049 - val_loss: 1.9998e-05 - val_root_mean_squared_error: 0.0045\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 798/1000\n", + "8/8 - 0s - loss: 2.4327e-05 - root_mean_squared_error: 0.0049 - val_loss: 1.9075e-05 - val_root_mean_squared_error: 0.0044\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 799/1000\n", + "8/8 - 0s - loss: 2.6520e-05 - root_mean_squared_error: 0.0051 - val_loss: 2.7350e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 800/1000\n", + "8/8 - 0s - loss: 2.7459e-05 - root_mean_squared_error: 0.0052 - val_loss: 2.5253e-05 - val_root_mean_squared_error: 0.0050\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 801/1000\n", + "8/8 - 0s - loss: 2.6364e-05 - root_mean_squared_error: 0.0051 - val_loss: 2.3362e-05 - val_root_mean_squared_error: 0.0048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 802/1000\n", + "8/8 - 0s - loss: 3.0160e-05 - root_mean_squared_error: 0.0055 - val_loss: 2.0507e-05 - val_root_mean_squared_error: 0.0045\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 803/1000\n", + "8/8 - 0s - loss: 4.5349e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.3733e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 804/1000\n", + "8/8 - 0s - loss: 5.0431e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.3684e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 805/1000\n", + "8/8 - 0s - loss: 4.4905e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.6785e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 806/1000\n", + "8/8 - 0s - loss: 2.9154e-05 - root_mean_squared_error: 0.0054 - val_loss: 2.0873e-05 - val_root_mean_squared_error: 0.0046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 807/1000\n", + "8/8 - 0s - loss: 2.8544e-05 - root_mean_squared_error: 0.0053 - val_loss: 1.9130e-05 - val_root_mean_squared_error: 0.0044\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 808/1000\n", + "8/8 - 0s - loss: 4.7407e-05 - root_mean_squared_error: 0.0069 - val_loss: 3.0701e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 809/1000\n", + "8/8 - 0s - loss: 5.2740e-05 - root_mean_squared_error: 0.0073 - val_loss: 3.9695e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 810/1000\n", + "8/8 - 0s - loss: 5.1045e-05 - root_mean_squared_error: 0.0071 - val_loss: 2.9132e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 811/1000\n", + "8/8 - 0s - loss: 3.6939e-05 - root_mean_squared_error: 0.0061 - val_loss: 2.3968e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 812/1000\n", + "8/8 - 0s - loss: 3.7493e-05 - root_mean_squared_error: 0.0061 - val_loss: 2.9264e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 813/1000\n", + "8/8 - 0s - loss: 4.2784e-05 - root_mean_squared_error: 0.0065 - val_loss: 2.6375e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 814/1000\n", + "8/8 - 0s - loss: 5.1059e-05 - root_mean_squared_error: 0.0071 - val_loss: 3.2842e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 815/1000\n", + "8/8 - 0s - loss: 4.5518e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.8921e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 816/1000\n", + "8/8 - 0s - loss: 4.4966e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.8411e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 817/1000\n", + "8/8 - 0s - loss: 4.3809e-05 - root_mean_squared_error: 0.0066 - val_loss: 4.0209e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 818/1000\n", + "8/8 - 0s - loss: 5.0863e-05 - root_mean_squared_error: 0.0071 - val_loss: 3.3948e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 819/1000\n", + "8/8 - 0s - loss: 7.0225e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.3855e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 820/1000\n", + "8/8 - 0s - loss: 6.9772e-05 - root_mean_squared_error: 0.0084 - val_loss: 7.1464e-05 - val_root_mean_squared_error: 0.0085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 821/1000\n", + "8/8 - 0s - loss: 6.7198e-05 - root_mean_squared_error: 0.0082 - val_loss: 4.7983e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 822/1000\n", + "8/8 - 0s - loss: 4.0594e-05 - root_mean_squared_error: 0.0064 - val_loss: 3.1901e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 823/1000\n", + "8/8 - 0s - loss: 3.3052e-05 - root_mean_squared_error: 0.0057 - val_loss: 2.3662e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 824/1000\n", + "8/8 - 0s - loss: 4.2235e-05 - root_mean_squared_error: 0.0065 - val_loss: 2.6043e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 825/1000\n", + "8/8 - 0s - loss: 6.3080e-05 - root_mean_squared_error: 0.0079 - val_loss: 3.8323e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 826/1000\n", + "8/8 - 0s - loss: 7.1857e-05 - root_mean_squared_error: 0.0085 - val_loss: 3.3724e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 827/1000\n", + "8/8 - 0s - loss: 1.1440e-04 - root_mean_squared_error: 0.0107 - val_loss: 6.5335e-05 - val_root_mean_squared_error: 0.0081\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 828/1000\n", + "8/8 - 0s - loss: 1.2260e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.1879e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 829/1000\n", + "8/8 - 0s - loss: 1.6565e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.0249e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 830/1000\n", + "8/8 - 0s - loss: 1.2595e-04 - root_mean_squared_error: 0.0112 - val_loss: 7.4607e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 831/1000\n", + "8/8 - 0s - loss: 1.6281e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.3172e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 832/1000\n", + "8/8 - 0s - loss: 2.5140e-04 - root_mean_squared_error: 0.0159 - val_loss: 1.2732e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 833/1000\n", + "8/8 - 0s - loss: 2.6273e-04 - root_mean_squared_error: 0.0162 - val_loss: 1.5790e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 834/1000\n", + "8/8 - 0s - loss: 2.1485e-04 - root_mean_squared_error: 0.0147 - val_loss: 1.5367e-04 - val_root_mean_squared_error: 0.0124\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 835/1000\n", + "8/8 - 0s - loss: 2.3457e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.2908e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 836/1000\n", + "8/8 - 0s - loss: 1.9157e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1748e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 837/1000\n", + "8/8 - 0s - loss: 1.8356e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5214e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 838/1000\n", + "8/8 - 0s - loss: 1.5844e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4575e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 839/1000\n", + "8/8 - 0s - loss: 1.0326e-04 - root_mean_squared_error: 0.0102 - val_loss: 8.2772e-05 - val_root_mean_squared_error: 0.0091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 840/1000\n", + "8/8 - 0s - loss: 1.0555e-04 - root_mean_squared_error: 0.0103 - val_loss: 7.4220e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 841/1000\n", + "8/8 - 0s - loss: 8.7467e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.0096e-05 - val_root_mean_squared_error: 0.0095\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 842/1000\n", + "8/8 - 0s - loss: 8.8923e-05 - root_mean_squared_error: 0.0094 - val_loss: 6.8713e-05 - val_root_mean_squared_error: 0.0083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 843/1000\n", + "8/8 - 0s - loss: 9.8277e-05 - root_mean_squared_error: 0.0099 - val_loss: 7.5085e-05 - val_root_mean_squared_error: 0.0087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 844/1000\n", + "8/8 - 0s - loss: 8.9178e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.0552e-04 - val_root_mean_squared_error: 0.0103\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 845/1000\n", + "8/8 - 0s - loss: 7.3226e-05 - root_mean_squared_error: 0.0086 - val_loss: 4.4225e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 846/1000\n", + "8/8 - 0s - loss: 8.2549e-05 - root_mean_squared_error: 0.0091 - val_loss: 4.5780e-05 - val_root_mean_squared_error: 0.0068\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 847/1000\n", + "8/8 - 0s - loss: 9.5910e-05 - root_mean_squared_error: 0.0098 - val_loss: 6.0125e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 848/1000\n", + "8/8 - 0s - loss: 1.3131e-04 - root_mean_squared_error: 0.0115 - val_loss: 9.9718e-05 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 849/1000\n", + "8/8 - 0s - loss: 1.1410e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.0077e-04 - val_root_mean_squared_error: 0.0100\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 850/1000\n", + "8/8 - 0s - loss: 9.0563e-05 - root_mean_squared_error: 0.0095 - val_loss: 8.9199e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 851/1000\n", + "8/8 - 0s - loss: 6.7005e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.3368e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 852/1000\n", + "8/8 - 0s - loss: 5.9841e-05 - root_mean_squared_error: 0.0077 - val_loss: 8.1034e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 853/1000\n", + "8/8 - 0s - loss: 5.1714e-05 - root_mean_squared_error: 0.0072 - val_loss: 4.3331e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 854/1000\n", + "8/8 - 0s - loss: 4.6709e-05 - root_mean_squared_error: 0.0068 - val_loss: 4.7021e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 855/1000\n", + "8/8 - 0s - loss: 4.1486e-05 - root_mean_squared_error: 0.0064 - val_loss: 2.9623e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 856/1000\n", + "8/8 - 0s - loss: 4.2771e-05 - root_mean_squared_error: 0.0065 - val_loss: 3.2702e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 857/1000\n", + "8/8 - 0s - loss: 5.3619e-05 - root_mean_squared_error: 0.0073 - val_loss: 2.6410e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 858/1000\n", + "8/8 - 0s - loss: 6.7040e-05 - root_mean_squared_error: 0.0082 - val_loss: 4.5165e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 859/1000\n", + "8/8 - 0s - loss: 1.0818e-04 - root_mean_squared_error: 0.0104 - val_loss: 7.1701e-05 - val_root_mean_squared_error: 0.0085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 860/1000\n", + "8/8 - 0s - loss: 1.0160e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.0415e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 861/1000\n", + "8/8 - 0s - loss: 8.9754e-05 - root_mean_squared_error: 0.0095 - val_loss: 7.4550e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 862/1000\n", + "8/8 - 0s - loss: 7.1391e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.9062e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 863/1000\n", + "8/8 - 0s - loss: 6.0932e-05 - root_mean_squared_error: 0.0078 - val_loss: 5.4148e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 864/1000\n", + "8/8 - 0s - loss: 5.4014e-05 - root_mean_squared_error: 0.0073 - val_loss: 4.1792e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 865/1000\n", + "8/8 - 0s - loss: 5.1752e-05 - root_mean_squared_error: 0.0072 - val_loss: 4.1361e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 866/1000\n", + "8/8 - 0s - loss: 5.3692e-05 - root_mean_squared_error: 0.0073 - val_loss: 4.5370e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 867/1000\n", + "8/8 - 0s - loss: 5.1057e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.4436e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 868/1000\n", + "8/8 - 0s - loss: 4.9805e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.0997e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 869/1000\n", + "8/8 - 0s - loss: 3.9057e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.4917e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 870/1000\n", + "8/8 - 0s - loss: 3.2218e-05 - root_mean_squared_error: 0.0057 - val_loss: 2.9173e-05 - val_root_mean_squared_error: 0.0054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 871/1000\n", + "8/8 - 0s - loss: 2.4554e-05 - root_mean_squared_error: 0.0050 - val_loss: 2.5473e-05 - val_root_mean_squared_error: 0.0050\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 872/1000\n", + "8/8 - 0s - loss: 1.8957e-05 - root_mean_squared_error: 0.0044 - val_loss: 1.7446e-05 - val_root_mean_squared_error: 0.0042\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 873/1000\n", + "8/8 - 0s - loss: 1.5801e-05 - root_mean_squared_error: 0.0040 - val_loss: 1.6917e-05 - val_root_mean_squared_error: 0.0041\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 874/1000\n", + "8/8 - 0s - loss: 1.7115e-05 - root_mean_squared_error: 0.0041 - val_loss: 1.4879e-05 - val_root_mean_squared_error: 0.0039\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 875/1000\n", + "8/8 - 0s - loss: 1.9370e-05 - root_mean_squared_error: 0.0044 - val_loss: 1.5559e-05 - val_root_mean_squared_error: 0.0039\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 876/1000\n", + "8/8 - 0s - loss: 2.6590e-05 - root_mean_squared_error: 0.0052 - val_loss: 2.1215e-05 - val_root_mean_squared_error: 0.0046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 877/1000\n", + "8/8 - 0s - loss: 3.2839e-05 - root_mean_squared_error: 0.0057 - val_loss: 3.1885e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 878/1000\n", + "8/8 - 0s - loss: 3.8788e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.1390e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 879/1000\n", + "8/8 - 0s - loss: 3.6522e-05 - root_mean_squared_error: 0.0060 - val_loss: 2.7838e-05 - val_root_mean_squared_error: 0.0053\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 880/1000\n", + "8/8 - 0s - loss: 3.3712e-05 - root_mean_squared_error: 0.0058 - val_loss: 2.7089e-05 - val_root_mean_squared_error: 0.0052\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 881/1000\n", + "8/8 - 0s - loss: 4.0119e-05 - root_mean_squared_error: 0.0063 - val_loss: 2.3781e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 882/1000\n", + "8/8 - 0s - loss: 4.8205e-05 - root_mean_squared_error: 0.0069 - val_loss: 3.3755e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 883/1000\n", + "8/8 - 0s - loss: 4.9353e-05 - root_mean_squared_error: 0.0070 - val_loss: 3.5021e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 884/1000\n", + "8/8 - 0s - loss: 5.5200e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.4070e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 885/1000\n", + "8/8 - 0s - loss: 5.4385e-05 - root_mean_squared_error: 0.0074 - val_loss: 3.8556e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 886/1000\n", + "8/8 - 0s - loss: 4.1839e-05 - root_mean_squared_error: 0.0065 - val_loss: 3.0291e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 887/1000\n", + "8/8 - 0s - loss: 3.7233e-05 - root_mean_squared_error: 0.0061 - val_loss: 2.5587e-05 - val_root_mean_squared_error: 0.0051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 888/1000\n", + "8/8 - 0s - loss: 3.1978e-05 - root_mean_squared_error: 0.0057 - val_loss: 2.5110e-05 - val_root_mean_squared_error: 0.0050\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 889/1000\n", + "8/8 - 0s - loss: 2.4482e-05 - root_mean_squared_error: 0.0049 - val_loss: 1.5826e-05 - val_root_mean_squared_error: 0.0040\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 890/1000\n", + "8/8 - 0s - loss: 2.7050e-05 - root_mean_squared_error: 0.0052 - val_loss: 2.2660e-05 - val_root_mean_squared_error: 0.0048\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 891/1000\n", + "8/8 - 0s - loss: 3.3672e-05 - root_mean_squared_error: 0.0058 - val_loss: 2.4363e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 892/1000\n", + "8/8 - 0s - loss: 3.5671e-05 - root_mean_squared_error: 0.0060 - val_loss: 1.6893e-05 - val_root_mean_squared_error: 0.0041\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 893/1000\n", + "8/8 - 0s - loss: 4.5283e-05 - root_mean_squared_error: 0.0067 - val_loss: 3.4648e-05 - val_root_mean_squared_error: 0.0059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 894/1000\n", + "8/8 - 0s - loss: 5.4353e-05 - root_mean_squared_error: 0.0074 - val_loss: 5.1622e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 895/1000\n", + "8/8 - 0s - loss: 5.6177e-05 - root_mean_squared_error: 0.0075 - val_loss: 3.3422e-05 - val_root_mean_squared_error: 0.0058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 896/1000\n", + "8/8 - 0s - loss: 3.7214e-05 - root_mean_squared_error: 0.0061 - val_loss: 3.0245e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 897/1000\n", + "8/8 - 0s - loss: 3.8759e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.8827e-05 - val_root_mean_squared_error: 0.0062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 898/1000\n", + "8/8 - 0s - loss: 5.9742e-05 - root_mean_squared_error: 0.0077 - val_loss: 3.0061e-05 - val_root_mean_squared_error: 0.0055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 899/1000\n", + "8/8 - 0s - loss: 8.1559e-05 - root_mean_squared_error: 0.0090 - val_loss: 5.9424e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 900/1000\n", + "8/8 - 0s - loss: 7.1086e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.9587e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 901/1000\n", + "8/8 - 0s - loss: 7.6814e-05 - root_mean_squared_error: 0.0088 - val_loss: 7.7506e-05 - val_root_mean_squared_error: 0.0088\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 902/1000\n", + "8/8 - 0s - loss: 7.0118e-05 - root_mean_squared_error: 0.0084 - val_loss: 6.4129e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 903/1000\n", + "8/8 - 0s - loss: 4.9169e-05 - root_mean_squared_error: 0.0070 - val_loss: 3.1535e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 904/1000\n", + "8/8 - 0s - loss: 3.8498e-05 - root_mean_squared_error: 0.0062 - val_loss: 3.0933e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 905/1000\n", + "8/8 - 0s - loss: 4.5814e-05 - root_mean_squared_error: 0.0068 - val_loss: 4.3656e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 906/1000\n", + "8/8 - 0s - loss: 4.8790e-05 - root_mean_squared_error: 0.0070 - val_loss: 2.1483e-05 - val_root_mean_squared_error: 0.0046\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 907/1000\n", + "8/8 - 0s - loss: 5.4954e-05 - root_mean_squared_error: 0.0074 - val_loss: 3.2995e-05 - val_root_mean_squared_error: 0.0057\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 908/1000\n", + "8/8 - 0s - loss: 6.9948e-05 - root_mean_squared_error: 0.0084 - val_loss: 7.3743e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 909/1000\n", + "8/8 - 0s - loss: 9.6912e-05 - root_mean_squared_error: 0.0098 - val_loss: 7.3701e-05 - val_root_mean_squared_error: 0.0086\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 910/1000\n", + "8/8 - 0s - loss: 1.1197e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.1151e-04 - val_root_mean_squared_error: 0.0106\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 911/1000\n", + "8/8 - 0s - loss: 1.1762e-04 - root_mean_squared_error: 0.0108 - val_loss: 6.4630e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 912/1000\n", + "8/8 - 0s - loss: 1.8830e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.3347e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 913/1000\n", + "8/8 - 0s - loss: 1.7176e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.1798e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 914/1000\n", + "8/8 - 0s - loss: 1.4644e-04 - root_mean_squared_error: 0.0121 - val_loss: 9.3872e-05 - val_root_mean_squared_error: 0.0097\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 915/1000\n", + "8/8 - 0s - loss: 1.0159e-04 - root_mean_squared_error: 0.0101 - val_loss: 8.1890e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 916/1000\n", + "8/8 - 0s - loss: 1.0674e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.2189e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 917/1000\n", + "8/8 - 0s - loss: 1.4636e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.0716e-04 - val_root_mean_squared_error: 0.0104\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 918/1000\n", + "8/8 - 0s - loss: 1.4008e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.8621e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 919/1000\n", + "8/8 - 0s - loss: 9.9543e-05 - root_mean_squared_error: 0.0100 - val_loss: 9.5688e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 920/1000\n", + "8/8 - 0s - loss: 9.4663e-05 - root_mean_squared_error: 0.0097 - val_loss: 7.7586e-05 - val_root_mean_squared_error: 0.0088\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 921/1000\n", + "8/8 - 0s - loss: 9.4938e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.8798e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 922/1000\n", + "8/8 - 0s - loss: 1.2054e-04 - root_mean_squared_error: 0.0110 - val_loss: 7.5396e-05 - val_root_mean_squared_error: 0.0087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 923/1000\n", + "8/8 - 0s - loss: 1.2344e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.1564e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 924/1000\n", + "8/8 - 0s - loss: 1.0243e-04 - root_mean_squared_error: 0.0101 - val_loss: 8.4994e-05 - val_root_mean_squared_error: 0.0092\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 925/1000\n", + "8/8 - 0s - loss: 8.8179e-05 - root_mean_squared_error: 0.0094 - val_loss: 4.5441e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 926/1000\n", + "8/8 - 0s - loss: 5.8491e-05 - root_mean_squared_error: 0.0076 - val_loss: 4.0330e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 927/1000\n", + "8/8 - 0s - loss: 6.6942e-05 - root_mean_squared_error: 0.0082 - val_loss: 4.1392e-05 - val_root_mean_squared_error: 0.0064\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 928/1000\n", + "8/8 - 0s - loss: 8.9851e-05 - root_mean_squared_error: 0.0095 - val_loss: 6.9396e-05 - val_root_mean_squared_error: 0.0083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 929/1000\n", + "8/8 - 0s - loss: 8.8738e-05 - root_mean_squared_error: 0.0094 - val_loss: 6.3711e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 930/1000\n", + "8/8 - 0s - loss: 1.3876e-04 - root_mean_squared_error: 0.0118 - val_loss: 9.1411e-05 - val_root_mean_squared_error: 0.0096\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 931/1000\n", + "8/8 - 0s - loss: 1.3233e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3820e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 932/1000\n", + "8/8 - 0s - loss: 1.3735e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5831e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 933/1000\n", + "8/8 - 0s - loss: 9.8280e-05 - root_mean_squared_error: 0.0099 - val_loss: 6.9310e-05 - val_root_mean_squared_error: 0.0083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 934/1000\n", + "8/8 - 0s - loss: 9.7514e-05 - root_mean_squared_error: 0.0099 - val_loss: 5.4480e-05 - val_root_mean_squared_error: 0.0074\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 935/1000\n", + "8/8 - 0s - loss: 1.3399e-04 - root_mean_squared_error: 0.0116 - val_loss: 8.9073e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 936/1000\n", + "8/8 - 0s - loss: 1.2657e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.5531e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 937/1000\n", + "8/8 - 0s - loss: 1.1143e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.2011e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 938/1000\n", + "8/8 - 0s - loss: 8.2309e-05 - root_mean_squared_error: 0.0091 - val_loss: 8.8097e-05 - val_root_mean_squared_error: 0.0094\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 939/1000\n", + "8/8 - 0s - loss: 6.2822e-05 - root_mean_squared_error: 0.0079 - val_loss: 4.7503e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 940/1000\n", + "8/8 - 0s - loss: 8.6025e-05 - root_mean_squared_error: 0.0093 - val_loss: 3.5560e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 941/1000\n", + "8/8 - 0s - loss: 9.1478e-05 - root_mean_squared_error: 0.0096 - val_loss: 8.1436e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 942/1000\n", + "8/8 - 0s - loss: 1.0500e-04 - root_mean_squared_error: 0.0102 - val_loss: 8.5130e-05 - val_root_mean_squared_error: 0.0092\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 943/1000\n", + "8/8 - 0s - loss: 8.3989e-05 - root_mean_squared_error: 0.0092 - val_loss: 9.5948e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 944/1000\n", + "8/8 - 0s - loss: 6.4732e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.4003e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 945/1000\n", + "8/8 - 0s - loss: 5.7470e-05 - root_mean_squared_error: 0.0076 - val_loss: 3.7374e-05 - val_root_mean_squared_error: 0.0061\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 946/1000\n", + "8/8 - 0s - loss: 4.7385e-05 - root_mean_squared_error: 0.0069 - val_loss: 3.1066e-05 - val_root_mean_squared_error: 0.0056\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 947/1000\n", + "8/8 - 0s - loss: 6.2973e-05 - root_mean_squared_error: 0.0079 - val_loss: 3.5883e-05 - val_root_mean_squared_error: 0.0060\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 948/1000\n", + "8/8 - 0s - loss: 6.5291e-05 - root_mean_squared_error: 0.0081 - val_loss: 6.2234e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 949/1000\n", + "8/8 - 0s - loss: 8.0685e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.1080e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 950/1000\n", + "8/8 - 0s - loss: 8.0729e-05 - root_mean_squared_error: 0.0090 - val_loss: 7.3084e-05 - val_root_mean_squared_error: 0.0085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 951/1000\n", + "8/8 - 0s - loss: 9.3224e-05 - root_mean_squared_error: 0.0097 - val_loss: 6.2410e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 952/1000\n", + "8/8 - 0s - loss: 9.4520e-05 - root_mean_squared_error: 0.0097 - val_loss: 5.0591e-05 - val_root_mean_squared_error: 0.0071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 953/1000\n", + "8/8 - 0s - loss: 9.5749e-05 - root_mean_squared_error: 0.0098 - val_loss: 7.9139e-05 - val_root_mean_squared_error: 0.0089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 954/1000\n", + "8/8 - 0s - loss: 1.1248e-04 - root_mean_squared_error: 0.0106 - val_loss: 7.5281e-05 - val_root_mean_squared_error: 0.0087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 955/1000\n", + "8/8 - 0s - loss: 1.0080e-04 - root_mean_squared_error: 0.0100 - val_loss: 7.3377e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 956/1000\n", + "8/8 - 0s - loss: 1.1416e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.2090e-05 - val_root_mean_squared_error: 0.0091\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 957/1000\n", + "8/8 - 0s - loss: 1.0351e-04 - root_mean_squared_error: 0.0102 - val_loss: 8.1333e-05 - val_root_mean_squared_error: 0.0090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 958/1000\n", + "8/8 - 0s - loss: 9.1449e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.6026e-05 - val_root_mean_squared_error: 0.0081\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 959/1000\n", + "8/8 - 0s - loss: 8.3702e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.4699e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 960/1000\n", + "8/8 - 0s - loss: 6.9484e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.1945e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 961/1000\n", + "8/8 - 0s - loss: 8.1453e-05 - root_mean_squared_error: 0.0090 - val_loss: 7.7652e-05 - val_root_mean_squared_error: 0.0088\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 962/1000\n", + "8/8 - 0s - loss: 7.4791e-05 - root_mean_squared_error: 0.0086 - val_loss: 6.9862e-05 - val_root_mean_squared_error: 0.0084\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 963/1000\n", + "8/8 - 0s - loss: 8.6078e-05 - root_mean_squared_error: 0.0093 - val_loss: 5.3309e-05 - val_root_mean_squared_error: 0.0073\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 964/1000\n", + "8/8 - 0s - loss: 8.8871e-05 - root_mean_squared_error: 0.0094 - val_loss: 7.9303e-05 - val_root_mean_squared_error: 0.0089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 965/1000\n", + "8/8 - 0s - loss: 8.7718e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.1320e-04 - val_root_mean_squared_error: 0.0106\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 966/1000\n", + "8/8 - 0s - loss: 9.7906e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.0592e-04 - val_root_mean_squared_error: 0.0103\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 967/1000\n", + "8/8 - 0s - loss: 8.5310e-05 - root_mean_squared_error: 0.0092 - val_loss: 7.4549e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 968/1000\n", + "8/8 - 0s - loss: 8.2448e-05 - root_mean_squared_error: 0.0091 - val_loss: 5.8625e-05 - val_root_mean_squared_error: 0.0077\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 969/1000\n", + "8/8 - 0s - loss: 6.2954e-05 - root_mean_squared_error: 0.0079 - val_loss: 4.4968e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 970/1000\n", + "8/8 - 0s - loss: 5.4364e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.3765e-05 - val_root_mean_squared_error: 0.0066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 971/1000\n", + "8/8 - 0s - loss: 5.0967e-05 - root_mean_squared_error: 0.0071 - val_loss: 3.9707e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 972/1000\n", + "8/8 - 0s - loss: 6.0467e-05 - root_mean_squared_error: 0.0078 - val_loss: 4.4346e-05 - val_root_mean_squared_error: 0.0067\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 973/1000\n", + "8/8 - 0s - loss: 6.6360e-05 - root_mean_squared_error: 0.0081 - val_loss: 4.0101e-05 - val_root_mean_squared_error: 0.0063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 974/1000\n", + "8/8 - 0s - loss: 8.7524e-05 - root_mean_squared_error: 0.0094 - val_loss: 6.5555e-05 - val_root_mean_squared_error: 0.0081\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 975/1000\n", + "8/8 - 0s - loss: 1.2172e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.0776e-04 - val_root_mean_squared_error: 0.0104\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 976/1000\n", + "8/8 - 0s - loss: 1.1051e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0735e-04 - val_root_mean_squared_error: 0.0104\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 977/1000\n", + "8/8 - 0s - loss: 1.0297e-04 - root_mean_squared_error: 0.0101 - val_loss: 6.0609e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 978/1000\n", + "8/8 - 0s - loss: 1.1216e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0364e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 979/1000\n", + "8/8 - 0s - loss: 1.2527e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.0684e-04 - val_root_mean_squared_error: 0.0103\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 980/1000\n", + "8/8 - 0s - loss: 1.5960e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.0182e-04 - val_root_mean_squared_error: 0.0101\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 981/1000\n", + "8/8 - 0s - loss: 1.1712e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.2098e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 982/1000\n", + "8/8 - 0s - loss: 9.4573e-05 - root_mean_squared_error: 0.0097 - val_loss: 6.1012e-05 - val_root_mean_squared_error: 0.0078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 983/1000\n", + "8/8 - 0s - loss: 7.1464e-05 - root_mean_squared_error: 0.0085 - val_loss: 6.9665e-05 - val_root_mean_squared_error: 0.0083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 984/1000\n", + "8/8 - 0s - loss: 5.9964e-05 - root_mean_squared_error: 0.0077 - val_loss: 5.0432e-05 - val_root_mean_squared_error: 0.0071\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 985/1000\n", + "8/8 - 0s - loss: 8.1943e-05 - root_mean_squared_error: 0.0091 - val_loss: 4.7275e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 986/1000\n", + "8/8 - 0s - loss: 8.9102e-05 - root_mean_squared_error: 0.0094 - val_loss: 4.8187e-05 - val_root_mean_squared_error: 0.0069\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 987/1000\n", + "8/8 - 0s - loss: 1.4367e-04 - root_mean_squared_error: 0.0120 - val_loss: 9.4286e-05 - val_root_mean_squared_error: 0.0097\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 988/1000\n", + "8/8 - 0s - loss: 1.4955e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.1760e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 989/1000\n", + "8/8 - 0s - loss: 1.5798e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.2528e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 990/1000\n", + "8/8 - 0s - loss: 1.3258e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.0305e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 991/1000\n", + "8/8 - 0s - loss: 1.5237e-04 - root_mean_squared_error: 0.0123 - val_loss: 6.4503e-05 - val_root_mean_squared_error: 0.0080\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 992/1000\n", + "8/8 - 0s - loss: 1.4115e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.0441e-04 - val_root_mean_squared_error: 0.0102\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 993/1000\n", + "8/8 - 0s - loss: 1.0500e-04 - root_mean_squared_error: 0.0102 - val_loss: 9.5261e-05 - val_root_mean_squared_error: 0.0098\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 994/1000\n", + "8/8 - 0s - loss: 1.0573e-04 - root_mean_squared_error: 0.0103 - val_loss: 7.8975e-05 - val_root_mean_squared_error: 0.0089\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 995/1000\n", + "8/8 - 0s - loss: 8.2885e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.2525e-05 - val_root_mean_squared_error: 0.0079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 996/1000\n", + "8/8 - 0s - loss: 6.4475e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.4020e-05 - val_root_mean_squared_error: 0.0086\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 997/1000\n", + "8/8 - 0s - loss: 4.8013e-05 - root_mean_squared_error: 0.0069 - val_loss: 4.1960e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 998/1000\n", + "8/8 - 0s - loss: 4.1386e-05 - root_mean_squared_error: 0.0064 - val_loss: 4.2283e-05 - val_root_mean_squared_error: 0.0065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 999/1000\n", + "8/8 - 0s - loss: 3.3998e-05 - root_mean_squared_error: 0.0058 - val_loss: 2.3990e-05 - val_root_mean_squared_error: 0.0049\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1000/1000\n", + "8/8 - 0s - loss: 3.9845e-05 - root_mean_squared_error: 0.0063 - val_loss: 5.1482e-05 - val_root_mean_squared_error: 0.0072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(SimpleRNN(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(SimpleRNN(50, return_sequences=True))\n", + "model.add(SimpleRNN(50, return_sequences=True))\n", + "model.add(SimpleRNN(1))\n", + "model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=1000, batch_size=100, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "train_yhat = model.predict(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", + "# invert scaling for actual\n", + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 63095.492327106855.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 3160.4812924616404.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 521984\n", + "1 400838\n", + "2 418377\n", + "3 610318\n", + " Count\n", + "0 488981\n", + "1 336030\n", + "2 381773\n", + "3 535746\n" + ] + } + ], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n" + ] + } + ], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 115829.72216361394.\n" + ] + } + ], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 55204.417561006834.\n" + ] + } + ], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/multivar_simple_gru.ipynb b/multivar_simple_gru.ipynb new file mode 100644 index 0000000..5060529 --- /dev/null +++ b/multivar_simple_gru.ipynb @@ -0,0 +1,5782 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + " king_all_copy, king_data= load_data(ismael_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1939-01-01\n", + "1 1939-01-02\n", + "2 1939-01-03\n", + "3 1939-01-04\n", + "4 1939-01-05\n", + " ... \n", + "24364 2020-12-25\n", + "24365 2020-12-26\n", + "24366 2020-12-27\n", + "24367 2020-12-28\n", + "24368 2020-12-29\n", + "Name: date, Length: 24369, dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "print(data_copy['date'])\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateking
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984 rows × 2 columns

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dateking
01939-01-316
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41939-05-3125159
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9792020-08-31105269
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984 rows × 2 columns

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" + ], + "text/plain": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = data_copy\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data[132:]" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateking
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1341950-03-3121
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852 rows × 2 columns

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Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearmonthupwellingnoinpgopdooni
019501-162.644-2.190-1.61-1.40
119502-1662.077-1.450-2.17-1.20
219503-493.091-0.970-1.89-1.10
319504-41.923-0.860-1.99-1.20
419505492.211-0.630-3.19-1.10
........................
8472020843-0.463-1.422-1.32-0.57
84820209-1-0.276-1.161-1.03-0.89
849202010101.612-1.476-0.62-1.17
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851202012-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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" + ], + "text/plain": [ + " year month upwelling noi npgo pdo oni \n", + "0 1950 1 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950 2 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950 3 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950 4 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950 5 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020 8 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020 9 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020 10 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020 11 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020 12 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", + "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", + "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", + "cov_data = load_cov_set(ismael_path_cov)\n", + "cov_data" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwelling
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............
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852 rows × 3 columns

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" + ], + "text/plain": [ + " date king upwelling\n", + "0 1950-01-31 0 -16\n", + "1 1950-02-28 0 -166\n", + "2 1950-03-31 21 -49\n", + "3 1950-04-30 6630 -4\n", + "4 1950-05-31 50638 49\n", + ".. ... ... ...\n", + "847 2020-08-31 105269 43\n", + "848 2020-09-30 254930 -1\n", + "849 2020-10-31 30917 10\n", + "850 2020-11-30 843 -43\n", + "851 2020-12-31 9 -97\n", + "\n", + "[852 rows x 3 columns]" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "upwelling = cov_data[\"upwelling\"]\n", + "master_data = master_data.join(upwelling)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoi
01950-01-310-162.644
11950-02-280-1662.077
21950-03-3121-493.091
31950-04-306630-41.923
41950-05-3150638492.211
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8472020-08-3110526943-0.463
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852 rows × 4 columns

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" + ], + "text/plain": [ + " date king upwelling noi\n", + "0 1950-01-31 0 -16 2.644\n", + "1 1950-02-28 0 -166 2.077\n", + "2 1950-03-31 21 -49 3.091\n", + "3 1950-04-30 6630 -4 1.923\n", + "4 1950-05-31 50638 49 2.211\n", + ".. ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463\n", + "848 2020-09-30 254930 -1 -0.276\n", + "849 2020-10-31 30917 10 1.612\n", + "850 2020-11-30 843 -43 1.998\n", + "851 2020-12-31 9 -97 5.098\n", + "\n", + "[852 rows x 4 columns]" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noi = cov_data[\"noi\"]\n", + "master_data = master_data.join(noi)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgo
01950-01-310-162.644-2.190
11950-02-280-1662.077-1.450
21950-03-3121-493.091-0.970
31950-04-306630-41.923-0.860
41950-05-3150638492.211-0.630
..................
8472020-08-3110526943-0.463-1.422
8482020-09-30254930-1-0.276-1.161
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852 rows × 5 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo\n", + "0 1950-01-31 0 -16 2.644 -2.190\n", + "1 1950-02-28 0 -166 2.077 -1.450\n", + "2 1950-03-31 21 -49 3.091 -0.970\n", + "3 1950-04-30 6630 -4 1.923 -0.860\n", + "4 1950-05-31 50638 49 2.211 -0.630\n", + ".. ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422\n", + "848 2020-09-30 254930 -1 -0.276 -1.161\n", + "849 2020-10-31 30917 10 1.612 -1.476\n", + "850 2020-11-30 843 -43 1.998 -1.710\n", + "851 2020-12-31 9 -97 5.098 -1.870\n", + "\n", + "[852 rows x 5 columns]" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npgo = cov_data[\"npgo\"]\n", + "master_data = master_data.join(npgo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdo
01950-01-310-162.644-2.190-1.61
11950-02-280-1662.077-1.450-2.17
21950-03-3121-493.091-0.970-1.89
31950-04-306630-41.923-0.860-1.99
41950-05-3150638492.211-0.630-3.19
.....................
8472020-08-3110526943-0.463-1.422-1.32
8482020-09-30254930-1-0.276-1.161-1.03
8492020-10-3130917101.612-1.476-0.62
8502020-11-30843-431.998-1.710-1.58
8512020-12-319-975.098-1.870-0.98
\n", + "

852 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date king upwelling noi npgo pdo\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19\n", + ".. ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni \n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oni = cov_data[\"oni \"]\n", + "master_data = master_data.join(oni)\n", + "master_data\n", + "# cov_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = master_data.rename(columns={\"oni \": \"oni\"})\n", + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kingupwellingnoinpgopdooni
date
1950-01-310-162.644-2.190-1.61-1.40
1950-02-280-1662.077-1.450-2.17-1.20
1950-03-3121-493.091-0.970-1.89-1.10
1950-04-306630-41.923-0.860-1.99-1.20
1950-05-3150638492.211-0.630-3.19-1.10
.....................
2020-08-3110526943-0.463-1.422-1.32-0.57
2020-09-30254930-1-0.276-1.161-1.03-0.89
2020-10-3130917101.612-1.476-0.62-1.17
2020-11-30843-431.998-1.710-1.58-1.27
2020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " king upwelling noi npgo pdo oni\n", + "date \n", + "1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + "... ... ... ... ... ... ...\n", + "2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " var1(t-6) var2(t-6) var3(t-6) var4(t-6) var5(t-6) var6(t-6) \\\n", + "6 0.000006 0.520913 0.710488 0.220877 0.329032 0.119048 \n", + "7 0.000006 0.079848 0.683284 0.332829 0.238710 0.166667 \n", + "8 0.000035 0.399240 0.731936 0.405446 0.283871 0.190476 \n", + "9 0.009241 0.566540 0.675895 0.422088 0.267742 0.166667 \n", + "10 0.070540 0.764259 0.689713 0.456883 0.074194 0.190476 \n", + "\n", + " var1(t-5) var2(t-5) var3(t-5) var4(t-5) ... var3(t-1) var4(t-1) \\\n", + "6 0.000006 0.079848 0.683284 0.332829 ... 0.632281 0.464448 \n", + "7 0.000035 0.399240 0.731936 0.405446 ... 0.567508 0.440242 \n", + "8 0.009241 0.566540 0.675895 0.422088 ... 0.572306 0.468986 \n", + "9 0.070540 0.764259 0.689713 0.456883 ... 0.591786 0.461422 \n", + "10 0.023221 0.703422 0.632281 0.464448 ... 0.461760 0.570348 \n", + "\n", + " var5(t-1) var6(t-1) var1(t) var2(t) var3(t) var4(t) var5(t) \\\n", + "6 0.182258 0.238095 0.045884 0.847909 0.567508 0.440242 0.000000 \n", + "7 0.000000 0.309524 0.056366 0.638783 0.572306 0.468986 0.108065 \n", + "8 0.108065 0.309524 0.286279 0.634981 0.591786 0.461422 0.201613 \n", + "9 0.201613 0.333333 0.006073 0.380228 0.461760 0.570348 0.279032 \n", + "10 0.279032 0.309524 0.000205 0.311787 0.606804 0.512859 0.354839 \n", + "\n", + " var6(t) \n", + "6 0.309524 \n", + "7 0.309524 \n", + "8 0.333333 \n", + "9 0.309524 \n", + "10 0.285714 \n", + "\n", + "[5 rows x 42 columns]\n" + ] + } + ], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 6)\n", + "(80,)\n", + "(712, 6, 6)\n", + "(712,)\n", + "(54, 6, 6)\n", + "(54,)\n" + ] + } + ], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "1/1 - 6s - loss: 0.1902 - root_mean_squared_error: 0.4361 - val_loss: 0.1280 - val_root_mean_squared_error: 0.3578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2/1000\n", + "1/1 - 0s - loss: 0.1436 - root_mean_squared_error: 0.3789 - val_loss: 0.0922 - val_root_mean_squared_error: 0.3036\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3/1000\n", + "1/1 - 0s - loss: 0.1013 - root_mean_squared_error: 0.3182 - val_loss: 0.0638 - val_root_mean_squared_error: 0.2526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4/1000\n", + "1/1 - 0s - loss: 0.0653 - root_mean_squared_error: 0.2555 - val_loss: 0.0448 - val_root_mean_squared_error: 0.2117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 5/1000\n", + "1/1 - 0s - loss: 0.0377 - root_mean_squared_error: 0.1941 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 6/1000\n", + "1/1 - 0s - loss: 0.0199 - root_mean_squared_error: 0.1409 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 7/1000\n", + "1/1 - 0s - loss: 0.0121 - root_mean_squared_error: 0.1100 - val_loss: 0.0482 - val_root_mean_squared_error: 0.2197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 8/1000\n", + "1/1 - 0s - loss: 0.0130 - root_mean_squared_error: 0.1142 - val_loss: 0.0625 - val_root_mean_squared_error: 0.2499\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 9/1000\n", + "1/1 - 0s - loss: 0.0195 - root_mean_squared_error: 0.1396 - val_loss: 0.0761 - val_root_mean_squared_error: 0.2759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 10/1000\n", + "1/1 - 0s - loss: 0.0274 - root_mean_squared_error: 0.1656 - val_loss: 0.0857 - val_root_mean_squared_error: 0.2928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 11/1000\n", + "1/1 - 0s - loss: 0.0335 - root_mean_squared_error: 0.1832 - val_loss: 0.0900 - val_root_mean_squared_error: 0.2999\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 12/1000\n", + "1/1 - 0s - loss: 0.0364 - root_mean_squared_error: 0.1907 - val_loss: 0.0891 - val_root_mean_squared_error: 0.2984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 13/1000\n", + "1/1 - 0s - loss: 0.0358 - root_mean_squared_error: 0.1892 - val_loss: 0.0842 - val_root_mean_squared_error: 0.2902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 14/1000\n", + "1/1 - 0s - loss: 0.0326 - root_mean_squared_error: 0.1806 - val_loss: 0.0768 - val_root_mean_squared_error: 0.2772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 15/1000\n", + "1/1 - 0s - loss: 0.0279 - root_mean_squared_error: 0.1669 - val_loss: 0.0683 - val_root_mean_squared_error: 0.2613\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 16/1000\n", + "1/1 - 0s - loss: 0.0226 - root_mean_squared_error: 0.1505 - val_loss: 0.0598 - val_root_mean_squared_error: 0.2445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 17/1000\n", + "1/1 - 0s - loss: 0.0178 - root_mean_squared_error: 0.1335 - val_loss: 0.0521 - val_root_mean_squared_error: 0.2283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 18/1000\n", + "1/1 - 0s - loss: 0.0140 - root_mean_squared_error: 0.1184 - val_loss: 0.0459 - val_root_mean_squared_error: 0.2143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 19/1000\n", + "1/1 - 0s - loss: 0.0116 - root_mean_squared_error: 0.1075 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 20/1000\n", + "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1022 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 21/1000\n", + "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1023 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 22/1000\n", + "1/1 - 0s - loss: 0.0113 - root_mean_squared_error: 0.1062 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 23/1000\n", + "1/1 - 0s - loss: 0.0125 - root_mean_squared_error: 0.1118 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 24/1000\n", + "1/1 - 0s - loss: 0.0137 - root_mean_squared_error: 0.1172 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 25/1000\n", + "1/1 - 0s - loss: 0.0147 - root_mean_squared_error: 0.1212 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 26/1000\n", + "1/1 - 0s - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 27/1000\n", + "1/1 - 0s - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 28/1000\n", + "1/1 - 0s - loss: 0.0148 - root_mean_squared_error: 0.1215 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 29/1000\n", + "1/1 - 0s - loss: 0.0139 - root_mean_squared_error: 0.1181 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 30/1000\n", + "1/1 - 0s - loss: 0.0129 - root_mean_squared_error: 0.1138 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 31/1000\n", + "1/1 - 0s - loss: 0.0119 - root_mean_squared_error: 0.1091 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 32/1000\n", + "1/1 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1048 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 33/1000\n", + "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1013 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 34/1000\n", + "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0992 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 35/1000\n", + "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0984 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 36/1000\n", + "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0987 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 37/1000\n", + "1/1 - 0s - loss: 0.0100 - root_mean_squared_error: 0.0999 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2088\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 38/1000\n", + "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1013 - val_loss: 0.0445 - val_root_mean_squared_error: 0.2111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 39/1000\n", + "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1026 - val_loss: 0.0451 - val_root_mean_squared_error: 0.2124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 40/1000\n", + "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1034 - val_loss: 0.0453 - val_root_mean_squared_error: 0.2128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 41/1000\n", + "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1036 - val_loss: 0.0450 - val_root_mean_squared_error: 0.2122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 42/1000\n", + "1/1 - 0s - loss: 0.0106 - root_mean_squared_error: 0.1031 - val_loss: 0.0445 - val_root_mean_squared_error: 0.2109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 43/1000\n", + "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1021 - val_loss: 0.0437 - val_root_mean_squared_error: 0.2090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 44/1000\n", + "1/1 - 0s - loss: 0.0102 - root_mean_squared_error: 0.1008 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 45/1000\n", + "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0994 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 46/1000\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0982 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 47/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1994\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 48/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1975\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 49/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 50/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 51/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 52/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 53/1000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 54/1000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0975 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 55/1000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 56/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 57/1000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0967 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 58/1000\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 59/1000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 60/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 61/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1979\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 62/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0951 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 63/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 64/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 65/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 66/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 67/1000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 68/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 69/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 70/1000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 71/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 72/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 73/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1974\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 74/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 75/1000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 76/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 77/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 78/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 80/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 81/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 82/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 83/1000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 84/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 85/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1971\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 86/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 87/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 88/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 89/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 90/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 91/1000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 92/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1976\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 93/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 94/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 95/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 96/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 97/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 98/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 99/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 100/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 101/1000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 102/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 103/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 104/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 105/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 106/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 107/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 108/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 109/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 110/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 111/1000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 112/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 113/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 114/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 115/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 116/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 117/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 118/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 119/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 120/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 121/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 122/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 123/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 124/1000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 125/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 126/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 127/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 128/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 129/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 130/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 131/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 132/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 133/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 134/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 135/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 136/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 137/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 138/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 139/1000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 140/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 141/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 142/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 143/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 144/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 145/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 146/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 147/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 148/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 149/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 150/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1956\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 151/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 152/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 153/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 154/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 155/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 156/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 157/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 158/1000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1953\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 159/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0903 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 160/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 161/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 162/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 163/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 164/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 165/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 166/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 167/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 168/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 169/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 170/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 171/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 172/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 173/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 174/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 175/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 176/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 177/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1948\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 178/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1948\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 179/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1948\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 180/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 181/1000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 182/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 183/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 184/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 185/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 186/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 187/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 188/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 189/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 190/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 191/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 192/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 193/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 194/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 195/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 196/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 197/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 198/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 199/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 200/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 201/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 202/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 203/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 204/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 205/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 206/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 207/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 208/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 209/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 210/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 211/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 212/1000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 213/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 214/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 215/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 216/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 217/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 218/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 219/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 220/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 221/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 222/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 223/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 224/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 225/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 226/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1935\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 227/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 228/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 229/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 230/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 231/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 232/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 233/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 234/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 235/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 236/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 237/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 238/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 239/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 240/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 241/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 242/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 243/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 244/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 245/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 246/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 247/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 248/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 249/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 250/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 251/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 252/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 253/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 254/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 255/1000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 256/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 257/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 258/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 259/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 260/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 261/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 262/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 263/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 264/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 265/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 266/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 267/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 268/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 269/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 270/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 271/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 272/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 273/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 274/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 275/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 276/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 277/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 278/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 279/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 280/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 281/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 282/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 283/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 284/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 285/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 286/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 287/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 288/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 289/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 290/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 291/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 292/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 293/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 294/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 295/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 296/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 297/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 298/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 299/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 300/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 301/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 302/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 303/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 304/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 305/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 306/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 307/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 308/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 309/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 310/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 311/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 313/1000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 314/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 315/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 316/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 317/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 318/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 319/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 320/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 321/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 322/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 323/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 324/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 325/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 326/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 327/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 328/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1912\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 329/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 330/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 331/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 332/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 333/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 334/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 335/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 336/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 337/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 338/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 339/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 340/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 341/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 342/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 343/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 344/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 345/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 346/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 347/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 348/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 349/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 352/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 353/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 354/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 355/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 356/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 357/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 358/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 359/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 360/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 361/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 362/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 363/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 364/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 365/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 366/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 367/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 368/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 369/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 370/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 371/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 372/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 373/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 374/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 375/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 376/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 377/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 378/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 379/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 380/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 381/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 382/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 383/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 384/1000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 385/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 386/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 387/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 391/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 392/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 393/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 394/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 395/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 396/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 397/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 398/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 399/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 400/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 401/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 402/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 403/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 404/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 405/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 406/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 407/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 408/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 409/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 410/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 411/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 412/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 413/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 414/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 415/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 416/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 417/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 418/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 419/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 420/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 421/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 422/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 423/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 424/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 425/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 426/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 427/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 428/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 429/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 430/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 431/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 432/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 433/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 434/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 435/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 436/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 437/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 438/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 439/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 440/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 441/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 442/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 443/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 444/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 445/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 446/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 447/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 448/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 449/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 450/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 451/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 452/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 453/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 454/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 455/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 456/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 457/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 458/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 459/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 460/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 461/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 462/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 463/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 464/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 465/1000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 466/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 467/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 468/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 469/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 470/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 471/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 472/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 473/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 474/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 475/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 476/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 477/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 478/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 479/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 480/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 481/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 482/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 483/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 484/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 485/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 486/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 487/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 488/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 489/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 490/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 491/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 492/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 493/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 494/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 495/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 496/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 497/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 498/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 499/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 500/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 501/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 502/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 503/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 504/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 505/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 506/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 507/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 508/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 509/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 510/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 511/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 512/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 513/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 514/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 515/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 516/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 517/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 518/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 519/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 520/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 521/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 522/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 523/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 524/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 525/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 526/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 527/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 528/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 529/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 530/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 531/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 532/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 533/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 534/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 535/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 536/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 537/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 538/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 539/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 540/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 541/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 542/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 543/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 544/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 545/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 546/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 547/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 548/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 549/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 550/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 551/1000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 552/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 553/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 554/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 555/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 556/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 557/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 558/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 559/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 560/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 561/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 562/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 563/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 564/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 565/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 566/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 567/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 568/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 569/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 570/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 571/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 572/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 573/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 574/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 575/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 576/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 577/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 578/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 579/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 580/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 581/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 582/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 583/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 584/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 585/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 586/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 587/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 588/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 589/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 590/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 591/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 592/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 593/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 594/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 595/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 596/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 597/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 598/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 599/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 600/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 601/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 602/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 603/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 604/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 605/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 606/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 607/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 608/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 609/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 610/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 611/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 612/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 613/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 614/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 615/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 616/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 617/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 618/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 619/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 620/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 621/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 622/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 623/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 624/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 625/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 626/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 627/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 628/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 629/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 630/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 631/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 632/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 633/1000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 634/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 635/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 636/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 637/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 638/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 639/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 640/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 641/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 642/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 643/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 644/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 645/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 646/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 647/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 648/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 649/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 650/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 651/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 652/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 653/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 654/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 655/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 656/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 657/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 658/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 659/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 660/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 661/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 662/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 663/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 664/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 665/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 666/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 667/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 668/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 669/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 670/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 671/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 672/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 673/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 674/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 675/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 676/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 677/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 678/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 679/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 680/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 681/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 682/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 683/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 684/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 685/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 686/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 687/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 688/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 689/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 690/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 691/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 692/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 693/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 694/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 695/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 696/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 697/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 698/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 699/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 700/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 701/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 702/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 703/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 704/1000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 705/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 706/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 707/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 708/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 709/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 710/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 711/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 712/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 713/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 714/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 715/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 716/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 717/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 718/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 719/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 720/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 721/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 722/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 723/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 724/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 725/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 726/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 727/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 728/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 729/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 730/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 731/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 732/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 733/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 734/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 735/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 736/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 737/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 738/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 739/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 740/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 741/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 742/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 743/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 744/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 745/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 746/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 747/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 748/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 749/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 750/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 751/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 752/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 753/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 754/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 755/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 756/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 757/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 758/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 759/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 760/1000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 761/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 762/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 763/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 764/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 765/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 766/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 767/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 768/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 769/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 770/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 771/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 772/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 773/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 774/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 775/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 776/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 777/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 778/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 779/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 780/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 781/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 782/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 783/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 784/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 785/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 786/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 787/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 788/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 789/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 790/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 791/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 792/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 793/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 794/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 795/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 796/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 797/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 798/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 799/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 800/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 801/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 802/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 803/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 804/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 805/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 806/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 807/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 808/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 809/1000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 810/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 811/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 812/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 813/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 814/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 815/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 816/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 817/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 818/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 819/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 820/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 821/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 822/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 823/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1818\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 824/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 825/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 826/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 827/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 828/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 829/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 830/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 831/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 832/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 833/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 834/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 835/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 836/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 837/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 838/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 839/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 840/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 841/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 842/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 843/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 844/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 845/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 846/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 847/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 848/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 849/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 850/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 851/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 852/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 853/1000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 854/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 855/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 856/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1807\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 857/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 858/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 859/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 860/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 861/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 862/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 863/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1804\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 864/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 865/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 866/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 867/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 868/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 869/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 870/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 871/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 872/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 873/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 874/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 875/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 876/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 877/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 878/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 879/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 880/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 881/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 882/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 883/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 884/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 885/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 886/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 887/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 888/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 889/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 890/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 891/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 892/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 893/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 894/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 895/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1793\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 896/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 897/1000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 898/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 899/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 900/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 901/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 902/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 903/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 904/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 905/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 906/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 907/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 908/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 909/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 910/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 911/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 912/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 913/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 914/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 915/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 916/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 917/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 918/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 919/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 920/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1785\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 921/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 922/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 923/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 924/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 925/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 926/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 927/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 928/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1782\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 929/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 930/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 931/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 932/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 933/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 934/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 935/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 936/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 937/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 938/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 939/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 940/1000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 941/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 942/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 943/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 944/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 945/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 946/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 947/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 948/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 949/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 950/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 951/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 952/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 953/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 954/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 955/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 956/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 957/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 958/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 959/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 960/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 961/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 962/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 963/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 964/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1768\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 965/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 966/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 967/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 968/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 969/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 970/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 971/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1765\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 972/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 973/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 974/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 975/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 976/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 977/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1762\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 978/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1762\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 979/1000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 980/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 981/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 982/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 983/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 984/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 985/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 986/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 987/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 988/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 989/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 990/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 991/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 992/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 993/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 994/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 995/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 996/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 997/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 998/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 999/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1000/1000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1751\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "# model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "# model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(GRU(1))\n", + "# model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=1000, batch_size=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "train_yhat = model.predict(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", + "# invert scaling for actual\n", + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 53364.70144205812.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 58259.14410631176.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 410131\n", + "1 332086\n", + "2 352166\n", + "3 333230\n", + " Count\n", + "0 488981\n", + "1 336030\n", + "2 381773\n", + "3 535746\n" + ] + } + ], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n" + ] + } + ], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 115829.72216361394.\n" + ] + } + ], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 109683.84377496077.\n" + ] + } + ], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/multivar_simple_lstm.ipynb b/multivar_simple_lstm.ipynb new file mode 100644 index 0000000..3fb3b3c --- /dev/null +++ b/multivar_simple_lstm.ipynb @@ -0,0 +1,3899 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np \n", + "import math\n", + "import pandas as pd \n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.optimizers import SGD\n", + "import tensorflow.keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "# plt.style.use('fivethirtyeight')\n", + "from pandas import read_csv\n", + "from pandas import DataFrame\n", + "from pandas import concat\n", + "from numpy import concatenate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Multivariable Dataset

\n", + "

Load Chinook Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data(pathname):\n", + " salmon_data = pd.read_csv(pathname)\n", + " salmon_data.head()\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", + " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", + " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", + " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", + " greater_than = king_data[king_greater]\n", + " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", + " king_all_copy = king_all\n", + " king_all_copy = king_all_copy.reset_index()\n", + " king_all_copy = king_all_copy.drop('index', axis=1)\n", + " return king_all_copy, king_data" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date king\n", + "0 1939-01-01 0\n", + "1 1939-01-02 0\n", + "2 1939-01-03 0\n", + "3 1939-01-04 1\n", + "4 1939-01-05 0\n", + "... ... ...\n", + "24364 2020-12-25 0\n", + "24365 2020-12-26 0\n", + "24366 2020-12-27 0\n", + "24367 2020-12-28 0\n", + "24368 2020-12-29 0\n", + "\n", + "[24369 rows x 2 columns]\n" + ] + } + ], + "source": [ + "chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", + "ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", + "abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", + "king_all_copy, king_data= load_data(ismael_path)\n", + "print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1939-01-01\n", + "1 1939-01-02\n", + "2 1939-01-03\n", + "3 1939-01-04\n", + "4 1939-01-05\n", + " ... \n", + "24364 2020-12-25\n", + "24365 2020-12-26\n", + "24366 2020-12-27\n", + "24367 2020-12-28\n", + "24368 2020-12-29\n", + "Name: date, Length: 24369, dtype: datetime64[ns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_copy = king_all_copy\n", + "print(data_copy['date'])\n", + "data_copy.set_index('date', inplace=True)\n", + "data_copy.index = pd.to_datetime(data_copy.index)\n", + "data_copy = data_copy.resample('1M').sum()\n", + "data_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " king\n", + "date \n", + "1939-01-31 6\n", + "1939-02-28 12\n", + "1939-03-31 121\n", + "1939-04-30 51410\n", + "1939-05-31 25159\n", + "... ...\n", + "2020-08-31 105269\n", + "2020-09-30 254930\n", + "2020-10-31 30917\n", + "2020-11-30 843\n", + "2020-12-31 9\n", + "\n", + "[984 rows x 1 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "(984, 1)" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [], + "source": [ + "data_copy.reset_index(inplace=True)\n", + "data_copy = data_copy.rename(columns = {'index':'date'})" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateking
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984 rows × 2 columns

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dateking
01939-01-316
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41939-05-3125159
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9792020-08-31105269
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984 rows × 2 columns

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" + ], + "text/plain": [ + " date king\n", + "0 1939-01-31 6\n", + "1 1939-02-28 12\n", + "2 1939-03-31 121\n", + "3 1939-04-30 51410\n", + "4 1939-05-31 25159\n", + ".. ... ...\n", + "979 2020-08-31 105269\n", + "980 2020-09-30 254930\n", + "981 2020-10-31 30917\n", + "982 2020-11-30 843\n", + "983 2020-12-31 9\n", + "\n", + "[984 rows x 2 columns]" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = data_copy\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [], + "source": [ + "master_data = master_data[132:]" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateking
1321950-01-310
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1341950-03-3121
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852 rows × 2 columns

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Load Covariate Data and Concat to Master_Data

" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [], + "source": [ + "def load_cov_set(pathname):\n", + " data = pd.read_csv(pathname)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearmonthupwellingnoinpgopdooni
019501-162.644-2.190-1.61-1.40
119502-1662.077-1.450-2.17-1.20
219503-493.091-0.970-1.89-1.10
319504-41.923-0.860-1.99-1.20
419505492.211-0.630-3.19-1.10
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8472020843-0.463-1.422-1.32-0.57
84820209-1-0.276-1.161-1.03-0.89
849202010101.612-1.476-0.62-1.17
850202011-431.998-1.710-1.58-1.27
851202012-975.098-1.870-0.98-1.19
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852 rows × 7 columns

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" + ], + "text/plain": [ + " year month upwelling noi npgo pdo oni \n", + "0 1950 1 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950 2 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950 3 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950 4 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950 5 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020 8 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020 9 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020 10 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020 11 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020 12 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", + "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", + "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", + "cov_data = load_cov_set(ismael_path_cov)\n", + "cov_data" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwelling
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............
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852 rows × 3 columns

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" + ], + "text/plain": [ + " date king upwelling\n", + "0 1950-01-31 0 -16\n", + "1 1950-02-28 0 -166\n", + "2 1950-03-31 21 -49\n", + "3 1950-04-30 6630 -4\n", + "4 1950-05-31 50638 49\n", + ".. ... ... ...\n", + "847 2020-08-31 105269 43\n", + "848 2020-09-30 254930 -1\n", + "849 2020-10-31 30917 10\n", + "850 2020-11-30 843 -43\n", + "851 2020-12-31 9 -97\n", + "\n", + "[852 rows x 3 columns]" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "upwelling = cov_data[\"upwelling\"]\n", + "master_data = master_data.join(upwelling)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoi
01950-01-310-162.644
11950-02-280-1662.077
21950-03-3121-493.091
31950-04-306630-41.923
41950-05-3150638492.211
...............
8472020-08-3110526943-0.463
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852 rows × 4 columns

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" + ], + "text/plain": [ + " date king upwelling noi\n", + "0 1950-01-31 0 -16 2.644\n", + "1 1950-02-28 0 -166 2.077\n", + "2 1950-03-31 21 -49 3.091\n", + "3 1950-04-30 6630 -4 1.923\n", + "4 1950-05-31 50638 49 2.211\n", + ".. ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463\n", + "848 2020-09-30 254930 -1 -0.276\n", + "849 2020-10-31 30917 10 1.612\n", + "850 2020-11-30 843 -43 1.998\n", + "851 2020-12-31 9 -97 5.098\n", + "\n", + "[852 rows x 4 columns]" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noi = cov_data[\"noi\"]\n", + "master_data = master_data.join(noi)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgo
01950-01-310-162.644-2.190
11950-02-280-1662.077-1.450
21950-03-3121-493.091-0.970
31950-04-306630-41.923-0.860
41950-05-3150638492.211-0.630
..................
8472020-08-3110526943-0.463-1.422
8482020-09-30254930-1-0.276-1.161
8492020-10-3130917101.612-1.476
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852 rows × 5 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo\n", + "0 1950-01-31 0 -16 2.644 -2.190\n", + "1 1950-02-28 0 -166 2.077 -1.450\n", + "2 1950-03-31 21 -49 3.091 -0.970\n", + "3 1950-04-30 6630 -4 1.923 -0.860\n", + "4 1950-05-31 50638 49 2.211 -0.630\n", + ".. ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422\n", + "848 2020-09-30 254930 -1 -0.276 -1.161\n", + "849 2020-10-31 30917 10 1.612 -1.476\n", + "850 2020-11-30 843 -43 1.998 -1.710\n", + "851 2020-12-31 9 -97 5.098 -1.870\n", + "\n", + "[852 rows x 5 columns]" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npgo = cov_data[\"npgo\"]\n", + "master_data = master_data.join(npgo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdo
01950-01-310-162.644-2.190-1.61
11950-02-280-1662.077-1.450-2.17
21950-03-3121-493.091-0.970-1.89
31950-04-306630-41.923-0.860-1.99
41950-05-3150638492.211-0.630-3.19
.....................
8472020-08-3110526943-0.463-1.422-1.32
8482020-09-30254930-1-0.276-1.161-1.03
8492020-10-3130917101.612-1.476-0.62
8502020-11-30843-431.998-1.710-1.58
8512020-12-319-975.098-1.870-0.98
\n", + "

852 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date king upwelling noi npgo pdo\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19\n", + ".. ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdo = cov_data[\"pdo\"]\n", + "master_data = master_data.join(pdo)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

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" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni \n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oni = cov_data[\"oni \"]\n", + "master_data = master_data.join(oni)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datekingupwellingnoinpgopdooni
01950-01-310-162.644-2.190-1.61-1.40
11950-02-280-1662.077-1.450-2.17-1.20
21950-03-3121-493.091-0.970-1.89-1.10
31950-04-306630-41.923-0.860-1.99-1.20
41950-05-3150638492.211-0.630-3.19-1.10
........................
8472020-08-3110526943-0.463-1.422-1.32-0.57
8482020-09-30254930-1-0.276-1.161-1.03-0.89
8492020-10-3130917101.612-1.476-0.62-1.17
8502020-11-30843-431.998-1.710-1.58-1.27
8512020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " date king upwelling noi npgo pdo oni\n", + "0 1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1 1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "2 1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "3 1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "4 1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + ".. ... ... ... ... ... ... ...\n", + "847 2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "848 2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "849 2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "850 2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "851 2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 7 columns]" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data = master_data.rename(columns={\"oni \": \"oni\"})\n", + "master_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Load and Concat NOI data

" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kingupwellingnoinpgopdooni
date
1950-01-310-162.644-2.190-1.61-1.40
1950-02-280-1662.077-1.450-2.17-1.20
1950-03-3121-493.091-0.970-1.89-1.10
1950-04-306630-41.923-0.860-1.99-1.20
1950-05-3150638492.211-0.630-3.19-1.10
.....................
2020-08-3110526943-0.463-1.422-1.32-0.57
2020-09-30254930-1-0.276-1.161-1.03-0.89
2020-10-3130917101.612-1.476-0.62-1.17
2020-11-30843-431.998-1.710-1.58-1.27
2020-12-319-975.098-1.870-0.98-1.19
\n", + "

852 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " king upwelling noi npgo pdo oni\n", + "date \n", + "1950-01-31 0 -16 2.644 -2.190 -1.61 -1.40\n", + "1950-02-28 0 -166 2.077 -1.450 -2.17 -1.20\n", + "1950-03-31 21 -49 3.091 -0.970 -1.89 -1.10\n", + "1950-04-30 6630 -4 1.923 -0.860 -1.99 -1.20\n", + "1950-05-31 50638 49 2.211 -0.630 -3.19 -1.10\n", + "... ... ... ... ... ... ...\n", + "2020-08-31 105269 43 -0.463 -1.422 -1.32 -0.57\n", + "2020-09-30 254930 -1 -0.276 -1.161 -1.03 -0.89\n", + "2020-10-31 30917 10 1.612 -1.476 -0.62 -1.17\n", + "2020-11-30 843 -43 1.998 -1.710 -1.58 -1.27\n", + "2020-12-31 9 -97 5.098 -1.870 -0.98 -1.19\n", + "\n", + "[852 rows x 6 columns]" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_data.set_index('date', inplace=True)\n", + "master_data.index = pd.to_datetime(master_data.index)\n", + "master_data" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "master_data.to_csv('master_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint_filepath = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/checkpoint'\n", + "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", + "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", + "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_filepath,\n", + " save_weights_only=True,\n", + " monitor='val_accuracy',\n", + " mode='max',\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's plot each series

" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# specify columns to plot\n", + "groups = [0, 1, 2, 3, 4, 5]\n", + "i = 1\n", + "# plot each column\n", + "plt.figure()\n", + "for group in groups:\n", + " plt.subplot(len(groups), 1, i)\n", + " plt.plot(values[:, group])\n", + " plt.title(dataset.columns[group], y=.5, loc='right')\n", + " i += 1\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Make Series into Train and Test Set with inputs and ouptuts

" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " var1(t-6) var2(t-6) var3(t-6) var4(t-6) var5(t-6) var6(t-6) \\\n", + "6 0.000006 0.520913 0.710488 0.220877 0.329032 0.119048 \n", + "7 0.000006 0.079848 0.683284 0.332829 0.238710 0.166667 \n", + "8 0.000035 0.399240 0.731936 0.405446 0.283871 0.190476 \n", + "9 0.009241 0.566540 0.675895 0.422088 0.267742 0.166667 \n", + "10 0.070540 0.764259 0.689713 0.456883 0.074194 0.190476 \n", + "\n", + " var1(t-5) var2(t-5) var3(t-5) var4(t-5) ... var3(t-1) var4(t-1) \\\n", + "6 0.000006 0.079848 0.683284 0.332829 ... 0.632281 0.464448 \n", + "7 0.000035 0.399240 0.731936 0.405446 ... 0.567508 0.440242 \n", + "8 0.009241 0.566540 0.675895 0.422088 ... 0.572306 0.468986 \n", + "9 0.070540 0.764259 0.689713 0.456883 ... 0.591786 0.461422 \n", + "10 0.023221 0.703422 0.632281 0.464448 ... 0.461760 0.570348 \n", + "\n", + " var5(t-1) var6(t-1) var1(t) var2(t) var3(t) var4(t) var5(t) \\\n", + "6 0.182258 0.238095 0.045884 0.847909 0.567508 0.440242 0.000000 \n", + "7 0.000000 0.309524 0.056366 0.638783 0.572306 0.468986 0.108065 \n", + "8 0.108065 0.309524 0.286279 0.634981 0.591786 0.461422 0.201613 \n", + "9 0.201613 0.333333 0.006073 0.380228 0.461760 0.570348 0.279032 \n", + "10 0.279032 0.309524 0.000205 0.311787 0.606804 0.512859 0.354839 \n", + "\n", + " var6(t) \n", + "6 0.309524 \n", + "7 0.309524 \n", + "8 0.333333 \n", + "9 0.309524 \n", + "10 0.285714 \n", + "\n", + "[5 rows x 42 columns]\n" + ] + } + ], + "source": [ + "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", + "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", + " n_vars = 1 if type(data) is list else data.shape[1]\n", + " df = DataFrame(data)\n", + " cols, names = list(), list()\n", + " # input sequence (t-n, ... t-1)\n", + " for i in range(n_in, 0, -1):\n", + " cols.append(df.shift(i))\n", + " names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # forecast sequence (t, t+1, ... t+n)\n", + " for i in range(0, n_out):\n", + " cols.append(df.shift(-i))\n", + " if i == 0:\n", + " names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n", + " else:\n", + " names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n", + " # put it all together\n", + " agg = concat(cols, axis=1)\n", + " agg.columns = names\n", + " # drop rows with NaN values\n", + " if dropnan:\n", + " agg.dropna(inplace=True)\n", + " return agg\n", + "\n", + "# load dataset\n", + "dataset = read_csv('master_data.csv', header=0, index_col=0)\n", + "values = dataset.values\n", + "# integer encode direction\n", + "encoder = LabelEncoder()\n", + "values[:,1] = encoder.fit_transform(values[:,1])\n", + "# ensure all data is float\n", + "values = values.astype('float32')\n", + "# normalize features\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled = scaler.fit_transform(values)\n", + "# frame as supervised learning\n", + "n_months = 6\n", + "n_features = 6\n", + "reframed = series_to_supervised(scaled, n_months, 1)\n", + "# drop columns we don't want to predict\n", + "# reframed.drop(reframed.columns[[13]], axis=1, inplace=True)\n", + "print(reframed.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" + ] + } + ], + "source": [ + "# split into train and test sets\n", + "values = reframed.values\n", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MOTH TO YEAR CHECK THIS\n", + "train = values[:n_train_months, :]\n", + "test = values[n_train_months:, :]\n", + "# split into input and outputs\n", + "n_obs = n_months * n_features\n", + "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n", + "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n", + "train_X = train_X.reshape((train_X.shape[0], n_months, n_features))\n", + "test_X = test_X.reshape((test_X.shape[0], n_months, n_features))\n", + "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": {}, + "outputs": [], + "source": [ + "#create train, test, dev split\n", + "X_train, X_dev, y_train, y_dev = train_test_split(train_X, train_y, test_size=0.10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 6, 6)\n", + "(80,)\n", + "(712, 6, 6)\n", + "(712,)\n", + "(54, 6, 6)\n", + "(54,)\n" + ] + } + ], + "source": [ + "print(X_dev.shape)\n", + "print(y_dev.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(test_X.shape)\n", + "print(test_y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/400\n", + "1/1 - 3s - loss: 0.0256 - root_mean_squared_error: 0.1601 - val_loss: 0.0638 - val_root_mean_squared_error: 0.2527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2/400\n", + "1/1 - 0s - loss: 0.0203 - root_mean_squared_error: 0.1424 - val_loss: 0.0572 - val_root_mean_squared_error: 0.2393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3/400\n", + "1/1 - 0s - loss: 0.0161 - root_mean_squared_error: 0.1268 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4/400\n", + "1/1 - 0s - loss: 0.0130 - root_mean_squared_error: 0.1140 - val_loss: 0.0477 - val_root_mean_squared_error: 0.2185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 5/400\n", + "1/1 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1047 - val_loss: 0.0447 - val_root_mean_squared_error: 0.2114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 6/400\n", + "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0991 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2063\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 7/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 8/400\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0978 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2011\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 9/400\n", + "1/1 - 0s - loss: 0.0100 - root_mean_squared_error: 0.1000 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 10/400\n", + "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1025 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 11/400\n", + "1/1 - 0s - loss: 0.0109 - root_mean_squared_error: 0.1045 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 12/400\n", + "1/1 - 0s - loss: 0.0111 - root_mean_squared_error: 0.1056 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 13/400\n", + "1/1 - 0s - loss: 0.0112 - root_mean_squared_error: 0.1057 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 14/400\n", + "1/1 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1051 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 15/400\n", + "1/1 - 0s - loss: 0.0108 - root_mean_squared_error: 0.1038 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1994\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 16/400\n", + "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1023 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 17/400\n", + "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1007 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 18/400\n", + "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0992 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 19/400\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0979 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 20/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 21/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2040\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 22/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 23/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0425 - val_root_mean_squared_error: 0.2062\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 24/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0961 - val_loss: 0.0429 - val_root_mean_squared_error: 0.2071\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 25/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0964 - val_loss: 0.0432 - val_root_mean_squared_error: 0.2079\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 26/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0967 - val_loss: 0.0435 - val_root_mean_squared_error: 0.2085\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 27/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2088\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 28/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0437 - val_root_mean_squared_error: 0.2090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 29/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0437 - val_root_mean_squared_error: 0.2090\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 30/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2087\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 31/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0970 - val_loss: 0.0434 - val_root_mean_squared_error: 0.2083\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 32/400\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0432 - val_root_mean_squared_error: 0.2078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 33/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0429 - val_root_mean_squared_error: 0.2072\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 34/400\n", + "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2065\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 35/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2058\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 36/400\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0957 - val_loss: 0.0421 - val_root_mean_squared_error: 0.2051\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 37/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0956 - val_loss: 0.0418 - val_root_mean_squared_error: 0.2045\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 38/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0416 - val_root_mean_squared_error: 0.2039\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 39/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 40/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 41/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2025\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 42/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 43/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 44/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 45/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 46/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 47/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0954 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 48/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 49/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 50/400\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 51/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 52/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 53/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2029\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 54/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2031\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 55/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 56/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 57/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2036\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 58/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 59/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 60/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2037\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 61/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0415 - val_root_mean_squared_error: 0.2036\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 62/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2035\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 63/400\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2034\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 64/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2032\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 65/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0412 - val_root_mean_squared_error: 0.2030\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 66/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0411 - val_root_mean_squared_error: 0.2028\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 67/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0945 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 68/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0410 - val_root_mean_squared_error: 0.2024\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 69/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 70/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2020\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 71/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0408 - val_root_mean_squared_error: 0.2019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 72/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2018\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 73/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 74/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 75/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 76/400\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 77/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 78/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 79/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 80/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 81/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 82/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 83/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 84/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 85/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 86/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 87/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2016\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 88/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2015\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 89/400\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0406 - val_root_mean_squared_error: 0.2014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 90/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2013\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 91/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0405 - val_root_mean_squared_error: 0.2012\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 92/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2010\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 93/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2009\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 94/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 95/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 96/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 97/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 98/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 99/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2004\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 100/400\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0930 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 101/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 102/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 103/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 104/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 105/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 106/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2001\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 107/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 108/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0400 - val_root_mean_squared_error: 0.1999\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 109/400\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 110/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0925 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1997\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 111/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 112/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1995\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 113/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1994\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 114/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1993\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 115/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1991\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 116/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1990\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 117/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1990\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 118/400\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1989\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 119/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 120/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 121/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1986\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 122/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1985\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 123/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1984\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 124/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1983\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 125/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0393 - val_root_mean_squared_error: 0.1982\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 126/400\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1981\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 127/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1979\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 128/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 129/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 130/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1975\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 131/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 132/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1973\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 133/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1971\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 134/400\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0908 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 135/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1969\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 136/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 137/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 138/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 139/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 140/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 141/400\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 142/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 143/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 144/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 145/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 146/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 147/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1951\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 148/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 149/400\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 150/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 151/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 152/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 153/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 154/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 155/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 156/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 157/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 158/400\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 159/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 160/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 161/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 162/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 163/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 164/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 165/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1920\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 166/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 167/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 168/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 169/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 170/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 171/400\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 172/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 173/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 174/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 175/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 176/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 177/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 178/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 179/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 180/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 181/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 182/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 183/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 184/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 185/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 186/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 187/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 188/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 189/400\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 190/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 191/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 192/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 193/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 194/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 195/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 196/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 197/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 198/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 199/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 200/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 201/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 202/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 203/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 204/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 205/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 206/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 207/400\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 208/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 209/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 210/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 211/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 212/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 213/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 214/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 215/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 216/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 217/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 218/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 219/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 220/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 221/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 222/400\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 223/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 224/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 225/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 226/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 227/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 228/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 229/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 230/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 231/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 232/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 233/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 234/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 235/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 236/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 237/400\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 238/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 239/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 240/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 241/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 242/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 243/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 244/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 245/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 246/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 247/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 248/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1853\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 249/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 250/400\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 251/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 252/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 253/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 254/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 255/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 256/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 257/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 258/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 259/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 260/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 261/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 262/400\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 263/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 264/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 265/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 266/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 267/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 268/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 269/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 270/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 271/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 272/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 273/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 274/400\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 275/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 276/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 277/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 278/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 279/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 280/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 281/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 282/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 283/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 284/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 285/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 286/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 287/400\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 288/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 289/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 290/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 291/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 292/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 293/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 294/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 295/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 296/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 297/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 298/400\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 299/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 300/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 301/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 302/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 303/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 304/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1796\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 305/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 306/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 307/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 308/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 309/400\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 310/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 311/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 312/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 313/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 314/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 315/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 316/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 317/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 318/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 319/400\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 320/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 321/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 322/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 323/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 324/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 325/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 326/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1762\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 327/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 328/400\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 329/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 330/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 331/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1751\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 332/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1750\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 333/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1745\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 334/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 335/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 336/400\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1742\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 337/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1738\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 338/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1736\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 339/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1735\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 340/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1731\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 341/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1732\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 342/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 343/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1729\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 344/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0803 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1718\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 345/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1730\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 346/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1708\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 347/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1739\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 348/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 349/400\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1730\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 350/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1714\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1699\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 352/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 353/400\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1697\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 354/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0797 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1701\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 355/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1717\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 356/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0796 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 357/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 358/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1706\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 359/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 360/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 361/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1694\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 362/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1682\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 363/400\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1699\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 364/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1684\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 365/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 366/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 367/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 368/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 369/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1683\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 370/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1670\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 371/400\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 372/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1671\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 373/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1667\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 374/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1675\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 375/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1662\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 376/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1668\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 377/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0780 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1665\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 378/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1657\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 379/400\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0778 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 380/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0778 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1653\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 381/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1657\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 382/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0776 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1655\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 383/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0775 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1648\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 384/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1654\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 385/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 386/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0772 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1647\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 387/400\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0771 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1642\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 388/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1639\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1641\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1633\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 391/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0768 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1637\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 392/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 393/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1632\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 394/400\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1624\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 395/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1626\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 396/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1620\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 397/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0762 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1619\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 398/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0761 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1615\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 399/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1613\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 400/400\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0758 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1610\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# design network\n", + "model = Sequential()\n", + "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "# model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "# model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", + "model.add(LSTM(1))\n", + "# model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", + "# fit network\n", + "# \n", + "history = model.fit(train_X, train_y, epochs=400, batch_size=2000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "# plot history\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='dev')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [], + "source": [ + "# make a prediction\n", + "yhat = model.predict(test_X)\n", + "train_yhat = model.predict(train_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", + "# invert scaling for actual\n", + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [], + "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", + "# invert scaling for actual\n", + "test_y = test_y.reshape((len(test_y), 1))\n", + "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", + "inv_y = scaler.inverse_transform(inv_y)\n", + "inv_y = inv_y[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(test,predicted):\n", + " plt.plot(test, color='red',label='Real Chinook Count')\n", + " plt.plot(predicted, color='blue',label='Predicted Chinook Count')\n", + " plt.title('Chinook Population Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Chinook Count')\n", + " plt.legend()\n", + " plt.show()\n", + "def plot_loss(history):\n", + " plt.plot(history.history['loss'])\n", + " plt.title('model loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('epoch')\n", + " plt.show()\n", + "def month_to_year(month_preds):\n", + " month_preds = month_preds[6:]\n", + " year_preds = []\n", + " for i in range(12, len(month_preds) + 1, 12): \n", + " salmon_count = np.sum(month_preds[i - 12:i])\n", + " year_preds.append(salmon_count)\n", + " year_preds = pd.DataFrame(year_preds, columns = [\"Count\"])\n", + " return year_preds\n", + "\n", + "def return_rmse(test, predicted):\n", + " rmse = math.sqrt(mean_squared_error(test, predicted))\n", + " print(\"The test root mean squared error is {}.\".format(rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 53646.549339169986.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y, inv_yhat)\n", + "return_rmse(inv_y, inv_yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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oSJLXLs+ECRMYP348zz//PM8++yxPPPEE48ePZ8yYMbz66qsA3HbbbZx99tkcfvjh9OrVq9U8x40bxwMPPMB5553Hvvvuy9ixY9m0adNOy9q3b19++9vfcvTRRzN+/HgmTpzIaaed1mK6g8/n4/nnn+f999/nz3/+c0yeIsLLL7/M9OnTGT58OGPGjOG2226jX79+nHHGGYwbN47x48dzzDHHcN9999GnTx8GDhzIOeecw7hx4/je977HhAkTWpX929/+Ni+//LINQrBYdgJJ5u7oFgFEngfeVko9aX7/H7DDE4RQppT6pYiMAf4BTEEHIbwLjFRKhUVkNvATYCY6COEhpdQbInIVsJ9S6goThHCmUuocE4QwF5hoxPgCmOT0CbXE5MmTVfyCdIsXL2bfffftlHthsbQHW/YsncI+++iw6yVL9HcAZ6aNTtIPIjJXKTU5Pj2lFpCI5APHA96e3XuA40Vkmdl2D4BSaiHwIrAIeAu4ykTAAVwJPA4sB1agAxAAngB6moCFa9ERdRhFcycw2/zd0ZrysVgsll0epeDWW2Hp0ti0FJHSIASlVAM6KMCbtgMdFZdo/7uAuxKkzwHGJkhvAs6OTzfbpgHT2i+1xWKx7KJs2wZ33AHTpsG6da3v38WkQx+QxWKxWLoDx9oJBFIrh8EqIIvFYtlTSOZuS8EM61YBWSwWy55GImWTgr4gq4AsFovFkhKsAtoN8C7HcPbZZ9PQ0NDhvLzLK/zwhz9k0aJFLe7b0RmhhwwZwvbt25ul19XV8aMf/cgdv3PEEUcwc+bMmIlN47nlllv43//+124ZknHbbbdx//33t7rf3/72N8aOHcuYMWMYPXp0m45pL3fffXen52mxpAtWAe0GeJdjyM7O5pFHHonZHg6HWzgyOY8//jijR49ucXtnL0nwwx/+kLKyMpYtW8bChQt56qmnEioqL3fccQfHHXdcp8nQVt58800eeOAB3nnnHRYuXMgXX3zhznXXmVgFZNmdsQpoN+Pwww9n+fLlfPDBBxx99NGcf/757LfffoTDYa6//noOOOAAxo0bx6OPPgroKXquvvpqRo8ezcknn8zWrVvdvI466iicgbdvvfUWEydOZPz48Rx77LEJlyTYtm0b3/nOdzjggAM44IAD+OSTTwDYsWMHU6dOZcKECfzoRz9KOCfbihUrmDlzJr/5zW/IyNDFctiwYZx88smAVqKXXXYZY8aMYerUqTQ2NgKxFluipRMAKioqOP300xk3bhwHHXQQX331VdJ0L3/961/51re+5Z7P4be//S33338//fr1AyA3N5fLLrsM0DN/H3TQQYwbN44zzjiDysrKZvdz+/btDBkyBGh5uYobb7zRnWj2e9/7Xlsev8XScVIQhJAuk5HuFqR4NQZCoRBvvvkmJ554IgCzZs1iwYIFDB06lMcee4ySkhJmz56N3+/n0EMPZerUqcybN4+lS5fy9ddfs2XLFkaPHs0ll1wSk++2bdu47LLLmDFjBkOHDqWiooKysrJmSxKcf/75/PznP+ewww5j7dq1nHDCCSxevJjbb7+dww47jFtuuYX//ve/PPbYY81kX7hwIfvvvz8+ny/htS1btoznnnuOv/71r5xzzjn861//4oILLmi2X6KlE2699VYmTJjAK6+8wnvvvceFF17I/PnzW0x3ePjhh3nnnXd45ZVXYiZTheTLPlx44YU89NBDHHnkkdxyyy3cfvvtPNDKQ0y0XMU999zDww8/nHBCWYulQyQLNEhBEIJVQLsBTisZtAV06aWX8umnnzJlyhR3qYV33nmHr776yrUWqqurWbZsGTNmzHCXKOjXrx/HHHNMs/w///xzjjjiCDevsrKyhHL873//i+kzqqmpoba2lhkzZrjLGJx88smUlpYmPD4ZQ4cOda9x0qRJrG5h+eBESyd8/PHH/Otf/wL0ZKU7duygurq6xXSAZ555hgEDBvDKK6+QlZXVZjmrq6upqqpyJ0+96KKLYpbEaIlEy1UMHDiwlaMslg6SAmsnEVYBdSIpWo3B7QOKp6CgwP2ulOKhhx7ihBNOiNnnjTfeQFopjEqpVvcBPUv2Z599Rl5eXrNtrR0/ZswYvvzySyKRiOuC8xK/nEO8Syx+P+/SCYlcfiLSYjrA2LFjmT9/PuvXr0+4XpKzREUihd0S3mUfWlryIV52i6XbsOOALF3FCSecwF/+8heCwSAA33zzDfX19RxxxBE8//zzhMNhNm3axPvvv9/s2IMPPpgPP/yQVatWAbrvBJovSTB16lQefvhh97ejFI844gieffZZQHfeO30iXoYPH87kyZO59dZbXcWwbNkydwbvncF7/g8++IBevXpRXFzcYjroWcUfffRRTj31VDZubD5R+k033cQvf/lLNm/eDOjF/h588EFKSkooLS11Z8h+5plnXGtoyJAhzJ07F6DFNYviycrKcp+ZxbLTpHjy6XisBbSH8MMf/pDVq1czceJElFKUl5fzyiuvcMYZZ/Dee++x3377sffeeydc5K28vJzHHnuMM888k0gkQu/evZk+fTrf/va3Oeuss3j11Vd56KGHePDBB7nqqqsYN24coVCII444gkceeYRbb72V8847j4kTJ3LkkUe2uLDf448/znXXXceIESPIz8+nZ8+e/N///d9OX/ttt93GxRdfzLhx48jPz3fXQGop3eGwww7j/vvv5+STT2b69OkxS1ecdNJJbNmyheOOO861EJ2+s6effporrriChoYGhg0bxpNPPgnAL37xC8455xyeeeaZNltOl19+OePGjWPixImusrRYdhdSvhzDroRdjsGSTtiyZ2k3mzZBv37Qp4/+DrD33rBs2Z63HIPFYrFYupE0MzisArJYLJY9jUQBB3taEIKI9BCRl0RkiYgsFpGDRaRMRKaLyDLzWerZ/yYRWS4iS0XkBE/6JBH52mx7UEwok4jkiMgLJn2miAzxHHOROccyEbloZ67DujEt3Y0tc5YOkWblJtUW0B+Bt5RSo4DxwGL0qqXvKqVGopfdvhFAREYD5wJjgBOBP4uIM2rxL8DlwEjzd6JJvxSoVEqNAP4A3GvyKgNuBQ5EL/F9q1fRtYfc3Fx27NhhKwRLt6GUYseOHeTm5qZaFMuuyp4+DkhEioEjgB8AKKUCQEBETgOOMrs9DXwA3ACcBjyvlPIDq8wy21NEZDVQrJT6zOT7N+B09LLcpwG3mbxeAh421tEJwHRnGW4RmY5WWs+19zoGDBjA+vXr2bZtW3sPtVg6TG5uLgMGDEi1GJZdlTRpMKcyDHsYsA14UkTGA3OBnwF7KaU2ASilNolIb7N/f+Bzz/HrTVrQfI9Pd45ZZ/IKiUg1eglwNz3BMe0iKysr4UBFi8ViSTvSRPE4pNIFlwlMBP6ilJoA1GPcbS2QyGZUSdI7ekzsSUUuF5E5IjLHWjkWi2WXxlFAaeKCS6UCWg+sV0rNNL9fQiukLSLSF8B8bvXs750cawCw0aQPSJAec4yIZAIlQEWSvJqhlHpMKTVZKTW5vLy8A5dpsVgsaYK1gDRKqc3AOhExI584FlgEvAY4UWkXAc5cLK8B55rItqHoYINZxl1XKyIHmf6dC+OOcfI6C3hP6WiBt4GpIlJqgg+mmjSLxWLZfbGzYcfwE+BZEckGVgIXo5XiiyJyKbAWOBtAKbVQRF5EK6kQcJVSyllp7UrgKSAPHXzwpkl/AnjGBCxUoKPoUEpViMidwGyz3x1OQILFYrHstiRywaXQKkqpAlJKzQeaTc+AtoYS7X8XcFeC9DlAszWblVJNGAWWYNs0YFo7xLVYLJbdg0RKJwWKKNXjgCwWi8XSXSRSMp0871t7sArIYrFY9hTSzAVnFZDFYrHsKaRZEIJVQBaLxbKnYMOwLRaLxZISkg1EtRaQxWKxWLocr7KxQQgWi8Vi6XLSJPzawSogi8Vi2VOwLjiLxWKxpAQbhGCxWCyWlGDDsC0Wi8WSEqwLzmKxWCwWq4AsFotlz8G64CwWi8WSEmwQgsVisVhSgrWALBaLxZISbBBCFBFZLSJfi8h8EZlj0spEZLqILDOfpZ79bxKR5SKyVERO8KRPMvksF5EHzdLcmOW7XzDpM0VkiOeYi8w5lonIRVgsFsvujnXBNeNopdT+SilnZdQbgXeVUiOBd81vRGQ0ekntMcCJwJ9FxGeO+QtwOTDS/J1o0i8FKpVSI4A/APeavMqAW4EDgSnArV5FZ7FYLHsce5oF1AKnAU+b708Dp3vSn1dK+ZVSq4DlwBQR6QsUK6U+U0op4G9xxzh5vQQca6yjE4DpSqkKpVQlMJ2o0rJYLJbdE2sBxaCAd0RkrohcbtL2UkptAjCfvU16f2Cd59j1Jq2/+R6fHnOMUioEVAM9k+TVDBG5XETmiMicbdu2degiLRbLbkZdHVxwAWzfnmpJ2keaBSFkdvsZYzlUKbVRRHoD00VkSZJ9E/SaoZKkd/SY2ESlHgMeA5g8eXJ6NR8sFktqeOIJePZZ6NkT/vjHVEvTdtJMAaXUAlJKbTSfW4GX0f0xW4xbDfO51ey+HhjoOXwAsNGkD0iQHnOMiGQCJUBFkrwsFotl9yVZFFwKSJkCEpECESlyvgNTgQXAa4ATlXYR8Kr5/hpwrolsG4oONphl3HS1InKQ6d+5MO4YJ6+zgPdMP9HbwFQRKTXBB1NNmsVisey+pJkFlEoX3F7AyyZiOhP4h1LqLRGZDbwoIpcCa4GzAZRSC0XkRWAREAKuUkqFTV5XAk8BecCb5g/gCeAZEVmOtnzONXlViMidwGyz3x1KqYquvFiLxWKxxJIyBaSUWgmMT5C+Azi2hWPuAu5KkD4HGJsgvQmjwBJsmwZMa5/UFovFsguTZhZQqqPgLBaLxdJd2CW5LRaLZRcnzcbTtBlrAVksFstuQppEk7WZRFFwznergCwWi8XSZexqLjgRubctaRaLxWLZhUlTC+j4BGnf6mxBLBaLxdLFpFnfVYth2CJyJfBjYJiIfOXZVAR80tWCWSwWi6WTSbMghGTjgP6BHtD5W8ySCIZaO2jTYrHs0SSqrD/6CL76Cq66qvvlaStpFoTQogJSSlWjZ48+z6y7s5fZv1BECpVSa7tJRovFYklPvBX5EUfoz11BAbWW1k20OhOCiFwN3AZsASImWQHjuk4si8VisXQ6u5ALzuEaYB8zRY7FYrFYdlV2wdmw16FdcRaLxWLZHUgTV1xbLKCVwAci8l/A7yQqpX7fZVJZLBaLpfPZBV1wa81ftvmzWCyWPZs0G0/TZtriguvGa2tVASmlbu8OQSwWi2WXI036UtpMWyygdFJAIvI+OuotBqXUMV0ikcVisVi6hjSz3NoShPAL4HrzdzMwH5jTWQKIiE9E5onIf8zvMhGZLiLLzGepZ9+bRGS5iCwVkRM86ZNE5Guz7UGzNDdm+e4XTPpMERniOeYic45lInIRFovFsruTzAWXAguoVQWklJrr+ftEKXUtcGAnyvAzYLHn943Au0qpkcC75jciMhq9pPYY4ETgz2aALMBfgMuBkebvRJN+KVCplBoB/AG41+RVBtxqrmMKcKtX0VksFstuTbIouHRSQMYicf56GcujT2ecXEQGACcDj3uSTwOeNt+fBk73pD+vlPIrpVYBy4EpItIXKFZKfaaUUsDf4o5x8noJONZYRycA05VSFUqpSmA6UaVlsVgsuydp5oJrSxTcXHQfkAAhYBXasugMHgB+iZ7g1GEvpdQmAKXUJhHpbdL7A5979ltv0oLme3y6c8w6k1dIRKqBnt70BMfEICKXo60rBg0a1L6rs1gsuydpVpG3mTRzwbUlCm5oV5xYRE4Btiql5orIUW05JEGaSpLe0WNiE5V6DHgMYPLkybtoqbNYLF3C7hQF1559Oom2RMFlAVcCZqY9PgAeVUoFd/LchwKnishJQC5QLCJ/B7aISF9j/fQFtpr91wMDPccPADaa9AEJ0r3HrBeRTKAEqDDpR8Ud88FOXo/FYrGkN2kWht2WKLi/AJOAP5u/SSZtp1BK3aSUGqCUGoIOLnhPKXUB8BrgRKVdBLxqvr8GnGsi24aigw1mGXddrYgcZPp3Low7xsnrLHMOBbwNTBWRUhN8MNWkWSwWy+5LW1xw3Uhb+oAOUEqN9/x+T0S+7CqBgHuAF0XkUvQMDGcDKKUWisiLwCJ0X9RVSqmwOeZK4CkgD72G0Zsm/QngGRFZjrZ8zjV5VYjIncBss98ddo0ji8ViIb1ccEBYRIYrpVYAiMgwINzKMe1CKfUBxgVmZt0+toX97gLuSpA+BxibIL0Jo8ASbJsGTOuozBaLxbLLBSOkmQuuLQroeuB9EVmJ7rwfDFzcpVJZLBaLpfNJM4XZlii4d0VkJLAPWgEtUUr5WznMYrFYdn92pyi4dLKAROQCQJRSzxiF85VJv0xE6pVS/+guIS0Wi8XSCSQKQohXOGkSBXcd8EqC9BfMNovFYrHsSqTZekDJFJBPKVUbn6iUqgGyuk4ki8VisXQbjjWUZuOAskSkID5RRIqwC9NZLBbLrkeazYSQTAE9AbwUt4TBEOB5s81isVj2TNpaSf/737B2bdfI8OmnsHp1+47ZVYIQlFL3i0gd8KGIFKLnSqsH7lFK7fRMCBaLxbLL01oU3He+A717w5YtnX/uQw/Vn+1RGMmWYUgBScOwlVKPAI8YBSSJ+oQsFovFkoStW1vfp7vY1WbDBlBK1XW1IBaLxWLpYhIplzQNQrBYLBZLe0mz2QZaJYXydkgBiUhOZwtisVgsuwUpsCTaTJoFIbRlSe5pcb8LgTe6TCKLxWJJd9pSkUci3SNLe9iFwrAdNojIXwDM2jnvAH/vUqksFotlVyBNOvPbzK5mASmlbgZqROQRtPL5nVLqyS6XzGKxWNKF996DL9u4DNquoIC8ijM+CKEbSTYZ6Zmen7OAm82nEpEzlVL/7mrhLBaLJS041ixR1h4XVne74JSCZctg772T79NaWppYQN/2/J0CzEPPAef83ilEJFdEZonIlyKyUERuN+llIjJdRJaZz1LPMTeJyHIRWSoiJ3jSJ4nI12bbg2Zpbszy3S+Y9JlxszpcZM6xTEQuwmKxWDqDVFlAzz4L++wDb7/dsePTaRyQUqqrF53zA8copepEJAv4WETeBM4E3lVK3SMiNwI3AjeIyGj0ktpjgH7A/0Rkb7Ms91+Ay4HP0QESJ6KX5b4UqFRKjRCRc4F7ge+KSBlwKzAZPcPDXBF5TSlV2cXXbLFYdndSpYDmztWfixbBCSck3ifN3IJtiYIbICIvi8hWEdkiIv8SkQE7e2KlcQa4Zpk/BZwGPG3SnwZON99PA55XSvmVUquA5cAUEekLFCulPlNKKeBvccc4eb0EHGusoxOA6UqpCqN0pqOVlsVi2VNZvx5+/nMIh1vfd3eMgkvHIATgSeA1tNXRH3jdpO00IuITkfnAVrRCmAnspZTaBGA+e5vd+wPrPIevN2n9zff49JhjlFIhoBromSSvRDJeLiJzRGTOtm3bOnilFosl7bnoInjgAfj447Yfs6tGwaWJ3G1RQOVKqSeVUiHz9xRQ3hknV0qFlVL7AwPQ1szYJLsnmvVPJUnv6DHxMj6mlJqslJpcXt4pl22xWNKRUEh/1tTAjBkdzydVCijNxvi0hbYooO0icoGxVnxmqe4dnSmEUqoK+ADtBtti3GqYT2cmv/XAQM9hA4CNJn1AgvSYY0QkEygBKpLkZbFY9lQcq+Dss+HII6GiomP5pNoCSjZD9y7ogrsEOAfYbP7OMmk7hYiUi0gP8z0POA5Ygnb3OVFpFwGvmu+vAeeayLahwEhglnHT1YrIQaZ/58K4Y5y8zgLeM/1EbwNTRaTURNlNNWkWi2VPx+/Xn4FAx45P5z6gtpAOUXAOSqm1wKldcO6+wNMi4kMrwheVUv8Rkc+AF0XkUmAtcLaRY6GIvAgsAkLAVSYCDuBK4CkgDx399qZJfwJ4RkSWoy2fc01eFSJyJzDb7HeHUqqDzR2LxbJb0NraPm0l1RZQMtLMAmpVAZmIt4eAQ9H9JB8DP1NKrU96YCsopb4CJiRI3wEc28IxdwF3JUifAzTrP1JKNWEUWIJt04BpibZZLBZLWruyOpp3W+TuRlIaBWexWCy7NMmUVFe64HZWAXVUuXYyKY2Cs1gslrQhvlLuqEtuV7eA0kwBdXkUnMWyy7NkSXotvWxpP7tSH1BHras065dqbxTcJjopCs5i2a3Yd18YMSLVUlg6k46Oq+mOKLi2TCoK8OijcP31zfdJk4GoqYyCs1h2L2prUy2BZWeIr5STVcRt6UNJlQXkle2KK/Tn//1f7D5e2VI4G3ZbouDKgcuAId79lVLWCrJYLLsvySr5VPeldIULLk2j4F5FzyDwP+C/nj+LxbK7EQrBd78LX3+daklST1sq5GSurF0lCi5+Qbp0soCAfKXUDV0uicViST1ffQUvvgjffAPz5qVamu4lXpnsrJWxq1lA7dmnk2iLBfQfETmpyyWxWCzpQ2dFhO1KtNQH1N4K2YZht5m2KKCfoZVQo4jUiEitiNR0tWAWS9rS2AgrV6ZaCktX41gZ6aiAdtYC8irbFIZmt6qAlFJFSqkMpVSeUqrY/C7uDuEslrTkjDNg+PBUS9E1tGWk/O5KSxZQeyv7dArD7kie6dAHJCKjlFJLRGRiou1KqS+6TiyLJY1520ycrtTuV1HvyQooHudeJFohNdWurM7sA0rTIIRrgcuB3yXYpoBjukQii2VXYXdUQJYoTiWfqLJP9YDOzuwDSqELrkUFpJS63Hwe3X3iWLqVhgbIyQGfL9WS7JpEIpDRlm5Uyy5Be1xwbbGAEh0XCsH8+TB5codEdGnrQNR4Um25xdGmt0dEDhGR80XkQuevqwWzdAMFBXDllamWYtdlV11wLBlpNldYSmmLBZSIZBX5r38NBxygw913hi6eDbuKEuoo6PLy0KoCEpFngPuBw4ADzN9Oqm9LynEK1l//mlo5dmV2x8p6T+4DaskC6sw+oLlz9eeWLe2Xz0tXzoSgFKVUMYTVHTtHO2iLBTQZOFQp9WOl1E/M30939sQiMlBE3heRxSKyUER+ZtLLRGS6iCwzn6WeY24SkeUislRETvCkTxKRr822B83S3Jjlu18w6TNFZIjnmIvMOZaJyEV0NbW1EAx2+WnazO7Yeu9udud7aBXQzltA7T2uPXRm2YsPQjDsoFfqLSBgAdCnC84dAq5TSu0LHARcJSKjgRuBd5VSI4F3zW/MtnOBMcCJwJ/Nct4Af0EHTIw0fyea9EuBSqXUCOAPwL0mrzLgVuBAYApwq1fRdQnFxXD66V16inaxO1ee3cXueA93R6uuo+xsH1BHJzNtCx0NJmjLcekQBScir6Oj3YqARSIyC/A725VSOzVDtlJqE3p5B5RStSKyGL3i6mnAUWa3p4EPgBtM+vNKKT+wSkSWA1NEZDVQrJT6zMj9N+B04E1zzG0mr5eAh411dAIwXSlVYY6ZjlZaz+3MNbXKG290afbtYnesPLub3fke7okWkOEFzqEPmzmyLS64VEXBdbTsOdeSKHgmkdxdrIyShWHf36Vn9mBcYxOAmcBeRjmhlNokIr3Nbv2Bzz2HrTdpQfM9Pt05Zp3JKyQi1UBPb3qCY+JluxxtXTFo0KCOXWA6sjtXnt1FCqKGupzd6Vrai1Em5/ICACpiAgUSvCvhMDzGFVwa9pEdvzFZuWjL/Q0E4Ic/hNtug2HDEu/T0efkXItHASkFQbLc64jJOoUuuA1ASCn1ofcPbRWtT3JcuxCRQuBfwDVKqWRT/CRqkqkk6R09JjZRqceUUpOVUpPLy3ejlcitAtp5kvUR7OrswRaQSxIX3FPz9+fH/IX/+/zwdh3nkuz+zpgBzzwDl1/e8j4dzds5zrPPPdVXkkOAyrosQM801V0kU0APAIlW2Gow23YaEclCK59nlVL/NslbRKSv2d4XcNY5Xg8M9Bw+ANho0gckSI85RkQy0ctKVCTJa88hQUHcZWlqgp/8BKqquve8u6MCshZQFOe5hsOUs5VTedXdtKWuAID6YFbzfHbWMk7mJos/Ryfk/WTtWQBsrckBoK6+++aJS6aAhiilmgWrK6XmoBen2ylMX8wTwGKl1O89m14DnKi0i8B96q8B55rItqHoYINZxl1XKyIHmTwvjDvGyess4D2llALeBqaKSKkJPphq0vYcdqdK86mn4OGH4ZZbuve8u4MCevZZeOSR6O9EDZOlS+G++7pXrlSQZCDqdsp53bMwdFNQxz/l+kIt59dRF5zzDJINEO9omUvkgjMOIefq6xo8aiGFfUC5SbbldcK5DwW+D3wtIvNN2q+Ae4AXReRSYC1wNoBSaqGIvAgsQkfQXaWUcnoHrwSeMnK9af5AK7hnTMBCBTqKDqVUhYjcCcw2+93hBCTsMezKlWY8IVMJJOos7kq6Y9LJruaCC/Sns3Rzont42GGwfTtcfTXk53efbKkmSQOjKaSrzlxfgqEVO+uCa6MF9DQXMoolHNjyXs1J0MBwVEwGeluMBdTFJFNAs0XkMqVUzEhFoxjm7uyJlVIfk7gvBuDYFo65C7grQfocYGyC9CaMAkuwbRowra3y7hTp6NbYlSvNluhud+LuYAHFk8gCqtlDV18x760KN3++jUGjgDKTKKAutoB+wNM6y9ZzbJ635/lGjCPMSQp4LymFFtA1wMsi8j2iCmcykA2c0aVS7W50d8u8LexOlWZnviQffaSf11FHtb5vsvVi6urg1lvhN7+BvM5wGHQTiRRQR9fFSUc2bdK97Imiy1roA2qoa9kCyktmAXVipFqL52gvTl0UYwFJzKZQNyqgFq9QKbVFKXUIcDuw2vzdrpQ6WCm1uUul2t1Ix8o+kVKcMQP699ezNuxKtHX6mA8/bH0KlCOOgKPbOP9uMgvo3nvh97+HP/85mtbYCNXVbcs7VSS6FqespGM5bi/9+iVdyylM8/6Pmtrm5apxZ11wyWiLAuroLAsJjnMUUCgc+9kdJLOAAFBKvQ+83w2y7L6k44ubqKX7q1/Bxo16tt7DE4SXpjutKaCjjtIt3xUrOud8ySoavxmzHfJ0Uo8dq1dSTWdLItmgy3Qsx52JCEE8UW3muhO1x5qMCy5TktyvroyC21nl5pFNKaN4Ivp83iKbDlPxWHYW64Lrfl5+Ga67rnl6Zy6l3d4+oF1hGe9k4fnecrxkiXZn7WYEvMNKzb1I1AXmuOAiiernnZ2KJ1Ef0KJFOtLT2SXchqmAEpGgLopaQPq3VUC7G+lY2aejTImoqYH//S/pLpGw4jEuwx/2GPRnnqldYA7eFykUgr//vW33YPt2XVn8+9/Nt+0pQQjx2wD23Ve7a3czEllATU3N9/OHtXKIqCRT2iRydynYRJ+2KQmvBTRhgh7r5sgZaKcCipfJawE5pzUWUDjSfS44q4C6g13FAkrHqfjPOQeOPx62bWtxl398sQ8/4jHunjO15Xy81/v738P3v6+VUGssXKg///jHlvPsaLhtOpLsWuK3pbMrsSOIJLSAQsHm1+lU1glvVxIL6HfrzqEfm1i2NqdlORL1AQUCMXk6P5MeD1zI0+zLouRKMb4PqBstoFb7gCydQDq2kHeVSnPBAv3p97e4S2W9fpl3NCUZo+JtBGw2MTTbt++cbLvjXHAJoqRc0rEcdzIxCkgpuPhiQu/7gMdj9gubfpOIit6njzmUr9mPK5OUizcrpgCwZnMOI1sSIlkQQiQCPh8Bf9ssoGcwa4dGQjq/hBZQ6hSQtYC6g3R8cbtyrZJEKKVH1HdR1tCK3uwKK7QzXHDTp0NFGo2BTjhORKgnP70s+VBIh7rvJOEw7NhhfsRZQCocgaeeIrSm+dSXjuLxuqsO52N+zF+SNkzcWQdUGyzNROOAzDMIJFtaLFF59EwrFC9b8yi4JHl3MlYBdQfp9OI6dLcFNG0ajBoF77czoLINSjF+KhGAjziMB4n6zLtE4XZ0wTKHujqYOhW+/e2dk6MzSXAtP+OPFFJP0J9GDanzzoOioti0qqqklnIifvlL6NXLBBrEKSBn/ciYfiGDo4CSuuAS9gE5ZTV5H9CbnEhjJIGbzlFACS6zMZjJPdxAMJRkiYjWXHAVFYQqapsf10VYBdQdtLWF/Oab8OKLXSuLQ3dPRjrbzHq0ZEnHjk8iZyIL6Ag+4mc8GE3wNgJaWAGy3exseLLjyF+0aOfkaCuRSNSlmWyfOB7lR0ArHd/dzUsvNU8rLdUKvR3861/60+li9Cogf8BUzAl6KtxHr5JU9gktII0z7U0iZq3oyUm8yY1fJJjEJRyGzZsJbGs+nuyeL6ZyE/cwbdZY/Rwfeii6Mb6x5JHNCaQIRwR69iT823uNjF3fcLYKqDtoqwV00knw3e+2O/v6+lY6JRPR3S44h44qvCSVfHTtjTaGnxoZ5q0vZ7/9Ysd5nM+znGPWg2mzTC3Iti5mkvYkxycb79ESHXlWv/897LcfzJzZ8j4J+oCCplIOB3eBGRFmzGjX7gV6UmvXC+q1dsIhfZ2JFFAkQR+QSxIF5FT2ycKo127XfZnr6ns23xiJQN++BH54ZWz6uHFUzVkGQEMgE154AX7609jjvJ8JouBcF5y53gwi1gLaLehIC/mll2DDhjbtWlgIhx7aiTJ1hVXUloLc0KDP/eijzY/zyvvcczGh2e3uAzI73vDGkSxYAJ98gl4uvU8fnuN8/sk5rcvqlSnBvfz3N2MYxDqmLxvS+vHtvd9Llmil9cor7TvOUTxr1nRIJjcarK0NqjfegPu7eF3LZOXq3Xf1dSTpe3QU0LZtNHPBOZeZyAXnRsElU0AJ3V2aZA3Gmnrd91OSE12Yp44ClrK3K5SfOPfc11+7XwXVvH8s2equBncgqlVAuxntVUChEJx9Nhx5ZJsPmTOn/TI9xUWsVEPdpLpQLv/krHZm1EbaoiW2mqWf7r67+Tbvi3P++To028narTMTuDzcQQ7NXzynAzkzE3j11Ran6dlAv8QVjXmuq9dmsDdLWcwod9OnG4YA8OWm3s2PcwVIEnGWjFmz9KfjP2orbXkGScpqKGC2tdXcPvlkuP76NgrXQZLNU/ePf+jPjz5KfOxHH5GfpTt6nIDIGAXUFguonWHYTn9LINjyM3AUUHF2tKPnFP7DKJaiTIRAoPk6rLEBDvFWdVILKC4IwVxvUo9CJ2EVUHfQ3iAEZ/9Vq/j4Y20YtERbA4Guu07HADhEQhEu5ikOiURfzitW3cA5/JMvv+mCyTPbUPkpBTfyW5YGE0wUmeQeui9eosH7oZZbfo4CSjbp8MqNuQxgA/etOy+xwMD7n+WyjL25jt9F8zauFp8kaXw48a6tKaDHH2+/sklEZymgYIIQrOpqvbRDZeVOCGgYPRoOOaRt+zrPNVH5SObiVAqOOILcr7RVWFlJMwvIcZPFKKBwGK6/nkhQP7uEgzaTKSCjuJLp8OoGbXEV50YV0IccpWUxzyCpAkI1e8aRUPsVkLWAdhfaawGZl2lVZBCHHw5XXdXyrm2dDeX3v4/1RDjjCLbQx01b6e8HJFd4O433xVAqpoCvWe/jXm7k1G3RMRe/qf0ZD3F18j4gp15NsM3fEJ1I8wXOYQXDXBmcsRzJumBWbtTLYk2vmtx8o5EpJ0t/VtHD3eTk7ctI8gK3VQFddhmc5bFMOzhg+D8bJyIot48hIclcNI4LLlHt+cADenE77+wTHWXxYvjss9i0GTPiBqgYHHkTydTaeBogVKenOQiFaO6CS2QBvfce3H8/qkoHASSyjN3+nQRlNkLrCqihScub5WtedvyNbVBAKtKsbLgBJAmer7Mcg6NMw+gWmVVAuwsdtIC2sBeg38eWcLxW7SXRQLZIW1rtHSVRQZ4yxfi/NE7DOuIpljfX3cBPeYj62rYooObnCDSZisYf5lxe4HA+iiqgNnjAgmFTGUiCys9UMHX1+mcO0Rarcy+Txhe0VQG1RGvHff11zBijB1acAsDiDcUtH9OWPqBEtadzM5OZkx1l5kztjr7ttpbPmyj8OoECuoF7dJBJXF+K8yha7QMyhc0po4kUkPu6d9AF1xTQeQdUc9dfmxQQqlnBc96DZhbQypXu14QWUBeTUgUkItNEZKuILPCklYnIdBFZZj5LPdtuEpHlIrJURE7wpE8Ska/NtgfN0tyY5btfMOkzRWSI55iLzDmWichFXXqhCVpC06frd3ztWnSncJ+oJeK8DQ3olmqy5WTaa60kazA6bqOkg+R2Fk/lf+acm/gwcpi7yVFAiWYYbqhvoSU2axZq7Tpv1jEEGnVezmDDbZRHZVi3EYDQgpZDw52WY1aiWY8dBVSn88slOmmYawF1hgsunra2SseNg4MPdn9u9ZcAUFwQvZZpXMwfiUZL+f1wFO8zs2bf5uImc8G1ZRG1jrJRP6eE4erttIDu4wYdZBKO7UtxHkVMFJwzOafXAjLXl0wBuYo6YRSc3t8J8U5Eg1+fIxBqfi/jFZA3VNpRQErR3AJyHlkkwn84mR1B0wgZPtyVaU8MQngKODEu7UbgXaXUSOBd8xsRGY1eUnuMOebPIuI8ob8AlwMjzZ+T56VApVJqBPAH4F6TVxlwK3AgMAW41avoOp0ECsgJ0Z83D+268HaAm5LvKKBkqyAnmiixGR5/er1prSe0gJyFqYJReW++GW6/vQ3niEcELrkkKmfQx8Nc5Zr5O3bAy5zJUXzo7uMMdMyiubXR4hiUAw8kaBRIQgXkuBrN7e1BlbvNcTUEK1pe/6ixSWea0AIyL6ezhLHXAnKipDJE6efvrSDdJudOWkBt4Ztv3K+OAgpGohXbpUzjGqLz3C3eWMKHHMVlS5rPJN4mC6gjIeWtkUCR1JPPagZ33AVn7r1TkYfDtBgFF+2Uj3buO2UnooC77op5hq6nMEnl7bWAGhtjG5KNAaOAwgkUUJOKkduXQAH5Q77mFpB5D7Y1FPBt/sN3197naiXnWhIGISilW8v//W+L17IzpFQBKaVmAPHzkJwGZq1Z/Xm6J/15pZRfKbUKWA5MEZG+QLFS6jOllAL+FneMk9dLwLHGOjoBmK6UqlBKVQLTaa4IO48ELrjKSl0gevQAsrIS7l9HIdAJCsjjT3eCFhK+r6Y4eF+O3/wmseejTTz5pPv1V3O/w094mNfmDwISy93YoCsMXwJrw6sw92EJZxMdsOu8jCpBL5DTB7R1m95WQnXUAnIUkKmQgwminWrqjAsuI6qAxrCAn/P7qAXUINHzh0Jwyy2E/Xp/X4bS/Tc5nrBZp2JMoIDm3PgSJ8kbBLZWNZPFpYN9QDVBXZACoZZfe8f6DKnmlV/ICejobgsogSI5iTcYyuqOKyBzXBO6jy+0VZvIyVxwgmpmAYUjGfDrXyfKOumsA953bNgwRY8e0fLdENDlMBBuLne8BZTpaazFKKB4C8g07rY26DplQ7C3Wxm0pIBcF9zUqXDKKc1k6QxSbQElYi+l1CYA8+nEsfYH1nn2W2/S+pvv8ekxxyilQkA10DNJXs0QkctFZI6IzNmWZEbmpCQoiJUrdLSQ75vFLSqganSLNZkLzqnIveUtHIa//jVxPeEqoAQWkOOCSzrRYQdZUKlvr9Nh30wBBYM01ettmQlGYLs+bOAb9uEloqPEXTdKpHlxdq5l63a9rYioteMqIPOiOxanl+o6vY/XAlrEGB7g554+II/r4vnn4c47CX+zHDCh4S+/HJtpEgX0gwfG8yYnsfST6ESpf+P7vIZnup62KKCEnc2m8kvQsnb3MdFS4QRVg7tUcxpYQDPQQxRca72dCijkj/UyhF57o9UoOG/filPZJ1oPyF1RVCnu5zrmsX80T+cd80yXs3mzEPQoJFcBOS44jyWVzAJyynNTUFtAIaLP2bk9lX59vaUZNe4IbOe4+CAEQXX5PJbpqIBaItHbppKkd/SY2ESlHlNKTVZKTS4vL2+ToM1IUBlUVenTBVdvcBVQDUVU0qNdCqjRjFXz1kV/umULl18Oj926AT7/PGb/pBaQ83K0NtX7rbfGzCS96K+f8OjhyZc22NDQA4AsXwsKKDubxvu0X9Kp7L0ejETK1N1mWqhe15KDo7hqvf00EtvSc+bOaqT5jXYsIF+iDlnXAsqIymFuntNCDiWQKerbaa6AnFant2/hIv7GabzWPJ9kBINMYg5/4sdRcZ3+h2DLr73TAR5M0AHu7QN6jnOZyRR3W3VjNj/iEaoD7Q/hf/vtVlZKTxIY0VCXRAE5BSiBAqqt0s/Aeebh7Pw2uOCiFbLrgotIswaku4J5WHE99zOReTph+XJXwQcSPAOnn7IhqPMLhH3aN+eRv6lRX5MTPOG1gJw0v1Fc3sGqTkNse5O2gHr4apspoFAk9r1QSJfPY5mOCmiLcathPp04r/XAQM9+A4CNJn1AgvSYY0QkEyhBu/xayqtriG9FzJhBtV+b/kGVCdm60PdnA2VUug+9Hj1M2xMohlKeMlFRQdNsPQLa+4598+5aAEKz58V0QkNrLriWI3ScunLrPz9E7rid109/wt12wOX7c8XHFySMknWo8OtrcVrfjY2ejaaiaPxGy51pFJBXxmRWmVNpJFRA5jjnujMJNXfBGQsooQIygwKbVHYzmdw+oAbPi9tC3jEksYAcBZTImmsPKhDkCyZxNX+KnpYkDQxn8bVAy4rTccFt2ADn8xwnE+0XuPOz43iMH/H03LFJ5ar7w18JL13u/q5ZU8mJJ7YyA1USS8aNjkwUBZckzLGmMs4CKtJdwM3CsPPz3Qo5jM99ZtE+IHHfXwfnsTb54847ciQB4wrzJ3jHtm0DKitpbDBWTiij2XKsySwgp/z6Qz4Ih2MseqcPdVuTnsC11FftvhQR1wUXG4QQISO25dcFAQnpqIBeA5yotIuAVz3p55rItqHoYINZxk1XKyIHmf6dC+OOcfI6C3jP9BO9DUwVkVITfDDVpHUN8QroyCPdmW6DEZ/bgqrDzO4bFwUXDKIj5X7xC275dYTMTFOJjB9P0+PPALHv5vY6XRB7FWkzw+tOccYJJreAlFcMINo6W7Zay3rHgjPdbQ1GUe7Y3vJYA6dCdVYt9VpAkYA+kfMCOS44b53iKJJESs5p6SWzgJxAgUQui6QKqMEozLA+hxPEAWa6fqIWkDdaKtqqbLn/IZkCqm9M8mq2wQXnr2tuMka8gyDr6mIW+VOROAWUqA/IZDl/iW48eVvfy6p6AVBe3HI4dLgpSNG1l/HTCdHBz0su+A0AW9Ym6cxMYsnU1UYDIx7lcmZxQLPzJiqPNZUhFB4FlJkLSjW3gHJyXAtbkeGWVSctoiS2f4/oY230J+jDccO+mz+7piZgyBAaqvXL2eYgBHOdToM1kQJy3nfHQi3IaIydBJHmFlCEjNgXrgsGCKY6DPs54DNgHxFZLyKXAvcAx4vIMuB48xul1ELgRWAR8BZwlVLKKVlXoleMWg6sAN406U8APUVkOXAtJqJOKVUB3AnMNn93mLSuIdEofMf941FA8fs7BSoQAC6+GH73O+6/R9cCNdsDsH6924nqrYu215uKNKN534ZT5yRsMLotZJ2Zd5YFp+J1LmVDU1mz47dtdnptg6xmMNVEx5s4/UuO+8ergJrqYhWQ44Lz7uO04OpqPMrcVEzOfQpFMnSo7sMPu7s4L2x9AivFuS9Op7z3PikFhELU1BrlZBoM3nviyuR1wTnXG6fcIggn8ibTOa4VCyhW3oQkUkB//CNMmuT+rKtqrqmVeb5+PzBuHJHee7nbmuqNS8qfTAGZiMJteh9vROHmet14UtKywq3boQvd443n6/Rt21i6Tt/zwX2SLKOQzAKq0zIpf4AreJQDmdX8uARr4NTXRPCT496TUCQDIpHYZxgRiERiGxYBE6ThWEUqo5kF9JM/DGPhwuiAUi/O+xoMN3++fj9QUxMtlxEfBAIxfQMJFVBc0FIgrC01572AaFltCOnjsglCbW1MP1FCC8irgKqrm8m8s6R0RVSlVIL5TQA4toX97wLuSpA+B2hm+yulmsDTWx27bRowrc3C7gxJOvICYV/z7XFh2IFtVVEXiakIa7f76YUnisdTTuoD+iXyR3Rh8xbEbdu0PIF/vATEju53LSDjHvBa/4GA/ue4pKpD0TwzCBPBx9ZNYdhfm2dDWc0oFuOMoXUqtEQKqKEmRD4eC8hEYnn3cVpw2ndvXmzjHnAtxYgPpkwhVN8EXK2Pc1xwxgLy+sWdFzZRH1AoBFkHTqFm3j3AIBrNvfQqIL8fsomzgOJdcCbvSkp5mxP5hEOpDQejJ4GEFlCDCcho8/I211wT87OuumXffSAArFrlWq6g+1LyiqEp6IQZG6XqMaRcN+wOfW3egA7H0msMNZ+4k3AYsrKoqdAZZBCBZ56BCy9kK9cCUFqcpLM7SR+Qo4AqKxK4h+LC3bXy0FVeQ22shRBW0twCCml/d7wCysJTSSsgO9t1XwO8/mlPvj4F3romgZJxrPWWLCA8Dc+wD4LBmHLpN+9BdBxQxL0+pzyHItJMATnvQX1In18BNDTEXm9EYK+9CG1pQQFVVUG/fs3k3hnS0QW3+xEO8xznsojmg/uCEd1aiWnlmFBLVwF9PBtyc2OOq60xCskooHC0IeR2QjY5laYpmGBmTnj9dQJfRGfPdcX0tpCJtdD922shJ4fa5/4DxA6wzEa/FVs36zRllmtc4lzv8uVuBeUokhgFVBvbgsuRALzxBk2D93b3CQSANWuoW+wJXjQuAedFC0YyoL4+1vXQFOsmayLXrchqjYXmVLLeFz0YBObNo8bsE1DGTepVQI6l2OCEcWe5eTvHxSu3JnLb5oIz5+noop9OJ3sinGfgLReNRuE1eS0gpWisimpAxwLaWtF8pLzj3nMiuGIw11uzQ99oH2F3YcJKdN+L6wQIh/mGkXrKJDfzJBaQscw3bEri6nRCruuilWljfawCCoUSKKAwzawiJ3rOVUAR7YLzVvY6P2gMtGwBOZFy3m4V552oNa74QFhbQN7n5Df7uOOXPP1SrgIKZzTvAzJlvN5YQEGlG4reBlnIWHzOcW4fUEEBHH00jBjR7Hp2FquAuoFIKML5PMck5gKx4XbBsG5leAtLTbUxlx0FRHYzP3N8FA9EFYczfsBvKk3vy7F1K7BxY8KpPJzIJ9cCWhSNbncWwKpZpaPfvArIeRnrjSKpr/a0mv73Pxg50n3hnErbG4TgKCCnMvIRhj/8wX1ZwbgQhgyh9pRzoweaTFwXnLnumJafU9kaBeQnB0Ri+sUc90SMBWQqW1eRRPQ1ehWCU2HUNTa3gLaj+0QcBeTKSFZz11AiBWQq1trqlqPvEvYBqQSuyjicZ+C9Tw21YRgzhqZPdBkNKR888ABNvaOxOk4ltqVSl6tG8vQNvvLKaD9egn4PwmH46U+pee0DwDxfo3Eq0K5c19ILBtmHbxjBiujxjqJO1AdUp+DJJ9n45pfxtwAiEe0GNve5akdUKTfURWKft1E2MWHYEW0BeRWQ2xfpWkA6CKHG4242p6ahKerecuaVi1pAzQNC/H49Fq3JyOVYQN7nFO+CS6SAgpGMFi2gBmMBhZTO26uA7n73IF5pPCFGAS1Z5tON3333beZq7AysAuoGNm83He/kxbQwwFRQoZBb+ULU9RVVQFnNLaAECigQAJ54Av8OXUsmdMFtVVBZmVABOa18VwGdc2l0m+kQdV60WAVkKiQzXY73RXdW4HRe2EQuuPoavb9TGTmWhFcBueMYPPfJUUCuCy6cASLNX9hbb6Xuoy/0b/PCOSHuEK1YY56LiVZy9nPm5fJMreZWmnWNvug1GqWw1QxfcxRvzBgjjwW0kb4xFVzUAtL30mvJOHpn9ooyPUFrIpzKKE4BeT0pzvONUUAVTbBoEU1bqvT+ER88+2xCpbyxQj+XJnL1CPlHHiFcq61RZxoZgEe5nJu5Q5/8oYeo+YOeZNZH2B3Q6TxPtzwkio5xHpBRQF6PdWMDcMklbH9rtpvmzDLy1taJ9KCaT5bohd3WrvEM9qyPfSbhMKBUTIUcDusw5B1EF4ZzvBMxCqhHj2YKCGL7gIKNISKIu7ifUy6csGrnHjjWD0Agoq2UmPIcNxDV6yaLuuC0BRTTB+RYQJX6/jZGcti8I6vZukJn1D3jqXdy2PesMfyn4ZguUT5gFVC3sG6Lfni5NEIgEDNrcjCgmimg6qpYC8ivcporINMy9h7n9wN33ukWTifizCmYZexg61YFFRWJFVAk1gKKeRmc8TQmzWl7e10IzkwGlds904Nk+FB4OsATWECOa8RRQCH0SG7vtTn1kvfeORrAdcGFM8Dni4v+UXDHHW4l0kQuRCLuuQCCweYWULBRy+S64CJaSXjHIvsDQvgPD9Lo97jg0AEH29BjxuItIL2DuZe1sDff8HhtNAbZGeDY6LhjPArIuWdTHzqFn/IQmxpKPMfBS3yHQJ2+UY6L1iGmP81URjEuuJpgzD0IKj2YMb5fDKIKqJG8ZoEuTqXb2AhX8Ci/4WYiQeOC8zZezNgC5zk0GWXuuG9jcB6+Ue7eSETHevWWlZ/9TCuUebXaZfTM5yMBWLM6qrka6yMx5TsU1i64zZ7Z4cNhPe7Hm+ZvUoTwETAVd0QJlJc3U0BKxVqDwYZYa8N1zdZEr7epiZh8HBect+zEjwNyLCBFnAsuzqsSCABvvUW9eT7P155C319dzJy4fmCILRcA84JjrALalVm7RReWnuwAv9+tnCCxAqqpig2rdCygRo9F4FQwzRTQxo3RcEyjgJzfQ1mlXXB+f8JR/34z1sUfFB1w4H0ZjALaRF8AmiJZ8LvfUZcRfYkbTKu9qiL6ogdUVoyycyygGEvChAw71+K4Ar0tT6cFF7PkQWMg5vqCEa2AYl0P+tOxSPzkQDgcq4BM/RajgKrqCZPhhsYHVKZ+djf+Lip3QKi+9jb3d4hMUIptlLtWYTCBW9CxgL5cWUQ9hawNRTt2nWv3J1BATsVb06Tv56dboz75mRzI2bzET6/Vx2/bHuue8yp8f6C5TA01Wrs4M7ArMghLZowVGgoq1BVXssF0UjeS51oyTqXlzGO2ztNVt25tbFnNJOQqINcCMmNmnEAFLxF/kEe5nLqwfj7evklHAXmfJ+hRCxIxLmHjClu53GMBNcB6z/DBUFj3f6xjIGXoMQdhk7aZPu6kn01NseUyrLQHI5EC8lqDoaY4BWQsoPqKaB+b3x9t4PkI6QZhG11wDeS7jbxgIgsooGfkiH/vZ3AE8VT3HBbzOxDyWQW0K+MooDIqwO93K0NowQIy0Y5OP4I/ovuAvIrLGf/gffECAVDBoPsyuJ3kpnIYyiq2bReUP+AqEodQyFhamJZbfX1MC9GZU20VQwFoCOeg/vhgjEJwBtB5JkmgNpjLBs8sR84UJN6R744Ccu5LUGkXnHP9zrVBnMKtjx0vFYpkQGZmXB+QQnnyTmQBua12rwKqbYxpCQZVJnzxBduqo+4yvx/32oqp1gooHHbvESS3gOav0hZMdSR6n53vbiCIpw/IGYbhRCvWhjwNEvOsXvmvrtg3m1DpLAKwYwe13/W4UxO44JyWuFf2BvKbWUC1jz5LvbkvTeS6CsjJy6l0vQpo+TIVk3ex1DbrA3IUkBPI4rlN/OfrwVzBo9w6R89HFqOATMOkklJyaWSUbxmg36GtAX1/xbg15873MZjVWs4GWGfGog9mNaGwoCKKdQxkMGsAPc1PBGETfd00f1Mk5j2MGAXkfVcAlFLU/fudqJwNoVhlbhomq5ZFFa7XAurJDgJh7YLzvgctKSCvUgyZPqAlnhV6AwFg8+ZmwRIxHgVDdTjWAgqQ1XyoSCdhFVA3sG6bZ9qMeAUUBEKhmAqxpga20JvNRknURXRL80kudvdpbNBujArKKDHjMfx++JLxrp/ZUUBO3sNYSSgk1NUqt/XntOzqZ7orYuhKpK4u1gJqjFVAEZWBP780pi+l0bgHVq6JtvxqAzm8wUnub38gA155hS3vRs/nrw+xlXIWMQaIuuC8L1UggQXUUB1E0YoLzq/7cQLkUEgtfnJRoVgLqKK2ebCGv9rvXn8OTbo1+vXXMe6YJr+wEW29DGE1G+nPkb89ka/Zz90nmQU0b7VWpl4FtCVsGh1O8ERNrAJyBgQD1AWjFZqjmJ1Ke/O26Oh99ZdH2P5+NOrRsUK9CtaxgFZ6os/qVEFMWQ2FcK93KCtpJA8lGQTJdEO6600U3Jo1UTmrt+uHtxxtsTWR26IFtMWjgJyG2Mrt+jk4HeiJLKBKSilnG38q/X+Anq3hDxu0a7PJWGXzFmZzIDPxEaKxUZflEqooo4JwWLh56QVU0JMD0P1J4VCExexLE3luAJG/iYQKyFsuAFSTn6qNUV9hsCmc0AL6Zmn0ev1Nyi3fvdjuWkBrGBzdxwQTOOdzFFDMczIW0OccxEj0bOiBIKCUa+E6bEgwBWaVP3ZAdpAsawHtyqzdqh9oPQXg98cUAkcBxVtAMzkQgFEspipcRMgf5k9cRT66UDfUK5RPK6A+bAa0AnrXp5dJEiLaldavH+sZQA5NDDBztjbVh1lghk1F8BGJQN1H89zzN/gzoa4u1rpqilBFCSsZTk+0idOQWxZnAQETJrBi2oduWm0gh9kcQB82Uc5WPVHiGWewZWUdA8x8sP76EPPNhI25NLoW0DbKXQXpuMm8rcH62giVlLoKNxjW/RaxYavRl26gOV8wJO619WMDFXVaAb3HMe5xdZVRS7IX23WARnU1XzGO3mjzzR8Q9wV2WsgzlvVlGpcA0IdN+ENaKa5miJv3tu3C5s0wf62WoSqiz9PUBNWq2H2WfPMNtd9EZ4iqr49ZXYH6YLRScK4nJ0tXUJu2O2M5fATzS2KtyQRWWUOdfr4LGcNQVup7QEGsMg1Fr3cky4jgw98Q5kvGu/tUN+ZAIMAn70ddSzUVIcJk8Bl6WqgmlQM+HxHEo4B0VbRlc/T6qqr054YqLWehT/sRvW5Jp/9uBz0po4KSTP1+vPZaNJ/GgA+/H1ZvzGJfFpNPAw2N8BYncjCf4SNMKCK8u11fx0VmAv1IKMLH6PWqjme6ljNeAUV0oEJ8X4oKhWP7ehvjlIRpmHw+20ehGU/VVBd0y8lIlrkDUdcyiBKqyKee6jofFzON901ZdRSQI1MBdQTDPlQwxDfszQQzD10wALUNPtZ4yiHEuiEdnGnCHBYwlrtnHJZ8vr4OYhVQN3DiWF3xOwpoNgfQjw0IEf0ChXRhyUW/YDV1GbzHMeTRwIm8RWWwkAVbytlGbx7jcnJppLEBloeG4CeXfc1wz0AAvpZx9GETg1mjW37Dh7OeAQxgPXkm/483DmMmB7nyhcMw83/RZmVjIANWrGA5IyhCh+QFmiLMNtOcHMu7ADTklLKI0e5xDQ3A/Pksr4m+oLVNWcxjAhOYRxkVVDToVuBqhjDchNo21YdjXrygiSZbxVBGol0qjtvoG6Jjg+prwjGuxJDpA1rEaFdxBZrC7os/iLX6fP6odTWC5eyozWYWBzCDIzmNV7TcFXEKKJJJXXWYb9ibg9ATvPqDGcxjAgXUMcS4dkC3rPdiM73YTkMwi31ZxC3c6W7vffBwDj0UFmw0FpDSFpA3wKHJL7DPPtT++ZmY+7vwkmgfVJ1xwb3yCtzLDQBkm9nGV2+OViKD77iE+/mF+zuhC64uzHscQwQfZ/JvAGoiRbzBSQxmNUKELZXZrgJyLIINm32uYtmHJVQ3ZcM55/D+3zdwODP0vawK8xYnsoEBDGCdtgQyMqih2O232FCRS01NrAvOiQRcW6nvz45Gbdk644kg2jBZzwD6s4HizEb3nuRKExP4gqagj+XLQSlhb74hnwaq63ysYhgH8xmZhAiFM2iKZPFtXiMf7esMBxUfcxh9sncwOlOXQ3+TYiYHko2fHlS6FtAsplDqWVlGhSOxkZaNIVYwHIASqgiGMwgG4fX3CzmTf5NBmIbKACsZRh4NDGaNGwU3kwPZh6Xk4Oe5z4bwlPGEHM17hMhCBaPKrR8bCUUy2FGbTTU9onVDUJixYTjxOAoox7OYYvw8gNOZyv9787CumInHKqDu4PLyl/kpf6ROiiAQ4FMO4Ug+JIsgwaDgb1K8w1QO4VNyaKK6NoOvGMd4vmQvthBQ2Xy0QbtGDmSmbsE1wP/CRwF6bRTQreav1Fj242sKqaPOnwmhEIvZlwGsd1fsfHXDZIQI13E/oFuad3xyDMNYwRgW0ODPRH372yxiNGNYCGgF9DkHIUQ4ig8AWB0eyJ3czGRmM5qF1DcIDeTxBRNdX/u2qiwWMZqJfEE529hWl8+OwsFsoQ8TMaHRDVoBZRJkMGv0GAURljGS0ehVMJ1Kcwmj6Gvmja2rVa5LqDdbCEYyUBk+3uVYxvEVWQQINDZXQP6Qtkj6sYG92EJFXQ7zmADAxTwJ6MkqHctpL7YQVJl8ta4URQYHo9dX2lSRzd+5gEP4NGY11C30YSiryKeBGn8OS8JaaU71TDe4ciUEwpn4CFGldEW1wjP0xXGfelvNDQ0we0khPaikkFrqAtkoBWecAauNazQU0h3gKzbm4zNztW2uzndbzH3ZiD+oKxhv63fTlgz+x3EUUstUdN/FKn8/PuMQfsSjDGUVC9cWsYyR+Ajppc2BtZuy+IyD6c96xrKAqqZc1r76BasYxqlmBu+aasWrnEYPKjmbf2oLKBh0rbbz+Af1TZnMnAmbN3mmy6mNMHs2vLhMP5utjdqyXbPSYwE1hd1rGcg6SkQ3mLZtg0Py5lFKJY1Bn2s57s035NHIjlptPRZSRyYhwhHBH8kilyZ3vsBwSLGcEYwdXEfOD7+vn0uT4r+czFFHCUWZjYRVBrX+bBYx2n0vADIioVgLqCnsKqC9+YZgOIPPHl9IVW0mp/MKQ1jNsm8U85jA3nxDDn78kSwWrspnNlM4l+fJwU9FfbRhcRz/A7R7fAXDySDMANYTUhms2KHP7SigYBBe2HQ4vdjGYUTn4qunkBKqYuZIBCjMj/1dlBtg8GA6HauAuoPZsymgnnqVT01FiHUMYj++JosggQD895sRrGIYl/IExdRQU+9jCaMYxRJK0bOHztk6iEyCDGUVeTTS2KCV1uCsDW4lXV8PC8OjGD86RC+2s60uj48qx/Il+3MWL7kW0OdVoxjOCtd1N3q04sumUXyXF+hBFQ0BH9M5npUM51tmWj1/k2IWUxjFEvqyCYCHtpzDegbyID8lnwZe+bCUAhrYQS/2YSkAM1f0Ikwm+zOfcraxtT6fRXufDsDE72nryd+ofe1DWUUB9TSpHIIRrSS8L9AOythGb7f1/easnvyKuwHYO2cNwYiPi2of4nMO5iKeJgc/gcZIMxecPyCsYDjDWeFaZcsYSQ5N7vlqKrVbxUeIiXxBBB/z1mk31hQz39hb8/aiilKu43dkERs+7CigjXXaijqdlznNnSM3yiF8yqZgL668QvHY7+sQItpKCAhvcQJ/5Bp33/rqIDM5kCnMosgooBWeyC7QA263bIG6pkzGsiBmWxk7KGebO/fdm3yLo3kPgDtf2pdZTOFAZupoTeCzGt0nN5EvGMkyVm4tYCFjGMFy9jZ9C2s25zCf/ZnMHHpQRbU/13WnHsbH5NBETY3iCyYymTkUOf1wgaD7XJyy0tQEr33UI3otNREuvzwqf40/FxoaWPH/nnDdVsGmMI3ksp1yBrKOYhWdr+yg7Hnk0kSTRwGNZBn5NLCtRlviBdRrCygiNEWyycHvUUAR6iikODdAbpZOW7a1hKWM4qRTMvBJhIgSFtYOQpERMzt4JBRppoBmcwAjWEYJ1YQiwqK/zQF0eZrAPD6ancuHHMnJ/JdsAgQimbwzX3sTvssLMSvuvsjZrqXWWB3gQ45kYu4iSqgmGM5gRYW2rkehVwsOBODzuv04jI/d4xz2PahH7LLjwIjBseV57ICqLlnqySqg7uDppyns34MQWcxfoB/0KJboCjIofLR2MHk0cBYvUUI163bksYl+MQro/c2jGJKxDh8R8mlg5YYcXufbfOfADeRMHgfA118pAuQwfmQD5RkVbKsv4K4NF9Hbt51Lil5yW+lLmwazL4vd2Yy3m5DdsSwgj0bq/Zn8OOcJcmnkCh4B9EjqBYxlPF+6BfjrmkHsxWYO5vNmhfon6NGAc9foSnsky7QFVF/Aohrd8p4wSitEf2OEOUxmMnO0dRfJY3VTH8JksjffaEsmKCxlHyDq/rntn2OYwwH8+sc7GJq5nvpgDs80nQPA2fyTbAL4m3QYra7YTR9YIINljGQ4K3RF7s9iOSMYzgp3gs3aGq1w9+NrN219lW6BO+62T5f2JJOga816cZTphjpt3ZzBy83uUS6NHM37VIRKeORR4bnXCzmRt+jDZvyBDF5Ad6I/bz7XLazlK8YxhVnawg1k8/kT2kL9EY9wHNOpb8xg/t3aIj6QmTHnG80iXeZCGaxkKEvYN0YpzmUyE/lCrxoLfFatGwhjWEgRtdQ3ZbKC4ezNN+69vPjxQ1nMaPZhKSVUU+3PdfsXneNqa7U1uy+L3Uo00BRx3XmOK3bVKpi/opjzeRaAyh0RlmnPFz2opC6Uw5rp3/CS+g6jWEIWAYL+CMsY6eaTG4ne4wnM0421YCYrVujZ4UuoIZ8GttdqSyKfBtMHlEFTOIvc8mJ8V/4I0C64OgopzA25Cyl+tVkrhPH7CxloBbS4fhAAhw/d4J57e6Qnr3Oq+zvYFOZTDuFQPtGej3AGy7aWkEcDfdlEXzaxpSKLCD4OZCbZBIjgY4eJuuzDZvfeDWINZ3salBWbtZvuyNNLySrMIRQWlleUIUTchkIgCCvDg9h3SBOZQ2L7fSZMiM5d6DBicGw4/LihHZwTqhWsAuoO+venYLCuiN/5VPvdpzCLXJpoDPj4amtf9uNrsglSTA2zVmu3y6hBje6Ej+saejEwVy+NlE8Dn39TSphMTttvJTlDdETM7Fn6JRk/qJJy3w62NRTwaf14zhk8i/xpD8e4iYax0lVABTm68nQqydUVxazwD2QMCyk2fUAVVRmsZihjWOhWpItrBrid787LALB++JGuG2f6siGAdn/1Zis7GvNYWDOAAl8jI/rpfGrqhPUMjHYQq1yWNUQ7u7MIEgjidogf0HttzO391U/ryZQwGxt1q++4nl/Q/91ndCuySbtRBvWoocAEcKytLWUzfRnHVxRQT0Mgi6Xsw0iWufe7uhpmMYUpzIrOdVeXTwF1FKJfxq01efRjI7n33E7pcbGd0I4FVB3Q/RZlVDRTQMNZ4d4/h8t5TLtfgsLnHMRpvMKR6KCOa39ThiKD03mFAuqpC+Yw6/06CqjjT1zFEczAH8hg7kOfALh9VQ7f4k2tlEMZfJJ1NBDtz3O4gL+7z/zz6lEUUUN/NuiKPJDBZvrQl03kEDtjwUiW0YMq6oM5rGEwZeygiDqKqaG6VqiliB5UuWWwqSnqAhxmgh4+NLErjkv5wxlCfT38c+ztnMhb1AVzuOTm/mymD3dwi67I/WG3H3IMC2Nmnp9Y9a62gEKZVFdDWbZ+/nk0st24sqIWUAZNkWxyM0P4LroAiFpAXgW0cKtWQMNHZpAhikgEVjb2JYMww955hHim9NRrHy1ensU2enPoyaVkSZhQSFhTU8pg1pCBink3x7DQLXPbarIpooYMlKuAytGdhU55unNaP/zkcuSBfjKzMgiFhQ21xZSzjTyayCJAfb0QJpPCYb3JLI4dC3ToodHZ+R3Gj4lVQGNGtHVW3PZhFVA3UZitC9QTbw9kXxbR//nfk0cjTYEMllT2dl0/JVSzrU4XkH3+9FMKRkbDJC/u+Tp8/LE+LqgLzMi+deTkaAvmiy8gkyCjBtbT21dBpT+f2kghpbkNkJkZoyTKqHAVkKA4n2fJm/cZ+TSwuVa39H/OH9wXYf4K3RE89tKDYirSgT1q4Uc/cvOewBf0v/JUsq+IjjsBPXV/eVYVYeXjy7oRDM7fRnau6YDeoaMEy9lGPg3UR/JY06Bf9CGsJpsAwYDiIw6nT2EdY78Xjbr6f/yGvOIssjKiFc89o58Bn08rIL9iKfuwz8BGco7REU1zturxH+Pvv9BVSkvYlxEsJ5cmMgmyeF0h1fRgf+a792BLfQGlVMZUFqVUQk4OZfmx69n0ZmvMfSqlstkgwMGsoT/RVvMoFnMCb5ODn8r6bL5h7xiLE+AQPmESX5BHI59tH8FDsw5iEnPxEXGv5Sl+QDlbXRcrwGU8xo3co+9JKIMvMiaRRwOjWMLrnOLuN4LlrgJSZLAvixF0ZVfXlMl2esXk67AXW1zlvVH6u1ZUMTVsqtDLHhRR61aiF06/gJ/zAHk00M/06X1tIsWdMOh3P9Ct8sOzZ2pLNZTLglX5XMI0vsVbZBKioUHcfru9+QbCYbdB5birm0KZ1C1ZR9GOVWCupbIxz/0e0weUEcCXqd+ncMhYQHmhqAuushc+QvQfIGT4hEggpI/zBcksLeIfnMfxI/R5Rvfezs2HaBfnrK/1+Q44qZzMHB/BoKLan+ta194y1Z8NbpnbXp3l3kvn3vViOzzzDHmX6GUtnvpQ9w8ffjhkZgvBUAb1jUJhZhO8+SZZBKk2C/DlF/nc63OYHDcZQo+8JgpjhzVRXNZ8eY7OwCqgbqIgW78Um6vzuIC/w5FHkkcT1U3ZbKzvwfCCLfC3v7kvP0DvcuW+TAB9cqugrMytkHJppE/PoFuRb9ycQRkVZBXmUJ5Z6R5XmB2EzMzYinPyCHxiJq70Z+vz9u8fU9kVHbAvGVOPJ5Mg89Zo62LMeePIHxk14Xtm18Gll7oKKJcmyM+PXcYVkJUrKc/QUUJfNY2kX0E1kpVJLo1sqDQL6LGdgp55NKlcNvh7kkGYvdjiKpJljGRcv20U5EaVTR6NkJ3triEE0CPPDxkZxgWnFdCogfVuJfLBJu3KG3DiWNeaAf3iS0YGA1nHh8t0dN1wVkQVUEMRpVTG+OLLqEiogLIJuAoBtAJy+o6OGKXjWQezxnVlAbzIOeTRRC5NrK0oJIKPoayikDo9oJSoVZODnx0B3b/kBEU4QR3LGclg1sQ879N5xW1FB8I+vgiOY3/mk0nYtUCc+5mLn2zR5+ttFiTOo5HtdXlE8LGXCUP/Hn93jyug3r2XG6W/W46LqGVjpVa8hdS5Mr22Zn8AjuE9934uWwb98ipcBbd2fQYi0LtpLYXUURPMY0d9rnv+LIK8uXAgTeSRS6O2ysJhvv7tf/mIwxB0eWwMZlG3vsqVz1vGHQsoGPHpPqCMIBmZzkShQhN5FOaGyM3WFlCVP598GsnIgAyfEPaHCIYzyMyIQFER5/E8h/fSfbKn77+aol66r2nRSv2595AAWTkZelBvMMdV2t5nlfPko2T30VGan63uSwk1sGyZu08vtkNmJvml0XFFJVTRo28eWdlCKCLUN2RQkOmHvDyyCVC1Xb8f+SVZzSZ1LY6bxi7LF3HvgUNmfuyccZ3FHq2AROREEVkqIstF5MauPJdjAYEZM1JcTJ40sbVOu+RKMuvB53NbOwCFRUJBdrRizc8OQVaW+yKVUolkZ5GT5yyjIPr4vLxYBZQTbGYB9eiXT+ZB0aZPCTVQXBxz/qJBpfCtb5FNgE3VBRRSy7ARGVoOQ2luY0zeOfhdBdRXNkVvwNChlGdqBVQVKaFvYS34fOTgZ4PpW+n1xH3k99b347VNUyhnGz4i2gXn14MNywoDFORFQ3XzaYCcHLJ8UaVUkh908165XU93s8+gRteN8uYGbUH12ssXoyQKqYNjjmEYK1lXpd/Koaxy+3e2NJVQWhAk+6u50et3LKDiWJdF1tv/Jb8o2mos/cHpjH7tXhTCYYN0METvsXvRf2p0GSunNZyDn4qGqIsoA+VaCY68XiV4NXoBvqP4kDMn6Nb3EFbHVGrO88kmQFPQx7zIOFdheZ+50zbum6Er+Zy8DDj66JhK23EBXcMDMffOKZcbZKCrgIqpYWONfqZF1MbI1INKHuGKmGsZ1LjUvcb6BqGgAKRe510fziOsfO75swiyurIHAB9wlM4gHGZo9gYOQ7shc2miKZxJXTDHlc/7HhRQTxG1VASLieAj1xfEV6LLoxPuXZgbJr9X1HrN9el3OSsLQgHdf5QpYT1YMzubpkp9jbmFPrJKdV6LVuXRn/XkF2ZoCygk1IbyKB7UA6ZNc2USIsikiTQU6QCNTXXFFGc1QFaWu09PdoDPR37P6IDR33EdFBaSmZ1BRaiYTfXFutHbuzdZBKky8zPml2SRGWcB5cfNypWZofBlxVo8WYVWAXUqIuID/gR8CxgNnCcio5Mf1XEcCwigT8Y2yMsjN8PPtgb99IsyG8Hnc1/cbPxk5/liW/tZWgE5/QbF1OiWUF40EqqYGigspDzbo8hygpCVFWsBDcjXrTbnuNxAMwVYVCyQleVaAAcyE19uFvlZ0WvpkdsIWVkeq6wJ8vIgM5MNOcOZ3HsNT+dfAUBvX3ScRN+iesjM1AqoVtv75b0U+Zk6769rh+houzlztAUU0IMtywqD5OZErzefBsjOJtMzO3dJfhAyMihnG7PWaxfmPkMDNGXEvmk9yjIoFE9rePRguPRShqIrcSHC4KXTyTlWu+42+0spy21AigrJNpVmKZWQnU3ugF4xeWfl+igojlqBpYVBGKhdf36ztEXBweMo6RGtDHr0K4CKCnLwo8z6OvnlhTBkiFvpOpWz8yzP6DWDAR433vAMLfug3G3k9okObs7NCMKECWQTYHtdLrUUM7RPI/TqFfPMee89uOwyxkW+BCC7KAeefTam0i7+2SVw110xaYXUubJtCfeKccHV+nXl5XXBATzODxnAhpi0XmzHR4QeJvim0L8d1qyJsVR7mYHQTsNgLzZHV0INh6MjWNHKpjGUTY0/h8JC4LnnYpRp/pN/pkefPLaEdEh4blYYX45+btXbdLkvzA+TNXSAO1Ymx6cbG9lZ2koKRjLIct6loiKaavRxuYVZDB5upkaq0v2FZGWRlecjGMqgNpxPUSFwwAHu88wkBHl5rPZFx+yUZDdB377u/S6lEnw+8sqiCqgHVVBQQGaOjwaVz8y6MRTkhGGvvSimhlXbtSIsKM0mK25Sg2YKyBchKyfOAiqwCqizmQIsV0qtVEoFgOeB07rqZE6hBijPrwcR8nIVW01/T9HAHuDzaZcOpmL1+SjIaW4BORVkiEzIyqKoULnzXRVTA6NHxyqg3FAzF1yPQSVk+jyKKy/YXAH10JMQOhXEMFZCdnaMTKV5TTEWkKuAevZEmhqZPexcLix8GYCBedFJ4kaUV8Fee+n+jiZ9D3r1ziBYGK00e5aEYexY7UrbUastoOIQkht9GfJodF9q/buBrFy9honXvTVkYJjDj451C2ZkoN0UhoLJo2HUKNft1J8NZBflUNTPrAmksijNaYD8fPeeOBbQ+MOKuI1b3bwyMyG/d6ErU25hprugV9NS3YDILc5GBg/ibF5kcv4iCntkQkkJ+VnR+1tw3qlw7bWx5eL998kp1zKVN6yBKVPgV7+CnBxylunQ6/xvH0vB+dHinFdeCF98QU6/Xmxv0nKVTBkFixfHWIGMHAljxzJS6dDonMywsdaj96mwSGD48FgFtGw+hQOjM2cUDyqFa66JWTU13gLq1S8HLr44RgH1ZAfce6/7DAqCVQAEBkYnXi3fywcXXOAqoJ7sgH/9C66/Xo9F+OADXQaXLSO3n5apIlBIUf8SOPbYWBfciL706JOjZ98AcvqUOtPb8blpvAwoa4RBg6LK37hys3P02luhuqZoY66oiFFV2k06emSQ/iPzGYvu3AqQDYMHk5WfTTAs1KpCigoj0LdvrALKz+fGk6NTJ5Xlazez5GmruJgabQF5rLLSLO1BycqJWi4FeWEoLeUw+ZSV9TpQKb9nHplZsdV+/DRvmT5FZm7su5JVZBVQZ9Mf8EyZyHqT1iUMO32c+32f3rp1l1deRENEt2KKRg+EiRPplakVQJAsKC2lcPQg97i80lzo0YNhBdo9sp1eMGQIGWNHR1ucGfUwciS9RkcHMBYWCQwaRElGtBXZc1Q5mYOiszAXlwiIUFIeLWhFvfNgv/3cF72XVGjL7fjD3X169CuAIUPIK9bNqpyMEEycCKecoqfP//xzmDQJgNKTD3GPGzUmEyZNIicjak2VjepNRf/o1C8FxT7tXivI4Z+bjyCCj9IBBXDCCe4++f3LQISskXogZgnVMGYM7LcfA4qiynTAyDwKDhnPHdwc81wKDxwTPd/gXjB6NGWZutLsxXYoLKRkWHROutLCgHZr+HSFU0Qt9OhBxt4juJU73P2ysiB/mH7pS6mEqVOhsBAGD3Ynbc3rUwJHHsmLfJfZDWOQ3uWQkUH+gGhFnr/PQNhvP7fyK/D54dBDyR6k8+7VsBbGjoW77oL99iOvRvefFJXnUnjMFDef3H30KMLssgJ3KYHiffrqhkJpVOnTpw8MHepW0tl7lUFBAfkjojNOFJZlw8EHxyqgsmwKD40GhxSXZcKdd1KcE3U9F/3zSXJ+dqX7u7wcmDaNzJNOcGeu6HncBLj8csqNlVNIHTz7LKde2CN63P794ZlndEPDeQa9eukRuaAXQezTB0aMIG+ktjor6ElhaRb06kVeXtTqLBhQSo8x0T7N3KF9XQX0v8ixlFLB8WcWwdChFGboe5JjLIjsnsVMZypP8EMyM01jbvx4ftj4ILM4gJO/FYFRo7g9U8+CEczKh379yBo6gIDKopYiikp8UFZGbolWLlkEoaiIQUcM4Q9mDFhegRZIlfSIXm9hIQVDojOOlPbQ5bEmN5qWlaentDqobzTSMn+vIjL3ip05PJ6s3Ewy82K1Ulf1AaGU2iP/gLOBxz2/vw88lGC/y4E5wJxBgwapDhOJKPX660o98YRSs2crpZQ698wmpcetK/XJO3VKKaX+cd86BUr5fBF9nN/v7rN18XallFJf/Xetm+YwpE+DAqW+f+JWpZRSdZtqonm/Wa13mjfPTauqjKh//TPk/n75wbVKKaX+9XiFm1a7pV4ppVSvHgEFSv3uZ2t0PqHocf97w6+UUuq3N1UrUOq7UyuiQn38sVJPPqlUZaX+XVGhDt17iwKlaqrCSimlxo7QcvcoDimllPrzw2E373PPCiqllJo4NnoPnn5aZ+X8/s/T+p7ceYuWcXifWqVCOq/H7tgUe58iERV+e7ra9NA/VdX8Vfpevr/d3efTT/Q9f/KWlQqUOnJijVJKqfmzA+4+d16nr6W4UMv5u0sX6mdrrtfZb948paY9rvcZO6gqus+SJer8g1YoUOqZZ5RS4bBSr76q1LRpSq1fr5RS6pc/rnXzWbBAH/b9qfpapv16uS47ZwUVKPV/58xSasMGvdOKFar2sX+o676zUtXVhFVNdcTNZ/WXVUoppX50SfRa3nzDyPTVV83u028uWKxAqSsu1/fyyd9F79PyhU36cb7xqZvm9yu1aGa0zP32dl0u7rxqowKlCvLCqqZGqc8+iT7fLZ/qa1EbNyoRLetdd+jznX7EDgVKHTJWy60aG93j1n2u79PYvXW5OG7/re4zV2+/rQvJokW6PP2uwT3u179oVEopdfuV0XLR2KjU449Gy/PfHver+vpo+fK+Y/sO1+/rpHEBpZRSxx8f3WfEMF2eVXW1Un//u1L/+pf7zD+etlSBUiMH6fP//JqI8mXo+/Db2/S9/NfD+j6VFQfcZ/CnK75UoNQPz61VSil1ylR9vc9fP0epcFiFw0pNHl2vehc3qJoZ85RSSk2YEH3mh07ReX38r81u2ty5Sv3oR82vz/t79Gil/vGP2LTp09VOAcxRCerhBAu47zGsBwZ6fg8ANsbvpJR6DHgMYPLkySp+e5sR0VaBh7wSj7XRR3fU9tpft8bCZrZc7yy0eQN0S3zoEV6xNcW982AzlO6tW0B5vaNxlIXGhcT++0f3LxEys6PmeslYnWfJkGhruKDczL3VoFtDvSYaa8wXPW7sRC1ffh99jkiJpzV96KH6z6G0lJc/Ntdboo3vnBITAddb5/mjKzP4sVnsM9/0oWTkRu/BAQfEXnf+QH1PnBZbv5GFOGPqppzaB27x7CxCxtTj6DM1mtRz76h1U1Bo+l3GGGtqgL6HJb2ircHSoT0AM+8cUHDA6GjPvedas7Igv1DvUzq4JLrPPvtQOgn4HHr2RPsBT40OWAQo2Cs6marjny8cri2eut66b8BZ66nkuAPAMWSHDaNw2DDuv0z/9K4cmt+3BICcwui1uP1P+0WtTkC7hyeMgr/ryUwB8gdE71NhT11u844+2E3LzobCvtEyN26yfmbnX9uX/86F667LoKgI9h4Vdbr0nGL6Ofr2xVnYsLSXPl/ZiDKYAQVGbu+CjL3Ga0dFVoE+R9HQcveZM9XzcCGmn6Swlxl8Oiw6c3VODvToGS3Px34rmzzPZNAHRy+RwrIcWBG9h94JojOzzXUVF8P3vhcjQ/mheiqmgOjz5+QK4Yi+9wVl+l7mDtUWZmaueT4iDDp5HDwC+x6gy0MkU58w77BJkKHdVx/M0gWkoGB/AAYNEubN01l8vUTnNfnk6OTHOTnNAlSbkZnZfJ/Wjukoe7ICmg2MFJGhwAbgXOD87hTAu8hpkXl3e5m+bK9XxMF5MQpN/fSTn0S3OdP0T5yoP72RloXR+sxFJLZQOaGYAwfG7gPRtXh6RusgDjsMPv4Y9torVjbv0s+JKC+P/Z1jdLBz3RkZcNJJ8MYb0crXu5rnqFGxxxeY+TQdP3bvqOeRsWPhssv0X0t4r8nJy6kMnXvhDVN1nouzDk2ie+vI4+QX/yzvuUfLduKJiY/1dgo7eQwYEM0XousFxYfQevGWgTLjdenrWQbKe+zf/hYtgxAtm44SG+6Zx9KRKSfOK+OkAwwZoj+HDYPPPmsuB8S0Y1wcGVzFm+D+OrI59yLZPUj0jnkVjIj21jn0i3qlAXjxxeh3RxYnT68CSrZcjpPnT3/a/DgnL+deeu/PySfD22/Dscfq30659D5X7z0HeOopPafgkUfCb34TzbuxUb9TY8Z0TAF10XJAe64CUkqFRORq4G10+2maUmphd8rgvAhZWdGXYNw4uOOOxJWT94VVcbbY974Hjz4aLaxevC/x009HF4xLpICGxS6GGENJSfT722/HLo2877760xlM2FbiFRBEKx/n5XJm4Z02LaoUHZyK8RizksLJJ0e3+Xzw2GNtO7/3fGeeCTffDL8wE0h7K7ixJmraUUDxFYBDVlb0OuIVUGEhXHFFyzJ5FZDz/Re/0N8vNeN726KAvDhlx1EM8cd+//ux+zuVpKOAxkW7MF2Z4p+Ft5x5lVk8770X21BIlEd8GQC47jr4b3S6NYYMgVmzoC7JLDFeZROft8PA5g4FF2+5dGRJpICSzZNWWKjvo3O/vGXOkc8J3BvticMViTXonHc+/r576dFDd7nG35PcXF2uQef5xz/Gbp8wAddyysxMEJhgLaDORyn1Bph5P1KAU/jGj48Wap9PV35eZszQL20y7r1X/yXCWzFceGH0u1ehOZVRZibss0+s5yxRPvn5sS/ywQfDgQfqYKz2kEgBOWlO/s5y0j16ND/esWAmTNAK0VvhtBdHhqws3QhwyMzUXqrvfCe2IobkFpAjf1nyPt9meO+rcz3Z2XDNNdF0RwHFV6atMXRo9LvXGorHqVAdBeRcT0NDbGV7yinRxpK3Qm7pvgAcfXTL2+KtFO/13X+//nO4+WZtoWyIRqE3w2sBtaSA4q0eL15l4dwvJ817vc7zaAmv0vAe51znCSfAeefB735Hi/z61zBzZqxbsCOcdJKeaqqxMfp8Z87Uq9gOHw7nn28toD0Cp/DFu6XiOfxw/bez54nH+1J4K/clSxLvn6xS8fl0wFt7cSoIrwJycCoKxwLyWmDvvw+bNyfev71ceKE7dKlFvvoqcXpHXHCt4W31J3JTQdQt2t6FKr0KNNn1xisggNWr9YShXl5/PfHxLVmGreHcT6fMtnT9oK3R++7TFWpLJFJA8e9DZqbO67vfbX689x1xvAOOm7k9CsiLV6k58hUWwj/+kfy4Qw+Fiork+7SV4uJYCzgrS1+f04h7N3aKQGsB7Y44hS+ZSd0ZtJS/U6huvLG5Pz8RydwqHcWZOzKZArruOnj4YW2ZORx1VOfJ8PTT7T9m8GC97HRL9y2ZC6412qJInQqvtef25puxyiAvD26/vXWrzFFAXldveXnrjSWHjq7gHN/P0toSANdfn3y7V9nE9y95aYvr2FFAzrpN3mv09lO2RiILKF1w7k28xWMtoN0Qp/B1xTobbeHww7XVMmVK6/tCcguoo9SacYpeBeQsfOVUQjff3NwtmWref1938np99l6ysnQfxVVXxfZLtYW2WA/PPgu//W3zALZ4EvUl3nJL87R4EllA3UF8GUtmAbWFtrjgEnHhhbB0aWya07/q9AN639v2KKBEFlC6YaPg9gAcBdTVFlBLiOh+m7bSFa01Zxnq/p4hwLfdpitvp9M0HRk6FJ54ouXtWVm68nz44fbn7XU1tsSYMfD3v7e+X0fpbgWUl6f7JBwl4Zx3Zxtn3nvpWECOAhg0qPn+Doms4p49tRvSsWgdNyh0XAGlmwXk0F19QHvyTAgpp6sVUGe7zLrCUtuqZ1yJiUTKytKh04mCDnYVdqbF2BYF1NU44fXeqLmu5K679KfTL9FZCsgJX/d+d967jjRwBg+Oyujtj+moCy5dLSAbBbcH4BTErlJA69e3Pi6nLWRlRcOOOxvHAkoWCrsrsjOuo3RQQMceC6++2vJYpZaYMEH3jbWXn/9c/zl0lgLyPgenEh09Gj79tO2u55Zwyi40HxaRDGsBec7TNdla2kNX9QG1dYxIa6xY0TzirLN46y148snOkzVd2JlGRbrci7gJGtrEF190zrmdcUKd0TCZPr15hbqzocwQHU/XXtI5CMHB9gHtAbRlYFk6MHBg11koJ5wQM7eoha5rbe5KfP/72j111lk7n9dxx+18Hom45JLoYOX24LWAOjp0oKvpLhec7QNKIbuKArJYupuMDD0uZ2ej4LqS665rX9+PQ1sH7KYSG4SwB+C4GbzjWyy7Nsmm2GkPr78OX37ZOXlZuo62jJ+LxwnwgPRtfHaXC05Ue3rP9nAmT56s5syZ06l5vv22nsfMul0sll0TR4m0tSoNhaLve7pWv5s2xU5RFA7vXF+1iMxVSk2OT7d9QCnG9n9YLLs2TzzR+oBgL441ka7WD0QVZGamniWiqwKlrAKyWCyWneCSS9p/zNKlHZ8vrztwlGNhYfMlUDoTq4AsFoulm9l771RLkByvAupKbBCCxWKxWGIoK4O77259GZidxVpAFovFYmnGTTd1/TlSYgGJyNkislBEIiIyOW7bTSKyXESWisgJnvRJIvK12fagiDYSRSRHRF4w6TNFZIjnmItEZJn5u8iTPtTsu8wc28HJ4y0Wi8XSUVLlglsAnAnM8CaKyGjgXGAMcCLwZxFxhqL9BbgcGGn+nFmqLgUqlVIjgD8A95q8yoBbgQOBKcCtIuKszHIv8Ael1Eig0uRhsVgslm4kJQpIKbVYKbU0wabTgOeVUn6l1CpgOTBFRPoCxUqpz5QeuPQ34HTPMc7k6S8Bxxrr6ARgulKqQilVCUwHTjTbjjH7Yo518rJYLBZLN5FuQQj9gXWe3+tNWn/zPT495hilVAioBnomyasnUGX2jc+rGSJyuYjMEZE527zT31osFotlp+iyIAQR+R/QJ8Gm/6eUerWlwxKkqSTpHTkmWV7NNyj1GPAY6JkQWtrPYrFYLO2jyxSQUqojc9CuB7zzLg8ANpr0AQnSvcesF5FMoASoMOlHxR3zAbAd6CEimcYK8uZlsVgslm4i3VxwrwHnmsi2oehgg1lKqU1ArYgcZPpwLgRe9RzjRLidBbxn+oneBqaKSKkJPpgKvG22vW/2xRzbkkVmsVgsli4iVWHYZ4jIeuBg4L8i8jaAUmoh8CKwCHgLuEopFTaHXQk8jg5MWAG8adKfAHqKyHLgWuBGk1cFcCcw2/zdYdIAbgCuNcf0NHlYLBaLpRuxs2G3AxHZBnRgwWEAeqHdf+lIuspm5Wo/6SpbusoF6StbusoF7ZdtsFKqPD7RKqBuQkTmJJqOPB1IV9msXO0nXWVLV7kgfWVLV7mg82RLtz4gi8VisewhWAVksVgslpRgFVD38ViqBUhCuspm5Wo/6SpbusoF6StbusoFnSSb7QOyWCwWS0qwFpDFYrFYUoJVQBaLxWJJCVYBdTEicqJZ22i5iNyYgvNPE5GtIrLAk1YmItPNekjTPctUtLgeUxfINVBE3heRxWZtqJ+lkWy5IjJLRL40st2eLrKZc/lEZJ6I/CfN5Fpt1uyaLyJz0kU2EekhIi+JyBJT3g5OE7n2MffK+asRkWvSRLafm7K/QESeM+9E58ullLJ/XfQH+NCzNgwDsoEvgdHdLMMRwERggSftPuBG8/1G4F7zfbSRMQcYamT3dZFcfYGJ5nsR8I05fzrIJkCh+Z4FzAQOSgfZzPmuBf4B/Cddnqc532qgV1xaymVDL7nyQ/M9G+iRDnLFyegDNgODUy0benWAVUCe+f0i8IOukKtLb+qe/oeeauhtz++bgJtSIMcQYhXQUqCv+d4XWJpIPvR8egd3k4yvAsenm2xAPvAFemHDlMuGnjz3XfSaVo4CSrlcJv/VNFdAKZUNKDaVqaSTXAnknAp8kg6yEV3Kpgw9YfV/jHydLpd1wXUtLa1JlGr2UnqCV8xnb5OeEnlFL6M+AW1ppIVsxs01H9iKXtgwXWR7APglEPGkpYNcoJc1eUdE5orI5Wki2zBgG/CkcVs+LiIFaSBXPOcCz5nvKZVNKbUBuB9YC2wCqpVS73SFXFYBdS3tWnsoDeh2eUWkEPgXcI1SqibZrgnSukw2pVRYKbU/2uKYIiJjk+zeLbKJyCnAVqXU3LYekiCtK5/noUqpicC3gKtE5Igk+3aXbJloF/RflFITgHrMhMUplit6QpFs4FTgn63tmiCtK8pZKXql6aFAP6BARC7oCrmsAupaWlrfKNVsEb3MOeZzq0nvVnlFJAutfJ5VSv07nWRzUEpVodeROjENZDsUOFVEVgPPA8eIyN/TQC4AlFIbzedW4GVgShrIth5YbyxYgJfQCinVcnn5FvCFUmqL+Z1q2Y4DVimltimlgsC/gUO6Qi6rgLqW2cBIERlqWjnnotcvSjXeNZS86yElXI+pKwQQEUEvg7FYKfX7NJOtXER6mO956BdySaplU0rdpJQaoJQagi5L7ymlLki1XAAiUiAiRc53dJ/BglTLppTaDKwTkX1M0rHo5V5Sfs88nEfU/ebIkErZ1gIHiUi+eU+PBRZ3iVxd3bm2p/8BJ6EjvFaglyPv7vM/h/bjBtEtlUvRayC9Cywzn2We/f+fkXUp8K0ulOswtJn+FTDf/J2UJrKNA+YZ2RYAt5j0lMvmOd9RRIMQUi4Xuq/lS/O30CnraSLb/sAc8zxfAUrTQS5zrnxgB1DiSUu5bMDt6EbXAuAZdIRbp8tlp+KxWCwWS0qwLjiLxWKxpASrgCwWi8WSEqwCslgsFktKsArIYrFYLCnBKiCLxWKxpASrgCyWNEREenpmSd4sIhvM9zoR+XOq5bNYOgMbhm2xpDkichtQp5S6P9WyWCydibWALJZdCBE5SqLrAN0mIk+LyDui1+I5U0TuE70mz1tmqiNEZJKIfGgmCX3bmU7FYkk1VgFZLLs2w4GT0ZNH/h14Xym1H9AInGyU0EPAWUqpScA04K5UCWuxeMlMtQAWi2WneFMpFRSRr9GLmr1l0r9GrwO1DzAWmK6n9cKHnprJYkk5VgFZLLs2fgClVEREgiraqRtBv98CLFRKHZwqAS2WlrAuOItl92YpUC4iB4NeAkNExqRYJosFsArIYtmtUUoFgLOAe0XkS/Ss44ekVCiLxWDDsC0Wi8WSEqwFZLFYLJaUYBWQxWKxWFKCVUAWi8ViSQlWAVksFoslJVgFZLFYLJaUYBWQxWKxWFKCVUAWi8ViSQn/H9B0um1UX79qAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 54361.83837951031.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 470161\n", + "1 310176\n", + "2 312240\n", + "3 383788\n", + " Count\n", + "0 488981\n", + "1 336030\n", + "2 381773\n", + "3 535746\n" + ] + } + ], + "source": [ + "preds = month_to_year(inv_yhat).astype(np.int64)\n", + "actual = month_to_year(inv_y).astype(np.int64)\n", + "print(preds)\n", + "print(actual)" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Count\n", + "0 498710\n", + "1 439060\n", + "2 294840\n", + "3 347600\n" + ] + } + ], + "source": [ + "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", + "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", + "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", + "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", + "print(traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 115829.72216361394.\n" + ] + } + ], + "source": [ + "return_rmse(actual, traditional)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 85071.57805195576.\n" + ] + } + ], + "source": [ + "return_rmse(actual, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/multivar_simple_rnn.ipynb b/multivar_simple_rnn.ipynb index 3f9b1a8..48fb66b 100644 --- a/multivar_simple_rnn.ipynb +++ b/multivar_simple_rnn.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 426, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,6 @@ "import tensorflow.keras\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, SimpleRNN\n", - "#\"/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import LabelEncoder\n", @@ -41,20 +40,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 427, "metadata": {}, "outputs": [], "source": [ "def load_data(pathname):\n", " salmon_data = pd.read_csv(pathname)\n", " salmon_data.head()\n", - " salmon_copy = salmon_data # Create a copy for us to work with \n", - " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, \n", - " inplace = True)\n", + " salmon_copy = salmon_data \n", + " salmon_copy.rename(columns = {\"mo\": \"month\", \"da\" : \"day\", \"fc\" : \"king\"}, inplace = True)\n", " salmon_copy['date']=pd.to_datetime(salmon_copy[['year','month','day']])\n", - "# print(salmon_copy)\n", " king_data = salmon_copy.filter([\"date\",\"king\"], axis=1)\n", - "# print(king_data)\n", " king_greater = king_data['date'].apply(pd.Timestamp) >= pd.Timestamp('01/01/1939')\n", " greater_than = king_data[king_greater]\n", " king_all = greater_than[greater_than['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2020')]\n", @@ -66,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 428, "metadata": {}, "outputs": [ { @@ -94,13 +90,13 @@ " chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/passBonCS.csv'\n", " ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/data.csv'\n", " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", - " king_all_copy, king_data= load_data(abdul_path)\n", + " king_all_copy, king_data= load_data(ismael_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 429, "metadata": {}, "outputs": [ { @@ -217,7 +213,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 17, + "execution_count": 429, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 430, "metadata": {}, "outputs": [ { @@ -263,7 +259,7 @@ "(984, 1)" ] }, - "execution_count": 18, + "execution_count": 430, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 431, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 432, "metadata": {}, "outputs": [ { @@ -391,7 +387,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 20, + "execution_count": 432, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 433, "metadata": {}, "outputs": [ { @@ -508,7 +504,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 21, + "execution_count": 433, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 434, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 435, "metadata": {}, "outputs": [ { @@ -635,7 +631,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 23, + "execution_count": 435, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 436, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 437, "metadata": {}, "outputs": [], "source": [ @@ -669,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 438, "metadata": {}, "outputs": [ { @@ -697,16 +693,6 @@ "print(master_data)" ] }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# type(data_copy['date'])\n", - "# # data_copy['date'].astype(p)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -716,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 439, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 440, "metadata": {}, "outputs": [ { @@ -893,7 +879,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 29, + "execution_count": 440, "metadata": {}, "output_type": "execute_result" } @@ -902,13 +888,13 @@ "ismael_path_cov = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/covariates.csv'\n", "chris_path_cov = '/Users/chrisshell/Desktop/Stanford/SalmonData/Environmental Variables/salmon_env_use.csv'\n", "abdul_path_cov= '/Users/abdul/Downloads/SalmonNet/salmon_env_use.csv'\n", - "cov_data = load_cov_set(abdul_path_cov)\n", + "cov_data = load_cov_set(ismael_path_cov)\n", "cov_data" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 441, "metadata": {}, "outputs": [ { @@ -1026,7 +1012,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 30, + "execution_count": 441, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 442, "metadata": {}, "outputs": [ { @@ -1169,7 +1155,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 31, + "execution_count": 442, "metadata": {}, "output_type": "execute_result" } @@ -1182,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 443, "metadata": {}, "outputs": [ { @@ -1324,7 +1310,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 32, + "execution_count": 443, "metadata": {}, "output_type": "execute_result" } @@ -1337,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 444, "metadata": {}, "outputs": [ { @@ -1491,7 +1477,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 33, + "execution_count": 444, "metadata": {}, "output_type": "execute_result" } @@ -1504,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 445, "metadata": {}, "outputs": [ { @@ -1670,7 +1656,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 34, + "execution_count": 445, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 446, "metadata": {}, "outputs": [ { @@ -1850,7 +1836,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 35, + "execution_count": 446, "metadata": {}, "output_type": "execute_result" } @@ -1860,80 +1846,6 @@ "master_data" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_cov_set(ismael_path_pdo)\n", - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = data_copy" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# pdo = pdo[:984]\n", - "# pdo\n", - "# master_data = master_data.join(pdo)\n", - "# # master_data\n", - "# # master_data = master_data[:984]\n", - "# # master_data = master_data.reindex(columns=[\"Date\", \"Month\", \"king\", \"PDO\"])\n", - "# # master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# # master_data.columns = ['year', 'month', 'king', 'pdo']\n", - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data['year']=pd.to_datetime(master_data[['year','month']])\n", - "# master_data.set_index('date', inplace=True)\n", - "# master_data.index = pd.to_datetime(master_data.index)\n", - "# master_data" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1943,71 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_noi = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/noi.csv'\n", - "# noi_data = load_cov_set(ismael_path_noi)\n", - "# noi_data = noi_data[:877]\n", - "# noi_data = noi_data.drop(labels=0, axis=0)\n", - "# noi_data.reset_index()\n", - "# print(noi_data)\n", - "# print(noi_data['noix'])\n", - "# # noi_data = noi_data.drop(columns=\"index\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# noi = noi_data[\"noix\"]\n", - "# # noi\n", - "# print(master_data)\n", - "# master_data = master_data[120:]\n", - "# print(master_data)\n", - "# master_data.reset_index()\n", - "# master_data = master_data.join(noi)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data = master_data.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data\n", - "# master_data = master_data.drop(labels=\"index\", axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# master_data.head(700)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 47, + "execution_count": 447, "metadata": {}, "outputs": [ { @@ -2171,7 +2019,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 47, + "execution_count": 447, "metadata": {}, "output_type": "execute_result" } @@ -2184,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 448, "metadata": {}, "outputs": [], "source": [ @@ -2200,7 +2048,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 449, "metadata": {}, "outputs": [], "source": [ @@ -2208,7 +2056,7 @@ "chris_checkpoint_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Checkpoint'\n", "abdul_checkpoint_path = '/Users/abdul/Downloads/SalmonNet/Checkpoint'\n", "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=abdul_checkpoint_path,\n", + " filepath=checkpoint_filepath,\n", " save_weights_only=True,\n", " monitor='val_accuracy',\n", " mode='max',\n", @@ -2252,12 +2100,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 450, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2293,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 451, "metadata": {}, "outputs": [ { @@ -2334,6 +2182,7 @@ ], "source": [ "# convert series to supervised learning\n", + "# series_to_supervised from Jason Brownlee's \"Multivariate Time Series Forecasting in Keras\"\n", "def series_to_supervised(data, n_in=6, n_out=1, dropnan=True):\n", " n_vars = 1 if type(data) is list else data.shape[1]\n", " df = DataFrame(data)\n", @@ -2379,7 +2228,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 452, "metadata": {}, "outputs": [ { @@ -2409,7 +2258,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 453, "metadata": {}, "outputs": [], "source": [ @@ -2418,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 454, "metadata": {}, "outputs": [ { @@ -2445,7 +2294,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 455, "metadata": {}, "outputs": [ { @@ -2453,121 +2302,121 @@ "output_type": "stream", "text": [ "Epoch 1/1000\n", - "8/8 - 5s - loss: 0.0552 - root_mean_squared_error: 0.2350 - val_loss: 0.0547 - val_root_mean_squared_error: 0.2339\n", + "8/8 - 2s - loss: 0.0312 - root_mean_squared_error: 0.1766 - val_loss: 0.0573 - val_root_mean_squared_error: 0.2395\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 2/1000\n", - "8/8 - 0s - loss: 0.0135 - root_mean_squared_error: 0.1162 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1981\n", + "8/8 - 0s - loss: 0.0149 - root_mean_squared_error: 0.1221 - val_loss: 0.0438 - val_root_mean_squared_error: 0.2093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 3/1000\n", - "8/8 - 0s - loss: 0.0172 - root_mean_squared_error: 0.1312 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2008\n", + "8/8 - 0s - loss: 0.0125 - root_mean_squared_error: 0.1120 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1981\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 4/1000\n", - "8/8 - 0s - loss: 0.0139 - root_mean_squared_error: 0.1178 - val_loss: 0.0484 - val_root_mean_squared_error: 0.2199\n", + "8/8 - 0s - loss: 0.0123 - root_mean_squared_error: 0.1110 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 5/1000\n", - "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0948 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "8/8 - 0s - loss: 0.0109 - root_mean_squared_error: 0.1045 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2005\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 6/1000\n", - "8/8 - 0s - loss: 0.0102 - root_mean_squared_error: 0.1012 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 7/1000\n", - "8/8 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1973\n", + "8/8 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 8/1000\n", - "8/8 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "8/8 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 9/1000\n", - "8/8 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", + "8/8 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 10/1000\n", - "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 11/1000\n", - "8/8 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 12/1000\n", - "8/8 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 13/1000\n", - "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 14/1000\n", - "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 15/1000\n", - "8/8 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 16/1000\n", - "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 17/1000\n", - "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 18/1000\n", - "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 19/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 20/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 21/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 22/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 23/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 24/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 25/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 26/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 27/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 28/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 29/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 30/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 31/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 32/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 33/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 34/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 35/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 36/1000\n", - "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 37/1000\n", - "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 38/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 39/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2576,121 +2425,121 @@ "output_type": "stream", "text": [ "Epoch 40/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 41/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 42/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 43/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 44/1000\n", - "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 45/1000\n", - "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 46/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 47/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 48/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 49/1000\n", - "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 50/1000\n", - "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 51/1000\n", - "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1754\n", + "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 52/1000\n", - "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1748\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 53/1000\n", - "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1743\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 54/1000\n", - "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1737\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1765\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 55/1000\n", - "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1732\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 56/1000\n", - "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1726\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 57/1000\n", - "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0296 - val_root_mean_squared_error: 0.1720\n", + "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 58/1000\n", - "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1714\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 59/1000\n", - "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1708\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 60/1000\n", - "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 61/1000\n", - "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0775 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1696\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 62/1000\n", - "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1690\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 63/1000\n", - "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1684\n", + "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1750\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 64/1000\n", - "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1678\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1749\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 65/1000\n", - "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1672\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 66/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1666\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1745\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 67/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1660\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 68/1000\n", - "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0750 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1654\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 69/1000\n", - "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1648\n", + "8/8 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1739\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 70/1000\n", - "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1642\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 71/1000\n", - "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1636\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1735\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 72/1000\n", - "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1631\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 73/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1625\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0300 - val_root_mean_squared_error: 0.1731\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 74/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1620\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 75/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1614\n", + "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0809 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 76/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1609\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0298 - val_root_mean_squared_error: 0.1725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 77/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1603\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 78/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1598\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0296 - val_root_mean_squared_error: 0.1721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2699,121 +2548,121 @@ "output_type": "stream", "text": [ "Epoch 79/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0721 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1593\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 80/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0718 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1588\n", + "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1716\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 81/1000\n", - "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1582\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0294 - val_root_mean_squared_error: 0.1714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 82/1000\n", - "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1577\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 83/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1572\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1709\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 84/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1568\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1707\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 85/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1563\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0291 - val_root_mean_squared_error: 0.1704\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 86/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1558\n", + "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0797 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 87/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1554\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0796 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1699\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 88/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1549\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 89/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1545\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1694\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 90/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1540\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1691\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 91/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0689 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n", + "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1689\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 92/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1532\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1686\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 93/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0684 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1527\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1683\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 94/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1523\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 95/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1519\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 96/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1515\n", + "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 97/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1511\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1671\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 98/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1507\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1668\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 99/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1502\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1665\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 100/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1498\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1662\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 101/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1494\n", + "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0778 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1659\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 102/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1490\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 103/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1485\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0775 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1653\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 104/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1481\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 105/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1476\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 106/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1472\n", + "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0771 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 107/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0652 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1467\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0269 - val_root_mean_squared_error: 0.1640\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 108/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1463\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1636\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 109/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1459\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1633\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 110/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1456\n", + "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0266 - val_root_mean_squared_error: 0.1630\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 111/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1452\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0765 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 112/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1448\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0264 - val_root_mean_squared_error: 0.1623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 113/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1444\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0762 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1620\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 114/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1439\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0761 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1617\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 115/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1434\n", + "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0759 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1613\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 116/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1428\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1610\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 117/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1422\n" + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1607\n" ] }, { @@ -2822,121 +2671,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 118/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1417\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0755 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 119/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1412\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1600\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 120/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1407\n", + "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1597\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 121/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1404\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0752 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1594\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 122/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1402\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0750 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 123/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1400\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1588\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 124/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1399\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 125/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1397\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 126/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1394\n", + "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 127/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1388\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 128/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1379\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0743 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1572\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 129/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1368\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1569\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 130/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1358\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 131/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 132/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n", + "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0738 - val_loss: 0.0243 - val_root_mean_squared_error: 0.1560\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 133/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0737 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1557\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 134/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1554\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 135/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1347\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1551\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 136/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1352\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1548\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 137/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1364\n", + "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1545\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 138/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1373\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0238 - val_root_mean_squared_error: 0.1542\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 139/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1361\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1539\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 140/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1332\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 141/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0235 - val_root_mean_squared_error: 0.1533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 142/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1530\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 143/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\n", + "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 144/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0652 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1327\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1525\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 145/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1369\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1522\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 146/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1331\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0722 - val_loss: 0.0231 - val_root_mean_squared_error: 0.1519\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 147/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0721 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1516\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 148/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1513\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 149/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1298\n", + "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0719 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1510\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 150/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0717 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1507\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 151/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0226 - val_root_mean_squared_error: 0.1504\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 152/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1284\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1501\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 153/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1282\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0714 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1498\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 154/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1269\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 155/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1271\n", + "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1492\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 156/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n" + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1490\n" ] }, { @@ -2945,121 +2794,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 157/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1444\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1487\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 158/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1356\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1484\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 159/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1481\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 160/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1315\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 161/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1302\n", + "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1475\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 162/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1472\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 163/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 164/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1276\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1466\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 165/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1262\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1463\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 166/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 167/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1271\n", + "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 168/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1454\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 169/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1235\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 170/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1241\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1448\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 171/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1233\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 172/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1442\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 173/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n", + "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1439\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 174/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1224\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0206 - val_root_mean_squared_error: 0.1436\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 175/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0687 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1433\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 176/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1192\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1430\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 177/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1181\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 178/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - val_loss: 0.0203 - val_root_mean_squared_error: 0.1423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 179/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1178\n", + "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 180/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1186\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1417\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 181/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1174\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1414\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 182/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1158\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0678 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 183/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1147\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0198 - val_root_mean_squared_error: 0.1407\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 184/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\n", + "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1404\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 185/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1143\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 186/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0672 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 187/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0505 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1394\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 188/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0193 - val_root_mean_squared_error: 0.1391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 189/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1138\n", + "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 190/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1116\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0667 - val_loss: 0.0191 - val_root_mean_squared_error: 0.1384\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 191/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1110\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 192/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 193/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1120\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 194/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n", + "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0661 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 195/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n" + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1366\n" ] }, { @@ -3068,121 +2917,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 196/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1142\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0658 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 197/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 198/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 199/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1101\n", + "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1350\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 200/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1107\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0652 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 201/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1342\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 202/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1183\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1338\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 203/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1334\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 204/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1184\n", + "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1330\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 205/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1127\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 206/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0175 - val_root_mean_squared_error: 0.1322\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 207/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1146\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0174 - val_root_mean_squared_error: 0.1318\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 208/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1313\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 209/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1309\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 210/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1070\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0636 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1305\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 211/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1094\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0169 - val_root_mean_squared_error: 0.1300\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 212/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0168 - val_root_mean_squared_error: 0.1296\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 213/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1292\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 214/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1068\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0166 - val_root_mean_squared_error: 0.1287\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 215/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1066\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0628 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 216/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1098\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - val_loss: 0.0163 - val_root_mean_squared_error: 0.1278\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 217/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0625 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1274\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 218/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 219/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1045\n", + "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0622 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1265\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 220/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0505 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1092\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 221/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 222/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1023\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1252\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 223/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1045\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1248\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 224/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1088\n", + "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1243\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 225/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1043\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1239\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 226/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 227/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1230\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 228/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1084\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 229/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1013\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0606 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1222\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 230/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1012\n", + "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1217\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 231/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1213\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 232/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1083\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1209\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 233/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0994\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0600 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1205\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 234/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1005\n" + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0144 - val_root_mean_squared_error: 0.1201\n" ] }, { @@ -3191,121 +3040,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 235/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1197\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 236/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", + "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1193\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 237/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1189\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 238/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1025\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - val_loss: 0.0140 - val_root_mean_squared_error: 0.1185\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 239/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1024\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1181\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 240/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1177\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 241/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1000\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1173\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 242/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0137 - val_root_mean_squared_error: 0.1170\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 243/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1166\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 244/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 245/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1035\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0134 - val_root_mean_squared_error: 0.1159\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 246/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0964\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1155\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 247/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0967\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1152\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 248/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1028\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 249/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0579 - val_loss: 0.0131 - val_root_mean_squared_error: 0.1145\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 250/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1141\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 251/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1138\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 252/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1018\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1134\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 253/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0940\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 254/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0993\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1128\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 255/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1010\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1124\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 256/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 257/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1118\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 258/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1018\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 259/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0946\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1112\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 260/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 261/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1105\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 262/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0943\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1102\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 263/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1099\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 264/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0980\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1096\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 265/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0120 - val_root_mean_squared_error: 0.1093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 266/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1090\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 267/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1087\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 268/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1084\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 269/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0897\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1081\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 270/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1079\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 271/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1076\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 272/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0914\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1073\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 273/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n" + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1070\n" ] }, { @@ -3314,121 +3163,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 274/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0934\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0552 - val_loss: 0.0114 - val_root_mean_squared_error: 0.1067\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 275/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 276/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0878\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1062\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 277/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1059\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 278/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0892\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1056\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 279/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1053\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 280/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1050\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 281/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0545 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 282/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0854\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1045\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 283/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 284/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0900\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1040\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 285/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1037\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 286/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0855\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0107 - val_root_mean_squared_error: 0.1034\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 287/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1032\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 288/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1029\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 289/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 290/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1024\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 291/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1021\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 292/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 293/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0863\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1016\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 294/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 295/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 296/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1008\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 297/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1006\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 298/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1003\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 299/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 300/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 301/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 302/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0809\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0993\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 303/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0991\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 304/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0989\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 305/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0797\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0986\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 306/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 307/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 308/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0979\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 309/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 310/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 311/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 312/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\n" + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0969\n" ] }, { @@ -3437,2182 +3286,2176 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 313/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0967\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 314/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 315/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0782\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 316/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0805\n", + "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0960\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 317/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0757\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0958\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 318/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0955\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 319/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0778\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0953\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 320/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0753\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0951\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 321/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0949\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 322/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0946\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 323/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 324/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 325/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0940\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 326/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 327/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0726\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0935\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 328/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0505 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 329/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0738\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0931\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 330/1000\n", - "8/8 - 0s - loss: 9.3258e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0929\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 331/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0926\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 332/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0745\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 333/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0922\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 334/1000\n", - "8/8 - 0s - loss: 9.7735e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0920\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 335/1000\n", - "8/8 - 0s - loss: 9.0910e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 336/1000\n", - "8/8 - 0s - loss: 8.9711e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 337/1000\n", - "8/8 - 0s - loss: 9.5609e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0707\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0914\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 338/1000\n", - "8/8 - 0s - loss: 9.5130e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 339/1000\n", - "8/8 - 0s - loss: 9.0582e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0909\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 340/1000\n", - "8/8 - 0s - loss: 7.8764e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", + "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 341/1000\n", - "8/8 - 0s - loss: 8.7060e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0905\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 342/1000\n", - "8/8 - 0s - loss: 8.5941e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 343/1000\n", - "8/8 - 0s - loss: 8.7772e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 344/1000\n", - "8/8 - 0s - loss: 9.1226e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 345/1000\n", - "8/8 - 0s - loss: 8.1288e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 346/1000\n", - "8/8 - 0s - loss: 7.8444e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0666\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0895\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 347/1000\n", - "8/8 - 0s - loss: 8.8392e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 348/1000\n", - "8/8 - 0s - loss: 8.6918e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0891\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 349/1000\n", - "8/8 - 0s - loss: 8.7677e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 350/1000\n", - "8/8 - 0s - loss: 8.0612e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0887\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 351/1000\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0885\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 351/1000\n", - "8/8 - 0s - loss: 8.1587e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 352/1000\n", - "8/8 - 0s - loss: 8.3820e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0657\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 353/1000\n", - "8/8 - 0s - loss: 8.4806e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0652\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 354/1000\n", - "8/8 - 0s - loss: 8.8138e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0879\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 355/1000\n", - "8/8 - 0s - loss: 7.9344e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 356/1000\n", - "8/8 - 0s - loss: 7.1315e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 357/1000\n", - "8/8 - 0s - loss: 8.1253e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0652\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 358/1000\n", - "8/8 - 0s - loss: 8.0199e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 359/1000\n", - "8/8 - 0s - loss: 8.0960e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 360/1000\n", - "8/8 - 0s - loss: 7.7969e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0640\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 361/1000\n", - "8/8 - 0s - loss: 7.3659e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 362/1000\n", - "8/8 - 0s - loss: 7.5068e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0864\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 363/1000\n", - "8/8 - 0s - loss: 7.6789e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 364/1000\n", - "8/8 - 0s - loss: 7.9662e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 365/1000\n", - "8/8 - 0s - loss: 7.6292e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 366/1000\n", - "8/8 - 0s - loss: 6.6004e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0612\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 367/1000\n", - "8/8 - 0s - loss: 7.1262e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", + "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0854\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 368/1000\n", - "8/8 - 0s - loss: 6.9936e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 369/1000\n", - "8/8 - 0s - loss: 6.7526e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0851\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 370/1000\n", - "8/8 - 0s - loss: 6.7834e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 371/1000\n", - "8/8 - 0s - loss: 6.3875e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 372/1000\n", - "8/8 - 0s - loss: 6.2242e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 373/1000\n", - "8/8 - 0s - loss: 6.0257e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 374/1000\n", - "8/8 - 0s - loss: 6.5424e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0609\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 375/1000\n", - "8/8 - 0s - loss: 6.3084e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0588\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 376/1000\n", - "8/8 - 0s - loss: 6.1300e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 377/1000\n", - "8/8 - 0s - loss: 7.6617e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0836\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 378/1000\n", - "8/8 - 0s - loss: 7.2421e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0616\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0467 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 379/1000\n", - "8/8 - 0s - loss: 8.6248e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0832\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 380/1000\n", - "8/8 - 0s - loss: 8.6025e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 381/1000\n", - "8/8 - 0s - loss: 9.0847e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0829\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 382/1000\n", - "8/8 - 0s - loss: 9.6324e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0464 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 383/1000\n", - "8/8 - 0s - loss: 8.1639e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0825\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 384/1000\n", - "8/8 - 0s - loss: 9.8303e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0635\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 385/1000\n", - "8/8 - 0s - loss: 9.3508e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 386/1000\n", - "8/8 - 0s - loss: 8.3476e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0820\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 387/1000\n", - "8/8 - 0s - loss: 9.1017e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 388/1000\n", - "8/8 - 0s - loss: 8.2601e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0817\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 389/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 390/1000\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 389/1000\n", - "8/8 - 0s - loss: 7.4481e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 390/1000\n", - "8/8 - 0s - loss: 7.7623e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 391/1000\n", - "8/8 - 0s - loss: 8.0290e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0811\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 392/1000\n", - "8/8 - 0s - loss: 7.4234e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 393/1000\n", - "8/8 - 0s - loss: 7.2038e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 394/1000\n", - "8/8 - 0s - loss: 7.4386e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 395/1000\n", - "8/8 - 0s - loss: 6.7740e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 396/1000\n", - "8/8 - 0s - loss: 6.4185e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 397/1000\n", - "8/8 - 0s - loss: 6.5367e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 398/1000\n", - "8/8 - 0s - loss: 6.0563e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0799\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 399/1000\n", - "8/8 - 0s - loss: 5.9046e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0798\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 400/1000\n", - "8/8 - 0s - loss: 5.9728e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 401/1000\n", - "8/8 - 0s - loss: 5.4746e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 402/1000\n", - "8/8 - 0s - loss: 5.6148e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0793\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 403/1000\n", - "8/8 - 0s - loss: 5.5981e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 404/1000\n", - "8/8 - 0s - loss: 5.0213e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 405/1000\n", - "8/8 - 0s - loss: 5.4298e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0788\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 406/1000\n", - "8/8 - 0s - loss: 5.3024e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 407/1000\n", - "8/8 - 0s - loss: 4.7362e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0785\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 408/1000\n", - "8/8 - 0s - loss: 5.1480e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 409/1000\n", - "8/8 - 0s - loss: 5.2365e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 410/1000\n", - "8/8 - 0s - loss: 4.8458e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 411/1000\n", - "8/8 - 0s - loss: 4.9412e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 412/1000\n", - "8/8 - 0s - loss: 6.4181e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 413/1000\n", - "8/8 - 0s - loss: 5.8841e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0777\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 414/1000\n", - "8/8 - 0s - loss: 6.4039e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 415/1000\n", - "8/8 - 0s - loss: 8.9251e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 416/1000\n", - "8/8 - 0s - loss: 7.3149e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 417/1000\n", - "8/8 - 0s - loss: 9.0868e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 418/1000\n", - "8/8 - 0s - loss: 8.5173e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 419/1000\n", - "8/8 - 0s - loss: 7.0639e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 420/1000\n", - "8/8 - 0s - loss: 6.9996e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 421/1000\n", - "8/8 - 0s - loss: 7.3041e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0767\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 422/1000\n", - "8/8 - 0s - loss: 7.5967e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 423/1000\n", - "8/8 - 0s - loss: 6.6620e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 424/1000\n", - "8/8 - 0s - loss: 6.6710e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 425/1000\n", - "8/8 - 0s - loss: 6.3248e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0761\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 426/1000\n", - "8/8 - 0s - loss: 5.3465e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0760\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 427/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 428/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 429/1000\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0754\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 427/1000\n", - "8/8 - 0s - loss: 5.5506e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 428/1000\n", - "8/8 - 0s - loss: 5.1805e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 429/1000\n", - "8/8 - 0s - loss: 4.6146e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 430/1000\n", - "8/8 - 0s - loss: 4.9479e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 431/1000\n", - "8/8 - 0s - loss: 5.0787e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 432/1000\n", - "8/8 - 0s - loss: 5.1604e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 433/1000\n", - "8/8 - 0s - loss: 5.0801e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 434/1000\n", - "8/8 - 0s - loss: 5.9879e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 435/1000\n", - "8/8 - 0s - loss: 6.3794e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 436/1000\n", - "8/8 - 0s - loss: 6.0499e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 437/1000\n", - "8/8 - 0s - loss: 7.0304e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 438/1000\n", - "8/8 - 0s - loss: 6.7636e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 439/1000\n", - "8/8 - 0s - loss: 6.4122e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 440/1000\n", - "8/8 - 0s - loss: 6.8039e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 441/1000\n", - "8/8 - 0s - loss: 6.5444e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0731\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 442/1000\n", - "8/8 - 0s - loss: 5.6416e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 443/1000\n", - "8/8 - 0s - loss: 6.2131e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0728\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 444/1000\n", - "8/8 - 0s - loss: 6.4697e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 445/1000\n", - "8/8 - 0s - loss: 5.7412e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0726\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 446/1000\n", - "8/8 - 0s - loss: 6.2568e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 447/1000\n", - "8/8 - 0s - loss: 6.3121e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0453\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0724\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 448/1000\n", - "8/8 - 0s - loss: 5.9116e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 449/1000\n", - "8/8 - 0s - loss: 6.1225e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 450/1000\n", - "8/8 - 0s - loss: 5.6189e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 451/1000\n", - "8/8 - 0s - loss: 5.0537e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 452/1000\n", - "8/8 - 0s - loss: 5.3553e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 453/1000\n", - "8/8 - 0s - loss: 4.5449e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0720\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 454/1000\n", - "8/8 - 0s - loss: 3.9566e-04 - root_mean_squared_error: 0.0199 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 455/1000\n", - "8/8 - 0s - loss: 4.2427e-04 - root_mean_squared_error: 0.0206 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 456/1000\n", - "8/8 - 0s - loss: 3.7063e-04 - root_mean_squared_error: 0.0193 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 457/1000\n", - "8/8 - 0s - loss: 3.2711e-04 - root_mean_squared_error: 0.0181 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0715\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 458/1000\n", - "8/8 - 0s - loss: 3.3818e-04 - root_mean_squared_error: 0.0184 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 459/1000\n", - "8/8 - 0s - loss: 3.2842e-04 - root_mean_squared_error: 0.0181 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 460/1000\n", - "8/8 - 0s - loss: 3.0099e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 461/1000\n", - "8/8 - 0s - loss: 3.0867e-04 - root_mean_squared_error: 0.0176 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 462/1000\n", - "8/8 - 0s - loss: 3.0050e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 463/1000\n", - "8/8 - 0s - loss: 2.8029e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0733\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 464/1000\n", - "8/8 - 0s - loss: 2.9002e-04 - root_mean_squared_error: 0.0170 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 465/1000\n", - "8/8 - 0s - loss: 2.7861e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 466/1000\n", - "8/8 - 0s - loss: 2.6559e-04 - root_mean_squared_error: 0.0163 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 467/1000\n", - "8/8 - 0s - loss: 2.7664e-04 - root_mean_squared_error: 0.0166 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0791\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 468/1000\n", - "8/8 - 0s - loss: 2.6313e-04 - root_mean_squared_error: 0.0162 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 469/1000\n", - "8/8 - 0s - loss: 2.6530e-04 - root_mean_squared_error: 0.0163 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0711\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 470/1000\n", - "8/8 - 0s - loss: 2.6878e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", + "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 471/1000\n", - "8/8 - 0s - loss: 2.6454e-04 - root_mean_squared_error: 0.0163 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", + "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 472/1000\n", - "8/8 - 0s - loss: 2.9984e-04 - root_mean_squared_error: 0.0173 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 473/1000\n", - "8/8 - 0s - loss: 2.7991e-04 - root_mean_squared_error: 0.0167 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0822\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 474/1000\n", - "8/8 - 0s - loss: 3.2006e-04 - root_mean_squared_error: 0.0179 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 475/1000\n", - "8/8 - 0s - loss: 3.9572e-04 - root_mean_squared_error: 0.0199 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", + "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 476/1000\n", - "8/8 - 0s - loss: 3.5318e-04 - root_mean_squared_error: 0.0188 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0773\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 477/1000\n", - "8/8 - 0s - loss: 4.5507e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n", + "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 478/1000\n", - "8/8 - 0s - loss: 5.3297e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0756\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 479/1000\n", - "8/8 - 0s - loss: 5.0517e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 480/1000\n", - "8/8 - 0s - loss: 5.1579e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", + "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 481/1000\n", - "8/8 - 0s - loss: 4.9780e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 482/1000\n", - "8/8 - 0s - loss: 4.6757e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 483/1000\n", - "8/8 - 0s - loss: 4.0479e-04 - root_mean_squared_error: 0.0201 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 484/1000\n", - "8/8 - 0s - loss: 3.3351e-04 - root_mean_squared_error: 0.0183 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 485/1000\n", - "8/8 - 0s - loss: 2.9028e-04 - root_mean_squared_error: 0.0170 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0367\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 486/1000\n", - "8/8 - 0s - loss: 2.6793e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0730\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 487/1000\n", - "8/8 - 0s - loss: 2.6890e-04 - root_mean_squared_error: 0.0164 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0705\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 488/1000\n", - "8/8 - 0s - loss: 2.7164e-04 - root_mean_squared_error: 0.0165 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 489/1000\n", - "8/8 - 0s - loss: 2.5040e-04 - root_mean_squared_error: 0.0158 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 490/1000\n", - "8/8 - 0s - loss: 3.3556e-04 - root_mean_squared_error: 0.0183 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 491/1000\n", - "8/8 - 0s - loss: 3.3715e-04 - root_mean_squared_error: 0.0184 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 492/1000\n", - "8/8 - 0s - loss: 3.3928e-04 - root_mean_squared_error: 0.0184 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0709\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 493/1000\n", - "8/8 - 0s - loss: 4.2700e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 494/1000\n", - "8/8 - 0s - loss: 4.0067e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 495/1000\n", - "8/8 - 0s - loss: 4.0558e-04 - root_mean_squared_error: 0.0201 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 496/1000\n", - "8/8 - 0s - loss: 4.0892e-04 - root_mean_squared_error: 0.0202 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0713\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 497/1000\n", - "8/8 - 0s - loss: 3.8430e-04 - root_mean_squared_error: 0.0196 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0675\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 498/1000\n", - "8/8 - 0s - loss: 3.4757e-04 - root_mean_squared_error: 0.0186 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 499/1000\n", - "8/8 - 0s - loss: 3.4089e-04 - root_mean_squared_error: 0.0185 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 500/1000\n", - "8/8 - 0s - loss: 3.3034e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 501/1000\n", - "8/8 - 0s - loss: 3.0380e-04 - root_mean_squared_error: 0.0174 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0676\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 502/1000\n", - "8/8 - 0s - loss: 3.1583e-04 - root_mean_squared_error: 0.0178 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0692\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 503/1000\n", - "8/8 - 0s - loss: 3.0253e-04 - root_mean_squared_error: 0.0174 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0669\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 504/1000\n", - "8/8 - 0s - loss: 3.0466e-04 - root_mean_squared_error: 0.0175 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 505/1000\n", - "8/8 - 0s - loss: 3.3215e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 506/1000\n", - "8/8 - 0s - loss: 2.9359e-04 - root_mean_squared_error: 0.0171 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0695\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 507/1000\n", - "8/8 - 0s - loss: 3.2695e-04 - root_mean_squared_error: 0.0181 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0664\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 508/1000\n", - "8/8 - 0s - loss: 3.9066e-04 - root_mean_squared_error: 0.0198 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 509/1000\n", - "8/8 - 0s - loss: 3.1034e-04 - root_mean_squared_error: 0.0176 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 510/1000\n", - "8/8 - 0s - loss: 3.7261e-04 - root_mean_squared_error: 0.0193 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 511/1000\n", - "8/8 - 0s - loss: 4.0157e-04 - root_mean_squared_error: 0.0200 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0663\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 512/1000\n", - "8/8 - 0s - loss: 4.3025e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0685\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 513/1000\n", - "8/8 - 0s - loss: 4.3661e-04 - root_mean_squared_error: 0.0209 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 514/1000\n", - "8/8 - 0s - loss: 3.7793e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 515/1000\n", - "8/8 - 0s - loss: 4.5438e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 516/1000\n", - "8/8 - 0s - loss: 4.3331e-04 - root_mean_squared_error: 0.0208 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 517/1000\n", - "8/8 - 0s - loss: 3.7506e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 518/1000\n", - "8/8 - 0s - loss: 5.2889e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 519/1000\n", - "8/8 - 0s - loss: 4.1039e-04 - root_mean_squared_error: 0.0203 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 520/1000\n", - "8/8 - 0s - loss: 4.2890e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0667\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 521/1000\n", - "8/8 - 0s - loss: 4.2762e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 522/1000\n", - "8/8 - 0s - loss: 4.3617e-04 - root_mean_squared_error: 0.0209 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 523/1000\n", - "8/8 - 0s - loss: 4.2974e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 524/1000\n", - "8/8 - 0s - loss: 3.8422e-04 - root_mean_squared_error: 0.0196 - val_loss: 9.9795e-04 - val_root_mean_squared_error: 0.0316\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0672\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 525/1000\n", - "8/8 - 0s - loss: 4.1120e-04 - root_mean_squared_error: 0.0203 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 526/1000\n", - "8/8 - 0s - loss: 4.6238e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 527/1000\n", - "8/8 - 0s - loss: 4.0925e-04 - root_mean_squared_error: 0.0202 - val_loss: 9.8931e-04 - val_root_mean_squared_error: 0.0315\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 528/1000\n", - "8/8 - 0s - loss: 3.7488e-04 - root_mean_squared_error: 0.0194 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 529/1000\n", - "8/8 - 0s - loss: 4.2756e-04 - root_mean_squared_error: 0.0207 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 530/1000\n", - "8/8 - 0s - loss: 3.9890e-04 - root_mean_squared_error: 0.0200 - val_loss: 9.6685e-04 - val_root_mean_squared_error: 0.0311\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0665\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 531/1000\n", - "8/8 - 0s - loss: 2.7654e-04 - root_mean_squared_error: 0.0166 - val_loss: 9.2286e-04 - val_root_mean_squared_error: 0.0304\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 532/1000\n", - "8/8 - 0s - loss: 3.3109e-04 - root_mean_squared_error: 0.0182 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0660\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 533/1000\n", - "8/8 - 0s - loss: 3.6188e-04 - root_mean_squared_error: 0.0190 - val_loss: 9.7467e-04 - val_root_mean_squared_error: 0.0312\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 534/1000\n", - "8/8 - 0s - loss: 3.1820e-04 - root_mean_squared_error: 0.0178 - val_loss: 9.2429e-04 - val_root_mean_squared_error: 0.0304\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0651\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 535/1000\n", - "8/8 - 0s - loss: 3.4208e-04 - root_mean_squared_error: 0.0185 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 536/1000\n", - "8/8 - 0s - loss: 3.3223e-04 - root_mean_squared_error: 0.0182 - val_loss: 8.8363e-04 - val_root_mean_squared_error: 0.0297\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0653\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 537/1000\n", - "8/8 - 0s - loss: 3.6349e-04 - root_mean_squared_error: 0.0191 - val_loss: 9.3682e-04 - val_root_mean_squared_error: 0.0306\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 538/1000\n", - "8/8 - 0s - loss: 3.6032e-04 - root_mean_squared_error: 0.0190 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 539/1000\n", - "8/8 - 0s - loss: 3.2088e-04 - root_mean_squared_error: 0.0179 - val_loss: 8.8860e-04 - val_root_mean_squared_error: 0.0298\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 540/1000\n", - "8/8 - 0s - loss: 3.5624e-04 - root_mean_squared_error: 0.0189 - val_loss: 9.3461e-04 - val_root_mean_squared_error: 0.0306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0652\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 541/1000\n", - "8/8 - 0s - loss: 3.6706e-04 - root_mean_squared_error: 0.0192 - val_loss: 9.8937e-04 - val_root_mean_squared_error: 0.0315\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 542/1000\n", - "8/8 - 0s - loss: 3.1464e-04 - root_mean_squared_error: 0.0177 - val_loss: 8.6491e-04 - val_root_mean_squared_error: 0.0294\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 543/1000\n", - "8/8 - 0s - loss: 2.6149e-04 - root_mean_squared_error: 0.0162 - val_loss: 9.3343e-04 - val_root_mean_squared_error: 0.0306\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 544/1000\n", - "8/8 - 0s - loss: 2.8164e-04 - root_mean_squared_error: 0.0168 - val_loss: 9.1143e-04 - val_root_mean_squared_error: 0.0302\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0641\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 545/1000\n", - "8/8 - 0s - loss: 2.8956e-04 - root_mean_squared_error: 0.0170 - val_loss: 8.5561e-04 - val_root_mean_squared_error: 0.0293\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 546/1000\n", - "8/8 - 0s - loss: 2.5492e-04 - root_mean_squared_error: 0.0160 - val_loss: 7.8150e-04 - val_root_mean_squared_error: 0.0280\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0644\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 547/1000\n", - "8/8 - 0s - loss: 2.5008e-04 - root_mean_squared_error: 0.0158 - val_loss: 8.5941e-04 - val_root_mean_squared_error: 0.0293\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 548/1000\n", - "8/8 - 0s - loss: 2.4589e-04 - root_mean_squared_error: 0.0157 - val_loss: 8.2726e-04 - val_root_mean_squared_error: 0.0288\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 549/1000\n", - "8/8 - 0s - loss: 2.4468e-04 - root_mean_squared_error: 0.0156 - val_loss: 7.7645e-04 - val_root_mean_squared_error: 0.0279\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0615\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 550/1000\n", - "8/8 - 0s - loss: 2.3253e-04 - root_mean_squared_error: 0.0152 - val_loss: 7.8872e-04 - val_root_mean_squared_error: 0.0281\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 551/1000\n", - "8/8 - 0s - loss: 1.9323e-04 - root_mean_squared_error: 0.0139 - val_loss: 7.3337e-04 - val_root_mean_squared_error: 0.0271\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 552/1000\n", - "8/8 - 0s - loss: 1.8068e-04 - root_mean_squared_error: 0.0134 - val_loss: 7.1699e-04 - val_root_mean_squared_error: 0.0268\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0629\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 553/1000\n", - "8/8 - 0s - loss: 1.6834e-04 - root_mean_squared_error: 0.0130 - val_loss: 7.3330e-04 - val_root_mean_squared_error: 0.0271\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 554/1000\n", - "8/8 - 0s - loss: 1.5809e-04 - root_mean_squared_error: 0.0126 - val_loss: 7.0565e-04 - val_root_mean_squared_error: 0.0266\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 555/1000\n", - "8/8 - 0s - loss: 1.9878e-04 - root_mean_squared_error: 0.0141 - val_loss: 6.9501e-04 - val_root_mean_squared_error: 0.0264\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 556/1000\n", - "8/8 - 0s - loss: 1.9563e-04 - root_mean_squared_error: 0.0140 - val_loss: 6.7361e-04 - val_root_mean_squared_error: 0.0260\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0633\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 557/1000\n", - "8/8 - 0s - loss: 1.7746e-04 - root_mean_squared_error: 0.0133 - val_loss: 7.0252e-04 - val_root_mean_squared_error: 0.0265\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 558/1000\n", - "8/8 - 0s - loss: 2.0276e-04 - root_mean_squared_error: 0.0142 - val_loss: 6.8725e-04 - val_root_mean_squared_error: 0.0262\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 559/1000\n", - "8/8 - 0s - loss: 1.9415e-04 - root_mean_squared_error: 0.0139 - val_loss: 6.6993e-04 - val_root_mean_squared_error: 0.0259\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 560/1000\n", - "8/8 - 0s - loss: 1.8965e-04 - root_mean_squared_error: 0.0138 - val_loss: 6.8747e-04 - val_root_mean_squared_error: 0.0262\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 561/1000\n", - "8/8 - 0s - loss: 1.8716e-04 - root_mean_squared_error: 0.0137 - val_loss: 6.3057e-04 - val_root_mean_squared_error: 0.0251\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 562/1000\n", - "8/8 - 0s - loss: 1.7675e-04 - root_mean_squared_error: 0.0133 - val_loss: 6.5008e-04 - val_root_mean_squared_error: 0.0255\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0624\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 563/1000\n", - "8/8 - 0s - loss: 2.0398e-04 - root_mean_squared_error: 0.0143 - val_loss: 6.6720e-04 - val_root_mean_squared_error: 0.0258\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 564/1000\n", - "8/8 - 0s - loss: 1.8341e-04 - root_mean_squared_error: 0.0135 - val_loss: 6.3702e-04 - val_root_mean_squared_error: 0.0252\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0611\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 565/1000\n", - "8/8 - 0s - loss: 2.0855e-04 - root_mean_squared_error: 0.0144 - val_loss: 6.6163e-04 - val_root_mean_squared_error: 0.0257\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 566/1000\n", - "8/8 - 0s - loss: 2.4980e-04 - root_mean_squared_error: 0.0158 - val_loss: 6.2601e-04 - val_root_mean_squared_error: 0.0250\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0623\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 567/1000\n", - "8/8 - 0s - loss: 2.1212e-04 - root_mean_squared_error: 0.0146 - val_loss: 6.9407e-04 - val_root_mean_squared_error: 0.0263\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 568/1000\n", - "8/8 - 0s - loss: 3.0921e-04 - root_mean_squared_error: 0.0176 - val_loss: 7.3695e-04 - val_root_mean_squared_error: 0.0271\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0635\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 569/1000\n", - "8/8 - 0s - loss: 2.7853e-04 - root_mean_squared_error: 0.0167 - val_loss: 6.5513e-04 - val_root_mean_squared_error: 0.0256\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 570/1000\n", - "8/8 - 0s - loss: 2.5489e-04 - root_mean_squared_error: 0.0160 - val_loss: 7.1118e-04 - val_root_mean_squared_error: 0.0267\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0644\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 571/1000\n", - "8/8 - 0s - loss: 3.2334e-04 - root_mean_squared_error: 0.0180 - val_loss: 6.4888e-04 - val_root_mean_squared_error: 0.0255\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 572/1000\n", - "8/8 - 0s - loss: 3.0073e-04 - root_mean_squared_error: 0.0173 - val_loss: 6.9618e-04 - val_root_mean_squared_error: 0.0264\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0632\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 573/1000\n", - "8/8 - 0s - loss: 3.3266e-04 - root_mean_squared_error: 0.0182 - val_loss: 7.2399e-04 - val_root_mean_squared_error: 0.0269\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 574/1000\n", - "8/8 - 0s - loss: 3.4541e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.6518e-04 - val_root_mean_squared_error: 0.0258\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0597\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 575/1000\n", - "8/8 - 0s - loss: 4.3103e-04 - root_mean_squared_error: 0.0208 - val_loss: 7.3348e-04 - val_root_mean_squared_error: 0.0271\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 576/1000\n", - "8/8 - 0s - loss: 4.4215e-04 - root_mean_squared_error: 0.0210 - val_loss: 8.2675e-04 - val_root_mean_squared_error: 0.0288\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0625\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 577/1000\n", - "8/8 - 0s - loss: 3.6276e-04 - root_mean_squared_error: 0.0190 - val_loss: 7.3452e-04 - val_root_mean_squared_error: 0.0271\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0616\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 578/1000\n", - "8/8 - 0s - loss: 5.1391e-04 - root_mean_squared_error: 0.0227 - val_loss: 9.5800e-04 - val_root_mean_squared_error: 0.0310\n", + "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0659\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 579/1000\n", - "8/8 - 0s - loss: 4.2748e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.5404e-04 - val_root_mean_squared_error: 0.0292\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0634\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 580/1000\n", - "8/8 - 0s - loss: 3.2486e-04 - root_mean_squared_error: 0.0180 - val_loss: 6.4379e-04 - val_root_mean_squared_error: 0.0254\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0655\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 581/1000\n", - "8/8 - 0s - loss: 1.9930e-04 - root_mean_squared_error: 0.0141 - val_loss: 6.3951e-04 - val_root_mean_squared_error: 0.0253\n", + "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 582/1000\n", - "8/8 - 0s - loss: 2.2703e-04 - root_mean_squared_error: 0.0151 - val_loss: 6.3264e-04 - val_root_mean_squared_error: 0.0252\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 583/1000\n", - "8/8 - 0s - loss: 2.1243e-04 - root_mean_squared_error: 0.0146 - val_loss: 5.8228e-04 - val_root_mean_squared_error: 0.0241\n", + "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 584/1000\n", - "8/8 - 0s - loss: 1.8163e-04 - root_mean_squared_error: 0.0135 - val_loss: 5.2718e-04 - val_root_mean_squared_error: 0.0230\n", + "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 585/1000\n", - "8/8 - 0s - loss: 1.9244e-04 - root_mean_squared_error: 0.0139 - val_loss: 5.5606e-04 - val_root_mean_squared_error: 0.0236\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 586/1000\n", - "8/8 - 0s - loss: 1.9012e-04 - root_mean_squared_error: 0.0138 - val_loss: 5.2952e-04 - val_root_mean_squared_error: 0.0230\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 587/1000\n", - "8/8 - 0s - loss: 1.9429e-04 - root_mean_squared_error: 0.0139 - val_loss: 5.2303e-04 - val_root_mean_squared_error: 0.0229\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 588/1000\n", - "8/8 - 0s - loss: 1.8684e-04 - root_mean_squared_error: 0.0137 - val_loss: 5.5868e-04 - val_root_mean_squared_error: 0.0236\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 589/1000\n", - "8/8 - 0s - loss: 1.4730e-04 - root_mean_squared_error: 0.0121 - val_loss: 4.7001e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 590/1000\n", - "8/8 - 0s - loss: 1.3690e-04 - root_mean_squared_error: 0.0117 - val_loss: 4.6593e-04 - val_root_mean_squared_error: 0.0216\n", + "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 591/1000\n", - "8/8 - 0s - loss: 1.4728e-04 - root_mean_squared_error: 0.0121 - val_loss: 4.8880e-04 - val_root_mean_squared_error: 0.0221\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 592/1000\n", - "8/8 - 0s - loss: 1.2106e-04 - root_mean_squared_error: 0.0110 - val_loss: 4.5753e-04 - val_root_mean_squared_error: 0.0214\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0620\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 593/1000\n", - "8/8 - 0s - loss: 1.4507e-04 - root_mean_squared_error: 0.0120 - val_loss: 4.7187e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 594/1000\n", - "8/8 - 0s - loss: 1.7336e-04 - root_mean_squared_error: 0.0132 - val_loss: 4.7010e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 595/1000\n", - "8/8 - 0s - loss: 1.5857e-04 - root_mean_squared_error: 0.0126 - val_loss: 4.7971e-04 - val_root_mean_squared_error: 0.0219\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 596/1000\n", - "8/8 - 0s - loss: 1.5980e-04 - root_mean_squared_error: 0.0126 - val_loss: 4.5332e-04 - val_root_mean_squared_error: 0.0213\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 597/1000\n", - "8/8 - 0s - loss: 1.5921e-04 - root_mean_squared_error: 0.0126 - val_loss: 4.7217e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 598/1000\n", - "8/8 - 0s - loss: 1.5311e-04 - root_mean_squared_error: 0.0124 - val_loss: 4.7286e-04 - val_root_mean_squared_error: 0.0217\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 599/1000\n", - "8/8 - 0s - loss: 1.5417e-04 - root_mean_squared_error: 0.0124 - val_loss: 4.0908e-04 - val_root_mean_squared_error: 0.0202\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 600/1000\n", - "8/8 - 0s - loss: 1.3651e-04 - root_mean_squared_error: 0.0117 - val_loss: 4.3103e-04 - val_root_mean_squared_error: 0.0208\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 601/1000\n", - "8/8 - 0s - loss: 1.4003e-04 - root_mean_squared_error: 0.0118 - val_loss: 4.5344e-04 - val_root_mean_squared_error: 0.0213\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 602/1000\n", - "8/8 - 0s - loss: 1.3339e-04 - root_mean_squared_error: 0.0115 - val_loss: 3.7599e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 603/1000\n", - "8/8 - 0s - loss: 1.1443e-04 - root_mean_squared_error: 0.0107 - val_loss: 3.8857e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 604/1000\n", - "8/8 - 0s - loss: 1.2618e-04 - root_mean_squared_error: 0.0112 - val_loss: 4.2189e-04 - val_root_mean_squared_error: 0.0205\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 605/1000\n", - "8/8 - 0s - loss: 1.1737e-04 - root_mean_squared_error: 0.0108 - val_loss: 3.6556e-04 - val_root_mean_squared_error: 0.0191\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 606/1000\n", - "8/8 - 0s - loss: 1.1349e-04 - root_mean_squared_error: 0.0107 - val_loss: 3.6989e-04 - val_root_mean_squared_error: 0.0192\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 607/1000\n", - "8/8 - 0s - loss: 1.3444e-04 - root_mean_squared_error: 0.0116 - val_loss: 3.7316e-04 - val_root_mean_squared_error: 0.0193\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 608/1000\n", - "8/8 - 0s - loss: 1.2370e-04 - root_mean_squared_error: 0.0111 - val_loss: 3.9813e-04 - val_root_mean_squared_error: 0.0200\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 609/1000\n", - "8/8 - 0s - loss: 1.9420e-04 - root_mean_squared_error: 0.0139 - val_loss: 4.2596e-04 - val_root_mean_squared_error: 0.0206\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 610/1000\n", - "8/8 - 0s - loss: 1.8627e-04 - root_mean_squared_error: 0.0136 - val_loss: 3.6844e-04 - val_root_mean_squared_error: 0.0192\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 611/1000\n", - "8/8 - 0s - loss: 2.1818e-04 - root_mean_squared_error: 0.0148 - val_loss: 4.4072e-04 - val_root_mean_squared_error: 0.0210\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 612/1000\n", - "8/8 - 0s - loss: 3.6773e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.2576e-04 - val_root_mean_squared_error: 0.0229\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 613/1000\n", - "8/8 - 0s - loss: 3.9972e-04 - root_mean_squared_error: 0.0200 - val_loss: 5.4015e-04 - val_root_mean_squared_error: 0.0232\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 614/1000\n", - "8/8 - 0s - loss: 4.8699e-04 - root_mean_squared_error: 0.0221 - val_loss: 5.7666e-04 - val_root_mean_squared_error: 0.0240\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 615/1000\n", - "8/8 - 0s - loss: 4.8615e-04 - root_mean_squared_error: 0.0220 - val_loss: 7.7410e-04 - val_root_mean_squared_error: 0.0278\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0354 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 616/1000\n", - "8/8 - 0s - loss: 6.9216e-04 - root_mean_squared_error: 0.0263 - val_loss: 6.1622e-04 - val_root_mean_squared_error: 0.0248\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 617/1000\n", - "8/8 - 0s - loss: 5.6968e-04 - root_mean_squared_error: 0.0239 - val_loss: 8.6108e-04 - val_root_mean_squared_error: 0.0293\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 618/1000\n", - "8/8 - 0s - loss: 4.7152e-04 - root_mean_squared_error: 0.0217 - val_loss: 6.4090e-04 - val_root_mean_squared_error: 0.0253\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 619/1000\n", - "8/8 - 0s - loss: 5.8695e-04 - root_mean_squared_error: 0.0242 - val_loss: 7.8550e-04 - val_root_mean_squared_error: 0.0280\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 620/1000\n", - "8/8 - 0s - loss: 4.3608e-04 - root_mean_squared_error: 0.0209 - val_loss: 6.9267e-04 - val_root_mean_squared_error: 0.0263\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 621/1000\n", - "8/8 - 0s - loss: 3.1353e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.1927e-04 - val_root_mean_squared_error: 0.0228\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0552\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 622/1000\n", - "8/8 - 0s - loss: 2.1553e-04 - root_mean_squared_error: 0.0147 - val_loss: 4.3980e-04 - val_root_mean_squared_error: 0.0210\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 623/1000\n", - "8/8 - 0s - loss: 2.2847e-04 - root_mean_squared_error: 0.0151 - val_loss: 5.5642e-04 - val_root_mean_squared_error: 0.0236\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 624/1000\n", - "8/8 - 0s - loss: 2.3662e-04 - root_mean_squared_error: 0.0154 - val_loss: 4.2373e-04 - val_root_mean_squared_error: 0.0206\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 625/1000\n", - "8/8 - 0s - loss: 1.7754e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.9499e-04 - val_root_mean_squared_error: 0.0199\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 626/1000\n", - "8/8 - 0s - loss: 1.6226e-04 - root_mean_squared_error: 0.0127 - val_loss: 4.0233e-04 - val_root_mean_squared_error: 0.0201\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 627/1000\n", - "8/8 - 0s - loss: 1.8257e-04 - root_mean_squared_error: 0.0135 - val_loss: 3.9482e-04 - val_root_mean_squared_error: 0.0199\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 628/1000\n", - "8/8 - 0s - loss: 2.1216e-04 - root_mean_squared_error: 0.0146 - val_loss: 3.8479e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 629/1000\n", - "8/8 - 0s - loss: 2.0411e-04 - root_mean_squared_error: 0.0143 - val_loss: 4.1405e-04 - val_root_mean_squared_error: 0.0203\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 630/1000\n", - "8/8 - 0s - loss: 1.9934e-04 - root_mean_squared_error: 0.0141 - val_loss: 3.3809e-04 - val_root_mean_squared_error: 0.0184\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0570\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 631/1000\n", - "8/8 - 0s - loss: 1.7683e-04 - root_mean_squared_error: 0.0133 - val_loss: 3.7223e-04 - val_root_mean_squared_error: 0.0193\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 632/1000\n", - "8/8 - 0s - loss: 1.2830e-04 - root_mean_squared_error: 0.0113 - val_loss: 3.6568e-04 - val_root_mean_squared_error: 0.0191\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 633/1000\n", - "8/8 - 0s - loss: 1.0764e-04 - root_mean_squared_error: 0.0104 - val_loss: 3.1130e-04 - val_root_mean_squared_error: 0.0176\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 634/1000\n", - "8/8 - 0s - loss: 1.1655e-04 - root_mean_squared_error: 0.0108 - val_loss: 3.1297e-04 - val_root_mean_squared_error: 0.0177\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 635/1000\n", - "8/8 - 0s - loss: 1.2520e-04 - root_mean_squared_error: 0.0112 - val_loss: 3.2968e-04 - val_root_mean_squared_error: 0.0182\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 636/1000\n", - "8/8 - 0s - loss: 1.0775e-04 - root_mean_squared_error: 0.0104 - val_loss: 2.9798e-04 - val_root_mean_squared_error: 0.0173\n", + "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 637/1000\n", - "8/8 - 0s - loss: 1.0772e-04 - root_mean_squared_error: 0.0104 - val_loss: 3.1267e-04 - val_root_mean_squared_error: 0.0177\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 638/1000\n", - "8/8 - 0s - loss: 1.4477e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.2840e-04 - val_root_mean_squared_error: 0.0181\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 639/1000\n", - "8/8 - 0s - loss: 1.3708e-04 - root_mean_squared_error: 0.0117 - val_loss: 2.8072e-04 - val_root_mean_squared_error: 0.0168\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 640/1000\n", - "8/8 - 0s - loss: 1.1580e-04 - root_mean_squared_error: 0.0108 - val_loss: 2.7434e-04 - val_root_mean_squared_error: 0.0166\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 641/1000\n", - "8/8 - 0s - loss: 1.4469e-04 - root_mean_squared_error: 0.0120 - val_loss: 3.3970e-04 - val_root_mean_squared_error: 0.0184\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 642/1000\n", - "8/8 - 0s - loss: 1.6126e-04 - root_mean_squared_error: 0.0127 - val_loss: 3.2431e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 643/1000\n", - "8/8 - 0s - loss: 1.3531e-04 - root_mean_squared_error: 0.0116 - val_loss: 2.9004e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 644/1000\n", - "8/8 - 0s - loss: 1.3786e-04 - root_mean_squared_error: 0.0117 - val_loss: 3.1633e-04 - val_root_mean_squared_error: 0.0178\n", + "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 645/1000\n", - "8/8 - 0s - loss: 1.1195e-04 - root_mean_squared_error: 0.0106 - val_loss: 2.7739e-04 - val_root_mean_squared_error: 0.0167\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 646/1000\n", - "8/8 - 0s - loss: 9.3790e-05 - root_mean_squared_error: 0.0097 - val_loss: 2.6080e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 647/1000\n", - "8/8 - 0s - loss: 9.0692e-05 - root_mean_squared_error: 0.0095 - val_loss: 2.5793e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 648/1000\n", - "8/8 - 0s - loss: 8.4792e-05 - root_mean_squared_error: 0.0092 - val_loss: 2.5908e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 649/1000\n", - "8/8 - 0s - loss: 1.0661e-04 - root_mean_squared_error: 0.0103 - val_loss: 2.3768e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 650/1000\n", - "8/8 - 0s - loss: 9.3996e-05 - root_mean_squared_error: 0.0097 - val_loss: 2.1827e-04 - val_root_mean_squared_error: 0.0148\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 651/1000\n", - "8/8 - 0s - loss: 1.1359e-04 - root_mean_squared_error: 0.0107 - val_loss: 2.8806e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 652/1000\n", - "8/8 - 0s - loss: 1.8816e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.8503e-04 - val_root_mean_squared_error: 0.0169\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 653/1000\n", - "8/8 - 0s - loss: 1.4522e-04 - root_mean_squared_error: 0.0121 - val_loss: 2.5352e-04 - val_root_mean_squared_error: 0.0159\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 654/1000\n", - "8/8 - 0s - loss: 1.7439e-04 - root_mean_squared_error: 0.0132 - val_loss: 3.0948e-04 - val_root_mean_squared_error: 0.0176\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 655/1000\n", - "8/8 - 0s - loss: 2.2321e-04 - root_mean_squared_error: 0.0149 - val_loss: 3.1291e-04 - val_root_mean_squared_error: 0.0177\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 656/1000\n", - "8/8 - 0s - loss: 2.1939e-04 - root_mean_squared_error: 0.0148 - val_loss: 3.3933e-04 - val_root_mean_squared_error: 0.0184\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 657/1000\n", - "8/8 - 0s - loss: 2.5396e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.7647e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 658/1000\n", - "8/8 - 0s - loss: 2.0202e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.3041e-04 - val_root_mean_squared_error: 0.0182\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 659/1000\n", - "8/8 - 0s - loss: 2.6174e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.8429e-04 - val_root_mean_squared_error: 0.0196\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 660/1000\n", - "8/8 - 0s - loss: 2.8181e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.8697e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 661/1000\n", - "8/8 - 0s - loss: 2.1400e-04 - root_mean_squared_error: 0.0146 - val_loss: 3.4214e-04 - val_root_mean_squared_error: 0.0185\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 662/1000\n", - "8/8 - 0s - loss: 3.5837e-04 - root_mean_squared_error: 0.0189 - val_loss: 5.3586e-04 - val_root_mean_squared_error: 0.0231\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 663/1000\n", - "8/8 - 0s - loss: 2.5894e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.9027e-04 - val_root_mean_squared_error: 0.0198\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 664/1000\n", - "8/8 - 0s - loss: 1.5511e-04 - root_mean_squared_error: 0.0125 - val_loss: 2.6604e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 665/1000\n", - "8/8 - 0s - loss: 1.1377e-04 - root_mean_squared_error: 0.0107 - val_loss: 2.5070e-04 - val_root_mean_squared_error: 0.0158\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 666/1000\n", - "8/8 - 0s - loss: 1.1913e-04 - root_mean_squared_error: 0.0109 - val_loss: 2.6258e-04 - val_root_mean_squared_error: 0.0162\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 667/1000\n", - "8/8 - 0s - loss: 1.0414e-04 - root_mean_squared_error: 0.0102 - val_loss: 2.3816e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 668/1000\n", - "8/8 - 0s - loss: 1.1612e-04 - root_mean_squared_error: 0.0108 - val_loss: 2.2915e-04 - val_root_mean_squared_error: 0.0151\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 669/1000\n", - "8/8 - 0s - loss: 1.2299e-04 - root_mean_squared_error: 0.0111 - val_loss: 2.3045e-04 - val_root_mean_squared_error: 0.0152\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 670/1000\n", - "8/8 - 0s - loss: 1.1392e-04 - root_mean_squared_error: 0.0107 - val_loss: 2.0219e-04 - val_root_mean_squared_error: 0.0142\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 671/1000\n", - "8/8 - 0s - loss: 1.0163e-04 - root_mean_squared_error: 0.0101 - val_loss: 2.1841e-04 - val_root_mean_squared_error: 0.0148\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 672/1000\n", - "8/8 - 0s - loss: 8.9131e-05 - root_mean_squared_error: 0.0094 - val_loss: 2.0358e-04 - val_root_mean_squared_error: 0.0143\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 673/1000\n", - "8/8 - 0s - loss: 7.6482e-05 - root_mean_squared_error: 0.0087 - val_loss: 1.9747e-04 - val_root_mean_squared_error: 0.0141\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 674/1000\n", - "8/8 - 0s - loss: 7.3904e-05 - root_mean_squared_error: 0.0086 - val_loss: 1.8930e-04 - val_root_mean_squared_error: 0.0138\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 675/1000\n", - "8/8 - 0s - loss: 6.4845e-05 - root_mean_squared_error: 0.0081 - val_loss: 1.7174e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 676/1000\n", - "8/8 - 0s - loss: 6.1318e-05 - root_mean_squared_error: 0.0078 - val_loss: 1.8358e-04 - val_root_mean_squared_error: 0.0135\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 677/1000\n", - "8/8 - 0s - loss: 6.3635e-05 - root_mean_squared_error: 0.0080 - val_loss: 1.5864e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 678/1000\n", - "8/8 - 0s - loss: 6.2347e-05 - root_mean_squared_error: 0.0079 - val_loss: 1.5539e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 679/1000\n", - "8/8 - 0s - loss: 6.6796e-05 - root_mean_squared_error: 0.0082 - val_loss: 1.6176e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 680/1000\n", - "8/8 - 0s - loss: 8.4785e-05 - root_mean_squared_error: 0.0092 - val_loss: 1.8728e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 681/1000\n", - "8/8 - 0s - loss: 1.0400e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.9558e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 682/1000\n", - "8/8 - 0s - loss: 1.0026e-04 - root_mean_squared_error: 0.0100 - val_loss: 1.7945e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 683/1000\n", - "8/8 - 0s - loss: 8.9763e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.6031e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 684/1000\n", - "8/8 - 0s - loss: 8.8493e-05 - root_mean_squared_error: 0.0094 - val_loss: 1.7624e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 685/1000\n", - "8/8 - 0s - loss: 8.0983e-05 - root_mean_squared_error: 0.0090 - val_loss: 1.7611e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 686/1000\n", - "8/8 - 0s - loss: 8.4012e-05 - root_mean_squared_error: 0.0092 - val_loss: 1.6583e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 687/1000\n", - "8/8 - 0s - loss: 9.4782e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.6276e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 688/1000\n", - "8/8 - 0s - loss: 9.5128e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.5225e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0505\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 689/1000\n", - "8/8 - 0s - loss: 1.0740e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.6950e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 690/1000\n", - "8/8 - 0s - loss: 1.0137e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.4495e-04 - val_root_mean_squared_error: 0.0120\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0534\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 691/1000\n", - "8/8 - 0s - loss: 9.6406e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.7879e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 692/1000\n", - "8/8 - 0s - loss: 1.7296e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8829e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 693/1000\n", - "8/8 - 0s - loss: 1.8979e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.2834e-04 - val_root_mean_squared_error: 0.0181\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 694/1000\n", - "8/8 - 0s - loss: 2.7172e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.2633e-04 - val_root_mean_squared_error: 0.0150\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 695/1000\n", - "8/8 - 0s - loss: 2.9754e-04 - root_mean_squared_error: 0.0172 - val_loss: 2.8896e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 696/1000\n", - "8/8 - 0s - loss: 2.5797e-04 - root_mean_squared_error: 0.0161 - val_loss: 4.1208e-04 - val_root_mean_squared_error: 0.0203\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 697/1000\n", - "8/8 - 0s - loss: 2.6368e-04 - root_mean_squared_error: 0.0162 - val_loss: 1.6427e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 698/1000\n", - "8/8 - 0s - loss: 2.0501e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.9219e-04 - val_root_mean_squared_error: 0.0171\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 699/1000\n", - "8/8 - 0s - loss: 3.0124e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.2675e-04 - val_root_mean_squared_error: 0.0181\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 700/1000\n", - "8/8 - 0s - loss: 2.8184e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.3967e-04 - val_root_mean_squared_error: 0.0184\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 701/1000\n", - "8/8 - 0s - loss: 5.5617e-04 - root_mean_squared_error: 0.0236 - val_loss: 5.5442e-04 - val_root_mean_squared_error: 0.0235\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 702/1000\n", - "8/8 - 0s - loss: 5.7361e-04 - root_mean_squared_error: 0.0240 - val_loss: 5.6937e-04 - val_root_mean_squared_error: 0.0239\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 703/1000\n", - "8/8 - 0s - loss: 4.1934e-04 - root_mean_squared_error: 0.0205 - val_loss: 3.7749e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 704/1000\n", - "8/8 - 0s - loss: 4.0338e-04 - root_mean_squared_error: 0.0201 - val_loss: 5.1208e-04 - val_root_mean_squared_error: 0.0226\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 705/1000\n", - "8/8 - 0s - loss: 2.9662e-04 - root_mean_squared_error: 0.0172 - val_loss: 3.8828e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 706/1000\n", - "8/8 - 0s - loss: 1.7647e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.9739e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 707/1000\n", - "8/8 - 0s - loss: 1.4772e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.8658e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 708/1000\n", - "8/8 - 0s - loss: 1.4176e-04 - root_mean_squared_error: 0.0119 - val_loss: 2.0430e-04 - val_root_mean_squared_error: 0.0143\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 709/1000\n", - "8/8 - 0s - loss: 1.9630e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1260e-04 - val_root_mean_squared_error: 0.0146\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 710/1000\n", - "8/8 - 0s - loss: 2.0105e-04 - root_mean_squared_error: 0.0142 - val_loss: 3.0426e-04 - val_root_mean_squared_error: 0.0174\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 711/1000\n", - "8/8 - 0s - loss: 1.7752e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.3926e-04 - val_root_mean_squared_error: 0.0155\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 712/1000\n", - "8/8 - 0s - loss: 1.7604e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.2338e-04 - val_root_mean_squared_error: 0.0149\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 713/1000\n", - "8/8 - 0s - loss: 1.9021e-04 - root_mean_squared_error: 0.0138 - val_loss: 3.3446e-04 - val_root_mean_squared_error: 0.0183\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 714/1000\n", - "8/8 - 0s - loss: 1.4006e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4529e-04 - val_root_mean_squared_error: 0.0121\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 715/1000\n", - "8/8 - 0s - loss: 1.0561e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.4140e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 716/1000\n", - "8/8 - 0s - loss: 1.4212e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.8491e-04 - val_root_mean_squared_error: 0.0136\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 717/1000\n", - "8/8 - 0s - loss: 1.6091e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7004e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 718/1000\n", - "8/8 - 0s - loss: 2.5141e-04 - root_mean_squared_error: 0.0159 - val_loss: 1.7487e-04 - val_root_mean_squared_error: 0.0132\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 719/1000\n", - "8/8 - 0s - loss: 2.7201e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.8926e-04 - val_root_mean_squared_error: 0.0197\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 720/1000\n", - "8/8 - 0s - loss: 2.5047e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.4272e-04 - val_root_mean_squared_error: 0.0156\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 721/1000\n", - "8/8 - 0s - loss: 1.4328e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.6327e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 722/1000\n", - "8/8 - 0s - loss: 1.5606e-04 - root_mean_squared_error: 0.0125 - val_loss: 2.1376e-04 - val_root_mean_squared_error: 0.0146\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 723/1000\n", - "8/8 - 0s - loss: 1.7109e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.5821e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0488\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 724/1000\n", - "8/8 - 0s - loss: 2.0355e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.4137e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 725/1000\n", - "8/8 - 0s - loss: 2.5310e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6171e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 9.4615e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 726/1000\n", - "8/8 - 0s - loss: 2.5708e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.3575e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 727/1000\n", - "8/8 - 0s - loss: 1.4574e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.6458e-04 - val_root_mean_squared_error: 0.0128\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 728/1000\n", - "8/8 - 0s - loss: 1.2338e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.9556e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 729/1000\n", - "8/8 - 0s - loss: 1.3552e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.4993e-04 - val_root_mean_squared_error: 0.0122\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 730/1000\n", - "8/8 - 0s - loss: 1.7210e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.3693e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 9.8302e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 731/1000\n", - "8/8 - 0s - loss: 2.0903e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.9048e-04 - val_root_mean_squared_error: 0.0170\n", + "8/8 - 0s - loss: 9.3414e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 732/1000\n", - "8/8 - 0s - loss: 2.5713e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.4112e-04 - val_root_mean_squared_error: 0.0155\n", + "8/8 - 0s - loss: 9.0998e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 733/1000\n", - "8/8 - 0s - loss: 1.6474e-04 - root_mean_squared_error: 0.0128 - val_loss: 2.2608e-04 - val_root_mean_squared_error: 0.0150\n", + "8/8 - 0s - loss: 9.2871e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 734/1000\n", - "8/8 - 0s - loss: 1.2684e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.6207e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 735/1000\n", - "8/8 - 0s - loss: 1.2332e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2734e-04 - val_root_mean_squared_error: 0.0113\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 736/1000\n", - "8/8 - 0s - loss: 1.8194e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5017e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 9.4826e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 737/1000\n", - "8/8 - 0s - loss: 2.0888e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.5137e-04 - val_root_mean_squared_error: 0.0159\n", + "8/8 - 0s - loss: 9.3996e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 738/1000\n", - "8/8 - 0s - loss: 3.0827e-04 - root_mean_squared_error: 0.0176 - val_loss: 2.5207e-04 - val_root_mean_squared_error: 0.0159\n", + "8/8 - 0s - loss: 9.7783e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 739/1000\n", - "8/8 - 0s - loss: 2.5088e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.6262e-04 - val_root_mean_squared_error: 0.0190\n", + "8/8 - 0s - loss: 9.3457e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 740/1000\n", - "8/8 - 0s - loss: 2.6904e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.2236e-04 - val_root_mean_squared_error: 0.0180\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 9.7798e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 741/1000\n", - "8/8 - 0s - loss: 2.0782e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.5647e-04 - val_root_mean_squared_error: 0.0160\n", + "8/8 - 0s - loss: 9.9817e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 742/1000\n", - "8/8 - 0s - loss: 2.7811e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.2310e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 743/1000\n", - "8/8 - 0s - loss: 2.2668e-04 - root_mean_squared_error: 0.0151 - val_loss: 4.0202e-04 - val_root_mean_squared_error: 0.0201\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 744/1000\n", - "8/8 - 0s - loss: 3.0249e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.7578e-04 - val_root_mean_squared_error: 0.0194\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 745/1000\n", - "8/8 - 0s - loss: 1.6729e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.9480e-04 - val_root_mean_squared_error: 0.0140\n", + "8/8 - 0s - loss: 9.7355e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 746/1000\n", - "8/8 - 0s - loss: 9.4346e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.2136e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 747/1000\n", - "8/8 - 0s - loss: 7.1229e-05 - root_mean_squared_error: 0.0084 - val_loss: 9.9335e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 9.8044e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 748/1000\n", - "8/8 - 0s - loss: 6.4599e-05 - root_mean_squared_error: 0.0080 - val_loss: 1.5306e-04 - val_root_mean_squared_error: 0.0124\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 749/1000\n", - "8/8 - 0s - loss: 7.4061e-05 - root_mean_squared_error: 0.0086 - val_loss: 9.6350e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 750/1000\n", - "8/8 - 0s - loss: 7.8498e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.0353e-04 - val_root_mean_squared_error: 0.0102\n", + "8/8 - 0s - loss: 9.8448e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 751/1000\n", - "8/8 - 0s - loss: 7.2383e-05 - root_mean_squared_error: 0.0085 - val_loss: 1.5293e-04 - val_root_mean_squared_error: 0.0124\n", + "8/8 - 0s - loss: 9.7664e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 752/1000\n", - "8/8 - 0s - loss: 8.3701e-05 - root_mean_squared_error: 0.0091 - val_loss: 1.0918e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 753/1000\n", - "8/8 - 0s - loss: 8.7125e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.0623e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 754/1000\n", - "8/8 - 0s - loss: 7.6783e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.2936e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 755/1000\n", - "8/8 - 0s - loss: 8.4097e-05 - root_mean_squared_error: 0.0092 - val_loss: 1.1559e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 756/1000\n", - "8/8 - 0s - loss: 9.7164e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.1725e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 757/1000\n", - "8/8 - 0s - loss: 9.2310e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.4180e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 9.6742e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 758/1000\n", - "8/8 - 0s - loss: 1.3557e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.7015e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 9.6242e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 759/1000\n", - "8/8 - 0s - loss: 1.5364e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.5539e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 9.4546e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 760/1000\n", - "8/8 - 0s - loss: 1.2801e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.7584e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 9.2747e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 761/1000\n", - "8/8 - 0s - loss: 2.2004e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.5842e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 9.8714e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 762/1000\n", - "8/8 - 0s - loss: 2.2380e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.6668e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 763/1000\n", - "8/8 - 0s - loss: 1.8858e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.6792e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 764/1000\n", - "8/8 - 0s - loss: 2.1358e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.7324e-04 - val_root_mean_squared_error: 0.0132\n", + "8/8 - 0s - loss: 9.2499e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 765/1000\n", - "8/8 - 0s - loss: 2.3872e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.5888e-04 - val_root_mean_squared_error: 0.0189\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 9.5332e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 766/1000\n", - "8/8 - 0s - loss: 2.5844e-04 - root_mean_squared_error: 0.0161 - val_loss: 1.6470e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 8.4107e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 767/1000\n", - "8/8 - 0s - loss: 1.9927e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.0736e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 8.1263e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 768/1000\n", - "8/8 - 0s - loss: 1.9238e-04 - root_mean_squared_error: 0.0139 - val_loss: 3.2440e-04 - val_root_mean_squared_error: 0.0180\n", + "8/8 - 0s - loss: 9.0986e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 769/1000\n", - "8/8 - 0s - loss: 1.8343e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.2925e-04 - val_root_mean_squared_error: 0.0114\n", + "8/8 - 0s - loss: 9.4570e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 770/1000\n", - "8/8 - 0s - loss: 1.1388e-04 - root_mean_squared_error: 0.0107 - val_loss: 7.4283e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 8.2983e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 771/1000\n", - "8/8 - 0s - loss: 1.1949e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.9374e-04 - val_root_mean_squared_error: 0.0139\n", + "8/8 - 0s - loss: 8.5862e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 772/1000\n", - "8/8 - 0s - loss: 1.6490e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7152e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 9.7932e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 773/1000\n", - "8/8 - 0s - loss: 1.1623e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.1350e-04 - val_root_mean_squared_error: 0.0107\n", + "8/8 - 0s - loss: 8.4013e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 774/1000\n", - "8/8 - 0s - loss: 9.4587e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.1718e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 8.2224e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 775/1000\n", - "8/8 - 0s - loss: 1.3598e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5792e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 8.5815e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 776/1000\n", - "8/8 - 0s - loss: 1.3536e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.8835e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 9.2230e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 777/1000\n", - "8/8 - 0s - loss: 1.5303e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.0887e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 8.8870e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 778/1000\n", - "8/8 - 0s - loss: 1.7087e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.2164e-04 - val_root_mean_squared_error: 0.0110\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 779/1000\n", - "8/8 - 0s - loss: 1.9341e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.9928e-04 - val_root_mean_squared_error: 0.0173\n", + "Epoch 779/1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8/8 - 0s - loss: 9.8624e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 780/1000\n", - "8/8 - 0s - loss: 1.8377e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.5406e-04 - val_root_mean_squared_error: 0.0124\n", + "8/8 - 0s - loss: 9.1789e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 781/1000\n", - "8/8 - 0s - loss: 8.5534e-05 - root_mean_squared_error: 0.0092 - val_loss: 8.7578e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 8.7242e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 782/1000\n", - "8/8 - 0s - loss: 6.5579e-05 - root_mean_squared_error: 0.0081 - val_loss: 1.1373e-04 - val_root_mean_squared_error: 0.0107\n", + "8/8 - 0s - loss: 8.4387e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 783/1000\n", - "8/8 - 0s - loss: 7.2078e-05 - root_mean_squared_error: 0.0085 - val_loss: 8.6472e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 9.0460e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 784/1000\n", - "8/8 - 0s - loss: 6.9741e-05 - root_mean_squared_error: 0.0084 - val_loss: 6.6677e-05 - val_root_mean_squared_error: 0.0082\n", + "8/8 - 0s - loss: 9.9138e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 785/1000\n", - "8/8 - 0s - loss: 7.0819e-05 - root_mean_squared_error: 0.0084 - val_loss: 1.0028e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 786/1000\n", - "8/8 - 0s - loss: 1.0266e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.2684e-04 - val_root_mean_squared_error: 0.0113\n", + "8/8 - 0s - loss: 9.0971e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0453\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 787/1000\n", - "8/8 - 0s - loss: 9.6328e-05 - root_mean_squared_error: 0.0098 - val_loss: 1.2338e-04 - val_root_mean_squared_error: 0.0111\n", + "8/8 - 0s - loss: 9.5636e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 788/1000\n", - "8/8 - 0s - loss: 8.6319e-05 - root_mean_squared_error: 0.0093 - val_loss: 7.9199e-05 - val_root_mean_squared_error: 0.0089\n", + "8/8 - 0s - loss: 9.8309e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 789/1000\n", - "8/8 - 0s - loss: 9.9853e-05 - root_mean_squared_error: 0.0100 - val_loss: 9.9561e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 8.5107e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 790/1000\n", - "8/8 - 0s - loss: 1.2731e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.7834e-04 - val_root_mean_squared_error: 0.0134\n", + "8/8 - 0s - loss: 9.2955e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 791/1000\n", - "8/8 - 0s - loss: 1.5184e-04 - root_mean_squared_error: 0.0123 - val_loss: 9.2417e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 792/1000\n", - "8/8 - 0s - loss: 1.1912e-04 - root_mean_squared_error: 0.0109 - val_loss: 8.2948e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 9.8696e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 793/1000\n", - "8/8 - 0s - loss: 1.3727e-04 - root_mean_squared_error: 0.0117 - val_loss: 2.0142e-04 - val_root_mean_squared_error: 0.0142\n", + "8/8 - 0s - loss: 8.6571e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0430\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 794/1000\n", - "8/8 - 0s - loss: 1.4267e-04 - root_mean_squared_error: 0.0119 - val_loss: 8.8959e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 795/1000\n", - "8/8 - 0s - loss: 8.9628e-05 - root_mean_squared_error: 0.0095 - val_loss: 6.2053e-05 - val_root_mean_squared_error: 0.0079\n", + "8/8 - 0s - loss: 9.5156e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 796/1000\n", - "8/8 - 0s - loss: 1.0262e-04 - root_mean_squared_error: 0.0101 - val_loss: 1.5844e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 8.5417e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 797/1000\n", - "8/8 - 0s - loss: 1.1609e-04 - root_mean_squared_error: 0.0108 - val_loss: 9.0273e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 9.0853e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 798/1000\n", - "8/8 - 0s - loss: 7.7357e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.0514e-05 - val_root_mean_squared_error: 0.0071\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 799/1000\n", - "8/8 - 0s - loss: 9.1259e-05 - root_mean_squared_error: 0.0096 - val_loss: 1.3384e-04 - val_root_mean_squared_error: 0.0116\n", + "8/8 - 0s - loss: 9.3801e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 800/1000\n", - "8/8 - 0s - loss: 1.3982e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2583e-04 - val_root_mean_squared_error: 0.0112\n", + "8/8 - 0s - loss: 9.6778e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 801/1000\n", - "8/8 - 0s - loss: 9.8192e-05 - root_mean_squared_error: 0.0099 - val_loss: 7.3017e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 802/1000\n", - "8/8 - 0s - loss: 8.9559e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.0964e-04 - val_root_mean_squared_error: 0.0105\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 9.4531e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 803/1000\n", - "8/8 - 0s - loss: 1.0508e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.0843e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 9.2147e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 804/1000\n", - "8/8 - 0s - loss: 7.8360e-05 - root_mean_squared_error: 0.0089 - val_loss: 1.0134e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 9.5981e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 805/1000\n", - "8/8 - 0s - loss: 6.5363e-05 - root_mean_squared_error: 0.0081 - val_loss: 7.2138e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 9.8561e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 806/1000\n", - "8/8 - 0s - loss: 6.5962e-05 - root_mean_squared_error: 0.0081 - val_loss: 8.0655e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 9.7029e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 807/1000\n", - "8/8 - 0s - loss: 7.3424e-05 - root_mean_squared_error: 0.0086 - val_loss: 1.0725e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 808/1000\n", - "8/8 - 0s - loss: 6.6687e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.1882e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 9.8673e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 809/1000\n", - "8/8 - 0s - loss: 5.0969e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.6166e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 9.3654e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 810/1000\n", - "8/8 - 0s - loss: 6.9242e-05 - root_mean_squared_error: 0.0083 - val_loss: 9.8351e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 9.0847e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 811/1000\n", - "8/8 - 0s - loss: 8.8051e-05 - root_mean_squared_error: 0.0094 - val_loss: 5.5209e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 9.3929e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 812/1000\n", - "8/8 - 0s - loss: 9.7087e-05 - root_mean_squared_error: 0.0099 - val_loss: 7.5805e-05 - val_root_mean_squared_error: 0.0087\n", + "8/8 - 0s - loss: 9.0115e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 813/1000\n", - "8/8 - 0s - loss: 1.3480e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.8320e-04 - val_root_mean_squared_error: 0.0135\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 814/1000\n", - "8/8 - 0s - loss: 1.6772e-04 - root_mean_squared_error: 0.0130 - val_loss: 9.6333e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 9.3189e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 815/1000\n", - "8/8 - 0s - loss: 1.1760e-04 - root_mean_squared_error: 0.0108 - val_loss: 6.0927e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 8.6079e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 816/1000\n", - "8/8 - 0s - loss: 1.5267e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.9968e-04 - val_root_mean_squared_error: 0.0141\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 8.5156e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 817/1000\n", - "8/8 - 0s - loss: 2.2010e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.3410e-04 - val_root_mean_squared_error: 0.0116\n", + "8/8 - 0s - loss: 9.0077e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 818/1000\n", - "8/8 - 0s - loss: 1.4437e-04 - root_mean_squared_error: 0.0120 - val_loss: 8.5483e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 8.3663e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 819/1000\n", - "8/8 - 0s - loss: 1.5333e-04 - root_mean_squared_error: 0.0124 - val_loss: 2.0795e-04 - val_root_mean_squared_error: 0.0144\n", + "8/8 - 0s - loss: 8.9042e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 820/1000\n", - "8/8 - 0s - loss: 1.5174e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.0636e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 8.6450e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 821/1000\n", - "8/8 - 0s - loss: 9.9600e-05 - root_mean_squared_error: 0.0100 - val_loss: 6.4842e-05 - val_root_mean_squared_error: 0.0081\n", + "8/8 - 0s - loss: 7.7968e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 822/1000\n", - "8/8 - 0s - loss: 1.0631e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.4975e-04 - val_root_mean_squared_error: 0.0122\n", + "8/8 - 0s - loss: 8.2020e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 823/1000\n", - "8/8 - 0s - loss: 1.0018e-04 - root_mean_squared_error: 0.0100 - val_loss: 1.0102e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 8.7989e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 824/1000\n", - "8/8 - 0s - loss: 6.1095e-05 - root_mean_squared_error: 0.0078 - val_loss: 6.4804e-05 - val_root_mean_squared_error: 0.0081\n", + "8/8 - 0s - loss: 8.4571e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 825/1000\n", - "8/8 - 0s - loss: 5.5702e-05 - root_mean_squared_error: 0.0075 - val_loss: 7.7638e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 8.2109e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 826/1000\n", - "8/8 - 0s - loss: 7.9884e-05 - root_mean_squared_error: 0.0089 - val_loss: 9.4503e-05 - val_root_mean_squared_error: 0.0097\n", + "8/8 - 0s - loss: 8.2353e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 827/1000\n", - "8/8 - 0s - loss: 7.6684e-05 - root_mean_squared_error: 0.0088 - val_loss: 6.8029e-05 - val_root_mean_squared_error: 0.0082\n", + "8/8 - 0s - loss: 7.4682e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 828/1000\n", - "8/8 - 0s - loss: 6.7463e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.4853e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 7.9977e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 829/1000\n", - "8/8 - 0s - loss: 9.0547e-05 - root_mean_squared_error: 0.0095 - val_loss: 9.2526e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 8.4748e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 830/1000\n", - "8/8 - 0s - loss: 1.0732e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.5137e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 9.1285e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 831/1000\n", - "8/8 - 0s - loss: 1.2875e-04 - root_mean_squared_error: 0.0113 - val_loss: 8.3319e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 8.1668e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 832/1000\n", - "8/8 - 0s - loss: 1.2349e-04 - root_mean_squared_error: 0.0111 - val_loss: 8.7857e-05 - val_root_mean_squared_error: 0.0094\n", + "8/8 - 0s - loss: 7.7325e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 833/1000\n", - "8/8 - 0s - loss: 1.4011e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.8492e-04 - val_root_mean_squared_error: 0.0136\n", + "8/8 - 0s - loss: 7.4411e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0418\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 834/1000\n", - "8/8 - 0s - loss: 1.2425e-04 - root_mean_squared_error: 0.0111 - val_loss: 8.4492e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 7.7177e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 835/1000\n", - "8/8 - 0s - loss: 5.8593e-05 - root_mean_squared_error: 0.0077 - val_loss: 5.3852e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 8.1368e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 836/1000\n", - "8/8 - 0s - loss: 6.1259e-05 - root_mean_squared_error: 0.0078 - val_loss: 9.2417e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 837/1000\n", - "8/8 - 0s - loss: 8.1212e-05 - root_mean_squared_error: 0.0090 - val_loss: 5.7935e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 8.8471e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0432\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 838/1000\n", - "8/8 - 0s - loss: 8.1982e-05 - root_mean_squared_error: 0.0091 - val_loss: 5.8640e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 8.1136e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 839/1000\n", - "8/8 - 0s - loss: 9.4341e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.3705e-04 - val_root_mean_squared_error: 0.0117\n", + "8/8 - 0s - loss: 8.2210e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 840/1000\n", - "8/8 - 0s - loss: 1.1712e-04 - root_mean_squared_error: 0.0108 - val_loss: 9.9687e-05 - val_root_mean_squared_error: 0.0100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 8.0751e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 841/1000\n", - "8/8 - 0s - loss: 7.7570e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.6409e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 7.9977e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 842/1000\n", - "8/8 - 0s - loss: 7.7060e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.0204e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 9.9786e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 843/1000\n", - "8/8 - 0s - loss: 9.2355e-05 - root_mean_squared_error: 0.0096 - val_loss: 8.4486e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 8.7947e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 844/1000\n", - "8/8 - 0s - loss: 6.2241e-05 - root_mean_squared_error: 0.0079 - val_loss: 6.0324e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 7.9335e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 845/1000\n", - "8/8 - 0s - loss: 5.0848e-05 - root_mean_squared_error: 0.0071 - val_loss: 7.2294e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 7.9541e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 846/1000\n", - "8/8 - 0s - loss: 7.9901e-05 - root_mean_squared_error: 0.0089 - val_loss: 9.9241e-05 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 7.7877e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 847/1000\n", - "8/8 - 0s - loss: 8.5088e-05 - root_mean_squared_error: 0.0092 - val_loss: 8.4345e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 7.4108e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 848/1000\n", - "8/8 - 0s - loss: 7.7872e-05 - root_mean_squared_error: 0.0088 - val_loss: 6.4017e-05 - val_root_mean_squared_error: 0.0080\n", + "8/8 - 0s - loss: 8.7811e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 849/1000\n", - "8/8 - 0s - loss: 1.0137e-04 - root_mean_squared_error: 0.0101 - val_loss: 9.8995e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 8.3144e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0434\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 850/1000\n", - "8/8 - 0s - loss: 1.3196e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.6655e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 7.3862e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 851/1000\n", - "8/8 - 0s - loss: 1.5455e-04 - root_mean_squared_error: 0.0124 - val_loss: 9.0940e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 7.4361e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 852/1000\n", - "8/8 - 0s - loss: 1.4790e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.0691e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 7.3761e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 853/1000\n", - "8/8 - 0s - loss: 1.7826e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.3636e-04 - val_root_mean_squared_error: 0.0154\n", + "8/8 - 0s - loss: 7.0627e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 854/1000\n", - "8/8 - 0s - loss: 1.5469e-04 - root_mean_squared_error: 0.0124 - val_loss: 8.1536e-05 - val_root_mean_squared_error: 0.0090\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 7.9810e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 855/1000\n", - "8/8 - 0s - loss: 9.2344e-05 - root_mean_squared_error: 0.0096 - val_loss: 5.6584e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 8.2182e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 856/1000\n", - "8/8 - 0s - loss: 1.2011e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.5868e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 7.1959e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 857/1000\n", - "8/8 - 0s - loss: 1.5371e-04 - root_mean_squared_error: 0.0124 - val_loss: 8.5795e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 7.2529e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 858/1000\n", - "8/8 - 0s - loss: 1.1317e-04 - root_mean_squared_error: 0.0106 - val_loss: 5.3923e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 7.2296e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 859/1000\n", - "8/8 - 0s - loss: 1.3100e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.7677e-04 - val_root_mean_squared_error: 0.0133\n", + "8/8 - 0s - loss: 7.1560e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 860/1000\n", - "8/8 - 0s - loss: 1.8183e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.3891e-04 - val_root_mean_squared_error: 0.0118\n", + "8/8 - 0s - loss: 7.8284e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 861/1000\n", - "8/8 - 0s - loss: 1.0573e-04 - root_mean_squared_error: 0.0103 - val_loss: 7.0027e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 8.8353e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 862/1000\n", - "8/8 - 0s - loss: 1.0305e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.0050e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 7.5569e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 863/1000\n", - "8/8 - 0s - loss: 9.1656e-05 - root_mean_squared_error: 0.0096 - val_loss: 8.2660e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 7.6236e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 864/1000\n", - "8/8 - 0s - loss: 5.4424e-05 - root_mean_squared_error: 0.0074 - val_loss: 6.5825e-05 - val_root_mean_squared_error: 0.0081\n", + "8/8 - 0s - loss: 7.4915e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 865/1000\n", - "8/8 - 0s - loss: 3.9700e-05 - root_mean_squared_error: 0.0063 - val_loss: 5.3962e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 7.5844e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 866/1000\n", - "8/8 - 0s - loss: 3.7783e-05 - root_mean_squared_error: 0.0061 - val_loss: 4.3955e-05 - val_root_mean_squared_error: 0.0066\n", + "8/8 - 0s - loss: 8.4219e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 867/1000\n", - "8/8 - 0s - loss: 3.9365e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.4565e-05 - val_root_mean_squared_error: 0.0059\n", + "8/8 - 0s - loss: 9.9710e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 868/1000\n", - "8/8 - 0s - loss: 3.7989e-05 - root_mean_squared_error: 0.0062 - val_loss: 4.5129e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 8.1323e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 869/1000\n", - "8/8 - 0s - loss: 5.9691e-05 - root_mean_squared_error: 0.0077 - val_loss: 7.8115e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 8.3353e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 870/1000\n", - "8/8 - 0s - loss: 7.3798e-05 - root_mean_squared_error: 0.0086 - val_loss: 8.6346e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 7.8531e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 871/1000\n", - "8/8 - 0s - loss: 8.7609e-05 - root_mean_squared_error: 0.0094 - val_loss: 5.4222e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 7.7351e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 872/1000\n", - "8/8 - 0s - loss: 1.1693e-04 - root_mean_squared_error: 0.0108 - val_loss: 8.5469e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 9.0569e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 873/1000\n", - "8/8 - 0s - loss: 1.3447e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.6331e-04 - val_root_mean_squared_error: 0.0128\n", + "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 874/1000\n", - "8/8 - 0s - loss: 1.2735e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.8855e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 7.9264e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 875/1000\n", - "8/8 - 0s - loss: 6.4465e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.3145e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 8.5656e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 876/1000\n", - "8/8 - 0s - loss: 9.3521e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.0612e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 7.7832e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 877/1000\n", - "8/8 - 0s - loss: 1.5154e-04 - root_mean_squared_error: 0.0123 - val_loss: 9.7968e-05 - val_root_mean_squared_error: 0.0099\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 7.0281e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 878/1000\n", - "8/8 - 0s - loss: 1.9111e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.0524e-04 - val_root_mean_squared_error: 0.0143\n", + "8/8 - 0s - loss: 8.1117e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 879/1000\n", - "8/8 - 0s - loss: 1.8296e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.6022e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 8.1093e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 880/1000\n", - "8/8 - 0s - loss: 1.2722e-04 - root_mean_squared_error: 0.0113 - val_loss: 9.8816e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 6.8791e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 881/1000\n", - "8/8 - 0s - loss: 7.1811e-05 - root_mean_squared_error: 0.0085 - val_loss: 5.5717e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 7.8356e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 882/1000\n", - "8/8 - 0s - loss: 8.6887e-05 - root_mean_squared_error: 0.0093 - val_loss: 1.1571e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 7.3649e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 883/1000\n", - "8/8 - 0s - loss: 1.2101e-04 - root_mean_squared_error: 0.0110 - val_loss: 8.3742e-05 - val_root_mean_squared_error: 0.0092\n", + "8/8 - 0s - loss: 6.2941e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 884/1000\n", - "8/8 - 0s - loss: 1.0857e-04 - root_mean_squared_error: 0.0104 - val_loss: 5.5412e-05 - val_root_mean_squared_error: 0.0074\n", + "8/8 - 0s - loss: 6.7190e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 885/1000\n", - "8/8 - 0s - loss: 1.2410e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.4269e-04 - val_root_mean_squared_error: 0.0119\n", + "8/8 - 0s - loss: 6.4207e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 886/1000\n", - "8/8 - 0s - loss: 1.2634e-04 - root_mean_squared_error: 0.0112 - val_loss: 9.4516e-05 - val_root_mean_squared_error: 0.0097\n", + "8/8 - 0s - loss: 6.3356e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 887/1000\n", - "8/8 - 0s - loss: 7.2234e-05 - root_mean_squared_error: 0.0085 - val_loss: 5.0198e-05 - val_root_mean_squared_error: 0.0071\n", + "8/8 - 0s - loss: 7.1803e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 888/1000\n", - "8/8 - 0s - loss: 7.7136e-05 - root_mean_squared_error: 0.0088 - val_loss: 1.0890e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.7103e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 889/1000\n", - "8/8 - 0s - loss: 1.1432e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.1877e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 6.2860e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 890/1000\n", - "8/8 - 0s - loss: 7.9900e-05 - root_mean_squared_error: 0.0089 - val_loss: 5.3012e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 6.8866e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 891/1000\n", - "8/8 - 0s - loss: 6.3047e-05 - root_mean_squared_error: 0.0079 - val_loss: 6.9715e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 6.2163e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 892/1000\n", - "8/8 - 0s - loss: 1.0668e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.2783e-04 - val_root_mean_squared_error: 0.0113\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 6.2591e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 893/1000\n", - "8/8 - 0s - loss: 1.4022e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5059e-04 - val_root_mean_squared_error: 0.0123\n", + "8/8 - 0s - loss: 6.8212e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 894/1000\n", - "8/8 - 0s - loss: 1.7497e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.0888e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.8751e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 895/1000\n", - "8/8 - 0s - loss: 2.2156e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.1711e-04 - val_root_mean_squared_error: 0.0147\n", + "8/8 - 0s - loss: 7.0025e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 896/1000\n", - "8/8 - 0s - loss: 2.3129e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1842e-04 - val_root_mean_squared_error: 0.0178\n", + "8/8 - 0s - loss: 7.8850e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 897/1000\n", - "8/8 - 0s - loss: 1.6025e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.1286e-04 - val_root_mean_squared_error: 0.0106\n", + "8/8 - 0s - loss: 7.0531e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 898/1000\n", - "8/8 - 0s - loss: 8.3775e-05 - root_mean_squared_error: 0.0092 - val_loss: 5.2794e-05 - val_root_mean_squared_error: 0.0073\n", + "8/8 - 0s - loss: 7.4991e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0381\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 899/1000\n", - "8/8 - 0s - loss: 9.0505e-05 - root_mean_squared_error: 0.0095 - val_loss: 1.0890e-04 - val_root_mean_squared_error: 0.0104\n", + "8/8 - 0s - loss: 6.9764e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 900/1000\n", - "8/8 - 0s - loss: 8.9822e-05 - root_mean_squared_error: 0.0095 - val_loss: 5.9135e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 6.6155e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 901/1000\n", - "8/8 - 0s - loss: 7.8707e-05 - root_mean_squared_error: 0.0089 - val_loss: 3.7198e-05 - val_root_mean_squared_error: 0.0061\n", + "8/8 - 0s - loss: 7.7413e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 902/1000\n", - "8/8 - 0s - loss: 9.3168e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.1719e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 8.3366e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 903/1000\n", - "8/8 - 0s - loss: 1.1883e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.0093e-04 - val_root_mean_squared_error: 0.0100\n", + "8/8 - 0s - loss: 7.1958e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 904/1000\n", - "8/8 - 0s - loss: 8.4542e-05 - root_mean_squared_error: 0.0092 - val_loss: 5.9062e-05 - val_root_mean_squared_error: 0.0077\n", + "8/8 - 0s - loss: 7.8991e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 905/1000\n", - "8/8 - 0s - loss: 8.4096e-05 - root_mean_squared_error: 0.0092 - val_loss: 9.0162e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 8.1211e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 906/1000\n", - "8/8 - 0s - loss: 1.3488e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.4342e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 7.1148e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 907/1000\n", - "8/8 - 0s - loss: 1.2330e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.4420e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 7.2748e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 908/1000\n", - "8/8 - 0s - loss: 1.3268e-04 - root_mean_squared_error: 0.0115 - val_loss: 4.8781e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 6.5155e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 909/1000\n", - "8/8 - 0s - loss: 1.2786e-04 - root_mean_squared_error: 0.0113 - val_loss: 7.7615e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 6.8368e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 910/1000\n", - "8/8 - 0s - loss: 1.4391e-04 - root_mean_squared_error: 0.0120 - val_loss: 2.1614e-04 - val_root_mean_squared_error: 0.0147\n", + "8/8 - 0s - loss: 7.8675e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 911/1000\n", - "8/8 - 0s - loss: 1.9435e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.5361e-04 - val_root_mean_squared_error: 0.0124\n", + "8/8 - 0s - loss: 7.5032e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 912/1000\n", - "8/8 - 0s - loss: 1.4516e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.1634e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 6.5800e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 913/1000\n", - "8/8 - 0s - loss: 1.2680e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.1073e-04 - val_root_mean_squared_error: 0.0105\n", + "8/8 - 0s - loss: 7.9411e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 914/1000\n", - "8/8 - 0s - loss: 1.3567e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3846e-04 - val_root_mean_squared_error: 0.0118\n", + "8/8 - 0s - loss: 7.5057e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 915/1000\n", - "8/8 - 0s - loss: 1.1416e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.3647e-05 - val_root_mean_squared_error: 0.0097\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 6.9231e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 916/1000\n", - "8/8 - 0s - loss: 7.7828e-05 - root_mean_squared_error: 0.0088 - val_loss: 3.7054e-05 - val_root_mean_squared_error: 0.0061\n", + "8/8 - 0s - loss: 6.6615e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 917/1000\n", - "8/8 - 0s - loss: 5.4481e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.5075e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 7.4480e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 918/1000\n", - "8/8 - 0s - loss: 5.2401e-05 - root_mean_squared_error: 0.0072 - val_loss: 8.2398e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 8.4743e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 919/1000\n", - "8/8 - 0s - loss: 5.4797e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.4709e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 9.4124e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 920/1000\n", - "8/8 - 0s - loss: 4.8398e-05 - root_mean_squared_error: 0.0070 - val_loss: 2.9260e-05 - val_root_mean_squared_error: 0.0054\n", + "8/8 - 0s - loss: 7.8393e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 921/1000\n", - "8/8 - 0s - loss: 4.8915e-05 - root_mean_squared_error: 0.0070 - val_loss: 4.6832e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 8.9836e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 922/1000\n", - "8/8 - 0s - loss: 6.5460e-05 - root_mean_squared_error: 0.0081 - val_loss: 6.7386e-05 - val_root_mean_squared_error: 0.0082\n", + "8/8 - 0s - loss: 8.8548e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 923/1000\n", - "8/8 - 0s - loss: 8.4551e-05 - root_mean_squared_error: 0.0092 - val_loss: 9.6654e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 7.5816e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 924/1000\n", - "8/8 - 0s - loss: 1.2022e-04 - root_mean_squared_error: 0.0110 - val_loss: 6.1582e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 8.2393e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 925/1000\n", - "8/8 - 0s - loss: 1.8189e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6235e-04 - val_root_mean_squared_error: 0.0127\n", + "8/8 - 0s - loss: 8.9936e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 926/1000\n", - "8/8 - 0s - loss: 2.0496e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.3531e-04 - val_root_mean_squared_error: 0.0153\n", + "8/8 - 0s - loss: 8.5089e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 927/1000\n", - "8/8 - 0s - loss: 2.0168e-04 - root_mean_squared_error: 0.0142 - val_loss: 9.2673e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 9.5263e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 928/1000\n", - "8/8 - 0s - loss: 1.5302e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4769e-04 - val_root_mean_squared_error: 0.0122\n", + "8/8 - 0s - loss: 9.8759e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0409\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 929/1000\n", - "8/8 - 0s - loss: 1.2166e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.8391e-04 - val_root_mean_squared_error: 0.0136\n", + "8/8 - 0s - loss: 8.9360e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 930/1000\n", - "8/8 - 0s - loss: 1.5049e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.5633e-04 - val_root_mean_squared_error: 0.0125\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 8.3825e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 931/1000\n", - "8/8 - 0s - loss: 1.3137e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.0528e-04 - val_root_mean_squared_error: 0.0103\n", + "8/8 - 0s - loss: 8.3113e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 932/1000\n", - "8/8 - 0s - loss: 1.4021e-04 - root_mean_squared_error: 0.0118 - val_loss: 6.8212e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 9.6266e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 933/1000\n", - "8/8 - 0s - loss: 1.7305e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.6875e-04 - val_root_mean_squared_error: 0.0130\n", + "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 934/1000\n", - "8/8 - 0s - loss: 1.9993e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.0330e-04 - val_root_mean_squared_error: 0.0143\n", + "8/8 - 0s - loss: 8.8811e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 935/1000\n", - "8/8 - 0s - loss: 2.0723e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.0238e-04 - val_root_mean_squared_error: 0.0101\n", + "8/8 - 0s - loss: 9.5490e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 936/1000\n", - "8/8 - 0s - loss: 2.7884e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.5846e-04 - val_root_mean_squared_error: 0.0161\n", + "8/8 - 0s - loss: 8.6896e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 937/1000\n", - "8/8 - 0s - loss: 2.8653e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.6098e-04 - val_root_mean_squared_error: 0.0162\n", + "8/8 - 0s - loss: 8.2492e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 938/1000\n", - "8/8 - 0s - loss: 2.8773e-04 - root_mean_squared_error: 0.0170 - val_loss: 2.6426e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 9.1933e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 939/1000\n", - "8/8 - 0s - loss: 1.9027e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1599e-04 - val_root_mean_squared_error: 0.0147\n", + "8/8 - 0s - loss: 9.4457e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 940/1000\n", - "8/8 - 0s - loss: 1.7609e-04 - root_mean_squared_error: 0.0133 - val_loss: 9.2419e-05 - val_root_mean_squared_error: 0.0096\n", + "8/8 - 0s - loss: 7.8915e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 941/1000\n", - "8/8 - 0s - loss: 1.6006e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7046e-04 - val_root_mean_squared_error: 0.0131\n", + "8/8 - 0s - loss: 9.9867e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 942/1000\n", - "8/8 - 0s - loss: 2.0224e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.8646e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 8.4249e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0402\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 943/1000\n", - "8/8 - 0s - loss: 2.4453e-04 - root_mean_squared_error: 0.0156 - val_loss: 5.6799e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 8.0754e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 944/1000\n", - "8/8 - 0s - loss: 2.6308e-04 - root_mean_squared_error: 0.0162 - val_loss: 1.6669e-04 - val_root_mean_squared_error: 0.0129\n", + "8/8 - 0s - loss: 8.1272e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 945/1000\n", - "8/8 - 0s - loss: 2.8116e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.2924e-04 - val_root_mean_squared_error: 0.0207\n", + "8/8 - 0s - loss: 8.0306e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 946/1000\n", - "8/8 - 0s - loss: 4.5011e-04 - root_mean_squared_error: 0.0212 - val_loss: 1.8382e-04 - val_root_mean_squared_error: 0.0136\n", + "8/8 - 0s - loss: 8.9517e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 947/1000\n", - "8/8 - 0s - loss: 3.9589e-04 - root_mean_squared_error: 0.0199 - val_loss: 4.4224e-04 - val_root_mean_squared_error: 0.0210\n", + "8/8 - 0s - loss: 9.6029e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 948/1000\n", - "8/8 - 0s - loss: 3.3970e-04 - root_mean_squared_error: 0.0184 - val_loss: 3.4316e-04 - val_root_mean_squared_error: 0.0185\n", + "8/8 - 0s - loss: 8.0193e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 949/1000\n", - "8/8 - 0s - loss: 2.8735e-04 - root_mean_squared_error: 0.0170 - val_loss: 2.6667e-04 - val_root_mean_squared_error: 0.0163\n", + "8/8 - 0s - loss: 8.1847e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 950/1000\n", - "8/8 - 0s - loss: 3.0669e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.0563e-04 - val_root_mean_squared_error: 0.0175\n", + "8/8 - 0s - loss: 7.4393e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 951/1000\n", - "8/8 - 0s - loss: 3.0172e-04 - root_mean_squared_error: 0.0174 - val_loss: 2.8020e-04 - val_root_mean_squared_error: 0.0167\n", + "8/8 - 0s - loss: 7.5228e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 952/1000\n", - "8/8 - 0s - loss: 4.5701e-04 - root_mean_squared_error: 0.0214 - val_loss: 3.6303e-04 - val_root_mean_squared_error: 0.0191\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 8.8043e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 953/1000\n", - "8/8 - 0s - loss: 3.0605e-04 - root_mean_squared_error: 0.0175 - val_loss: 1.8860e-04 - val_root_mean_squared_error: 0.0137\n", + "8/8 - 0s - loss: 7.3083e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 954/1000\n", - "8/8 - 0s - loss: 2.9196e-04 - root_mean_squared_error: 0.0171 - val_loss: 1.5564e-04 - val_root_mean_squared_error: 0.0125\n", + "8/8 - 0s - loss: 7.1996e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 955/1000\n", - "8/8 - 0s - loss: 2.2637e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.1289e-04 - val_root_mean_squared_error: 0.0146\n", + "8/8 - 0s - loss: 6.8546e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 956/1000\n", - "8/8 - 0s - loss: 1.4026e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5792e-04 - val_root_mean_squared_error: 0.0126\n", + "8/8 - 0s - loss: 6.7724e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 957/1000\n", - "8/8 - 0s - loss: 9.2768e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.4083e-05 - val_root_mean_squared_error: 0.0080\n", + "8/8 - 0s - loss: 7.2465e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 958/1000\n", - "8/8 - 0s - loss: 5.9903e-05 - root_mean_squared_error: 0.0077 - val_loss: 6.9051e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 6.8626e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 959/1000\n", - "8/8 - 0s - loss: 3.0673e-05 - root_mean_squared_error: 0.0055 - val_loss: 4.7544e-05 - val_root_mean_squared_error: 0.0069\n", + "8/8 - 0s - loss: 6.3241e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 960/1000\n", - "8/8 - 0s - loss: 2.4833e-05 - root_mean_squared_error: 0.0050 - val_loss: 3.1311e-05 - val_root_mean_squared_error: 0.0056\n", + "8/8 - 0s - loss: 6.5268e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 961/1000\n", - "8/8 - 0s - loss: 2.1703e-05 - root_mean_squared_error: 0.0047 - val_loss: 2.7588e-05 - val_root_mean_squared_error: 0.0053\n", + "8/8 - 0s - loss: 6.4749e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 962/1000\n", - "8/8 - 0s - loss: 2.1461e-05 - root_mean_squared_error: 0.0046 - val_loss: 2.4802e-05 - val_root_mean_squared_error: 0.0050\n", + "8/8 - 0s - loss: 6.4312e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 963/1000\n", - "8/8 - 0s - loss: 2.3103e-05 - root_mean_squared_error: 0.0048 - val_loss: 2.6911e-05 - val_root_mean_squared_error: 0.0052\n", + "8/8 - 0s - loss: 6.4407e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 964/1000\n", - "8/8 - 0s - loss: 2.7573e-05 - root_mean_squared_error: 0.0053 - val_loss: 3.8487e-05 - val_root_mean_squared_error: 0.0062\n", + "8/8 - 0s - loss: 5.5230e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 965/1000\n", - "8/8 - 0s - loss: 3.4415e-05 - root_mean_squared_error: 0.0059 - val_loss: 2.8194e-05 - val_root_mean_squared_error: 0.0053\n", + "8/8 - 0s - loss: 5.9374e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 966/1000\n", - "8/8 - 0s - loss: 3.9314e-05 - root_mean_squared_error: 0.0063 - val_loss: 3.1214e-05 - val_root_mean_squared_error: 0.0056\n", + "8/8 - 0s - loss: 6.0984e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 967/1000\n", - "8/8 - 0s - loss: 4.4508e-05 - root_mean_squared_error: 0.0067 - val_loss: 4.5350e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 6.6604e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 968/1000\n", - "8/8 - 0s - loss: 5.0022e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.6965e-05 - val_root_mean_squared_error: 0.0069\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "8/8 - 0s - loss: 6.2296e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Epoch 969/1000\n", - "8/8 - 0s - loss: 5.0641e-05 - root_mean_squared_error: 0.0071 - val_loss: 5.7197e-05 - val_root_mean_squared_error: 0.0076\n", + "8/8 - 0s - loss: 5.5552e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 970/1000\n", - "8/8 - 0s - loss: 6.3706e-05 - root_mean_squared_error: 0.0080 - val_loss: 2.6452e-05 - val_root_mean_squared_error: 0.0051\n", + "8/8 - 0s - loss: 5.4448e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 971/1000\n", - "8/8 - 0s - loss: 6.6385e-05 - root_mean_squared_error: 0.0081 - val_loss: 5.2159e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 5.5418e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 972/1000\n", - "8/8 - 0s - loss: 6.9201e-05 - root_mean_squared_error: 0.0083 - val_loss: 9.0101e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 6.2532e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 973/1000\n", - "8/8 - 0s - loss: 8.0140e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.0837e-05 - val_root_mean_squared_error: 0.0078\n", + "8/8 - 0s - loss: 7.0696e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 974/1000\n", - "8/8 - 0s - loss: 6.4610e-05 - root_mean_squared_error: 0.0080 - val_loss: 5.2502e-05 - val_root_mean_squared_error: 0.0072\n", + "8/8 - 0s - loss: 5.8880e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 975/1000\n", - "8/8 - 0s - loss: 6.7077e-05 - root_mean_squared_error: 0.0082 - val_loss: 4.1545e-05 - val_root_mean_squared_error: 0.0064\n", + "8/8 - 0s - loss: 5.7173e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 976/1000\n", - "8/8 - 0s - loss: 8.1803e-05 - root_mean_squared_error: 0.0090 - val_loss: 7.4052e-05 - val_root_mean_squared_error: 0.0086\n", + "8/8 - 0s - loss: 5.6544e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 977/1000\n", - "8/8 - 0s - loss: 1.0355e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.1820e-04 - val_root_mean_squared_error: 0.0109\n", + "8/8 - 0s - loss: 5.3618e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 978/1000\n", - "8/8 - 0s - loss: 1.4746e-04 - root_mean_squared_error: 0.0121 - val_loss: 7.2804e-05 - val_root_mean_squared_error: 0.0085\n", + "8/8 - 0s - loss: 6.7194e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 979/1000\n", - "8/8 - 0s - loss: 1.3123e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.1702e-04 - val_root_mean_squared_error: 0.0108\n", + "8/8 - 0s - loss: 6.6079e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 980/1000\n", - "8/8 - 0s - loss: 1.2515e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.4411e-04 - val_root_mean_squared_error: 0.0120\n", + "8/8 - 0s - loss: 5.9953e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 981/1000\n", - "8/8 - 0s - loss: 1.2661e-04 - root_mean_squared_error: 0.0113 - val_loss: 7.6928e-05 - val_root_mean_squared_error: 0.0088\n", + "8/8 - 0s - loss: 6.0885e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0339\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 982/1000\n", - "8/8 - 0s - loss: 6.8180e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.4679e-05 - val_root_mean_squared_error: 0.0080\n", + "8/8 - 0s - loss: 6.0368e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0349\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 983/1000\n", - "8/8 - 0s - loss: 5.4304e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.8452e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 5.8867e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 984/1000\n", - "8/8 - 0s - loss: 5.9816e-05 - root_mean_squared_error: 0.0077 - val_loss: 5.6751e-05 - val_root_mean_squared_error: 0.0075\n", + "8/8 - 0s - loss: 7.2765e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 985/1000\n", - "8/8 - 0s - loss: 8.4292e-05 - root_mean_squared_error: 0.0092 - val_loss: 9.0957e-05 - val_root_mean_squared_error: 0.0095\n", + "8/8 - 0s - loss: 6.5310e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 986/1000\n", - "8/8 - 0s - loss: 1.2467e-04 - root_mean_squared_error: 0.0112 - val_loss: 8.0552e-05 - val_root_mean_squared_error: 0.0090\n", + "8/8 - 0s - loss: 6.7090e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 987/1000\n", - "8/8 - 0s - loss: 1.1125e-04 - root_mean_squared_error: 0.0105 - val_loss: 9.8476e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 6.6311e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 988/1000\n", - "8/8 - 0s - loss: 1.1176e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.2346e-04 - val_root_mean_squared_error: 0.0111\n", + "8/8 - 0s - loss: 6.7161e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 989/1000\n", - "8/8 - 0s - loss: 1.1235e-04 - root_mean_squared_error: 0.0106 - val_loss: 8.6506e-05 - val_root_mean_squared_error: 0.0093\n", + "8/8 - 0s - loss: 7.7622e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 990/1000\n", - "8/8 - 0s - loss: 6.2669e-05 - root_mean_squared_error: 0.0079 - val_loss: 7.3998e-05 - val_root_mean_squared_error: 0.0086\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "8/8 - 0s - loss: 8.6178e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 991/1000\n", - "8/8 - 0s - loss: 5.0656e-05 - root_mean_squared_error: 0.0071 - val_loss: 4.6262e-05 - val_root_mean_squared_error: 0.0068\n", + "8/8 - 0s - loss: 7.6753e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 992/1000\n", - "8/8 - 0s - loss: 5.6202e-05 - root_mean_squared_error: 0.0075 - val_loss: 4.5314e-05 - val_root_mean_squared_error: 0.0067\n", + "8/8 - 0s - loss: 7.8977e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 993/1000\n", - "8/8 - 0s - loss: 7.1259e-05 - root_mean_squared_error: 0.0084 - val_loss: 8.3427e-05 - val_root_mean_squared_error: 0.0091\n", + "8/8 - 0s - loss: 7.2666e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 994/1000\n", - "8/8 - 0s - loss: 9.3099e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.9029e-05 - val_root_mean_squared_error: 0.0083\n", + "8/8 - 0s - loss: 6.1394e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 995/1000\n", - "8/8 - 0s - loss: 8.1970e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.3711e-05 - val_root_mean_squared_error: 0.0080\n", + "8/8 - 0s - loss: 7.5353e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 996/1000\n", - "8/8 - 0s - loss: 8.3448e-05 - root_mean_squared_error: 0.0091 - val_loss: 9.7956e-05 - val_root_mean_squared_error: 0.0099\n", + "8/8 - 0s - loss: 6.7547e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 997/1000\n", - "8/8 - 0s - loss: 9.7425e-05 - root_mean_squared_error: 0.0099 - val_loss: 9.5813e-05 - val_root_mean_squared_error: 0.0098\n", + "8/8 - 0s - loss: 6.4319e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 998/1000\n", - "8/8 - 0s - loss: 6.4177e-05 - root_mean_squared_error: 0.0080 - val_loss: 7.0199e-05 - val_root_mean_squared_error: 0.0084\n", + "8/8 - 0s - loss: 6.1533e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 999/1000\n", - "8/8 - 0s - loss: 5.4054e-05 - root_mean_squared_error: 0.0074 - val_loss: 4.3619e-05 - val_root_mean_squared_error: 0.0066\n", + "8/8 - 0s - loss: 6.0413e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 1000/1000\n", - "8/8 - 0s - loss: 6.4858e-05 - root_mean_squared_error: 0.0081 - val_loss: 4.9654e-05 - val_root_mean_squared_error: 0.0070\n", + "8/8 - 0s - loss: 6.5935e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5627,10 +5470,10 @@ "# design network\n", "model = Sequential()\n", "model.add(SimpleRNN(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n", - "model.add(SimpleRNN(50, return_sequences=True))\n", - "model.add(SimpleRNN(50, return_sequences=True))\n", + "# model.add(SimpleRNN(50, return_sequences=True))\n", + "# model.add(SimpleRNN(50, return_sequences=True))\n", "model.add(SimpleRNN(1))\n", - "model.add(Dense(1))\n", + "# model.add(Dense(1))\n", "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", "# fit network\n", "# \n", @@ -5644,38 +5487,52 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 456, "metadata": {}, "outputs": [], "source": [ "# make a prediction\n", "yhat = model.predict(test_X)\n", - "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))" + "train_yhat = model.predict(train_X)\n" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 457, + "metadata": {}, + "outputs": [], + "source": [ + "test_X = test_X.reshape((test_X.shape[0], n_months*n_features))\n", + "train_X = train_X.reshape((train_X.shape[0], n_months*n_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 458, "metadata": {}, "outputs": [], "source": [ "# invert scaling for forecast\n", - "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", - "inv_yhat = scaler.inverse_transform(inv_yhat)\n", - "inv_yhat = inv_yhat[:,0]\n", + "inv_yhat_train = concatenate((train_yhat, train_X[:, -5:]), axis=1)\n", + "inv_yhat_train = scaler.inverse_transform(inv_yhat_train)\n", + "inv_yhat_train = inv_yhat_train[:,0]\n", "# invert scaling for actual\n", - "test_y = test_y.reshape((len(test_y), 1))\n", - "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", - "inv_y = scaler.inverse_transform(inv_y)\n", - "inv_y = inv_y[:,0]" + "train_y = train_y.reshape((len(train_y), 1))\n", + "inv_y_train = concatenate((train_y, train_X[:, -5:]), axis=1)\n", + "inv_y_train = scaler.inverse_transform(inv_y_train)\n", + "inv_y_train = inv_y_train[:,0]" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 459, "metadata": {}, "outputs": [], "source": [ + "# invert scaling for forecast\n", + "inv_yhat = concatenate((yhat, test_X[:, -5:]), axis=1)\n", + "inv_yhat = scaler.inverse_transform(inv_yhat)\n", + "inv_yhat = inv_yhat[:,0]\n", "# invert scaling for actual\n", "test_y = test_y.reshape((len(test_y), 1))\n", "inv_y = concatenate((test_y, test_X[:, -5:]), axis=1)\n", @@ -5685,14 +5542,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 79, + "execution_count": 460, "metadata": {}, "outputs": [], "source": [ @@ -5726,12 +5576,12 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 461, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5745,7 +5595,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 77656.691868763.\n" + "The test root mean squared error is 87677.129880032.\n" ] } ], @@ -5756,12 +5606,42 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 462, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test root mean squared error is 17301.78071760245.\n" + ] + } + ], + "source": [ + "plot_predictions(inv_y_train, inv_yhat_train)\n", + "return_rmse(inv_y_train, inv_yhat_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 463, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5778,7 +5658,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 464, "metadata": {}, "outputs": [ { @@ -5786,10 +5666,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 665158\n", - "1 260097\n", - "2 372892\n", - "3 280885\n", + "0 805148\n", + "1 223192\n", + "2 318407\n", + "3 357216\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5807,7 +5687,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 465, "metadata": {}, "outputs": [ { @@ -5826,14 +5706,14 @@ "bs_chris_path = '/Users/chrisshell/Desktop/Stanford/SalmonData/Use Data/Forecast Data Update.csv'\n", "bs_ismael_path = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/forecast_data_17_20.csv'\n", "bs_abdul_path = '/Users/abdul/Downloads/SalmonNet/Forecast Data Update.csv'\n", - "baseline_data = pd.read_csv(bs_abdul_path)\n", + "baseline_data = pd.read_csv(bs_ismael_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 466, "metadata": {}, "outputs": [ { @@ -5850,14 +5730,14 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 467, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 159559.67324170604.\n" + "The test root mean squared error is 192731.806527231.\n" ] } ], @@ -5865,154 +5745,6 @@ "return_rmse(actual, preds)" ] }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# def create_train_test(king_all):\n", - "# king_training_parse = king_all['date'].apply(pd.Timestamp) <= pd.Timestamp('12/31/2015')\n", - "# king_training = king_all[king_training_parse]\n", - "# king_training = king_training.reset_index()\n", - "# king_training = king_training.drop('index', axis=1)\n", - " \n", - "# king_test_parse = king_all['date'].apply(pd.Timestamp) > pd.Timestamp('12/31/2015')\n", - "# king_test = king_all[king_test_parse]\n", - "# king_test = king_test.reset_index()\n", - "# king_test = king_test.drop('index', axis=1)\n", - "# print(king_test.shape)\n", - " \n", - "# # Normalizing Data\n", - "# king_training[king_training[\"king\"] < 0] = 0 \n", - "# # print('max val king_train:')\n", - "# print(max(king_training['king']))\n", - "# king_test[king_test[\"king\"] < 0] = 0\n", - "# # print('max val king_test:')\n", - "# print(max(king_test['king']))\n", - "# king_train_pre = king_training[\"king\"].to_frame()\n", - "# # print(king_train_norm)\n", - "# king_test_pre = king_test[\"king\"].to_frame()\n", - "# scaler = MinMaxScaler(feature_range=(0, 1))\n", - "# king_train_norm = scaler.fit_transform(king_train_pre)\n", - "# king_test_norm = scaler.fit_transform(king_test_pre)\n", - "# print('king_test_norm')\n", - "# print(king_test_norm.shape)\n", - "# print('king_train_norm')\n", - "# print(king_train_norm.shape)\n", - "# #king_train_norm = (king_training[\"king\"] - np.min(king_training[\"king\"])) / (np.max(king_training[\"king\"]) - np.min(king_training[\"king\"]))\n", - "# #print(type(king_train_norm))\n", - "# #king_train_norm = king_train_norm.to_frame()\n", - "# x_train = []\n", - "# y_train = []\n", - "# x_test = []\n", - "# y_test = []\n", - "# y_test_not_norm = []\n", - "# y_train_not_norm = []\n", - " \n", - "# # Todo: Experiment with input size of input (ex. 30 days)\n", - " \n", - "# for i in range(6,924): # 30\n", - "# x_train.append(king_train_norm[i-6:i])\n", - "# y_train.append(king_train_norm[i])\n", - "# for i in range(6, 60):\n", - "# x_test.append(king_test_norm[i-6:i])\n", - "# y_test.append(king_test_norm[i])\n", - " \n", - "# # make y_test_not_norm\n", - "# for i in range(6, 60):\n", - "# y_test_not_norm.append(king_test['king'][i])\n", - "# for i in range(6,924): # 30\n", - "# y_train_not_norm.append(king_training['king'][i])\n", - " \n", - "# return x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'create_train_test' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test_not_norm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train_not_norm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_train_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_copy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mx_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mx_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'create_train_test' is not defined" - ] - } - ], - "source": [ - "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", - "x_train = np.array(x_train)\n", - "x_test = np.array(x_test)\n", - "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)).astype(np.float32)\n", - "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n", - "y_train = np.array(y_train)\n", - "y_test = np.array(y_test)\n", - "y_test_not_norm = np.array(y_test_not_norm)\n", - "print(y_test.shape)\n", - "y_test_not_norm = y_test_not_norm.reshape((y_test_not_norm.shape[0], 1))\n", - "print(y_test_not_norm.shape)\n", - "y_train_not_norm = np.array(y_train_not_norm)\n", - "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n", - "print(y_train_not_norm.shape)\n", - "print(y_train.shape)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# def load_pdo(pathname):\n", - "# pdo_data = pd.read_csv(pathname)\n", - "# # print(pdo_data.head())\n", - "# return pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ismael_path_pdo = '/Users/ismaelcastro/Documents/Computer Science/CS Classes/CS230/project/pdo.csv'\n", - "# pdo_data = load_pdo(ismael_path_pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo = pdo_data[\"PDO\"]\n", - "# data_copy = data_copy.join(pdo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pdo_data" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "# print(data_copy)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -6044,7 +5776,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4,