diff --git a/.ipynb_checkpoints/daily_nn-checkpoint.ipynb b/.ipynb_checkpoints/daily_nn-checkpoint.ipynb index 36478c1..c116f92 100644 --- a/.ipynb_checkpoints/daily_nn-checkpoint.ipynb +++ b/.ipynb_checkpoints/daily_nn-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -16,24 +16,20 @@ "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", "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": 8, + "execution_count": 5, "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", @@ -41,7 +37,6 @@ " 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": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -103,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -121,20 +116,14 @@ " \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", + " # Create lists to be filled \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -142,8 +131,7 @@ " 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(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", " y_train.append(king_train_norm[i])\n", @@ -162,23 +150,14 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 8, "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" + "(1824, 2)\n" ] } ], @@ -190,19 +169,17 @@ "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", + "\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": 107, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -284,112 +261,100 @@ "text": [ "(22365, 180)\n", "Epoch 1/25\n", - "224/224 [==============================] - 1s 2ms/step - loss: 0.0015\n", + "224/224 [==============================] - 2s 2ms/step - loss: 9.6369e-04\n", "Epoch 2/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 5.2724e-04\n", + "224/224 [==============================] - 1s 2ms/step - loss: 4.2687e-04\n", "Epoch 3/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.3932e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 3.5946e-04\n", "Epoch 4/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.1974e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 3.0601e-04\n", "Epoch 5/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.1543e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 2.5082e-04\n", "Epoch 6/25\n", - "224/224 [==============================] - 0s 1ms/step - loss: 2.8840e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5926e-04\n", "Epoch 7/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4203e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.6381e-04\n", "Epoch 8/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.6688e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 2.7281e-04\n", "Epoch 9/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4260e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.7279e-04\n", "Epoch 10/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.3346e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.7465e-04\n", "Epoch 11/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2799e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 3.1394e-04\n", "Epoch 12/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.7529e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5299e-04\n", "Epoch 13/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4860e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3594e-04\n", "Epoch 14/25\n", - "224/224 [==============================] - 1s 2ms/step - loss: 2.6579e-04\n", + "224/224 [==============================] - 1s 2ms/step - loss: 2.4796e-04\n", "Epoch 15/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.6130e-04A: 0s - loss: 2.6334e-\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3770e-04\n", "Epoch 16/25\n", - "224/224 [==============================] - 1s 3ms/step - loss: 2.5171e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3713e-04\n", "Epoch 17/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5540e-04\n", + "224/224 [==============================] - 1s 3ms/step - loss: 2.4587e-04A: 0s - loss: 2.418\n", "Epoch 18/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5533e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3999e-04\n", "Epoch 19/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2185e-04\n", + "224/224 [==============================] - 1s 3ms/step - loss: 2.1717e-04\n", "Epoch 20/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2341e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.2080e-04\n", "Epoch 21/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.3676e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.1802e-04\n", "Epoch 22/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2324e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5899e-04\n", "Epoch 23/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.0010e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.0676e-04\n", "Epoch 24/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5782e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 1.9265e-04\n", "Epoch 25/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.1594e-04\n" + "224/224 [==============================] - 0s 2ms/step - loss: 2.4804e-04\n" ] } ], "source": [ - "# train single_layer_rnn_model\n", + "# train nn_model\n", "print(x_train.shape)\n", "model, nn_train_preds, nn_test_preds, history_nn, y_train, y_test = create_nn_model(x_train, y_train, x_test, y_test, scaler)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# global var for baseline\n", - "y_test_year = day_to_year(y_test)" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "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_abdul_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)" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 12, "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 478.7304444895119.\n" + "The root mean squared error is 454.5654304135976.\n" ] } ], @@ -401,24 +366,26 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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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 1417.8878610870483.\n" + "The root mean squared error is 1400.910400461662.\n" ] } ], @@ -429,17 +396,19 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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vM7Mqwme/yNA06l9E8l22ZzJnAtvc/aCZvR/4DHAoumpNDxr1LyL5LtuQuQloM7OTgL8DdgK3R1araSJVHkySGTybTUQk/2QbMt3hUyxXA99w928AieiqNT0kZ8bp6E7T1N6d66qIiOREtiHTbGafAj4A3GdmMaAoumpND70DMvVcGRHJV9mGzHuADoLxMnuBBcA/R1araSKpqWVEJM9lFTJhsHwfmGVmbwPa3V3XZIahUf8iku+ynVbm3cDjwKXAu4E/mNm7oqzYdHC4u0whIyL5KdtxMn8PnOrudQBmlgR+Cfw4qopNB4l4ISVFBbqNWUTyVrbXZAp6AybUOIKyecvMSCU06l9E8le2ZzI/N7P7gR+Gn98DrIumStNLKhHXhX8RyVtZhYy7f8LMLgFeRzAx5i3ufk+kNZsmUuVxtu1tznU1RERyItszGdz9buDuCOsyLaUSJfzuuYZcV0NEJCcyhoyZNQODzYligLt7eSS1mkaSiTjN7d20d/VQUhTLdXVERCZUxpBxd00dM0apfgMyF88uzXFtREQmlu4Qi1iqvHdApm5jFpH8o5CJWN+ZjG5jFpE8pJCJ2OHuMp3JiEj+UchErLK0mMIC05mMiOQlhUzECgqMpB7DLCJ5KtKQMbMLzGybmdWY2XWDbDczuyHcvtnMVg5X1syqzOwBM3sufK8ccMzFZtZiZh+Psm0joZARkXwVWciEDza7EbgQWA5cbmbLB+x2IbAsfF1D8Jjn4cpeBzzo7suAB8PP/X0N+Nm4N2gMUom4ZmIWkbwU5ZnMaUCNu293907gToLHN/e3GrjdA48BFWY2f5iyq4HbwuXbgIt7D2ZmFwPbgS1RNWo0kokSPR1TRPJSlCGzAHip3+facF02+2QqO9fd9wCE7ykAMysDPgn8Y6ZKmdk1ZrbBzDbU19ePqEGjlUrEaWztpLsnPSHfJyIyWUQZMjbIuoFT1Ay1TzZlB/pH4Gvu3pJpJ3e/xd1XufuqZDI5zCHHR6o8jjs0tHROyPeJiEwWWU+QOQq1wKJ+nxcCu7PcpzhD2X1mNt/d94Rda73PuTkdeJeZfQWoANJm1u7u3xqX1ozB4ccwtzNvVkmOayMiMnGiPJNZDywzs6VmVgxcBqwdsM9a4IrwLrMzgENhF1imsmuBK8PlK4F7Adz9HHevdvdq4OvAFydDwMCR85eJiOSTyM5k3L3bzK4F7gdiwK3uvsXM1oTbbyZ48NlFQA3QBlyVqWx46OuBu8zsauBF4NKo2jBeUuWaWkZE8lOU3WW4+zoGPEEzDJfeZQc+km3ZcH0jcN4w3/u5UVQ3MnNmxjHTJJkikn804n8CFMUKqCot1pmMiOQdhcwESSbiuiYjInlHITNBUuUakCki+UchM0FSmr9MRPKQQmaCLKosZV9TO8/ua851VUREJoxCZoJ84MwllM8o4u/veYp0erjJC0REpgeFzASpKivm0xcez/odB/jRxpeGLyAiMg0oZCbQpasWclp1FV/62TM0tuj6jIhMfwqZCWRmfOEdK2jt6OYL67bmujoiIpFTyEywZXMTXHPu0fxk0y4eeb4h19UREYmUQiYHPvrGZSyuKuUzP32aju6eXFdHRCQyCpkcKCmK8U8Xr2B7fSvffmh7rqsjIhIZhUyOvP7YJG87cT7f+nUNLzS05ro6IiKRUMjk0P9923LisQI++9OnCSakFhGZXhQyOZQqL+HvLjiO39c0sPbJgQ8NFRGZ+hQyOfbe05dw0qIK/um//8Shtq5cV0dEZFwpZHIsVmB84eIV7G/t5Mv3P5Pr6oiIjCuFzCSwYsEsrnrdUn7whxfZuPNArqsjIjJuFDKTxN+8+Vjmzyrh7+95iq6edK6rIyIyLhQyk0RZvJDP/fkJPLO3mVt//0KuqyMiMi4UMpPIW06Yx5uOn8vXf/kctQfacl0dEZExU8hMMv+4+gTM4B/u3aKxMyIy5SlkJpkFFTP4P286lgefqeP+LftyXR0RkTFRyExCV72umuPnl/O5tVto6ejOdXVEREZNITMJFcYK+OI7VrCvuZ1P/ngz7V2aqVlEpiaFzCR1yuJKPnnBq7nvqT286+ZHdCOAiExJCplJbM3rj+H/X7GKnY1tvP2bv+d3z9XnukoiIiOikJnk3rR8LmuvPZtUooQrb32cf/tNje46E5EpQyEzBSydU8ZPPnwWF71mPl/5+Tb+8o5NNLdrMk0RmfwUMlNEWbyQb15+Cp956/E8sHUfF9/4MDV1LbmulohIRgqZKcTM+N/nHM0dV5/OwbYuLr7xYX7+9N5cV0tEZEgKmSnozGNm898fO5tjUjNZc8dGvvzzZ+hJ6zqNiEw+Cpkpav6sGdz1oTN47+mLuek3z/PB7z7O/tbOXFdLROQICpkpLF4Y44vveA1fvuQ1/OGF/bz9m7/n6V2Hcl0tEZE+kYaMmV1gZtvMrMbMrhtku5nZDeH2zWa2criyZlZlZg+Y2XPhe2W4/jQzeyJ8PWlm74iybZPJe05dzI8+dCbuzjtveoTvPbpDtzmLyKQQWciYWQy4EbgQWA5cbmbLB+x2IbAsfF0D3JRF2euAB919GfBg+BngaWCVu58MXAB828wKI2repHPSogr+66Nnc+bRs/nsvVu46j/WU9fcnutqiUiei/JM5jSgxt23u3sncCewesA+q4HbPfAYUGFm84cpuxq4LVy+DbgYwN3b3L13NskSIO/+Kz97Zpz/uOpUPr/6BB59vpG3fO23uvtMRHIqypBZALzU73NtuC6bfTKVnevuewDC91TvTmZ2upltAZ4C1vQLHfrtc42ZbTCzDfX102+aFjPjijOrue9j57CwspQ1d2zkEz96UrM5i0hORBkyNsi6gWcXQ+2TTdlX7uD+B3c/ATgV+JSZlQyyzy3uvsrdVyWTyeEOOWW9KjWTu//yLK79s1dx96ZaLvrG79i4c3+uqyUieSbKkKkFFvX7vBDYneU+mcruC7vUCN/rBn6xu28FWoEVY6j/lFdcWMDH33Icd33oTBzn0psf5V/u30ZXTzrXVRORPBFlyKwHlpnZUjMrBi4D1g7YZy1wRXiX2RnAobALLFPZtcCV4fKVwL0A4b6F4fIS4DhgR2Stm0JWVVex7mPncMnKhXzr1zW8898e0ZQ0IjIhIguZ8HrItcD9wFbgLnffYmZrzGxNuNs6YDtQA/w78OFMZcMy1wNvNrPngDeHnwHOBp40syeAe4APu3tDVO2bahIlRfzzpSdx8/tXUnugjbd983e61VlEImf5/I/MqlWrfMOGDbmuxoSra2rnEz/ezEPP1vOG45J85ZITSZW/4vKViMigzGyju6/Kal+FTP6FDIC7873HdvKF+7bS0Z3GDAoLjAIzCguMWN+r4IjPvcvHzy/nqtdVc8riylw3RUQm2EhCJm8GK8qRem91ft2r5rBu8x66etJ0p52e8NW37E5PT/A57cF7Z3cPv95Wx9ond3PK4gquPnspF5wwj8KYZikSkSPpTCZPz2TGqqWjm7s31vLdh19gR2MbR80q4cqzqrns1MXMKi3KdfVEJELqLsuSQmbsetLOr56p49bfv8Cj2xspLY7xrtcu5INnVXN0cmauqyciEVDIZEkhM7627D7Edx/ewdondtOVTvPG41JcffZSzjxmNmaDja8VkalIIZMlhUw06prbueOxF/n+YztpbO3k1fMSfPCsalLlcVo7eni5s4fWzm7aOnto6+w+Yl3/bcWxAi5YMY/VJy8gmYjnulkiElLIZEkhE632rh7WPrGbWx9+gWf2Ng+6T6zAKC2OUVZcSGlxjNJ4jNJwuaGlg6d3NRErMF5/bJJLVi7kvONTlBTFJrglItKfQiZLCpmJ4e5s2d1Ed9opK44xozdU4jGKYwUZu9Ke29fM3Zt28dM/7mJvUzvlJYW87aSjuGTlQlYurlA3nEgOKGSypJCZOnrSziPPN/CTTbv42dN7aO9Ks3ROGe88ZQHvWLmAhZWlE1KP9q4emtq7KC8p0hmV5C2FTJYUMlNTS0c3657aw0821fLY9mBm6TOOruKSlQs5felsigsLKC4soChmwfIwZ0sAXT1pGlo62NfUwd5D7dQ1t7OvqZ19TR3he7B86OWuvjJlxTEqy4qpKiumorSYqtKi4HNpcd/6ytJiKsuKWFJVxoxihZJMDwqZLClkpr6X9rfx0z/u4u5NtexobBtyv6KYURQr6AudolgB8cICYgXGgbYuGls7GPhXIVZgpBJxUuUlzE3EmVtewrxZJZSXFNLU3s3+1k4OtHayv63/e9egz+6pLC3iY+ct432nL6G4UINWZWpTyGRJITN9uDt/fOkg2+tb6exO09WTprM7TWf43v9zV0+aju40XT1OV3eayrIiUokS5paXMLc8Hr6XUFVWTKxg5Nd8OrvTHGwLQmd/aycNLZ385/oXebimkerZpVx34at5ywnzdD1JpiyFTJYUMjJR3J3fPFvPF+/bynN1LaxaUsmn33o8KzX3m0xBIwkZnbeLTAAz48+OS/GzvzqHL73zNezc38Y7/+0RPvKDTbyYoZtPZKrTmYzOZCQHWju6ueW327nlt9vpTqe54sxqPvrGV1FRWpzrqokMS91lWVLISK7ta2rnq794lh9tfImZ8UI+dt4yPnDmEuKFQ9+J1tTexYuNbexobGVnYxs7w/f6lg4qS4tJzowzJ1FMcmYJyUT8iNecmcUZjy2SDYVMlhQyMlk8s7eJL617hoeerWdR1Qw+fv5xLKws7QuQnY2t7Nzfxs7GNva3dh5RNpmIs6SqlLnlJRx8uZP65g7qmzs40NY16HeVlxT2hc6iylJOXFTByQsrOG5eQne+SVYUMllSyMhk89tn6/niuq1HTMNjBkfNmkH1nFIWV5VRPbuUJbNLWTK7jMVVpZTFB38sVGd3msbWDhqaO6lvae8Ln/rmDhpaOqlrbmd7fSuNYWgVFxawfH45Jy+q4MSFszhpUQVLZ5dRMIo77GR6U8hkSSEjk1FP2vnNtjr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" - ], - "text/plain": [ - " Count\n", - "0 461289\n", - "1 318556\n", - "2 361129\n", - "3 506459" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# RNN_test_year = day_to_year(RNN_test_preds)\n", - "# RNN_test_year" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 39, + "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 24270.60509134455.\n" - ] - } - ], - "source": [ - "# # test RMSE with baseline and RNN\n", - "# return_rmse(y_test_year, traditional)\n", - "# return_rmse(y_test_year, RNN_test_year)" - ] + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/.ipynb_checkpoints/monthly_nn-checkpoint.ipynb b/.ipynb_checkpoints/monthly_nn-checkpoint.ipynb new file mode 100644 index 0000000..2478a55 --- /dev/null +++ b/.ipynb_checkpoints/monthly_nn-checkpoint.ipynb @@ -0,0 +1,6671 @@ +{ + "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", + "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": 2, + "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['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": 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", + "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(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "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": 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" + ] + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape\n", + "forecast_set = data_copy" + ] + }, + { + "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": 77, + "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", + " \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", + " # create lists to be filled \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": 78, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "\n", + "# Make arrays for train and test \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])).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "\n", + "# Make arrays for y train and test for testing of normalization \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": 79, + "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": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def create_nn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create nn 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(Dense(32, input_dim = (x_train.shape[1]), activation = 'relu'))\n", + " model.add(Dense(16, activation = 'relu'))\n", + " model.add(Dense(8, activation = 'relu'))\n", + " model.add(Dense(1))\n", + " model.compile(loss='mean_squared_error', optimizer = 'adam')\n", + " \n", + " history = model.fit(x_train, y_train, epochs = 3000, batch_size = 100)\n", + " \n", + " # Predictions\n", + " nn_train_predict = model.predict(x_train)\n", + " nn_test_predict = model.predict(x_test)\n", + " \n", + " # Descale \n", + " nn_train_predict = scaler.inverse_transform(nn_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " nn_test_predict = scaler.inverse_transform(nn_test_predict)\n", + " nn_test_predict = nn_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return model, nn_train_predict, nn_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(918, 6)\n", + "Epoch 1/3000\n", + "10/10 [==============================] - 0s 980us/step - loss: 991432680.7273\n", + "Epoch 2/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1120946321.4545\n", + "Epoch 3/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 956586932.3636\n", + "Epoch 4/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 980322176.0000\n", + "Epoch 5/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 734022644.3636\n", + "Epoch 6/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 870754330.1818\n", + "Epoch 7/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1026949143.2727\n", + "Epoch 8/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 964317201.4545\n", + "Epoch 9/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1026203031.2727\n", + "Epoch 10/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 831096314.1818\n", + "Epoch 11/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 905880186.1818\n", + "Epoch 12/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1010551424.0000\n", + "Epoch 13/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 864442644.3636\n", + "Epoch 14/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 851433117.0909\n", + "Epoch 15/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1153174312.7273\n", + "Epoch 16/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1181130711.2727\n", + "Epoch 17/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 937398516.3636\n", + "Epoch 18/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1151585931.6364\n", + "Epoch 19/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 949975674.1818\n", + "Epoch 20/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 959467589.8182\n", + "Epoch 21/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 873340334.5455\n", + "Epoch 22/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1083537303.2727\n", + "Epoch 23/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1003179095.2727\n", + "Epoch 24/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 886005582.5455\n", + "Epoch 25/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1031505402.1818\n", + "Epoch 26/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 995987194.1818\n", + "Epoch 27/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 904443880.7273\n", + "Epoch 28/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 972847115.6364\n", + "Epoch 29/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1065960389.8182\n", + "Epoch 30/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1037026507.6364\n", + "Epoch 31/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 864433122.9091\n", + "Epoch 32/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 935894778.1818\n", + "Epoch 33/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1079479505.4545\n", + "Epoch 34/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1096584770.9091\n", + "Epoch 35/3000\n", + "10/10 [==============================] - 0s 5ms/step - loss: 1205697233.4545\n", + "Epoch 36/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 1060432581.8182\n", + "Epoch 37/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 999479232.0000\n", + "Epoch 38/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1042761093.8182\n", + "Epoch 39/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1045574568.7273\n", + "Epoch 40/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 817358894.5455\n", + "Epoch 41/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 936149178.1818\n", + "Epoch 42/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1000115979.6364\n", + "Epoch 43/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 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[==============================] - 0s 2ms/step - loss: 844480663.2727\n", + "Epoch 54/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 940181474.9091\n", + "Epoch 55/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 898198528.0000\n", + "Epoch 56/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1048184296.7273\n", + "Epoch 57/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1012406341.8182\n", + "Epoch 58/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 832115642.1818\n", + "Epoch 59/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 987409216.0000\n", + "Epoch 60/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1007196386.9091\n", + "Epoch 61/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1186307991.2727\n", + "Epoch 62/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 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1966/3000\n", + "10/10 [==============================] - 0s 944us/step - loss: 411091537.4545\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1967/3000\n", + "10/10 [==============================] - 0s 942us/step - loss: 429936721.4545\n", + "Epoch 1968/3000\n", + "10/10 [==============================] - 0s 965us/step - loss: 419120503.2727\n", + "Epoch 1969/3000\n", + "10/10 [==============================] - 0s 949us/step - loss: 388783640.7273\n", + "Epoch 1970/3000\n", + "10/10 [==============================] - 0s 935us/step - loss: 454318270.5455\n", + "Epoch 1971/3000\n", + "10/10 [==============================] - 0s 930us/step - loss: 370506493.0909\n", + "Epoch 1972/3000\n", + "10/10 [==============================] - 0s 888us/step - loss: 435339749.8182\n", + "Epoch 1973/3000\n", + "10/10 [==============================] - 0s 939us/step - loss: 460076334.5455\n", + "Epoch 1974/3000\n", + "10/10 [==============================] - 0s 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+ "Epoch 2021/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 503989410.9091\n", + "Epoch 2022/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 404691003.6364\n", + "Epoch 2023/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 438835496.7273\n", + "Epoch 2024/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 413435249.4545\n", + "Epoch 2025/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 446061748.3636\n", + "Epoch 2026/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 371707693.0909\n", + "Epoch 2027/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 439129335.2727\n", + "Epoch 2028/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 558768741.8182\n", + "Epoch 2029/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 473509757.0909\n", + "Epoch 2030/3000\n", + "10/10 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1ms/step - loss: 375462917.8182\n", + "Epoch 2068/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 513336939.6364\n", + "Epoch 2069/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 392168296.7273\n", + "Epoch 2070/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 457336337.4545\n", + "Epoch 2071/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 423277832.7273\n", + "Epoch 2072/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 461529920.0000\n", + "Epoch 2073/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 374213029.8182\n", + "Epoch 2074/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 377051096.7273\n", + "Epoch 2075/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 454389890.9091\n", + "Epoch 2076/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 413186202.1818\n", + "Epoch 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"stream", + "text": [ + "Epoch 2761/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 341636135.2727\n", + "Epoch 2762/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 377531825.4545\n", + "Epoch 2763/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 385356349.0909\n", + "Epoch 2764/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 481805015.2727\n", + "Epoch 2765/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 368764256.0000\n", + "Epoch 2766/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 294939844.3636\n", + "Epoch 2767/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 296057818.1818\n", + "Epoch 2768/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 427876634.1818\n", + "Epoch 2769/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 389751115.6364\n", + "Epoch 2770/3000\n", + 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2882/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 355024221.0909\n", + "Epoch 2883/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 402237984.0000\n", + "Epoch 2884/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 357764973.0909\n", + "Epoch 2885/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 373675697.4545\n", + "Epoch 2886/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 306364363.6364\n", + "Epoch 2887/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 480334938.1818\n", + "Epoch 2888/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 414684241.4545\n", + "Epoch 2889/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 307328287.2727\n", + "Epoch 2890/3000\n", + "10/10 [==============================] - 0s 7ms/step - loss: 306246748.3636\n", + "Epoch 2891/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 377225975.2727\n", + "Epoch 2892/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 387999739.6364\n", + "Epoch 2893/3000\n", + "10/10 [==============================] - 0s 954us/step - loss: 415129169.4545\n", + "Epoch 2894/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 371255880.7273\n", + "Epoch 2895/3000\n", + "10/10 [==============================] - 0s 886us/step - loss: 404470676.3636\n", + "Epoch 2896/3000\n", + "10/10 [==============================] - 0s 870us/step - loss: 389820258.9091\n", + "Epoch 2897/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 358752279.2727\n", + "Epoch 2898/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414616317.0909\n", + "Epoch 2899/3000\n", + "10/10 [==============================] - 0s 891us/step - loss: 452401195.6364\n", + "Epoch 2900/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 301824334.5455\n", + "Epoch 2901/3000\n", + "10/10 [==============================] - 0s 978us/step - loss: 383501253.8182\n", + "Epoch 2902/3000\n", + "10/10 [==============================] - 0s 908us/step - loss: 321237400.7273\n", + "Epoch 2903/3000\n", + "10/10 [==============================] - 0s 923us/step - loss: 488517841.4545\n", + "Epoch 2904/3000\n", + "10/10 [==============================] - 0s 875us/step - loss: 358912637.0909\n", + "Epoch 2905/3000\n", + "10/10 [==============================] - 0s 975us/step - loss: 328033476.3636\n", + "Epoch 2906/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 362653413.8182\n", + "Epoch 2907/3000\n", + "10/10 [==============================] - 0s 988us/step - loss: 490793518.5455\n", + "Epoch 2908/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 354435711.2727\n", + "Epoch 2909/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414829757.0909\n", + "Epoch 2910/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 317251516.3636\n", + "Epoch 2911/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 327235684.3636\n", + "Epoch 2912/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 274291697.4545\n", + "Epoch 2913/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 426120194.9091\n", + "Epoch 2914/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 320020247.2727\n", + "Epoch 2915/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 314426277.8182\n", + "Epoch 2916/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 396820192.0000\n", + "Epoch 2917/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 342402946.9091\n", + "Epoch 2918/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 375880939.6364\n", + "Epoch 2919/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 335807611.6364\n", + "Epoch 2920/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 356718109.0909\n", + "Epoch 2921/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 302283051.6364\n", + "Epoch 2922/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 448345437.0909\n", + "Epoch 2923/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 312370779.6364\n", + "Epoch 2924/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 457482187.6364\n", + "Epoch 2925/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 467051130.1818\n", + "Epoch 2926/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 377873652.3636\n", + "Epoch 2927/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 404772069.8182\n", + "Epoch 2928/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 373026999.2727\n", + "Epoch 2929/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 409139733.8182\n", + "Epoch 2930/3000\n", + "10/10 [==============================] - 0s 965us/step - loss: 476271360.0000\n", + "Epoch 2931/3000\n", + "10/10 [==============================] - 0s 962us/step - loss: 450123514.1818\n", + "Epoch 2932/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 335306373.8182\n", + "Epoch 2933/3000\n", + "10/10 [==============================] - 0s 934us/step - loss: 376473024.0000\n", + "Epoch 2934/3000\n", + "10/10 [==============================] - 0s 941us/step - loss: 313878594.9091\n", + "Epoch 2935/3000\n", + "10/10 [==============================] - 0s 949us/step - loss: 410917029.8182\n", + "Epoch 2936/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 391376637.0909\n", + "Epoch 2937/3000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10/10 [==============================] - 0s 893us/step - loss: 349614248.7273\n", + "Epoch 2938/3000\n", + "10/10 [==============================] - 0s 983us/step - loss: 475278490.1818\n", + "Epoch 2939/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 393699752.7273\n", + "Epoch 2940/3000\n", + "10/10 [==============================] - 0s 946us/step - loss: 404036852.3636\n", + "Epoch 2941/3000\n", + "10/10 [==============================] - 0s 939us/step - loss: 310075509.8182\n", + "Epoch 2942/3000\n", + "10/10 [==============================] - 0s 957us/step - loss: 310417889.4545\n", + "Epoch 2943/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414358536.7273\n", + "Epoch 2944/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 344871547.6364\n", + "Epoch 2945/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 356274475.6364\n", + "Epoch 2946/3000\n", + "10/10 [==============================] - 0s 979us/step - loss: 357627610.1818\n", + "Epoch 2947/3000\n", + "10/10 [==============================] - 0s 954us/step - loss: 308476590.5455\n", + "Epoch 2948/3000\n", + "10/10 [==============================] - 0s 948us/step - loss: 344861485.0909\n", + "Epoch 2949/3000\n", + "10/10 [==============================] - 0s 967us/step - loss: 367587838.5455\n", + "Epoch 2950/3000\n", + "10/10 [==============================] - 0s 918us/step - loss: 366758368.0000\n", + "Epoch 2951/3000\n", + "10/10 [==============================] - 0s 930us/step - loss: 350354638.5455\n", + "Epoch 2952/3000\n", + "10/10 [==============================] - 0s 970us/step - loss: 363771441.4545\n", + "Epoch 2953/3000\n", + "10/10 [==============================] - 0s 957us/step - loss: 385292110.5455\n", + "Epoch 2954/3000\n", + "10/10 [==============================] - 0s 947us/step - loss: 407589152.0000\n", + "Epoch 2955/3000\n", + "10/10 [==============================] - 0s 908us/step - loss: 342974936.7273\n", + "Epoch 2956/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 416040910.5455\n", + "Epoch 2957/3000\n", + "10/10 [==============================] - 0s 989us/step - loss: 341894946.9091\n", + "Epoch 2958/3000\n", + "10/10 [==============================] - 0s 903us/step - loss: 355141790.5455\n", + "Epoch 2959/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 329033154.9091\n", + "Epoch 2960/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 417409678.5455\n", + "Epoch 2961/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 379748576.0000\n", + "Epoch 2962/3000\n", + "10/10 [==============================] - 0s 897us/step - loss: 356126507.6364\n", + "Epoch 2963/3000\n", + "10/10 [==============================] - 0s 937us/step - loss: 410452302.5455\n", + "Epoch 2964/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 294462520.7273\n", + "Epoch 2965/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 326169524.3636\n", + "Epoch 2966/3000\n", + "10/10 [==============================] - 0s 973us/step - loss: 330078720.0000\n", + "Epoch 2967/3000\n", + "10/10 [==============================] - 0s 991us/step - loss: 332932516.3636\n", + "Epoch 2968/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 493803624.7273\n", + "Epoch 2969/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 472380657.4545\n", + "Epoch 2970/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 464569550.5455\n", + "Epoch 2971/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 419182048.0000\n", + "Epoch 2972/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 431501954.9091\n", + "Epoch 2973/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 421149061.8182\n", + "Epoch 2974/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 340897744.0000\n", + "Epoch 2975/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 347571086.5455\n", + "Epoch 2976/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 416178693.8182\n", + "Epoch 2977/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 297465639.2727\n", + "Epoch 2978/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 358959688.7273\n", + "Epoch 2979/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 390126804.3636\n", + "Epoch 2980/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 326467739.6364\n", + "Epoch 2981/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 305814135.2727\n", + "Epoch 2982/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 424695109.8182\n", + "Epoch 2983/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 301992577.4545\n", + "Epoch 2984/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 339711704.7273\n", + "Epoch 2985/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 464452765.0909\n", + "Epoch 2986/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 299091185.4545\n", + "Epoch 2987/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 331288641.4545\n", + "Epoch 2988/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 433518656.0000\n", + "Epoch 2989/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 345901474.9091\n", + "Epoch 2990/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414442202.1818\n", + "Epoch 2991/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 335004619.6364\n", + "Epoch 2992/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 330958909.0909\n", + "Epoch 2993/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 411063717.8182\n", + "Epoch 2994/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 477360256.0000\n", + "Epoch 2995/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 313871268.3636\n", + "Epoch 2996/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 365627371.6364\n", + "Epoch 2997/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 319629053.0909\n", + "Epoch 2998/3000\n", + "10/10 [==============================] - 0s 938us/step - loss: 454736247.2727\n", + "Epoch 2999/3000\n", + "10/10 [==============================] - 0s 925us/step - loss: 372288258.9091\n", + "Epoch 3000/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 338687688.7273\n" + ] + } + ], + "source": [ + "# train nn model \n", + "print(x_train.shape)\n", + "model, nn_train_preds, nn_test_preds, history_nn, y_train, y_test = create_nn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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evTuGhoZK77OqNGjQABcXFy5evIi3t3exyfDOnTsK51kw8G7NmjUKrdGcnBwWL16s9LFtbGxo37498fHxxd5GlZmZqdA7UfBa3bp1S+ljFLx/Fy1apNBtnZuby5w5c4DS/8/+bcQ11PfQN998w4sXL1i2bBndunXjk08+oXXr1tSpU4fU1FRiYmKIj4+vNk+wUVFRYdOmTTg7OzNu3DgOHjwo3YdakPR8fX2lW2bg5YMEjh8/zvHjx7Gzs6NHjx7k5uZy6NAh7t69y7Bhw0q8ZaaAjY0Nhw8fxtnZmeHDh7N9+3aFpF0RXrecBgYG5OTkYGdnR69evcjJySEoKIgHDx7g5uamMGBJXV2diRMnsnTpUjp27Ejfvn2Bly1AuVxOixYt+L//+78yy9q6dWtsbW05dOgQ3bt3x9bWlpSUFI4fP07Tpk2LTXCdOnVCVVUVX19fHj9+LF1ndHV1LfEeR1NTU1asWMHUqVNxcHDA2dkZfX19oqOjOXXqFMbGxqxZs0ap+q0OVq5cyY0bN1ixYgX+/v7Y2dmhr6/PgwcPuHbtGmfPnmXp0qXSk45sbW1xdXVl8+bNtG/fnn79+kn3odarVw8DAwOlb53x8/PDycmJlStXEhwcTMeOHZHJZCQlJREaGsrevXul6/wODg4cPHiQyZMn079/f7S0tKhXrx6urq4l7n/QoEGEhIQQEBCAra0tffr0ke5DvXbtGp06dXpnJseoCCKhvqc8PT1xdnZm69at/PHHH+zbt4+nT5+ira1N8+bNWbFiRZFbVKpS69atCQ8PZ9WqVYSHh3PixAnq1atHnz59mDZtmkIXJ7wctn/gwAE2bdrE/v372bp1K6qqqlhaWjJz5kzpm3VZmjdvTnBwMP3792fUqFFs3rz5je9lrIhyqqurc/DgQRYvXkxgYCD//PMPH374IdOmTSv2GbOenp5oamqyfft2du7cyQcffECfPn2YN28eI0aMUKqsMpmMvXv3smTJEn777Tf8/PwwNDRk1KhReHp6FjvquEmTJmzbto1169axe/duqTU8dOjQUh8aMGbMGBo1asT69es5cuQIWVlZGBoa4urqiqenZ4U/YKMy1alTh8OHD7Nr1y4CAgI4fPgw2dnZ6OrqYmpqyvz584t8aVqxYgVNmjRh69at0uvl5OQk3aupLFNTUyIiItiwYQOHDx/mhx9+QF1dHWNjY0aOHKlwi9GIESO4c+cO+/fvx8fHh9zcXBo2bFhqQoWXSdvOzo5du3axa9cu8vPzady4MYsWLWLcuHFK35f+b6CSlpam/DPYBEGoctra2jRs2LDERy4KglA1xDVUQRAEQagAIqEKgiAIQgUQCVUQBEEQKoAYlCQI75h39bFsgvBvJ1qogiAIglABREIVBEEQhAogEqogCIIgVACRUKux8swt+r4TdaUcUU/KE3WlHFFP/yMSqiAIgiBUgCpLqFu2bMHOzo6GDRvSsGFDunXrxrFjx6T1crkcb29vmjVrhoGBAX369OHy5csK+8jJyWH69Ok0atQIIyMjhg0bVmSez7S0NFxdXTE1NcXU1FSaRulVt27dwsXFBSMjIxo1aoSXlxfPnz9XiLl06RK9e/fGwMAAS0tLVqxYIU3WLQiCIAhVllCNjIxYuHAhERERhIWF0bFjR7744gvpId3r1q3Dx8eHFStWEBoaiq6uLgMGDODJkyfSPmbNmsWhQ4fYtm0bwcHBPHnyBBcXF4WpisaOHUtcXBwBAQEEBgYSFxeHm5ubtD4vLw8XFxcyMzMJDg5m27ZtBAUFSTMlwMuZPwYMGICenh6hoaEsX76c9evXs2HDhrdQU4IgCMK7oFo9y9fc3JwFCxYwevRomjVrxtdffy1NC/Xs2TOaNm3K4sWLGTNmDOnp6TRp0gQfHx+GDh0KvJwiytramsDAQBwdHUlISKBdu3aEhIRga2sLQFRUFL169eLs2bM0bdqU33//naFDh3Lx4kVMTEwA8Pf3Z9KkSSQmJlK3bl22bdvGt99+y9WrV6WZ6VetWsUPP/xAfHx8sRMLV4TExESaNm1aKfv+txF1pRxRT8oTdaUcUU//Uy0e7JCXl8cvv/xCVlYWbdu2JSkpiQcPHtClSxcpRlNTEzs7O6KjoxkzZgyxsbHk5uYqxJiYmGBhYUF0dDSOjo7ExMRQu3ZthVkwbG1t0dLSIjo6mqZNmxITE4OFhYWUTAEcHR3JyckhNjaWjh07EhMTQ/v27aVkWhCzdOlSkpKSMDc3L/c5v3jxgqysrFJjatasqTBfoVAyUVfKeZ/rSU1NDS0traouhvAvVqUJ9dKlS3Tv3p3s7Gy0tLTYvXs3VlZWREdHA6Crq6sQr6ury7179wBISUlBJpMVmbNTV1eXlJQUKUZHR0ehBamiokKDBg0UYgofR0dHB5lMphBjZGRU5DgF60pLqCWNgKtVqxYffPBBqa3bwuUSSibqSjnvcz1lZmZy7969co19ECNYlVPd6unKlVpkZspo3foJqhV8YbO01niVJtSmTZsSGRlJeno6QUFBuLu7c/jwYWl94WQjl8vL7F4tHFNcvDIxhZcXV5bSti1QXOWnp6dTt27dMrfNzs6mZs2apcYIL4m6Us77XE8aGhqoq6uXOg/rq0RXpnKqWz1t9lPHa0YtAAYPyGbr9py3duwqvW2mRo0aNGrUiFatWrFgwQKsra3ZuHEj+vr6AFILscCjR4+kb9h6enrk5eWRmppaasyjR48UvpHK5XJSU1MVYgofJzU1lby8vFJjHj16BLz+N/7Kuu4qCELxxP/c+6EgmQIEHqzJnTtv73WvVveh5ufn8/z5c8zMzNDX1ycsLExal52dTVRUlHQ91MbGBnV1dYWYO3fuSAORANq2bUtmZiYxMTFSTExMDFlZWQoxCQkJCrfbhIWFoaGhgY2NjRQTFRVFdna2QoyhoSFmZmaVUBOCIAhCRbgd8wDkcsjOhvz8Sj1WlSXUb7/9ltOnT5OUlMSlS5dYuHAhJ0+eZMiQIaioqODu7s73339PUFAQ8fHxeHh4oKWlxeDBgwGoV68eI0eOZP78+YSHh3PhwgXc3NywsrKic+fOAFhYWNC1a1emTJnC2bNniYmJYcqUKfTo0UPqoujSpQuWlpaMGzeOCxcuEB4ezvz58xk1ahR169YFYPDgwWhqauLh4UF8fDxBQUF8//33eHh4iG+9giAI1ZjKgwfUGj6cegYGaHXpgsqDB5V2rCpLqA8ePMDV1ZVPP/2U/v3789dffxEYGEi3bt0AmDx5Mh4eHkyfPh0HBwfu37/PgQMHqFOnjrSPZcuW4eTkxJgxY+jZsydaWlrs27cPmUwmxWzZsoUWLVowcOBABg0aRIsWLfDz85PWy2Qy/P39qVWrFj179mTMmDE4OTmxZMkSKaZevXocPHiQe/fu4eDgwPTp0xk/fjwTJkx4CzX1/omMjERbW7tId355JSUloa2tzfnz598opjLs2bMHY2Pjt3pMQXgfqZ0+jfrRoy9/j41Fw8en0o5Vre5DfV+kp6crNTCiug0gcXd3Z+/evcDLLyKGhoZ0796d+fPno62tXWHHiYyMpG/fvly/fr3IKO5X3bx5kzVr1hAWFsbDhw/R19enVatWjB8/nnbt2pGUlETLli0JCwujVatWxe4jLy+PR48eoaOjg5ra2xujt2fPHry8vIo82assKSkprFmzhmPHjnH37l10dHSwsrLC1dWV7t27l7l9Rb6n3N3d+eeff/D396+Q/b0Nyv7vQfUbbFNdVbd60tZWfH1PYo8eKQTTm7bE0J4zpFfSnMLV4j5U4d3RuXNn/Pz8ePHiBQkJCUyYMIH09HS2bdv2Vstx/vx5+vfvz0cffcTq1av58MMPefHiBb/99hteXl5EREQotR+ZTCYNgqvukpKS6NmzJ7Vr12bBggW0aNGC/Px8IiIimDp1qvSUMUEQ/ucOxvTgGFnURpU8QumCTSUdq1oNShKqPw0NDfT19TE2NqZLly4MGDCA0NBQhZjdu3fTrl079PX1adOmDT4+PuS/Mhhgw4YN2NnZYWRkhKWlJRMnTizyfOXSyOVyPDw8MDMz49ixY/Tq1Qtzc3NatGjB1KlT+fXXXxXik5OTcXZ2xtDQkHbt2ikMZCvc5VvQ3RwREYGjoyOGhoZ07tyZ2NhYhX0GBQVhZ2eHnp4eVlZWrF69WmE0eVpaGuPGjcPMzAwDAwP69+9f5FnUr0pLS6NHjx4MHDiwxAd+eHp6IpfLCQsLY8CAATRt2hQLCwtcXV05efKkFHfr1i2++OILTExMMDExYcSIEQotYW9vb9q3b6+w78Jd0AUxP//8MzY2NpiYmDB8+HCpG97b25u9e/dy7NgxtLW10dbWJjIyssTzE4Sqsph5ZFEbgHxkjGF7pR1LJNRqpJ62tsKPvoFBkWUV+fOm/v77b06cOIG6urq0bOfOnSxevJjZs2cTHR3NkiVLWLduHVu3bpViVFVV8fb2Jioqii1btnDu3Dm8vLyUPm5cXByXL19m0qRJCtfLCxTufl6yZAlubm6cPHmSVq1a8Z///IfMzMxSj7Fw4UIWLFhAREQEH3zwAa6urlLCjI2NZfTo0Tg5OXH69GkWLFjAd999x+bNm6Xt3d3dOXfuHD/99BMnTpxAU1OTwYMH8+zZsyLHun//Pr1798bQ0JB9+/YV+zSfx48fc/z4cb7++mtq165d4jnL5XK++OILHj58SFBQEIcOHeL+/ft88cUX5Z7MITk5mQMHDrB7924OHDhAXFwcixcvBmDixIkMGDCAzp07k5CQoDC6XhCqk//DWuHvmzSqtGOJLl+hXI4fP46xsTF5eXnSbURLly6V1q9atYqFCxfSv39/4OXzmW/evMm2bdtwdXUFwMPDQ4o3MzNj0aJFDB8+HF9fX1SVeKzJjRs3APjoo4+UKrOHhwe9evUCYP78+ezbt4+LFy8WaaW9as6cOXTs2BEALy8vevbsyd27dzE2NsbHxwd7e3tmz54NQJMmTbh+/Trr1q3Dzc2N69evc/ToUY4cOYK9vT0Afn5+WFtbExAQwKhRoxTOZcCAATg6OrJ69eoSz//GjRvI5fIyzzk8PJz/+7//4/z589ItXVu3bqVVq1ZERERIz7RWxosXL9i4caN0zXH06NHs2bMHgNq1a1OzZk2px0IQBNFCFcrJzs6OyMhITpw4IQ2EGTduHPDyYRe3b99mypQpGBsbSz8LFy7k5s2b0j4iIiJwdnamefPmmJiYMHLkSJ4/f84DJYezl7elZWVlJf1uaGgIwMOHD5XexsDAQGGb4lpj7du35+7du2RkZJCQkICqqipt27aV1terV4/mzZtz5coVadnz58/p2bMn3bp1Y+3ataV+mVD2nBMSEorcH21ubo6hoaHCsZXRsGFDhQE8BgYG0gNNBKG6OHpUjREjarFqlQYvXlRtWUQLVSiXWrVq0ajRyy6TlStX4uTkxMqVK5k1a5Z0nXTt2rUldv8lJyfj4uLCqFGjmD17Nh988AEXLlzgq6++KjIHbUkaN24MwNWrV2nZsmWZ8a92SRfcN1xWgiptm9IegamiolLqvl/dTl1dHQcHB3777TeSk5MxNTUtcbvGjRujoqLC1atXSy13WWWDl13uhcv4ophPolfroGD7/Eq+MV4QyiMpSYXPP395ieTwYXU++KBqb1oRCbUaKTyUu7rdNlOcGTNmMGTIEEaPHo2hoSFGRkbcvHmTzz//vNj48+fP8/z5c7y9vaXrnyEhIeU65scff0yzZs3473//y8CBA4tcR01LS6vQ23gKa9asGWfOnFFYFhUVhbGxMXXq1KFZs2bk5+cTExMjdflmZGQQHx/P8OHDpW1UVFTYtGkT48aNo2/fvhw+fJiGDRsWe8z69evj6OjIli1bcHNzK3IdteCcmzVrxt27d0lKSpJaqX///Tf37t2jWbNmANLkEK8m34sXL5a7HmrUqKEw97AgvG0rFip+eZw2TbOEyLdDdPkKb+Szzz6jWbNmrF69GoCZM2fy3//+Fx8fHxITE4mPj2fv3r2sXbsWeNnSys/PZ+PGjfz9998EBgbi6+tbrmOqqKjg4+PD33//TY8ePQgJCeHvv//m0qVLrFu3Dmdn5wo/z1eNH+SlDuoAACAASURBVD+eU6dO4e3tzbVr19i/fz8+Pj5MmjQJeHmOvXv3ZsqUKZw+fZpLly7h6upKnTp1GDJkiMK+VFVV8fX1pV27djg5OXHr1q0Sj1swktjBwYFffvmFxMRErl69yrZt2+jQoQPw8ramFi1a4OrqSmxsLOfPn+frr7+mZcuW0jXhDh068PjxY9asWcPNmzf58ccfi4yMVoapqSmXL18mMTGR1NRUcnNzy70PQXgTD/+8XdVFUCASqvDGxo8fz65du0hOTmbUqFFs2LABf39/OnToQK9evdi5c6fUWmrRogXLly9n48aN2Nra8uOPP0ojR8ujTZs2hIeH89FHHzF16lQ+++wzXFxcOHfuHKtWraroU1RgY2PDjh07OHToEO3bt2fhwoV888030qArgI0bN9K6dWs+//xzHB0defbsGYGBgQpz6hZQVVVl06ZNtGvXjr59+5aYVM3NzYmIiKBz584sWLAAe3t7+vXrx9GjR/nuu++Al1829uzZg46ODk5OTvTt2xc9PT327NkjtUYtLCxYu3YtO3bswN7envDwcKZOnVruevjyyy/56KOPcHBwoHHjxkVa7YJQ2VTKGK3/toknJVWBd/VJSdWZqCvlvO/1JJ6UVPGqsp5cGidwLLVt2YGFpKWlV0JpRAtVEARBECqESKiCIAiCUAFEQhUEQRDeSXJ59Zo+UyRUQRAEQagAIqEKgiAIQgUQCVUQBEF4R1Wvm1REQhUEQRCECiASqiAIgiBUAJFQBUEQBKECiIQqVEu//vqrwgPu9+zZg7GxcZWUxcXFBXd39zfej7u7Oy4uLm8cUxmsra1Zv379Wz+uIPybiIQqKM3d3R1tbW20tbVp0KABLVu2ZO7cuWRlZVX6sQcOHEhsbKzS8W87Qcjlcn788Ue6deuGiYkJDRs2pGPHjqxbt46MjAyl97N8+XL8/PwqsaQVKygoiL59+2JqaoqRkRF2dnYsXry4zPlmK1pSUhLa2tqcP3/+rR5XEF4lEqpQLp07dyYhIYHY2Fjmzp3Ltm3bmDdvXrGxL168KPdk4CXR1NREV1e3QvZVGdzc3PDy8qJbt278+uuvnDx5kjlz5hAZGcmhQ4eU3k+9evUqdeq5irR48WJGjx6NtbU1/v7+nDlzBm9vb5KTk9m2bVtVF08Q3roqS6hr167FwcGBhg0b0rhxY1xcXIiPj1eIebVFVPDTtWtXhZicnBymT59Oo0aNMDIyYtiwYdy5c0chJi0tDVdXV0xNTTE1NcXV1ZW0QnOP3rp1CxcXF4yMjGjUqBFeXl5FJry+dOkSvXv3xsDAAEtLS1asWFFhCeNdoaGhgb6+PiYmJgwZMoQhQ4Zw5MgRALy9vWnfvj179uzBxsYGPT09srKySE9PZ/LkyTRp0gQTExN69+5dpCWxd+9eWrRogaGhIS4uLqSkpCisL67L99ixYzg6Okqvh4uLC9nZ2fTp04dbt24xb9486X1TIDo6mt69e2NoaIilpSVTp05VaEE+ffoUd3d3jI2Nadq0KWvWrCmzTg4ePMj+/fvZvHkzXl5etGnTBjMzM3r06EFgYCB9+vRRiN+0aROWlpaYmZnh4eHB06dPpXWFu3z79OnDtGnTWLRoEY0aNaJJkybMnTtXYaLvtLQ0xo0bh5mZGQYGBvTv35/Lly8rHDMoKAg7OztMTU2xsrKSpoIrib+/Pw0bNiQ4OLjY9efOnWPNmjUsWrSIZcuW0b59e0xNTenUqRNbtmxh3LhxUuz27dtp1aoVurq6tGrVip07dyrsS1tbu8j0cYV7GLS1tdmxYwdffvklRkZGtGzZEn9/f2l9wUTzDg4OaGtrF6lzQXgbqiyhnjx5kq+++opjx44RFBSEmpoazs7OPH78WCGuoEVU8BMQEKCwftasWRw6dIht27YRHBzMkydPcHFxUZj4eOzYscTFxREQEEBgYCBxcXG4ublJ6/Py8nBxcSEzM5Pg4GC2bdtGUFAQc+bMkWIyMjIYMGAAenp6hIaGsnz5ctavX8+GDRsqrE60tesp/BgY6BdZVpE/FaFmzZoK82AmJSURGBjIjh07OHnyJBoaGri4uHDv3j38/f35448/sLOzo1+/fty/fx+AP//8Ew8PD0aPHk1kZCQ9e/Zk2bJlpR73+PHjDB8+HAcHB8LDw/n555/p0KED+fn57N69G2NjY7y8vKT3Dbz8QjRw4EB69erFyZMn2bVrFxcvXmTChAnSfufNm0d4eLg0R2hcXBynT58utSz79++nSZMm9OvXr9j1ryb0qKgoLl++zC+//ML27ds5fPhwmfPBBgQEIJPJ+O2331i1ahWbNm3iwIED0np3d3fOnTvHTz/9xIkTJ9DU1GTw4ME8e/YMgNjYWEaPHo2TkxNhYWEsWLCA7777js2bNxd7PF9fX7y8vNi3bx+9e/cu8Zy1tLQU/o+KO+dDhw4xffp03N3diYqKYty4cUybNo2jR4+Wes7FWblyJb179+bkyZMMHDiQCRMmkJycDEBoaCgAP//8MwkJCezevbvc+xeEN6VWVQd+9QMBwM/PD1NTU86cOUOvXr2k5QUtouKkp6eza9cufHx8cHBwkPZjbW1NeHg4jo6OJCQkcPz4cUJCQmjXrh0A3333Hb169ZKmHQoNDeXy5ctcvHgRExMTABYuXMikSZOYN28edevWJSAggGfPnrFp0yY0NTVp3rw5V69eZePGjUyYMEGaa/J9cu7cOQIDA+nUqZO07Pnz5/j5+aGnpwdAREQEFy9e5Nq1a9JcoHPnziUkJAR/f38mT56Mr68vnTp1wtPTE4AmTZrw119/sWvXrhKPvWrVKvr378/cuXOBl9OStW7dGoBatWqhqqpKnTp1FN47//3vfxkwYAATJ06Ulq1Zs4aOHTvy8OFDNDU12bVrFxs2bMDR0REAHx8fmjdvXmo93LhxQ+npq+rUqcPatWtRU1PDwsICZ2dnIiIiSp2P1MLCQvpy16RJE3bu3ElERASDBw/m+vXrHD16lCNHjmBvbw/8738gICCAUaNG4ePjg729PbNnzyY7OxsrKyuuX7/OunXriiTEpUuXsmPHDoKCgqRWX0nnbG5ujrq6eqnnu2HDBlxcXKS5Yps0aUJsbCzr1q1T+D9XhouLi9R6nzNnDr6+vkRFRWFqaoqOjg4AH3zwQYmfF4JQ2arNNdTMzEzy8/OLXD+KioqiSZMmtGnThkmTJikMdoiNjSU3N5cuXbpIy0xMTLCwsCA6OhqAmJgYateuLSVTAFtbW7S0tBRiLCwspGQK4OjoSE5OjjQQJiYmhvbt2ytMEO3o6Mi9e/dISkqqwJqo3o4fP46xsTH6+vp069YNOzs7Vq5cKa03MjKSkinAhQsXePr0KU2aNMHY2Fj6uXz5Mjdv3gQgISGBTz/9VOE4hf8uLC4uTiGRK+PChQvs379foRw9e/YE4ObNm9y8eZPnz5/Ttu3/5lesXbs2VlZWpe63PN3+FhYWqKn973usgYFBmQN4Ch//1W0SEhJQVVVVKHO9evVo3rw5V65ckWJeff8DtG/fnrt37yp0d/v6+uLn50dISEipyRSUP+eSjl1QtvJ4tR7U1NTQ0dF564OfBKE0VdZCLWzmzJlYW1srfDB07dqVvn37YmZmRnJyMkuWLKFfv36Eh4ejoaFBSkoKMplM+nZaQFdXV7oGl5KSgo6OjkILUkVFhQYNGijEFB7woqOjg0wmU4gxMjIqcpyCdebm5sWeV2JiYpFlNWvWRENDo5joiumGVVZ2dna54vPy8rC1tWX16tWoqalhYGAgtVCys7N58eIFmpqaCvvNyclBV1e3yDUyeJmssrOzyc/P58WLFwrbvXjxQqGMubm5yOVyhZjc3FyFv1/9XS6XF1mfl5fH8OHDi+2mNDAw4Pr161KZX90uPz+fvLy8Euvrww8/JCEhocz6zMvLQ1VVtUiZXt134b/z8/NRUVEpUp6C+iq4zp+dna1wmePVMhcu/6vbPX/+nOzsbORyOZ9++ilhYWHs27ePadOmlXou5ubmnD59moyMDGrUqFFqbOHXNjc3V+GcVFRUitR5bm5ukdev8Ov/avlzcnKAoq9dYRkZGUWuz5emuP9foaiqqqdX3/Pl8SblLa03qlok1NmzZ3PmzBlCQkKQyWTS8kGDBkm/W1lZYWNjg7W1NceOHSvxehW8/McrnEBfJ6bw8sIxBd/SS+vuLa7y09PTqVmzZpHlhWeRz87OLjau4pRv3zKZjNq1a2NpaVnsejU1NVRVVRXK/Mknn7Bs2TI0NTVL/NJhaWlJbGyswnYFPQMFy9TV1VFRUZH+/vjjjzl9+jRjx44FitaVhoZGkbLY2NiQmJhYYvmbNWuGuro6cXFxWFhYAJCVlcWVK1do1KhRia+Fi4sL//nPf/jtt9+KfV+mpaWhra2NTCZDJpMp7KdwnRWOUVVVRU1NTWGbV2Osra3Jz88nLi5O6vLNyMjgypUrjBgxgpo1a2Jpacmff/5JzZo1pXo6d+4cxsbGNGjQAHj5Hi7oBXJ2dkZdXR0vL69izxdg2LBhbN26lR9//FHhGnThc7awsODcuXP85z//kdb9+eefNGvWTDqnBg0a8M8//0h/p6Sk8ODBA9TV1RXOu0aNGgp/q6ioSDF16tSR6rO0/5m6devSsGHDEte/quCSkFC6qqwnmWrCa21XWeWt8i7fWbNm8fPPPxMUFFTiB24BQ0NDjIyMuHHjBgB6enrk5eWRmpqqEPfo0SOp9ainp8ejR48UuqjkcjmpqakKMYW/taamppKXl1dqzKNHjwCq9e0cVa1z587Y2toyfPhwfv/9d/7++29iYmJYtmyZNNjHzc2N8PBw1q5dy/Xr19m5cyeHDx8udb/Tpk3jl19+YcmSJVy5coUrV67g4+MjjZg1NTUlKiqKu3fvSu+PyZMn89dffzFlyhQuXLjAjRs3CAkJ4ZtvvgFetphHjhzJt99+S1hYGJcvX2bChAkKI2qLM2DAAAYNGoSrqysrV67kr7/+Ijk5mePHjzN06FBpFHRlaNy4Mb1792bKlCmcPn2aS5cu4erqSp06dRgyZAgA48eP59SpU3h7e3P9+nX279+Pj48PkyZNKrK/1q1bc/DgQTZs2MCqVatKPO4nn3zC5MmTmT9/vvSFODk5mcjISFxdXaWBVhMnTsTf358tW7Zw/fp1/Pz8CAgIUDh2x44d2bp1K+fPn+fChQt4eHiU+4ukrq4umpqanDhxgpSUFNLT08veSBAqWJUm1BkzZhAYGEhQUBAfffRRmfGpqancu3dPGnRgY2ODuro6YWFhUsydO3cUrtu0bduWzMxMYmJipJiYmBiysrIUYhISEhRutwkLC0NDQwMbGxspJioqSqE7KSwsDENDQ8zMzN6gFv7dVFRU2L9/P5999hmTJ0/m008/ZcyYMVy7dg1DQ0Pg5fXS9evX88MPP2Bvb8+hQ4eYOXNmqfvt3r07u3fv5vfff6djx44MHDiQyMhIVFVfvqVnz57N7du3adWqFY0bNwagRYsWBAcHk5ycjJOTEx06dGDRokUKX4gWL15Mhw4dGDFiBH379sXS0hI7O7syz3Hr1q0sX76cY8eO0bdvX+zt7Vm4cCH29val9qZUhI0bN9K6dWs+//xzHB0defbsGYGBgdL1fhsbG3bs2MGhQ4fo3LkzCxcu5JtvvpEGChXWpk0bDh48yPr160tNqgsXLuSHH37gwoULDB06FFtbW7y8vGjYsKHUc+Dk5MTKlSvZuHEj7dq1w9fXlzVr1igMSFqyZAnm5uY4OTnx5ZdfMnLkSKnlrCw1NTVWrFjBrl27aNasGcOHDy/X9oJQEVTS0tKq5EZKT09P/P392b17N82aNZOWa2lpUbt2bTIzM1m+fDn9+vVDX1+f5ORkFi1axJ07d4iOjpa6eKZOncrRo0fZtGkT9evXZ86cOaSlpRERESF1Hw8ePJi7d++ybt065HI533zzDQ0bNpTuY8vLy+Ozzz5DR0eHJUuW8PjxY9zd3XFycpI+UNLT0/n000/p0KEDnp6eXLt2jfHjx+Pl5aUwalQZ6enp1KtX9vXSyu/y/fcQdaWc972elP3fA9Hlq6yqrKehHybw2+O2ZQcWUvjyWkWpsmuoW7duBaB///4Ky2fMmMGsWbOQyWTEx8ezb98+0tPT0dfX57PPPmP79u1SMgVYtmwZMpmMMWPGkJ2dTceOHfH19VW4FrtlyxZmzJjBwIEDAejVq5fCyFSZTIa/vz+enp707NmTmjVrMnjwYJYsWSLF1KtXj4MHD+Lp6SndPD5+/Phirx8JgiAI758qa6G+z0QLteKJulLO+15PooVa8aqynoZ8mMDv1aiFWuWDkgRBEATh9VSvB+qIhCoIgiC8o6pXB6tIqIIgCIJQAURCrSLv2yw1glDVxP+cUNlEQq0CWlpapKWliX9wQXiLnj59+l4PyBIqX7V49OD7Rk1NjTp16ig8mLw4GRkZ1K1b9y2V6t0m6ko573M9qamplfAMbUGoGCKhVhE1NbUyh++npKQo/dzR952oK+WIehKEyiO6fAVBEAShAiidUFu2bElwcHCJ65WZQ1EQBEEQ/q2UTqjJyclkZWWVuD4rK4tbt25VSKEEQRAE4V1Tri7f0ub9vHbtmsIzdgVBEAThfVLqoKSffvqJvXv3Sn+vXr2anTt3FolLS0sjPj6eHj16VHwJBUEQBOEdUGpCzcrK4sGDB9Lf6enpRSZbVlFRoVatWnz55ZdlzmEpCIIgCBWmmt3KX2pC/frrr/n6668B+Pjjj1m+fDm9e/d+KwUTBEEQhHeJ0vehxsXFVWY5BEEQBKF8qtdkM+V/sMOTJ0+4ffs2jx8/LvbRefb29hVSMEEQBEEo1bvU5fuqx48fM2PGDA4ePEheXl6R9XK5HBUVFf75558KLaAgCIIgvAuUTqhTpkzh8OHDfP3119jb26OtrV2Z5RIEQRCEd4rSCfX48eO4ubmxdOnSyiyPIAiCILyTlH6wQ40aNWjcuHFllkUQBEEQlFbNLqEqn1D79+/P77//XpllEQRBEIR3ltIJdeLEidy/f59x48Zx9uxZ7t+/z8OHD4v8CIIgCML7SOlrqG3atEFFRYXY2Fj2799fYpwY5SsIgvBuUr15E43ly5FrapIzdy7yBg2qukjvFKUTqpeXV6kPxy+vtWvXcujQIa5du0aNGjX45JNPWLBgAc2bN5di5HI5y5cvZ+fOnaSlpdGmTRtWr16NpaWlFJOTk8PcuXP5+eefyc7OpmPHjqxZswZjY2MpJi0tDS8vL0JCQgDo2bMnK1euVBipfOvWLTw9PYmMjKRmzZoMHjyYJUuWUKNGDSnm0qVLTJ8+nb/++ov69eszevToCq8XQRCEqnKy3wam3ZqPJs/YlPAdFker9yDU6vbJq3RCnTVrVoUe+OTJk3z11Ve0bt0auVzOsmXLcHZ2Jjo6mvr16wOwbt06fHx88PHxoWnTpqxcuZIBAwZw9uxZaWabWbNmERwczLZt26hfvz5z5szBxcWFiIgIZDIZAGPHjuX27dsEBASgoqLCpEmTcHNzw9/fH4C8vDxcXFyoX78+wcHBPH78GHd3d+RyOatWrQIgIyODAQMGYGdnR2hoKImJiYwfP55atWoxceLECq0bQRCEt02efJuxtxZyl5eNkWlRTzlcxWUqS3UblFTuJyVVlAMHDij87efnh6mpKWfOnKFXr17I5XI2bdrEN998Q//+/QHYtGkTTZs2JTAwkDFjxpCens6uXbvw8fHBwcFB2o+1tTXh4eE4OjqSkJDA8ePHCQkJoV27dgB899139OrVi8TERJo2bUpoaCiXL1/m4sWLmJiYALBw4UImTZrEvHnzqFu3LgEBATx79oxNmzahqalJ8+bNuXr1Khs3bmTChAmilSoIwjstOUlFSqYAJ/kMSK+6Ar2DlE6oK1asKDNGRUUFLy+v1ypIZmYm+fn5UjdsUlISDx48oEuXLlKMpqYmdnZ2REdHM2bMGGJjY8nNzVWIMTExwcLCgujoaBwdHYmJiaF27dpSMgWwtbVFS0uL6OhomjZtSkxMDBYWFlIyBXB0dCQnJ4fY2Fg6duxITEwM7du3R1NTUyFm6dKlJCUlYW5u/lrnLQiCUB0U8yRZoZyUTqjLly8vcZ2Kior06MHXTagzZ87E2tqatm3bAkjTxunq6irE6erqcu/ePQBSUlKQyWTo6OgUiUlJSZFidHR0FFqQKioqNGjQQCGm8HF0dHSQyWQKMUZGRkWOU7CupISamJioXAWU4E23f5+IulKOqCflvU91lXL7H6C5wjJlz7+q6ikvL7/soGK8SXmbNm1a4rpyPcu3sPz8fJKTk/Hz8yM6OprAwMDXKuDs2bM5c+YMISEh0nXPAoW7UgsSd2kKxxQXr0xM4eXFlaW0baH0yi9LQZe0UDZRV8oR9aS8962u1O7cLrJMmfOvynqSyRJea7vKKq/S96EWu7GqKubm5nh7e2NmZvZaE4zPmjWLn3/+maCgIIVWnr6+PoDUQizw6NEjqWWop6dHXl4eqamppcY8evRIYWYcuVxOamqqQkzh46SmppKXl1dqzKNHj4CirWhBEIR3jhgH8sbeKKG+6rPPPuPYsWPl2mbGjBkEBgYSFBTERx99pLDOzMwMfX19wsLCpGXZ2dlERUVJ10NtbGxQV1dXiLlz5w4JCQlSTNu2bcnMzCQmJkaKiYmJISsrSyEmISGBO3fuSDFhYWFoaGhgY2MjxURFRZGdna0QY2hoiJmZWbnOWxAEodoRF1HfWIUl1MTExGLnRy2Jp6cnP/30E1u3bkVbW5sHDx7w4MEDMjMzgZfdqO7u7nz//fcEBQURHx+Ph4cHWlpaDB48GIB69eoxcuRI5s+fT3h4OBcuXMDNzQ0rKys6d+4MgIWFBV27dmXKlCmcPXuWmJgYpkyZQo8ePaRmf5cuXbC0tGTcuHFcuHCB8PBw5s+fz6hRo6hbty4AgwcPRlNTEw8PD+Lj4wkKCuL777/Hw8NDjPAVBOGd905+jFWz7wBKX0M9depUscvT09OJjIxky5YtODs7K33grVu3Aki3xBSYMWOGdM/r5MmTefbsGdOnT5ce7HDgwAHpHlSAZcuWIZPJGDNmjPRgB19fX4VrsVu2bGHGjBkMHDgQgF69erFy5UppvUwmw9/fH09PT3r27KnwYIcC9erV4+DBg3h6euLg4IC2tjbjx49nwoQJSp+zIAhCdfUuNlDl1ezRDippaWlKVWP9+vVLHNwjk8kYNGgQK1asEPOkVqD3bVDEmxB1pRxRT8p73+rqZvhtWjlbKSxLS0vn8WMVkpJUaNYsn5o1i25XlfU0yOwqJ9I/Lfd2aWmVc3+t0i3UQ4cOFVmmoqKCtrY2pqamCq1GQRAE4d139aoqTk5apKSo0rx5HseOZVKdPupVqlmfr9IJtUOHDpVZDkEQBKGa+fbbmqSkvBxqEx8v48cfazB+/PMqLlX1Ve5HDz558oSTJ0+SnJwMgKmpKR06dBAtVEEQhHdYcYOSgoPVFf7et08k1NKUK6H6+fmxZMkSsrKyFEb0amlpMW/ePNzc3Cq8gIIgCELlU2pQUv7rPZmoslS3QUlKJ9R9+/Yxc+ZM2rRpg7u7OxYWFsjlcq5evYqvry+zZs2ifv36DB06tDLLKwiCIFQR1cepgGaZcW/PO3oN1cfHh3bt2nH48GHU1P63mbW1Nf3798fJyYn169eLhCoIgvAvpfrPY6pXQq1elH6wQ2JiIgMHDlRIpgXU1NQYOHAg165dq9DCCYIgCMK7QumEqqWlJc0AU5wHDx5Qq1atCimUIAiCILxrlE6oXbp0wc/Pj8jIyCLrTp48yebNm3F0dKzQwgmCIAjCu0Lpa6gLFizg9OnT9O/fn48//lh6mP3Vq1eJi4vD0NCQBQsWVFpBBUEQhKqlolK9BgFVN0q3UE1MTIiMjMTDw4OnT58SFBREUFAQT58+Zfz48URGRmJsbFyZZRUEQRCqkFxevW5TqW7KdR/qBx98wJIlSxQeGi8IgiC8+97Fh+NXN2W2UM+ePcv58+dLjTl//jx//vlnhRVKEARBeMtERn1jpSbUyMhIevToQUJCQqk7SUhIoHv37kRHR1do4QRBEIS3Q6n5UEWPb6lKTag7duzA2tqaYcOGlbqTYcOG0bJlS2mOU0EQBOHdokwDtbrN7lLdlJpQz5w5Q9++fZXaUZ8+fYiKiqqQQgmCIAhvlzxfJMs3VWpCffjwIYaGhkrtyNDQkJSUlAoplCAIgvCWiXz6xkpNqLVr1+aff/5Rakf//PMPtWvXrpBCCYIgCG+ZGJT0xkpNqNbW1gQHByu1o+DgYFq0aFEhhRIEQRDeLtHl++ZKTajDhg3jzJkzbNiwodSd+Pj4EB0dzRdffFGhhRMEQRDeDmUaqCLllq7UBzsMGzaMgwcPMn/+fEJDQ3FxccHKyoratWuTmZlJfHw8+/btIzw8nG7duuHi4vK2yi0IgiBUIOVG+QqlKTWhqqiosGvXLubMmcPOnTsJDw9XWC+Xy1FTU+Orr75i8eLFlVlOQRAEoRKJLt83V+aTkjQ0NFi9ejVxcXF8//33uLu7M2LECNzd3Vm3bh0XLlxg1apV1KxZs9wHP3XqFMOGDcPS0hJtbW327NmjsN7d3R1tbW2Fn65duyrE5OTkMH36dBo1aoSRkRHDhg3jzp07CjFpaWm4urpiamqKqakprq6upKWlKcTcunULFxcXjIyMaNSoEV5eXjx//lwh5tKlS/Tu3RsDAwMsLS1ZsWIFcnEhXxCEf4N38bOsmhVZ6Wf5GhoaMmrUqAo9eFZWFs2bN+fzzz9n3LhxxcZ07twZPz8/6e8aNWoorJ81axbBwcFs27aN+vXrM2fOHFxcXIiIiEAmkwEwduxYbt++TUBAACoqKkyaNAk3Nzf8/f0ByMvLw8XFhfr16xMcBKCV2gAAIABJREFUHMzjx49xd3dHLpezatUqADIyMhgwYAB2dnaEhoaSmJjI+PHjqVWrFhMnTqzQehEEQXjb3s18Wr06ocv1cPyK1r17d7p37w6Ah4dHsTEaGhro6+sXuy49PZ1du3bh4+ODg4MDAH5+flhbWxMeHo6joyMJCQkcP36ckJAQ2rVrB8B3331Hr169SExMpGnTpoSGhnL58mUuXryIiYkJAAsXLmTSpEnMmzePunXrEhAQwLNnz9i0aROampo0b96cq1evsnHjRiZMmICKUs/tEgRBqJ5El++bU3r6tqoSFRVFkyZNaNOmDZMmTeLhw4fSutjYWHJzc+nSpYu0zMTEBAsLC+m5wjExMdSuXVtKpgC2trZoaWkpxFhYWEjJFMDR0ZGcnBxiY2OlmPbt26OpqakQc+/ePZKSkirn5AVBEN4SpQYliflQS1WtE2rXrl3x9fXl119/ZcmSJZw7d45+/fqRk5MDQEpKCjKZDB0dHYXtdHV1pac2paSkoKOjo9CCVFFRoUGDBgoxurq6CvvQ0dFBJpOVGlPwt3hClCAI77p3scu3uqnSLt+yDBo0SPrdysoKGxsbrK2tOXbsGP369StxO7lcXiSBvk5M4eWFYwoGJJXW3ZuYmFjiOmW86fbvE1FXyhH1pLz3qa7u3Su7YSDPz+fy5UROndKmTp0XtG6dCVRdPeXn5b3Wdm9S3qZNm5a4rlon1MIMDQ0xMjLixo0bAOjp6ZGXl0dqaioNGjSQ4h49eoSdnZ0U8+jRI4UEKpfLSU1NlVqYenp6RaaeS01NJS8vTyGmcEv00aNHAEVarq8qrfLLUnCNtySq166hvmcP+U2akDt8uJLzL/07lVVXwkuinpT3vtVVTmzZMSqqqixc2JKQEHUAFi9+Rs+el6qsnlRlpU8tWpLKKq/SXb5hYWFlxnh7e79RYcqSmprKvXv3pEFKNjY2qKurK5Ttzp07JCQkSNdM27ZtS2ZmJjExMVJMTEwMWVlZCjEJCQkKt9uEhYWhoaGBjY2NFBMVFUV2drZCjKGhIWZmZpV30iXJyqJ2ly7U/O47ao0fTw0xdZ4gCG9AmRGzN3OMpWQKMG+eZinR5ZOeDlO/fMrgTk8J/y2/wvb7NimdUEeMGMHp06dLXD937lzpFhNlZWZmEhcXR1xcHPn5+dy+fZu4uDhu3bpFZmYmc+fOJSYmhqSkJCIjIxk2bBi6uro4OTkBUK9ePUaOHMn8+fMJDw/nwoULuLm5YWVlRefOnQGwsLCga9euTJkyhbNnzxITE8OUKVPo0aOH9C2lS5cuWFpaMm7cOC5cuEB4eDjz589n1KhR1K1bF4DBgwejqamJh4cH8fHxBAUF8f333+Ph4VElI3xr7NqFSkaG9Lfm9OlvvQyCIPyLKDHK95+8/2/vvsOjKP4Hjr/37tIbJSEYQ1AEQxekg5QQpP/oCqiIIF2kCKF8QRCMtFhAQLqFoiARFARBgaB0LAQQMKD0lpBAQnpyd/v74+DC5S7JJVxLMq/n4XnYu9nducnufnZmZ2fKWG33Hw67zuc/PMGek0/Qt687yfeL30NdswNqhw4d6NevH3/++afRd++88w5Lly7lvffeK9TOT5w4QatWrWjVqhXp6enMnTuXVq1aMWfOHJRKJWfPnuWVV16hYcOGjBw5kqpVq/Lzzz/j5eWl38acOXPo2rUrgwYNomPHjnh4eLBx40b9O6gAq1atonbt2vTq1YvevXtTu3Ztg3dblUolmzZtwt3dnY4dOzJo0CC6du1KeHi4Po2Pjw9bt27l1q1bhISEEBYWxltvvcXo0aML9ZstRSF6FguCYEH27pS0eHct/f8zZRe+fb/4XePMfoa6atUqXnvtNXr37s22bduoW7cusiwzcuRIvv32WyIiIhgyZEihdt6yZUujEYsetWXLlgK34erqSkRERL6147Jly7Jy5cp8t1OpUiX9QA95qVWrFj/99FOBebKVLJw4RAsqc4UqXLJ3dgRBKMbsHVBzS78UBzxl72wUitk1VKVSyVdffcXzzz9Pr169OHnyJAMHDmTz5s0sXry40MFUeDxaWeIFDtKWKGpyll9oV/BKgiAIeXG0iFoMFaqXr7OzMxs2bKB3796EhoaiUChYs2YNPXr0sFb+hDzsuFyH32kMQCauDGAdRevvJgiC4IAjJRXDtxbyDKhbt27Nc6V+/fpx+vRpOnfujCzLBml79uxp2RwKJv1207DbdywVgST7ZEYQhGLPESuov/6qZPVqF4KDNYSFZeLiYu8c5S/PgDp48GAkSTI5m8rDz7/99lu+/fZbg89FQBUEQXB8ajV8/LELv/2molOnbJp62jtHhhLS3OnVywONRmL7dic8PGD8+Ex7ZytfeQbU7du32zIfgiAIgg3t2KFizhzdtJsHD6qY9WZZO+fI0OIjTdBoc5p9Z81yLb4B9YUXXrBlPgRBEAQbGjzY3WB55hrHGhVKrVUWnMjBmqmLNPTgmTNnuHr1KgBBQUHUqlWrgDUEQRAER6LRFL9OP46uUAF1x44dTJ06levXrwM5A8xXqlSJOXPm0KVLF6tkUjAmy+JkEAShlHOwy6DZAXXPnj28/vrrBAQE8O677xIcHIwsy5w/f54vvviCgQMHsmnTJkJDQ62ZX6EAyhMnkG7fRh0aCs7O9s6OIAiC9RTXJt8FCxYQHBzM7t27DYb+69KlC0OGDKFDhw5ERESIgGpHThs24P7WWwCoW7QgdccOO+dIEARHoDh9GikpCU3z5qBw6GmwizWzS/bvv//m1VdfNQimD3l5efHqq69y6tQpi2ZOKJyHwRRAdegQyj/+sGNuBEFwBM5ffIFXy5Z4du2K29Ch9s6ORZkzQ44tmR1QnZycSEtLy/P71NRUnJyc8vxesD3FP//YOwuCINiZ2/jx+v/f++4Ad07ctGNuSjazA2qzZs1YtWoV//33n9F3Fy9eZPXq1fpJvQXrkxzt4YEgCA5tCW8RwE2qh1TjiwX3C16hWHCs66DZz1BnzpxJhw4daNasGZ06ddLPJXr+/Hl2796Nq6srM2fOtFpGBUEQhKLRIvE2S/TL4+dUsmNuSi6zA2qNGjWIiopi1qxZ7N27l23btgHg4eFBx44deffdd6latarVMioIgiAUjbpoQw44PEdrqStUKT/zzDOsXbsWrVZLfHw8AL6+vihErzFBEATBxoptpySDlRQK3NzccHNzE8HUTvI6kKJ5jp/oSPZj3JEq//iDf+oO5q9qQ1BuE2M6C0Jx52iBp6QqVDS8evUqw4cPp0qVKlSuXJnKlStTpUoVRowYoR+KULCfdbzG8/xFZ36iI7uKvJ2lg8/R9Op3tL2zmXHDZNBoTCfUanX/BEFwaNqi1Z2EQjK7lC9cuECbNm2IjIykfv36jBgxguHDh/P888+zefNmQkJC+Pfff62ZV6EAr7MO+cGfdB+h/HnVv0jbmXJ1tP7/azIGkPjXZaM0qh9/xLtyZbwDA3HasqVI+xEEwTZEQLUNs9sFZ82ahSzLREVFUbduXYPvTp8+Tffu3Zk1axbr1q2zeCaFojlzsxx1C05WoIREJ8rk+sx95Eik5GQA3IYOJbtHDzECiyA4KNHkaxtmXwEPHjzI8OHDjYIpQJ06dRg6dCgHDhywaOaEx2RicvgikSSk+HhUu3cj3bih++hBMAWQNBrIdOx5CoXHp7h4EY927fCqXh3nNWvsnR2hEEpSDVURHY1q927IzrZ3VoyYXUPNysrC29s7z+99fHzIysqySKYEBxMfj2eLfihiY5F9fEj55Rd750iwA5f581E9GM7SddIksnv2RC5Xzs65EsxRUgKq07p1uL/9NgDZISHAPPtmKBezS7lmzZps2rSJ9PR0o+8yMzPZtGkTNWvWtGjmhMckWaaZx+WrL1DExuo2mZSE64wZFtmuULw4b9qk/7+k0eAUGWnH3AiFUVKafB8GUwCnqCiklBQ75saY2QH1nXfe4fTp04SEhLBq1Sr279/P/v37WblyJa1bt+bvv/9mwoQJhdr5oUOH6NevHzVq1KBMmTJs2LDB4HtZlpk7dy7Vq1enYsWKdOnShXPnzhmkyczMJCwsjCpVqhAQEEC/fv248aBZ8qHExESGDRtGUFAQQUFBDBs2jMTERIM0165do2/fvgQEBFClShUmTZpkVOM+c+YMnTt3pmLFitSoUYP58+cjW6pZ1QxaLajVNtudntORI4bLu0z0ILZhOQgOwkI3bIL1lZQaqgz8SBdWMYQUPBzuumN2KXfu3JmVK1eSmJjIpEmT6NWrF7169WLy5MkkJiaycuVKOnXqVKidp6amUrNmTebNm4ebm5vR94sWLWLp0qXMnz+fffv24efnR8+ePUl+5Pnd1KlT2b59O2vWrGHnzp0kJyfTt29fNI+86jFkyBBOnTrF5s2biYyM5NSpUwwfPlz/vUajoW/fvqSkpLBz507WrFnDtm3bmDZtmj7N/fv36dmzJxUqVGDfvn3MmzePxYsXs2RJznBe1nT2rIL69b3w9fVhxgxXm+wzPyl4EM40ZjGD+3g53IEtPD6NBk6eVHD7dk7gvEtZLvK0g41PYz7p7l3ce/XCOygI1/Hj834lrIQpKQH1Iybwf/zIMFbRhv0OV/Mu1Nv/ffr0oUePHkRHR+vfOw0KCqJevXqoVIUfSKB9+/a0b98egFGjRhl8J8syy5YtY9y4cXTv3h2AZcuWUa1aNSIjIxk0aBBJSUmsW7eOpUuXEhISAsCKFSuoU6cO+/fvJzQ0lJiYGPbs2cOuXbto0qQJAJ988gmdOnXiwoULVKtWjX379nHu3DlOnz5NYGAgoOvVPGbMGN599128vb3ZvHkz6enpLFu2DDc3N2rWrMn58+f57LPPGD16NJKV79ZnzXLlyhXdSfHppy60r1S0V2Is5TXW8wM9ADhMc74VAbVEkWV4+WV39u51wsND5ptvUvEghB58TzLeDORLlhFv72wWmvMXX+C0bx8ALl98QXaPHmhat7ZzrqzP0QJPUYXxof7/f9IQN/KeAc0eCn3bolKpaNiwob6G2rBhwyIF04JcuXKF2NhY2rZtq//Mzc2N5s2bc+zYMQCio6PJzs42SBMYGEhwcLA+zfHjx/H09NQHU4CmTZvi4eFhkCY4OFgfTAFCQ0PJzMwkOjpan6ZZs2YGNenQ0FBu3brFlStXLP77c9u923BqvJ+v1bL6Ph8yNV7mw2AK8DMdyMoUAbUkOXBAyd69umMuNVVi9Gh3BvEFyeg6Jn7FG5yN9bVnFovE9f33DZdnz7ZTTmyrpNRQc0vH3d5ZMFDoSBgTE8Ply5e5d++eyeeH/fv3t0jGYh90gvHz8zP43M/Pj1u3bgEQFxeHUqmkfPnyRmni4uL0acqXL29Qg5QkCV9fX4M0ufdTvnx5lEqlQZqAgACj/Tz87qmnnjL5Oy5cuGD2b85//YaFXvf+/aQi7t9wX1oURDCR7+lBG/bzHu8ZrfHvhYs43bXvwf24ZV1amFNOW7Y8CXjql69eVQCVDdLsjC5Dp2JW5rnPooyMjHzLoyQcUw2xfkC1TDkV/hpXVI+T34czrZlidkC9cuUKw4cP5/jx43l2xJEkyWIB9dFtPkqW5QKbV3OnMZXenDS5PzeVl/zWhfwLvyAPm6SLytvb57HWf+gALZlEBACHaUFdThmlefqpp3Cr6PPY+yqqxy2r0sLccipXzqXANJ5eXsWyzP8hmJM8RwhRlHN1zfM3lKRjyppNvrIMzz5bvMrJWn9XswPq+PHjOXXqFB988AEtWrSgTJncY+dYlr+/7hlhXFycQVNsfHy8vmZYoUIFNBoNCQkJ+Pr6GqR5ONl5hQoViI+PNwigsiyTkJBgsJ2Hzb8PJSQkoNFoDNI8rK0+uh8wrkWXNCNZZrDcj015pBRKCikuDgiydzYs7hiNacN+MnCjIrc4qXmNgm8dij9r1lBlrXjc85DZpXzkyBHefvttRo4cSd26dfWvoOT+ZymVK1fG39+fqKgo/WcZGRkcOXJE/zy0Xr16ODk5GaS5ceMGMTEx+jSNGzcmJSWF48eP69McP36c1NRUgzQxMTEGr9tERUXh4uJCvXr19GmOHDlCRkaGQZonnniCypUNm8IchoVuStU4FZhGVotB8ksSxfnzBaZxtLkozTGMlWSg6wdxmydYFPuKnXNkG9YMqFpN8TsOrMXsUvbx8TF6Vvm4UlJSOHXqFKdOnUKr1XL9+nVOnTrFtWvXkCSJkSNHsnDhQrZt28bZs2cZNWoUHh4e9OnTR5+nAQMGMGPGDPbv38/JkycZPnw4tWrVok2bNgAEBwfTrl07xo8fz++//87x48cZP348HTp00Ff727ZtS40aNRgxYgQnT55k//79zJgxg9dff10/OlSfPn1wc3Nj1KhRnD17lm3btrFw4UJGjRpl9R6+RVWUXBX1blPcpZZCDnrc5+cUzxks/3K/SR4pSxar9vIVp76e2QH1lVde4fvvv7fozk+cOEGrVq1o1aoV6enpzJ07l1atWjFnzhwAxo4dy6hRowgLCyMkJITbt2+zZcsWvLy89NuYM2cOXbt2ZdCgQXTs2BEPDw82btyIUqnUp1m1ahW1a9emV69e9O7dm9q1a7NixQr990qlkk2bNuHu7k7Hjh0ZNGgQXbt2JTw8XJ/Gx8eHrVu3cuvWLUJCQggLC+Ott95i9OicmVkcThFeZZE1RatpFoe71CPb7jG4+TU+GHCFzAzHz6+jkyRRhsWFVTslZatxWbAA99deQ/XTT9bbTzFg9jPUF198kaioKP7v//6PQYMGERgYaBC0HmrQoIHZO2/ZsqXRiEWPkiSJqVOnMnXq1DzTuLq6EhERQURERJ5pypYty8qVK/PNS6VKldi0Kf9ng7Vq1eKnYnTAyEjMmePC2rXO1K6tYfnydHx9878IFjUwOvprqIlxWXR//QmycIGz4PLGESZuFENllnbFr45dNNYMqL5btnJ68X5OU4cuP47F7XRt5EqVrLY/R2Z2QO3cubP+/4cOHTL6/mGnn7t371omZ8JjO3urHIvX60ZVun1bwapVGqZOzX9WmCLXUB38GepX75wnixb65fBdzZhIkh1z5NhkuXSEmuL4HLgorNnke2bxeXpxFC1K/LnNmTkzUS1bYLX9OTKzA+rSpUutmQ/BChbvrWewPH++q1FA1Whg2jRXIiOdaNBAw6cfFm1KpLyeoUqXL4NCgWzBDmtFce96RsGJhFKntDRbW7OG+iob0KJrrYylIsv/bIoDPwizKrMD6iuvlI7ecKXN0aNKli/XvTiwe7eCdfWK9hKBbKKC6vLhh7iGhyMrFGQsWEDWkCGPk9XHUlKGXnMkjtoZTzBmzYCa+sgAIABHE6ubDKiXL0tcvaqgSRMNLiX0XaWSOR6VYLZp0wwH2g+f75VHyvwZNRVnZuL6oFOXpNXiNnFikbYrCNZVOm4K7H1D+euvSpo29aJbN086dvQosXMS5FlDnT9/PpIkMXHiRBQKBfPnzy9wY5IkMWnSJItmUCgecndmkh7MCHSJp1ChphLX7ZEtPUfvNOVoSkt5lZZnqLYdy9c4eI8c6U5Ghu7zEydU7Nihols3O8xFaWV5BtR58+YhSRLjxo3D2dmZefMKnhldBNTSy+gCLMvMYSrTmIMCDUt5C8sOSikIFlDSK6gPTkxbBlRTz6Vv3jTc/4EomW7dbJUj28kzoN67dy/fZaFksFRNJHcv36wsmIbufWItSkaynP527FVr7yYvS1KcOQNubmirVLFrPkpC7a4k/IZ82SGgmkMV8w9QvMb/NYdjlbJgc1JaukW2k7uXb1Ky8TvK9lRSLpuuU6fi1aIFng0b4vTll/bNTDHplCRdvoz0yLCiJiUnQ0bx7Qmu1cJ33znx9ddOZGU98sWDgOpoN5SK+/cNlkvKIwYRUEs56eZNi2xHm/u9xZJyhjiSxERclukmKpC0WtzHjSvypj77zJmgIG+aNfPkn3+MLwNSCfn7uYSH412vHl7PPYfThg2m03z0Ed5BQXgHB6Pcv9+2GbSQyZNdefNNd0aNcufNNx+ZRtEeTb7mpMl9uSghQ5cWqpTXrl1LaGgozzzzDOXKlTP6Z+mxfgXrk7UWGpBBK3PxooJvvnHi8mVJBFQrUFy3TMeu+HiJ//3Pjfv3Jc6dUxIe7mqUpqg1Go0GvvzSiQ8/dCEhwc61ouRkXD/8EABJrcb9rbeMkkhqNVfe38RgeQ1hSdNRj5lm61xaxKpVOe+hbN/uhL4C+OD8drQmXylXdkpKQDX7PdTZs2ezcOFCatWqxUsvvWT16duE4uWf/1x4/SVPUlMlPD1lvltpYkhJWbZbM2FeI//895+C2FiJJk00mBhJ0+Gs51XeZjGepPA1r1C3CNvYvt1w9qAffyx4NiFTJAnu3ZPYvVtFcLCW+vU1hIe78MknugC9ebMTR4+m2K1lWHH7dsGJ0tN5kV+49mCqurtXy/GRFlaudObQoacYOVJJ8+bF7x2P9HQJb2/ZYZt8c7/DXBzGAjeH2QF1/fr1dO7cmfXr11szP0Ix9e7CJ0lN1Z0kKSkS733oa5xIq8VeUctUhfmHH1S8+aY7arVE+/bZfPttmu0zVghZ2RJvsoYsXEikLG+zmF+LsJ28Gg9u3ZKIjZWoU8e8VouMbCWtGiq4luCOQtLyzVeJ+mAKEBOj5I8/lDRqZKeAZEYryVGaonnkMvgFg6n7VTpTprgBbuzcKXP2bDL+/sX0gu8gvXyN0xguF3XIU0djdimnpqbSrl07a+ZFeIQsw8WLrsTFOdadZV7O/udmsHzkL3ejNI520rzxhi6YAvz8sxMnTjh2FfXCFVfd4P4PnEQ3tOThw0qWLnXm0qUHp3NGBk6bNqHas8f0htTG7/8dPKikYUMv2rTxondvd7Na7Ncfqsa1BN0oOVpZwbChxn9zRz9+NSbqFOPH5xzLGo3EokXFeFgfB+3lmzs7JaXJ1+xSbtq0KWfOnLFmXoRHDB7sRt++talf34v9+613obdlU5CjBdTczcDRh/OfOMDeTAW5fftUdOniwbRpbrRq5cmdOxIevXvjPnw4Hn364LxokdE6ir//Nvrs7bfd9C0M+/c7EXXt2QLzczLuSYPlpEzjZ7GO0BE4GxVqin4O3bzpAD+ikKQU3cAq9mnyNatbEnfvSqxc6cwvv6gcfnINc5kdUCMiIti9ezfr169HFh1OrCo6WsHWrc4ApKZKjB5tfOdvKbYcG9yez0nMuaCoTkbbICdFZ+q0GzHCTX9jkJwssXB6CqpHZoNymznTaB0pzbhp+9Ilw4Bz4o5lpt9S3LPv7FMrGYonKfgSz090tOi279+HK1cku/e/M7V/Kf3B63B26JRkzru9WhS0bePOpEluvPSSB1+ucytwneIgz2eoTZoYz2SflZXFmDFjmDRpEgEBAUbzoUqSxNGjRy2fy1LmwAHDP8v16w7WXFNE9rwLNeuaZ+8rI+jehZQkzB09PC7O8Nj485hxGun2bWRfX1CZ3WXCYpQX/wPq23y/AJmZMBzdPMhZuPAma4q0HVO3YidOKOnTx52EBAVdu2azbl2a3Wrjpg5b/Q2kgzb5/vhvLS4n5RyPU2d42zE3lpPnGebr62vUE8vPz4+qVataPVOlnS2v67YMIY7erGPv5kmnr7/Gbfx4UCpJW7YMdffuBt+b85xJceM6F6jKAibhSzzTCce7enXUjRqR+t13uu3Y8ACzZZkmJuqeix88qKJzZzWTXzJsgr5FQJG2q7gTCxhe8CdNciUhQRekfvzRiUOHlLzwgn06X5l6800fQGWZLJyIIMy2mSrApaSS+YplngF1x44dtsyH8AhHqChZg12bfM2ZMNueAVWjwW3UKA7RAieyaTxsGPdzBVSzZGfTln1cR9dke5dyrGAEqt9/x3ntWujQwcIZz58tA+r69c7s3697BWjbNidq+Ze1yHaVly9DrheUfv/d8NK5Y4eT3QKqRm18XumDrCzzDh+zg642y09pmWPWFMdqBxB0Yu/YOwdWYaqGlZ6uGwhgyxYnq95ImLNtS177b92S2LLFiYsXzTzFkpN5h49pyUGacoz3MqcaJTHnNxymhT6YAqxkuP7/Lg8GObAlW46VO3264XO4uasCLbLd3IMQmExj5RuH+HiJGzdM70SbbRzIHw2oS20+3Xfx68RlKfkeKrGxsTRq1Ij3338/3428//77NG7cmPj4eItmrrSSLvxrs33ZsvefqRpq//7ujBvnzuDB7sycadxL1KYsdFW8dUuiRRNXBg9254Vmbvz9d8FX5KwsWMh4/fJsjDsTPe4Nhz2GE5QUEk7ffINH9+64vPee7sFmsVPwcWHJgCpdu4bzypUoH/RH+f57FbVqeVGrljfh4cbP1k0GVAd/vFJS5XumL1++nLt37zKugDFDx44dS0JCAitWrLBo5kora173FKdP49miBV41a+K0ebP1dmSCnOskv3ZN0jfRAXz6qfXe9zPnxsFStamP303n7n1dL+20TBXTRhU86Hpaug3v6m0YV5U3ruE+ciSqX3/FdeFCnIvhwDCWDJZz5rjw9NNedO7sYfJ1HPleIjMa/EbApCH06Kjk7ncHGDzYncxMXdoPP3QlMVF3A3b+vIKUFNBkGwfPh6+oSZYaWrQQSvwMPvnIN6D+/PPP9OrVCy8vr3w34u3tTe/evfnpp58smrnSypoB1XX2bOQz/6C+eQe3d94BG75QnfvctvtYr1ayY7th7/dfT/kVvJKpP0PuA6EYvvzu8rXhgPRuEyaYtd7Ro0oaN/akfn1P9u61fe/kR1kqoJ4/r2DBAlfu3VNw+LCKZcuMbyBPTtvGoqxRJFKWvbRj3dt/o9UaZuDUKSUvvuhJ48ZeNGvmxaX/jPelbw0qqR0yHFS+AfXSpUvUrl3brA3VqlWLixcvWiRTD82dO5cyZcoY/Hv22ZwXzmVZZu7cuVQqk63MAAAgAElEQVSvXp2KFSvSpUsXzp07Z7CNzMxMwsLCqFKlCgEBAfTr148buaZySkxMZNiwYQQFBREUFMSwYcNITDQci/batWv07duXgIAAqlSpwqRJk8gymCfJcqx5Dhz+JZMnuIU7aXycPBSybNcEl7uGKtuwk5JZZWqhK6ecbTwS0d69Kp591otnnvFi+3bjAGFy0AuNBrVa13tVq8V0d85CZcz2F1mFVs3HjKceJxjCKlLwMGu9yZPdOH9eyaVLSsaNc7NrXLDUM9TcIy4tXmwcUGfvbGawPDNtilGaDavUnDypu2m7dk3BvI+NXznRn2sioNpUvoeKJElozTyJtVqt0Ws2llCtWjViYmL0/w4fPqz/btGiRSxdupT58+ezb98+/Pz86NmzJ8nJyfo0U6dOZfv27axZs4adO3eSnJxM37590WhynjsMGTKEU6dOsXnzZiIjIzl16hTDh+d05tBoNPTt25eUlBR27tzJmjVr2LZtG9OmWWdmCms+1xzFZ8TjhwYVE/mIRGw3yYHRM9T/jG/AnNatQ3HhgsX3bV6ZWubiY2pf48e7ERenICFBwdixbmQnpfFhs910KH+BiGY/k51oPNhC7C2Z0IZannrKh55t1aSmPX4fwrJ79+K0detjb8dcp6jLBD7mJPVYwxDW8KZZ6z0MGKALGvfu2a81w5zrmjmXPrNimxlpNm03PGd/2mM88MujnZJszd6vn9lTvmdoUFAQf/75p1kb+uuvvwgKCrJIph6lUqnw9/fX//P11Q26Lssyy5YtY9y4cXTv3p2aNWuybNkyUlJSiIyMBCApKYl169Yxe/ZsQkJCqFevHitWrODMmTPsfzDvYUxMDHv27GHhwoU0adKExo0b88knn7B7924uPLiw79u3j3PnzrFixQrq1atHSEgIs2bNYu3atdzPNVGuJVjzHDhLLYPlq1S23s5yMeookZVtlMb97bfxbNUKRUyMjXJleaYC6tWrOafa3bsKfpgdQ/i5lzmmacgH517il8WXjNb57H/xnLyse/Xj1+jybIx8zNFkNBoqf/DB422jkKYy12B5HMZDIZpD0tpxxhdz3rgyo6XHnPPaYqf+hf90TRuihmpT+QbUDh068N1333H+/Pl8N3L+/HkiIyPp2NGyQ3sBXL58mRo1alC3bl0GDx7M5cuXAbhy5QqxsbG0bdtWn9bNzY3mzZtz7JhuuJjo6Giys7MN0gQGBhIcHKxPc/z4cTw9PQ1GhmratCkeHh4GaYKDgwkMzOmGHxoaSmZmJtHRVhiuroSeA0avzWhMXySl9HRc333XBjnKtV8L3VmbUxsevuYFg+W3vu9ilGbR9uoGy18drWWUpjCk1FRUj7Te2MKjg/nnR7pyBae1a1H8/bfpGGCFG1dzSQoJkpJwXr0a1Y4dJoOU6vQpi+zLrPelzeAaNgmvRo1Qmhi32Vbu3ZNITeXxH1UUI/kG1NGjR+Ph4cH//d//ERkZiTrXLBVqtZrIyEi6deuGl5cXo0db9n2nhg0b8tlnn7F582Y+/fRTYmNjad++PXfv3iU2NhbQjd70KD8/P+Li4gCIi4tDqVQaTXyeO0358uUNmnUkScLX19cgTe79lC9fHqVSqU9jSSX1pjJ3k6+szrvW4fTzzxbdt02foZoRUE3NclJaSTdvcqfFANaN+Zt/24xD+t1Eq5g9n6FKMp4dO5I9cRbKVwfjvHSpURpFemrBG0pLLzCJpX6mFgWKO3dwtdJjqfxIksyMGa48/bQ3tWp5cfC30tMGnO9Z7evry+bNm3n11VcZNmwYY8aMoWrVqnh6epKSksK///5LRkYGTzzxBBs3bjQKXI/rxRdfNFhu2LAh9erV4+uvv6ZRo0aA8fMNWZYLfOaRO42p9Oakye/zhy4U4XlgmonBy0uCq5evkuyVU0OKux2bb/rCll1+6TPN6ECWnJxUpL9XbrL81GNvoyTLXcaqd1fQKuUAyXjjpM7ix6FvAm0N0ly8eBlvrXGHpjNnPFAoZGrUSAMaWifDt24yPXkA85iKH3Fsnd4TMAxUGZmZBR472SfTgZYGn1375Reemj0bp4QEbowciVZjmZGsHg49qMzVSdMWLqeUI/LBK3CJiQreHuts8zwU5HHO82rVquX5XYG3yfXr1+fIkSN88cUX7Nq1i5iYGJKTk/Hy8qJu3bp06tSJN954Ax8fnyJn0Fyenp5Ur16dixcv0rWrbiituLg4g6bY+Ph4fW2yQoUKaDQaEhIS9M9eH6Zp3ry5Pk18fLxBAJVlmYSEBIPtPGz+fSghIQGNRmNUc80tv8LPi5ubbZvlbCXgiSd5osqTZGaCmxskS9fzTV+Ysrtw4UK+6Z2dTxe4DW8v7yL9vXKTsePzPhPUKPmICZyiLkNYbe/sGJXxxIMhJD8YKzcbZ965MtlonUqVnqJCNcPOOLNGJ/PJet25P3XoDaN1LOVWannW8RIAd6jAWBPPgV1dXAs8dpwxPgaD163D6bTu86fnzkXCsgHVHv5IqmmwfOmK9WbLKipLnOemmFXqPj4+jBs3jl27dnHp0iXi4+O5dOkSu3btYuzYsTYJpgAZGRlcuHABf39/KleujL+/P1FRUQbfHzlyRP88tF69ejg5ORmkuXHjBjExMfo0jRs3JiUlhePHj+vTHD9+nNTUVIM0MTExBq/bREVF4eLiQr169Sz+O207d6HtxMWrePFFDwICfHi5sxZ5wadW25fizBmUBw/qn9+Y82zqcVp8L24+ybnVv4NWi9bB/n7LGMkU5vM1r9KOPdymor2zZGBPumGt7QzGr+ppsjT8+quSd991Zd8+FWo1+mAKMHfVk0brWCx/WsPa8p8ma8Iy27apaN/eg+HD3Uz2Sjb12MFp2zb9/6W0NLBQ61RJvYY4Ood+kDN9+nQ6duxIYGAg8fHxREREkJaWRv/+/ZEkiZEjR/LRRx9RrVo1qlatyocffoiHhwd9+vQBdDcCAwYMYMaMGfj5+VG2bFmmTZtGrVq1aNOmDQDBwcG0a9eO8ePHs2jRImRZZvz48XTo0EF/F9O2bVtq1KjBiBEjCA8P5969e8yYMYPXX38db2/LTztUUp+hLvval7/+0h1yvxwpSzA9LbbtmzedWb/elcBALcOd1uA5fgySLJPVsyfpX3xh1QvM2lcPMHZHZ2QUjF6xE2hW4Dq2NIbF+v9rUTIf4xqgrSn++w9SU9HWqWPWDcif0c68NsYT0L2/ueXrBGtnsVASszwYPNgdtVri+HHw85MJDzccIcuW57WjTddWWjh0QL158yZDhgzRN9k2bNiQX375Rf96ztixY0lPTycsLIzExEQaNGjAli1bDEZ2mjNnDkqlkkGDBpGRkUGrVq1Yvny5wVyuq1atYvLkyfTq1QuATp06sWDBAv33SqWSTZs2MXHiRDp27Iirqyt9+vQhPDzcKr+7pAbUzT+VM1hewttGaWQKP7R2djYMHlydhIQHk7Jzh5kPunc4b91KholJtk0pag11zI6cmTyWXOhctI3YkLk9b63F6auvcBs/HkmrJXPoULS8V+A6YeH+BsvjJtumVcxcX5xthuaRVpAlS1x4++1MJk504+pVBe+8U/Dwk2C5mqUIqPYhJSYmltDLd/E1t8sJ5h9qY+9s2EU2KlQPnkEm5RqtKi8//KBi4EDDDiuPXpiyevRg6D9hrP2neb7bWTlgDy8vbmT0+YkTShISJEJC1CiVxuuVKeNYF3dHlztoBErXuSFbr8nWXgYPzuTzz3U3L87OMm3LnWDX7ecN0uQui1b8ygFaPfa+j9CUpuj6fZTmsXXzkpiYZJXtitsYB1RSa6jmKMqd9Y0b+a/j/P33SOlm1BBMVFHXrFQSEuJJnz4evPaSLpo6RUbi3rcvLgsWgNp4mEGhcLQWevfS0TwMpgBZWZJRMDXFkjXUPYQyjk8ssj3BPCKgOqBSHE/NCqiKf/5Bun1bvyynFNyRQ7pypcA0pm5kJkzy1P//p32eXPzlMtFD1vDO7i5EzrmCasPXBW5XyF9pbp6M5jmC+QcfEvmMkRYLqH/QkBfZwyLynymsNFJYsRd+6T2SBYekQUkcfhylCekpGlQ//IBq2zZdb92sLNz79cOraVO86tbVjVoDSNGmR6k5SV1+oBvpuJp1oTJnQJdjQ7+mJQdYwtsMYD07xh4o1O8TjJXmgDqdcM4TzH18GMsii42tPRbr9aAv7pRWDKgO3SmptCrNTb4nqE93fuAu5ald+yrHE4fjhnFzrZSVhcuSJai7dDE5H+RWetCHSLQoqccJ6ph4BzA3c8r9/cQxaMl5kPoqG/JJLZhylUpM4CPu480HTCvVAXUHOR3a1DiZfGVIsCwVat3ds8Lyx50IqA6oNAfUcSzkLroRt/5ODGItrzOclSbTqo4cAUzXLPvzjT7wRVOfK2ZMAqDVSiiio3HesAFNnTpkDxhglOYmhp1n1DgZpRHyN5olbKcbAOeogQYTPb0EwUqUaHTjiFshoJbeW0MHZqkBsouj3C/Nb6IvANd5kkRyetPeoiLHaExWFsgmAmomrgbL9yhnnCgX+X4Kihe7E7XqKrfGLMSji/GA9cLjexhMAa4RRCJl7ZgboVTKY2KOxyUCqgMqzTXU3JRomEgElbjOk9xgJ504ShOCiaEpx+haO4GsbMvcgKijjtAo+zCd2EVt/ubwYVFzEoQSyUq980WTrwMSATXHNSqxB90kCWl4MI8ppOCpH/v1eFwVMtIsU2Dr0vtwDt04pBm4MYrPLLJdQRAch4wkAmppIsbhzBGD4Zygpl56P5XyjEX29RcNDJZFBxFBKJkkrdYqryeKJl8HpNGKgCoIgmAN1qyhioDqgDSluFOSIAiC1YmAWnpoNSKgCoIgWIOooZYyoslXEATBOjQozRsWrQhEQHVA2aKGKgiCYBVqVEiihlp6ZGSLzteCIAjWoEWJNksE1FIjUy0GFBAEQbAWU+N/W4IIqA4oXS3GhxUEQbAWdaYYerDUyFCLJl9BEARr0WSJGmqpkZktmnwFQRCsRS0CaumRkSl6+QqCIFiLaPItRTKzxJ9FEATBWrSiU1LpIZ6hCoIgWI9o8i1F0r0r2DsLgiAIJZYm2zpzZIqAWkirV6+mbt26+Pv707p1aw4fPmzxfaRrXS2+TUEQBEFH1FAdwJYtW5gyZQoTJkzgt99+o3Hjxrz00ktcu3bNovvp3TvLotsTBEEQcoiA6gCWLl3KK6+8wsCBAwkODiYiIgJ/f38+//xzi+7n448zqKK6YtFtCoIgCDqa9GyrbFcEVDNlZWURHR1N27ZtDT5v27Ytx44ds/j+FJJ12vgFQRBKO018olW2K7qTmikhIQGNRoOfn5/B535+fsTFxeW53oULF4q0PxfXcmCdmyhBEIRS7XyDJqiKeG2uVq1ant+JgFpIkmQ46IIsy0afPSq/ws9PpXpw5kCRVhUEQRDyERAQRLVqlh/cQTT5mql8+fIolUqj2mh8fLxRrdUSKjztZvFtCkVTpox1OjAIhefsXLwehbi6GubXt7w4lhyBlaZDFQHVXM7OztSrV4+oqCiDz6OiomjSpInF91epUvE/8Ro1Mj5qa9Y0vCtcsSKNzz9Po3Jl3e9t2VLNxYv3uXw5ibfeyqRLl2xefrngXs9PP51eYJrgYOM70iFDMg2Wq1c3TPPFF2msX59Gy5ZqevTIIjr6PgkJSXTooGuPd3KS2bMnhaNHk6lfX63/DQMHGuY5LCyDO3eSuHw5iTVr0pg6NYMPPyw4zzt2pDB0aCbPPaehfftsnn664LvqF15QU65czvHj76/lyJFkNm5MxcVFt37r1sZ/mxdfLNozhpAQw/WeeKLgY/e335J56qmc37J1ayq+vjnrtW+fTWCg4XaOHUvms8/S8fKS8fKSWbUqjWnTMgzSBAQYrtOsmZWunMCffyZToULO/n74IcVg2dNT5tChFH2e2rXL5vc/UgzO7fLltXz3XSouLpa/Ufj00zR6986ibFktVatqzDp2LOnHH1PYsCGVJUvS2LgxlX79HOftBWsFVCkxMbF43fLZ0ZYtWxg+fDgfffQRTZo04fPPP2f9+vUcOXKEoKAgi+7rxg2JFi3cSEzUTeXWurWaX381bKF3dpbJyircuL+BgVrmzUtn3Dg3EhIkBg3K4quvnNFocrbj56flzp3C32v9+msyBw6ouHJFwZAhWQQEaKld25ukJN22Fy1K45VXslm/3pk7dyT698+iUiXd4Sc/OApNtZ6npcGLL3py5oySJ57QsmNHKqtWObNsmYs+zahR1/nss0CD9UJCsomK0pVfy5Zqpk3LoGNHT/33Bw4kU6eOlr/+UnL2rIJOndRkZ0PPnh6cO6ekVSs1kZGpODsb50mWISZGQUCAFm9v09/v2KHi3DklvXpl88wzpoNMWJgrq1bpfsesWen4+sqsWOFC9eoaFixIp0wZw/R796ro3dtDvxwamo2LC+zcqfudvr5aTp1KJiNDYs8eFc89pyE4OGffFy5coFq1aqSkQEiIJxcuKAkI0LJvXwr+/jJ79qiQZWjXTk3fvu788kvOVIJVqmi4eNFw4oaTJ+9TubLhJSQ5Gdavd8bLS6Z//2zmzHHho49y3q3+5ptUOnVSI8uQmQmuD7765x8FW7c6Ubu2hi5d1Ny4IbF2rTOVKmkZMCBbf2xkZIBWC+7uuuW7dyUyMiAgQGbzZieGDXNDliVat1bz/fepfP21E2+9pUtcubKWK1cMj+13380gMtKJc+eUlCmjZffuVJo08TJIs3ZtKgMHuiPLukxERqbSrp3ueLl0SUG1alokCY4eVTJvngvlysmEh2cQECCTmanLo7+/jEIB165JfPONM/7+Wl59NRuVSvfZtWsKGjbU8MEHrixalHNsL1uWRliYGykpun23b59NcLCWxYtz0oSFZRARYfj+emJiksHyzz+rePllD6xh9OhMli93Rq2WqFNHw+bNqVSsaHhcZGfDN984EROjZNSoTA4fVjF0qHu+261fX81//ym5fz/nwnD+/H2iolQcPqw7vj/6yIUbNwp3vfr++xTatLH8DYYIqIW0evVqFi1aRGxsLDVq1GDOnDm0aNHCKvs6cOAyUVHBeHvLvPxyNjVrGl65P/44nXfeyWka3rUrxSBgAEyZksG8eTkn2ldfpdK9uxqNRneS+/nJLFnizPTpuu1IksyCBRmEheXf5BwQoOXmzZyDWKmUuXjxPj4+hukuXlTw7bdOBAdr6d49G0UR20Sys3UBLChIF8Du34eXXvLg2DEVXbpkM336Sd58sz5nz+ou+C+/nMWcORksXuyCLMPYsZmULy8TFaVi/34VHTpk07y56RMqOxvu35coV042GeAt7cIFBQoFeQbdR2VlQZcuHvz+uwofH5kdO1Lw8ZGZPt2Nu3clJk3KoGXLvC8UDwMq6O7Sz53TBQNXE2OJHDyopGvXnOPp559TGDjQnVu3dH/E4GANx46lFJjnW7ck+vd35+RJJf36ZfPZZ+lWLdezZxXcvq2gVSs1qgf3oImJcOaMkkaNNFSoYHiQfvddKs2bqzl7VkmVKlrKlpXp29ed3bt1NxNPP63hzz9T2L9fxQ8/ONG6tZpevazXY/DyZYmePT24dElJSEg2mzensX27E7Nnu+DrK7NkSTre3jJvvunO338rGTo0k7CwTGrU8OLePd3fZsyYTGbPNqy9q9XQpIkn//2nO0c+/TSNa9cU+kD8xBNaRo/OZNq0/M/9SpW0XLuWcyJ363aHtWuduXlTIjNT4umnzWtdy8yECRPc+PlnFc2bq1m5Mp3XX3dn1y5duXfpks2GDWlcvizx889O+Ptr6dZNbXTsNGqkuzEsjIc3RJYmAqoDe/Til54OTzxheCG4di2JfftU/Pabik6d1LRrp2bcOFe+/FJ35zpjRgb9+2fRu7cHZ88qCQ3NZuPGNJxyzV+ekQFz5rgSHa3ktdeyqFRJS6dOhoHZ319LbGzOSTRtWgbx8RIrVrjg6iozbVoGb79t+yad9HRwc9OVlbv7syxe7IKXl8zYsZl4eha8fnGUlQXnzysIDNQa1WAL8ugxZY5t21RERalo315Np05qdu9WERbmhpOTzKefptOihfl3+Wo1+gBnT0OHurF5s67Zwd9fV6N3cTFMc+uWxIQJWSgUPkyZkkHt2rZ9BJOVlVOrNffm48wZBcuXu/Dkk1rGjMnU1+AflZSka8145hktjRtr0Ghg1Spnrl9X8OabWRw4oGTMmPxrjf/7XwbHjinZu9eJKlU0LFp0hpYtKxfhVxpLSYHPP3dGpYI33sgy+Rtye/55T6OWk4LUrKnh8OGCbwYLSwRUB/boxU+WdQfOpUu6A6dKFd1dc+6TTZbhyBElbm5Qv77uYqdW6w5UHx/TTaq5qdVQq5aXPoCOGpVJ1apafW3Y21vm9GldbTQ1VbeOh3VaksxW2EBRWolygvh4iffecyU+XmLixEwaNjR9U1Aay+rrr50YNSr/KLZtWwovvKAhIUHCx0fmyhX7ltNzz3kZNeMXpHp1DUePWj6gOsD9omAOSYJPPkln/HhdUPvkE9PNZpKEUVOmSkWhajIqFezcmcqyZc5UqCAzenQmbm7g7i4TE6PglVey9U279g6kglBYD5tNBWN37hhfVCZMyNA/A69TR0PLlhokCfz8HKMullWEhrHcnSMtRQTUYqRNGw0nTlj+rsqUZ57R8uGHhs9g+vUTI00IQkmmNNFyOm1aJs8+q+XOHYnXXsuySb+Cwnj4TD8//ftn8c03umZ+SZKZNi2zgDWKRgRUQRAEAYBu3bL1HRQBpk/PQKGAvn2L98305MkZeHrK/P23koEDs8zqAFgU4j1UQRAEAYCgIJnPP0+jdWs1I0ZkMnKkdWpylpT7XeTcy6B7pSoiIoOffkq1akubCKiCIAiCXq9e2fzwQyrz5mUUiz4SAwdm0aiRGkmS6ds3i1atjF+HMfUuuTWIJl9BEASh2KpQQeaXX1LRakGhgOho+9UTRQ1VEARBKPYeDhrz3HNagoJynpG+8Ybtmq1FDVUQBEEoMSRJ967s0qUulC2rG+TFVkRAFQRBEEqUp57SdUKyNdHkKwiCIAgWIAKqIAiCIFiACKiCIAiCYAEioAqCIAiCBYiAKgiCIAgWIAKqIAiCIFiAmA9VEARBECxA1FAFQRAEwQJEQBUEQRAECxABVRAEQRAsQARUQRAEQbAAEVAFQRAEwQJEQHVAq1evpm7duvj7+9O6dWsOHz5s7yzZ1Mcff0xISAiVKlXimWeeoW/fvpw9e9YgjSzLzJ07l+rVq1OxYkW6dOnCuXPnDNJkZmYSFhZGlSpVCAgIoF+/fty4ccOWP8WmPvroI8qUKUNYWJj+M1FOOW7fvs2IESN45pln8Pf3p0mTJhw8eFD/vSgrHY1GQ3h4uP4aVLduXcLDw1GrcybuFmVlmgioDmbLli1MmTKFCRMm8Ntvv9G4cWNeeuklrl27Zu+s2czBgwd588032b17N9u2bUOlUtGjRw/u3bunT7No0SKWLl3K/Pnz2bdvH35+fvTs2ZPk5GR9mqlTp7J9+3bWrFnDzp07SU5Opm/fvmg0Gnv8LKv6/fff+eqrr6hVq5bB56KcdBITE+nQoQOyLPPtt99y7NgxFixYgJ+fnz6NKCudhQsXsnr1aubPn8/x48eZN28eq1at4uOPP9anEWVlmngP1cGEhoZSq1YtPv30U/1nzz//PN27d2fmzJl2zJn9pKSkEBQUxIYNG+jUqROyLFO9enWGDh3KxIkTAUhPT6datWq8//77DBo0iKSkJKpWrcrSpUt5+eWXAbh+/Tp16tQhMjKS0NBQe/4ki0pKSqJ169YsWrSIBQsWULNmTSIiIkQ5PWL27NkcOnSI3bt3m/xelFWOvn37UrZsWZYvX67/bMSIEdy7d49NmzaJssqHqKE6kKysLKKjo2nbtq3B523btuXYsWN2ypX9paSkoNVqKVOmDABXrlwhNjbWoJzc3Nxo3ry5vpyio6PJzs42SBMYGEhwcHCJK8tx48bRvXt3WrdubfC5KKccO3bsoEGDBgwaNIiqVavywgsvsHLlSmRZV58QZZWjadOmHDx4kPPnzwPwzz//cODAAV588UVAlFV+xATjDiQhIQGNRmPQDAXg5+dHXFycnXJlf1OmTKFOnTo0btwYgNjYWACT5XTr1i0A4uLiUCqVlC9f3ihNSSrLr776iosXL7JixQqj70Q55bh8+TJr1qxh1KhRjBs3jtOnTzN58mQAhg0bJsrqEePGjSMlJYUmTZqgVCpRq9VMnDiRIUOGAOK4yo8IqA5IkiSDZVmWjT4rLf73v/9x9OhRdu3ahVKpNPiuKOVUksrywoULzJ49m59++glnZ+c805X2cgLQarXUr19f/9jkueee4+LFi6xevZphw4bp04my0vXj2LhxI6tXr6Z69eqcPn2aKVOmEBQUxOuvv65PJ8rKmGjydSDly5dHqVQa3cHFx8cb3Q2WBlOnTuW7775j27ZtPPXUU/rP/f39AfItpwoVKqDRaEhISMgzTXF3/PhxEhISaNasGeXLl6d8+fIcOnSI1atXU758ecqVKweIcgLdMRMcHGzw2bPPPsv169f134MoK4AZM2YwevRoevfuTa1atejXrx9vvfUWn3zyCSDKKj8ioDoQZ2dn6tWrR1RUlMHnUVFRNGnSxE65so/JkycTGRnJtm3bePbZZw2+q1y5Mv7+/gbllJGRwZEjR/TlVK9ePZycnAzS3Lhxg5iYmBJTll26dOHw4cMcOHBA/69+/fr07t2bAwcOULVqVVFODzRt2pR///3X4LN///2XSpUqAeKYelRaWppRa5BSqUSr1QKirPKjnDJlynv2zoSQw8vLi7lz51KxYkVcXV2JiIjg8OHDLFmyBB8fH3tnzyYmTpzIxo0b+fLLLwkMDCQ1NZXU1FRAd9MhSRIajZoamX8AAARtSURBVIZPPvmEqlWrotFomDZtGrGxsSxcuBAXFxdcXV25ffs2q1atonbt2iQlJTF+/Hi8vb2ZNWsWCkXxv5d0dXXFz8/P4N/mzZsJCgri1VdfFeX0iMDAQObPn49CoaBixYr8+uuvhIeHM378eBo0aCDK6hExMTFs2rSJqlWr4uTkxIEDB3j//ffp1asXoaGhoqzyIZ6hOphevXpx9+5dIiIiiI2NpUaNGnz77bcEBQXZO2s2s3r1agC6d+9u8PnkyZOZOnUqAGPHjiU9PZ2wsDASExNp0KABW7ZswcvLS59+zpw5KJVKBg0aREZGBq1atWL58uVGd98lmSgnneeff54NGzYwe/ZsIiIiCAwM5H//+5++ow2IsnpowYIFfPDBB0yYMIH4+Hj8/f0ZOHAgkyZN0qcRZWWaeA9VEARBECygZNa7BUEQBMHGREAVBEEQBAsQAVUQBEEQLEAEVEEQBEGwABFQBUEQBMECREAVBEEQBAsQAVUQhDzNnTtXP8uPIAj5EwM7CEIpY26AXLp0qZVzIgglixjYQRBKmU2bNhksf/nll/zxxx8sWbLE4PMmTZoQGBiIWq3G1dXVllkUhGJJBFRBKOVGjhzJli1b9PNcCoJQNOIZqiAIeTL1DLVOnTr07t2bI0eOEBoaSsWKFWnatKl+ZpE9e/bQqlUr/P39ad68OceOHTPa7u3btxk7dizVq1enQoUKPP/88yxatAhZFvf3QvElAqogCIV25coVBg0aRNu2bZk5cyapqan079+fLVu2MGbMGLp168b06dOJi4tjwIABZGZm6te9c+cO7dq1Y/fu3QwcOJD58+fTsGFDZs6cqZ/8QBCKI9EpSRCEQvv333/ZsWMHLVq0AKBu3bp06dKF4cOHc/jwYapVqwbopk0bNGgQu3bt0s8eFB4eTmZmJocOHaJChQoADBo0iIoVK7JkyRJGjhxJ5cqV7fPDBOExiBqqIAiFVrVqVX0wBWjYsCEAjRs31gdTgAYNGgBw+fJlAGRZ5ocffqBDhw4olUoSEhL0/0JDQ9FqtRw6dMh2P0QQLEjUUAVBKLTAwECDZRcXF1xcXHjyyScNPvf29gYgMTERgPj4eBITE1m/fj3r1683ue34+Hgr5FgQrE8EVEEQCi2vSaLz+vxhZyOtVgtAnz59eO2110ymrVKligVyKAi2JwKqIAg24+vri7e3N2q1mjZt2tg7O4JgUeIZqiAINqNUKunWrRs//vgj0dHRRt8nJSWRnZ1th5wJwuMTNVRBEGzqvffe49ChQ3Ts2JEBAwZQs2ZNkpOTOXv2LNu3b+evv/7C39/f3tkUhEITAVUQBJvy9fVl7969REREsGPHDr788kt8fHyoWrUqU6ZMoWzZsvbOoiAUiRh6UBAEQRAsQDxDFQRBEAQLEAFVEARBECxABFRBEARBsAARUAVBEATBAkRAFQRBEAQLEAFVEARBECxABFRBEARBsAARUAVBEATBAkRAFQRBEAQLEAFVEARBECzg/wGokhesEVh1vQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The root mean squared error is 7141.462663113151.\n" + ] + } + ], + "source": [ + "# plot train of nn_model\n", + "# plot results \n", + "plot_predictions(y_train, nn_train_preds)\n", + "return_rmse(y_train, nn_train_preds)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "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 36717.236100895294.\n" + ] + } + ], + "source": [ + "# plot test nn \n", + "# get results \n", + "plot_predictions(y_test, nn_test_preds)\n", + "return_rmse(y_test, nn_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot loss\n", + "plot_loss(history_nn)" + ] + } + ], + "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/multivar_gru-checkpoint.ipynb b/.ipynb_checkpoints/multivar_gru-checkpoint.ipynb index 50d3a48..ea84e1f 100644 --- a/.ipynb_checkpoints/multivar_gru-checkpoint.ipynb +++ b/.ipynb_checkpoints/multivar_gru-checkpoint.ipynb @@ -13,10 +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", + "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", "#\"/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": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "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/data.csv'\n", + " abdul_path = '/Users/abdul/Downloads/SalmonNet/passBonCS.csv'\n", " king_all_copy, king_data= load_data(chris_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "(984, 1)" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -391,7 +391,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -508,7 +508,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -635,7 +635,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -699,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -716,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 16, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -901,13 +901,14 @@ "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", + "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": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1025,7 +1026,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 17, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1038,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1168,7 +1169,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1323,7 +1324,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1336,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1490,7 +1491,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1503,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1669,7 +1670,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 21, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1684,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1849,7 +1850,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 22, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1868,7 +1869,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1879,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1888,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1904,7 +1905,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1914,7 +1915,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1923,7 +1924,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1942,7 +1943,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1958,7 +1959,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1973,7 +1974,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1982,7 +1983,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1992,7 +1993,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2006,7 +2007,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2170,7 +2171,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 34, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2183,7 +2184,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -2199,14 +2200,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 41, "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", + " filepath=abdul_checkpoint_path,\n", " save_weights_only=True,\n", " monitor='val_accuracy',\n", " mode='max',\n", @@ -2250,7 +2252,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2291,7 +2293,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2377,46 +2379,38 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" - ] - } - ], + "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", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MONTH 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": 40, + "execution_count": 59, "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": 41, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2433,9 +2427,9 @@ } ], "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)" @@ -2443,7 +2437,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -2451,121 +2445,121 @@ "output_type": "stream", "text": [ "Epoch 1/1000\n", - "8/8 - 8s - loss: 0.0128 - root_mean_squared_error: 0.1132 - val_loss: 0.0447 - val_root_mean_squared_error: 0.2115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 2/1000\n", - "8/8 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2011\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 3/1000\n", - "8/8 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0989 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2064\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 4/1000\n", - "8/8 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 5/1000\n", - "8/8 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0962 - val_loss: 0.0414 - val_root_mean_squared_error: 0.2035\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 6/1000\n", - "8/8 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 7/1000\n", - "8/8 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0404 - val_root_mean_squared_error: 0.2011\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 8/1000\n", - "8/8 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0396 - val_root_mean_squared_error: 0.1990\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 9/1000\n", - "8/8 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1986\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 10/1000\n", - "8/8 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1969\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 11/1000\n", - "8/8 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\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", "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: 0.0914 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1948\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 13/1000\n", - "8/8 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\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", "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: 0.0908 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\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", "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: 0.0905 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\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", "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: 0.0903 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 17/1000\n", - "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 18/1000\n", - "8/8 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 19/1000\n", - "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 20/1000\n", - "8/8 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 21/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 22/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 23/1000\n", - "8/8 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 24/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\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 25/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 26/1000\n", - "8/8 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\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 27/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\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 28/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 29/1000\n", - "8/8 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\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 30/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 31/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 32/1000\n", - "8/8 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\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 33/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 34/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 35/1000\n", - "8/8 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 36/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1843\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 37/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 38/1000\n", - "8/8 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 39/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2574,121 +2568,121 @@ "output_type": "stream", "text": [ "Epoch 40/1000\n", - "8/8 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 41/1000\n", - "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\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 42/1000\n", - "8/8 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1807\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 43/1000\n", - "8/8 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\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 44/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 45/1000\n", - "8/8 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 46/1000\n", - "8/8 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\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", "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: 0.0828 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\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", "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: 0.0823 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\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", "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: 0.0305 - val_root_mean_squared_error: 0.1747\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 50/1000\n", - "8/8 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0301 - val_root_mean_squared_error: 0.1736\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", "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: 0.0809 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1724\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 52/1000\n", - "8/8 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1712\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 53/1000\n", - "8/8 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1698\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 54/1000\n", - "8/8 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1683\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 55/1000\n", - "8/8 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0278 - val_root_mean_squared_error: 0.1667\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 56/1000\n", - "8/8 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0780 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 57/1000\n", - "8/8 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1633\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 58/1000\n", - "8/8 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0261 - val_root_mean_squared_error: 0.1616\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 59/1000\n", - "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1599\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 60/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0251 - val_root_mean_squared_error: 0.1583\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 61/1000\n", - "8/8 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0753 - val_loss: 0.0246 - val_root_mean_squared_error: 0.1567\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 62/1000\n", - "8/8 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0241 - val_root_mean_squared_error: 0.1553\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 63/1000\n", - "8/8 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0743 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1539\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 64/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0738 - val_loss: 0.0232 - val_root_mean_squared_error: 0.1525\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 65/1000\n", - "8/8 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1511\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 66/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1497\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 67/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1483\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 68/1000\n", - "8/8 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0719 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 69/1000\n", - "8/8 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1456\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 70/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1442\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 71/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 72/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1413\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 73/1000\n", - "8/8 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1399\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 74/1000\n", - "8/8 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1385\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 75/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0188 - val_root_mean_squared_error: 0.1371\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 76/1000\n", - "8/8 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1356\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 77/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0180 - val_root_mean_squared_error: 0.1343\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 78/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0177 - val_root_mean_squared_error: 0.1329\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2697,121 +2691,121 @@ "output_type": "stream", "text": [ "Epoch 79/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0173 - val_root_mean_squared_error: 0.1316\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 80/1000\n", - "8/8 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1303\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 81/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1291\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 82/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0662 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1280\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 83/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0161 - val_root_mean_squared_error: 0.1270\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 84/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1260\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 85/1000\n", - "8/8 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0653 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1251\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 86/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - val_loss: 0.0154 - val_root_mean_squared_error: 0.1242\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 87/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1234\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 88/1000\n", - "8/8 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0644 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1226\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 89/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1219\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 90/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0147 - val_root_mean_squared_error: 0.1211\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 91/1000\n", - "8/8 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0636 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 92/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1196\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 93/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0632 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1189\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 94/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - val_loss: 0.0139 - val_root_mean_squared_error: 0.1181\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 95/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0138 - val_root_mean_squared_error: 0.1175\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 96/1000\n", - "8/8 - 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: 0.0082 - root_mean_squared_error: 0.0908 - 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 97/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1161\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", "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: 0.0620 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1150\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", "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: 0.0620 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 100/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1131\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 101/1000\n", - "8/8 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 102/1000\n", - "8/8 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1116\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 103/1000\n", - "8/8 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - val_loss: 0.0160 - val_root_mean_squared_error: 0.1267\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 104/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 105/1000\n", - "8/8 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1460\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", "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: 0.0655 - val_loss: 0.0155 - val_root_mean_squared_error: 0.1246\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 107/1000\n", - "8/8 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1216\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 108/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1203\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 109/1000\n", - "8/8 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1149\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 110/1000\n", - "8/8 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0616 - val_loss: 0.0132 - val_root_mean_squared_error: 0.1148\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 111/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1136\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", "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: 0.0602 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1140\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 113/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0124 - val_root_mean_squared_error: 0.1114\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 114/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1124\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 115/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0592 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 116/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 117/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0122 - val_root_mean_squared_error: 0.1103\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" ] }, { @@ -2820,121 +2814,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 118/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 119/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0119 - val_root_mean_squared_error: 0.1091\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 120/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\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 121/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - val_loss: 0.0118 - val_root_mean_squared_error: 0.1086\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 122/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0117 - val_root_mean_squared_error: 0.1083\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 123/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1075\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 124/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 125/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1072\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 126/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1071\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 127/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 128/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1057\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 129/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 130/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0578 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1063\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 131/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0570 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1060\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 132/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1048\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 133/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\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 134/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1058\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 135/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1065\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 136/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1047\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 137/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 138/1000\n", - "8/8 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1062\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 139/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - val_loss: 0.0115 - val_root_mean_squared_error: 0.1073\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 140/1000\n", - "8/8 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1052\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 141/1000\n", - "8/8 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - val_loss: 0.0110 - val_root_mean_squared_error: 0.1051\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 142/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0113 - val_root_mean_squared_error: 0.1064\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 143/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1044\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 144/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0561 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1030\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 145/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1044\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 146/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - val_loss: 0.0108 - val_root_mean_squared_error: 0.1037\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 147/1000\n", - "8/8 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0553 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 148/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1027\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 149/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0550 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1024\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 150/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0549 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1022\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 151/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\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", "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: 0.0546 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1015\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 153/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\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 154/1000\n", - "8/8 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1013\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 155/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1010\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 156/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1007\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" ] }, { @@ -2943,121 +2937,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 157/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0541 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1006\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 158/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\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 159/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0100 - val_root_mean_squared_error: 0.1001\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 160/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0999\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 161/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0100 - val_root_mean_squared_error: 0.0998\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 162/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 163/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0992\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 164/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\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 165/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\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 166/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 167/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 168/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 169/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 170/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0981\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 171/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 172/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0972\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 173/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\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 174/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0975\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", "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: 0.0520 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0973\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 176/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0962\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 177/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0963\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 178/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 179/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0975\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 180/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0956\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 181/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0952\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 182/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 183/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 184/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0955\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 185/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 186/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0990\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 187/1000\n", - "8/8 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0978\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 188/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0980\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 189/1000\n", - "8/8 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0969\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 190/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0534 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0974\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 191/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0938\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 192/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 193/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0933\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 194/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0932\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 195/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0932\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" ] }, { @@ -3066,121 +3060,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 196/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0919\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 197/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 198/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0908\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 199/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0915\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 200/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0903\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 201/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0904\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 202/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0896\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 203/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0892\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 204/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0891\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 205/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0880\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 206/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0883\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 207/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 208/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 209/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0861\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 210/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0866\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 211/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 212/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0857\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 213/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\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", "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: 0.0493 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 215/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\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", "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: 0.0499 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 217/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0840\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 218/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 219/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 220/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 221/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0505 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0876\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 222/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0861\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 223/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0826\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 224/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 225/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 226/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 227/1000\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", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - 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 228/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 229/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 230/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0783\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 231/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0771\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 232/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0764\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 233/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 234/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0765\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" ] }, { @@ -3189,121 +3183,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 235/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\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", "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: 0.0443 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0737\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 237/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 238/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\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", "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: 0.0444 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 240/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0752\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", "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: 0.0451 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 242/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 243/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0738\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", "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: 0.0471 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0747\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", "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: 0.0472 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0766\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 246/1000\n", - "8/8 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 247/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 248/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 249/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0787\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 250/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 251/1000\n", - "8/8 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 252/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0750\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 253/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 254/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0699\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 255/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 256/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0678\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 257/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 258/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 259/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\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", "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: 0.0407 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 261/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 262/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0648\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 263/1000\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", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - 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 264/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 265/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 266/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0626\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 267/1000\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", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - 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 268/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\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", "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: 0.0420 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\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", "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: 0.0431 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 271/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 272/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 273/1000\n", - "8/8 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0597\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" ] }, { @@ -3312,121 +3306,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 274/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0641\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 275/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0640\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", "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: 0.0439 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0613\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 277/1000\n", - "8/8 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 278/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 279/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\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", "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: 0.0408 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\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", "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: 0.0033 - val_root_mean_squared_error: 0.0572\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 282/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 283/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 284/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\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", "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: 0.0380 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 286/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 287/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0534\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", "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: 0.0387 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\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", "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: 0.0389 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 290/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 291/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 292/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\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", "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: 0.0405 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 294/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 295/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 296/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 297/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 298/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 299/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 300/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0540\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 301/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 302/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 303/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\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", "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: 0.0361 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 305/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 306/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 307/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 308/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 309/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 310/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 311/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\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", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\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" ] }, { @@ -3435,121 +3429,121 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 313/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 314/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 315/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 316/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 317/1000\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", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0210 - val_root_mean_squared_error: 0.1449\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 318/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 319/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0394 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 320/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 321/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0480\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 322/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 323/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 324/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\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", "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: 0.0353 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 326/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\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", "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: 0.0341 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\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", "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: 0.0341 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 329/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 330/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\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", "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: 0.0336 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 332/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\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", "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: 0.0334 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 334/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 335/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 336/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0446\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 337/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 338/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 339/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 340/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 341/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 342/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 343/1000\n", - "8/8 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 344/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 345/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0434\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 346/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 347/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 348/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 349/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\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", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\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", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\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" ] }, { @@ -3558,2059 +3552,2053 @@ "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 352/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 353/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 354/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 355/1000\n", - "8/8 - 0s - loss: 9.6523e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 356/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 357/1000\n", - "8/8 - 0s - loss: 9.9345e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 358/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 359/1000\n", - "8/8 - 0s - loss: 9.8481e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 360/1000\n", - "8/8 - 0s - loss: 9.8708e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 361/1000\n", - "8/8 - 0s - loss: 9.7191e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 362/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 363/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 364/1000\n", - "8/8 - 0s - loss: 9.7586e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 365/1000\n", - "8/8 - 0s - loss: 9.9016e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 366/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 367/1000\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", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0178 - val_root_mean_squared_error: 0.1332\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 368/1000\n", - "8/8 - 0s - loss: 9.6503e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 369/1000\n", - "8/8 - 0s - loss: 9.9501e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 370/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 371/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 372/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0402\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 373/1000\n", - "8/8 - 0s - loss: 9.9858e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 374/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 375/1000\n", - "8/8 - 0s - loss: 9.5309e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 376/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0434\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 377/1000\n", - "8/8 - 0s - loss: 9.7711e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 378/1000\n", - "8/8 - 0s - loss: 9.6087e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 379/1000\n", - "8/8 - 0s - loss: 9.9991e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 380/1000\n", - "8/8 - 0s - loss: 9.0055e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 381/1000\n", - "8/8 - 0s - loss: 9.2014e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 382/1000\n", - "8/8 - 0s - loss: 9.2451e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 383/1000\n", - "8/8 - 0s - loss: 9.6486e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 384/1000\n", - "8/8 - 0s - loss: 8.7558e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 385/1000\n", - "8/8 - 0s - loss: 8.8936e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\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 386/1000\n", - "8/8 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 387/1000\n", - "8/8 - 0s - loss: 9.6824e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\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", - "8/8 - 0s - loss: 9.0488e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\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", - "8/8 - 0s - loss: 9.8502e-04 - root_mean_squared_error: 0.0314 - 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" + "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 390/1000\n", - "8/8 - 0s - loss: 9.9773e-04 - root_mean_squared_error: 0.0316 - 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 391/1000\n", - "8/8 - 0s - loss: 8.2372e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 392/1000\n", - "8/8 - 0s - loss: 9.6726e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 393/1000\n", - "8/8 - 0s - loss: 9.1999e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 394/1000\n", - "8/8 - 0s - loss: 7.9653e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 395/1000\n", - "8/8 - 0s - loss: 7.9777e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 396/1000\n", - "8/8 - 0s - loss: 7.7308e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 397/1000\n", - "8/8 - 0s - loss: 7.1454e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 398/1000\n", - "8/8 - 0s - loss: 7.3915e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 399/1000\n", - "8/8 - 0s - loss: 7.2592e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 400/1000\n", - "8/8 - 0s - loss: 7.2877e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 401/1000\n", - "8/8 - 0s - loss: 6.9985e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 402/1000\n", - "8/8 - 0s - loss: 7.7031e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 403/1000\n", - "8/8 - 0s - loss: 7.5499e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 404/1000\n", - "8/8 - 0s - loss: 7.1449e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 405/1000\n", - "8/8 - 0s - loss: 7.9211e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 406/1000\n", - "8/8 - 0s - loss: 7.7459e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 407/1000\n", - "8/8 - 0s - loss: 7.0836e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 408/1000\n", - "8/8 - 0s - loss: 8.6420e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 409/1000\n", - "8/8 - 0s - loss: 7.9743e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 410/1000\n", - "8/8 - 0s - loss: 6.9904e-04 - root_mean_squared_error: 0.0264 - val_loss: 9.7235e-04 - val_root_mean_squared_error: 0.0312\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 411/1000\n", - "8/8 - 0s - loss: 7.2899e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 412/1000\n", - "8/8 - 0s - loss: 6.9790e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 413/1000\n", - "8/8 - 0s - loss: 6.4784e-04 - root_mean_squared_error: 0.0255 - val_loss: 9.7066e-04 - val_root_mean_squared_error: 0.0312\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 414/1000\n", - "8/8 - 0s - loss: 6.4562e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 415/1000\n", - "8/8 - 0s - loss: 6.4883e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 416/1000\n", - "8/8 - 0s - loss: 6.1696e-04 - root_mean_squared_error: 0.0248 - val_loss: 9.4447e-04 - val_root_mean_squared_error: 0.0307\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 417/1000\n", - "8/8 - 0s - loss: 5.9953e-04 - root_mean_squared_error: 0.0245 - val_loss: 9.3899e-04 - val_root_mean_squared_error: 0.0306\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 418/1000\n", - "8/8 - 0s - loss: 6.2097e-04 - root_mean_squared_error: 0.0249 - val_loss: 9.6826e-04 - val_root_mean_squared_error: 0.0311\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 419/1000\n", - "8/8 - 0s - loss: 6.1923e-04 - root_mean_squared_error: 0.0249 - val_loss: 9.5634e-04 - val_root_mean_squared_error: 0.0309\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 420/1000\n", - "8/8 - 0s - loss: 5.8568e-04 - root_mean_squared_error: 0.0242 - val_loss: 8.9103e-04 - val_root_mean_squared_error: 0.0299\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 421/1000\n", - "8/8 - 0s - loss: 6.4628e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 422/1000\n", - "8/8 - 0s - loss: 6.4476e-04 - root_mean_squared_error: 0.0254 - val_loss: 9.9146e-04 - val_root_mean_squared_error: 0.0315\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 423/1000\n", - "8/8 - 0s - loss: 6.1921e-04 - root_mean_squared_error: 0.0249 - val_loss: 9.6239e-04 - val_root_mean_squared_error: 0.0310\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 424/1000\n", - "8/8 - 0s - loss: 6.8664e-04 - root_mean_squared_error: 0.0262 - val_loss: 9.8569e-04 - val_root_mean_squared_error: 0.0314\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 425/1000\n", - "8/8 - 0s - loss: 6.4674e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\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", "Epoch 426/1000\n", - "8/8 - 0s - loss: 6.5690e-04 - root_mean_squared_error: 0.0256 - val_loss: 9.8951e-04 - val_root_mean_squared_error: 0.0315\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 427/1000\n", - "8/8 - 0s - loss: 6.6581e-04 - root_mean_squared_error: 0.0258 - val_loss: 8.7582e-04 - val_root_mean_squared_error: 0.0296\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", + "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", + "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": [ - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 428/1000\n", - "8/8 - 0s - loss: 8.1597e-04 - root_mean_squared_error: 0.0286 - 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 429/1000\n", - "8/8 - 0s - loss: 7.9359e-04 - root_mean_squared_error: 0.0282 - 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 430/1000\n", - "8/8 - 0s - loss: 6.8987e-04 - root_mean_squared_error: 0.0263 - val_loss: 9.5958e-04 - val_root_mean_squared_error: 0.0310\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 431/1000\n", - "8/8 - 0s - loss: 8.5470e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 432/1000\n", - "8/8 - 0s - loss: 7.8093e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 433/1000\n", - "8/8 - 0s - loss: 6.5334e-04 - root_mean_squared_error: 0.0256 - val_loss: 8.5069e-04 - val_root_mean_squared_error: 0.0292\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 434/1000\n", - "8/8 - 0s - loss: 6.4139e-04 - root_mean_squared_error: 0.0253 - val_loss: 9.0096e-04 - val_root_mean_squared_error: 0.0300\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 435/1000\n", - "8/8 - 0s - loss: 6.5584e-04 - root_mean_squared_error: 0.0256 - val_loss: 8.9375e-04 - val_root_mean_squared_error: 0.0299\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 436/1000\n", - "8/8 - 0s - loss: 6.8794e-04 - root_mean_squared_error: 0.0262 - val_loss: 8.6369e-04 - val_root_mean_squared_error: 0.0294\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 437/1000\n", - "8/8 - 0s - loss: 5.7437e-04 - root_mean_squared_error: 0.0240 - val_loss: 8.0599e-04 - val_root_mean_squared_error: 0.0284\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 438/1000\n", - "8/8 - 0s - loss: 6.3349e-04 - root_mean_squared_error: 0.0252 - val_loss: 7.8533e-04 - val_root_mean_squared_error: 0.0280\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 439/1000\n", - "8/8 - 0s - loss: 6.2436e-04 - root_mean_squared_error: 0.0250 - val_loss: 8.0893e-04 - val_root_mean_squared_error: 0.0284\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 440/1000\n", - "8/8 - 0s - loss: 6.4134e-04 - root_mean_squared_error: 0.0253 - val_loss: 8.5903e-04 - val_root_mean_squared_error: 0.0293\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 441/1000\n", - "8/8 - 0s - loss: 7.5197e-04 - root_mean_squared_error: 0.0274 - val_loss: 8.0953e-04 - val_root_mean_squared_error: 0.0285\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 442/1000\n", - "8/8 - 0s - loss: 7.2979e-04 - root_mean_squared_error: 0.0270 - val_loss: 9.7244e-04 - val_root_mean_squared_error: 0.0312\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 443/1000\n", - "8/8 - 0s - loss: 8.5224e-04 - root_mean_squared_error: 0.0292 - val_loss: 7.8196e-04 - val_root_mean_squared_error: 0.0280\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 444/1000\n", - "8/8 - 0s - loss: 7.3425e-04 - root_mean_squared_error: 0.0271 - val_loss: 9.6091e-04 - val_root_mean_squared_error: 0.0310\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 445/1000\n", - "8/8 - 0s - loss: 9.3079e-04 - root_mean_squared_error: 0.0305 - val_loss: 7.7913e-04 - val_root_mean_squared_error: 0.0279\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 446/1000\n", - "8/8 - 0s - loss: 9.9997e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 447/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0342\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 448/1000\n", - "8/8 - 0s - loss: 8.9571e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 449/1000\n", - "8/8 - 0s - loss: 9.2668e-04 - root_mean_squared_error: 0.0304 - val_loss: 8.5818e-04 - val_root_mean_squared_error: 0.0293\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 450/1000\n", - "8/8 - 0s - loss: 9.1692e-04 - root_mean_squared_error: 0.0303 - val_loss: 9.1768e-04 - val_root_mean_squared_error: 0.0303\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 451/1000\n", - "8/8 - 0s - loss: 9.3373e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 452/1000\n", - "8/8 - 0s - loss: 9.4756e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 453/1000\n", - "8/8 - 0s - loss: 9.3730e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 454/1000\n", - "8/8 - 0s - loss: 8.7322e-04 - root_mean_squared_error: 0.0296 - val_loss: 9.7043e-04 - val_root_mean_squared_error: 0.0312\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 455/1000\n", - "8/8 - 0s - loss: 8.1515e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 456/1000\n", - "8/8 - 0s - loss: 7.7441e-04 - root_mean_squared_error: 0.0278 - val_loss: 9.6517e-04 - val_root_mean_squared_error: 0.0311\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 457/1000\n", - "8/8 - 0s - loss: 6.3482e-04 - root_mean_squared_error: 0.0252 - val_loss: 9.0304e-04 - val_root_mean_squared_error: 0.0301\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 458/1000\n", - "8/8 - 0s - loss: 7.4082e-04 - root_mean_squared_error: 0.0272 - val_loss: 9.1314e-04 - val_root_mean_squared_error: 0.0302\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 459/1000\n", - "8/8 - 0s - loss: 6.4931e-04 - root_mean_squared_error: 0.0255 - val_loss: 9.5756e-04 - val_root_mean_squared_error: 0.0309\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 460/1000\n", - "8/8 - 0s - loss: 7.2662e-04 - root_mean_squared_error: 0.0270 - val_loss: 9.7487e-04 - val_root_mean_squared_error: 0.0312\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 461/1000\n", - "8/8 - 0s - loss: 6.4809e-04 - root_mean_squared_error: 0.0255 - val_loss: 9.5272e-04 - val_root_mean_squared_error: 0.0309\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 462/1000\n", - "8/8 - 0s - loss: 7.9359e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 463/1000\n", - "8/8 - 0s - loss: 7.1491e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\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", "Epoch 464/1000\n", - "8/8 - 0s - loss: 8.3751e-04 - root_mean_squared_error: 0.0289 - 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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - 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 465/1000\n", - "8/8 - 0s - loss: 8.0258e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 466/1000\n", - "8/8 - 0s - loss: 9.6469e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 467/1000\n", - "8/8 - 0s - loss: 9.3977e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 468/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 9.9232e-04 - val_root_mean_squared_error: 0.0315\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 469/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 470/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 471/1000\n", - "8/8 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 472/1000\n", - "8/8 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 473/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 474/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 475/1000\n", - "8/8 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 476/1000\n", - "8/8 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 477/1000\n", - "8/8 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 478/1000\n", - "8/8 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 479/1000\n", - "8/8 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 480/1000\n", - "8/8 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0729\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 481/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 482/1000\n", - "8/8 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 483/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 484/1000\n", - "8/8 - 0s - loss: 9.2329e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 485/1000\n", - "8/8 - 0s - loss: 7.6904e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 486/1000\n", - "8/8 - 0s - loss: 7.3386e-04 - root_mean_squared_error: 0.0271 - val_loss: 9.4933e-04 - val_root_mean_squared_error: 0.0308\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 487/1000\n", - "8/8 - 0s - loss: 6.2301e-04 - root_mean_squared_error: 0.0250 - val_loss: 8.5922e-04 - val_root_mean_squared_error: 0.0293\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 488/1000\n", - "8/8 - 0s - loss: 6.1014e-04 - root_mean_squared_error: 0.0247 - val_loss: 8.3650e-04 - val_root_mean_squared_error: 0.0289\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 489/1000\n", - "8/8 - 0s - loss: 5.6525e-04 - root_mean_squared_error: 0.0238 - val_loss: 7.8830e-04 - val_root_mean_squared_error: 0.0281\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 490/1000\n", - "8/8 - 0s - loss: 5.4509e-04 - root_mean_squared_error: 0.0233 - val_loss: 7.6658e-04 - val_root_mean_squared_error: 0.0277\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 491/1000\n", - "8/8 - 0s - loss: 5.2340e-04 - root_mean_squared_error: 0.0229 - val_loss: 7.3207e-04 - val_root_mean_squared_error: 0.0271\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 492/1000\n", - "8/8 - 0s - loss: 5.0435e-04 - root_mean_squared_error: 0.0225 - val_loss: 7.1101e-04 - val_root_mean_squared_error: 0.0267\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 493/1000\n", - "8/8 - 0s - loss: 4.8917e-04 - root_mean_squared_error: 0.0221 - val_loss: 6.8278e-04 - val_root_mean_squared_error: 0.0261\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 494/1000\n", - "8/8 - 0s - loss: 4.7536e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.6512e-04 - val_root_mean_squared_error: 0.0258\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 495/1000\n", - "8/8 - 0s - loss: 4.6310e-04 - root_mean_squared_error: 0.0215 - val_loss: 6.4345e-04 - val_root_mean_squared_error: 0.0254\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 496/1000\n", - "8/8 - 0s - loss: 4.5249e-04 - root_mean_squared_error: 0.0213 - val_loss: 6.2916e-04 - val_root_mean_squared_error: 0.0251\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 497/1000\n", - "8/8 - 0s - loss: 4.4277e-04 - root_mean_squared_error: 0.0210 - val_loss: 6.1284e-04 - val_root_mean_squared_error: 0.0248\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 498/1000\n", - "8/8 - 0s - loss: 4.3437e-04 - root_mean_squared_error: 0.0208 - val_loss: 5.9990e-04 - val_root_mean_squared_error: 0.0245\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 499/1000\n", - "8/8 - 0s - loss: 4.2649e-04 - root_mean_squared_error: 0.0207 - val_loss: 5.8665e-04 - val_root_mean_squared_error: 0.0242\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 500/1000\n", - "8/8 - 0s - loss: 4.1945e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.7501e-04 - val_root_mean_squared_error: 0.0240\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 501/1000\n", - "8/8 - 0s - loss: 4.1308e-04 - root_mean_squared_error: 0.0203 - val_loss: 5.6382e-04 - val_root_mean_squared_error: 0.0237\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 502/1000\n", - "8/8 - 0s - loss: 4.0719e-04 - root_mean_squared_error: 0.0202 - val_loss: 5.5358e-04 - val_root_mean_squared_error: 0.0235\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - 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 503/1000\n", - "8/8 - 0s - loss: 4.0168e-04 - root_mean_squared_error: 0.0200 - val_loss: 5.4365e-04 - val_root_mean_squared_error: 0.0233\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 504/1000\n", - "8/8 - 0s - loss: 3.9668e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.3426e-04 - val_root_mean_squared_error: 0.0231\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 505/1000\n", - "8/8 - 0s - loss: 3.9194e-04 - root_mean_squared_error: 0.0198 - val_loss: 5.2557e-04 - val_root_mean_squared_error: 0.0229\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 506/1000\n", - "8/8 - 0s - loss: 3.8747e-04 - root_mean_squared_error: 0.0197 - val_loss: 5.1692e-04 - val_root_mean_squared_error: 0.0227\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 507/1000\n", - "8/8 - 0s - loss: 3.8329e-04 - root_mean_squared_error: 0.0196 - val_loss: 5.0898e-04 - val_root_mean_squared_error: 0.0226\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 508/1000\n", - "8/8 - 0s - loss: 3.7933e-04 - root_mean_squared_error: 0.0195 - val_loss: 5.0114e-04 - val_root_mean_squared_error: 0.0224\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 509/1000\n", - "8/8 - 0s - loss: 3.7550e-04 - root_mean_squared_error: 0.0194 - val_loss: 4.9382e-04 - val_root_mean_squared_error: 0.0222\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 510/1000\n", - "8/8 - 0s - loss: 3.7190e-04 - root_mean_squared_error: 0.0193 - val_loss: 4.8660e-04 - val_root_mean_squared_error: 0.0221\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 511/1000\n", - "8/8 - 0s - loss: 3.6841e-04 - root_mean_squared_error: 0.0192 - val_loss: 4.7976e-04 - val_root_mean_squared_error: 0.0219\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 512/1000\n", - "8/8 - 0s - loss: 3.6509e-04 - root_mean_squared_error: 0.0191 - val_loss: 4.7313e-04 - val_root_mean_squared_error: 0.0218\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 513/1000\n", - "8/8 - 0s - loss: 3.6181e-04 - root_mean_squared_error: 0.0190 - val_loss: 4.6671e-04 - val_root_mean_squared_error: 0.0216\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 514/1000\n", - "8/8 - 0s - loss: 3.5875e-04 - root_mean_squared_error: 0.0189 - val_loss: 4.6050e-04 - val_root_mean_squared_error: 0.0215\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 515/1000\n", - "8/8 - 0s - loss: 3.5569e-04 - root_mean_squared_error: 0.0189 - val_loss: 4.5449e-04 - val_root_mean_squared_error: 0.0213\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 516/1000\n", - "8/8 - 0s - loss: 3.5279e-04 - root_mean_squared_error: 0.0188 - val_loss: 4.4870e-04 - val_root_mean_squared_error: 0.0212\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 517/1000\n", - "8/8 - 0s - loss: 3.4988e-04 - root_mean_squared_error: 0.0187 - val_loss: 4.4298e-04 - val_root_mean_squared_error: 0.0210\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 518/1000\n", - "8/8 - 0s - loss: 3.4716e-04 - root_mean_squared_error: 0.0186 - val_loss: 4.3752e-04 - val_root_mean_squared_error: 0.0209\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 519/1000\n", - "8/8 - 0s - loss: 3.4439e-04 - root_mean_squared_error: 0.0186 - val_loss: 4.3213e-04 - val_root_mean_squared_error: 0.0208\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 520/1000\n", - "8/8 - 0s - loss: 3.4179e-04 - root_mean_squared_error: 0.0185 - val_loss: 4.2698e-04 - val_root_mean_squared_error: 0.0207\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 521/1000\n", - "8/8 - 0s - loss: 3.3912e-04 - root_mean_squared_error: 0.0184 - val_loss: 4.2180e-04 - val_root_mean_squared_error: 0.0205\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 522/1000\n", - "8/8 - 0s - loss: 3.3667e-04 - root_mean_squared_error: 0.0183 - val_loss: 4.1694e-04 - val_root_mean_squared_error: 0.0204\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 523/1000\n", - "8/8 - 0s - loss: 3.3407e-04 - root_mean_squared_error: 0.0183 - val_loss: 4.1199e-04 - val_root_mean_squared_error: 0.0203\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 524/1000\n", - "8/8 - 0s - loss: 3.3173e-04 - root_mean_squared_error: 0.0182 - val_loss: 4.0741e-04 - val_root_mean_squared_error: 0.0202\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 525/1000\n", - "8/8 - 0s - loss: 3.2917e-04 - root_mean_squared_error: 0.0181 - val_loss: 4.0254e-04 - val_root_mean_squared_error: 0.0201\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 526/1000\n", - "8/8 - 0s - loss: 3.2700e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.9831e-04 - val_root_mean_squared_error: 0.0200\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 527/1000\n", - "8/8 - 0s - loss: 3.2443e-04 - root_mean_squared_error: 0.0180 - val_loss: 3.9350e-04 - val_root_mean_squared_error: 0.0198\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 528/1000\n", - "8/8 - 0s - loss: 3.2241e-04 - root_mean_squared_error: 0.0180 - val_loss: 3.8965e-04 - val_root_mean_squared_error: 0.0197\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 529/1000\n", - "8/8 - 0s - loss: 3.1979e-04 - root_mean_squared_error: 0.0179 - val_loss: 3.8468e-04 - val_root_mean_squared_error: 0.0196\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 530/1000\n", - "8/8 - 0s - loss: 3.1805e-04 - root_mean_squared_error: 0.0178 - val_loss: 3.8146e-04 - val_root_mean_squared_error: 0.0195\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 531/1000\n", - "8/8 - 0s - loss: 3.1530e-04 - root_mean_squared_error: 0.0178 - val_loss: 3.7611e-04 - val_root_mean_squared_error: 0.0194\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 532/1000\n", - "8/8 - 0s - loss: 3.1394e-04 - root_mean_squared_error: 0.0177 - val_loss: 3.7380e-04 - val_root_mean_squared_error: 0.0193\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 533/1000\n", - "8/8 - 0s - loss: 3.1095e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.6751e-04 - val_root_mean_squared_error: 0.0192\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 534/1000\n", - "8/8 - 0s - loss: 3.1036e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.6685e-04 - val_root_mean_squared_error: 0.0192\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 535/1000\n", - "8/8 - 0s - loss: 3.0709e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.5885e-04 - val_root_mean_squared_error: 0.0189\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 536/1000\n", - "8/8 - 0s - loss: 3.0803e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.6106e-04 - val_root_mean_squared_error: 0.0190\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 537/1000\n", - "8/8 - 0s - loss: 3.0460e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.4985e-04 - val_root_mean_squared_error: 0.0187\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 538/1000\n", - "8/8 - 0s - loss: 3.0921e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.5714e-04 - val_root_mean_squared_error: 0.0189\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", "Epoch 539/1000\n", - "8/8 - 0s - loss: 3.0672e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.4121e-04 - val_root_mean_squared_error: 0.0185\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 540/1000\n", - "8/8 - 0s - loss: 3.2123e-04 - root_mean_squared_error: 0.0179 - val_loss: 3.5663e-04 - val_root_mean_squared_error: 0.0189\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - 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 541/1000\n", - "8/8 - 0s - loss: 3.2357e-04 - root_mean_squared_error: 0.0180 - val_loss: 3.3612e-04 - val_root_mean_squared_error: 0.0183\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 542/1000\n", - "8/8 - 0s - loss: 3.7025e-04 - root_mean_squared_error: 0.0192 - val_loss: 3.6154e-04 - val_root_mean_squared_error: 0.0190\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 543/1000\n", - "8/8 - 0s - loss: 3.8896e-04 - root_mean_squared_error: 0.0197 - val_loss: 3.4620e-04 - val_root_mean_squared_error: 0.0186\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 544/1000\n", - "8/8 - 0s - loss: 5.5415e-04 - root_mean_squared_error: 0.0235 - val_loss: 3.9310e-04 - val_root_mean_squared_error: 0.0198\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 545/1000\n", - "8/8 - 0s - loss: 6.0870e-04 - root_mean_squared_error: 0.0247 - val_loss: 4.3206e-04 - val_root_mean_squared_error: 0.0208\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 546/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 7.6254e-04 - val_root_mean_squared_error: 0.0276\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 547/1000\n", - "8/8 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 9.2012e-04 - val_root_mean_squared_error: 0.0303\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 548/1000\n", - "8/8 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 549/1000\n", - "8/8 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 550/1000\n", - "8/8 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0762\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 551/1000\n", - "8/8 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0682 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1193\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 552/1000\n", - "8/8 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0694\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 553/1000\n", - "8/8 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0561\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 554/1000\n", - "8/8 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 555/1000\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", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - 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 556/1000\n", - "8/8 - 0s - loss: 9.0206e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 557/1000\n", - "8/8 - 0s - loss: 7.6261e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 558/1000\n", - "8/8 - 0s - loss: 6.4336e-04 - root_mean_squared_error: 0.0254 - val_loss: 8.8665e-04 - val_root_mean_squared_error: 0.0298\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 559/1000\n", - "8/8 - 0s - loss: 5.9232e-04 - root_mean_squared_error: 0.0243 - val_loss: 7.8656e-04 - val_root_mean_squared_error: 0.0280\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 560/1000\n", - "8/8 - 0s - loss: 5.3926e-04 - root_mean_squared_error: 0.0232 - val_loss: 7.0255e-04 - val_root_mean_squared_error: 0.0265\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 561/1000\n", - "8/8 - 0s - loss: 4.7488e-04 - root_mean_squared_error: 0.0218 - val_loss: 6.6642e-04 - val_root_mean_squared_error: 0.0258\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 562/1000\n", - "8/8 - 0s - loss: 4.4753e-04 - root_mean_squared_error: 0.0212 - val_loss: 6.0010e-04 - val_root_mean_squared_error: 0.0245\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 563/1000\n", - "8/8 - 0s - loss: 4.2177e-04 - root_mean_squared_error: 0.0205 - val_loss: 5.5211e-04 - val_root_mean_squared_error: 0.0235\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 564/1000\n", - "8/8 - 0s - loss: 3.8977e-04 - root_mean_squared_error: 0.0197 - val_loss: 5.1328e-04 - val_root_mean_squared_error: 0.0227\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 565/1000\n", - "8/8 - 0s - loss: 3.7334e-04 - root_mean_squared_error: 0.0193 - val_loss: 4.6953e-04 - val_root_mean_squared_error: 0.0217\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 566/1000\n", - "8/8 - 0s - loss: 3.6308e-04 - root_mean_squared_error: 0.0191 - val_loss: 4.4317e-04 - val_root_mean_squared_error: 0.0211\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 567/1000\n", - "8/8 - 0s - loss: 3.4778e-04 - root_mean_squared_error: 0.0186 - val_loss: 4.2981e-04 - val_root_mean_squared_error: 0.0207\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 568/1000\n", - "8/8 - 0s - loss: 3.3437e-04 - root_mean_squared_error: 0.0183 - val_loss: 3.9680e-04 - val_root_mean_squared_error: 0.0199\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 569/1000\n", - "8/8 - 0s - loss: 3.3022e-04 - root_mean_squared_error: 0.0182 - val_loss: 3.8507e-04 - val_root_mean_squared_error: 0.0196\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 570/1000\n", - "8/8 - 0s - loss: 3.2179e-04 - root_mean_squared_error: 0.0179 - val_loss: 3.7441e-04 - val_root_mean_squared_error: 0.0193\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 571/1000\n", - "8/8 - 0s - loss: 3.1211e-04 - root_mean_squared_error: 0.0177 - val_loss: 3.6224e-04 - val_root_mean_squared_error: 0.0190\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 572/1000\n", - "8/8 - 0s - loss: 3.0840e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.4596e-04 - val_root_mean_squared_error: 0.0186\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 573/1000\n", - "8/8 - 0s - loss: 3.0431e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.4296e-04 - val_root_mean_squared_error: 0.0185\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 574/1000\n", - "8/8 - 0s - loss: 2.9826e-04 - root_mean_squared_error: 0.0173 - val_loss: 3.3588e-04 - val_root_mean_squared_error: 0.0183\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 575/1000\n", - "8/8 - 0s - loss: 2.9280e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.2520e-04 - val_root_mean_squared_error: 0.0180\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 576/1000\n", - "8/8 - 0s - loss: 2.9128e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.1737e-04 - val_root_mean_squared_error: 0.0178\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 577/1000\n", - "8/8 - 0s - loss: 2.8802e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.1699e-04 - val_root_mean_squared_error: 0.0178\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 578/1000\n", - "8/8 - 0s - loss: 2.8322e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.1044e-04 - val_root_mean_squared_error: 0.0176\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - 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 579/1000\n", - "8/8 - 0s - loss: 2.7945e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.0107e-04 - val_root_mean_squared_error: 0.0174\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 580/1000\n", - "8/8 - 0s - loss: 2.7913e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.9818e-04 - val_root_mean_squared_error: 0.0173\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 581/1000\n", - "8/8 - 0s - loss: 2.7601e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.9765e-04 - val_root_mean_squared_error: 0.0173\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 582/1000\n", - "8/8 - 0s - loss: 2.7204e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.9154e-04 - val_root_mean_squared_error: 0.0171\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 583/1000\n", - "8/8 - 0s - loss: 2.6860e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.8344e-04 - val_root_mean_squared_error: 0.0168\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 584/1000\n", - "8/8 - 0s - loss: 2.6919e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.8180e-04 - val_root_mean_squared_error: 0.0168\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 585/1000\n", - "8/8 - 0s - loss: 2.6647e-04 - root_mean_squared_error: 0.0163 - val_loss: 2.8274e-04 - val_root_mean_squared_error: 0.0168\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 586/1000\n", - "8/8 - 0s - loss: 2.6314e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.7672e-04 - val_root_mean_squared_error: 0.0166\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 587/1000\n", - "8/8 - 0s - loss: 2.5913e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.6940e-04 - val_root_mean_squared_error: 0.0164\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 588/1000\n", - "8/8 - 0s - loss: 2.6008e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.6656e-04 - val_root_mean_squared_error: 0.0163\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 589/1000\n", - "8/8 - 0s - loss: 2.5883e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.7022e-04 - val_root_mean_squared_error: 0.0164\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 590/1000\n", - "8/8 - 0s - loss: 2.5573e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.6562e-04 - val_root_mean_squared_error: 0.0163\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 591/1000\n", - "8/8 - 0s - loss: 2.5179e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.5816e-04 - val_root_mean_squared_error: 0.0161\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 592/1000\n", - "8/8 - 0s - loss: 2.5042e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.5301e-04 - val_root_mean_squared_error: 0.0159\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 593/1000\n", - "8/8 - 0s - loss: 2.5264e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.5583e-04 - val_root_mean_squared_error: 0.0160\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 594/1000\n", - "8/8 - 0s - loss: 2.4963e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.5908e-04 - val_root_mean_squared_error: 0.0161\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 595/1000\n", - "8/8 - 0s - loss: 2.4732e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.5009e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 596/1000\n", - "8/8 - 0s - loss: 2.4255e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.4339e-04 - val_root_mean_squared_error: 0.0156\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 597/1000\n", - "8/8 - 0s - loss: 2.4403e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.4008e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 598/1000\n", - "8/8 - 0s - loss: 2.4689e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.4771e-04 - val_root_mean_squared_error: 0.0157\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 599/1000\n", - "8/8 - 0s - loss: 2.4314e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.5106e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 600/1000\n", - "8/8 - 0s - loss: 2.4217e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.3790e-04 - val_root_mean_squared_error: 0.0154\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 601/1000\n", - "8/8 - 0s - loss: 2.3580e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.3201e-04 - val_root_mean_squared_error: 0.0152\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 602/1000\n", - "8/8 - 0s - loss: 2.3951e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.2918e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 603/1000\n", - "8/8 - 0s - loss: 2.4569e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.4088e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 604/1000\n", - "8/8 - 0s - loss: 2.4076e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.5124e-04 - val_root_mean_squared_error: 0.0159\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 605/1000\n", - "8/8 - 0s - loss: 2.4307e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.3132e-04 - val_root_mean_squared_error: 0.0152\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 606/1000\n", - "8/8 - 0s - loss: 2.3476e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.2441e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 607/1000\n", - "8/8 - 0s - loss: 2.3672e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.2274e-04 - val_root_mean_squared_error: 0.0149\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 608/1000\n", - "8/8 - 0s - loss: 2.5353e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.3231e-04 - val_root_mean_squared_error: 0.0152\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 609/1000\n", - "8/8 - 0s - loss: 2.4695e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.6417e-04 - val_root_mean_squared_error: 0.0163\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 610/1000\n", - "8/8 - 0s - loss: 2.5263e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.3937e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 611/1000\n", - "8/8 - 0s - loss: 2.5018e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.2055e-04 - val_root_mean_squared_error: 0.0149\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 612/1000\n", - "8/8 - 0s - loss: 2.3869e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.2863e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 613/1000\n", - "8/8 - 0s - loss: 2.7380e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.2332e-04 - val_root_mean_squared_error: 0.0149\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 614/1000\n", - "8/8 - 0s - loss: 2.7524e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.8680e-04 - val_root_mean_squared_error: 0.0169\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 615/1000\n", - "8/8 - 0s - loss: 2.7392e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.7692e-04 - val_root_mean_squared_error: 0.0166\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - 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 616/1000\n", - "8/8 - 0s - loss: 2.9565e-04 - root_mean_squared_error: 0.0172 - val_loss: 2.2351e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 617/1000\n", - "8/8 - 0s - loss: 2.5996e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.5114e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 618/1000\n", - "8/8 - 0s - loss: 3.2041e-04 - root_mean_squared_error: 0.0179 - val_loss: 2.2625e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 619/1000\n", - "8/8 - 0s - loss: 3.3719e-04 - root_mean_squared_error: 0.0184 - val_loss: 3.3362e-04 - val_root_mean_squared_error: 0.0183\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 620/1000\n", - "8/8 - 0s - loss: 3.3029e-04 - root_mean_squared_error: 0.0182 - val_loss: 3.2528e-04 - val_root_mean_squared_error: 0.0180\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 621/1000\n", - "8/8 - 0s - loss: 3.7948e-04 - root_mean_squared_error: 0.0195 - val_loss: 2.4975e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 622/1000\n", - "8/8 - 0s - loss: 3.1016e-04 - root_mean_squared_error: 0.0176 - val_loss: 2.8363e-04 - val_root_mean_squared_error: 0.0168\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 623/1000\n", - "8/8 - 0s - loss: 4.5464e-04 - root_mean_squared_error: 0.0213 - val_loss: 2.5870e-04 - val_root_mean_squared_error: 0.0161\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 624/1000\n", - "8/8 - 0s - loss: 3.9325e-04 - root_mean_squared_error: 0.0198 - val_loss: 4.6426e-04 - val_root_mean_squared_error: 0.0215\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 625/1000\n", - "8/8 - 0s - loss: 5.0299e-04 - root_mean_squared_error: 0.0224 - val_loss: 2.8308e-04 - val_root_mean_squared_error: 0.0168\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 626/1000\n", - "8/8 - 0s - loss: 3.7498e-04 - root_mean_squared_error: 0.0194 - val_loss: 3.4399e-04 - val_root_mean_squared_error: 0.0185\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 627/1000\n", - "8/8 - 0s - loss: 5.4125e-04 - root_mean_squared_error: 0.0233 - val_loss: 3.1148e-04 - val_root_mean_squared_error: 0.0176\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 628/1000\n", - "8/8 - 0s - loss: 4.5573e-04 - root_mean_squared_error: 0.0213 - val_loss: 4.6114e-04 - val_root_mean_squared_error: 0.0215\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 629/1000\n", - "8/8 - 0s - loss: 5.3285e-04 - root_mean_squared_error: 0.0231 - val_loss: 3.8079e-04 - val_root_mean_squared_error: 0.0195\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 630/1000\n", - "8/8 - 0s - loss: 4.0891e-04 - root_mean_squared_error: 0.0202 - val_loss: 2.7678e-04 - val_root_mean_squared_error: 0.0166\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 631/1000\n", - "8/8 - 0s - loss: 4.6604e-04 - root_mean_squared_error: 0.0216 - val_loss: 4.7393e-04 - val_root_mean_squared_error: 0.0218\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 632/1000\n", - "8/8 - 0s - loss: 4.1638e-04 - root_mean_squared_error: 0.0204 - val_loss: 3.2468e-04 - val_root_mean_squared_error: 0.0180\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 633/1000\n", - "8/8 - 0s - loss: 3.5030e-04 - root_mean_squared_error: 0.0187 - val_loss: 3.3330e-04 - val_root_mean_squared_error: 0.0183\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 634/1000\n", - "8/8 - 0s - loss: 3.6642e-04 - root_mean_squared_error: 0.0191 - val_loss: 2.6792e-04 - val_root_mean_squared_error: 0.0164\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 635/1000\n", - "8/8 - 0s - loss: 3.1541e-04 - root_mean_squared_error: 0.0178 - val_loss: 2.8312e-04 - val_root_mean_squared_error: 0.0168\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 636/1000\n", - "8/8 - 0s - loss: 3.0110e-04 - root_mean_squared_error: 0.0174 - val_loss: 2.8888e-04 - val_root_mean_squared_error: 0.0170\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 637/1000\n", - "8/8 - 0s - loss: 2.8097e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.4175e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 638/1000\n", - "8/8 - 0s - loss: 2.6959e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.4825e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 639/1000\n", - "8/8 - 0s - loss: 2.4993e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.1440e-04 - val_root_mean_squared_error: 0.0146\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 640/1000\n", - "8/8 - 0s - loss: 2.5050e-04 - root_mean_squared_error: 0.0158 - val_loss: 1.9833e-04 - val_root_mean_squared_error: 0.0141\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 641/1000\n", - "8/8 - 0s - loss: 2.5747e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.3986e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 642/1000\n", - "8/8 - 0s - loss: 2.7092e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.2718e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 643/1000\n", - "8/8 - 0s - loss: 2.5062e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.1494e-04 - val_root_mean_squared_error: 0.0147\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 644/1000\n", - "8/8 - 0s - loss: 2.7309e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.0590e-04 - val_root_mean_squared_error: 0.0143\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 645/1000\n", - "8/8 - 0s - loss: 2.6546e-04 - root_mean_squared_error: 0.0163 - val_loss: 2.4155e-04 - val_root_mean_squared_error: 0.0155\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 646/1000\n", - "8/8 - 0s - loss: 2.8656e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.2520e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 647/1000\n", - "8/8 - 0s - loss: 2.7129e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.3378e-04 - val_root_mean_squared_error: 0.0153\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 648/1000\n", - "8/8 - 0s - loss: 2.8462e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.3540e-04 - val_root_mean_squared_error: 0.0153\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 649/1000\n", - "8/8 - 0s - loss: 2.6241e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.4987e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 650/1000\n", - "8/8 - 0s - loss: 2.7266e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.2784e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 651/1000\n", - "8/8 - 0s - loss: 2.4970e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.4535e-04 - val_root_mean_squared_error: 0.0157\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 652/1000\n", - "8/8 - 0s - loss: 2.4117e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.2742e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 653/1000\n", - "8/8 - 0s - loss: 2.1702e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.1905e-04 - val_root_mean_squared_error: 0.0148\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - 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 654/1000\n", - "8/8 - 0s - loss: 2.1007e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.9342e-04 - val_root_mean_squared_error: 0.0139\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 655/1000\n", - "8/8 - 0s - loss: 1.9934e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.0086e-04 - val_root_mean_squared_error: 0.0142\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 656/1000\n", - "8/8 - 0s - loss: 1.9108e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7539e-04 - val_root_mean_squared_error: 0.0132\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 657/1000\n", - "8/8 - 0s - loss: 1.8299e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.7317e-04 - val_root_mean_squared_error: 0.0132\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 658/1000\n", - "8/8 - 0s - loss: 1.8384e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.6826e-04 - val_root_mean_squared_error: 0.0130\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 659/1000\n", - "8/8 - 0s - loss: 1.8335e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6894e-04 - val_root_mean_squared_error: 0.0130\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 660/1000\n", - "8/8 - 0s - loss: 1.8941e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.6075e-04 - val_root_mean_squared_error: 0.0127\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 661/1000\n", - "8/8 - 0s - loss: 1.8944e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.6589e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 662/1000\n", - "8/8 - 0s - loss: 1.9406e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.6892e-04 - val_root_mean_squared_error: 0.0130\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 663/1000\n", - "8/8 - 0s - loss: 1.9176e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.5457e-04 - val_root_mean_squared_error: 0.0124\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 664/1000\n", - "8/8 - 0s - loss: 2.1922e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.7652e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 665/1000\n", - "8/8 - 0s - loss: 2.2600e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.7891e-04 - val_root_mean_squared_error: 0.0134\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 666/1000\n", - "8/8 - 0s - loss: 2.4123e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.8242e-04 - val_root_mean_squared_error: 0.0135\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 667/1000\n", - "8/8 - 0s - loss: 2.3328e-04 - root_mean_squared_error: 0.0153 - val_loss: 1.6655e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 668/1000\n", - "8/8 - 0s - loss: 2.6732e-04 - root_mean_squared_error: 0.0164 - val_loss: 2.0967e-04 - val_root_mean_squared_error: 0.0145\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 669/1000\n", - "8/8 - 0s - loss: 2.6336e-04 - root_mean_squared_error: 0.0162 - val_loss: 1.9954e-04 - val_root_mean_squared_error: 0.0141\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 670/1000\n", - "8/8 - 0s - loss: 2.8706e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.1775e-04 - val_root_mean_squared_error: 0.0148\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 671/1000\n", - "8/8 - 0s - loss: 2.5688e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.0360e-04 - val_root_mean_squared_error: 0.0143\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 672/1000\n", - "8/8 - 0s - loss: 2.4801e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.0539e-04 - val_root_mean_squared_error: 0.0143\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 673/1000\n", - "8/8 - 0s - loss: 2.1069e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.7588e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 674/1000\n", - "8/8 - 0s - loss: 2.1876e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.8603e-04 - val_root_mean_squared_error: 0.0136\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 675/1000\n", - "8/8 - 0s - loss: 2.0318e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.9206e-04 - val_root_mean_squared_error: 0.0139\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 676/1000\n", - "8/8 - 0s - loss: 1.9176e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.5496e-04 - val_root_mean_squared_error: 0.0124\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 677/1000\n", - "8/8 - 0s - loss: 1.6943e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.4611e-04 - val_root_mean_squared_error: 0.0121\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 678/1000\n", - "8/8 - 0s - loss: 1.7889e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.6410e-04 - val_root_mean_squared_error: 0.0128\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 679/1000\n", - "8/8 - 0s - loss: 1.7406e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.6563e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 680/1000\n", - "8/8 - 0s - loss: 1.7868e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.3795e-04 - val_root_mean_squared_error: 0.0117\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 681/1000\n", - "8/8 - 0s - loss: 1.7535e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.5946e-04 - val_root_mean_squared_error: 0.0126\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 682/1000\n", - "8/8 - 0s - loss: 1.7514e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.5948e-04 - val_root_mean_squared_error: 0.0126\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 683/1000\n", - "8/8 - 0s - loss: 1.7039e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.3651e-04 - val_root_mean_squared_error: 0.0117\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 684/1000\n", - "8/8 - 0s - loss: 1.8909e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.4034e-04 - val_root_mean_squared_error: 0.0118\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 685/1000\n", - "8/8 - 0s - loss: 2.0360e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.0267e-04 - val_root_mean_squared_error: 0.0142\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 686/1000\n", - "8/8 - 0s - loss: 2.0892e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.5305e-04 - val_root_mean_squared_error: 0.0124\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 687/1000\n", - "8/8 - 0s - loss: 2.0257e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.3006e-04 - val_root_mean_squared_error: 0.0114\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 688/1000\n", - "8/8 - 0s - loss: 2.4047e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.6700e-04 - val_root_mean_squared_error: 0.0129\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", "Epoch 689/1000\n", - "8/8 - 0s - loss: 2.4778e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.5081e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 690/1000\n", - "8/8 - 0s - loss: 2.9435e-04 - root_mean_squared_error: 0.0172 - val_loss: 1.6365e-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": [ + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - 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 691/1000\n", - "8/8 - 0s - loss: 3.2279e-04 - root_mean_squared_error: 0.0180 - val_loss: 2.1713e-04 - val_root_mean_squared_error: 0.0147\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 692/1000\n", - "8/8 - 0s - loss: 3.7796e-04 - root_mean_squared_error: 0.0194 - val_loss: 2.3622e-04 - val_root_mean_squared_error: 0.0154\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 693/1000\n", - "8/8 - 0s - loss: 3.5053e-04 - root_mean_squared_error: 0.0187 - val_loss: 2.8630e-04 - val_root_mean_squared_error: 0.0169\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 694/1000\n", - "8/8 - 0s - loss: 3.6636e-04 - root_mean_squared_error: 0.0191 - val_loss: 2.0832e-04 - val_root_mean_squared_error: 0.0144\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 695/1000\n", - "8/8 - 0s - loss: 4.7545e-04 - root_mean_squared_error: 0.0218 - val_loss: 4.6590e-04 - val_root_mean_squared_error: 0.0216\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 696/1000\n", - "8/8 - 0s - loss: 5.1219e-04 - root_mean_squared_error: 0.0226 - val_loss: 4.4200e-04 - val_root_mean_squared_error: 0.0210\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 697/1000\n", - "8/8 - 0s - loss: 3.8094e-04 - root_mean_squared_error: 0.0195 - val_loss: 3.2917e-04 - val_root_mean_squared_error: 0.0181\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 698/1000\n", - "8/8 - 0s - loss: 2.5152e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.4234e-04 - val_root_mean_squared_error: 0.0156\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 699/1000\n", - "8/8 - 0s - loss: 2.1174e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.5580e-04 - val_root_mean_squared_error: 0.0160\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 700/1000\n", - "8/8 - 0s - loss: 1.9943e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.9629e-04 - val_root_mean_squared_error: 0.0140\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 701/1000\n", - "8/8 - 0s - loss: 1.6746e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.6785e-04 - val_root_mean_squared_error: 0.0130\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 702/1000\n", - "8/8 - 0s - loss: 1.8515e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8931e-04 - val_root_mean_squared_error: 0.0138\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 703/1000\n", - "8/8 - 0s - loss: 1.7549e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.6611e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 704/1000\n", - "8/8 - 0s - loss: 1.7623e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.2831e-04 - val_root_mean_squared_error: 0.0113\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 705/1000\n", - "8/8 - 0s - loss: 2.0265e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.7578e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 706/1000\n", - "8/8 - 0s - loss: 1.8692e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.8115e-04 - val_root_mean_squared_error: 0.0135\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 707/1000\n", - "8/8 - 0s - loss: 2.0466e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.2431e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 708/1000\n", - "8/8 - 0s - loss: 2.6304e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.1070e-04 - val_root_mean_squared_error: 0.0145\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 709/1000\n", - "8/8 - 0s - loss: 2.9889e-04 - root_mean_squared_error: 0.0173 - val_loss: 3.0372e-04 - val_root_mean_squared_error: 0.0174\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 710/1000\n", - "8/8 - 0s - loss: 2.7261e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.0623e-04 - val_root_mean_squared_error: 0.0144\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 711/1000\n", - "8/8 - 0s - loss: 2.1393e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.4766e-04 - val_root_mean_squared_error: 0.0122\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 712/1000\n", - "8/8 - 0s - loss: 2.8974e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.1652e-04 - val_root_mean_squared_error: 0.0178\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 713/1000\n", - "8/8 - 0s - loss: 2.9393e-04 - root_mean_squared_error: 0.0171 - val_loss: 2.5796e-04 - val_root_mean_squared_error: 0.0161\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 714/1000\n", - "8/8 - 0s - loss: 3.4603e-04 - root_mean_squared_error: 0.0186 - val_loss: 2.2398e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 715/1000\n", - "8/8 - 0s - loss: 3.5887e-04 - root_mean_squared_error: 0.0189 - val_loss: 3.2622e-04 - val_root_mean_squared_error: 0.0181\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 716/1000\n", - "8/8 - 0s - loss: 3.2755e-04 - root_mean_squared_error: 0.0181 - val_loss: 2.3021e-04 - val_root_mean_squared_error: 0.0152\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 717/1000\n", - "8/8 - 0s - loss: 2.6440e-04 - root_mean_squared_error: 0.0163 - val_loss: 1.6521e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 718/1000\n", - "8/8 - 0s - loss: 3.7596e-04 - root_mean_squared_error: 0.0194 - val_loss: 3.5074e-04 - val_root_mean_squared_error: 0.0187\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 719/1000\n", - "8/8 - 0s - loss: 3.8280e-04 - root_mean_squared_error: 0.0196 - val_loss: 3.6668e-04 - val_root_mean_squared_error: 0.0191\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 720/1000\n", - "8/8 - 0s - loss: 4.1301e-04 - root_mean_squared_error: 0.0203 - val_loss: 3.6534e-04 - val_root_mean_squared_error: 0.0191\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 721/1000\n", - "8/8 - 0s - loss: 3.8770e-04 - root_mean_squared_error: 0.0197 - val_loss: 3.6705e-04 - val_root_mean_squared_error: 0.0192\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 722/1000\n", - "8/8 - 0s - loss: 3.5835e-04 - root_mean_squared_error: 0.0189 - val_loss: 2.8827e-04 - val_root_mean_squared_error: 0.0170\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 723/1000\n", - "8/8 - 0s - loss: 3.2353e-04 - root_mean_squared_error: 0.0180 - val_loss: 2.7556e-04 - val_root_mean_squared_error: 0.0166\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 724/1000\n", - "8/8 - 0s - loss: 4.2329e-04 - root_mean_squared_error: 0.0206 - val_loss: 3.1175e-04 - val_root_mean_squared_error: 0.0177\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 725/1000\n", - "8/8 - 0s - loss: 4.4998e-04 - root_mean_squared_error: 0.0212 - val_loss: 3.7386e-04 - val_root_mean_squared_error: 0.0193\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 726/1000\n", - "8/8 - 0s - loss: 3.9646e-04 - root_mean_squared_error: 0.0199 - val_loss: 3.6094e-04 - val_root_mean_squared_error: 0.0190\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 727/1000\n", - "8/8 - 0s - loss: 3.7078e-04 - root_mean_squared_error: 0.0193 - val_loss: 2.9706e-04 - val_root_mean_squared_error: 0.0172\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 728/1000\n", - "8/8 - 0s - loss: 3.3884e-04 - root_mean_squared_error: 0.0184 - val_loss: 2.4661e-04 - val_root_mean_squared_error: 0.0157\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - 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 729/1000\n", - "8/8 - 0s - loss: 2.7621e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.2558e-04 - val_root_mean_squared_error: 0.0150\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 730/1000\n", - "8/8 - 0s - loss: 2.8894e-04 - root_mean_squared_error: 0.0170 - val_loss: 2.5310e-04 - val_root_mean_squared_error: 0.0159\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 731/1000\n", - "8/8 - 0s - loss: 2.7249e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.2946e-04 - val_root_mean_squared_error: 0.0151\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 732/1000\n", - "8/8 - 0s - loss: 2.4444e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.0935e-04 - val_root_mean_squared_error: 0.0145\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 733/1000\n", - "8/8 - 0s - loss: 2.1109e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.1382e-04 - val_root_mean_squared_error: 0.0146\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 734/1000\n", - "8/8 - 0s - loss: 1.7210e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.5654e-04 - val_root_mean_squared_error: 0.0125\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 735/1000\n", - "8/8 - 0s - loss: 1.5905e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4844e-04 - val_root_mean_squared_error: 0.0122\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 736/1000\n", - "8/8 - 0s - loss: 1.4836e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6015e-04 - val_root_mean_squared_error: 0.0127\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 737/1000\n", - "8/8 - 0s - loss: 1.3122e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.1858e-04 - val_root_mean_squared_error: 0.0109\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 738/1000\n", - "8/8 - 0s - loss: 1.2950e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2309e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 739/1000\n", - "8/8 - 0s - loss: 1.3246e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2596e-04 - val_root_mean_squared_error: 0.0112\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 740/1000\n", - "8/8 - 0s - loss: 1.2765e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2792e-04 - val_root_mean_squared_error: 0.0113\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 741/1000\n", - "8/8 - 0s - loss: 1.2356e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.1288e-04 - val_root_mean_squared_error: 0.0106\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 742/1000\n", - "8/8 - 0s - loss: 1.2030e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.1610e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 743/1000\n", - "8/8 - 0s - loss: 1.1866e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.1580e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 744/1000\n", - "8/8 - 0s - loss: 1.1504e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.1122e-04 - val_root_mean_squared_error: 0.0105\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 745/1000\n", - "8/8 - 0s - loss: 1.1372e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.1143e-04 - val_root_mean_squared_error: 0.0106\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 746/1000\n", - "8/8 - 0s - loss: 1.1208e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0806e-04 - val_root_mean_squared_error: 0.0104\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 747/1000\n", - "8/8 - 0s - loss: 1.1099e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0536e-04 - val_root_mean_squared_error: 0.0103\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 748/1000\n", - "8/8 - 0s - loss: 1.1165e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0680e-04 - val_root_mean_squared_error: 0.0103\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 749/1000\n", - "8/8 - 0s - loss: 1.0930e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0586e-04 - val_root_mean_squared_error: 0.0103\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 750/1000\n", - "8/8 - 0s - loss: 1.1012e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0386e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 751/1000\n", - "8/8 - 0s - loss: 1.0773e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0244e-04 - val_root_mean_squared_error: 0.0101\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 752/1000\n", - "8/8 - 0s - loss: 1.1158e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.0445e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 753/1000\n", - "8/8 - 0s - loss: 1.0863e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0627e-04 - val_root_mean_squared_error: 0.0103\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 754/1000\n", - "8/8 - 0s - loss: 1.0628e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.0086e-04 - val_root_mean_squared_error: 0.0100\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 755/1000\n", - "8/8 - 0s - loss: 1.0601e-04 - root_mean_squared_error: 0.0103 - val_loss: 9.8271e-05 - val_root_mean_squared_error: 0.0099\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 756/1000\n", - "8/8 - 0s - loss: 1.0599e-04 - root_mean_squared_error: 0.0103 - val_loss: 1.0307e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 757/1000\n", - "8/8 - 0s - loss: 1.1063e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.0468e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 758/1000\n", - "8/8 - 0s - loss: 1.0670e-04 - root_mean_squared_error: 0.0103 - val_loss: 9.5891e-05 - val_root_mean_squared_error: 0.0098\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 759/1000\n", - "8/8 - 0s - loss: 1.0731e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0356e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 760/1000\n", - "8/8 - 0s - loss: 1.0846e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.0227e-04 - val_root_mean_squared_error: 0.0101\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 761/1000\n", - "8/8 - 0s - loss: 1.0675e-04 - root_mean_squared_error: 0.0103 - val_loss: 9.4297e-05 - val_root_mean_squared_error: 0.0097\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 762/1000\n", - "8/8 - 0s - loss: 1.1794e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.0819e-04 - val_root_mean_squared_error: 0.0104\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 763/1000\n", - "8/8 - 0s - loss: 1.1219e-04 - root_mean_squared_error: 0.0106 - val_loss: 1.1186e-04 - val_root_mean_squared_error: 0.0106\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", "Epoch 764/1000\n", - "8/8 - 0s - loss: 1.0681e-04 - root_mean_squared_error: 0.0103 - val_loss: 9.3271e-05 - val_root_mean_squared_error: 0.0097\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 765/1000\n", - "8/8 - 0s - loss: 1.1551e-04 - root_mean_squared_error: 0.0107 - val_loss: 9.3767e-05 - val_root_mean_squared_error: 0.0097\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - 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 766/1000\n", - "8/8 - 0s - loss: 1.2298e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2275e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 767/1000\n", - "8/8 - 0s - loss: 1.4276e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.1725e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 768/1000\n", - "8/8 - 0s - loss: 1.4131e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.0020e-04 - val_root_mean_squared_error: 0.0100\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 769/1000\n", - "8/8 - 0s - loss: 1.6004e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7689e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 770/1000\n", - "8/8 - 0s - loss: 1.6685e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.3141e-04 - val_root_mean_squared_error: 0.0115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 771/1000\n", - "8/8 - 0s - loss: 1.7300e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.2863e-04 - val_root_mean_squared_error: 0.0113\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 772/1000\n", - "8/8 - 0s - loss: 2.2131e-04 - root_mean_squared_error: 0.0149 - val_loss: 1.7194e-04 - val_root_mean_squared_error: 0.0131\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 773/1000\n", - "8/8 - 0s - loss: 2.1663e-04 - root_mean_squared_error: 0.0147 - val_loss: 1.9619e-04 - val_root_mean_squared_error: 0.0140\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 774/1000\n", - "8/8 - 0s - loss: 2.2646e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.3982e-04 - val_root_mean_squared_error: 0.0118\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 775/1000\n", - "8/8 - 0s - loss: 3.1627e-04 - root_mean_squared_error: 0.0178 - val_loss: 1.5322e-04 - val_root_mean_squared_error: 0.0124\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 776/1000\n", - "8/8 - 0s - loss: 3.6863e-04 - root_mean_squared_error: 0.0192 - val_loss: 3.5581e-04 - val_root_mean_squared_error: 0.0189\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 777/1000\n", - "8/8 - 0s - loss: 5.1920e-04 - root_mean_squared_error: 0.0228 - val_loss: 3.8538e-04 - val_root_mean_squared_error: 0.0196\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 778/1000\n", - "8/8 - 0s - loss: 5.3016e-04 - root_mean_squared_error: 0.0230 - val_loss: 2.9677e-04 - val_root_mean_squared_error: 0.0172\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 779/1000\n", - "8/8 - 0s - loss: 6.1836e-04 - root_mean_squared_error: 0.0249 - val_loss: 4.9748e-04 - val_root_mean_squared_error: 0.0223\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 780/1000\n", - "8/8 - 0s - loss: 4.6451e-04 - root_mean_squared_error: 0.0216 - val_loss: 4.3320e-04 - val_root_mean_squared_error: 0.0208\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 781/1000\n", - "8/8 - 0s - loss: 3.9165e-04 - root_mean_squared_error: 0.0198 - val_loss: 2.7640e-04 - val_root_mean_squared_error: 0.0166\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 782/1000\n", - "8/8 - 0s - loss: 3.3472e-04 - root_mean_squared_error: 0.0183 - val_loss: 3.5921e-04 - val_root_mean_squared_error: 0.0190\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 783/1000\n", - "8/8 - 0s - loss: 3.4624e-04 - root_mean_squared_error: 0.0186 - val_loss: 1.9114e-04 - val_root_mean_squared_error: 0.0138\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 784/1000\n", - "8/8 - 0s - loss: 3.5042e-04 - root_mean_squared_error: 0.0187 - val_loss: 2.8589e-04 - val_root_mean_squared_error: 0.0169\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 785/1000\n", - "8/8 - 0s - loss: 3.6765e-04 - root_mean_squared_error: 0.0192 - val_loss: 3.9003e-04 - val_root_mean_squared_error: 0.0197\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 786/1000\n", - "8/8 - 0s - loss: 3.1901e-04 - root_mean_squared_error: 0.0179 - val_loss: 2.7252e-04 - val_root_mean_squared_error: 0.0165\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 787/1000\n", - "8/8 - 0s - loss: 3.4316e-04 - root_mean_squared_error: 0.0185 - val_loss: 2.3090e-04 - val_root_mean_squared_error: 0.0152\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 788/1000\n", - "8/8 - 0s - loss: 2.8005e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.1989e-04 - val_root_mean_squared_error: 0.0148\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 789/1000\n", - "8/8 - 0s - loss: 2.6405e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.5087e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 790/1000\n", - "8/8 - 0s - loss: 1.9381e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.9084e-04 - val_root_mean_squared_error: 0.0138\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 791/1000\n", - "8/8 - 0s - loss: 1.9202e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1000e-04 - val_root_mean_squared_error: 0.0145\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 792/1000\n", - "8/8 - 0s - loss: 1.6456e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6506e-04 - val_root_mean_squared_error: 0.0128\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 793/1000\n", - "8/8 - 0s - loss: 2.0116e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.7801e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 794/1000\n", - "8/8 - 0s - loss: 1.8282e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.8434e-04 - val_root_mean_squared_error: 0.0136\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 795/1000\n", - "8/8 - 0s - loss: 1.7963e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.8391e-04 - val_root_mean_squared_error: 0.0136\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 796/1000\n", - "8/8 - 0s - loss: 1.5106e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.4645e-04 - val_root_mean_squared_error: 0.0121\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 797/1000\n", - "8/8 - 0s - loss: 1.3037e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3250e-04 - val_root_mean_squared_error: 0.0115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 798/1000\n", - "8/8 - 0s - loss: 1.1828e-04 - root_mean_squared_error: 0.0109 - val_loss: 1.1436e-04 - val_root_mean_squared_error: 0.0107\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 799/1000\n", - "8/8 - 0s - loss: 1.1543e-04 - root_mean_squared_error: 0.0107 - val_loss: 1.0413e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 800/1000\n", - "8/8 - 0s - loss: 1.0488e-04 - root_mean_squared_error: 0.0102 - val_loss: 1.0746e-04 - val_root_mean_squared_error: 0.0104\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 801/1000\n", - "8/8 - 0s - loss: 9.5927e-05 - root_mean_squared_error: 0.0098 - val_loss: 8.7273e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 802/1000\n", - "8/8 - 0s - loss: 9.4098e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.0015e-05 - val_root_mean_squared_error: 0.0089\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 803/1000\n", - "8/8 - 0s - loss: 9.8363e-05 - root_mean_squared_error: 0.0099 - val_loss: 1.0187e-04 - val_root_mean_squared_error: 0.0101\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - 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 804/1000\n", - "8/8 - 0s - loss: 9.4910e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.6199e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 805/1000\n", - "8/8 - 0s - loss: 9.2165e-05 - root_mean_squared_error: 0.0096 - val_loss: 8.5364e-05 - val_root_mean_squared_error: 0.0092\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 806/1000\n", - "8/8 - 0s - loss: 1.0154e-04 - root_mean_squared_error: 0.0101 - val_loss: 8.9583e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 807/1000\n", - "8/8 - 0s - loss: 9.6404e-05 - root_mean_squared_error: 0.0098 - val_loss: 9.2516e-05 - val_root_mean_squared_error: 0.0096\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 808/1000\n", - "8/8 - 0s - loss: 9.4489e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.7673e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 809/1000\n", - "8/8 - 0s - loss: 9.6650e-05 - root_mean_squared_error: 0.0098 - val_loss: 9.6339e-05 - val_root_mean_squared_error: 0.0098\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 810/1000\n", - "8/8 - 0s - loss: 9.4490e-05 - root_mean_squared_error: 0.0097 - val_loss: 1.0019e-04 - val_root_mean_squared_error: 0.0100\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 811/1000\n", - "8/8 - 0s - loss: 9.7908e-05 - root_mean_squared_error: 0.0099 - val_loss: 6.8005e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 812/1000\n", - "8/8 - 0s - loss: 9.4639e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.5707e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 813/1000\n", - "8/8 - 0s - loss: 1.0203e-04 - root_mean_squared_error: 0.0101 - val_loss: 8.9361e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 814/1000\n", - "8/8 - 0s - loss: 1.1376e-04 - root_mean_squared_error: 0.0107 - val_loss: 8.1127e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 815/1000\n", - "8/8 - 0s - loss: 1.1672e-04 - root_mean_squared_error: 0.0108 - val_loss: 1.0475e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 816/1000\n", - "8/8 - 0s - loss: 1.2327e-04 - root_mean_squared_error: 0.0111 - val_loss: 8.7840e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 817/1000\n", - "8/8 - 0s - loss: 9.9184e-05 - root_mean_squared_error: 0.0100 - val_loss: 9.2870e-05 - val_root_mean_squared_error: 0.0096\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 818/1000\n", - "8/8 - 0s - loss: 1.1277e-04 - root_mean_squared_error: 0.0106 - val_loss: 8.8928e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 819/1000\n", - "8/8 - 0s - loss: 1.2834e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2534e-04 - val_root_mean_squared_error: 0.0112\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 820/1000\n", - "8/8 - 0s - loss: 1.3467e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2397e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 821/1000\n", - "8/8 - 0s - loss: 1.9399e-04 - root_mean_squared_error: 0.0139 - val_loss: 8.0246e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 822/1000\n", - "8/8 - 0s - loss: 1.9110e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.7144e-04 - val_root_mean_squared_error: 0.0131\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 823/1000\n", - "8/8 - 0s - loss: 2.7541e-04 - root_mean_squared_error: 0.0166 - val_loss: 1.3224e-04 - val_root_mean_squared_error: 0.0115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 824/1000\n", - "8/8 - 0s - loss: 2.4514e-04 - root_mean_squared_error: 0.0157 - val_loss: 1.5942e-04 - val_root_mean_squared_error: 0.0126\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 825/1000\n", - "8/8 - 0s - loss: 2.4299e-04 - root_mean_squared_error: 0.0156 - val_loss: 1.7742e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 826/1000\n", - "8/8 - 0s - loss: 1.8170e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.7887e-04 - val_root_mean_squared_error: 0.0134\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 827/1000\n", - "8/8 - 0s - loss: 1.8093e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.0165e-04 - val_root_mean_squared_error: 0.0101\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 828/1000\n", - "8/8 - 0s - loss: 1.8495e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.3714e-04 - val_root_mean_squared_error: 0.0154\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 829/1000\n", - "8/8 - 0s - loss: 1.8280e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.2365e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 830/1000\n", - "8/8 - 0s - loss: 2.4670e-04 - root_mean_squared_error: 0.0157 - val_loss: 1.4954e-04 - val_root_mean_squared_error: 0.0122\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 831/1000\n", - "8/8 - 0s - loss: 2.9203e-04 - root_mean_squared_error: 0.0171 - val_loss: 2.0823e-04 - val_root_mean_squared_error: 0.0144\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 832/1000\n", - "8/8 - 0s - loss: 3.9752e-04 - root_mean_squared_error: 0.0199 - val_loss: 3.2942e-04 - val_root_mean_squared_error: 0.0182\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 833/1000\n", - "8/8 - 0s - loss: 3.6493e-04 - root_mean_squared_error: 0.0191 - val_loss: 1.4769e-04 - val_root_mean_squared_error: 0.0122\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 834/1000\n", - "8/8 - 0s - loss: 3.4168e-04 - root_mean_squared_error: 0.0185 - val_loss: 2.9172e-04 - val_root_mean_squared_error: 0.0171\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 835/1000\n", - "8/8 - 0s - loss: 3.1311e-04 - root_mean_squared_error: 0.0177 - val_loss: 1.4617e-04 - val_root_mean_squared_error: 0.0121\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 836/1000\n", - "8/8 - 0s - loss: 2.9044e-04 - root_mean_squared_error: 0.0170 - val_loss: 2.4924e-04 - val_root_mean_squared_error: 0.0158\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 837/1000\n", - "8/8 - 0s - loss: 2.7243e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.4299e-04 - val_root_mean_squared_error: 0.0156\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 838/1000\n", - "8/8 - 0s - loss: 1.8092e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.4368e-04 - val_root_mean_squared_error: 0.0120\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", "Epoch 839/1000\n", - "8/8 - 0s - loss: 1.4099e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.2506e-04 - val_root_mean_squared_error: 0.0112\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 840/1000\n", - "8/8 - 0s - loss: 1.3343e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2740e-04 - val_root_mean_squared_error: 0.0113\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - 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 841/1000\n", - "8/8 - 0s - loss: 1.0657e-04 - root_mean_squared_error: 0.0103 - val_loss: 8.5590e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 842/1000\n", - "8/8 - 0s - loss: 9.6223e-05 - root_mean_squared_error: 0.0098 - val_loss: 8.8375e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 843/1000\n", - "8/8 - 0s - loss: 8.8209e-05 - root_mean_squared_error: 0.0094 - val_loss: 8.1065e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 844/1000\n", - "8/8 - 0s - loss: 8.2781e-05 - root_mean_squared_error: 0.0091 - val_loss: 6.7712e-05 - val_root_mean_squared_error: 0.0082\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 845/1000\n", - "8/8 - 0s - loss: 8.5261e-05 - root_mean_squared_error: 0.0092 - val_loss: 6.6967e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 846/1000\n", - "8/8 - 0s - loss: 7.8927e-05 - root_mean_squared_error: 0.0089 - val_loss: 7.5087e-05 - val_root_mean_squared_error: 0.0087\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 847/1000\n", - "8/8 - 0s - loss: 9.4902e-05 - root_mean_squared_error: 0.0097 - val_loss: 6.3247e-05 - val_root_mean_squared_error: 0.0080\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 848/1000\n", - "8/8 - 0s - loss: 9.7035e-05 - root_mean_squared_error: 0.0099 - val_loss: 7.3273e-05 - val_root_mean_squared_error: 0.0086\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 849/1000\n", - "8/8 - 0s - loss: 9.9881e-05 - root_mean_squared_error: 0.0100 - val_loss: 8.3453e-05 - val_root_mean_squared_error: 0.0091\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 850/1000\n", - "8/8 - 0s - loss: 9.9830e-05 - root_mean_squared_error: 0.0100 - val_loss: 7.4672e-05 - val_root_mean_squared_error: 0.0086\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 851/1000\n", - "8/8 - 0s - loss: 1.0648e-04 - root_mean_squared_error: 0.0103 - val_loss: 7.9074e-05 - val_root_mean_squared_error: 0.0089\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 852/1000\n", - "8/8 - 0s - loss: 1.0912e-04 - root_mean_squared_error: 0.0104 - val_loss: 1.1088e-04 - val_root_mean_squared_error: 0.0105\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 853/1000\n", - "8/8 - 0s - loss: 9.9286e-05 - root_mean_squared_error: 0.0100 - val_loss: 7.3429e-05 - val_root_mean_squared_error: 0.0086\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 854/1000\n", - "8/8 - 0s - loss: 8.9024e-05 - root_mean_squared_error: 0.0094 - val_loss: 6.4916e-05 - val_root_mean_squared_error: 0.0081\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 855/1000\n", - "8/8 - 0s - loss: 1.1106e-04 - root_mean_squared_error: 0.0105 - val_loss: 1.1230e-04 - val_root_mean_squared_error: 0.0106\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 856/1000\n", - "8/8 - 0s - loss: 1.2616e-04 - root_mean_squared_error: 0.0112 - val_loss: 9.5960e-05 - val_root_mean_squared_error: 0.0098\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 857/1000\n", - "8/8 - 0s - loss: 1.5586e-04 - root_mean_squared_error: 0.0125 - val_loss: 7.0866e-05 - val_root_mean_squared_error: 0.0084\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 858/1000\n", - "8/8 - 0s - loss: 1.5856e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.3712e-04 - val_root_mean_squared_error: 0.0117\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 859/1000\n", - "8/8 - 0s - loss: 1.9623e-04 - root_mean_squared_error: 0.0140 - val_loss: 8.6841e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 860/1000\n", - "8/8 - 0s - loss: 1.8577e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.0107e-04 - val_root_mean_squared_error: 0.0101\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 861/1000\n", - "8/8 - 0s - loss: 1.8680e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.3701e-04 - val_root_mean_squared_error: 0.0117\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 862/1000\n", - "8/8 - 0s - loss: 1.4930e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.0749e-04 - val_root_mean_squared_error: 0.0104\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 863/1000\n", - "8/8 - 0s - loss: 1.6707e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.0594e-04 - val_root_mean_squared_error: 0.0103\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 864/1000\n", - "8/8 - 0s - loss: 1.8986e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.3011e-04 - val_root_mean_squared_error: 0.0152\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 865/1000\n", - "8/8 - 0s - loss: 1.5575e-04 - root_mean_squared_error: 0.0125 - val_loss: 9.6657e-05 - val_root_mean_squared_error: 0.0098\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 866/1000\n", - "8/8 - 0s - loss: 1.3972e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.1182e-04 - val_root_mean_squared_error: 0.0106\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 867/1000\n", - "8/8 - 0s - loss: 1.7307e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.5347e-04 - val_root_mean_squared_error: 0.0124\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 868/1000\n", - "8/8 - 0s - loss: 1.9996e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.3462e-04 - val_root_mean_squared_error: 0.0116\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 869/1000\n", - "8/8 - 0s - loss: 2.0303e-04 - root_mean_squared_error: 0.0142 - val_loss: 8.4125e-05 - val_root_mean_squared_error: 0.0092\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 870/1000\n", - "8/8 - 0s - loss: 2.1099e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.7651e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 871/1000\n", - "8/8 - 0s - loss: 2.3748e-04 - root_mean_squared_error: 0.0154 - val_loss: 1.1594e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 872/1000\n", - "8/8 - 0s - loss: 2.2771e-04 - root_mean_squared_error: 0.0151 - val_loss: 1.5466e-04 - val_root_mean_squared_error: 0.0124\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 873/1000\n", - "8/8 - 0s - loss: 2.1354e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.7572e-04 - val_root_mean_squared_error: 0.0133\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 874/1000\n", - "8/8 - 0s - loss: 1.7401e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.3960e-04 - val_root_mean_squared_error: 0.0118\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 875/1000\n", - "8/8 - 0s - loss: 1.7417e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.3247e-04 - val_root_mean_squared_error: 0.0115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 876/1000\n", - "8/8 - 0s - loss: 1.8096e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0507e-04 - val_root_mean_squared_error: 0.0143\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 877/1000\n", - "8/8 - 0s - loss: 1.4727e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.1249e-04 - val_root_mean_squared_error: 0.0106\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 878/1000\n", - "8/8 - 0s - loss: 1.3452e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.0428e-04 - val_root_mean_squared_error: 0.0102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - 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 879/1000\n", - "8/8 - 0s - loss: 1.5597e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.1859e-04 - val_root_mean_squared_error: 0.0109\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 880/1000\n", - "8/8 - 0s - loss: 1.7043e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.6720e-04 - val_root_mean_squared_error: 0.0129\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 881/1000\n", - "8/8 - 0s - loss: 1.7398e-04 - root_mean_squared_error: 0.0132 - val_loss: 8.7929e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 882/1000\n", - "8/8 - 0s - loss: 1.4498e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.0043e-04 - val_root_mean_squared_error: 0.0100\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 883/1000\n", - "8/8 - 0s - loss: 2.0416e-04 - root_mean_squared_error: 0.0143 - val_loss: 1.2577e-04 - val_root_mean_squared_error: 0.0112\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 884/1000\n", - "8/8 - 0s - loss: 2.7138e-04 - root_mean_squared_error: 0.0165 - val_loss: 1.4131e-04 - val_root_mean_squared_error: 0.0119\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 885/1000\n", - "8/8 - 0s - loss: 2.5972e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.1143e-04 - val_root_mean_squared_error: 0.0145\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 886/1000\n", - "8/8 - 0s - loss: 2.3461e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.1933e-04 - val_root_mean_squared_error: 0.0148\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 887/1000\n", - "8/8 - 0s - loss: 1.5551e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.2165e-04 - val_root_mean_squared_error: 0.0110\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 888/1000\n", - "8/8 - 0s - loss: 1.0185e-04 - root_mean_squared_error: 0.0101 - val_loss: 8.9761e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 889/1000\n", - "8/8 - 0s - loss: 9.8901e-05 - root_mean_squared_error: 0.0099 - val_loss: 6.4218e-05 - val_root_mean_squared_error: 0.0080\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 890/1000\n", - "8/8 - 0s - loss: 9.3269e-05 - root_mean_squared_error: 0.0097 - val_loss: 8.0310e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 891/1000\n", - "8/8 - 0s - loss: 8.0485e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.2399e-05 - val_root_mean_squared_error: 0.0079\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 892/1000\n", - "8/8 - 0s - loss: 8.1693e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.1014e-05 - val_root_mean_squared_error: 0.0078\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 893/1000\n", - "8/8 - 0s - loss: 7.6959e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.6134e-05 - val_root_mean_squared_error: 0.0075\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 894/1000\n", - "8/8 - 0s - loss: 7.4540e-05 - root_mean_squared_error: 0.0086 - val_loss: 6.6245e-05 - val_root_mean_squared_error: 0.0081\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 895/1000\n", - "8/8 - 0s - loss: 7.1218e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.2036e-05 - val_root_mean_squared_error: 0.0072\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 896/1000\n", - "8/8 - 0s - loss: 5.7506e-05 - root_mean_squared_error: 0.0076 - val_loss: 4.2334e-05 - val_root_mean_squared_error: 0.0065\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 897/1000\n", - "8/8 - 0s - loss: 6.0466e-05 - root_mean_squared_error: 0.0078 - val_loss: 4.6830e-05 - val_root_mean_squared_error: 0.0068\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 898/1000\n", - "8/8 - 0s - loss: 5.5902e-05 - root_mean_squared_error: 0.0075 - val_loss: 4.2599e-05 - val_root_mean_squared_error: 0.0065\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 899/1000\n", - "8/8 - 0s - loss: 4.8985e-05 - root_mean_squared_error: 0.0070 - val_loss: 3.5217e-05 - val_root_mean_squared_error: 0.0059\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 900/1000\n", - "8/8 - 0s - loss: 5.1255e-05 - root_mean_squared_error: 0.0072 - val_loss: 4.3613e-05 - val_root_mean_squared_error: 0.0066\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 901/1000\n", - "8/8 - 0s - loss: 5.6255e-05 - root_mean_squared_error: 0.0075 - val_loss: 4.1822e-05 - val_root_mean_squared_error: 0.0065\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 902/1000\n", - "8/8 - 0s - loss: 5.5236e-05 - root_mean_squared_error: 0.0074 - val_loss: 5.2141e-05 - val_root_mean_squared_error: 0.0072\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 903/1000\n", - "8/8 - 0s - loss: 5.6576e-05 - root_mean_squared_error: 0.0075 - val_loss: 3.2433e-05 - val_root_mean_squared_error: 0.0057\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 904/1000\n", - "8/8 - 0s - loss: 5.2162e-05 - root_mean_squared_error: 0.0072 - val_loss: 3.8048e-05 - val_root_mean_squared_error: 0.0062\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 905/1000\n", - "8/8 - 0s - loss: 5.7423e-05 - root_mean_squared_error: 0.0076 - val_loss: 4.0744e-05 - val_root_mean_squared_error: 0.0064\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 906/1000\n", - "8/8 - 0s - loss: 5.9206e-05 - root_mean_squared_error: 0.0077 - val_loss: 4.3595e-05 - val_root_mean_squared_error: 0.0066\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 907/1000\n", - "8/8 - 0s - loss: 6.9023e-05 - root_mean_squared_error: 0.0083 - val_loss: 3.3844e-05 - val_root_mean_squared_error: 0.0058\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 908/1000\n", - "8/8 - 0s - loss: 6.7556e-05 - root_mean_squared_error: 0.0082 - val_loss: 6.0637e-05 - val_root_mean_squared_error: 0.0078\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 909/1000\n", - "8/8 - 0s - loss: 7.2037e-05 - root_mean_squared_error: 0.0085 - val_loss: 3.4689e-05 - val_root_mean_squared_error: 0.0059\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 910/1000\n", - "8/8 - 0s - loss: 6.4422e-05 - root_mean_squared_error: 0.0080 - val_loss: 6.3651e-05 - val_root_mean_squared_error: 0.0080\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 911/1000\n", - "8/8 - 0s - loss: 6.3936e-05 - root_mean_squared_error: 0.0080 - val_loss: 3.3798e-05 - val_root_mean_squared_error: 0.0058\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 912/1000\n", - "8/8 - 0s - loss: 6.2466e-05 - root_mean_squared_error: 0.0079 - val_loss: 4.1528e-05 - val_root_mean_squared_error: 0.0064\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 913/1000\n", - "8/8 - 0s - loss: 7.5876e-05 - root_mean_squared_error: 0.0087 - val_loss: 6.2674e-05 - val_root_mean_squared_error: 0.0079\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", "Epoch 914/1000\n", - "8/8 - 0s - loss: 1.0144e-04 - root_mean_squared_error: 0.0101 - val_loss: 7.4592e-05 - val_root_mean_squared_error: 0.0086\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 915/1000\n", - "8/8 - 0s - loss: 1.1576e-04 - root_mean_squared_error: 0.0108 - val_loss: 5.8873e-05 - val_root_mean_squared_error: 0.0077\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - 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 916/1000\n", - "8/8 - 0s - loss: 1.3686e-04 - root_mean_squared_error: 0.0117 - val_loss: 8.0444e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 917/1000\n", - "8/8 - 0s - loss: 1.1690e-04 - root_mean_squared_error: 0.0108 - val_loss: 5.8649e-05 - val_root_mean_squared_error: 0.0077\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 918/1000\n", - "8/8 - 0s - loss: 1.2871e-04 - root_mean_squared_error: 0.0113 - val_loss: 8.7326e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 919/1000\n", - "8/8 - 0s - loss: 1.5794e-04 - root_mean_squared_error: 0.0126 - val_loss: 8.0762e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 920/1000\n", - "8/8 - 0s - loss: 1.8508e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.1683e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 921/1000\n", - "8/8 - 0s - loss: 1.8546e-04 - root_mean_squared_error: 0.0136 - val_loss: 7.1042e-05 - val_root_mean_squared_error: 0.0084\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 922/1000\n", - "8/8 - 0s - loss: 1.5801e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4002e-04 - val_root_mean_squared_error: 0.0118\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 923/1000\n", - "8/8 - 0s - loss: 1.3874e-04 - root_mean_squared_error: 0.0118 - val_loss: 8.3540e-05 - val_root_mean_squared_error: 0.0091\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 924/1000\n", - "8/8 - 0s - loss: 1.5850e-04 - root_mean_squared_error: 0.0126 - val_loss: 9.8353e-05 - val_root_mean_squared_error: 0.0099\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 925/1000\n", - "8/8 - 0s - loss: 1.6225e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.3205e-04 - val_root_mean_squared_error: 0.0115\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 926/1000\n", - "8/8 - 0s - loss: 1.9825e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.5931e-04 - val_root_mean_squared_error: 0.0126\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 927/1000\n", - "8/8 - 0s - loss: 2.5510e-04 - root_mean_squared_error: 0.0160 - val_loss: 8.1388e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 928/1000\n", - "8/8 - 0s - loss: 2.5082e-04 - root_mean_squared_error: 0.0158 - val_loss: 1.6332e-04 - val_root_mean_squared_error: 0.0128\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 929/1000\n", - "8/8 - 0s - loss: 2.5690e-04 - root_mean_squared_error: 0.0160 - val_loss: 1.1910e-04 - val_root_mean_squared_error: 0.0109\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 930/1000\n", - "8/8 - 0s - loss: 2.3029e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.4290e-04 - val_root_mean_squared_error: 0.0120\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 931/1000\n", - "8/8 - 0s - loss: 1.8438e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.4260e-04 - val_root_mean_squared_error: 0.0119\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 932/1000\n", - "8/8 - 0s - loss: 1.6281e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.2961e-04 - val_root_mean_squared_error: 0.0114\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 933/1000\n", - "8/8 - 0s - loss: 2.0575e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.0043e-04 - val_root_mean_squared_error: 0.0142\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 934/1000\n", - "8/8 - 0s - loss: 2.0970e-04 - root_mean_squared_error: 0.0145 - val_loss: 1.9657e-04 - val_root_mean_squared_error: 0.0140\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 935/1000\n", - "8/8 - 0s - loss: 1.6140e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8630e-04 - val_root_mean_squared_error: 0.0136\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 936/1000\n", - "8/8 - 0s - loss: 1.3895e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2061e-04 - val_root_mean_squared_error: 0.0110\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 937/1000\n", - "8/8 - 0s - loss: 1.3716e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1800e-04 - val_root_mean_squared_error: 0.0109\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 938/1000\n", - "8/8 - 0s - loss: 1.2894e-04 - root_mean_squared_error: 0.0114 - val_loss: 9.4676e-05 - val_root_mean_squared_error: 0.0097\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 939/1000\n", - "8/8 - 0s - loss: 1.1326e-04 - root_mean_squared_error: 0.0106 - val_loss: 6.6784e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 940/1000\n", - "8/8 - 0s - loss: 1.1935e-04 - root_mean_squared_error: 0.0109 - val_loss: 6.1311e-05 - val_root_mean_squared_error: 0.0078\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 941/1000\n", - "8/8 - 0s - loss: 1.3227e-04 - root_mean_squared_error: 0.0115 - val_loss: 9.7408e-05 - val_root_mean_squared_error: 0.0099\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 942/1000\n", - "8/8 - 0s - loss: 1.2888e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.0354e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 943/1000\n", - "8/8 - 0s - loss: 9.4578e-05 - root_mean_squared_error: 0.0097 - val_loss: 5.9575e-05 - val_root_mean_squared_error: 0.0077\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 944/1000\n", - "8/8 - 0s - loss: 8.8512e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.1306e-05 - val_root_mean_squared_error: 0.0096\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 945/1000\n", - "8/8 - 0s - loss: 9.2683e-05 - root_mean_squared_error: 0.0096 - val_loss: 6.0971e-05 - val_root_mean_squared_error: 0.0078\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 946/1000\n", - "8/8 - 0s - loss: 9.3894e-05 - root_mean_squared_error: 0.0097 - val_loss: 6.8709e-05 - val_root_mean_squared_error: 0.0083\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 947/1000\n", - "8/8 - 0s - loss: 1.1197e-04 - root_mean_squared_error: 0.0106 - val_loss: 9.4126e-05 - val_root_mean_squared_error: 0.0097\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 948/1000\n", - "8/8 - 0s - loss: 1.2749e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.0490e-04 - val_root_mean_squared_error: 0.0102\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 949/1000\n", - "8/8 - 0s - loss: 1.0167e-04 - root_mean_squared_error: 0.0101 - val_loss: 4.6384e-05 - val_root_mean_squared_error: 0.0068\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 950/1000\n", - "8/8 - 0s - loss: 8.3910e-05 - root_mean_squared_error: 0.0092 - val_loss: 6.1489e-05 - val_root_mean_squared_error: 0.0078\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 951/1000\n", - "8/8 - 0s - loss: 8.0118e-05 - root_mean_squared_error: 0.0090 - val_loss: 5.7754e-05 - val_root_mean_squared_error: 0.0076\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 952/1000\n", - "8/8 - 0s - loss: 8.4003e-05 - root_mean_squared_error: 0.0092 - val_loss: 7.0592e-05 - val_root_mean_squared_error: 0.0084\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 953/1000\n", - "8/8 - 0s - loss: 7.8236e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.8472e-05 - val_root_mean_squared_error: 0.0076\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - 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 954/1000\n", - "8/8 - 0s - loss: 7.0227e-05 - root_mean_squared_error: 0.0084 - val_loss: 5.7347e-05 - val_root_mean_squared_error: 0.0076\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 955/1000\n", - "8/8 - 0s - loss: 7.3716e-05 - root_mean_squared_error: 0.0086 - val_loss: 5.7788e-05 - val_root_mean_squared_error: 0.0076\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 956/1000\n", - "8/8 - 0s - loss: 8.9041e-05 - root_mean_squared_error: 0.0094 - val_loss: 7.9568e-05 - val_root_mean_squared_error: 0.0089\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 957/1000\n", - "8/8 - 0s - loss: 8.0734e-05 - root_mean_squared_error: 0.0090 - val_loss: 7.5325e-05 - val_root_mean_squared_error: 0.0087\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 958/1000\n", - "8/8 - 0s - loss: 6.9696e-05 - root_mean_squared_error: 0.0083 - val_loss: 3.9963e-05 - val_root_mean_squared_error: 0.0063\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 959/1000\n", - "8/8 - 0s - loss: 5.9113e-05 - root_mean_squared_error: 0.0077 - val_loss: 5.7814e-05 - val_root_mean_squared_error: 0.0076\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 960/1000\n", - "8/8 - 0s - loss: 6.5779e-05 - root_mean_squared_error: 0.0081 - val_loss: 3.5101e-05 - val_root_mean_squared_error: 0.0059\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 961/1000\n", - "8/8 - 0s - loss: 6.1043e-05 - root_mean_squared_error: 0.0078 - val_loss: 5.5245e-05 - val_root_mean_squared_error: 0.0074\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 962/1000\n", - "8/8 - 0s - loss: 6.7569e-05 - root_mean_squared_error: 0.0082 - val_loss: 3.8735e-05 - val_root_mean_squared_error: 0.0062\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 963/1000\n", - "8/8 - 0s - loss: 6.7751e-05 - root_mean_squared_error: 0.0082 - val_loss: 5.7050e-05 - val_root_mean_squared_error: 0.0076\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 964/1000\n", - "8/8 - 0s - loss: 8.5382e-05 - root_mean_squared_error: 0.0092 - val_loss: 3.5622e-05 - val_root_mean_squared_error: 0.0060\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 965/1000\n", - "8/8 - 0s - loss: 8.8722e-05 - root_mean_squared_error: 0.0094 - val_loss: 8.6944e-05 - val_root_mean_squared_error: 0.0093\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 966/1000\n", - "8/8 - 0s - loss: 1.0029e-04 - root_mean_squared_error: 0.0100 - val_loss: 5.2632e-05 - val_root_mean_squared_error: 0.0073\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 967/1000\n", - "8/8 - 0s - loss: 8.1248e-05 - root_mean_squared_error: 0.0090 - val_loss: 6.1730e-05 - val_root_mean_squared_error: 0.0079\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 968/1000\n", - "8/8 - 0s - loss: 8.8418e-05 - root_mean_squared_error: 0.0094 - val_loss: 4.1923e-05 - val_root_mean_squared_error: 0.0065\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 969/1000\n", - "8/8 - 0s - loss: 7.7816e-05 - root_mean_squared_error: 0.0088 - val_loss: 5.6047e-05 - val_root_mean_squared_error: 0.0075\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 970/1000\n", - "8/8 - 0s - loss: 9.9702e-05 - root_mean_squared_error: 0.0100 - val_loss: 7.7860e-05 - val_root_mean_squared_error: 0.0088\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 971/1000\n", - "8/8 - 0s - loss: 9.5950e-05 - root_mean_squared_error: 0.0098 - val_loss: 3.9684e-05 - val_root_mean_squared_error: 0.0063\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 972/1000\n", - "8/8 - 0s - loss: 6.9255e-05 - root_mean_squared_error: 0.0083 - val_loss: 6.6998e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 973/1000\n", - "8/8 - 0s - loss: 6.9674e-05 - root_mean_squared_error: 0.0083 - val_loss: 3.3185e-05 - val_root_mean_squared_error: 0.0058\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 974/1000\n", - "8/8 - 0s - loss: 6.9009e-05 - root_mean_squared_error: 0.0083 - val_loss: 8.1445e-05 - val_root_mean_squared_error: 0.0090\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 975/1000\n", - "8/8 - 0s - loss: 7.9782e-05 - root_mean_squared_error: 0.0089 - val_loss: 3.2603e-05 - val_root_mean_squared_error: 0.0057\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": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 976/1000\n", - "8/8 - 0s - loss: 7.5102e-05 - root_mean_squared_error: 0.0087 - val_loss: 7.7928e-05 - val_root_mean_squared_error: 0.0088\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 977/1000\n", - "8/8 - 0s - loss: 9.2946e-05 - root_mean_squared_error: 0.0096 - val_loss: 7.1823e-05 - val_root_mean_squared_error: 0.0085\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 978/1000\n", - "8/8 - 0s - loss: 1.0523e-04 - root_mean_squared_error: 0.0103 - val_loss: 9.0218e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 979/1000\n", - "8/8 - 0s - loss: 1.2987e-04 - root_mean_squared_error: 0.0114 - val_loss: 6.7157e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 980/1000\n", - "8/8 - 0s - loss: 1.3446e-04 - root_mean_squared_error: 0.0116 - val_loss: 8.8951e-05 - val_root_mean_squared_error: 0.0094\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 981/1000\n", - "8/8 - 0s - loss: 1.7033e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.4989e-04 - val_root_mean_squared_error: 0.0122\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 982/1000\n", - "8/8 - 0s - loss: 1.8090e-04 - root_mean_squared_error: 0.0134 - val_loss: 5.0167e-05 - val_root_mean_squared_error: 0.0071\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 983/1000\n", - "8/8 - 0s - loss: 1.3295e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.1734e-04 - val_root_mean_squared_error: 0.0108\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 984/1000\n", - "8/8 - 0s - loss: 1.3401e-04 - root_mean_squared_error: 0.0116 - val_loss: 7.5709e-05 - val_root_mean_squared_error: 0.0087\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 985/1000\n", - "8/8 - 0s - loss: 1.2778e-04 - root_mean_squared_error: 0.0113 - val_loss: 6.6762e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 986/1000\n", - "8/8 - 0s - loss: 1.6355e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.2307e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 987/1000\n", - "8/8 - 0s - loss: 1.4732e-04 - root_mean_squared_error: 0.0121 - val_loss: 8.4239e-05 - val_root_mean_squared_error: 0.0092\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 988/1000\n", - "8/8 - 0s - loss: 1.4966e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.2724e-04 - val_root_mean_squared_error: 0.0113\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", "Epoch 989/1000\n", - "8/8 - 0s - loss: 1.6026e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.2491e-04 - val_root_mean_squared_error: 0.0112\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 990/1000\n", - "8/8 - 0s - loss: 1.2624e-04 - root_mean_squared_error: 0.0112 - val_loss: 9.9349e-05 - val_root_mean_squared_error: 0.0100\n", - "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - 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 991/1000\n", - "8/8 - 0s - loss: 1.3400e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2431e-04 - val_root_mean_squared_error: 0.0111\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 992/1000\n", - "8/8 - 0s - loss: 1.4268e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.1467e-04 - val_root_mean_squared_error: 0.0107\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 993/1000\n", - "8/8 - 0s - loss: 1.2067e-04 - root_mean_squared_error: 0.0110 - val_loss: 1.2110e-04 - val_root_mean_squared_error: 0.0110\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 994/1000\n", - "8/8 - 0s - loss: 9.8976e-05 - root_mean_squared_error: 0.0099 - val_loss: 9.0305e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 995/1000\n", - "8/8 - 0s - loss: 8.8226e-05 - root_mean_squared_error: 0.0094 - val_loss: 9.1174e-05 - val_root_mean_squared_error: 0.0095\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 996/1000\n", - "8/8 - 0s - loss: 9.3500e-05 - root_mean_squared_error: 0.0097 - val_loss: 5.8974e-05 - val_root_mean_squared_error: 0.0077\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 997/1000\n", - "8/8 - 0s - loss: 1.0454e-04 - root_mean_squared_error: 0.0102 - val_loss: 4.7789e-05 - val_root_mean_squared_error: 0.0069\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 998/1000\n", - "8/8 - 0s - loss: 1.2861e-04 - root_mean_squared_error: 0.0113 - val_loss: 6.7213e-05 - val_root_mean_squared_error: 0.0082\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 999/1000\n", - "8/8 - 0s - loss: 1.5823e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.1036e-04 - val_root_mean_squared_error: 0.0105\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", "Epoch 1000/1000\n", - "8/8 - 0s - loss: 1.9264e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.1082e-04 - val_root_mean_squared_error: 0.0105\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", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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GmEJjzKcAxphKYDOQHcH6o2/gaCjJD/9uUxoUSqkeJpygzwb2tHhdwNFh3eE6IpILTARWdrZIWw0YBcYPBzu4FIL27Sqleqhwgj5UE7V1qrW7jogkAf8GvmuMqQj5ISILRWSViKwqLi4Oo6xu0jTyJuzuG23RK6V6lnCCvgAY0uJ1DrAv3HVExIMV8k8bY55v60OMMQ8bY6YYY6ZkZmaGU3v3SBsOLk8YB2S1Re8o+g1NOUg4Qf8JMEJE8kTEC1wBtL5z8kvANcHRN9OBcmNMoYgI8Biw2Rjz24hW3l1ivJAxIvwWvfbR93L676ecJ6ajFYwxPhG5CXgdcAOPG2M2isj1weWLgCXAXCAfqAG+Htz8NOCrwGcisjY4705jzJLI7kaUDRgNBR/bXYVSSh2TDoMeIBjMS1rNW9TiuQFuDLHdcpzQRBowCjb8C+orITbZ7mqUUqpT9MzYcDQfkN0Sxsq9/++aUspZNOjDMWCUNW3vgKwevHMY/fdUzqFBH46UYeBJDO+ArB6M7d303085kAZ9OFwuq1W//7N2VtIWoFKqZ9KgD9fgCVC4DgKBDlbUFqFSqmfRoA/X4InQUAml20Mv1z56Z9B/R+VAGvThGjzJmu79tP31TEctfqWU6l4a9OHKOAE8CbBvTfvrGb2/bK+mB2OVA2nQh8sdA4PGtxP0wa/8eiNxpVQPo0HfGdmToXBt+9em1xa9UqqH0aDvjLzTwVcHBZ8cvazpIF6Ho3KUUqp7adB3xrBTQVzwxXtHLysP3ndFW/TOoKNvlINo0HdGXH/ImgA7lh69rHC9NdU++l5OD8Yq59Gg76wRX4I9H0PlgSPnBxqtqbbolVI9jAZ9Z42ZDxjY3OreK/5g0Id7E3GllOomGvSdlXmiNaZ+04tHzg/4rGl1D7rfrVJKoUHfeSIwej7s+gAqCg/Pb2rRVx0IvZ3qZfRgrHIODfpjMX6BdamD9c8entfUR98y/JVSqgfQoD8WGcfD0FNgzdPWMLxA4PA1bg5+DnUV9tanjp1eAkE5kAb9sZrwFSjZZo3AaeqfHzgOMFCx19bSVA9UUQjv/lLH5ytbaNAfqzHzrbtOrX3qcLdNQqo1ra+0ry7VNdEK4ue/Bct+Bfs6uPqpUlGgQX+sYpOtsN/w/OGumngNeueIcBdOQ7U11Ra9soEGfVdMvBoaqmDdYut1fJo11aB3AA1k5Rwa9F0x9BTr0sVv/8x6ndAU9HowtteK+sFYPdirup8GfVeIwNhLDr/2JlrTZb/Wr+hKqR7DUUH/2ob9bC7s5tb09BuOfJ2aa13Jcst/u7cOpZRqg6OC/rvPreGFNd08tDEm9vDz2jKY96D1/MUbu7cO1cPpNzxlH0cFvcflotFvw40/zgn20TdUQe4M63ldGRRv7f5aVGREq+tNu+iVDRwV9DFuwee3oeU0bSGMvwLOvM16nTPVmv7nBu2r73U0iZXzOCzoXfjsuJWfNwEu+QskD7Jef+NN6JcNe1fBzve7vx6llGrBUUHvcQmNdrToWxOBG1eCOxb+9Q04tNPuipRSfZijgj7G7cJnRx99KLHJcNlfoboI/jr3yLBf9VcoWG1baSocPaDBoFSExNhdQCTFuIXGQA/6BT3xfDj1ZvjwAfjDSXDad61hlyXbrOXXvgJ5p9tbowot0sdW9FiNspGjWvQeVw9q0Tc59x4475fW8w9+fzjkARZfaU9Nqm3NZ8ZqMCvncFTQ2zbqpj0i1klVVyw+ellDJWx7q/trUh2LeAvcROl9leqYw4Le1bO6bpqIwIlz4badcPNa+OE2GDzRWvb0peBrsLU81UK0g1iDXtkgrKAXkdkislVE8kXk9hDLRUQeCC5fLyKTWix7XESKRGRDJAsPxeOSntd101J8KqTlQdIAmP2rw/OfugQq9tlXlwohSoFsevD/T+VYHQa9iLiBB4E5wGjgShEZ3Wq1OcCI4GMh8FCLZX8DZkei2I70yK6btgw9Gf63GM67F3avgN+Phw8eAF+93ZUpiF7LW4Ne2SCcFv00IN8Ys8MY0wA8C8xrtc484EljWQGkiEgWgDHmPaA0kkW3xeN20WjHCVPHKsYLp9wAN3xkjb5588fwyNnwxXt2V9Z3RftgrAa9skE4QZ8N7GnxuiA4r7PrtEtEForIKhFZVVxc3JlNm23cV8Ga3WWU1zQe0/a2yRgBVz8Pl/8d6svhiQutETlFmw+vU7kfHp8Dv8q1bkauoktb9MpBwhlHH+riH61/C8JZp13GmIeBhwGmTJlyTL9lpdXWQc01ew4xc+SAY3kL+4jA6Itg+Cx4/7ew/LewdQkMPRUGnAirHj+87j2p8OMScDvqNIgeRoNeOUc4LfoCYEiL1zlA6yOH4azTbf67vtCuj+662GQ456dwwwpIHwG7Pzwy5Jsc/Lz7a+tLonXClAa9skE4Qf8JMEJE8kTEC1wBvNRqnZeAa4Kjb6YD5caYbk/b7597AgD/XF2A6e3D2AaMghs/hjn/F3r5izeEnq96Ng16ZYMOg94Y4wNuAl4HNgP/MMZsFJHrReT64GpLgB1APvAI0JxCIrIY+AgYKSIFIvKNCO9DsxtnHd/8/LlP9rSzZi/hcsHJC+G2XTDm4iOX7VsDAb89dTlasBcyWidM6Rm3ygZhdfIaY5ZghXnLeYtaPDdAyFsqGWO67Tx/t0tY+5NzmXDPm9z76hbmTcgm3uvuro+PnvgUuOxvMHo+pA6Dh2da83+dBz/aoX31URGtPnoNetX9HHVmLEBKgpfHrp1CeW0j3356NdX1PrtLipwx860zau8stE6+qiuHv5xhd1XOpKNulIM4LugBzh41kF9ePI73tx3kskUfsWlfN98wPNq8CVb/PUDRRtj6qr31OJIGvXIORwY9wFUnD+XRa6ewv6KOC/74Prf+ax1fHKy2u6zISRoAX/m39fz566yza5usXQzb37WnLqfQFr1yEMcGPcCskQN49wczufbUXF5cu4+zf7OUG5/5lK37K+0uLTJGnGO17OvL4fHzYO+n1vz/XA9/nw97Pra3vl5Ng145h6ODHqB/goefXjiG5bedxXVnDmfZ1mJm/+E9vrN4DbtLauwur+syR8LMO6znj8yCV287vOzNn9hTU2/WfAWESI+jb5pq0Kvu5/igb5KZHMtts09k+W2z+PaZw3l78wFm/+E9/r26wO7Sum7m7TDkZOv5ykWH5+tJVZ0X7UExOupG2aDPBH2TlAQvt84+kTe/fybjc/rzg3+u4/9e39L7T7D6n9chOevIeTUlsPJhe+rp9bTrRjlHnwv6Jtkp8fz9GydzxdQhPPjudu55ZVPvDnsR+MEWyJ5ivZ7yP9b01R/ZV1MvVFlvXRCvrjFKw3I16JUN+vSZNh63i3svGUe8181fP9hJ/3gP3z3nBLvL6ppvvW1Na0ph7TPgq7P67ef8qv3tFAAb9pZzCrC9qJIx0fiA3tyYUL1Wn23RNxERfnLBaC6dlMPv39rGu1uL7C4pMhLS4KsvWM9XLoLqg0evo5c7PooJHo0NdTnWrr6zNdGfuep+fT7owQr7X1w8lhMHJfP959ZSWF5rd0mRkT0F4tOs5y2vbQ9W3/09qVBb1v119QraR6+cQ4M+KM7j5sGvTKLBF+D6pz6lrrHzFwwrqqiLQmVdEOOFm4Nj65+4wLoQGsAX7x/uu69yyDeYCJNo9bBo0CsbaNC3MDwzid9cPoF1e8r44T/X4Q+E/9v+3/WFTPvl26za2S13TQxffCrMust6/saP4T83WqHf5KM/2VNXTxfppNfr0SsbadC3MnvsIO6YcyKvrC/kxy9uCHskzmsb9wOw51APPAnrzFvh5Oth5/uw9qkjl336hHbfhCARP2Gq6RuiHoxV3a9Pj7ppy3VnDqestpGHlm7HJfCzi8bidrV/eG5vMODjPT30sshTvgEl2yH/zaOXle2yLoWsCJimg7GRDeT9ZTUMAm3RK1toi74Nt543kuvOPI6nVuzmlmfX0OBr/xd0U6F1hcyahh56M5DME+Dqf4Vetn9D99bSgwXE+kPtCkT2BvPVwfH5GvTKDhr0bRAR7pgzqrkb5yuPrmj3YGtdo/ULXN1Tg77JwmWQM9V6fs7PICEdlt6rQy2bWT8HV6Ahou/qDr6v3++g+yOoXkODvgPXnTmcP145kQ17Kzj/j8v5MP/o8eiBFgdta3r6jU4GT4BvvgW37YQZ34WzfwLle2Dj83ZX1iM0HYMVf31E39cf/FWrru6Bx3CU42nQh+HCkwbznxtPIzk2hqseXcn3/7H2iNZ9Zd3hcL/31S12lNh58anWdNxlkDYcnv+WdU37jx+B3Svtrc1GTX+yxRfZobKNwcNh/noH3RNB9Rp6MDZMIwcls+SW0/njO9t4+L0dvLK+kCumDuFbpx/Xu89q9ybCgqfgoVOsa9q3NOI8mPcn6yYnfYb1jyn+yHbdNP0X0aBXdtAWfSfEedz86LwTefv7M7lkYjaLP97NGf/3LvMeXA7AOaOsQHzv82I7y+y8AaNg5p1Hz9/2Otw/AgI9/LhDBElz0Ee26yYm2EcfaNCgV91Pg/4YDE1P4L5Lx/PerbO4adbxpCZ4uerkoSw8YzgA1zz+MT99cUPIs2vLaxq5efEa9pX1oMssiMDM2+CyJ0Iv37u6e+uxk4lOiz6GYPeetuiVDbTrpguy+sfzgy+N5AdfGtk87//NH8uP/7OBJz7axbOf7GHCkBRGD+5HbnoiO4qreOKjXQC8tG4fH91xFln94zv1mUUVdaQleolxR+Fv9Jj5cMIBWP5bWPEQZJ1knWT12Llw5z7w1VuPhmrr8gopQyNfg82azpZwRalFbxr1YKzqfhr0EfbV6cOYP2Ewr27Yz7KtxXy2t5yVX4S+LMJVj6zk118ez4QhKXjCCO5XPyvk209/yqnD03nmW9MjXbrFEwez7rQeAHf3t6a/HHz0uneXR6cGGwWCLfpIB71brG93okGvbKBBHwXJcR4unzKEy6cMAaCirpH1e8o5WFVPXkYiowf34/lPC/jlki1ctugjvG4Xxw9I4sSsZEYN6seJWcmMzupHaoIXV/CM3A/zD/Ltp60LlH24vYRGf6DNPw7+gCFgTFh/PDp08xp4YGLoZTs/gNzTuv4ZPYjP3zSOPrJB78EKek9tiMtFKxVlGvTdoF+chxkjMo6Yt2DqUOaMy+KdzUVs3l/BlsJKPsg/yPOf7m1eJzk2hoRYN6XVDTT6jxzas2xrMeeMHhjy8xY+uYq3txTx6y+Pb/5j09or6/cxcWgq2SkddB2lHQc3rYI/T4dAq3ME/jYXRnzJeoy6EGKTobEWEEhMb/99eyif3wrkGF9kj6G4g0GfWPWFdRxAIn/Fe6XaokFvo35xHuZPzGY+2c3zSqsb2FxYwebCCrYdqGJ3aQ2ZyY00+gzzJ2azYOoQJv2/N/nmk6uYODSF88dlcfHEbNISvYgIf3pnG29vsS49fOu/1nPZ5BykVagUVdRx0zPWJYv/e/MMxgzu336hGSPg1h3girGC/LN/WmfT1pXDtjesx5IfHrnNhQ/A5Gu7/kPqLvlvgbjxB1v0bl/kDpo2+ALNffReXxXUlR0+j0GpbiA98T6pU6ZMMatWrbK7jB7rzU0HuP/1rWw9UNk8LzbGRX2L6/EM7h/HvvI6LpmYzbdnDmfEwGQAdpfUcNGDyymrsa69csH4LP501aSjPuNgVT0et4v+8Z62CynZDouvhINbQy+f8BU444fWt4KeLngs4n0zgdNlLVWuZJJ+UhCRt95dUkPGA7lUEs9AKYMbP7GuPaRUBInIamPMlFDLtEXfC507eiDnjh5IUUUdG/dV8PmBSlbsKOGjHSXUNQZYdPVkTjkunV8s2cQ/Vxfw4rp9fHNGHtmp8fzkxY0AnHFCJhmJXpZ+Xkxdo5+4FlfdPFTdwJSfv0V2SjyvfGcGqYne0IWkD4ebPobPX4e1T0P/ITB8Fjx1qbV87dPWA8AdC00HOM//DUz9ZrR+PF1yuqwFIC5QE7Eulr1ltQzET6FJt4K+6oAGvepW2qJ3uPUFZfz6ta18sP1g8xm8F4zP4oErJrI8/yDXPP4xY7P78eerJpOTGs/m/RWc/8DyI95j0z3nkeCNYVdJNdsOVF5vK9oAAA6tSURBVFHd4OP8cVkhh3gGAgZXfTm8dTes/mv7xY27HCZcBenHg8sN/QZbNzX/+8Xg9ljX5GlStgc8CdHr+787RPfVXfvB07nhr6G8vG4fF74wig+9p3Bqw0fw5cdh7KVdfl+lWmqvRa9B30fsKqmmtLoBb4yL0Vn9mvvtf/vm5zzw9jbA6v7xBwy+VnfWOn98Fj/60khm3r+0eV5yXAxfOzWXG2cdT5zHTb3Pzwuf7uW+17Zw59xR1rEBXx1U7IPt71g3J88aD+/eCwc+C6/opEFWP/979wdv3CFwd4ubpHz+hnVWb0roA85hK1wPfzn9qNnmexuR/jlde29gyev/Ze5HV/HagG8xu+gR/F+6F/epN3T5fZVqSYNetckYw5ubDrBhbzkHKupJS/Jy1bShpCZ6qW/0c9ZvllFe2/G12eM8ruZLNQOcN2Ygv7x4HOlJsfj8Ad7PP8gr6wq58KQszjwhE1n1GBzcBmsXQ30nxuPP+B5MWwibXzl839uflFrfCJoEAvD6nXDcmTByzpHb+33WwdDEDOvA8iePwhv/G/KjKi95muTxF4Rc1hlvP/Mbzv78Hv592ktcuPxi6qfeQPIFP+/y+yrVkga9OmYFh2r4+ItSymoamZKbypjB/XG7hPyiSr733Do+23tkSD/5P9O4/42trC8oRwTy0hMpKKs96sYt15wyjBnHZ5Ac5yEntpo9xeWU1wXYXdbAvqoA18W8zOB1fwy/0O9vgdpSEJd1jKAiOEw1eFJXRWU5+b87nwlssW4qknUSpI+ADaFvxlJl4qgZfAoDrvtP+DW04a/3fYev1z3Je5etZ/g/ZhGfdzJpX1vc5fdVqiUNehVV9T4/xtB8QPdARR0PLd1OwaFaCg7VUFxZT2JsDF+dPoz8oiqeW7Un7PceIQUMlEMAXJT8ORcPrcWzbUn4xZ16M2bNU0htxzdt3zXrjwx79zv4LvozT774GtfKf3Ff9AeYePUxH5Q1xvDEjy/nEvf7lN28g/d/dzULPO8T84NNkJR5TO+pVCga9KpHKaqsY0thJR9uL6He5yctwYvfGIyBzORYXt1QyK6SGoyxRqyEkiNF1BsPl7uXcXpqKQMSXGSWrmZj7EkMq1pLg4lhmKvoqO0OujJ5wz2DKfUfk2+y+bv/XPaaDP785RMYO3lG83q3P/MBF275Eae5NmLSj0dGnAc5k2HQeOifE/ZB2oJDNQR+Nx53xnFk3/wGdz38L3667zokezKeK5+OWNj7AwaXcNQ5Ez3dJztLyctIJCMp1u5Ser0uB72IzAb+ALiBR40x97VaLsHlc4Ea4GvGmE/D2TYUDXrVWml1A/lFVSz5rJCiyjqq6/2UVjfgcQu7SmooqT58tUmPW5rPJB4nO/DgY2L8fq694kqGnjCBspoGJtxz+Cbpr3xnBmOzjxx1c7Cqnqv+8gGjSt7kK95lTGAbXg5/Rp03jfrEwfjj0jDxqZj4NExCGpKQjis+lZjYeIiJ4/kPPuNr+39Bwck/IWfOD9iwt5y/LfoVP3c/golJoHLERcRnj8ObNRrvgBFIQpo14igMZeXlLFmxnuTlP2egHGK47GPN+J9y6oVfJ8F77COny2oaePvV5+m36SmyLv8NY0cGL9pnDIeqaskvKKR+12o+q0yChkrOOnMWI3M6f8+CJ5cs40srr+UTzzSGnnYZA8fNYlBGRscbtvDp9n1U1PmYcWJOdC7014t0KehFxA18DpwLFACfAFcaYza1WGcu8B2soD8Z+IMx5uRwtg1Fg151hjGGfeV11Df6GZqWcMQv/I7iKganxB9xnkC4GnwB3t58gGWfF5O//xBxpVsYULuDwVJCthxksJSQKpWkUkV/qaKfhP72UedKIO7G5dZ5B8DW/ZX85R8vMbf4EU5xbSJRjryuTjVxVEkSdRJPoyuWBokjgOA2PhrFgzvQSKb/AAMI3R1VZFIojMmmKhCLO9BIozsWb0J/qhOH0BiXTr0f6kr34autICM5FldyFiY+lfqaSpIq8hlbtZwMrGMbtcSycdB8Uio/5/jqNSE/74BJYZt3NJJ3BgkDh+NNSsPl8eKJiSHG7SaAYMSNuFzUNRrqdq5g9549zC/68xHvU2YSyU+cTH3GaOJzp5IybDxxHjfiEgLEUFVTTW1VBdtXvcGh0mLmB94io8E6FlNDHKtP+RPJx0+nX3J/XJWFVDcG2L93J7vLGpl88CUqY9KorK7CuLwMSnRTk5DF+oJD5FFI7rw7ScnIxhPjwhvjwuN24XW7aPAHKKqop6Ckkv37dpJ4aDOV+3dQWhtgiPsQ/VLTYdzlJHjdZGZkUlFTy7DBWbgFXCJ4YlzU19dR53ch4gLjY29RKUUb3sXXL4eYmiI8yQMYNDibbWVC6e6NXDV/Xvj/SVvoatCfAtxtjDkv+PoOAGPMvS3W+Quw1BizOPh6KzATyO1o21A06FVP5Q8Yqup8VNQ1Ulnno6reR4MvQL3PT0NDPdQcwtQdItBQS6ChluPTYjhh7DRi+g866r32l9exad8hSvbuIL5sGwnVu3HVVxDTUIG3sRy3r4YYfy0xgXrcwZvZxpk6XMaPyyUYl5d+7nri3IIcfzZu00BBeQP+gztIrt6FuFzUxvTD+BtJaCgl1ZQ136Q8gFDvSsAbqG2e11pp2kQaSvcwiKMvxHYwdSLVWSeTEtNI1Ref0L9qO0mm85eN8A2bQcwu67yNMncaKf6Oj6V0JGAEl3S+S7rWeKnFSz1eBEMAIYF64mkgVjoeedbEb6T5HsENeEmSWupNDLHS8f2kD0l/4m/PJy62jZMU29HVM2OzgZZHzwqwWu0drZMd5rZNRS4EFgIMHeq865wrZ3C7hP4JHvontNW9Ev7/3UH94xjUPwtGZQGRuQrosPYW+uqhvhL8jbiSBhLvckEggKkuwld9yLrCpjfRug5PfAppWN+WGsoK8dSXIilDwOUBTzwZxpDhssKsP0DAT/HuLRw6uB9fdSk+nw+/308g4MdlAmACQACvC1ITYkgbNo64lCxi+h++zlMKEGisp762hoINS6k7kE+j3xDAjddXRUxcAt7YBNJi/aSNPA1TeQAZcCLExFPjC3Dgs6X4D+3EX1NBfVwGSaaKZKklfUA2DXiojelHqqnAnz2V8opyAvs3EjtgONUkULLxLVwNVYivDvHVETCGQMBwMCYBb3wi3vgk0rwBYhrKiB08Bk98P8yQ6ew/UEhg54dU11Tjq63EYxqoCnjA34i3sQLjb8ArhoAnEfz1JPjKqU8fw+DK9dQmDaEx+2SqiaeqeA8ZlDFo8vnEeCJ/wYJw3jHU0Z3Wfy7bWiecba2ZxjwMPAxWiz6MupRSnRETaz1acrmQ5EF4ko/+xgHWwV1v6mBgcOsFrd7HTWbuGDJzx3SpRJcnlnhPLCNOvbjDdVtWkADknfmVNteNCz7AOliYBjD2HACSgUHjz+p0rQJkZRwPY44+2S4cice01bEJJ+gLgJanHuYA+8JcxxvGtkoppaIonMPUnwAjRCRPRLzAFcBLrdZ5CbhGLNOBcmNMYZjbKqWUiqIOW/TGGJ+I3AS8jvWt53FjzEYRuT64fBGwBGvETT7W8Mqvt7dtVPZEKaVUSHrClFJKOUB7o2769hkGSinVB2jQK6WUw2nQK6WUw2nQK6WUw/XIg7EiUgzsOsbNMyDEOdvOpvvcN+g+O19X9neYMSbk5VB7ZNB3hYisauvIs1PpPvcNus/OF6391a4bpZRyOA16pZRyOCcG/cN2F2AD3ee+QffZ+aKyv47ro1dKKXUkJ7bolVJKtaBBr5RSDueYoBeR2SKyVUTyReR2u+uJFBEZIiLvishmEdkoIrcE56eJyJsisi04TW2xzR3Bn8NWETnPvuq7RkTcIrJGRF4Jvnb0PotIioj8S0S2BP+9T+kD+/y94P/rDSKyWETinLbPIvK4iBSJyIYW8zq9jyIyWUQ+Cy57QKT13V/aYYzp9Q+sSyBvB47DutnJOmC03XVFaN+ygEnB58lYN1sfDfwauD04/3bgV8Hno4P7HwvkBX8ubrv34xj3/fvAM8ArwdeO3mfgCeCbwederLvrOXafsW41+gUQH3z9D+BrTttn4AxgErChxbxO7yPwMXAK1s2tXgXmhFuDU1r004B8Y8wOY0wD8CxwbLdS72GMMYXGmE+DzyuBzVi/IPOwgoHgdH7w+TzgWWNMvTHmC6x7BEzr3qq7TkRygPOBR1vMduw+i0g/rEB4DMAY02CMKcPB+xwUA8SLSAzWHQH34bB9Nsa8B7S+63mn9lFEsoB+xpiPjJX6T7bYpkNOCfq2bk7uKCKSC0wEVgIDjXUXL4LTAcHVnPKz+D1wKxBoMc/J+3wcUAz8Ndhd9aiIJOLgfTbG7AXuB3YDhVh3pnsDB+9zC53dx+zg89bzw+KUoA/7JuS9lYgkAf8GvmuMqWhv1RDzetXPQkQuAIqMMavD3STEvF61z1gt20nAQ8aYiUA11lf6tvT6fQ72S8/D6qIYDCSKyNXtbRJiXq/a5zC0tY9d2nenBH04NzDvtUTEgxXyTxtjng/OPhD8OkdwWhSc74SfxWnARSKyE6sb7iwReQpn73MBUGCMWRl8/S+s4HfyPp8DfGGMKTbGNALPA6fi7H1u0tl9LAg+bz0/LE4JesfehDx4ZP0xYLMx5rctFr0EXBt8fi3wYov5V4hIrIjkASOwDuL0GsaYO4wxOcaYXKx/y3eMMVfj7H3eD+wRkZHBWWcDm3DwPmN12UwXkYTg//OzsY5BOXmfm3RqH4PdO5UiMj34s7qmxTYds/uIdASPbM/FGpGyHbjL7noiuF8zsL6irQfWBh9zgXTgbWBbcJrWYpu7gj+HrXTiyHxPfAAzOTzqxtH7DEwAVgX/rf8DpPaBff4ZsAXYAPwda7SJo/YZWIx1DKIRq2X+jWPZR2BK8Oe0HfgTwSsbhPPQSyAopZTDOaXrRimlVBs06JVSyuE06JVSyuE06JVSyuE06JVSyuE06JVSyuE06JVSyuH+P297PMHfoplJAAAAAElFTkSuQmCC\n", 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AGKgshjydLkEp5V+biV5EXMBjwFRgJHC5iIxsUm0qMMz7mAk87i2vBiYZY0YDY4ApIjKhY0LvweJS4YzfwubPYP3b9u7Zf0+C938T7MiUUiEokBb9eCDXGLPZGFMDzAGmN6kzHXjOWIuAJBHp590u89aJ8D5MRwXfo2VfB71HwXu/hh3e+XAWPRbcmJRSISmQRD8A2OGznectC6iOiLhEZDmwF/jIGPPNIUerGrjCYdosOLCrcbnHHZx4lFIhK5BEL37KmrbKW6xjjHEbY8YAGcB4ETna74eIzBSRHBHJKSgoCCAsRUY2HP/jxmW7VwQnFqVUyAok0ecBA322M4Bd7a1jjCkGPgOm+PsQY8xsY0y2MSY7PT09gLAUAGc06Zd/8gw7XYJSSnkFkugXA8NEJEtEIoHLgHlN6swDrvKOvpkAlBhjdotIuogkAYhIDDAZ0Dl3O1JsCvzo48ZlpbuDE4tSKiS1meiNMXXAzcAHwDrgFWPMGhG5UURu9FZ7F9gM5AJPAjd5y/sB80VkJfYL4yNjzNsdfA5q4PEw/oaG7bqa4MWilAo5YkzoDYLJzs42OTk5wQ6j+7mnl32e8FM4+w8Q5gpuPEqpLiMiS4wx2f726Z2xTjLtEfu86DH44q/BjUUpFTI00TvJ2KsaXm/6FNy1wYtFKRUyNNE7Tb/R9nn71zD3R8GNRSkVEjTRO80PXml4vfZNyF8TvFiUUiFBE73TJPSFmZ81bD9+UtBCUUqFBk30TtT/OLhrT8P2R78PXixKqaDTRO9UETFwu/cO2YUPw8pX7JTGSqkeRxO9k8X3hp98ZV+/dj18cp8me6V6IE30TtdnFPxyM/QbA1/+Db54KNgRKaW6mCb6niAuFa54FYZOsmvNzv9zsCNSSnUhTfQ9RXxv+N6TkHkqLHgAXrkKPJ5gR6WU6gKa6HuSuDS4fA4cc4kdY//ipVBbFeyolFKdTBN9TxMVb1v2J90CuR/Bc9OgvDDYUSmlOpEm+p5IBM6+3z7yFtvFSvZvDXZUSqlOoom+JzvpFpjxApTvgzlXQElesCNSSnUCTfQ93Yhz4ftPQ9Fmm+wP6OpUSjmNJnoFw6fAhY9DwXp48RKoLg12REqpDqSJXlmjLoQZz0P+Wph7vQ69VMpBNNGrBsPOgnP+CN+9Bx/rRGhKOUV4sANQIeaEG6FgA3z1CPQaCCfc0PYxSqmQpi161ZgITH0QjpwCH/wGtn8T7IiUUodJE71qLjwSLvoX9MqAV6+xwy+VUt2WJnrlX0wSXPocVBTatWc97mBHpJQ6RJroVcv6jYbzHoLNn8FXs1qu9+z58PZtXRaWUqp9NNGr1h33QxhxPix4EIp3NN+/7SvY+gXkPNX1sSmlAqKJXrVOBKb82a5M9f4djffVlMMzU4MTl1IqYJroVduSBsHEX8P6t2H9uw3lddWN6829vmvjUkoFRBO9CsyJN0P6CNuqr620ZU0T/apXuj4upVSbNNGrwLgi7Pj64m2w0Hthtk4XLVGqO9BErwI35HQYdZFdZLxwk/9Ev3lB18ellGqVJnrVPuf8CVyRMO9nUFvRfP9z0+yXgFIqZGiiV+2T2N9OfLbtS3hykv86NeVdG5NSqlWa6FX7HfdDGHxyy/sX/sMOx1RKhYSAEr2ITBGRDSKSKyJ3+NkvIjLLu3+liIz1lg8Ukfkisk5E1ojIrR19AioIRGDaIy3vXz0XNn7UdfEopVrVZqIXERfwGDAVGAlcLiIjm1SbCgzzPmYCj3vL64D/Z4w5CpgA/NTPsao7Sh3a+v4XL4H173RNLEqpVgXSoh8P5BpjNhtjaoA5wPQmdaYDzxlrEZAkIv2MMbuNMUsBjDGlwDpgQAfGr4Lpl5vs/PUXzLLTJDQ15wewf2uXh6WUaiyQRD8A8J3kJI/mybrNOiKSCRwH+J3gXERmikiOiOQUFBQEEJYKurg0mPp/MO5quOwFuGUpRCU2rvPoeCjaon32SgVRIIle/JQ1/V/bah0RiQfmAj83xhzw9yHGmNnGmGxjTHZ6enoAYamQkzoUfrXZjrWv566GWWPg3iR45/ZgRaZUjxZIos8DBvpsZwC7Aq0jIhHYJP+CMea1Qw9VdQuuCLjkWbhybvN9i5+ErV92eUhK9XSBJPrFwDARyRKRSOAyYF6TOvOAq7yjbyYAJcaY3SIiwFPAOmPM3zo0chXajpgMP1sGw85uXP7sebBS58RRqiu1meiNMXXAzcAH2Iuprxhj1ojIjSJyo7fau8BmIBd4ErjJW34y8ENgkogs9z7O7eiTUCEqZQhc8SrcUwLf+3dD+WvXw/t3grs2eLEp1YOICcGLZNnZ2SYnJyfYYaiOdmA3fPYnWPpcQ9mNX0LfY4IXk1IOISJLjDHZ/vbpnbGq6yT2szda3eQz8OqJU2D+n3VNWqU6kSZ61fV6j4DfFsCYK+z2ggfgvhR7N63HA/u3BTc+pRxGu25UcO3LhUfHNS+//lMY4KdcKeWXdt2o0JV2BPxuX/Py+X/u+liUcihN9Cr4XBHwm10w8ISGsq1fwCtXw57VwYtLKYfQRK9CQ2Qc/OhDOxTznD/Z1avWvgFPnGz77ZVSh0wTvQo9x10JR05p2L4vGb75l31duR/u6QWr9SZrpQKliV6Fnuhe8IOX4ddbGy7IvvcrWPMG7F5ht7/4a7CiU6rbCQ92AEq1KCbZjr4pK4CHjoBXr27YFx4dvLiU6ma0Ra9CX3w6HH9947KKwobWfU+z4C+2+0qvXagAaaJX3cN5D8HkeyBpkF2zdv8W+Ndp8KeMYEfW9b70zg9Y4WdYqlJ+aKJX3ccpv4Cfr4Kz7msoqymFtfMaJkirKILcj4MTX2cryYNdy22XFsCBnUENR3UfmuhV9xObAv9vA6SPsNuv/BD+kGZfv3wlPH8xlBcGL77O8vdRMPt0e7EaoHRPcONR3YYmetU9JfS1I3N8ffg72LbQvt67putj6jLeBd1qyhuK8tfCI9n2F41STWiiV91Xcib8fj/0GmS3v5rVsK8wNyghdZq6mobXFd5fK3VVDWVf/BUKN9qJ4d6+TRd3UY1oolfdW1gY/GIVTHmgcfnbv4Av/9E4QXZnNWUNr8v32ufayoYy8bbyC3Mh5ym7uItSXprolTNM+AncvhF+taWh7OO77Y1WTuDbTWO8wyp9E329/Vu7JBzVvTgq0V/zzLe88I3OZd5jxfe2F2p/W9CwVu2SZ2zL3tc3s+Hpqd2rP9u3RV/Pt+umvt++aFOXhKO6F0cl+sVbithcUN52ReVs4ZFw0b8g0TvG/uO74ZP7oH7thcVPwvavYPXctt9rwYPw+o1t1+tsNX7+XddW+Gx4z813JE4IrjWhgsNRiT4yPIyaOr1bUGFb9retgYl32u0v/gqvXgP/vQj2fWfLtn7R9vvM/yOseKnTwgxIXY3/i8u1Pi36+u6c8oKGMn9fDqpHctRcN5roVTMT74BjL4WFD8OSZxvv27XcrlUb5gpGZIF75xew7Pnm5b4t+vobxtxNRueUF0BKVufGp0Keo1r0Ea4wat2a6FUTKUPggofhx582lA0+BYq32bVqt3/T8rHB5q6DDe+1sK+24bW/xdXfuAlmjbFTO6sezVGJPjI8jGpN9KolGePgJ1/DjQthms+Y+3k3Q9Fmm1RbsnkBFHzX+TF63LBrWcP2Eyc3jJtvVtc30fuJfduX9rlEp0ro6ZyV6F1h1GrXjWpNn5HQ92hIHQpXv2XL9n0Hs46zCb8lz02Dx47v/Pi++BvMngh5OXa7YH3LdX27afwl+no6J06P56xEHx5GjbboVaCyTrOLm9Rb8ZKd/rd4u932N069s+1ebp8P7Gq7ru8vEN/WfVPlOstlT+esRO/Si7GqnWKS4bd74ZTbGsr+cQzsy4Wy/K6Pp370TCAXiD21tqvn43tan+CsLghfWCqkOCrR68VYdUjCo2Dy3fau2iPOsmWPjoOHRzevu2sZvDYT5v+5c2KpT/Tr32m7rrsGNs+HL//eMGTUH99hmKpHclSi1+GV6rDEpsCV/4NblkLGeP91Zk+ElS/Dggc6Z977+puclr9g559vTWsXj30FowtKhRTHJfpqTfTqcKUOhWvfg7Pvh5mfwZ0tXMx8/mL454ktT5xmjF3ub/uiwO9SNT7/fv1dYI3q1fDaXQNhEW2/p3bd9HjOSvQuvRirOogrHE66BfofB1HxcHcxpB/VvN7etTD/ftjyefN9r98A9yXD0+fA4n8H9rm+id5fiz0qoeG1p7Zh1kpf4TGNtw+1RV+2V++udQhHJfqYSBdVNX5uHFHqcInAjz6EqQ/ChU/A0EkN+xY+DP+5AHYsbnzMSp+FUd69HfauC+CDfFr+dX761iNjG1676/z/mohokui3LYRV/wvgs5t4aBg8dXb7j1Mhx1GJPi7SRbkmetVZohPhhBtgzOVw5WsNF27rPTUZnpxkZ8XMebr58f+cAAv+0vpn+Lbo875tvl98/su6a/x/GUTENt7evQLm/qj1zwU7tPSlyxuX5a9u+zgV8hw1101sVDiVmuhVVxCxF253LIZ9G+Dje+2CIDuXwIOtzC0z/34Yfz3EJPnf75vo3/5F8/2+N1B5au1F26aatujbY8O7h36sClkBtehFZIqIbBCRXBG5w89+EZFZ3v0rRWSsz76nRWSviHR60yAu0kWN26NDLFXXGXg8HHcl3LYOzv97432DT/Z/zPw/tvx+rV20PWJy4+2izf4Tc0t3yRpjh1oufLjxPDnK8dpM9CLiAh4DpgIjgctFZGSTalOBYd7HTOBxn33PAlM6Iti2xETaHyhfbCzgQJX+Q1ZdyBUO2dfBPSX2wu0Nn8O179rJ1E76WeO63862c+GX7YXK4sb7WrtweuVcuPiptmPZv8V/eW0lfP0IfPR7WPpc6++hc9k7SiAt+vFArjFmszGmBpgDTG9SZzrwnLEWAUki0g/AGPM50CVL+cRF2rsJr3s2h+ueWdxGbaU6iQj0895sNe4aOPsPcNM3MOp7DXX+d5292Pl/g+38NvX2bWz9vaMSDz2uJc9AqfduX9/RNFUltn/el+9smLVVdibMptMyLHsBPn/o0ONRXSaQRD8A2OGznecta2+dVonITBHJEZGcgoKCtg/wIyG6YUxxzrb97C93yMLQqvvrPQIueQZ+vx8m/bbxvk/utYn22yehuqT190nsb5/HXtW4vOmQSn8++I1dXQsaT7Hgb14d37lzNrxrrwV8cFfjOm/eBJ/+oe3PVUEXyMVYPwN1afq7LpA6rTLGzAZmA2RnZx/S78YByY3/sb+5fCfjBqewu6SS045MJzoixBeYUM4XFgan/RJGnG9b8v2OhbXz7Aibd29v+bjJ99jnvkfDD16FzJPturile2DctbbraM8qu2TiiPPtHPQf393y+9W32DfNh/9e2Hifuw7e9JnJszPuAFZdKpBEnwcM9NnOAJo2AQKp0+kGpdhhZRnJMaTERXLPW2sP7ktPiOIH4wfhChME+6UwKCWWPonRpMZHEhvpqAFIKtT1Pgou9rauT7oFcj+B579nb8oaPgWOvcz2tZfuhlVz4QSfdWuP9I5tP+qCxu/Z9xi44lX7uni7TfRHToHTfw3bv7Yt+nof/c6O8CnY0Dy2Hd/Aap9x9/Uje8RRo7F7lECy22JgmIhkATuBy4AfNKkzD7hZROYAJwAlxpjdHRppAFLiInnsB2M5YUgKm/aWcf876zj9yHTGDEziqS+38PAnLfd/xkeF0zsxigFJMWSmxpGZFkdWWizD+ybSv1c04u8ORKU6yhFn2gu5vnqPsM/Z17X//ZIGwYwXYPBJdg6fAWNh+FTbgv/2SShY13KLv6WZM33/D+zf1v6YVNC0meiNMXUicjPwAeACnjbGrBGRG737nwDeBc4FcoEK4Nr640XkJWAikCYiecDdxpgAhg4cmvOO7QdAWnwUb91yysHyySP7UFJRS1yUizqPYWdxJduLKig4UE1heQ17S6vIP1BF3v5K3li+k9KqhiFqafFRjBnYi7GDkzlzRB+O7BOviV+FvqPOb7ydMsQ+jpgMz54HJTv8H2cCGJ788LGHH5/qMmJCcBhVdna2ycnJCdrnG2MoKq9hy75y1u4+wPIdxazYUcymAjtSISM5hnOP6cdlxw9kSHp80OJU6rA0HWlTL6E/lPrpeU3OstM5j7qo8bFNf4mooBCRJcaYbH/7tGPaDxEhNT6K1PgosjNTuOpEW55/oIpP1+/lo7X5PP3lFmZ/vpmThqZyzUmZnHlUH1xh2spXDuAvyYO9ZvDqNXaxFl/G+J9cTYUMbdEfor0Hqnh1SR4vLNrGrpIqBqbEcPWJmVySPZBeMQFMHatUKKittLNr7l5x6O/x08WQfmTHxaQOSWstek30h6nO7eGjtfk8s3Ar324tItIVxvC+CRyT0YsxGUmMHpjEEb3jtbWvQlfxDvj2XzD4FDtmful/2nd8yhD42bLOiU0FTBN9F1m9s4S3Vuxi1c4SVu0sOXhBNy7SxbEZSRw9IJHhfRMZ0TeBI3rH67h+FZqWvwRv3AhDJtqVtoadDWvfgK8fbfmY29ZDYr+uilD5oYk+CDwew5bCclbsKGbZ9mKW7yhmQ37pwaUOXWFCVlocw/smcFTfhINfABnJMTqiR4Wm1XNhybP+F1kBuxpX/+O6MiLlQxN9iKhze9haWMGGPaWs33OA9XtK2bCnlO1FFQfrxEeFM7xvAkf3T2R8Virjs1JIT4gKYtRK+agus1Mxl+2F137cfP8vN0FcWtfHpTTRh7qy6jq+yy9l/e5SNuw5wLo9pazeWUKFd279I/vEM2VUX6Yc3Y+j+iVoi1+FBo/bTojWdP79oZPgkmchuoXhm4fzea9cBaMvb36PgNJE3x3Vuj2s3lnCN1uKmL9+L4u3FuExMDQ9jqtPyuTisRnERenoWBUiVrwMr89sXPbDN2DoGR33GSV58PdR9rWO3W9GE70D7Cur5sM1+by8eDsr8kpIi4/ktrOGc2l2BuEunYNEhYgdi+2Sir6O+yGMuhAyjret/PJCiIyDiOj2vXdeDvz7TPv6+0/D0Rd3SMhOoYneYZZsK+KB99azeOt+Rg9M4rfnHcWD76/nrJF9uP7UIdq1o4Jv06fw34ta3u+KhKvfhvThLS+r2NQrV9vRP/XO+C2cdrverOWlid6BjDHMW7GL37+5hpLKxqtpvf/zUxnR9zAWqFCqo9RV2ymTd6+0Y/RXv9Z4zv2oXra/fdRFkHU6hEe2/F7/mQZbFjQuu/ptyDq1Y2ItzbezhfYf0zHv18U00TtY/oEq7ntrLe+saj5Z6D0XjOSKCYOJ0K4dFWry19rpjzd9Cnu904lLmE32fUbZ2TYzT7MjeOpb7I+dALFpdkbOzx9seK+fr4akgc0/o73+NhIO7LRLQXbDXwma6HuAqlo3P3tpGR+uzW+27/vjMjj9yHTGDk7WKZdV6KkqgfXvwNaFdi78os1gvAujxPexiT2hPyx6DI6/Hs79C9yb1HB82pHw028PPznXT9T2qy12auduRhN9D1Pn9vD+mj3c/eYaymvqMAaqvTdqpcVHMTqjF6P6JzKyv33Wm7RUSPG4YdtXkL8adi6FbQttSzs2Fa6aZ1fZqihqPqxz/A12rd7qAyAuGDkd4nu3/AWQv9bOvZ8+3M6vXz/18tn3Q+owu2rXoBPtheNuQBN9D1fn9hyclmH59mJW7SxhU0EZHu9ffWJ0OCP7JzKyn038owYkMjQ9PuAun4/X5nPKsDSd0kF1DmNsYo+Ibpx017wBu5bCwodbPjaqF6QOhQHj7Iif3kdBQl/bTfTM1LY/u98YuGFBm9VCgSZ61UxljZsN+aWs2VXCml0HWLvrAOv3HKCq1rb8I8PDGN4ngZH9EhnWJ57hfRM4sk8CvROiGrX+P16bz4+fs39X7/zsFEb17+CbZJQKxL5c2P4VxPe1LfFN86GiEOqq7Pj7/DVQU3Zo7516BIRF2Okdhk6yF2vj0u3IocjYDj2Nw6GJXgWkzu1hy75ym/h3H2DNrhLW7S6lqLzmYJ3EaDtFw7A+CRyRHs99b69t9B6Xjx/I/Rceo7N1qtBTV237/8vywV0LSYNta3//Vti1zK64VbITyvZATQVs+qTt94xMsP35vQZCRIy9eBybastiU+2Io8JcSOgHyZkQkwIDx4O7xv5SSezf0LW09L+2G2ng+EM6PU306rDsK6vmu/xSNuaX8V1+qfdR1mxYp6/RA5OorfNwxoh0sjNT6JsYTVF5DWMHJRMTqV08qhuoLLZj/CuK7LDOwlz7BRCVYC8gR8bZfRX77GLsddVQvg8qi6C2oq13bxDVC5IHwZ5Vdvt3+8DV/jUtdIUpdVjS4qNIi4/ipKENk1UZYygorabG7aGkspZVeSXk7i3jw7X5HJvRi4W5+9hfUcva3QeATQePi4lwkZYQyVF9Ezk2oxeDU+PISI4hIzmWtPhIRISi8hpiI13a56+Cq/5GrtgUO86/PWoqbMJ319prA1sW2OsCO5dAeDREJ9kviLzFEBZuu5cAJv6mI8/gIG3Rq05T5/ZQUFbN9sIK8kuriQoP4/PvCthaWM6OospGs3aCvS6QEBVOYXkNafFRnDmiN/HR4Uw9ui/RES6G9YknKtzFkm37iXSFcUyGXg9Qqp523aiQVF5dx479FezcX0ne/kp2FldSVl1Hn4RoXvx2GyWVtQcvDgNER4QxrHcCq3aWEB4mXHtyJjGR4Uwb3Y+UuChS4iLZVVzJ/A17mT5mAPGtTPr2xrKdTBiSSt9e7ZxvJUhy95ayMq+E743NAOyXqM5xpHxpolfdjsdjMMDGvaVsL6yg1m1YvLWIdbsP0D8phgXfFTS6SAwwMCWG/eW1lFXXkRYfxbjBSaTERXHMgF4MTY8jIyWWvonRLN2+n0ue+Jqxg5J47aaTW43jq037uGPuKu6+YCRl1XVMHzOgE8+6ZcPuepdat2H0wCR+fEoWt7y0jJeun8CJQ1MB25X2yKe5nD2qDyP6JuLxGML0gniPooleOU5VrZvqWg+l1bV8/t0+CkrtBeOYSBcDkmLI2VbE5oJydpdUtfo+sZEuhvWOZ9zgFDKSY0hPiCI9IYq+idHERrmY9shC9hxoeI9Tjkjj7FF9+GhtPk9dfTyR4S23qmvdHnL3lpEWH0VCdHhA1xzq3B6u+08OI/sl8vPJw/jNa6t4bdlOv3UHJMXw8W2nE+4Sht31HmC7vzJTYymvdvP6T08iyuUiMSZcb4jrATTRqx6rqLyGTQVllFTUkl9axb7SGuKiXJw6LJ25S/NYu+sAew5UsXN/JZW17na/f1p8JCP6JhLuEs4e2Zek2AiSYyMRgQffX8/S7cUH6/74lCz2V9SybMd+MlPjuPuCkQxObXzX5eqdJZz/yJcATD6qNx+v23tY51/vwYuPZWT/RI4eEPh1DbfH4PaYVr/MVOjQRK9UG4wxFJXXUFheQ/6BKvIPVFNZ6yZM4MIxA4iLCmfWJxvZsKeUcYOT2V9RwyOf5gK2FZ0eH8XO4spD+uwIl3DFCYMZ0TeBJdv28+qSvBbrPnzZGG6ds/yQPgegV0wEmWlxxEW6eOrq4w8Odd1VXMmM2V9TU+fhm99Mptbt4aJ/LmT1zgMsvGMSz329lR+fMqTRspZujyF3bxnD+yYccjyq42iiV6oTGWMwBjbkl+L2GIor7P0F9d1AK/KKiYlw0a9XNBGuMB76cAOvLfXfHdOaNfeeQ1xUOCf86WMGp8bx6OXH8ad31/HG8l3N6p46LI0vNu5jYEoMxw9O8dv9k54QRUFpdbPy0QOTWLGj2G8Ml48fyI2nD+WbzUX8au5KAJ66Opszj+rT7vM5FJsLynglJ4+fTBxKr5j2jzV3Mk30SoUgYwwiQp3bw97SanYUVVBR46Z/Ugz7yqrpFRPByrwSKmrqmDi8N0f0jm/xvdweQ63bw4Y9pewrq2bi8N7Uuj1EuMJwhQmLNhce/AJ6ftE2vszd12KiPxSZqbGkxEVy+9nD6dMrmugIF30SovyODLK/mKo4NiOp3Z8z419f882WIgBuO+tIfjJxqE7D7aWJXinVjMdj2FlcSXWdh22F5YSFCV9vKmRU/0T+tyQPt3f/uEHJLN5WxI6i9ndNJUSHk54QRVS4iwiX4DGGdbvtL59xg5OJjXRxVL9EesVEkB4fRVREGClxkcRFhRMXGU50RBgxES7CXWF8vamQn764tNlnHNE7nv5JMYzPTObEoWmM6p94SDfbFZZVM/vzzdxw+lBS4lpZACVEaaJXSnUaYwz7ymrYuLeUmjoPW/eVU1Xnoby6jgOVtewrq6G6zkOdx4MxMKp/IuXVdcxbsYvk2Ei2FpYfnEm1LQOSYvjrpaP59dyVbCtseZqB8DBhQHIM/XpFkxgdQUR4GGEiRIeHERkehgjs3F/Jl7n76J8Uc/DXE4ArTMhIjmHvgWpS4yM5c0RvBqbYycvCREiIDqdXjH3P6HAXURFhRIWHER3hIsr7/hFh9vPcxrBu9wEGpcSSGBNBYnQ4HgOVtW72lFSSmRp38FfPwtx9fJdfylUnZh7SXFGa6JVSIcvjMRyoqqW0qo6qWjdF5TVU1LiprHVTVeumosaN22OIcIUxeWRveifYm9zKq+v4Zkshg1Pj+Py7AhZvLWJbYQX9k2LsrwcPFJZXU1pVR43bfslU1bqpqfNggOjwMEoqaymvcZMUG0FxRS0j+iZgDJRW1bKvvIaaOg8idv6xzhATYb8o7J9BHVlpcbx366mH9ItE57pRSoWssDAhKTaSpNj2dZfERYUzaYS9CDw0PZ5rT85q44hDY4xhd0kVHmOoc5uDXzw1bg81dR6q6uw9HdXe5/ryeqnxkewpqSJMhNLqOsLDBFeYEBPhYsf+Cjwee60mwiXMOH5Qp8zxpIleKaVaISL0T4oJdhiHJaDL1SIyRUQ2iEiuiNzhZ7+IyCzv/pUiMjbQY5VSSnWuNhO9iLiAx4CpwEjgchEZ2aTaVGCY9zETeLwdxyqllOpEgbToxwO5xpjNxpgaYA4wvUmd6cBzxloEJIlIvwCPVUop1YkCSfQDgB0+23neskDqBHKsUkqpThRIovc3oLPpYKOW6gRyrH0DkZkikiMiOQUFBQGEpZRSKhCBJPo8YKDPdgbQdHKNluoEciwAxpjZxphsY0x2enp6AGEppZQKRCCJfjEwTESyRCQSuAyY16TOPOAq7+ibCUCJMWZ3gMcqpZTqRG2OozfG1InIzcAHgAt42hizRkRu9O5/AngXOBfIBSqAa1s7tlPORCmllF8hOQWCiBQA2w7x8DRgXweG0x3oOfcMes7OdzjnO9gY47ffOyQT/eEQkZyW5ntwKj3nnkHP2fk663x1ImellHI4TfRKKeVwTkz0s4MdQBDoOfcMes7O1ynn67g+eqWUUo05sUWvlFLKhyZ6pZRyOMckeqfOey8iA0VkvoisE5E1InKrtzxFRD4SkY3e52SfY+70/jlsEJFzghf94RERl4gsE5G3vduOPmcRSRKR/4nIeu/f94k94Jx/4f13vVpEXhKRaKeds4g8LSJ7RWS1T1m7z1FExonIKu++WSIS+MKyxphu/8DedbsJGAJEAiuAkcGOq4POrR8w1vs6AfgOO7f/g8Ad3vI7gP/zvh7pPf8oIMv75+IK9nkc4rnfBrwIvO3ddvQ5A/8Bfux9HQkkOfmcsTPZbgFivNuvANc47ZyB04CxwGqfsnafI/AtcCJ2ssj3gKmBxuCUFr1j5703xuw2xiz1vi4F1mH/g0zHJga8zxd6X08H5hhjqo0xW7DTUozv0qA7gIhkAOcB//Ypduw5i0giNiE8BWCMqTHGFOPgc/YKB2JEJByIxU566KhzNsZ8DhQ1KW7XOXrX90g0xnxtbNZ/zueYNjkl0feIee9FJBM4DvgG6GPsxHF4n3t7qznlz+IfwK8Aj0+Zk895CFAAPOPtrvq3iMTh4HM2xuwEHgK2A7uxkyF+iIPP2Ud7z3GA93XT8oA4JdEHPO99dyUi8cBc4OfGmAOtVfVT1q3+LETkfGCvMWZJoIf4KetW54xt2Y4FHjfGHAeUY3/St6Tbn7O3X3o6touiPxAnIle2doifsm51zgE47LU9/HFKog943vvuSEQisEn+BWPMa97ifO/PObzPe73lTvizOBmYJiJbsd1wk0TkeZx9znlAnjHmG+/2/7CJ38nnPBnYYowpMMbUAq8BJ+Hsc67X3nPM875uWh4QpyR6x857772y/hSwzhjzN59d84Crva+vBt70Kb9MRKJEJAu7YPu3XRVvRzDG3GmMyTDGZGL/Lj81xlyJs895D7BDRIZ7i84E1uLgc8Z22UwQkVjvv/MzsdegnHzO9dp1jt7unVIRmeD9s7rK55i2BfuKdAde2T4XOyJlE3BXsOPpwPM6BfsTbSWw3Ps4F0gFPgE2ep9TfI65y/vnsIF2XJkPxQcwkYZRN44+Z2AMkOP9u34DSO4B53wvsB5YDfwXO9rEUecMvIS9BlGLbZn/6FDOEcj2/jltAh7FO7NBIA+dAkEppRzOKV03SimlWqCJXimlHE4TvVJKOZwmeqWUcjhN9Eop5XCa6JVSyuE00SullMP9f/Jbc+LyVaCwAAAAAElFTkSuQmCC\n", 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" ] @@ -5632,7 +5620,7 @@ "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", + "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", @@ -5642,7 +5630,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -5653,7 +5641,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -5670,7 +5658,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -5690,7 +5678,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -5719,17 +5707,24 @@ "\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))" + " print(\"The root mean squared error is {}.\".format(rmse))" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 127, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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9w/p+wvVbt25dioqKiv8OXl/ij90ft8Phkl1WN4EajCMhNG/enPvvv5+77rqLRo0a0bVrV15//XXAHq7z588HYPPmzXTsaOtzn3322aj9Xnnlldx6663FkVhFRUXcc889lR7voEGD+PLLL1m/fj2FhYW8/PLLDBkyJGw72PqPp59+mh9//JHbb7+9TJ/Dhg1j165dPPHEE8Vts2fP5ssvv2Tw4MG8+uqrFBYWkpOTw/Tp0xk4cCBdunThhx9+YNeuXWzevJlPP/006tibNm1KXl5epX8DR83ECZjqpKgI/Jttx47EjiXFOfDAAznggAN45ZVXePHFF3nqqac44IAD6NmzJ+++a1HwkydP5qSTTuLwww+ndevWUfvs06cP9913H6eccgr7778/vXr1YvXq1ZUea/v27bnttts44ogjOOCAA+jXrx/HHXdc2HaftLQ0XnnlFT7//HMefvjhUn2KCG+//TbTpk2jW7du9OzZk8mTJ9OhQwdOOOEE+vTpwwEHHMCwYcO44447aNeuHZ07d+bkk0+mT58+nHbaaRx44IFRx37sscfy9ttvOyd/iiKR1OxUYsCAARr3gmN5eeDbsefPhz594vt9ScSSJUvYf//9Ez0MRwrirr34IiJzVXVAqG1Og6lOfPMYOBOZw+Go9TgBU50EChhnInM4HLUcJ2CqE6fBOByOFMIJmOrEaTAOhyOFcAKmOnEajMPhSCGcgKlOnAbjcDhSCCdgqhOnwSSUwHT9J510Etu3b69wX4Hp98877zx++OGHsPtWNKNwZmYm69evL9O+detWLrjgguL1K4MHD2bmzJmlEmcGc/311/NJFdchmjx5MnfddVfU/Z577jl69epFz5496dGjR0zHlJdbb721yvt0VB4nYKoTp8EklMB0/fXr1+fRRx8ttb2wsDDMkZF58skn6dGjR9jtVZ2y/rzzzqNly5YsXbqUxYsXM2XKlJCCKJAbb7yRI488ssrGECsffvgh9913H1OnTmXx4sV89913xbnWqhInYJITJ2Cqky1boK6X/s1pMAnl8MMPZ9myZXzxxRccccQRnHrqqfTu3ZvCwkKuvPJKDjroIPr06cNjjz0GWAqZSy65hB49ejB69GjWrVtX3NfQoUPxF+l+9NFH9OvXjwMOOIDhw4eHTFmfk5PDH//4Rw466CAOOuggvvrqKwA2bNjAiBEjOPDAA7ngggtC5gT75ZdfmDlzJjfffDN16tjtu9deezF69GjAhOT5559Pz549GTFiBDu8iUygxhUqtT7Axo0bOf744+nTpw8HH3wwCxYsiNgeyBNPPMHRRx9d/H0+t912G3fddRcdOnQAoGHDhpx//vmAZY4++OCD6dOnDyeccAKbNm0q83uuX7+ezMxMIHw5g2uuuaY4kelpp50Wy7/fUU24ZJfVyZYt0LIl5OSktAaT4Gz9FBQU8OGHHzJq1CgAZs2axaJFi+jatSuPP/44zZs3Z/bs2ezatYtDDz2UESNG8P333/PTTz+xcOFC1q5dS48ePTjnnHNK9ZuTk8P555/P9OnT6dq1Kxs3bqRly5ZlUtafeuqp/P3vf+ewww5jxYoVjBw5kiVLlnDDDTdw2GGHcf311/P+++/z+OOPlxn74sWL6du3L2lhSj0sXbqUl19+mSeeeIKTTz6ZN998k9NPP73MfqFS60+aNIkDDzyQd955h88++4wzzzyTefPmhW33efDBB5k6dSrvvPNOqWSdELkswJlnnskDDzzAkCFDuP7667nhhhu4L8o/MVQ5g9tvv50HH3wwZMJSR2JxAqY62bIFmje3ipYpLGAShT/LBdNgzj33XL7++msGDhxYnIp/6tSpLFiwoHi2v3nzZpYuXcr06dOLU9h36NCBYcOGlen/22+/ZfDgwcV9tWzZMuQ4Pvnkk1I+my1btpCXl8f06dOL09yPHj2aFi1alPscu3btWnyO/fv3JysrK+R+oVLrz5gxgzfffBOwZJgbNmxg8+bNYdsBnn/+eTp16sQ777xDvXr1Yh7n5s2byc3NLU7OOWHChFIlE8IRqpxB586dY/5eR/XiBEx1smWL5SLbuDGlTWQJytZf7IMJpklA2QRV5YEHHmDkyJGl9vnggw8QkYj9q2rUfcCyLH/zzTc0atSozLZox/fs2ZP58+dTVFRUbCILJDjdf7DJKni/wNT6oUxyIhK2HaBXr17MmzeP7OzskPVy/BIGoQRyOALLAoQrCRA8dkdy4nww1YkvYBo2dBpMkjJy5EgeeeQR8vPzAfj555/Ztm0bgwcP5pVXXqGwsJDVq1fz+eeflzn2kEMO4csvv+S3334DzHcBZVPWjxgxggcffLD4b1/oDR48mBdffBEw57jvkwikW7duDBgwgEmTJhU/+JcuXVqcAboyBH7/F198QevWrWnWrFnYdrCs1I899hhjx45l1apVZfq89tprueqqq1izZg1gxdzuv/9+mjdvTosWLYozLD///PPF2kxmZiZz584FCFuzJph69eoV/88cyYPTYKqTLVuga1do1CilNZhk5rzzziMrK4t+/fqhqrRp04Z33nmHE044gc8++4zevXuzzz77hCzi1aZNGx5//HFOPPFEioqKyMjIYNq0aRx77LGMGzeOd999lwceeID777+fiy++mD59+lBQUMDgwYN59NFHmTRpEqeccgr9+vVjyJAh7LnnniHH+OSTT3L55Zez995707hxY1q1asWdd95Z6XOfPHkyZ599Nn369KFx48bFNXDCtfscdthh3HXXXYwePZpp06aVKm1wzDHHsHbtWo488shiDc/3XT377LNceOGFbN++nb322otnnnkGgCuuuIKTTz6Z559/PmbNZ+LEifTp04d+/foVC0NH4nHp+j2qJV1/165w+OHw/ffQvTukUFlZlzLdkSjctRdfXLr+ZME3kTkNxuFwVAWTJsGXXyZ6FGGJm4ARkc4i8rmILBGRxSLyN699soj8LiLzvNcxAcdcKyLLROQnERkZ0N5fRBZ62+4Xz8MoIg1E5FWvfaaIZAYcM0FElnqvCfE6z5hRdT4Yh8NRdajCrbdCjH6qRBBPH0wBcLmqficiTYG5IjLN23avqpbKFyEiPYDxQE+gA/CJiOyjqoXAI8BE4FvgA2AU8CFwLrBJVfcWkfHAv4E/iUhLYBIwAFDvu99T1bJe0+pi504oKCjRYAJX9acIsUZZORxVRa12AWzfbs+UbdsSPZKwxE2DUdXVqvqd9zkPWAJ0jHDIccArqrpLVX8DlgEDRaQ90ExVv1G7Wp4Djg84xvc4vgEM97SbkcA0Vd3oCZVpmFBKHL5ASVENpmHDhmzYsKF23/COpEJV2bBhAw0bNkz0UOJDbq69VyKnXrypligyz3R1IDATOBS4RETOBOZgWs4mTPh8G3BYtteW730Obsd7XwmgqgUishloFdge4pjAcU3ENKOwETtVRqCASUEfTKdOncjOziYnJyfRQ3GkEA0bNqRTp06JHkZ88Ba7JrMGE3cBIyLpwJvApaq6RUQeAW7CTFc3AXcD5wChbCcaoZ0KHlPSoPo48DhYFFnkM6kkKa7B1KtXL+RCPIfDUUFqgAYT1ygyEamHCZcXVfUtAFVdq6qFqloEPAEM9HbPBgJzPnQCVnntnUK0lzpGROoCzYGNEfpKHMEaTIoJGIfDUcX4AiaJNZh4RpEJ8BSwRFXvCWhvH7DbCcAi7/N7wHgvMqwr0B2YpaqrgTwROdjr80zg3YBj/AixccBnnp/mY2CEiLQQkRbACK8tcaS4iczhcFQxNUCDiaeJ7FDgDGChiMzz2q4DThGRvpjJKgu4AEBVF4vIa8APWATaxV4EGcBFwBSgERY99qHX/hTwvIgswzSX8V5fG0XkJmC2t9+NqroxLmcZKyluInM4HFVMKvtgVHUGoX0hH0Q45hbglhDtc4AypfpUdScQMgWrqj4NPB3reONOsAZTUGCvui5bj8PhqAA1QINxK/mrCz/Zoa/BgDOTORyOipPKPhhHEFu2QL160KCBaTDgBIzD4ag4gQImSdeXOQFTXfhpYkRKNBjnhzEWLYJvvkn0KByOmoXvgykqgt27EzuWMDgHQHXhCxhwGkww//wn/PYbzJ+f6JE4HDUHX4MB02KCylUnA06DqS4CBYzTYEqTm5uSudkcjkoRKGCS1NHvBEx14TSY8OTlwdatiR6Fw1GzCNZgkhAnYKoLp8GEJy8vaW8QhyNp2bwZ2rSxz06DSXFCaTBOwBh5efZbFBZG39fhcBi5udDRy+GbpBM0J2CqC2ciC49vHkvSWZjDkXTs3GmvDh3s7yS9d5yAqS6ciSw0RUUlAiZJZ2EOR9Lhhyj7AiZJ7x0nYKqD/HwTJk6DKUvgjeEc/Q5HbAQLGKfBpDCBaWLAaTCB+L8NJO0szOFIOvwIMueDcZRKdAlOgwkkUGtJ0pvE4Ug6fAHjNBhHGQHjNJgSAjUYZyJzOGIjWMAk6eTMCZjqIFjA1KsHdeo4DQaciczhqAi+D6ZNG0hLcxpMShMsYPyEl06DcQLG4agIvgbTogU0aZK0944TMNVBsIABVzbZJ9As5kxkDkds5Oaa5tKkCTRu7DSYlCaUgHEajOE0GIej/OTmQvPmZg1xGkyKE06DcQLGCRiHoyJs3gx77GGfnQaT4mzZUjLT8HEmMsMXMPXrOxOZwxErubklAiaJNRhXcKw6CKxm6eNMZMbWrTYDa9QoaW8ShyPp8E1k4DSYlCcwD5mP02CMvDxo2tRmYU6DcThio4ZoMHETMCLSWUQ+F5ElIrJYRP7mtbcUkWkistR7bxFwzLUiskxEfhKRkQHt/UVkobftfhFTBUSkgYi86rXPFJHMgGMmeN+xVEQmxOs8YyKUgHEajJGXB+np9krSm8ThSDoCfTBNmqSkBlMAXK6q+wMHAxeLSA/gGuBTVe0OfOr9jbdtPNATGAU8LCJpXl+PABOB7t5rlNd+LrBJVfcG7gX+7fXVEpgEDAIGApMCBVm14zSY8ARqME7AOByxEajBNG6ctPdO3ASMqq5W1e+8z3nAEqAjcBzwrLfbs8Dx3ufjgFdUdZeq/gYsAwaKSHugmap+o6oKPBd0jN/XG8BwT7sZCUxT1Y2qugmYRolQqn6cBhOerVtNwKSnOxOZwxELBQV2r/g+mBTVYIrxTFcHAjOBtqq6GkwIARnebh2BlQGHZXttHb3Pwe2ljlHVAmAz0CpCX8Hjmigic0RkTk5OTiXOMApOgwmP02AcsbJmTUnIfyrjp4lJZQ3GR0TSgTeBS1U10tUhIdo0QntFjylpUH1cVQeo6oA2fm3reOA0mPD4PhgnYBzROOYYuOaaRI8i8QQLmCZNYPdu02ySjLgKGBGphwmXF1X1La95rWf2wntf57VnA50DDu8ErPLaO4VoL3WMiNQFmgMbI/SVGJwGEx5fg3EmMkc0VqyA7Ozo+9V2/DxkgRoMJKWZLJ5RZAI8BSxR1XsCNr0H+FFdE4B3A9rHe5FhXTFn/izPjJYnIgd7fZ4ZdIzf1zjgM89P8zEwQkRaeM79EV5b9VNUZA/RUALGaTAlPhinwTgioWoP1sDMD6mKL2ACfTCQlPdPPBdaHgqcASwUkXle23XA7cBrInIusAI4CUBVF4vIa8APWATaxapa6B13ETAFaAR86L3ABNjzIrIM01zGe31tFJGbgNnefjeq6sY4nWdk/Fl5KBNZQYG96qboelfVEgHjOy5VSy9IdTjAHp6Fhc4HAzVKg4n6ZBORf6vq1dHaglHVGYT2hQAMD3PMLcAtIdrnAL1CtO/EE1Ahtj0NPB1pjNVCqDxkULqqZXp69Y4pWdi2zQRKeroJmMJCsyU3aJDokTmSDf+h6jSY0D4YSEoNJhYT2VEh2o6u6oHUWsIJGFfVsuRh4ZvIIClvEkcS4AsYp8HUDg1GRC4C/gzsJSILAjY1Bb6K98BqDbFoMKlKoIDxf4etW6Fly8SNyZGcOA2mBP+3aNrU3pN4chbJRPYS5uu4DW+1vUdewvwZNRGnwYTH9081bVrih0rCm8SRBPgP1e3bU9tvCfZbNGtmBccgqTWYsCYyVd2sqlmqegoW9puPrSVJF5E9q2uANR6nwYTHmcgcseILGHDh7IF5yCCp751YnPyXAJOBtUCR16xAn/gNqxbhNJjw+AImPb0kcizVHx6O0AQKmC1bSj9gU43APGSQ1BpMLHrmpcC+qrohzmOpnTgNJjyBGoxPEs7CHElAoIBJdT9MYC0YSGoNJpYospVYji9HRfAFTOBDFJwGA6V9MEl8kziSgGANJpWpZRrMr8AXIvI+sMtvDFqd7wjHli328PQdcj5OgymtwRR6a2qdicwRCqfBlBDsg/GfJUk4OYtFwKzwXvW9l6M8hMpDBiUXRSprMP6DokkTyM+3z0l4kziSgNxcqFPHUi85Daa0gBFJ2rLJUQWMqt5QHQOptYQTMM5EZgKmUSMLOfVNZE6DcYQiNxfat4fff09tDaaoyDSYQB8MJG0uv1iiyD4ndKr7YXEZUW0jmgaTyiYyPw8ZmMCtUycpbxJHEpCbC3vuaQImlTWYvDxLrxQcRVdTNRjgioDPDYE/YskoHbHgNJjw+Kn6wdT8JJ2FOZKA3Fzo3x+++Sa1NZjgPGQ+SXrvxGIimxvU9JWIfBmn8dQ+tmyBjIyy7U6DKSk25tOkiTOROUKTmwtt2tjELJU1mOA8ZD41VYMRkcDEUHWA/kC7uI2othFOg6lb10xCToMp+Ts9PSlnYY4E49eC2WMPu15SWYMJrgXjU1M1GGAuJWWIC4DfgHPjOahaRTgBI+KqWm7darNSnyS9SRwJxq8Fs8cedi85DSa0BpOTU92jiUosJrKu1TGQWolqeAEDpu6nugaz114lf7uyyY5QBD5UU12DieSDycqq7tFEJRYTWT2souRgr+kL4DFVzY/juGoHO3bYzCucgEl1DSaUD2azSxrhCCJQwDRrltoCpob5YGJJFfMI5nd52Hv199oc0QiXh8zHaTClfTDOye8IRbAG40xktcoHc5CqHhDw92ciMj9eA6pVRBMwqazBqJZeBwPOye8ITbAG8/PPiRxNYsnNNW2lXr3S7TVYgykUkW7+HyKyF1AYvyHVImIRMKmqwWzfbkImWINxAsYRjNNgSgjOQ+bTpIndU0VFZbclkFg0mCuBz0XkVyySrAtwdlxHVVtwJrLwBNaC8XEmMkconJO/hOA8ZD5+RuWdO0s+JwGxRJF9KiLdgX0xAfOjqu6KcpgDYtNgArPEphKhasGkp5vALSqyNUIOB5T2OzRrltplk4NrwfgElrtIIgET9i4WkdNF5AwAVd2lqgtUdT5wpoicGq1jEXlaRNaJyKKAtski8ruIzPNexwRsu1ZElonITyIyMqC9v4gs9LbdL2KlD0WkgYi86rXPFJHMgGMmiMhS7zWhvD9KleE0mPAE1oLx8W+SJLQlOxJIbq5dG/XqlVwvqarpRtNgkuzeiTRNvBx4J0T7q962aEwBRoVov1dV+3qvDwBEpAcwHujpHfOwiPgFVB4BJgLdvZff57nAJlXdG7gX+LfXV0tgEjAIGAhMEpEWMYy36nFO/vCE0mBcRmVHKAIfqv69lKp+mEg+GEg6H2YkAZOmqmWMnaq6BagXYv/g/aYDG2Mcx3HAK56m9BuwDBgoIu2BZqr6jaoq8BxwfMAxz3qf3wCGe9rNSGCaqm5U1U3ANEILuvgTrpqlTyprMOFMZJB0N4kjwQQKGP96SVU/TDgNJkm1/0gCpp6INAluFJGmVK7w2CUissAzofmaRUesNLNPttfW0fsc3F7qGFUtwMo6t4rQVxlEZKKIzBGROTnxSLOwZQs0aGCvUDgNpqyTH5yAcZTGaTCGn5MtlA/GN5El2b0TScA8BbwR5NvIBF7xtlWER4BuQF9gNXC333WIfTVCe0WPKd2o+riqDlDVAW0Cc2JVFZHSxIDTYMCZyBzRcRqMsWOHBTfUBhOZqt4FvAt8KSIbRGQ98CXwX1W9syJfpqprVbVQVYuAJzAfCZiW0Tlg107AKq+9U4j2UseISF2gOWaSC9dX9RNNwKSyBhPKye9MZI5QOA3GCJcmBmqkkx9VfVRVu2BrX7qqahdVrXCaGM+n4nMC4EeYvQeM9yLDumLO/FmquhrIE5GDPf/KmZjQ84/xI8TGAZ95fpqPgREi0sIzwY3w2qqfWDSYggJ7pRqRTGROg3EE4jQYI5KASVINJqZAclUt9x0vIi8DQ4HWIpKNRXYNFZG+mMkqC7jA63+xiLwG/ICVBLhYVf1sARdhEWmNgA+9F5iZ7nkRWYZpLuO9vjaKyE3AbG+/G1U11mCDqiUWDQZMiwl80KYCeXkmYAPXMjgNxhFMYC0YcBoMRPbBJJkGE7eVSqp6SojmsL4bVb0FuCVE+xygV4j2ncBJYfp6Gng65sHGiy1boFOn8Nt9AbNjR2oKmODouiSdhTkSSGAtGEhtDSZcqn5I2nunQsulRSRMWJSjFLGYyCA1Hf3BiS7BmcgcZQk2C9Wrl7plk2ubDwZsRX7Q3+nAB3EbUW2iPCayVMNpMI5YCPVQTdV8ZJEETFqaLYdIsnsnFg3mdxF5BMBzmk8FXojrqGoLToMJT3CxMTB/TBLeJI4EEuqhmqplkyP5YCApU/ZHFTCq+i9gi4g8igmXu1X1mbiPrKaza5e9nAYTmlAaDLiMyh45ObBhQ6JHkQQ4DaaEzZuhfv2SiWkwSVjuIlKyyxP9FzALOBj4HlCvzREJ/wZwGkxoQvlgwBUd8xg/Hs49N9GjSAKcBlOCH00nodaSk5QaTKQosmOD/v4ey0F2LBZm/Fa8BlUriJboEpwGE06DcQKGxYuhbdtEjyIJCKfBrF6diNEklnB5yHyS8N4JK2BU1RUVqwzREl1CamswoXww4Exk2OWwdm34iWpKEcrv0KwZ/PRTQoaTUMLlIfNJQg0mliiyTiLytlfbZa2IvCkiERZ3OACnwURC1ZnIIrB8ub3n5CRdBdzqJ7AWjE8q+2BqmAYTSxTZM1halg5YVuL/eG2OSMQiYFJVg/GrVjonf0iysuy9sBA2bUroUBJPKLNQs2apKWCimchqogYDtFHVZ1S1wHtNAeKQeriW4YcARbogUlWDCZVJ2cdpMMUCBmDduoQNIzkI9VBt2rSkbHIqUQN9MLEImPVe+eQ073U64AIoo/H77/beMWQpGiMwVUwqEUnAJOFNUt04ARNAOA0GUk/TrY0+GOAc4GRgjfca57U5IpGdDS1blqRwCEWqmshCZVL2cSYysrKgjndnOgGTG1qDgdQKVd61yywdNUyDiZrsUlVXAGOrYSy1i+xs6Nw58j716lmKh1QzkYWqBePjTGRkZUGvNmtYsLadEzC5ubDffqXbfA0mlfwwkRJd+vgajGrShCC6KLJ4kZ0dOZOyTypWtYxmIisogN27q3dMSURWFvTf9j+EIidgQpmFUlGDiZSHzKdJE4sMSaJ7x0WRxYtYBUwqVrWMJmAgZc1k27fbGphu2xfQus7G1BYwwbVgfFJRg4mWhwySMqOyiyKLBzt32iIGp8GEJpIPJsWLjvlrYDKLfqWNrmPdOk3sgBJJcC0Yn1TUYGIxkSVhNnIXRRYPVq2yd6fBhMZpMGHxI8gyySJD17JudQqvtAxnFkrFomOxmMhqqAYTGEW2GhdFFp3sbHt3GkxoIjn5k3AWVp34AqYrv5HBOtatKYy4f60m3EM1Fcsmx2IiS8J7x0WRxYPyCJhU1WAaNrT6L8GkuIksKwvqpxXQrnCNCZj1FSo6WztwGkwJNVSDiSpgRKQNcD6QGbi/qjotJhy+gIm0yNInFTWYcIkuwZnIsqBL4xzq5CkZrCM3ry67d1sZkJQj3EM1Fcsmb95si6PC3TdQMzUY4F3gf8AnQArr6+UgO9tU2UiZlH0aNSq5kVKFcKn6wWkwWZBZZwV0707GUgshy8mJba5S64g0a0+1hJfRasFAzdRggMaqenXcR1KbiDVEGUzApFpti3CZlCEpZ2HVSVYWHLf7Zxg0qFjArFvnBEwZUq3oWLQ0MZCU904sBt7/isgxcR9JbaI8AiZVTWTRBEwKmsi2bzdhkrljCey3Hxnpdl2k7FqYSI7tVNNgNmyw1FOR8DWYGiZg/oYJmR0iskVE8kQk6tRBRJ72Vv8vCmhrKSLTRGSp994iYNu1IrJMRH4SkZEB7f1FZKG37X4R0xFFpIGIvOq1zxSRzIBjJnjfsVREJsT4W1QdEQTMV1/BI48ENKSqkz+cLTmFTWTFa2DIgr32IqO1hSintIBp3Di0AyrVNJjly2HPPSPv40/OkshEFlXAqGpTVa2jqo1UtZn3d4QiJ8VMAUYFtV0DfKqq3YFPvb8RkR7AeKCnd8zDIpLmHfMIMBHo7r38Ps8FNqnq3sC9wL+9vloCk4BBwEBgUqAgizv5+bBmTVgB8+CDcOWVtkgZcBpMMA0bmp05BQVM4BoYunYlo53dniktYMJFTaWSBqNqAqZLl8j71SQNRkT28977hXpF61hVpwMbg5qPA571Pj8LHB/Q/oqq7lLV34BlwEARaQ80U9VvVFWB54KO8ft6AxjuaTcjgWmqulFVNwHTKCvo4sfq1XZBhBEwy5fb/7/Yr5+KGkwkH4xIymZUDhYwzTqkU192OwETilTSYNavN60kMzPyfvXrW/LcJNJgIjn5L8M0h7tDbFNgWAW+r62qrgZQ1dUikuG1dwS+Ddgv22vL9z4Ht/vHrPT6KhCRzUCrwPYQx5RCRCZi58ie0dTPWImyBmbFCntfuRJatMBpMKHwMipv2wYNGoReLlMbKV4DU38LZGQgbTPIkBxyclLRw4/TYHx822k0DUbEtJiaoMGo6kTv/YgQr4oIl0iEir3TCO0VPaZ0o+rjqjpAVQe0aVNF6dUiCJjdu0uyyKz0RWCjRpZvKVWq86lG9sEANGmC5m2lZ0/497+rb2iJJisLujRcS529Mu1hkZFBRtEa1q1N0XQxToMxYhUwYNp/DdFgihGRP1B2oeVzFfi+tSLS3tNe2gO+8p8NBBZP6QSs8to7hWgPPCZbROoCzTGTXDYwNOiYLyow1ooRQcD8/nuJ76VYwAQWHYtl3UxNZ8cOKCqKfK5NmrByfSOWL4dFi8LvVtvIyoJMWQ577WUNbdvaav5VBUAKrrTMzYV99w29rWlTu5YKCmq/ilteAVMTNBgfEXkeuAs4DDjIew2o4Pe9B/hRXROwRZx++3gvMqwr5syf5ZnT8kTkYM+/cmbQMX5f44DPPD/Nx8AIEWnhOfdHeG3VQ3a2/ZNDhFb61wkEaTCQOn6YSHnIfNLTWZDTDiipPJ0KZGUpmTt/gq5drSEjwwTM2sSOK2FE02AgNXx1WVl2v7SIIVYpycomxyL6BwA9vId3zIjIy5gm0VpEsrHIrtuB10TkXGAFcBKAqi4WkdeAH4AC4GJV9bMGXIRFpDUCPvReAE8Bz4vIMkxzGe/1tVFEbgJme/vdqKrBwQbxww9RDrHi1ve/pKWVKDopVzY5UiZlnyZNWLjC/A6+SbG2Y2tghEyWlmgwGRlk8CvrNqYlU5HC6iFcLRifwJT9kfJz1Qb8CLJYLoAk02BiETCLgHZYJuWYUdVTwmwaHmb/W4BbQrTPAXqFaN+JJ6BCbHsaeDrmwVYlEdbA+BpM374prMHEImDS01mQlwmYgEmFh2upNTBdD7Y/PBPZjl1pbNsW2W1V6whXC8YnlYqOxRKi7FNTNBgR+Q/mHG8K/CAis4Bd/nZVdRmWQ5GdDcNCx0CsWAFt28Lee8PcuV6jL2BSTYOJ4uRfsH1vwH6WzZtr/yQ1OEQZKDaRga2FSSkBEy17cCoVHVu+HA49NLZ9mzSBjdVnsIlGJA3mrmobRW2hsNCm3BE0mC5doHNnePddb2aeaiayGHwwuxo256f8vejWDX75xX7SlBQwzZuTUXcTFJiA8S1nKUE0AZMqGszmzfZbRFsD45NkGkwkJ//vQIGqfhn4wrSa7AjHpS5r1piQiUHA7Nxp66eciawsS3btRSF1GeUtj00FP0xWFtSvk0+71oUlqooIGS0tfD3lFls6DcYoTwQZJJ0PJpKAuQ8INT3Y7m1zBBMhRFnVTGR77mkCBjw/TKppMDEImAVbMgEYNcLWf6SKgOnSYC11unUt1Z7hLUV2AiaIVNFgyitgapAGk6mqC4IbPad7ZtxGVJOJIGByckxJ6dKlZHN2NqmrwURwKCzc1IkG7GTwALtRkjZU+b//NVtnFZCVBZn8VmIe82jT3qzYTsAE4TSY0NQgDaZhhG2NqnogtYIIAsYPUa6xGszLL8P111e+nxh8MAty2tGTxTRL20bz5kmswUyaBJdeWiVdlVkD49GwQ0uayRYnYIJJlbLJWVn2jGjbNrb9Gze2lCFJkhkkkoCZLSLnBzd6a1jmhtjfkZ1tybNatSqzKXAikpFhVV9XrqTmaDDPPAM33QRLllSun7w8+43q1Qu7y4JVrenDAti6lQ4dkljALF9uD4DAFbQVoHgNjP5a1pOfkUEbzWHdunItQ6v5RKoFA6lTNtlP0x9rnH6SpeyPJGAuBc4WkS9E5G7v9SVwHlYjxhFMDIss99zTSmt36lTDNBj/BO67r3L9REl0mZMDazY3pjcLYds2OnZMUgGzbZsVgQL48stKdVV6DUxpDcZCldeyblWKVSuPVAvGp1mz2q/BlGcNDCRd2eRIyS7XquofgBuALO91g6oeoqprqmd4NYwoiyzT00uyPXTuXIM0GD9CIS0NnnvOpEBFiZLocuFCe+/DAti2LXk1mECtpZICplSIcrAG4+cjW5OCAiZabHrTpqmhwZRHwCRZ2eRYCo59rqoPeK/PqmNQNZYIAsaPIPOVm2IBUxM0mJwcG9/EiSYIH3204n1F0WAWeGElgSay1astP2ZS4UuFdu2qTsDUWVnioPPx85Hl1PJUBsHEKmBqswazY4dXQzsz9mNqigbjKCdFRRbuFGUNjE+nTrZ7UVo90wySWYPxzWOjRsHRR1tZzoqON1KxMUyDyWiZTwY5xRpMfr63ZiiZ8DWY006z1aCVCHUrXgPTuV7ZzMCegMnJrZd8QjaexCJganvK/vJGkEHN02AcMZKTY0/CKBqMT+fOtvu6dZiZLJk1GP9C33NPuPxyG/RLL1Wsrxg0mD777bY/PAEDSWgmW77cHM3jx9vfldBisrKgS/3VZdbAAMUmsqIiSaYMIPHHaTAVEzBOg6mlRAhR3rbNZuCB10mZUOWaIGC6dLE8a336wD33lBS3KQ8RfDCFhVb/pXdPr9+tW+noFXNMSgHTuTMceKBFOlVSwGQW/VrWwQ/QunWpfGQpg9NgnAbjCCDGNTA+pQRMo0bJbyJLT7cbXgQuuwwWL4Zp08rfVwQN5pdf7Gfo09e7LJNZg8nKMtt4Whocdhh88UXFu/pNydz9c+hkY/Xrk5Fukw8nYIJIBQ0mLY3imyAWnAZTS4lBwNRoDSawHsX48ebcvuee8vcVwQdT7OAfUL9433ZWdyz5BEygU23oUPj5Z4tGKCfbt8O6HAkdouyR0cY0upQRMNFqwfjUdg0mK8seFOWp2Ok0mFpKdrZdCH7yqABCabqtWplcqTEaTODgGzSAv/wFPv64fDWNVSNqMAsW2Bqh/XvXtfUP27ZRvz60aZNkAmbXLhMm/m8yZIi9T59e7q4iroHxyGhrgj1lBEy0WjA+gWWTayPlDVEGp8HUWrKzoWNHe0IG4S8had++pE0kaLFlsmswgfY9gAsuMMFYnoWXO3fagyOMD2bhQthnH29pUHp68SysQ4cky0fmV4vzb/4DD7SHXQX8MBHXwHi06tgQoSh1BEy0NDE+tT3hZUUEjNNgailRFll26lRW0+3cOSDhZbJqMP6K9eALvVUrOOsseP55WBtj0fgomZQXLLD4AcBuFC9vWdIttixWOzLtvW5dKwhVGQHTcK2paiFIa9eG1rLBCZhgakg+sp9+gk2bynnQ7t120ZdXwPjr6pwGU8uIYZFlMKUWWyarBhMqQsHn0kvtRnjssdj6ipDoMi8Pfv0Vevf2GgKywiZduhhfKgTe/EOGwA8/lDvLwU8/QaO0XbTr1iR8vqmMDDJ0LTlrU2QhTHk1mCT2w+zaBYMGwRVXhNi4e3f4A7OzbW1deRZZgllQGjd2GkytQjWqBhNqItK5sz04Cxs2SV4NJlKo5D77wODB8NZbsfUVQYNZvNjeizWYIBPZ2rVJZGpfvrwkoZxPBf0ws2ZB/waLqbNXZvid/NX8q/LLP9aaSC3SYKZPt6KU06YFRfVPn24CMlwkZkVClH2cgKllbNxoAiI4zQfmcsjODq/BFBbCam2XvBpMtAt99GiYP78kii4SEQRMcQRZGBOZauyWuLizfLkNKjAj9IABdmOXI1x592747jtlUP6MyPWQ/Xxka1Mko3It8sG8/769r1xpGjpgEueMM0y9ef750AdWRsA0aeJMZLWKCCHKq1aZEAmnwQCsLOyQvBrMihXmYwiMUAhkzBh79++kSEQoNrZggcmd4t8pSIOBJDKTLV9e1nRRrx784Q/l8sMsWAC7dgkD82eEjSADSjSYDWkVG29No7waTJKayFStJt0++9jfn/mZHC+5xKJWBg2C994LbSrzBUyISWtUUl2DEZEsEVkoIvNEZI7X1lJEponIUu+9RcD+14rIMhH5SURGBrT39/pZJiL3i5gRW0QaiMirXvtMEcmM6wmVc5Glj7/7yvwk12A6dbIwuFDsv789bGMRMBF8MAsWmP+l2A0RpMFAEkWSZWWFnjEMHWqhcH4a/yjMnGnvg5gZk4DJ3Vovotm+1hCtFoxPkmswP/9si4f/9jebn332GfDqq/DCC/Cvf8E//2naTLHkCSAryy78Bg3K/8VOgwHgCFXtq6oDvL+vAT5V1e7Ap97fiEgPYDzQExgFPCwi/tPuEWAi0N17jfLazwU2qerewL3Av+N6JhEETCRN15+cZO9qnbwCJlyEgo+IaTGffBL9HMKYyFTtuVxsHoNSTv6k0mAKCuz/Heof6vth/ve/mLqaORPaNt/BnqyIyUQGlauUUGOIpRYMJL0G89//2vuYMXDEEfD5p4XoBRea5vKPf8CRR5qmHsqHWZEQZZ9U12DCcBzwrPf5WeD4gPZXVHWXqv4GLAMGikh7oJmqfqOqCjwXdIzf1xvAcF+7iQvZ2TbD95edBxCYJzKYPfaw5+jKHa2T10QWy4U+erQJl2j+h59/Nud4y5almrOz7ZlSHEEGpUxkGRn28yaFgIlk8zzoIIsIjNFMNmsWDGq/EoHI0UJNm5JR1+Jckz5U+bLL4N+VnM+tXBk2ZLsUSe7kf/996NXL7v1hQ4tYm5PGkt3dzO9St65dK2PGwDvv2DUVSGUEjNNgUGCqiMwVkYleW1tVXQ3gvftL4jsCKwOOzfbaOnqfg9tLHaOqBcBmoEwdYxGZKCJzRGROTmWmhtnZpgOHMCOtWGFLRvz1T6W/3wtV3trSLrD8JIsSKigwu1S0C33oUJs1+VO2UOzebWWXx4wpo8GUcfBDKROZL7uTQsAEr4EJpEEDOOSQmATMpk0Wojyo8GvYd9+IRdgQIaOlhdAltYApKLBaQf/4R/kyPARSVGQmo8GDo++bxGWTN282RdZ3UQ5b/gwAnx33f9C9e8mOf/yjqaWBWm9hoQlZp8FUmENVtR9wNHCxiES6mkJpHhqhPdIxpRtUH1fVAao6oE0sM6ZwRAlRjmRh6twZVubtYX8kmxbjz9YjnQDYTX7kkTZlC5dh+a237On45z+X2eRXsSylwTRpYg8sz+mQNIstQ62BCWTIEJg3r8SPEIbZs+194PLXYeTIiPtCSQaipBYwS5aYJltYaKmEKpJte/58Sz1+1FGx7Z+kZZOnTrXLd/RoYNEiut51MZmN1/L57j+U3vHoo+3+efPNkrbVq22yWd41MD6prsGo6irvfR3wNjAQWOuZvfDe/VspGwgMpegErPLaO4VoL3WMiNQFmgPxq6YRZZFlpIlI586wcrPnrEw2P0x5QiVHj7b9/QUtwTz8MHTrFvLBMX26ybBSPl1/Rh/gh0kKARPJ5gkmYFSj+mFmzgQR5aDdM2ITMO1NO05qATNnjr1feqmZS197rfx9+OtCjjwytv2TtGzy+++bJfjgg4FzzoHmzTlibDM+/1xKF45r0sQK+b39dknZ1sqEKENqazAi0kREmvqfgRHAIuA9YIK32wTgXe/ze8B4LzKsK+bMn+WZ0fJE5GDPv3Jm0DF+X+OAzzw/TdWjaupsCAGjGt2U2qkTrMlrzG7qJZ8GEyoNdDhGj7b3UNFkCxfaA/eii8rkanvtNfjwQ6vGXArfphgQSZYUUWTLl5s60ahR6O0HH2wz0qlTI3Yzcybs12IdzevvLAkOiEDTDk1pwM7kFzDp6XDHHdCvnxWn8yMHY2XaNFNlw4XFB5OEGkxREXzwgcmNuqtWmLp6zTUMG92ITZtMSSvFH/9oF/esWfZ3ZQVMimswbYEZIjIfmAW8r6ofAbcDR4nIUuAo729UdTHwGvAD8BFwsar6HrGLgCcxx/8vwIde+1NAKxFZBlyGF5EWF7ZssdlCCAGTm2v3VzQTmaqwmvbJq8HEEovfsSP07RtawDzyiPknzjqrVPPq1SZzBg6Eq68OOiYoaV/HjiXrWRNKqDUwgTRsaGaPN98kXI1jVc/BX/QNHH54aAddENLOW2y5LokXW86ZA/37m2/kwQftoXnzzbEfv2OHTURiNY9BUmows2ebW2X0aODTT63xqKM44gj7WCYqecwY+818M1lVCZgkqLFd7QJGVX9V1QO8V09VvcVr36Cqw1W1u/e+MeCYW1S1m6ruq6ofBrTPUdVe3rZLfC1FVXeq6kmqureqDlTVX8uOpMpOyGLaDz+8zKZYrpPixZZ0ToKnZxDLl1s0j58CPBpjxsBXX1Gqtm9enkXNjB9v0Q4eqnD++XYfPPdciJIXIUxkUKGSK1VLuDUwgZx8sg30q6/CdpGTA4NyP4rJPAZARgZtyGHd78mSLyeI3bttaj7AW3VwyCE2objnHotmiIUZM2x1e3kETBJqMO+/b4r6qFFY+H7bttCzJx072qLLzz8POmCPPcwk+NZbdmNkZUHr1jFNPELi369J8DxJpjDlmskee8CNN9o0PIhIiyx9SgmYZNNgoqyB2bTJZmn33us1jB5ts6aPPy7Z6YUXTI0Lcu4/9ZTdiP/+twVRlSGEiQwS7IcpKoruVAMTtA0bhvVBlFpgWQ4Bk8E61q1OUgGzeLEJB1/AANx+u5kS//rX2Bz+U6fa2pdYIsh8klCD+e9/LalDyxZqGszw4cUriIcNsyDDMgGjJ55ouWTmzw9pV1+0qOS6iUoSpex3AiaO1AoNJszgc3NhxAizNV9+uTcrO+ggm3n54cqq5tzv39+2efz2G/z977b47JJLwnx30E2SFAJm3Tp7iEYTMOnpcMwxZvIIXt+APSgapu2mV7sNQaFzEfDzkeXEbzlXpfAd/IECpm1bm3xNnQrvvhv6uECmTbOyB7FqzJB0Gszvv8P333vmscWLLYFeQMDCsGE2Z5o7N+jA444ztefNN8vcd9u3m9X1hBNitHolUdExJ2DiyIoVNpGNFAHdtCk0Ty9IPg0mQoTCli2m/s+fDy+/bGH9p58O6zel2YP1o48sRnPGDJt6XXRR8QyuqMgsJ3XqwJQpIeuzGb6JLJk0mEhrYII56aSwZrKZ3yr95TvqjRwWPkV/ML4Gs6lehaJ/482Ob+fzduPTOOvmbkyZErDh4ottteGll0a+vteutQuqPOYxSDoN5oMP7H3MGMw8BqbBeAwdau9lzGRt2liwRwgBc+edFqi6enUIwRQKp8GkBv4amGjPkE5t85NPwGzcaDOgIBNZXp7NpubOhddfN9fKq6/a0oWzzgI9ZrQd++23pr00bw6nnFJ8/H33WVjy//1flOU1QTdJy5YWJ5DQSLLyOF99M9nrr5dqLs6gXPBV7OYxKBYwO3enlTswK15s3Wr/+5NPhjZT7uDE7S/wwgvChRcGuF3q1jWH//LlAbbUEAQ4w8tFs2ZJVTb5/fftuu7ZEzun7t1LXeht2pjSGir9GCeeWLKWyJvErFxpZuSRI23B8XvvxTAIp8GkBtHSePl07lBENp2Sy0QW4mG6daspKDNnwiuvmFYPFjx29912c/3fr8faQ+Xpp202dvbZxRf8jBlw3XUwdixMmEBkgpz8IkmwFibaIstAfDPZG2+UMpMtXAi7dtdhELPK9zBt06Y4H1kyhCq/+KI9LMePh+nTlTN4nmnjnyQry9wuEycGmHOGDIFjj7WpeLgFqNOm2SziwAPLN5AkShezc6cpLaNHgxTk21qgAO3FZ9iwkniGUpxwQsln7xq75hr7HR97DA47LDZLo9NgUoRY0wl17qTJp8EERShs22aT8q+/hpdestD9QC6+2ATOVZMaMfeAcywtTH4+XHghGzfCBReY77ZtW3j88RgsQ0FOfkgCAbN8ObRoUZLFNxonnwxr1pQykxU7+HtvN39VrNStS0ZTm4AkWsCommule3dzWP/+zhweKbqQI8e1oFMnuOsu01KfeirgoBtvNOFyzz2hO5w2zR7G4bJ2hyOJBMyXX5bcJ8yebdduiAWjRxxhwqiM075jR4u+A+jShW++sXvtiivsOTJ2rE1QfvstykD8e2fz5sqeUqVxAiZO7Nxpz5aYNJg9hXW0ZVdeEuViX76ctWTw/Mx9OO0009j/9z+LOD755LK7i5jS0q4d/CnrdrbQFB02nCnf7Mu++9rD5u9/N5dM27YxfH+jRtZpwCwsUQJm3jzLrF6UFUMEWSCjR5eJJpv5v91ksJY9jz2g3OPIaGWaUKIFzJw5lrf0r3+1SUPa96Ud/OecY76GK68MCCvv29f8Uvfea/bUQH780Wyf5TWPQVKVTX7/fbtsjzgCU2VEKF78EsCQIeZ7DGkmO/10aNKEosy9itP8X+Ot4jv2WHv/z3+iDKRnT4vGK0fxu3jhBEwl8RfyB4cd+hn8Y9JgMu3fkL0meDFI9bNtmz1M+996Iu1Yy5kXN+WTT8yp//HHcOqp4Y9t2dJMJ79t2oMz0l5iyNrXOPtsm+l+952Z0UKUggmNiD081qwpbkqUgLnlFns99v3A8gmY9HQTMgHRZDOn72IQM5FR5fC/eGS0s+sk0QLm+efNHzZunNcwZ45pY95sSsS01J07LSVZMZMn2wV2xx2lO/TTw1REwCSJBqNq/pHhw70kD598YtkMgjKHg61s6NcvjIC56CLIyuKF95oxe7ZFevvW4u7drfxSVD9MeroN5N13K5YPripRVfdSpX///loR1q9Xtf+iauvWqr17q44cqTpqlLV99ln0Pj75uEBB9fNznqvQGKqS66+3cQ9uvUhvaX2Pzp2rWlhYvj5uvNH6aNlS9ckny398MSefbJ3s3Kmqqv/+t/W7ZUsF+6sAeXmqjRqppqUVaTNyNfucf5Wvg1desUF/8YVu3Ggfb65/g+ru3eUey84/nqp12a1XXVXuQ6uM3btV27RRPemkgMY+feyCD+LWW+1833knoPH00+0HXbWqpG3MGNW9967YgL76yr7ko48qdnwVMX++DeOJJ9Qumnr1VK++Ouz+V11lu2zbVnZbXp5q+/aqAweWvXeuvlq1bl3VTZuiDOiRR2xAixaV91TKDTBHwzxXnQZTSRo0sNna5Mnml+ja1YKoFi40k2osyxw6dTG788qchvEdbBRUzZpzxBHwZeZZXNf/Y/r1ixBKHIbrrrN+fvoJzj23/McXM2GC/Zhe+plEhCq//765xp64dxu7qc9fZ59Rvg5Gj7Yp7euvM2e2zSYHDSi01CDlpEH7lhyW9i0ffhh933jx8ceWheD0072G7dttvUfg+hePK66wEgx//nOAO2DSJAulu+02+zvfc4ZXRHuBpNFgfK1izBjMlpyfH9LB73PEEbbLyJH2U8yaVRILcvvtZlq8776y987YsRYw99FHUQbk29NiCjuLI+EkT6q9KqrBVAXbttlk45ZD30/YGFRVFyywcTzyiNo0deLEhI5H8/NV27VTHTtWVU0bjFUrrCpOPNFmkwWzv9PbuUpB9e23y9nJH/+o2rat3vTXtSoUau49T1VsMDfdpHdyuYLq8uUV66Ky/OlPqq1aqe7a5TV8/XUINaWEmTNVRVQvuki1qMjG/daRD+k/6tyqo4Zs14P226IbaKH61lsVG1B2tn3/bbdV7Pgq4qCDVAcN8v64/HLVBg1Ut28Pu39BgVkLevcusYA0b26XeoMGqqeeGv64Nm1UTzklhkENGBAwqPhBBA0m4Q/2ZHklUsCoqraUDXrufjMSOoZ//Uu1Th3VtVnb7dK4+eaEjkdVVa+4wmwCa9fqjz/asF54oXq+essW1YYNVf/yF1V9+23dTV3t032bduyounlzOTp69VVV0DHtZuv+LFb99deKDeixx/QH9iuZBFQzubn2e1x8cUDj/ffbPyU7O+xxl16qxSZk/2GaRr7u23y1guqLcloMNp8IDB6s2rFjsSm1uvn9d2+CeIvXcMABqkccEfPxa9eqvvyy6nnnqXbtqrrHHqorVoTf/5xzTBhFtbL6turVq2MeS0WIJGCciSxJOKrhDKb8dHB01TdOqNqawCFDIGNnOdL0x5sJE8wm8NJL1W4i++9/zVF98slAVhb1KOCJB3ezapUVbQzHsmUWZVW8rGn0aLRhI2au2ZOBTX80O2pFyMhgP34ks8Ou4hXj1cmbb9o5nRFoJZwzx0IH/X9OCG66yQICjj0WHnrI1uDmXXgVi7d2oVWdjUxtfYp5vivKP/5hUWjPPVfxPiqBnxnpuOMw++H8+bHXs8GqP4wfD088YenI1q+PnMB87NiSipkR8ReqRao0G2/CSZ5UeyVag9nSf6j2bfCDpqcX6fffV//3lzKPTZ1qf3z5ZfUPJBT9+6v27auqqunpNiOuDo4/XrVDB8/Reumlqk2aqBYV6V//amafr78u2beoyPzMw4eXzNJBNSPDLBVj2s9WUH14yMsVH5Bnjrp4TJY2bqy6Y0elT7FcDB2q2r27nWsxPXqYk768rFql2rCh/omXtX365tJ9lpeiIrtGunUzs2oFeO451W+/rdjXjx6tutde3u/iB3VUtLMY2LrVNMm//S3KjkVFql26VOz/Uw5wGkzy0/Sai3l/13D2qL+d0aMt9Lk6ef11cyieeCKVr0dR1Zx1li1GmT+/2kKVt2yxQmgnneQ5Wv1VsyLcfLMFcEycaD7uF1+0BeijRlmmj9tvt8n0zTfD8cdblYJf6uxDJ1Yy8oxYFgGFwaubPLr7z2zfXr3LHFassO87/fSARbJbt9oJh3DwR6V9e7jkEkYwldVbm4UthBoTIqbF/PJLmdQ8sbBkiSnKp59e/owzW7daRPLYsd7v8sknlh6pf/9yjyNWmjQxBSlqFLKIDeyTTxK3qj+c5Em1V6I1GH8WtqDdUdqsWZH26mU27+r66v32CzAb//Of5oyp4Gywylm/3mI6//53PeII1UMPjf9XvvCCTUS/+spr6NdP9eiji7e/955tb9rU3nv0UH3mmQDndzBFRapffKGVmqrn5amCbr/oMm3USPWSSyreVXm57TY7z19+CWicPt0a//vfinW6dauueOg9BdW7767kAAsL7Z/Qq1e54+LHj7fLHex/WB7eektLB5507ap63HHl66QCPP64fe+CBVF2/OQTjRSEURXgnPw1QMCoqk6bpgo67aI3tW5d1SOPDP3AysuzG71SZoUASpnHVFXPPFO1c+eq6byqOPFE1TZt9LRTCrVr1/h/3dixqp06BTyrWrZUvfDCUvtcdJHqsGGq//lPJdb6lJdzzlGtU0dHH7pRu3atumsgEkVF9uz+wx+CNtxzj1aFE3n//W3tWKV5/nkbz7vvxnzIokVm7rz6aptDdO1aviVKZ51lTvndu9VuSlB94IHyj72crFqlscXh7N5tEQFnnx23sTgBU1MEjKpJldatdcoj2xXsWf/xx6q3324hovvsYzcE2I15662VD1ktjh5b6zUMGaJ62GGVPZOq5d13VUGvPGGpNmgQ3wdrbq5q/fqqf/+71+BpDnr77fH70ljJzVXt3FkfzpikoLpkSfy/8rvvgiYgPqeealK4kvz1r7b2stI+pfx8kxADB8Z8gZx8svn11q83Rax4sWQMFBRYZFxxSLGvVvzwQ8XGX04GDrRXVMaPt9jmgoK4jMMJmJokYGbNsn/LpEk6aZKWchh36WKO5xtuUP2//zMZ4G8bOtRWzZfXrFbGPKZqN2m4QPxE4S0hv7fPUwqqGzbE76uefdZ+02++8RoWLbKGlyvhoK9Kpk3T5XRWUL3rrvh/3d//bhbKMr/5PvvYBVlJ/Af7J59UuivVRx+NuTNfc//HP+zvoiJbNrLnnhFMnQHMmGHHv/qqmue9Xz8Ll64OtVJNe4HSSRFC8vLLqqDbPv1G333Xfprvv7dQ6FCZBMqLEzA1ScCoqo4bp5qerkVr1uoHH6h++mn4B+ovv1i4e/fu9t9s2NAmLB98EJsLpYx5rKDA1p1ce22VnU6Vceml+mraKQqqCxfG72tGj7aHTPFz4v337UcKDBtLNBdfrL1YoEccuCmuX5Ofr9q2bQg5kpurVbVWys+sUiUpcHbutJWxMaxD+eMfVZs1K31vffyxhtbWQuCne8ldn28XTZ06FViFW3H8e/ehh6LsuGmTFqTV1xGZP5WasPqvhg1VR4yo+DicgKlpAubHH1XT0mKIQyyhqMhWTV98sbkLwBbBX3655UkKRxnz2MqVdvCjj1bqFOLCvHk6gz8omL/944+r3vexcaM9NC6/PKDxjjvsN/n996r9ssqwdatetcejWpfdmpudV+Xd5+ebVdLPqffmm0E7+GkVqigH2NChxZHolefuu6NOCObNs13+FZRarqjIgkg6dYpustt3X9Wjjioyv1gCVr/60dnNmqkuXhx530ldn1VQvfNOizV5802z6N12m61lvvPOio/DCZiaJmBUbVlv/fqqv/1W7kN37bKJ1PHH28MSzImalVV6v5DmMT954AcfVGb0cWNX7/56ZdtntVUrLTYb3nijycWq4JlnrN+ZM9VSfVx2mTm9eveuRk9+bHz5wHwF1dePeqzK+vztNwsi7NChZJIyeXKQ+b6gwCY/oJqTUyXf6yfGXLOmCjrLy7N8NhHWf5xwgvm+N24su+3TTzWqr97PKvHAqP/Yh3/+s/LjrgDLl5uGmZkZMEkM4qOPVEWKdALPaNGPP1X5GFJWwACjgJ+AZcA1kfZNOgGzcqXprhMmVKqbnBybnaSn2+uhh0qek2XMY0uW2PdB9ClRorj3XlXQndffoq88sE6PPNKGW6eORXRdcokFN73zjpnRtm4tX/dHH21Cq+ibb036goWL5VW9llBZ8vNV92iwTc/mKVPnKkhBgYVdjxplslRE9ZhjbJJSKqKqoED1xRdt6g4hMyhXlNmzNWIaoKIim2k/F2vC8Ztusg4PPtjGHOBU8YMWJk8O/11DhpilLVw6sTvvtD6y2NM0mGryu4Ri1iwLkjjkkLJa1/LlJmt777tLt9GocqpKGFJSwABpwC/AXkB9YD7QI9z+SSdgVFWvvNLu9uuuU33pJfPMRUigF4msLLOzgqVuWrrUN48V6dqbHrPl5v6TeuzYCqWTrxY2bjR7im9APuww/eWG5/W6S7fqgQeauSDYxty8uflUevUy88cxx1iywAkTLHrz3HNVzz9f9YILVOvWLdIrB31hv0PnzhY6nsT8aVyBtk3L0cL2HS0Z1ptv2uQghrxcOTkWGJeZab9Tx46qkyaFiEoMFiy9eqm+8UaVanQFBfYgPPPM0Nv9BfJdusT4tfn5lidtn320WBWbNEl11SodO9ZCiyMFxHzxhR12772htx/eI0cP4HvzvSTBerHXX7fxnnJKiazbtcuCFpo2Vf3pJ7WyCoMG2T+4CvO2RRIwYttrHyJyCDBZVUd6f18LoKq3hdp/wIABOmfOnGocYQxs3Gh13WfPLilwLmLlJbt1s5Tv/rJqkdKfA/H+x6owZeVw/v7DeewuqkuTtJ30zv+ezxhmK49PP92SIrVrVz3nVxl+/RVeecWW0f/wg5XaHTQIbdSYTUXN+XVnB37Z0YFfdrRn7a4WbC5owuaCxmwpaMzm/MZszm9CvqZRpEIRdexd69CgaDsfFhxFj3P/YBXSmjdP9JlG5LnnbBX67I7HM+D3gILtdeqQl9mbBc0PZ3edhhRQlwLqkk89dml93l93EK+sOpxdRfUZ2moBl2S+z9i2M6knBSWyuajI3n/91RKs9epl6fZPPLESNRjCM368lVr+/ffSl/CGDVZoa9cuy7AwfTocfniMnRYVwdSp8MAD8MEHzK07kAEFM7lxryn8q9tLdh7+K+i+OfLbm1i4pQuv97ud9DrbaVJnB+l1trO7qC57z3iGf3SYwo0/jy8pUZxgbr8drr3W/kWTJ1vF0QcesOQG48YBN9xgG3yaN7d7vW1by8Zw990V+l4RmauqIdM51GYBMw4YparneX+fAQxS1UsC9pkITATYc889+y/3U6QkGzt3wtKlltPCf2VllRSQ8B8I/udQBNw8q/LbcOGK6/jP5sE8cczbnHfnvtCjR3zPIV6oWvGdl16Cr76y38R/OPqvaL9J4HujRlaj9phjqmf8lSQnx54PkybBpMu3ws8/s2LGCh54pTWPz+nHlvzGIY9Lr7ONM1u+z59bv0bPRr+WbPAnKv4DV8QqJF5wgRU8ioNg8Xn6aasftHChyTKfs8+GF16wmvcjRsBpp8Fjj5W///n/WcGJZzRm07b6ZPX7I83qbC25Pvx7KYCvt/Zh8M9PUEjoSrOzp25iwFEtyj+QOKFqv98zz9hv9swzVqb8nnu8HXbuhE8/tWIza9bA2rUlr65dYcqUCn1vqgqYk4CRQQJmoKr+JdT+SanBxBFVm/j36FFW4XHULA4+2J6TDz5oD5M33rD2ceNMKU1PN2W3bt2S965dy1G+uppYudKqLt99N1x2mbV98onVIrvuOitbfcYZlhx4zRor9hcrzz1nMrJVK/t9Dj44tuOWLbO53LZtlnds2zZ77bGHpchLtntn924Twl9+CX/4g+WPq0Btu3IRScAk3FcSrxdwCPBxwN/XAteG2z8pfTAORwz4ZT/AfFBXXJG4gmSVJTBtzLZttuZ3n31KnNf+OpUyYdNh2LnTMvyARUuGi7SqTWzYYG7b6oqqJ4IPJrTuVzuYDXQXka7A78B44NTEDsnhqHrOOMNmrGPHmmkk2TST8nDUUVaCfOdOuP56+O03O7eGXjXxYcPMbfDCC17m7wisWGHZsGfNgquvtuzWdWvzE8+jZUvT9pKBWvtzq2qBiFwCfIxFlD2tqpVJCu5wJCWZmWZKqg2MGAH332/16O+918xagweXbK9bF045xQqXbdxoD9NQzJ8Pw4ebyeitt+CEE6pl+I4gaq0Pprykmg/G4UhGtm41oZGfbyVjliwpG8j33XcW9PjYY1aTJ5j8fAuKyskxH8Q++1TL0FOWSD4YV3DM4XAkDenpcOih9vnhh0NHiR94oIUtv/BC6D7uvBMWLLDjnXBJLE7AOByOpOKf/4TbbrNqoKEQsei4//3PIrwC+fFHW+4xblz44x3VhxMwDocjqRg+3JYiReK00+z9pZdK2oqK4Pzzbd3jAw/Eb3yO2HECxuFw1Di6dDHn//PPl6yjffRRmDHD1tHUhGQUqYATMA6Ho0Zy+ulmEvvuOwtJvvpqOPJIWwDpSA6cgHE4HDWSceOgfn3TYi66yExkjz+efKvrU5lauw7G4XDUblq0gDFj4JFHbL3LPfdYChxH8uA0GIfDUWM5/XQTLgMHWvZgR3LhBIzD4aixjB5tiTGff94qNjiSC2ciczgcNZb69StcxsRRDTgNxuFwOBxxwQkYh8PhcMQFJ2AcDofDERecgHE4HA5HXHACxuFwOBxxwQkYh8PhcMQFJ2AcDofDERecgHE4HA5HXHAlkz1EJAdYXokuWgPrq2g4yUoqnCOkxnm6c6w9JPo8u6hqm1AbnICpIkRkTri61LWFVDhHSI3zdOdYe0jm83QmMofD4XDEBSdgHA6HwxEXnICpOh5P9ACqgVQ4R0iN83TnWHtI2vN0PhiHw+FwxAWnwTgcDocjLjgB43A4HI644ARMJRGRUSLyk4gsE5FrEj2eqkJEnhaRdSKyKKCtpYhME5Gl3nuLRI6xsohIZxH5XESWiMhiEfmb115rzlNEGorILBGZ753jDV57rTlHHxFJE5HvReS/3t+18RyzRGShiMwTkTleW9KepxMwlUBE0oCHgKOBHsApItIjsaOqMqYAo4LargE+VdXuwKfe3zWZAuByVd0fOBi42Pv/1abz3AUMU9UDgL7AKBE5mNp1jj5/A5YE/F0bzxHgCFXtG7D2JWnP0wmYyjEQWKaqv6rqbuAV4LgEj6lKUNXpwMag5uOAZ73PzwLHV+eYqhpVXa2q33mf87CHU0dq0XmqsdX7s573UmrROQKISCdgNPBkQHOtOscIJO15OgFTOToCKwP+zvbaaittVXU12MMZyEjweKoMEckEDgRmUsvO0zMdzQPWAdNUtdadI3AfcBVQFNBW284RbHIwVUTmishEry1pz7NuogdQw5EQbS7uu4YhIunAm8ClqrpFJNS/teaiqoVAXxHZA3hbRHoleEhVioiMAdap6lwRGZrg4cSbQ1V1lYhkANNE5MdEDygSToOpHNlA54C/OwGrEjSW6mCtiLQH8N7XJXg8lUZE6mHC5UVVfctrrnXnCaCqucAXmG+tNp3jocBYEcnCzNTDROQFatc5AqCqq7z3dcDbmJk+ac/TCZjKMRvoLiJdRaQ+MB54L8FjiifvARO8zxOAdxM4lkojpqo8BSxR1XsCNtWa8xSRNp7mgog0Ao4EfqQWnaOqXquqnVQ1E7sHP1PV06lF5wggIk1EpKn/GRgBLCKJz9Ot5K8kInIMZv9NA55W1VsSO6KqQUReBoZiqcDXApOAd4DXgD2BFcBJqhocCFBjEJHDgP8BCymx3V+H+WFqxXmKSB/M8ZuGTShfU9UbRaQVteQcA/FMZFeo6pjado4ishemtYC5N15S1VuS+TydgHE4HA5HXHAmMofD4XDEBSdgHA6HwxEXnIBxOBwOR1xwAsbhcDgcccEJGIfD4XDEBSdgHI4EICKtvIy480RkjYj87n3eKiIPJ3p8DkdV4MKUHY4EIyKTga2qeleix+JwVCVOg3E4kggRGRpQz2SyiDwrIlO9OiAnisgdXj2Qj7w0N4hIfxH50kuA+LGfNsThSDROwDgcyU03LA39ccALwOeq2hvYAYz2hMwDwDhV7Q88DdSKbBKOmo/LpuxwJDcfqmq+iCzE0r185LUvBDKBfYFeWGZdvH1WJ2CcDkcZnIBxOJKbXQCqWiQi+VriNC3C7l8BFqvqIYkaoMMRDmciczhqNj8BbUTkELDyAyLSM8FjcjgAJ2AcjhqNV6p7HPBvEZkPzAP+kNBBORweLkzZ4XA4HHHBaTAOh8PhiAtOwDgcDocjLjgB43A4HI644ASMw+FwOOKCEzAOh8PhiAtOwDgcDocjLjgB43A4HI648P9+Q8aIc5awHgAAAABJRU5ErkJggg==\n", 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" ] @@ -5743,7 +5738,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The Test root mean squared error is 46728.36363494874.\n" + "The test root mean squared error is 62766.86208502063.\n" ] } ], @@ -5754,12 +5749,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 128, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -5776,7 +5771,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -5784,10 +5779,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 423269\n", - "1 310257\n", - "2 291678\n", - "3 296869\n", + "0 318737\n", + "1 299330\n", + "2 82255\n", + "3 212019\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5805,7 +5800,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -5824,21 +5819,21 @@ "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_abdul_path)\n", "traditional = pd.DataFrame(baseline_data[\"Count\"])\n", "print(traditional)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The Test root mean squared error is 115829.72216361394.\n" + "The test root mean squared error is 115829.72216361394.\n" ] } ], @@ -5848,14 +5843,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The Test root mean squared error is 132440.2172935019.\n" + "The test root mean squared error is 237086.54136675494.\n" ] } ], @@ -5865,7 +5860,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -5928,24 +5923,18 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 134, "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" + "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 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\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" ] } ], @@ -5969,7 +5958,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5981,7 +5970,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5991,7 +5980,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -6001,7 +5990,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -6010,20 +5999,13 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": null, "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/other_regression_methods_day-checkpoint.ipynb b/.ipynb_checkpoints/other_regression_methods_day-checkpoint.ipynb index 5b2e6ca..d6faef6 100644 --- a/.ipynb_checkpoints/other_regression_methods_day-checkpoint.ipynb +++ b/.ipynb_checkpoints/other_regression_methods_day-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 187, + "execution_count": 32, "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", @@ -32,13 +31,10 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 33, "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", @@ -46,7 +42,6 @@ " 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 +55,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -108,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -126,20 +121,14 @@ " \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", + " # Create list\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -147,7 +136,6 @@ " 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", @@ -156,7 +144,7 @@ " 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", + " # make y_test_not_norm for testing \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", @@ -167,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -180,7 +168,7 @@ "max val king_test:\n", "32446\n", "(1824, 1)\n", - "(22365, 180, 1)\n" + "(22365, 180)\n" ] } ], @@ -188,40 +176,16 @@ "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", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1])).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", + "\n", + "# create non-norm for testing \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)" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(22365, 180)\n", - "(1644, 180)\n" - ] - } - ], - "source": [ - "x_train_overall = x_train.reshape((x_train.shape[0], x_train.shape[1]))\n", - "x_test_overall = x_test.reshape((x_test.shape[0], x_test.shape[1]))\n", - "print(x_train_overall.shape)\n", - "print(x_test_overall.shape)" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n" ] }, { @@ -233,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -256,20 +220,12 @@ "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": 210, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -332,7 +288,7 @@ } ], "source": [ - "lr_train, lr_test, y_train, y_test = create_linear_model(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "lr_train, lr_test, y_train, y_test = create_linear_model(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, lr_train)\n", "plot_predictions(y_test, lr_test)\n", @@ -342,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +306,7 @@ " '''\n", " creating a basic Ridge Regression model (L2)\n", " '''\n", - " rr = Ridge(alpha=0.01)\n", + " rr = Ridge(alpha=0.1)\n", " rr.fit(x_train, y_train)\n", " train_preds_rr = rr.predict(x_train)\n", " test_preds_rr = rr.predict(x_test)\n", @@ -369,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -413,7 +369,7 @@ } ], "source": [ - "rr_train, rr_test, y_train, y_test = create_RR_model(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "rr_train, rr_test, y_train, y_test = create_RR_model(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, rr_train)\n", "plot_predictions(y_test, rr_test)\n", @@ -423,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -431,23 +387,42 @@ " '''\n", " creating lasso regression (L1)\n", " '''\n", - " lasso = Lasso(alpha=0.01)\n", + " lasso = Lasso(alpha=0.0001)\n", " lasso.fit(x_train, y_train)\n", " train_preds_lasso = lasso.predict(x_train)\n", + " print(train_preds_lasso)\n", " test_preds_lasso = lasso.predict(x_test)\n", + " print(train_preds_lasso.shape)\n", + " train_preds_lasso = train_preds_lasso.reshape(train_preds_lasso.shape[0], 1)\n", + " test_preds_lasso = test_preds_lasso.reshape(test_preds_lasso.shape[0], 1)\n", + " #Descale \n", + " \n", + " train_preds_lasso = scaler.inverse_transform(train_preds_lasso)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " test_preds_lasso = scaler.inverse_transform(test_preds_lasso)\n", + " test_preds_lasso = test_preds_lasso.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", "\n", " \n", - " return train_preds_lasso, test_preds_lasso " + " return train_preds_lasso, test_preds_lasso, y_train, y_test " ] }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 82, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00835204 0.00929928 0.00851214 ... 0.00212162 0.00212162 0.00213496]\n", + "(22365,)\n" + ] + }, { "data": { - "image/png": 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VsHKls51BRGSaqnb2O2YtAsMwjBLHFIFhGEY+mDoVTjwRtm/PtySmCAzDMPLCBRfAu+9Chl2vpIIpAsMwjELDRg0ZhmGUKHkaNWSKwDAMo8QxRZAhvG6ozzrrLDZt2pRyWl630pdeeilz5syJGTdVD5zNmjVj1apVUeEbN27k8ssvrxr/361bNyZPnhzmwC6S2267jffffz9pGeIxaNAghgwZkjDe008/TZs2bWjdujWtWrUKdE6yDB48OONpGoYveRrOb4ogQ3jdUNetW5dhw4aFHa9I0a3siBEjaNWqVczjmXbFfOmll9KwYUPmz5/P7NmzGTlypK/C8HLHHXdw/PHHZ0yGoLzzzjvcf//9jB07ltmzZ/Pll19W+VLKJKYIjJyTYxORKYIs0LVrVxYsWMCECRM45phjOPfcc2nbti0VFRVcf/31HHbYYbRr145HH30UcFxPDBgwgFatWnHSSSexYsWKqrSOPvpo3Al07777Lh07dqR9+/Ycd9xxvq6YV65cyRlnnMFhhx3GYYcdxieffALA6tWr6dGjB4ceeiiXX365r8+fhQsXMnnyZO6880522MEpGi1atOCkk04CHGV22WWX0bp1a3r06MHmzZuB8BaMn8togDVr1nDaaafRrl07jjjiCGbOnBk33Mtjjz3GiSeeWHU9l7vuuoshQ4bQpEkTAOrVq8dll10GOJ5WjzjiCNq1a0fv3r1Zu3ZtVH6uWrWKZs2aAbHddN90001VDgXPO++8II/fMGoc+XRDnRXy7IWa8vJy3nnnHXr2dJZr/uKLL5g1axbNmzdn+PDh7LbbbkyZMoWtW7fyu9/9jh49evDVV1/xzTff8PXXX7N8+XJatWrFxRdfHJbuypUrueyyy5g4cSLNmzdnzZo1NGzYMMoV87nnnstf/vIXjjrqKH788UdOOOEE5s6dy+23385RRx3FbbfdxltvvcXw4cOjZJ89ezYdOnSgVq1avvc2f/58XnjhBR577DH69OnDq6++yh//+MeoeH4uowcOHMihhx7KmDFj+PDDDzn//POZPn16zHCXhx56iLFjxzJmzJgwp3kQ3931+eefz4MPPkj37t257bbbuP3227k/wUP0c9N9991389BDD/k6DjSMjOBnDsqxiajoFEG+cL8awWkRXHLJJXz66ad06dKlysX02LFjmTlzZtXX8/r165k/fz4TJ06scs3cpEkTjj322Kj0P//8c7p161aVVsOGDX3leP/998P6FDZs2MDPP//MxIkTq9w3n3TSSeyxxx5J32Pz5s2r7rFTp04sWrTIN56fy+hJkybx6quvAo5TutWrV7N+/fqY4QDPPPMMTZs2ZcyYMdSpUyewnOvXr2fdunVVTvIuuOCCMFfgsfBz073//vsnOMswUsTP/JOnUUNFpwjy5IW6qo8gkp133rlqW1V58MEHOeGEE8LivP3220iCAqCqCeOA45X0s88+Y6eddoo6luj81q1bM2PGDCorK6tMQ14i3VhHmmoi43ldRvuZokQkZjhAmzZtmD59OmVlZb7rNbiuuf0UZyy87q5jubqOlN0wih3rI8ghJ5xwAo888gjbQ1PKv/32W3755Re6devGqFGjqKioYOnSpYwfPz7q3COPPJKPPvqI70Nrmq5ZswaIdsXco0cPHnrooap9Vzl169aN5557DnA6WV2buZeWLVvSuXNnBg4cWFVBz58/v8pjajp4rz9hwgQaNWrErrvuGjMcHC+ujz76KKeccgpLlkSvYnrzzTdzww03sGzZMsBZ9OeBBx5gt912Y4899qjySPrMM89UtQ6aNWvGtGnTAAKv51ynTp2qZ2YYxUjRtQgKmUsvvZRFixbRsWNHVJXGjRszZswYevfuzYcffkjbtm35zW9+47vYS+PGjRk+fDinn346lZWV7LXXXowbN46TTz6ZM888k9dff50HH3yQBx54gCuvvJJ27dpRXl5Ot27dGDZsGAMHDqRv37507NiR7t27c8ABB/jKOGLECP7617/y61//mvr167Pnnnty7733pn3vgwYN4qKLLqJdu3bUr1+/ag2GWOEuRx11FEOGDOGkk05i3LhxYS67e/XqxfLlyzn++OOrWkxu38pTTz1F//792bRpEy1atODJJ58E4LrrrqNPnz4888wzgVsS/fr1o127dnTs2LFKaRlG2hSQ52dzQ20YaWBlz0iZ1q1hzhyYNcvZBmjcGFatghUrnO0MYm6oDcMwjJiYIjAMw8gnBTB8tGgUQU0zcRk1HytzRloU0PDRolAE9erVY/Xq1fZiGjlDVVm9ejX16tXLtyiGkTZFMWqoadOmlJWVsXLlynyLYpQQ9erVo2nTpvkWwzDSpigUQZ06dXwnHBmGYRiJKQrTkGEYRlFgbqgNwzCMfGCKwDAMI594WwE2asgwDMPIB6YIDMMw8kmeWgFeTBEYhmGUOKYIDMMwSpysKgIR6Ski34jIAhG5yef4biLypojMEJHZInJRNuUxDMOoERSLryERqQUMBU4EWgF9RaRVRLQrgTmq2h44Gvi3iNTNlkyGYRgFTRGOGuoCLFDV71R1GzAKODUijgINxFmbcBdgDWDrAxqGUToUgI+0bCqC/YDFnv2yUJiXh4BDgCXA18DVqloZmZCI9BORqSIy1fwJGYZRFBTAaCGXbCoCv7uMVH0nANOBJkAH4CER2TXqJNXhqtpZVTs3zvCqPYZhGKVONhVBGbC/Z78pzpe/l4uA0eqwAPgeODiLMhmGYRgRZFMRTAEOFJHmoQ7gc4A3IuL8CBwHICJ7AwcB32VRJsMwjMInx/0GWXNDrarlIjIAeA+oBTyhqrNFpH/o+DDgH8BIEfkax5R0o6quypZMhmEYBYNfZZ+nfoOsrkegqm8Db0eEDfNsLwF6ZFMGwzCMgsZb+ZsbasMwjBKkAFoGpggMwzDyQYkMHzUMwzBqAKYIDMMwCo1i8TVkGIZhJEkR+hoyDMMwagCmCAzDMEocUwSGYRj5pMi9jxqGYRixsOGjhmEYJU4BtARcTBEYhmHkE7+WgQ0fNQzDKFFs+KhhGIaRD0wRGIZhlDimCAzDMPKJtz/A3FAbhmGUEPH6A8wNtWEYRgI2biyo4ZcZx0YNGYZhxGHZMmjQAO65J9+SZJ5CHTUkIv8KEmYYhpETfvrJ+X/xxfzKUUQEaRH83ifsxEwLYhiGEYgCcs1QLMRcvF5E/gxcAbQQkZmeQw2AT7ItWMGhCtu3Q926+ZbEMAyo+X0EQeUvL4d166BRo6yJEq9F8DxwMvBG6N/9dVLVP2ZNokLluutgxx0dZWAYRv4otRbBlVdC48aweXPWLhFTEajqelVdpKp9gTJgO6DALiJyQNYkKlSGDXP+t23LrxyGYTjU9BZBPIXmvbeXXnL+t2zJmigxTUMuIjIAGAQsBypDwQq0y5pUhmEYsSjmFkGe7i2hIgCuAQ5S1dXZFsYwDCMwNb1FkCxZvN8go4YWA+uzJkFNodQKnWEUKsXcIsgTQVoE3wETROQtYKsbqKr3ZU2qQsYKoWEUBqX2cZbFuieIIvgx9Ksb+hmGYRhFREJFoKq350IQwzCMksSvZRM0LEMEGTU0HmeUUBiqemxWJDIMw4iHayKp6aahoKaeHJijg5iGrvNs1wPOAMqDJC4iPYH/ArWAEap6t0+co4H7gTrAKlXtHiRtwzBKlFLop/PeYw4UXhDT0LSIoE9E5KNE54lILWAojq+iMmCKiLyhqnM8cXYHHgZ6quqPIrJXUtIbhlG61PQWQQHJH8Q01NCzuwPQCdgnQNpdgAWq+l0onVHAqcAcT5xzgdGq+iOAqq4IKHfuKaCHZhglTbG1CBLdT4GYhqbh9BEIjknoe+CSAOfthzMHwaUMODwizm+AOiIyAceZ3X9V9enIhESkH9AP4IAD8uzdotgKoWHUVOzjLGMEMQ01TzFtvxoz8snVxmlhHAfsBHwmIp+r6rcRMgwHhgN07tzZnr5hlDKl8DGW43WMg5iG6gB/BrqFgiYAj6pqIjecZcD+nv2mwBKfOKtU9RfgFxGZCLQHvqXQsK8PoybhltdirjSL5Z303kehrlAGPILz1f5w6NcpFJaIKcCBItJcROoC5+C4tPbyOtBVRGqLSH0c09HcoMIbhhGDbt1ghyJdibZYlFsNGz56mKq29+x/KCIzEp2kquUhz6Xv4QwffUJVZ4tI/9DxYao6V0TeBWbieDYdoaqzkr+NHFAshc8oDSZNyrcERg0iiCKoEJGWqroQQERaABVBElfVt4G3I8KGRezfC9wbTFzDMIwSJZ8zi4HrgfEi8h1OB/CvgIuyJlGhUiz2SMMoFkrtncyn0zlV/UBEDgQOwlEE81R1a4LTihczERlGfrF3MOPEW7z+j4Co6jOhin9mKPwyEflFVZ/PlZCGYRhRFHOLIMf3Fm9YwV+BMT7hL4aOGYZh5J5ibhEU4PDRWqr6c2Sgqm7AcRBXmhTzV4hh1CTsXcwY8RRBHRHZOTJQRBpgC9QYhpEvirlFkCflFk8RPA68IiLN3IDQ9qjQMcMwjPxRzC2CHCu7mJ3FqjpERDYCH4nILjh+gn4B7lbVIDOLi4tiLnSGYZQ0cYePhiZ/DQspAvHrMzAMwzAyTI4/PINMKENVN2ZbEMMwjEAUcx+B373lQCkUqVeqGsD27fCzNbAMI2WKxVxbAPeRkiIQkR0zLUiNIVMP7Q9/gF13zUxahlFKFEuLoIC8jyZUBCLyRMT+LkQ4kjNSYOzYfEtgGDUb70fZtm1wxBHF6XW1QExDP4nIIwAisgcwFng2q1LlkltucTRuZWW+JTEMIwjuF7K3glywACZPhn798iNTKsxI6M0/ZyRUBKr6d2CDiAzDUQL/VtUnsy5ZrvjXv5x/b6EqL4dVq8LjFYAdzzAMisc0FI8cr1oWUxGIyOnuD/gCOAL4CtBQWHHhzfjLL4fGjWFr6TpZNYyCp1g+zrwVfZ5GDcUbPnpyxP5XOD6GTsaZXDY6W0LlFL9m5ksvOf/btsGOOewX//pr2LABfve73F3TMGoafpWl33tcEykrc/5XroT99w8/lo+FaVS1NBafidfsynWhatcuP9c1jJpOTVYEfjLPmwcdOzrbBTJqqKmIvCYiK0RkuYi8KiJNsy5ZrvGzyXnDamIBMwyj5lMgo4aeBN4AmgD7AW+GwooDv0q/kFoJhmH44/cuFsv7WUAL07g0VtUnVbU89BsJNM6yXLkjaKVfCiMVDKMmUMx9BPHI4r0FUQSrROSPIlIr9PsjsDprEuWLRJV+MRcww6iJFPOHWqKRRBkmiCK4GOgDLAv9zgyFFQfxviSs8jeMwqPYKv1E5Hn4aEgG/RE4JeuS5It4zUzDMAqXUvt4y6dpqCRHDcULMwwjv5RaH0GBmIZKY9SQX5gNHzWMwqWY+wi8FMjw0eIeNeSHDR81jJrJli35liAzFODw0dIbNeQXVsxfHIZRk/B7V8ePd/5/+im3smQLd1axlzwPH/WOGlpKKY0a8mItAcMoXNauzbcEqfPDD9XbLVs6/3Xq5FQEGzVkX/qGUTMplhb7F1/AqaeGhxXa4vUi0hi4DGjmja+qxdMqgMS+hgzDKFxqsiIoAPc2QUxDrwO7Ae8Db3l+CRGRniLyjYgsEJGb4sQ7TEQqROTMIOlmlGR9DRUqgwbB1VfnWwrDyB2l8qGWgw/ThC0CoL6q3phswiJSCxgK/B4oA6aIyBuqOscn3r+A95K9RkYIqoELvdDdfrvz/9//5lcOw8gHNfHjzSXoHKY8twj+JyK9Uki7C7BAVb9T1W3AKOBUn3hXAa8CK1K4RuYogOaZYZQ8Z5wBF13kLBdbURE/brG8i4nqHrcjPM+K4GocZbBZRDaIyM8isiHAefsBiz37ZaGwKkRkP6A3MCxeQiLST0SmisjUlStXBrh0Epj3USMdKipgzJjiqZTyzejRMHKkM2qmVav4ce39zBhBFq9voKo7qOpOqrpraH/XAGn7PZnIt+V+4EZVjav6VXW4qnZW1c6NG2dpLpt1FhupcN990Ls3vPxyviUpPr79Nt8S5Iby8mDx8tFHICIHq+o8EfGZ2QCq+mWCtMsA76KbTYElEXE6A6PEqXgbAb1EpFxVxySUPFME7Sw2pYtmAV8AACAASURBVGD48eOPzv+yZfmVoxQplhbB9u3B4uWps/haoB/wb59jChybIO0pwIEi0hz4CTgHODcsEdXm7raIjAT+l1MlkIhCqPzbt4eFC2HjxnxLYvhRkyugmkCLFvDxx7Cfx6rs917uEMTKXaDsuWeweHlavL5f6P+YVBJW1XIRGYAzGqgW8ISqzhaR/qHjcfsFck6hfl3MnJlvCYwgFMJHQzHy/ffw7LNwY9IDF2sOQeubPA8fRUR+S/SEsqcTnaeqbwNvR4T5KgBVvTCILBkn6MI09qIbflh/UmGzdi3ssUe+pYiPt+zMnx8d5hcvwwRZj+AZYAhwFHBY6Nc5axLlGluYxkgHKyvZZ/FiePHF6PBErfjp06FhQ3jmmezJlgmCVvB5Hj7aGfidql6hqleFfv+XNYnyRQFM6jBqMIVeLu64A8aOzbcUqTF0KJxzTvw4forANavW1PuOJM+moVnAPjieR4uPoKOGRAr/ZTdyT01pEQwc6PzX5DKsGp7fQe+lpjwjLzn+CI03fPRNnNFBDYA5IvIFsLVaJi0Oj6QF0FFj1DAqK51fbc/rY+Ujd2zb5vyvTrAsSk15JgVgGorXIhiStasWImYGMoJy/PHOQijeL1QrK9nHze+XXnL2vS4o4n3QFXqLoMAVwU/A3qr6iTdQRLqFjhUHxeJ91Mgd7mpYYGUll7jvaGVl9LFimwRaQEtV3g/87BO+KXSsOCgW76P54ocfoKws31LkHysfhUuhK+sOHYLFy1OLoJmqRs1mUtWpItIsaxLlC/M1lBrNmjn/pZpXhV7JFBPFWsaaNg0WL0/DR+vFObZTpgXJG8mahoq1MBrpYeUi+xSiaUg1/ev4ne/niC5PimCKiFwWGSgilwDTsiZRIWH9BkYirFzkjngVYb46i4cPd/wcLc3w6PrRo6PD8mQaugZ4TUTOo7ri7wzUxVlDoLgw76NGOlj5yB0FMMqmiqdDnna++w723Te1NPzk9Gv15Mnp3HLgtyJyDNAmFPyWqn6YNWnygZ9pyN1OlPFbt8KmTYXvy8TIHtaflDsKsUWQCfzua/fdg8XLEAlnFqvqeGB8ong1Fr8XeXFoYbVvvqnuDPWjVy/48EOrBEqZQq9kiolk37Oa/F4e6+PlP8++hoqbeF90P/wQ/9wPAzaOKiuDLz5h1ExqcqVT0/DL62JrEaQTLwVMEQR1Q50O/fpB3bqZSQtg3TrI9NrNRmoUeiVTTMR7H/0Wpik25WyKIIsk+yKn8jAefzz5c+LRqBHstVdm0ywVfvwR3ngjPKyyEn75Jb10i63SKUTi9d2l2n+QLpl47onux+2DNEWQAwqgeRYYr48VIzk6d4ZTTw0Pu+462GUX2Lw5+fSsszj3lGpemyLIIkFfZHvhiwM/k9pTTzn/mzYln56ZhnJHvBZBvkxDsYaab90aHR6LAnB4aYogXgXfuXN0PL/xvYZhHwjZp6YMH73rLqhXD9ascVrvZ5wBn3+eXBqTJ0eHWYsgi/gpgoMOcv533jk6nmF4sXKRewrdjOtOMluxApYscWYJn3giDB7sb9b1k3PixGDxMoQpAjP5GJnAyk/2STWPc91Z7Be2bh3ceiu8/HKw+F5yUEeZIkg2k2viC79lCzz8sJm1soF9SMRn0iQnj5YtSz+teH0E+XYL47eEpki0XH4DElRh1Sro0cM/vch0s4ApApcC6LDJGrfdBlde6f81UuxUVsK998YfHpqt5/zyy3DCCdlJu6bw3/86/x9/nN3r5LuPwM9Fjd91Y/kQeuQRGDcuPHzVKmcOktvxbIogi2R6HsGf/wwNGqQuTzZw13b92W+doSLn1Vfhhhvgppv8j3/wAaxdm51r9+kDY8dmJ+2aRrbG22f7momI9+WejCKIpFUruOUWeOyx6o8YUwQ5IFOZPGwYbNyYmbQyRSl3aLpN8XXr/I8ff3z1dir5VMp5m2uSNQ0FOZYN4imCoPVM167RcU0RZJFSWpimJsn+7LPQpEn6k+dSteGrOq6Fk4lvJMeUKc4Xb7bI1cI0scL8+giCtghybKo2RRDU11BN/vKribJffrmz2MeWLemlk+q9P/44tGzpdHZmI/1SIV7+dOni2MCDku8WQWWlY7ePdY3ly53/77+Pfd1YisAvbmSYKYIsUgqjhlxqsuzpkuy9uxN65s7NTvpG8rh5HHSpynRYswb+8Y/wa/3zn9C4Mfz0k79sU6cmlimdr39TBFkkF95H882PPzr/fuugFjuu64Fkn6V7XqIhtzZ8NHdkqrM4iE+pq65yRtu991512OuvO//eZSm9zz/yesm0CHLYH+CHKYJMtAjSfWgDB8J++6WXRjzckSuJzBzpsm0bXHZZZtdvTTdvU62oXUVQUxz8rVwJH32Ubylik+2KLahpaMwYqF8fvvwyfnruSB2vadKvE9g1FfmtMeyHnw+iArBGZFURiEhPEflGRBaISNT4PRE5T0Rmhn6fikj7bMoTQ8jYx3LVgXzHHc5U9GyTjpzx7KMu//sfjBjhfE0VGtlqERQK3bvD0UfnW4rskqyvIb/4b7/t/E+ZEuyasSaKuXz7rfP/wAP+50bK5edDKDJN77UShWWIrCkCEakFDAVOBFoBfUWkVUS074HuqtoO+AcwPFvyJCSfLYJCobw89hfwXXc59tGystjnu5Vnrk1Q27dH22ddkmkReOMkaxpKxLZtzrjwDRuCxU+WoH0ZNZkVK5z/ZN837zNyvc8uWhTsXO+13LIQ9JmnM4/A7/yaqAiALsACVf1OVbcBo4AwR/Cq+qmqurN5PgeaZlEex83w//4XHpaJPoKaoggSyVmnDhx2mP8xdzEXv44yl3x9Rd98syP37NnRx9I1DWWqDDzzjKNMb7stOTmSpdDKYiY7cd0F3dNxMTFmjPPvzniORbITxfyuFdQ1dpF3Fu8HLPbsl4XCYnEJ8I7fARHpJyJTRWTqynSWaLzySjj5ZJgxw5u4818KLYIgcn71Vepp1Krl/Ofarj5tmvPvfjF6yXZnbtD0t21z/pPxU58K6SrhX36B117LjCyZJh9O54K6jkhHOcUKi5yY+uKLseVMk2wqAr/c932SInIMjiK40e+4qg5X1c6q2rlx48apS7RggfPv1zxPZx5BTVEE337r2O9TqSyCvEzptAjWr4fzzos9AzgeQWzHyZqGghKZL3XqwMUXp59uqqR7rT//GU4/HaZPz4w8mcTv3rZscZ6B6/o5UfxMXD8ZxRJ0Qplfms8/H77/6afBr5sk2VQEZcD+nv2mQFSPqIi0A0YAp6rq6izKE19r+1X6QbV3TelQnD4dHnooPXtyvJcrnZE2//mPU/Dvv9//+PPPw0svxU8j3qScVBVBsi3F8nJ48snoY7kaNZNuWXRnU2faL1Um7t9vQpnbCozn0ynIZK0gx93rBjX5+HlcjVXGggwfraGmoSnAgSLSXETqAucAYauGi8gBwGjgT6r6bRZlCcf7kJNVBMXAwoXZSdc1DaVSGSXK6/POg7PPTj7dVJ9lZEUwZkzsDul00s0U7v3VlOGuqRBvZnG8+H4EfQ5BTUN+vPlmdFgyncU5JGuKQFXLgQHAe8Bc4CVVnS0i/UWkfyjabcCewMMiMl1EMvCmxRUqOixZk08BPLS0SWVt3iCk0yKI97WVbBpeYimCZJ9t796xO9JTkStbTJgQHRZZ+fz0Exx6qP+Q5UJ2mZGsInDJdIsgaB7tsEN801DLlrHPLaIWAar6tqr+RlVbquo/Q2HDVHVYaPtSVd1DVTuEfp3jp5i2QM5/ok6cYnc7kUplm+0+gmSH5nkJMpkoW6ahIOnnsny4ndJeIofzPvqoYyYcMSI6bqZlzaRiyaRs6bYIPvssPWXi3V62LJg8NVURFCyJTEMuxdIi6N07fD+dr+54cwQyMTojUrZ08zsZmYIODkg1/UIhsuW2aFH2zIWZxK9FkC0FnKhF4E5Mi3e9RPMI3P6YSy9NnFaWKU1F4CVoH0FNHjVUt274fjotgm7d0pfHj3RaBKl0YPuds3Yt/PBD8LSDxkums3jTJhgwILMTzyKvHdlya94cfv3rzF3v22+r/fIUAqmYhly8eecto0GeZzrrEfjFu9F3UGVGMEVQbGagnj0TF/J0WgTZIhMtAve+zz7b8RTpTS+IuapVK2jWLDytoNeMR6IPiv79q8OHDYOhQ6vlzwZBynw6LZ2DDoLTTgsPy+SooUzET7ePIIgsfn0E6eTDrrumfm4CCrBGyCLJdCbGCguabr5wvSXef3/1RKvIwliI5ozIFkE6Mr70Evztb+mnA7kpA48+Gp1Oqm46gnyFun0DixdHxw3KypVwzjm5Xf40WdOQS6ZaBF5FEOTDItMtgixSWorAJZ3ho4VU6cfjL3+BzjH63rPdIkglj2J15GeqjyDIqCG/81JNP5lreQnaglm82Ll25Pj5vfeOjhuZluuWPKjHTD/uvNOZ6frEE6mnkSyZHD4aj3XrEo9880s70n9RUF9DBUBpKgIv+e4jyIViibyGO94/GYJUjNnoLE6XdCdaZer5BE0nnmJ5+WXH8d+2bc6oFaj+undt/LvsEvza8dbWztR9F8KoIa8MDRs6/3vs4R+3stI55udmI5FpaM2a2Nf1ph8Ev/T9RoRliNJSBJm0MWbq+vloYSxdmvzkKNfMlC1idRZnq0UQ9Lx4pPJl6qa7dau/+SeevFdf7bgCX7kyOl6mP1RybT4sL3ec8sUjEy2Ck092/t3lJCMJUlGr+rcYIp+n30dNrPSDlPubb04sW4qUliJwSWceQaFU5skQeb+XXhp/ctTmzdX39O67zvleZ2ndu2dexnRaBEHMMpHpxnvhx46tftETpZ2sacjdrlcPjjkmOm4805Brj4933UIvnytXRvuTqqiAZ5913HTHw72Ptm2jw1x69ow+z82rLVvit4Ig/mAFb+vy4YdjXyfWvp+8yfBt9pwvlKYi8JLv/oBC6DzavDm82Vm/vuOTCPy/0iZOjJ9eKrLGahEkY9KJ1xT3++KKJecJJ8T2eRQrjVS+oP1WjItnynIrMdVohZFKOW7SJHjcRNx3n//9uPMT3L6MvfZyzFteateG4QGWInFl2z/kwuzgg2PH8WOnneDVV+NfI8hzrKwMttylnzyF8L77UFqKIOiooVwqh0IoGPXrQ7t24WGJHLz5kejreNAgxxW4H5Ff7onMAI88knhxkVtuqX7xk1EEQYnXh+TdD9py8J4XL15lZWZaBHXqxJclGf76V+jaNTrcXQns2Werw/xMYm6fRzxUnU7yeHNOkh1RFEkQE02y/T1eYnVC57nVVlqKwCXozOKg1NS+hGXLqoebfvNN+ulFFvxPPoEzz3Re/K1b4fbbw5vUL79c7aEx1pet35fx+vVwxRVw3HHx5bnrrmpPoH4veCZGcAR5Ll5FkIggC+JUVlYfd23d2fp4+e9//V1SJ5tmJlatmz8fDjgABg+OLYefXO4oqUwR1M7vZ+aMpQiCTHjMIrVzerVCJJf9AX4VQrZbBMcfDx984H/sqKMSuxZI5rqRcU85xRlJ0bcvvPJK+LGff4Y+fRx778yZ0S0C92Xzu7770kSO0ogna6qKYMOG6uUNIwk6qizWOZGsXx9s+KhqdeXsLrIUrxyns/7ENdfETjfXuO7TP/88dhyvnK6L6kTmoGSJlZ+R4fFaBJFzOCLPNdNQjsnE8NGgrmXTqfRTLRixlADEVwLpunp4553qijpSCUB1nrnmnVT6CJKZfBYZ56GHgtml77zTsWv7EdlHEOsZBTUNeU0+8e7bG88llRZBOiONMjmqKihDhiSO4823nXZK/5ruPXzwQXhnfRD8ykRFhbMK4AEHRIfnkdJqEcTrIwgaP5kwv0otcgx/sYxMgnAZe/WKHzdWX0AQ01C868Yi8lnccEPic4Jc1yt3LFkrKoKPLgoSr6ICjjzS2f7Vr6qvH+u8WEN/MzXjNhaZnjy1ZUviOF659gutjHvOOfHP2bYNvvwSjjjC//jSpU7L2iXWUp6ReeJnGiov9x/9E5lXsYa3ZonSUgTxCNpZnOjceGHptBzyoQiWLYOPPw4eP1EHrxc3j70jYbzh8RSBO/xw7Vr/NONdL5PUqwf77ONsf/IJzJvnHy9e564X1WDrPldUVLeygjjJi2VKSadMufLFm5yYaUUQ5L3we4/r1Yuf7l/+4vRdzZsHjRqFH6usdNZx9uI3Ourmm4MNH431XCPzyjXJ5YjSNA1l2sVEIZuG0iHRl1Qkroxffx08rovbae363YmlCAYPTs2tQbbcarid3ePHQ5s2/nGCNvuTUQRz5oSHxSuzse49kVKKZMoUZ6U2qH4u8RRBpj2QRlbIfrhyv/RSdf9JIoXkjmxavz7a66vf6DK/vGnSBDp1Cg+LZRqK1++VJ6xFELTSz0QfQTrL1OVaEYgk7wrZlTGII7LI+3G/bGfNcv5jdRbfemtyMrnk09FeMqahIIqgvDx86Oe4cam1huKZhiLl3LYNunSpPuaOo1+6NPZ1zzor9rFMEauS9i5rGlQRzJsXPcfBO0IrUXqR7t7TaRHkmNJsEQTtGM50iyDZlsPy5fHjZZvIr85ERJpq4hHrfnbYwVlCMWgfgdfp2ubNsZfhLBRFEA9VZ3IVVA+3XLwYFiwIj1deHl5h9ehRrUCT6QdL1EfgrbR23DE8nvu1/49/+KedC/y+uP3KS9Cv7S+/DJaearSvItXoQQWmCAqUZL6AkiFopZ+swynX/hzrGgCrV2fnZdy4MdytRCK++sqZMxCUWJ1h27Y5nXzu/W7eHN8v0gknVA+lPPFE2Hln/3gbNiT2ZZMtvM892RbBAQfAgQeGx4unWCLTv/pqZ76GHxs3hn9sQPUM8/XrYfv22LLm2ZRRRRCzTVBZP/00cfpuWP36weNGyuIXz+/aOaS0FIGL90HE6+DMd4sgURjA5ZfDbbf5H0uHRFPo7703vDLym3QUixtucBZPD0L//okXjQ9iwho3LrEvmyC4dudkSMY05LYIEvURxFqkJDL9Bx6I/WxWrgz/2ACYPNn5/9vfEssQ65q5JJOKwDURefEzDQXtN6hbN9odS6yJdemsDZEBSksRxJuS7v6vW1e48wiWLoXTTw8Pz9bCIIlkjRx+mUzT9t57g8f95JPgcXNBhw7Jn1NR4cyrgNiT08DfNORHeXls+3umKuUVK2Kb+iorw593kGd/xBHZURiZNA0FSR8c82WQL/26dasHQSSSJbJvItbAgyxRWorAxfvAvA/mrbcc25/7ZRCrQp44MdqXTLxruFRWOiNqvKM44pl8ItMbODB6DHO2bIvJvrTpuBCIpxgKdCGPpKioqF4E5ssvY8d7771qRZFIEcRi8mT/hdWTZdmyaudukVRWhr83QZ7RmWdm3pzk10egGu23P5nr+imWyLCLL/ZXBJH4nbttm/+7dcop4fsXXFDdOZ8DSmvUkN9XvftSqcKf/xx+LFZlOGpU+H6sFsG4cU5HnjfeY4/5m6Yiz/3NbxJfI56M6ZJsBRxkaF8sMjG5q5AJmpcXXVS9ncgsEyvNROPPJ0wIJks8Iq8ftKLNRr+CnyKIdHOdTFkOYvL5+9+jw8vLo8OGDIluQa5f73/dyLopx30wpdsi2LIFnn46XBFE2uliVdIzZ4aHxVIEL74YHS9Ixa3q70cnHXNTsuSyRVDspPJSxztn7drUW0p+ayC4BO3niSzHQR0WZrqMxGoR1I74vo2Vl5s2Qfv20ed78Xtnly+PTtPvGl9/7d/hHmQEWUUFfPFF4ngZorQUgdePy8knO80vt/MvmUo28us31gghv0IaqUTS7SzOFsleL94Ik1KnoqL6a99dISsR8SrNvn2rh4tmkqAd+JWVcNBBznabNsH60kRy0yJIpo9gxozEH3V+73HHjtHPJ9ZoIL9ZyEHeLb/nP2hQ4vNSpLQUgYsqvP9+dJhfPHCa094VqyJXOYp1rl+hCnquX1gqI1ZSJdkvzr/9LTtyFAMVFdVrCu++e/BzYqGa1WULE1JR4QzVBedjyl0HOMh5mSTWzN3IshvrukHG+Uf2h4Bzz5EVdSzFHdniCIqZhnJAMl/ckyY5zWmvw6vICT7uV4N3luWyZf5fK6lObFL1H9dfKH0ERmy8X5V33x3u3CxWefj++8z70c8U3rIhEtzkk+nK7euvo8v/9u3Bx+774adEIsO2b69W7ImuEennKHLUn0tkOSgvj14syloEGSaZ4Z5LlkSHnXtudHpDhoQv/Ve/frBmazItgnh+XTJNoUwYKgYef9xZdN7FHasfjyVLqj2LFhqRZSOIIvjqq+yYs/wUgV9l7lee4y1t6j3XTxFEDiwJuuDM6NHBlFJFRXoDMJKkZEYNbdsGm7bVB3aDn2vBbr+C9Z7RBRtrO8e8/FwL1hMevnI7HNETnn+rOmy9wOsfhcdbUwm/1AkPWwcs2xIdFnndtRodtg6oaBB9rntPmWb7ztlJtxR5+LnQRhr52f3U9M7PJGsVNu8I7Aab6jplPZFsz77p/Nx4fuU+FTbsEJ7Ot8uj35+tO8Gq8ujr+b3z6yU8bGPt6LANO4DUjY4XKQvAlnrRYZH1wu97RL/bG2vDwlVR5+64OTPLLEQims9ZgSnQuXNnnRrP5UAMXn7ZWRDLMAyjpnLjjY51MRVEZJqqdvY7VjItgvYHb+U/3Ojs7LW3s0TiB54O44suhicj3BtffAk88Xh42OX9ndm8zz9XHXb9DXDvPeHxbrwJ3nwj3HHbrX+Df94ZHu+fg+HWCNcHl/eHR4eFh91+BwyMcCXxn/vhzn9ETz4zjFzR6yRnzsv9/8nP9a8cAEMfCg+7bSDccXt42OC74JaIDvYBV8FDD4aHNdkPlvwUHnb1NfDf+8PDzjgjegnM//s/x6WHl4MOhm8i1qnocza85Bla3uFQaLJv+ETArt3g4wj3FEDnP9wfFZYRVDVrP6An8A2wALjJ57gAD4SOzwQ6JkqzU6dOmhJ33+2OxFd95BHVe++t3gfV228P3wfVZ56JDnvjDdXnngsPmzo1Ot5nn6n+9rfhYXPmRMcrK4sOmz8/WDzV6DD72S+Xv/32U/3Xv/J3/fHjo8MWLYoOW7UqOuz116PD2rcP3z/qKNWJE6PjPfBA+H7jxqoffhge1rat6vHHh4ddeqnqqFHhYaefHl3/9Orlf79pAEyNVa9mrbNYRGoBQ4ETgVZAXxFpFRHtRODA0K8f8Ei25GHkyOrtTZuiO4r8Rmj86U/RYZ9/Dh99FB721FPR8W6+OdqjYOSQVfB3dOW30lXkeGewkT1G/vnpJ8dekS/cBeq9RLqYAP8Obb+1FPbdN3x/0iR/B4yRncArV0Z37n79dfS8kREjolvwo0dHL8OZCTchyRBLQ6T7A44E3vPs3wzcHBHnUaCvZ/8bYN946abcIli2LH9fLfbL3+/hh8P3d9mlenvvvdNP/5578n+PxfZ74on4xz/4IPsyHHts4ji1a/uHX3ll9fY++zj/F18cHe/mm6u3d95Z9ZBD/NP75pvq7cGDU6v/nAo2ZovANzATP+BMYIRn/0/AQxFx/gcc5dn/AOjsk1Y/YCow9YADDkg5I1RV9YgjnNs++GDVWrVUP/3U+QfVPn2c/+bNVevWdbb33df5v/pq1RtucPa7dq1+MG7FcsYZ1WG33aZ69tmqPXs6+199pTpsmOpZZ6meeaZznblzq+NfdJGT7vDhzn7DhtXHTjqpetutuJ5+2vnv0aP6mFvhtWql+v77ql98UX1s9WonzN1//XXV3/1OtV071XPPVV2wQLV3b9X+/R0z19//rlqnjhO3dWvVjh1VO3Rw9r/8sjqdu+5y7g2cOAsXqo4b58jcrl14U/mCC8KV8R57VJvY6tdX3bBB9cknq1+uU05xXp6uXR15KipUDz/cOXbttaoffVSd1iefOGHgxN20ybkfUK2sVB06tFrejRtVTz3VeQarV6uuX18t/+efqx54oJMnK1c65r0LL3TMiNOmqf7lL6off+yUozZtVB980Nn+/HNH7hUrnDRB9Z13nHx1TQOqqm+/7Wy/+67qK6842zfdVP3cL75YdelSR7kcdJDzLM47T/WKK1RfftmR3b3n5cud/K1f3zE1/Pij6ptvVueHqmrTps7+9u2qW7Y4Zfjaa528HD1aqyrdp5923oFDDnHMFI0bV1c67vUmTHDu390fMKA6z0H1j39UPfFE1aeeUr3qqurwuXNVzz9fdccdnXfNNcfcfbcj0+mnqx59tCNvv37OsbIyZ79+fcfspKp6zjnVlaeq6ubNqr/+teo116j+5z+qa9eqLlmiuueeTprXXKO6bp3qzJnOeeedp/qnP6l+/73q5MlOWN++Tnrl5dXPo29f1TvvdMrNkCHV73bTpqozZqhedll1eX70UUeWr792nvNhhzkyVVZWP9M+fZxnXFnplJcGDZz0/vQn59yJE1VbtFC95BLn+ak68dx3+ZZbnLA33nDCnn025aovniLI2qghETkLOEFVLw3t/wnooqpXeeK8BdylqpNC+x8AN6jqtFjppjpqyDAMo5SJN2oomxPKygCvH9umwJIU4hiGYRhZJJuKYApwoIg0F5G6wDnAGxFx3gDOF4cjgPWqGmc1bMMwDCPTZG0egaqWi8gA4D2gFvCEqs4Wkf6h48OAt4FeOMNHNwEXxUrPMAzDyA5ZnVCmqm/jVPbesGGebQWuzKYMhmEYRnxK0+mcYRiGUYUpAsMwjBLHFIFhGEaJY4rAMAyjxKlxbqhFZCXwQ4qnNwJWJYxVOlh+RGN5Eo7lRzg1OT9+paqN/Q7UOEWQDiIyNdbMulLE8iMay5NwLD/CKdb8MNOQYRhGiWOKwDAMo8QpNUUwPN8CFBiWH9FYnoRj+RFOUeZHSfURGIZhGNGUWovAMAzDiMAUgWEYRolTMopARHqKyDciskBEbsq3PNlERBaJyNciMl1EpobCPfmpcAAABFxJREFUGorIOBGZH/rfwxP/5lC+fCMiJ3jCO4XSWSAiD4hELvRcmIjIEyKyQkRmecIydv8isqOIvBgKnywizXJ5f8kSIz8GichPoTIyXUR6eY4VbX6IyP4iMl5E5orIbBG5OhResuUDyN5SlYX0w3GDvRBoAdQFZgCt8i1XFu93EdAoIuwe4KbQ9k3Av0LbrUL5sSPQPJRPtULHvsBZe1qAd4AT831vAe+/G9ARmJWN+weuAIaFts8BXsz3PaeQH4OA63ziFnV+APsCHUPbDYBvQ/dcsuVDVUumRdAFWKCq36nqNmAUcGqeZco1pwJPhbafAk7zhI9S1a2q+j3O2hBdRGRfYFdV/UydEv2055yCRlUnAmsigjN5/960XgGOK+TWUoz8iEVR54eqLlXVL0PbPwNzgf0o4fIBpWMa2g9Y7NkvC4UVKwqMFZFpItIvFLa3hlZ/C/3vFQqPlTf7hbYjw2sqmbz/qnNUtRxYD+yZNcmzxwARmRkyHbmmkJLJj5DJ5lBgMiVePkpFEfhp42IeN/s7Ve0InAhcKSLd4sSNlTelkmep3H8x5M0jQEugA7AU+HcovCTyQ0R2AV4FrlHVDfGi+oQVXX6UiiIoA/b37DcFluRJlqyjqktC/yuA13BMY8tDzVlC/ytC0WPlTVloOzK8ppLJ+686R0RqA7sR3PRSEKjqclWtUNVK4DGcMgIlkB8iUgdHCTynqqNDwSVdPkpFEUwBDhSR5iJSF6cD5408y5QVRGRnEWngbgM9gFk493tBKNoFwOuh7TeAc0IjHZoDBwJfhJrHP4vIESH75vmec2oimbx/b1pnAh+G7MQ1BrfSC9Ebp4xAkedHSPbHgbmqep/nUGmXj3z3VufqB/TCGSGwELg13/Jk8T5b4IxymAHMdu8Vx0b5ATA/9N/Qc86toXz5Bs/IIKAzTgWxEHiI0Ez0Qv8BL+CYO7bjfJ1dksn7B+oBL+N0HH4BtMj3PaeQH88AXwMzcSqufUshP4CjcMw0M4HpoV+vUi4fqmouJgzDMEqdUjENGYZhGDEwRWAYhlHimCIwDMMocUwRGIZhlDimCAzDMEocUwSGEQMR2dPjnXOZx1vnRhF5ON/yGUamsOGjhhEAERkEbFTVIfmWxTAyjbUIDCNJRORoEflfaHuQiDwlImPFWQfidBG5J+Sn/t2QOwPXd/1HIUeA70XM7DWMvGKKwDDSpyVwEo774WeB8araFtgMnBRSBg8CZ6pqJ+AJ4J/5EtYwIqmdbwEMowh4R1W3i8jXOIsgvRsK/xpoBhwEtAHGhdzS18Jx+WAYBYEpAsNIn60AqlopItu1uuOtEucdE2C2qh6ZLwENIx5mGjKM7PMN0FhEjgTHDbKItM6zTIZRhSkCw8gy6iyPeibwLxGZgePx8rf5lcowqrHho4ZhGCWOtQgMwzBKHFMEhmEYJY4pAsMwjBLHFIFhGEaJY4rAMAyjxDFFYBiGUeKYIjAMwyhx/h9ZrRahsAe08AAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -473,13 +448,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 0.04915541842199333.\n", - "The root mean squared error is 0.09078446501653406.\n" + "The root mean squared error is 538.4020947589681.\n", + "The root mean squared error is 1356.7620677164293.\n" ] } ], "source": [ - "lasso_train, lasso_test = create_lasso(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "lasso_train, lasso_test, y_train, y_test = create_lasso(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, lasso_train)\n", "plot_predictions(y_test, lasso_test)\n", diff --git a/.ipynb_checkpoints/other_regression_methods_month-checkpoint.ipynb b/.ipynb_checkpoints/other_regression_methods_month-checkpoint.ipynb index 134b7fc..921ad69 100644 --- a/.ipynb_checkpoints/other_regression_methods_month-checkpoint.ipynb +++ b/.ipynb_checkpoints/other_regression_methods_month-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,13 +33,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "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", @@ -47,7 +44,6 @@ " 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", @@ -61,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -109,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -208,7 +204,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 80, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -257,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -297,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -315,24 +311,14 @@ " \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", - "# 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", + " # Create empty lists \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -341,7 +327,6 @@ " y_train_not_norm = []\n", " x_train_not_norm = []\n", " x_test_not_norm = []\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", @@ -350,7 +335,7 @@ " 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", + " # Making the non-norm for testing \n", " for i in range(6, 60):\n", " x_test_not_norm.append(king_test['king'][i-6:i])\n", " y_test_not_norm.append(king_test['king'][i])\n", @@ -370,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -385,7 +370,6 @@ } ], "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, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm, x_test_not_norm, x_train_not_norm = create_train_test(data_copy)\n", "x_train = np.array(x_train)\n", "x_test = np.array(x_test)\n", @@ -393,20 +377,18 @@ "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", + "\n", + "# Getting non-normalized data for testing\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", "x_test_not_norm = np.array(x_test_not_norm)\n", "x_test_not_norm = np.reshape(x_test_not_norm, (x_test_not_norm.shape[0],x_test_not_norm.shape[1],1))\n", "x_train_not_norm = np.array(x_train_not_norm)\n", "x_train_not_norm = np.reshape(x_train_not_norm, (x_train_not_norm.shape[0],x_train_not_norm.shape[1],1)).astype(np.float32)\n", "\n", + "# Shape checks \n", "print(x_train.shape)\n", "print(x_test.shape)\n", "\n", @@ -416,31 +398,7 @@ }, { "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2],\n", - " [3, 4]])" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = [[1, 2], [3, 4]]\n", - "a = np.array(a)\n", - "a.reshape((-1, 1))\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 99, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -455,6 +413,7 @@ } ], "source": [ + "# Create copies for models \n", "x_train_lr = x_train.reshape((x_train.shape[0], x_train.shape[1]))\n", "print(x_train_lr.shape)\n", "x_test_lr = x_test.reshape((x_test.shape[0], x_test.shape[1]))\n", @@ -480,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -502,16 +461,7 @@ "\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" + " print(\"The root mean squared error is {}.\".format(rmse))" ] }, { @@ -589,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -597,7 +547,7 @@ " '''\n", " creating a basic Ridge Regression model (L2)\n", " '''\n", - " rr = Ridge(alpha=0.01)\n", + " rr = Ridge(alpha=0.1)\n", " rr.fit(x_train, y_train)\n", " train_preds_rr = rr.predict(x_train)\n", " test_preds_rr = rr.predict(x_test)\n", @@ -615,12 +565,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -646,8 +596,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 27009.22197757247.\n", - "The root mean squared error is 68350.03056015464.\n" + "The root mean squared error is 27009.219641017073.\n", + "The root mean squared error is 68350.22719746032.\n" ] } ], @@ -662,77 +612,43 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "# def create_lasso_copy(x_train, y_train, x_test, y_test, scaler): \n", - "# '''\n", - "# creating a basic Ridge Regression model (L2)\n", - "# '''\n", - "# transformer_x = RobustScaler().fit(x_train)\n", - "# transformer_y = RobustScaler().fit(y_train) \n", - "# x_rtrain = transformer_x.transform(x_train)\n", - "# y_rtrain = transformer_y.transform(y_train)\n", - "# x_rtest = transformer_x.transform(x_test)\n", - "# y_rtest = transformer_y.transform(y_test)\n", - "\n", - "# #Fit Train Model\n", - "# lasso = Lasso()\n", - "# lasso_alg = lasso.fit(x_rtrain,y_rtrain)\n", - "\n", - " \n", - "# # train_score = lasso_alg.score(x_rtrain,y_rtrain)\n", - "# # test_score = lasso_alg.score(x_rtest,y_rtest)\n", - "\n", - "# # print (\"training score:\", train_score)\n", - "# # print (\"test score:\", test_score)\n", - "\n", - "# # example = [[10,100]]\n", - "# train_preds_rr = transformer_y.inverse_transform(lasso.predict(x_rtrain).reshape(-1, 1))\n", - "# test_preds_rr = transformer_y.inverse_transform(lasso.predict(x_rtest).reshape(-1, 1))\n", - "# # train_preds_rr = scaler.inverse_transform(train_preds_rr)\n", - "# y_train = scaler.inverse_transform(y_train)\n", - "# # test_preds_rr = scaler.inverse_transform(test_preds_rr)\n", - "# test_preds_rr = test_preds_rr.astype(np.int64)\n", - "# y_test = scaler.inverse_transform(y_test)\n", + "def create_lasso(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " creating a basic lasso regression model (L1)\n", + " '''\n", + " # Fit and predict \n", + " lasso = Lasso(alpha = 0.0001)\n", + " lasso.fit(x_train, y_train)\n", + " train_preds_lasso = lasso.predict(x_train)\n", + " test_preds_lasso = lasso.predict(x_test)\n", " \n", - "# return train_preds_rr, test_preds_rr, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "def create_lasso(x_train, y_train, x_test, y_test):\n", - " lassoModel = Lasso(alpha = 0.1)\n", - " lassoModel.fit(x_train, y_train)\n", + " # Reshape\n", + " train_preds_lasso = train_preds_lasso.reshape(train_preds_lasso.shape[0], 1)\n", + " test_preds_lasso = test_preds_lasso.reshape(test_preds_lasso.shape[0], 1)\n", " \n", - " train_preds = lassoModel.predict(x_train)\n", - " test_preds = lassoModel.predict(x_test)\n", + " # Descale \n", + " train_preds_lasso = scaler.inverse_transform(train_preds_lasso)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " test_preds_lasso = scaler.inverse_transform(test_preds_lasso)\n", + " test_preds_lasso = test_preds_lasso.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", " \n", - " return train_preds, test_preds\n", + " return train_preds_lasso, test_preds_lasso, y_train, y_test\n", " " ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 30, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chrisshell/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1983623331840.0, tolerance: 434337824.0\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -758,14 +674,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 65816.70042312301.\n", - "The root mean squared error is 61579.33041874606.\n" + "The root mean squared error is 70010.61354592441.\n", + "The root mean squared error is 68346.74540545818.\n" ] } ], "source": [ - "# lasso_train, lasso_test, y_train, y_test = create_lasso_copy(x_train_rr, y_train, x_test_rr, y_test, scaler)\n", - "lasso_train, lasso_test = create_lasso(x_train_not_norm, y_train_not_norm, x_test_not_norm, y_test_not_norm)\n", + "lasso_train, lasso_test, y_train, y_test = create_lasso(x_train_lasso, y_train, x_test_lasso, y_test, scaler)\n", + "# lasso_train, lasso_test = create_lasso(x_train_not_norm, y_train_not_norm, x_test_not_norm, y_test_not_norm)\n", "#lasso_train, lasso_test = create_lasso(x_train_lasso, y_train, x_test_lasso, y_test, scaler)\n", "\n", "plot_predictions(y_train_not_norm, lasso_train)\n", diff --git a/daily_nn.ipynb b/daily_nn.ipynb index 36478c1..c116f92 100644 --- a/daily_nn.ipynb +++ b/daily_nn.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -16,24 +16,20 @@ "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", "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": 8, + "execution_count": 5, "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", @@ -41,7 +37,6 @@ " 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": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -103,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -121,20 +116,14 @@ " \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", + " # Create lists to be filled \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -142,8 +131,7 @@ " 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(180,22545): # 30\n", " x_train.append(king_train_norm[i-180:i])\n", " y_train.append(king_train_norm[i])\n", @@ -162,23 +150,14 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 8, "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" + "(1824, 2)\n" ] } ], @@ -190,19 +169,17 @@ "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", + "\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": 107, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -284,112 +261,100 @@ "text": [ "(22365, 180)\n", "Epoch 1/25\n", - "224/224 [==============================] - 1s 2ms/step - loss: 0.0015\n", + "224/224 [==============================] - 2s 2ms/step - loss: 9.6369e-04\n", "Epoch 2/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 5.2724e-04\n", + "224/224 [==============================] - 1s 2ms/step - loss: 4.2687e-04\n", "Epoch 3/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.3932e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 3.5946e-04\n", "Epoch 4/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.1974e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 3.0601e-04\n", "Epoch 5/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 3.1543e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 2.5082e-04\n", "Epoch 6/25\n", - "224/224 [==============================] - 0s 1ms/step - loss: 2.8840e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5926e-04\n", "Epoch 7/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4203e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.6381e-04\n", "Epoch 8/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.6688e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 2.7281e-04\n", "Epoch 9/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4260e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.7279e-04\n", "Epoch 10/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.3346e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.7465e-04\n", "Epoch 11/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2799e-04\n", + "224/224 [==============================] - 0s 1ms/step - loss: 3.1394e-04\n", "Epoch 12/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.7529e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5299e-04\n", "Epoch 13/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.4860e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3594e-04\n", "Epoch 14/25\n", - "224/224 [==============================] - 1s 2ms/step - loss: 2.6579e-04\n", + "224/224 [==============================] - 1s 2ms/step - loss: 2.4796e-04\n", "Epoch 15/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.6130e-04A: 0s - loss: 2.6334e-\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3770e-04\n", "Epoch 16/25\n", - "224/224 [==============================] - 1s 3ms/step - loss: 2.5171e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3713e-04\n", "Epoch 17/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5540e-04\n", + "224/224 [==============================] - 1s 3ms/step - loss: 2.4587e-04A: 0s - loss: 2.418\n", "Epoch 18/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5533e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.3999e-04\n", "Epoch 19/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2185e-04\n", + "224/224 [==============================] - 1s 3ms/step - loss: 2.1717e-04\n", "Epoch 20/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2341e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.2080e-04\n", "Epoch 21/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.3676e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.1802e-04\n", "Epoch 22/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.2324e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.5899e-04\n", "Epoch 23/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.0010e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 2.0676e-04\n", "Epoch 24/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.5782e-04\n", + "224/224 [==============================] - 0s 2ms/step - loss: 1.9265e-04\n", "Epoch 25/25\n", - "224/224 [==============================] - 0s 2ms/step - loss: 2.1594e-04\n" + "224/224 [==============================] - 0s 2ms/step - loss: 2.4804e-04\n" ] } ], "source": [ - "# train single_layer_rnn_model\n", + "# train nn_model\n", "print(x_train.shape)\n", "model, nn_train_preds, nn_test_preds, history_nn, y_train, y_test = create_nn_model(x_train, y_train, x_test, y_test, scaler)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# global var for baseline\n", - "y_test_year = day_to_year(y_test)" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "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_abdul_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)" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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VpV69eri6unL16lV8fHzyDXb3799Xus+8gW2rVq1Sqk1mZGSwePFita9tb29Pu3btuHbtWr6PGaWmpiq1LuS9Vvfu3VP7Gnnv30WLFik1K2dmZjJnzhyg8P+zqkj0YVYxkydPJisri6VLl/LJJ5/w4Ycf0qpVK/T09EhISCAqKopr165VmhlYZDIZGzdupH///ri7u3Pw4EHpOcy8oObn5yc9UgK5D8qHhYURFhaGo6Mj3bp1IzMzk+DgYP755x+GDRtW4CMleezt7Tl06BD9+/dn+PDhbNu2TSkol4Y3zaeJiQkZGRk4OjrSo0cPMjIyCAoK4tGjR4wfP15pQJCWlhZff/01S5YsoUOHDvTp0wfIrcEpFAqaN2/O77//XmReW7VqhYODA8HBwXTt2hUHBwfi4+MJCwujSZMm+Qawjh07oqGhgZ+fH8+ePZP6+dzc3Ap8xs/S0pJly5YxdepUnJ2d6d+/P8bGxpw/f54zZ85gbm7OqlWr1CrfymD58uXcvn2bZcuWERAQgKOjI8bGxjx69IibN29y4cIFlixZIs3U4+DggJubG5s2baJdu3b07dtXeg6zdu3amJiYqP1oib+/P71792b58uWEhITQoUMH5HI5d+/e5cSJE+zZs0fqZ3d2dubgwYNMmjSJfv36oaOjQ+3atXFzcyvw/IMGDSI0NJT9+/fj4OBAr169pOcwb968SceOHd+axRdKiwiYVZCnpyf9+/dny5Yt/Prrr+zdu5fnz5+jr6/P+++/z7Jly1Qe4ahIrVq1Ijw8nBUrVhAeHs7x48epXbs2vXr1Ytq0aUpNkJA7rP3AgQNs3LiRffv2sWXLFjQ0NGjWrBkzZ86UvhkX5f333yckJIR+/foxcuRINm3aVOJn+Uojn1paWhw8eJDFixcTGBjI06dPee+995g2bVq+c5x6enpSo0YNtm3bxo4dO6hbty69evVi3rx5fPbZZ2rlVS6Xs2fPHry9vfnll1/w9/fH1NSUkSNH4unpme+oXSsrK7Zu3cratWvZtWuXVJsdOnRooQ/FjxkzhkaNGrFu3ToOHz5MWloapqamuLm54enpWeoTSJQlPT09Dh06xM6dO9m/fz+HDh0iPT0dQ0NDLC0tmT9/vsqXomXLlmFlZcWWLVuk16t3797Ss4rqsrS0JCIigvXr13Po0CG+++47tLS0MDc35/PPP1d6BOezzz7j/v377Nu3D19fXzIzM6lfv36hARNyg7KjoyM7d+5k586d5OTk0LhxYxYtWoS7u7vaz2VXFbLExET15xETBKFM6evrU79+/QKnBBQEoeKIPkxBEARBUIMImIIgCIKgBhEwBUEQBEENYtCPIFQib/O0YYJQ1YkapiAIgiCoQQRMQRAEQVCDCJiCIAiCoAYRMCtIcdaWFHKJMiseUV7FI8qr+N61MhMBUxAEQRDUIAKmIAiCIKhBBExBEARBUIMImIIgCIKgBjFxQRnIysoiLS2t0DTVq1dXWq9OKJoos+J518pLR0cHTU3xkSaUHfHuKmVZWVmkpKSgr6+PTCYrMJ22tjbVq1cvx5y9/USZFc+7VF4KhYLExET09PRE0Kyinj6VcemSHFvbbExMKmaRLfHOKmVpaWlFBktBEEqXTCZDX1+f5OTkQtfiFN5Ojx7J6NRJlwcPNKhTJ4djx9Kwssop93yIPswyIIKlIJQ/8X9Xda1erc2DB7nh6tkzDRYsqJiWExEwBUEQhEotMEA5VB0+rFUh+RABUxAEQajUZM+fV3QWABEwhXJ26tQp9PX1SUhIKNF57t69i76+PpcuXSpRmrKwe/duzM3Ny/WaglClZWZWdA4AETCF//Hw8EBfXx99fX0MDAxo3rw5U6dOrbD1Gf/66y+++uorbG1tMTIyws7Oji+++ILz58+rfQ4LCwtiYmKws7Mrw5yWnvj4eGbMmIG9vT1GRkY0a9aMwYMH88svv5R7Xjw8PHB1dS336wpCZSZGyQqSTp064e/vT1ZWFjExMXz11VckJSWxdevWcs3HpUuX6NevH02bNmXlypXY2NiQlpZGSEgIXl5eREREqHUeuVyOsbFxGee2dNy9e5fu3bujq6vLggULaN68OTk5OURERDB16lR+//33is6iILzzRA1TkGhra2NsbIy5uTmdO3dmwIABnDhxQinNrl27aNu2LcbGxrRu3RpfX19ycv5/ePf69etxdHTEzMyMZs2a8fXXXxerlqpQKJgwYQINGjTg6NGj9OjRg/fee4/mzZszceJEfv75Z6X0cXFx9O/fH1NTU9q2bcvJkyelfa83yeY1B0dERODi4oKpqSmdOnUiOjpa6ZxBQUE4OjpiZGSEra0tK1euRKH4/+e+EhMTcXd3p0GDBpiYmNCvXz+uX79e4D0lJibSrVs3Bg4cWOCEFp6enigUCk6ePMmAAQNo0qQJ1tbWuLm5cfr0aSndvXv3GDFiBBYWFlhYWPDZZ59x//59ab+Pjw/t2rVTOvfrTcR5aX788Ufs7e2xsLBg+PDhUjO5j48Pe/bs4ejRo1Krw6lTpwq8P0F4V4iAWU5q6+sr/RibmKhsK82fkrpz5w7Hjx9HS+v/R6Pt2LGDxYsXM3v2bM6fP4+3tzdr165ly5YtUhoNDQ18fHyIjIxk8+bNXLx4ES8vL7Wve+XKFa5fv87EiRORy+Uq+/Vfuzdvb2/Gjx/P6dOn+eCDD/jXv/5FampqoddYuHAhCxYsICIigrp16+Lm5iYFxOjoaEaPHk3v3r05e/YsCxYsYM2aNWzatEk63sPDg4sXL/LDDz9w/PhxatSoweDBg3nx4oXKtR4+fEjPnj0xNTVl79696OjoqKR59uwZYWFhjBs3Dl1d3QLvWaFQMGLECB4/fkxQUBDBwcE8fPiQESNGKAV0dcTFxXHgwAF27drFgQMHuHLlCosXLwbg66+/ZsCAAXTq1ImYmBhiYmJo27Ztsc4vCKVJRsVMVPA60SQrSMLCwjA3Nyc7O5v09HQAlixZIu1fsWIFCxcupF+/fgA0bNiQv/76i61bt+Lm5gbAhAkTpPQNGjRg0aJFDB8+HD8/PzQ0iv5+dvv2bQCaNm2qVp4nTJhAjx49AJg/fz579+7l6tWrKrWsV82ZM4cOHToA4OXlRffu3fnnn38wNzfH19cXJycnZs+eDYCVlRW3bt1i7dq1jB8/nlu3bnHkyBEOHz6Mk5MTAP7+/tjZ2bF//35GjhypdC8DBgzAxcWFlStXFnj/t2/fRqFQFHnP4eHh/P7771y6dIkGDRoAsGXLFj744AMiIiLo1KmTWmUGuTNSbdiwQXrIf/To0ezevRsAXV1dqlevLrU4CIKQSwRMQeLo6MjatWt58eIFO3bs4M6dO7i7uwPw5MkT/v77b6ZMmcK0adOkY7KyspRqNxEREaxZs4YbN26QnJxMdnY2L1++5NGjR5iamhaZh+LWlGxtbaXf887/+PFjtY8xMTGRjjE3NycmJoauXbsqpW/Xrh3Lli0jOTmZmJgYNDQ0aNOmjbS/du3avP/++/z555/StpcvX9K9e3f69u3LypUrC82PuvccExODqampFCwh90uLqakpf/75Z7ECZv369ZVmxDExMeHJkydqHy8I7yLRJCtIatasSaNGjbC1tWX58uU8f/6c5cuXA0j9lKtXr+bUqVPST2RkJOfOnQNym/lcXV1p2rQp27dvJzw8nPXr1wO5AUQdjRs3BuDGjRtqpX+1yThvppeiAlBhxygUigJnjJHJZIWe+9XjtLS0cHZ25pdffiEuLq7Q/DRu3BiZTFbkPReVN8htEn89j1lZWSrpXy2DvONf7YsWBEGVqGGWk6TXBr6kp6dX+omxZ8yYwZAhQxg9ejSmpqaYmZnx119/8emnn+ab/tKlS7x8+RIfHx+p/zE0NLRY12zRogU2Njb85z//YeDAgSr9mImJiSr9mKXJxsZG+gKQJzIyEnNzc/T09LCxsSEnJ4eoqCipSTY5OZlr164xfPhw6RiZTMbGjRtxd3enT58+HDp0iPr16+d7zTp16uDi4sLmzZsZP368Sj9m3j3b2Njwzz//cPfuXamWeefOHR48eICNjQ0A9erVIz4+XiloXr16tdjlUK1aNbKzs4t9nCCUmEKB5okToKFBVqdOUImmPBQ1TKFAH3/8MTY2NlKT4syZM/nPf/6Dr68vsbGxXLt2jT179rB69Wogt6aUk5PDhg0buHPnDoGBgfj5+RXrmjKZDF9fX+7cuUO3bt0IDQ3lr7/+4o8//mD9+vX079+/1O/zVV9++SVnzpzBx8eHmzdvsm/fPnx9fZk4cSKQe489e/ZkypQpnD17lj/++AM3Nzf09PQYMmSI0rk0NDTw8/Ojbdu29O7dm3v37hV43byRuM7Ozvz000/ExsZy48YNtm7dSvv27YHcx36aN2+Om5sb0dHRXLp0iXHjxtGyZUupT7Z9+/Y8e/aMVatWcefOHb7//nuVkcXqsLS05Pr168TGxpKQkEBmJXlwXKj6qnt6ojNoEDoDBlB97lyg8gz6EQFTKNSXX37Jzp07iYuLY+TIkaxfv56AgADat29Pjx492LFjh1Tbad68Od9++y0bNmzAwcGB77//Xhp5WRytW7cmPDycpk2bMnXqVNq0aYOrqyuXLl1ixYoVpX2LSuzt7dm+fTvBwcG0a9eOhQsXMnnyZGlQE8CGDRto1aoVn376KS4uLrx48YLAwEBq1Kihcj4NDQ02btxI27Zt6dOnT4FBs2HDhtLAnQULFuDk5ETfvn05cuQIa9asAXK/TOzevRsDAwN69+5Nnz59MDIyYvfu3VKTrLW1NatXr2b79u04OzsTHh7O1KlTi10Oo0aNomnTpjg7O9O4cWOVWrcglImsLLRfee5b29e3AjOjSpaYmFghoXvz5s1s27ZN+gCxsbHB09OTbt26Abn9Nd9++y07duwgMTGR1q1bs3LlSpo1ayadIyMjg7lz5/Ljjz+Snp5Ohw4dWLVqldIzZ4mJiXh5eUlNg927d2f58uVKzXr37t3D09OTU6dOUb16dQYPHoy3tzfVqlUr9n0lJSWptbzQ29AkW9mIMiued7G81P3/y09sbCxNmjQp5RxVbaVeZi9eUPu1wYFJiYk0rZNBvMJIaXtiYvkvjl5hNUwzMzMWLlxIREQEJ0+epEOHDowYMUKa0WTt2rX4+vqybNkyTpw4gaGhIQMGDCAlJUU6x6xZswgODmbr1q2EhISQkpKCq6urUt/L2LFjuXLlCvv37ycwMJArV64wfvx4aX92djaurq6kpqYSEhLC1q1bCQoKYs6cOeVXGIIgCEKlV2E1zPw0bNiQBQsWMHr0aGxsbBg3bhyenp4AvHjxgiZNmrB48WLGjBlDUlISVlZW+Pr6MnToUAD+/vtv7OzsCAwMxMXFRXrgOjQ0FAcHByB3AEePHj24cOECTZo04dixYwwdOpSrV69iYWEBQEBAABMnTiQ2NpZatWoV6x5EDbPsiDIrnnexvEQNs3yJGmYFyM7O5scffyQtLY02bdpw9+5dHj16ROfOnaU0NWrUwNHRUZp8Ozo6mszMTKU0FhYWWFtbS2mioqLQ1dVVmqXEwcEBHR0dpTTW1tZSsARwcXEhIyNDZco0QRAE4d1VoY+V/PHHH3Tt2pX09HR0dHTYtWsXtra2UjAzNDRUSm9oaMiDBw+A3JUd5HI5BgYGKmni4+OlNAYGBkrPrslkMmnofV6a169jYGCAXC6X0hQkNjZWZVveDCnqyJtNR1CfKLPiedfKKzk5ucj/28Lk9z8tFK40y0yWnk7rfM4vQ/WRrLJ4rYqqLVdowGzSpAmnTp0iKSmJoKAgPDw8OHTokLT/9Ye0C3twu6A0+aVXJ01h21/N/+uSkpLUagZ7F5vLSkqUWfG8i+VVq1atAp93LYpoki2+smiSfV2TJk1QkJHv9vJWoU2y1apVo1GjRnzwwQcsWLAAOzs7NmzYIM1f+fo3xSdPnki1QSMjI7Kzs1UWIn49zZMnT5Qe4lYoFCQkJCilef06CQkJZGdnq9Q8BUEQhHdXpejDzJOTk8PLly9p0KABxsbGSks1paenExkZKfVH2tvbo6WlpZTm/v37SisrtGnThtTUVKKioqQ0UVFRpKWlKWSksekAACAASURBVKWJiYlRWiLp5MmTaGtrY29vX6b3KwiCILyimHNJl7cKa5L95ptv6Nq1K+bm5qSmphIYGMjp06fZt28fMpkMDw8PVq1aRZMmTbCysmLlypXo6OgwePBgIHfC688//5z58+djaGhInTp1mDNnDra2ttIk1NbW1nTp0oUpU6awdu1aFAoFU6ZMoVu3blJ1vnPnzjRr1gx3d3e8vb159uwZ8+fPZ+TIkcUeISsIgiBUXRUWMB89eoSbmxvx8fHUqlULW1tb6XEQgEmTJvHixQumT58uTVxw4MAB9PT0pHMsXboUuVzOmDFjpIkL/Pz8lOYf3bx5MzNmzGDgwIEA9OjRQ5pQHEAulxMQEICnpyfdu3dXmrhAEARBEPJUqucwqwLxHGbRfv75Z0aNGkXi/yak3717N15eXkrN4vkpizJzdXWlbt26bNy4sUTn8fDw4OnTpwQEBJQoTWnKKy87Ozvc3Nz4+uuvy+W6FUk8h1m+yus5TOs66TxSKK/N+s4+hylUPA8PD/T19dHX16devXq0bNmSuXPnkpaWVubXHjhwYLGeebWzs2PdunVlmCNlCoWC77//nk8++QQLCwvq169Phw4dWLt2LcnJyWqf59tvv8Xf378Mc1q6goKC6NOnD5aWlpiZmeHo6MjixYuLXG+0tN29exd9fX0uXbpUrtcVhNeJgClIOnXqRExMDNHR0cydO5etW7cyb968fNO+vnB0SdSoUaNSj0geP348Xl5efPLJJ/z888+cPn2aOXPmcOrUKYKDg9U+T+3atct0abLStHjxYkaPHo2dnR0BAQGcO3cOHx8f4uLi2PrK5NiC8C4RAVOQaGtrY2xsjIWFBUOGDGHIkCEcPnwYAB8fH9q1a8fu3buxt7fHyMiItLQ0kpKSmDRpElZWVlhYWNCzZ0+VmsCePXto3rw5pqamuLq6qjzGs3v3bqUJ8wGOHj2Ki4sLJiYmvPfee7i6upKenk6vXr24d+8e8+bNk2rEec6fP0/Pnj0xNTWlWbNmTJ06VakG+Pz5czw8PDA3N6dJkyasWrWqyDI5ePAg+/btY9OmTXh5edG6dWsaNGhAt27dCAwMpFevXkrpN27cSLNmzWjQoAETJkzg+fPn0j4PDw9cXV2lv3v16sW0adNYtGgRjRo1wsrKirlz5yot5JyYmIi7uzsNGjTAxMSEfv36cf36daVrBgUF4ejoiJGREba2ttJSYQUJCAigfv36hISE5Lv/4sWLrFq1ikWLFrF06VLatWuHpaUlHTt2ZPPmzbi7u0tpt23bxgcffIChoSEffPABO3bsUDqXvr6+yvJir7cQ6Ovrs337dkaNGoWZmRktW7ZUarZu2bIlAM7Ozujr66uUuSCUFxEwy4m+fm2lHxMTY5VtpflTGqpXr660DuLdu3cJDAxk+/btnD59Gm1tbVxdXXnw4AEBAQH8+uuvODo60rdvXx4+fAjAb7/9xoQJExg9ejSnTp2ie/fuLF26tNDrhoWFMXz4cGl5quDgYNq3b09OTg67du3C3NwcLy8vYmJiiImJAXJnjRo4cCA9evTg9OnT7Ny5k6tXr/LVV19J5503bx7h4eHSGpFXrlzh7NmzheZl3759WFlZ0bdv33z3vxqwIyMjuX79Oj/99BPbtm3j0KFDRa4Hun//fuRyOb/88gsrVqxg48aNHDhwQNrv4eHBxYsX+eGHHzh+/Dg1atRg8ODBvPjfA97R0dGMHj2a3r17c/bsWRYsWMCaNWvYtGlTvtfz8/PDy8uLvXv30rNnzwLvWUdHR2mRgvzuOTg4mOnTp+Ph4UFkZCTu7u5MmzaNI0eOFHrP+Vm+fDk9e/bk9OnTDBw4kK+++oq4uDgATpw4AcCPP/5ITEwMu3btKvb5hbfL35jzN+ZFJyxnFTrTj1B5Xbx4kcDAQDp27Chte/nyJf7+/hgZ5U6CHBERwdWrV7l586a0FuTcuXMJDQ0lICCASZMm4efnR8eOHaVJ9K2srPjvf//Lzp07C7z2ihUr6NevH3P/t3gs5K61mTeIRUNDAz09PWmCC4D//Oc/DBgwQGlgy6pVq+jQoQOPHz+mRo0a7Ny5k/Xr10sjsX19fXn//fcLLYfbt2+rPahBT0+P1atXo6mpibW1Nf379yciIqLQ9Sitra2llXGsrKzYsWMHERERDB48mFu3bnHkyBEOHz6Mk5MTAP7+/tjZ2bF//35GjhyJr68vTk5OzJ49WzrHrVu3WLt2LaNGjVK61pIlS9i+fTtBQUFSra2ge27YsCFaWlqF3u/69etxdXWV1gq1srIiOjqatWvX0qNHj6IL7BWurq5S7XvOnDn4+fkRGRmJpaWlNP1l3bp1lV5zoQpSKFjLRKaQuwbsv5nMqCIOKU8iYAqSsLAwzM3NycrKIjMzk549eyo9gmNmZiYFS4DLly/z/PlzrKyslM6Tnp7OX3/9BUBMTAzdu3dX2v/RRx8VGjCvXLnC8OHDi5X3y5cvc/v2bQ4ePChty2uW/Ouvv6hRowYvX76kTZs20n5dXV1sbW0LPW9x+mmtra3R1Pz/fykTExN+++23Qo95/fomJibSoJqYmBg0NDSU8ly7dm3ef/99/vzzTylN165dlc7Rrl07li1bRkpKijSq2M/Pj5SUFE6ePEnjxo0LzZO69xwTE8OIESNUrv0mNcxXy0FTUxMDA4NyH1wkVDyFAiazVvp7Ev9hFEkUPklp+REBU5A4Ojqydu1aNDU1MTU1Valh6OjoKP2dk5ODkZFRvh+Qec/LltbAoKLk5OQwcuRIJkyYoLLP1NT0jSdqbty4MTdu3FAr7evlJZPJirz/wo4p7Ni8eY4Lm1/51e0ODg6EhYURGBjIjBkzCs1T48aNiYyM5OXLl0Uuop7ftV+fp/n1+8jKylI55k3KTqh6Xum+r5REH2Y5SUxMUvp5+PCRyrbS/HkTNWvWpFGjRlhaWhbZHAe5gzHi4+PR0NCgUaNGSj95o15tbGxUallF1bpatGhBREREgfurVaumtEh4Xl6uX7+uko9GjRpRo0YNGjVqhJaWFhcuXJCOSUtL49q1a4XmZciQIdy6dYugoKB89+c9S1oWbGxsyMnJUZraMTk5mWvXrmFtbS2lOXfunNJxkZGRmJubo6urK22zt7fn4MGD+Pr6KrUa5GfIkCGkpaUV2A+ad8/W1tb5XtvGxkb6u169elJ/NuTOD/3q3+rIC9qvv+aCUN5EwBTeWKdOnXBwcGD48OEcO3aMO3fuEBUVxdKlS6XBNOPHjyc8PJzVq1dz69YtduzYobQiTX6mTZvGTz/9hLe3N3/++SfXr1/H19dXGnFqaWlJZGQk//zzjzT5/qRJk/jvf//LlClTpObZ0NBQJk+eDOQ2v37++ed88803nDx5kuvXr/PVV18pjUjNz4ABAxg0aBBubm4sX76c//73v8TFxREWFsbQoUOlUcRloXHjxvTs2ZMpU6Zw9uxZ/vjjD9zc3NDT02PIkCEAfPnll5w5cwYfHx9u3rzJvn378PX1ZeLEiSrna9WqFQcPHmT9+vWsWLGiwOt++OGHTJo0ifnz5zN79mzOnTtHXFwcp06dws3NTRrI9PXXXxMQEMDmzZu5desW/v7+7N+/X+naHTp0YMuWLVy6dInLly8zYcKEYk8+YWhoSI0aNTh+/Djx8fEkJZX/A+tCRascrQ0iYApvTCaTsW/fPj7++GMmTZrERx99xJgxY7h58yam/5ut46OPPmLdunV89913ODk5ERwczMyZMws9b9euXdm1axfHjh2jQ4cO9OrVi1OnTqGhkft2nT17Nn///TcffPCB1B/XvHlzQkJCiIuLo3fv3rRv355FixYpPd+5ePFi2rdvz2effUafPn1o1qwZjo6ORd7jli1b+Pbbbzl69Ch9+vTBycmJhQsX4uTkVODo2dKyYcMGWrVqxaeffoqLiwsvXrwgMDBQGmRlb2/P9u3bCQ4Opl27dixcuJDJkydLA3Fe17p1aw4ePMi6desKDZoLFy7ku+++4/LlywwdOhQHBwe8vLyoX78+Y8eOBaB3794sX76cDRs20LZtW/z8/Fi1apXSgB9vb28aNmxI7969GTVqFJ9//jn16tUrVhloamqybNkydu7ciY2NTbH7t4W3SBFLKlY0MTVeKRNT45UdUWbF8y6Wl5gar3yVdpllp77AwMJEaVtiYhLN6rzggUJ1e3kTNUxBEAShcqjkA71EwBQEQRAqhUoeL0XAFARBECo3RSV5ElMETEEQBEFQgwiYgiAIQqVQyQfJioBZFsQMJYJQ/sT/XdUlE89hVk06OjokJiaKf15BKEcKhYLExESV6RsFoTSJuWRLmaamJnp6ekrrMOYnOTmZWrVqlVOuqgZRZsXzrpWXnp6e0uT3glDaxLurDGhqahb58HR8fDz169cvpxxVDaLMikeUl/C2qewNcxXWJLt69WqcnZ2pX78+jRs3xtXVVWUibA8PD/T19ZV+unTpopQmIyOD6dOn06hRI8zMzBg2bBj3799XSpOYmIibmxuWlpZYWlri5uamMmn2vXv3cHV1xczMjEaNGuHl5cXLly/L5uYFQRCEt47aAbNly5aEhIQUuD80NLTQRWlfd/r0ab744guOHj1KUFAQmpqa9O/fn2fPniml69SpEzExMdLP/v37lfbPmjWL4OBgtm7dSkhICCkpKbi6uiqtbDB27FiuXLnC/v37CQwM5MqVK0qryWdnZ+Pq6kpqaiohISFs3bqVoKAgaWFfQRAEQVC7STYuLo60tLQC96elpXHv3j21L3zgwAGlv/39/bG0tOTcuXNKkzdra2sXuMp6UlISO3fuxNfXF2dnZ+k8dnZ2hIeH4+LiQkxMDGFhYYSGhtK2bVsA1qxZQ48ePaR5EE+cOMH169e5evUqFhYWQO7k0xMnTmTevHnvVD+QIAhCZfNWjpItaKFagJs3b0qLBr+J1NRUcnJy0NfXV9oeGRmJlZUVrVu3ZuLEiUqrsEdHR5OZmUnnzp2lbRYWFlhbW3P+/HkAoqKi0NXVlYIl5C6mq6Ojo5TG2tpaCpYALi4uZGRkEB0d/cb3JAiCIJRMZerXLLSG+cMPP7Bnzx7p75UrV7Jjxw6VdImJiVy7do1u3bq9cUZmzpyJnZ0dbdq0kbZ16dKFPn360KBBA+Li4vD29qZv376Eh4ejra1NfHw8crkcAwMDpXMZGhoSHx8P5A58MDAwUFkFvl69ekppXl0GCsDAwAC5XC6lEQRBEN5thQbMtLQ0Hj16JP2dlJSksuCuTCajZs2ajBo1qsh1DguSt0htaGgocrlc2j5o0CDpd1tbW+zt7bGzs+Po0aOFrkOoUChUAuSbpClsO+QubVMSJT3+XSTKrHhEeRWPKK/iK80yy0lNB0zzOb9ZmV43T1FLlRUaMMeNG8e4ceMAaNGiBd9++y09e/YsvdyRO2jnwIEDBAcH07Bhw0LTmpqaYmZmxu3btwEwMjIiOzubhIQEpUVpnzx5Ii0MbGRkxJMnT5QCpEKhICEhQapVGhkZSc2zeRISEsjOzlapeb6qJOvAibX3ik+UWfGI8ioeUV7FV9pllpX8XGVb7vkL2l6+1O7DvHLlSqkHyxkzZhAYGEhQUBBNmzYtMn1CQgIPHjyQBgHZ29ujpaXFyZMnpTT3798nJiZG6rNs06YNqampREVFSWmioqJIS0tTShMTE6P0OMrJkyfR1tbG3t6+VO5VEARBKJwiJ/8OS5mscnRkFnvigpSUFP7++2+ePXuW7/RvTk5Oap3H09OTgIAAdu3ahb6+vtT0q6Ojg66uLqmpqXz77bf07dsXY2Nj4uLiWLRoEYaGhvTu3RuA2rVr8/nnnzN//nwMDQ2pU6cOc+bMwdbWlk6dOgFgbW1Nly5dmDJlCmvXrkWhUDBlyhS6desmfUPp3LkzzZo1w93dHW9vb549e8b8+fMZOXKkGCErCIIgAMUImM+ePWPGjBkcPHhQ6RnHPHlNnk+fPlXrfFu2bAGgX79+SttnzJjBrFmzkMvlXLt2jb1795KUlISxsTEff/wx27ZtUxqNu3TpUuRyOWPGjCE9PZ0OHTrg5+en1Be6efNmZsyYwcCBAwHo0aMHy5cvl/bL5XICAgLw9PSke/fuVK9encGDB+Pt7a1u8QiCIAglVdCYkcpRwUSWmJioVlZGjx7NoUOHGDduHE5OTiqPf+Rp3759qWawqhL9JcUnyqx4RHkVjyiv4ivtMstMeo5hA+VBP8+eJdG8bhr3FcoDfxITk0rtuupSu4YZFhbG+PHjWbJkSVnmRxAEQRAkCgUgo1LUMtUe9FOtWjUaN25clnkRBEEQ1BAbq8Gvv8rJyqronLxb1A6Y/fr149ixY2WZF0EQBKEIQUGatGunS9++ugwapFOpZsKp6tQOmF9//TUPHz7E3d2dCxcu8PDhQx4/fqzyIwiCIJSdkSN1yMrKHRwTEaHJ+fPyIo54e1T24K92H2br1q2RyWRER0ezb9++AtOpO0pWEARBKLlLl+Q4OKg+ufBWquQRU+2A6eXlVeg0cYIgCIJQlakdMGfNmlWW+RAEQRCESq1Yy3sJgiAIQrmqRM20atcwly1bVmQamUyGl5dXiTIkCIIgCK+qLAtIqx0wv/322wL3yWQyaWo8ETAFQRDKTyWqgJWJynR/xZpL9nU5OTnExcXh7+/P+fPnCQwMLNXMCYIgCO+OyhQc81OiPkwNDQ0aNmyIj48PDRo0eOMFpAVBEIQ3pMip6ByUnkoeMUtt0M/HH3/M0aNHS+t0giAIghrk1/+s6Cy8M0otYMbGxua7PqYgCIJQdmSpqRWdhTJX0KCfu3dlDBxYEycnXQ4fLvbyzsWm9hXOnDmT7/akpCROnTrF5s2b6d+/f6llTChCTg6av/wC1aqR5exc8DpygiBUae9yPWXxouqcOKEFwHi3GsTeTKFGjbK7ntoBs3fv3vnO9KNQKJDL5QwaNEitR0+E0lHj66+ptns3AOmTJ5PxzTcVmyFBEIRyFvhjNen31DQNfvlFk379ym4JF7UDZnBwsMo2mUyGvr4+lpaW6OnplWrGhEKkpUnBEqD6v/8tAqYgvLPe4Srm6/66C5iX2enVDpjt27cvs0wIxSNLS6voLAiCUElUpc6YfJuXi9HmLHv2jEoRMPOkpKRw+vRp4uLiALC0tKR9+/aihikIgiCUusrUR1usgOnv74+3tzdpaWlKI2J1dHSYN28e48ePL/UMCvkQA3wEQfifShRPSq6A6Kju1HgyWdmWhtoBc+/evcycOZPWrVvj4eGBtbU1CoWCGzdu4Ofnx6xZs6hTpw5Dhw4ty/wKULm+cgmCULHegY8DRSVpeFb7OUxfX1/atm1LaGgogwYNonnz5tjZ2TFo0CCOHDlCmzZtWLdundoXXr16Nc7OztSvX5/GjRvj6urKtWvXlNIoFAp8fHywsbHBxMSEXr16cf36daU0GRkZTJ8+nUaNGmFmZsawYcO4f/++UprExETc3NywtLTE0tISNzc3EhMTldLcu3cPV1dXzMzMaNSoEV5eXrx8+VLt+xEEQRCqNrUDZmxsLAMHDkRTU7VSqqmpycCBA7l586baFz59+jRffPEFR48eJSgoCE1NTfr37680Z+3atWvx9fVl2bJlnDhxAkNDQwYMGEBKSoqUZtasWQQHB7N161ZCQkJISUnB1dWV7Oz/X4F87NixXLlyhf379xMYGMiVK1eUmo+zs7NxdXUlNTWVkJAQtm7dSlBQEHPmzFH7fsqVTEYOMn6mL4fp+S58wRQEQShSWddD1W6S1dHR4dGjRwXuf/ToETVr1lT7wgcOHFD629/fH0tLS86dO0ePHj1QKBRs3LiRyZMn069fPwA2btxIkyZNCAwMZMyYMSQlJbFz5058fX1xdnaWzmNnZ0d4eDguLi7ExMQQFhZGaGgobdu2BWDNmjX06NGD2NhYmjRpwokTJ7h+/TpXr17FwsICgIULFzJx4kTmzZtHrVq11L6v8jKOzXzHFwBMZg3fVGx2BEEQKlxZ91apXcPs3Lkz/v7+nDp1SmXf6dOn2bRpEy4uLm+ckdTUVHJyctDX1wfg7t27PHr0iM6dO0tpatSogaOjI+fPnwcgOjqazMxMpTQWFhZYW1tLaaKiotDV1ZWCJYCDgwM6OjpKaaytraVgCeDi4kJGRgbR0dFvfE9lJSMDKVgC/JspFZgbQRCE0lHZh2eoXcNcsGABZ8+epV+/frRo0YKmTZsCcOPGDa5cuYKpqSkLFix444zMnDkTOzs72rRpAyDVZg0NDZXSGRoa8uDBAwDi4+ORy+UYGBiopImPj5fSGBgYKM1SJJPJqFevnlKa169jYGCAXC6X0uQnNjb2TW61xMe/uJcMWJVqXt4W78p9lhZRXsXzdpTXh0p/paSkVmi+S/PaL5++ACyUtsXGxiJTmBZwXeWySHz2tET5adKkSaH71Q6YFhYWnDp1itWrV/PLL78QFBQE5D6H+eWXXzJlyhTq1q37RpmcPXs2586dIzQ0FLlcrrTv9en48haqLszraQqa0q+oNIVth6ILtzB5zcFvIlmeUKp5eVuUpMzeRaK8iudtLS9dXd0Ky3dpl9mL+BSVbU2aNAFZqspo4PyuW6du3TIti2I9h1m3bl28vb3x9vYutQzMmjWLAwcOEBwcTMOGDaXtxsbGQG7t79Wm0idPnki1QSMjI7Kzs0lISKBevXpKaRwdHaU0T548UQqQCoWChIQEpfPkNc/mSUhIIDs7W6XmWRlU9mYLQRCE0qLIKc4HXtl+OBbZh3nhwgUuXbpUaJpLly7x22+/FfviM2bMIDAwkKCgIKmJN0+DBg0wNjbm5MmT0rb09HQiIyOl/kh7e3u0tLSU0ty/f5+YmBgpTZs2bUhNTSUqKkpKExUVRVpamlKamJgYpcdRTp48iba2Nvb29sW+rzInJi4QBCGP+DgoN4UGzFOnTtGtWzdiYmIKPUlMTAxdu3ZVqaUVxtPTkx9++IEtW7agr6/Po0ePePToEan/W9tNJpPh4eHBv//9b4KCgrh27RoTJkxAR0eHwYMHA1C7dm0+//xz5s+fT3h4OJcvX2b8+PHY2trSqVMnAKytrenSpQtTpkzhwoULREVFMWXKFLp16yZV3Tt37kyzZs1wd3fn8uXLhIeHM3/+fEaOHFkpR8gKgiBIRIuTJOZ+7TI9f6EBc/v27djZ2TFs2LBCTzJs2DBatmzJli1b1L7wli1bSElJoV+/flhbW0s/r05+MGnSJCZMmMD06dNxdnbm4cOHHDhwQGne2qVLl9K7d2/GjBlD9+7d0dHRYe/evUp9oZs3b6Z58+YMHDhQmnTB399f2i+XywkICKBmzZp0796dMWPG0Lt371JtehYEoXRpREdTc8QIakyc+L9Jt4WqSt2p8Z6mapdpPgrtwzx37hxjxoxR60S9evVi+/btal/49Zl28iOTyZg1axazZs0qME316tVZsWIFK1asKDBNnTp12LRpU6HXql+/PgEBAUXmSRCESiA7G50hQ9B4/Dj375cveeHnV7F5Ekqs5OMzKrAP8/Hjx5iaqg7nzY+pqWmhj2AIgiCUFvnp0/8fLIFqe/dWYG6EykKhKNsO3UIDpq6uLk+fPlXrRE+fPkVXV7dUMiUUToySFd51svT0is5C5SE+EMpNoQHTzs6OkJAQtU4UEhJC8+bNSyVTgiAIwjuohMG/rJf3KjRgDhs2jHPnzrF+/fpCT+Lr68v58+cZMWJEqWZOKIB4rEQQhP+pLEtflZXKVIEudNDPsGHDOHjwIPPnz+fEiRO4urpia2uLrq4uqampXLt2jb179xIeHs4nn3yCq6treeVbEARBeEeoO0q2rBUaMGUyGTt37mTOnDns2LGD8PBwpf0KhQJNTU2++OILFi9eXJb5FF5Vmb5yCYJQsar650Elur8ip8bT1tZm5cqVTJs2jWPHjhETE0NKSgp6enrSpABmZmblkVdBEATJNZqxmHkYkIA3c6t4w+S7oRLFxnypPZesqakpI0eOLMu8COoSfZjCOy4nB7pxlL+pD0AytVhXxDHC26uy9NOqvR6mUHkUbzJiQah6Ltw0kIIlwE7El3mh7ImAKQjCW+d5hrzoRMI7p22TJ2V6fhEw30aiSVYQhKqogE5MdUfJ6mhnlWZuVIiAKQiCIFQJZV2XEAFTUCF7+BD5xYuQmVnRWRGE/Ilu/HdGZRo5q3bAfHWR5oL4+PiUKDNCxZOfP4/eRx+h6+KCTq9ekJ1d0VkSBOEdUeLgWMbRVe2A+dlnn3H27NkC98+dO7fQJbYENWVm5o6ZL0wZvidqTJ6MLCUFAM2oKDQPHSq7iwnCG6tE1Y4KlhcjNI8cQffjj6k5aBCyu3crNlMVpKwfP1E7YHbr1o1hw4Zx8eJFlX1Tp07F19eXb775pjTz9m5RKKju6UltQ0N0P/oIjdjYQtOqte0NyK9fV/pb69ixUjmvIJQqMfBNWXo6Nd3ckF+9itbx41RftKiic1R6FIpKMzWe2gFz8+bNODk5MWjQIK5cuQLkTo3n7u7O9u3bWbFiBRMnTiyzjFZ1Gpcvo71lCwDyW7eo7u39/zszMpAlJFSuxnxBqEjiX0GJZliY1DIEUO3HHyswN29O9vJlyY6vLIN+5HI5O3bsoFWrVgwcOJDLly8zatQo9u/fz7p16xg7dmxZ5rPKq7Zrl9LfWj//DIDGrVvotm1LrcaNqTlihOhTFARBVVbZPk5RXjSPHy/ZCSpLHyZAtWrV2L17N02bNsXFxYXQ0FC2bt0qlvUqQ9pLliC/cwcArZAQNI8ezT9hWb1R8s778iWaR4+i8b/WBUEQKg9ZFWl9kmVkVHQWClXgXLIHDx4s8KBhw4Zx9epVevbsiUKhUEo7YMCA0s3hO67agQPKf+/YAYvblm8mFAp0+vVDMzIShUzGC39/MocOLd88CIJQsKIGXMCz6wAAIABJREFUCr7tKsn9FRgw//WvfyGTyVDk880lb/u+ffvYt2+f0nYRMMteeX+ZlJ87h2ZkJJD7TbammxtJImAKQqWgUFBlxjeU9DZkGmXbiVlgwAwODi7TCwOcOXOGdevWcfnyZR48eICvr69S866Hhwd79uxROubDDz8kLCxM+jsjI4O5c+fy448/kp6eTocOHVi1ahXm5uZSmsTERLy8vAgNDQWge/fuLF++HH19fSnNvXv38PT05NSpU1SvXp3Bgwfj7e1NtWrVyur2lVXWUX8yGfLff6/oXAiCUJgqEjDzo1AUYwHpMi6HAgNm+/bty/TCAGlpabz//vt8+umnuLu755umU6dO+Pv7S3+/HsBmzZpFSEgIW7dupU6dOsyZMwdXV1ciIiKQy3MnaB47dix///03+/fvRyaTMXHiRMaPH09AQAAA2dnZuLq6UqdOHUJCQnj27BkeHh4oFIrK92ypQlGmj5UIgvC2UVSaJsuKVtYfg2qvh/mqP/74g7i4OAAsLS2xtbV9o4t37dqVrl27AjBhwoR802hra2NsbJzvvqSkJHbu3Imvry/Ozs4A+Pv7Y2dnR3h4OC4uLsTExBAWFkZoaCht2+b2/a1Zs4YePXoQGxtLkyZNOHHiBNevX+fq1atYWFgAsHDhQiZOnMi8efOoVavWG93f2yqR2jzCGCtu/u/rXenUfhUKSEuD6tVB843eeYIgvE4GoFCwkPksYQ7v8RcHGYB5UQcKxVasUbKHDx+mRYsWfPzxx4wYMYLhw4fz8ccf07JlSw4fPlwmGYyMjMTKyorWrVszceJEHj9+LO2Ljo4mMzOTzp07S9ssLCywtrbm/PnzAERFRaGrqysFSwAHBwd0dHSU0lhbW0vBEsDFxYWMjAyio6PL5L7eWBl/hYqmJU25gQ0xdOMo2TmlEywzM2HEiJpYWNSmQwdd/v67kjZBC2+FyrKgcGVx94ku37CQTKpxA2tms7Sis1Qlqf09PywsjJEjR2JmZsa8efOwtrZGoVBw48YNtm3bxqhRowgICMDFxaXUMtelSxf69OlDgwYNiIuLw9vbm759+xIeHo62tjbx8fHI5XIMDAyUjjM0NCQ+Ph6A+Ph4DAwMkL1SS5LJZNSrV08pjaGhodI5DAwMkMvlUpr8xBY2G48aXj2+flISr9ejY2Nj+fC1bc9TU7lz5w7QWDntjRulUm2bxloeYwTAcbqwP/Y8HS0f06CQvKvjxAl9QkKsALh2Tc4336QzY0ZcsfNX0jJ/11TV8kpISFDZVhr3+naUl/KnQkpqGt+dUP70+Jn+xMb+Vi65Kc0yq/FKhSjPrZs3AUOV7bnXVS6LZ8+elig/TZo0KXS/2p+wy5cvx9ramqNHj6Knpydt79WrF2PHjqVbt26sWLGiVAPmoEGDpN9tbW2xt7fHzs6Oo0eP0rdv3wKPUygUKgHyTdIUth2KLtzC5DUH56leu7Za56+po0ODBg3zT1sKAfNXlPNx5EknhhidVytvhZkwQUfp78BAI7Zs0S7WOV4vM6FwVbm8Hhqkqmwr6b2+reWlq6vLpXjVBtjyuJfSLrMMQ9XA2LhxYyBZZXt+161bp06Z3rfaTbK///47I0aMUAqWefT09BgxYoQ0ZV5ZMTU1xczMjNu3bwNgZGREdna2yrfNJ0+eSDVGIyMjnjx5ovR4jEKhICEhQSnN6zXJhIQEsrOzVWqeFU4M7hGEKisrC7y8qmNrq4ebWw1SVb8X5EPB309VP5ffSiVfrqRUslEQtQOmlpYWz58/L3B/WloaWlpapZKpgiQkJPDgwQNpEJC9vT1aWlpKS4/dv3+fmJgYqc+yTZs2pKamEhUVJaWJiooiLS1NKU3M/7V35nFRVe0D/94Z9k0REBdEX5Nwj9TU7NXcSq3UNEutt8Uyl94y/eWSaVpqbthir6ZppqUtpllZmbS4ZIqQJWIuSCGEqCAIKCjbzP39MTk6zB24wywMcL6fD58P994z5z733HPPc5bnPE9SEhkZGcY0u3fvxtPTk6ioKIc+kxErDGsUTayFIhXUIWprdd+zx401azzJyNDw2WcebNumrk2tLeVhaW26xjlfv/3221m7di1//fWX2bWUlBTee+89evToYdXNCwoKSExMJDExEb1ez5kzZ0hMTCQ9PZ2CggJmz55NfHw8aWlp7Nu3j1GjRhESEsJ9990HQL169Xj00UeZM2cOe/bs4ciRI4wfP5527drRu3dvACIjI+nfvz9Tpkzh119/JT4+nilTpjBgwADj0L1v3760adOGCRMmcOTIEfbs2cOcOXN47LHHnGcha0WNd+bHIcuS6+4RFdRdammdfP55b5PjSZN8qkkS10KtwnS0i0DVi15z585lwIAB3H777QwaNMiobE6dOkVMTAxeXl7MnTvXqpsfPnyYwYMHG48XLVrEokWLGD16NG+88QbHjx/n008/JT8/n9DQUHr27Mn69etNpoUXLlyIVqtlzJgxRscFq1evNu7BBEOklRkzZjB8+HAABg0axNKlS43XtVotmzdvZurUqQwcONDEcYErcI5G/MVNdOY33GpLV1IgEJhRldgKsixRa8K3KNqbqFeYpTqrNn5YjWqF2aZNG3bv3s2rr77KTz/9xPbt2wHw9fVl4MCBvPzyy7Rq1cqqm/fs2ZO8vDyL17eV86OqhJeXF9HR0RU6GAgMDGTNmjUV5tOsWTOjI4NqQZIowJfvGMRN/EUnDgPwO7fSl13kU592/MEB3TTl3ztIkUrYbx+mQGAvXGWKzt5U7TOW/1GaNR+9jc9xPs+78kQ2YJVZ5U033cSHH36IXq8nOzsbgODgYDQax2r1ukCZXkM34jhOOzTo2MoI+gLPsoJ8DC78jtGeDWcHcp8YZQocyPnzErt3u9Ghg4727V3Tg0xt/QSq+ly1pTh2nwo3Oyfl5qruIBUWOdYjSpU0nUajwdvbG29vb6Es7cQXpztxHIPHJD1aRrAVgFhM14W3Z9/hVLnEBvG6xcWLEnfc4cfEiT706ePHgQPayn8ksBu1tSOgluxC8xGidPmyaoW5Lb6FnSUyxSpt9/fffzN+/HhatmxJ8+bNad68OS1btmTChAlGV3mCqnHoQguTYz21rKHS1/GWoIawYoUHOTmGZqG0VOK55xw7xVVlxDJBrUSxw+DmplphlpS5yBpmcnIyAwYMID8/n969exs9/SQnJ7NlyxZ++OEHYmJirF7HFBiw+fN31BqmJCPboXGSci+CmS8jgauxb59pk/DXX7Ws4+bKlJYiV6VjKVN75mQVkP39kShSm9qhsqhWmK+++iqyLLN79246duxocu3o0aMMHTqUV199lY0bN9pdyLqA+vA1VO2jqiL22laiOX8OoTAFdqOWzV1qUlLwefBByIlFyQ1cZdSW0rhS4ti9/Laievz6yy+/MH78eDNlCdChQweefvpp9u3bZ1fhBLWH2mLFJxA4As/XXkP711913mbg6DnlzoKrWEWrVpglJSUVbuKvV68eJSUldhGqLmLzIM7BPW49EvHcRqqZG3ZBraK4hnzDtWwN0+Pzz4GqG9nV5g6prJdVK0wvih0qi2qF2bZtWzZv3szVq1fNrhUXF7N582batm1rV+HqEpKk0pMFsuKUrKP05bWKOowv6EY8kSSxnevOJigthby8WjdFVlfRnj1T3SIIrEZ2+Ib96qSoWOIkrVWlvb99kkNlUV3K//d//8fRo0fp06cPa9euZc+ePezZs4c1a9Zw55138scff/DCCy84UtZajdr+obOnbGQkDqc3ZDtDASjBk4f4DADp77/x69mTei1a4DNqlEF5WsBVplQElVCBv2hXwpnr+M5E6ft2//DDSn+XeLahI8RxOkqt25mzbuhUmtt4upXZV6ByqDb6ueeee1izZg2zZ89m+vTpxrBXsiwTGhrKmjVrGDRokMMErfWoHGFaWt43TFs4hvjURibHxXgBxXi++SbakycBcI+Jwe3bbym7/35l+er42kyNoXbqoRqD0nfiM2kSl+66C7lxY9W/qVW4kJ9tq9wijBgxgvvvv5+EhATjvsvw8HCioqJws0MsxrqMNVXe2bOflpaLPNevNz1+5x2LClMgsCe1bAnTiCXl57lqFUXz5ileO5FZiVWtTmeIG+ZpXfxZV8GVOgRWazk3Nze6dOlCly5dKk8scBoOU6KSNVteLKcTU7ICu1JLNaYl5eC+cSNFL7wACoHmi3WW98pKqan4jhqF9uRJSkaO5OqqVVDDvLNZ03Y4up2xWmEmJSWRmppKbm6uSVDma4wePdouggmUsVghHDjs1Ghsz9uVeomCmo/iGmZhIZ4rV0JJCcXPPgv16ztfMBtR+k6+ZCgnctswuufDBP7+ldKPLOIVHc3PJxtxmEEM37yN4If3obvzTjtKbF/UGj9axFXCe6WlpTF+/Hji4+MVFSWAJElCYVYRazrMTp+SVZtQWMoKqhGfiRNx/yeKkltsLIXfflvNElnHaVpwGfOte8P4EoA3/r7Ayc3bgTGq84z5KJ8h7AZgHnM49c5EPF1YYSpSE9cwp0yZQmJiIq+99hp33HEH9Wtg782VscbTj1KtcFxFsY+nHzElK3A015QlgNv+/XDpEjgrALyNxNKd/vxYYZpsQti0xb/CNOUZyfWQhfnU562U+5lRJQmdg+0tjYuMMGNjY5k0aRITJ050pDx1FpunIhyIPdYwBa6L265deL72GnJQEFKJsmFJTUQqK6sx3bSnWcsVfCtNl5yrYOBTgZa5io/JcUJ+S2tFq3Y83FwnxJxqhVmvXj2CgoIcKUudxtYpWUfuS1NtIyAUZs2jpASfMWOQ8vMBkJhTzQKpROGDuYwfS5lOCR5MZymu7ZXUlGO0V5nS/BuTCgpV36cmWhL4+bqOwlRtLvXwww/z5ZdfOlKWOo1LV2TVXhVqhsLMzJQ4cUJTU8R1KG579xqVZY1C4eU9xocs4GWWMoNhfFENQjkeRRd4xerdwUmS6ygfJep7m0clsTQYUBw4uMoa5l133cXu3bsZPHgwY8aMISwsDK3W3Jy5c+fOdhWwrnCu0Nxc3CJOXcO0YvRbAzTQnk2ZPDIpjEK9D8M6JLF+X6PKf1SbKXOsZxRn8iXDjP/voxeXLp3Gv0E1CuQ0rNl24doEeJkrf6uaFQf3B6zy9HON/fv3m12XZRlJkrh48aJ9JKtjxGX+S3VaxQrkIGUlI6lew5RqgMKcMDWEQr1hXeeLo5FM2n2GW/tYZ0ghqBmUlbm6erATVnx2tcn4Tqm5aRrg2NkS1Qpz5cqVdr/5/v37+d///seRI0c4d+4cK1eu5JFHHjFel2WZxYsX88EHH5CXl0fnzp1ZtmwZbdq0MaYpLi5m9uzZfP755xQVFdGrVy9ef/11mjZtakyTl5fH9OnT2blzJwADBw5k6dKlJpa+6enpTJ06lX379uHl5cWIESNYsGABHh4edn9uR+AoXVXbgiCcLwo0OT647k9u7XNrNUkjqCqq9vXWQucGSgrPpfY4l5TguWIF0vnzFD/zDHKLFjZnaaltUzrvqdXZfL+KUK0wH374YbvfvLCwkLZt2zJ69GgmTJhgdn358uWsXLmSlStXEhERwdKlSxk2bBi//vor/v6GUcHMmTPZsWMH69atIzAwkFmzZjFy5Ej27t1rnDIeO3YsZ86cYcuWLUiSxKRJkxg/fjybNxtMrnU6HSNHjiQwMJAdO3aQm5vLxIkTkWWZ6Ohouz+3bchOnfqUEEY/tZp/3lkxHmgcPZ/lZER1NMfR1vgZU1Yy7qO7yKApcza/xfDTr1jlWUhJ+VujMB3tlL9afSTdfffdzJkzh6FDh6IpV6iyLLNq1SomT57M0KFDadu2LatWraKgoICtW7cCkJ+fz8aNG5k3bx59+vQhKiqKd999l2PHjrFnzx7A4Jnoxx9/5K233qJbt2507dqVN998k5iYGJKTkwHYtWsXJ06c4N133yUqKoo+ffrw6quv8uGHH3Lp0iWnlIWkU98zUqxUDqooMhV31HOpTwK3UEzFI/GTxeqnnJ2KaFVZxgv4cIVGnOcAd1S3OKpQNbVYC9+t4hNZMcB09Fj0tY8i2M+/SeVfTMhfypVv99mcpzVtm76eY/0DWBxhLlmyBEmSmDp1KhqNhiVLllSamSRJTJ8+3S6CpaWlkZmZSd++fY3nvL296dGjB3FxcYwZM4aEhARKS0tN0oSFhREZGUlcXBz9+vUjPj4ePz8/unXrZkzTvXt3fH19iYuLIyIigvj4eCIjIwkLCzOm6devH8XFxSQkJNCrVy+7PFNFaAouq0tYSd1x27EDtz17KBs4kLIbyqXqWF7DTOJmerOH8zTmFhL4WTfeeO34cQ06HXToYBi15OmtMGpyIjV11m7XLjfmzvXC31/mrbeucvPNVRsd5hZ6MI1lAFxEbBtzdbKv+pmds2ZK1tFrmB9zfUmtBE+++qkeowdX8AMVWDPC1Ps51h7BosJcvHgxkiQxefJkPDw8WLx4caWZ2VNhZmZmAhASYrpRNyQkhHPnzgGQlZWFVqs12x8aEhJCVlaWMU1QUJAxHNk1OYODg03SlL9PUFAQWq3WmMbRqA4gffkSyOaVQpbh6g+xPPOwJ9/zOves2cH/dibi1b2j7bJplD/Il1jIeQwhh44QxfsX72cM8MYbnsyb5wXA1KlFzJ7t2CjotlATByE6HYwb5012tmFWZtYsL7ZsqVocy8MpNdOMVI2SqInvtjJi08LMztXQPp8i1mwVUUzr4NKwqDBzc3MrPHYWUrkhwDVr3Ioon0YpvZo0FZ0HjFO6VcXk97KXheumUWEuyMGk/50OtDU5n5KSQuK4n/gCw5rrFh6i/9Oz6LTN2+L9tfn5BP70E8VNm3LZOAI3vV9ZaQn5Cvv0kpOT2cYDJuc+uDiYfycnM2/e9TyWLfPigQf+MMv3+vNZh61lXl6OgsJCO+TpHK41EMeP+5Cdff39//CDu8VnqOzZcnJyKr2vK5ZPjgpr/NS0NC5K2VblW33Pqi76U3qJ+TaosrIyAr0KyC0yHX0qtR96Xandn9E0P9P75eflW3W/QoUA5mfOZAA3K9z3T8B0G+PFnIs2PV9ERESF1102iGVoaChgGP3dOFWanZ1tHA02bNgQnU5HTk4OwcHBJml69OhhTJOdnW2iIGVZJicnxySfuLg4k/vn5OSg0+nMRp43UlnhVkRycrLJ7yXNGSi3jKmUfwK3EtbsuNn5li1bckeuqYHSs+lzuRBxVVmAsjL8unRBm5oKwJV33qFUwbDLzd2DwPrm/jiVZJNxUzwfHq5cTtaWX/kyswf+vj52z9MRLFjgyeuvexIeLvPCC+abu5WeQU15nW1Q+VKAK5ZPRoPKvdu0aN6coIjAStNdwxH1yxlo3dxw05p37JWexd1Na9dnrKzMAgLqWXU/b2/ztq1p4yaKaW+6qZXZufqBDRz6Dl02MFrz5s0JDQ1l9+7dxnNFRUXExsYa1yOjoqJwd3c3SZORkUFSUpIxTdeuXSkoKCA+Pt6YJj4+nsLCQpM0SUlJZGRkGNPs3r0bT09PoqKiHPqc17B1bcHa6Qn3r74yKksAn2eesZhW7TqfznWrU40mNVVi2TIvZFkiLU3DK6+Yz0Zc46fNeUy57yyfvplTK6ckr6Hme6nNz2+KFWuYLuyz2oBtTllcxtMPwIcffsgHH3xgjIdZHkmSVE3xXKOgoICUlBQA9Ho9Z86cITExkcDAQJo1a8bEiRN5/fXXiYiIoFWrVixbtgxfX19GjBgBGPzbPvroo8yZM4eQkBDjtpJ27drRu3dvACIjI+nfvz9Tpkxh+fLlyLLMlClTGDBggLEn0rdvX9q0acOECRNYsGABubm5zJkzh8cee4wAJ0U7cITxSUUKU3PihLpMrAhWopMtBLJVaQGcnKxh4UJPvL1h7twiQkNd/eN2Dl98YWqBfPGicsckaV8OD4w3ONde/wssz0/n5lccLV314FJ7D6sZSZJVKwrnOy4wvKeCAti82YOgID1Dh5ZZbFMUXf9ZwKVd482bN4+33nqLdu3a8eCDD9olvNfhw4cZPPi6CdWiRYtYtGgRo0ePZtWqVTz//PNcvXqVadOmGR0XbNu2zbgHE2DhwoVotVrGjBljdFywevVqE7d9a9euZcaMGQwfPhyAQYMGsXTpUuN1rVbL5s2bmTp1KgMHDjRxXOAs2nuc4nCJOgfMavcfVdao7KIPbzOJmznFq8xVTCPhnBGmLMPDD/uQnGx4bxcvSnz6adUMWayhJljJqm0EXnnGdHp1xvJuPP6K6xpcOZqa8G7tgSs/5rXYycPu9+HXQwZ3+DNmFDFzpnK9tMaJmUsrzE2bNnHPPfewadMmu928Z8+e5OXlWbwuSRIzZ85k5syZFtN4eXkRHR1doYOBwMBA1qxZU6EszZo1MzoyqA6iPI+zsWC4XfOsSGHmXvViIDsp/Wf/pA9XmGIpD5Utj8URpgrS0iSjsgTYudM5sSYKil0/poXaRuCvc6ZGH0WyF1B3FWZtnJJtRTJ/omQ/oO4bdbZylZA5migZlSXAkiVezDs7DreYGH7t+AQ/3D6Lnv0koqL0ihK6kvN11UOCwsJC+vfv70hZBCpR8tmqvIZp+fWuO9LNqCwBXuUVm+Uqk7VVdgxfUlI9/eTEsw2r5b7WoKb8/vpLw6mymxySt8B10Hood0pVT8k6fQ1T4tz35ss/Hhs38kdWKD1/nM/L8wO4q58Px/5QbgMsjjCVZtUcXKFVK8zu3btz7NgxR8pSt1E5igsg3y7O1/OKfSpP9A+1eWrr77wA+vXzpXnzAJYu9axucRRR82qHDq08+LA9SE2VuOceXzp29GfTJtcfndcV1H6j1REgQUpS3uYxibeNnfZSnZZZ/y1RnJO1JPJTT5nXeWvWQKuCaoUZHR1NTEwMmzZtcrgWr4uoXYxvxZ+K5619Jar9hkrqe6WyJUFUCFddVepwRmN++82N/HyJhQu9OH3a9Sx91ZTNmTPOkXvxYi8OHHDj7781TJniTQUrKtVOdRkGSRcu4Dt0KAHh4XhNnw56028tIUHD4MG+DB/uQ1KSde/N0jO1ClJnbOn0EaYEeo3yyt9eepsc/3wkULGue3y4kQhOmZ2P+d68w1Zta5g3upK7RklJCZMmTWL69Ok0adLELB6mJEkcPHjQ/lLWBVR2Ee3VCFj34Ti+4XGVPtj773swf775PsfqxB5lk5MjkZ4u0aaNHk8bBtKffnp9Gr+0VOLLL9154olS2wWsRXi89x55e/8glQjar/kA7bBh6G6/3Xh9/HgfkpIMbefkyRLffVf5ntJryCXKZf3vFn8Tl95MRQ7O70Scu6Jup4GErNi+Hdx+kWQFxwVKVJvCDA4ONvNyExISQqtW5ptFBXbAijdtaRAnoVdct9TGxuL93/8ilZRwNTqaskGDVMfQ0+s11s3JVnUlPuMsEKn+PnUI2QrH/Er88YeGIUN8uXhRwy236IiJKcDrn62ctnbA5GKhLMvz15Kv6c0Jsgkx+FheMB2+3QJAXh5GZQkQG2ud7xhL70t1jPcb/j9zRsLHBxo0qJqWkWVDp0mvrzggybEs9UHalSR5hlVWyeRILL6tb7/91rF3FpigVidV1MAZemjmeE+bhvaf/a7ezz/P5QEDVI8wv/n7FoZLVkQcqGKNlQrV97IdSXWNdE+e1LBpkweRkTr+859Sk/qgOZEEdKpy3i+95G3cu3nkiJbNm915/HH7KDq3PxIB2/0VqyE3VyIxUUPbtnpV76m63uULvE42Bg9hR4hi/fl7eOKfa3q9axgEvPiiF6tXe+LrK7N+/RXuvrvMqt8XFcETT/iwc2dnunQp45NPrhASolzgemc6NKnN4b0EN2DjlKxhhKlcWbR//GH8X5OVhZSVpboxKda522W6uKNHxY4SZKkWVcW8PAqfnkl6/2fRfv9DpckvXYJ+/fxYscKT557zYf16U0cF8iWVkWws8PPPpv3ibdvsGBS9zLEBe69x/rzEv//tx9ChfvTo4cfpLPOoHa5CDANNjjdf6Ge3vK2JF2khA86ckVi92jAvX1goMW6cZX/TYNgTPXu2F7NmeZGTY7j/9u3uxq1fhw658e67luuU0v7sEpQNxmw12qnWbSWZmZncdtttzJ8/v8JM5s+fT9euXcnOts7RseA6ao1+ZCRFc2pJlh3mxcOafJVNvdX80DUWMe0hxq+Tt9FmyyI6HNrIY6PckPMrjqm6bp0nhYXXG4r/+z/TBqxYZ19rVHvGTnWWEcmbb3qSkWFori5c0DB/c+VOPpzv1cYZmCsUazz9yEj89pup7UleXsWd1aee8mbFCk9WrvTkiScM1vULF5ouhC9bpuyuUXviBFLcIbPz3TG3dZFQ/xyWcPQbr7CkVq9ezcWLF5k8eXKFmTz//PPk5OTw7rvv2lU4gXrkCuJWXiCYCazicTaQwr9Atm7lSvUSphX7pWozz3x5L5cwxP/8Qn8/CUv2Vpg+La3iAv7yeGu7yQbARftFHnLWBOMnn5iOYHILK7dccpE+mOmoSSHyj1V5udvYeZKsL5fdu6/fc98+N4OnS5V5uP8apzgle1hhiaEMd9vX1KsWFlY1FSrM77//nuHDh5u4olMiICCABx54gO+++86uwtUp7LDZ0ZLCfIp1vMsEPuRxhvLVP2etGDVaIVtNV472aGTLW/T9dKypTfldKrLv/lDp7Dmr0u/dq2XZMk+OHVNqLlz4fbvgBmJJIXyVHXJ12GhaMUizHjRX1dkcyEjosMIDmI0fYLVOyZ4+fZr27dX5N23Xrp3RkbqgYlJTJZ58sjUREf6sXGndepKMZNFxgaWP5muGGP//gw6cy7LOMs9T5cyBpX2YNUmJHjtWdfd+lrhwxZd77vElJCSA8eO9Ka12w9Lr7+PS1crrwtChfixY4EXfvubrhs5SSRpNzalDjsTRG/PN7qczkC9lAAAgAElEQVQzH7LJMkgqg2xIyJQ5MYpktSpMSZLQ69WNcfV6faWBnQUGXn/di6NH/bhwQcOsWd6cP2/FCM7CnEpFRj/lsbbB1pSo35doSZlXNtVib6VaWAirV3uwcaO72mApAOzda/3HXVICGza4s2GDOyUl5tfXHurCgQNulJZKbN7swa5d1t7Dvt/Vje8oM69ig48bKS5WkMNJn3xVRlCu0lFzvBTWxL8CxUpqKbmSwlQ4Z/l21o0wbZ6SdbDGrPDLDQ8P57fffmPMmDGVZvT7778THh5uN8FqMxs3mo4qN23yINgODY81hkPWfGPW5esaI8xRg3TsSzQogz8OXGDJKjtahpbjmWe82brVkP/PP5s3RnrZtF+6YIEXAwYUOEyeyrBrmyLLaI4cAW9v9Der21xeFTQlxYB6d46ui41TjkoKxUp9qTl8BMp52bGYXuHb1evUGxj+RD+28JB6+Ww1+qnONcwBAwbw+eefc+qUuVuiGzl16hRbt25l4MCBFaYTKCNJ1mw8Vq6q1owwHYmzlKP75s34PP44Hu+9Z/aVZaTL7EtsYDx+95MQh8pyTVmCui0b1k7E2LtE9bLEJ5+4s2SJp1WzG0r8+lkG/e/0ZEjXAv567Qs7SWiOdKUK+3RdccbLxpfppTGPPmOt1y65VP2eS0WFWaY3BFpQwcc8ovpe9sDR7hArVJjPPvssvr6+DB48mK1bt1JWZlrQZWVlbN26lSFDhuDv78+zzz7rUGFrM1a9aCvXMM2SWqnUbFbEau6n0LVs186f774znwTR/vorPuPH4/7VV3hPnYpbOWOz/F//qrKo1lKVHnH5dlyqZJ+lvRuB/ZduYeJEHxYt8uKN76vuEAFgfdljxNONPfRharTjZpiqNCVb/f1HuzPE90ezc7IskVmg3vn+nynqlwQUFaYeUvXNVedhDfW8bQxHV53RSoKDg9myZQtarZZx48YRHh5Or169uOeee+jVqxfh4eGMGzcOrVbLZ599RlBQkEOFra1Ily9xtEilr0RLjaeVI0xr0t7Pl6rTKirjKlbijAwNzz/vXd53Nd5Tp3KYKF7jJfbSC59x40yuS2XWeS2xBXt8n5obHEvYm++/d57BxW76OizvKnUZFF5OXJyWmBjD1gj3jRvxb9ECv06d0B4+bLOMqrCx7+MpmU/5bz37bzYm3Krq9zJQWqjeiEFfZj7HqXTOXnRokmVbBql/20cQC1T6Nd16663Exsayfv16du7cSVJSEpcvX8bf35+OHTsyaNAgnnjiCerVq+dQQWszbsf+oBj1a2w2z/PL1s0MVRRX0zSdhFLOtkzTZmVpyMyUaNz4eh5/pbnTnV8owRMJPXsL7jR1zubEoUXVRpiV/0iW4dw5ifr1bdvM/dBDzgn7pYarV+F///MkL0/iueeKTd5pZUiSlZUWOJXiwX+e9yU7W2LOnGLOn5d46SXDuvZ9g64y6rtYpnKcwLxc1k+KRvv+f627QTXg7Agsio5IHLjsYqsVsNu+fcBQ+wijlL+aRPXq1WPy5MmVOjAQVB2rDGuUzusd5+lHNTLKRj9IlX8IFWmF0lJurKovXZlNCZ7/5K3hKdbx641ZXbmqXmYbUWlEbvWPRo3yISbGnaZN9eRerR1uA6dP9zYavP34oxtxcQUVLjOePKnh6FEtvXqVValuD3m0ifH/J580NRj65jtvvuEjAM7TmBePPka01XcwIMuwZIknn3ziQadO5rMbJt9sNc8TW4oIYgnFWAoOHGHaWj6O9lvrvPkaQSVY4a/VQqWybkrW/lhy21dRj/TsWQkfn4rlNmz2vh4iKK6si8l1g6OA6x5UpMxMdQLbgar0tjW5ucCNrsTM84iJMXhXueYOriZSUgKLF3ty6JAbDz1UYmIdfuqUlmPHNLRvf73xLS6GAwfcaNZMT16exL33+lJcLBEcrCdfVna9Zi/20Acwd+GmhiNHNCxebJAvLc1x1tj2QMYKC0Msr2E6Cllrm0py9AhcKMwaiOXwXipHqQYPA3aV6Z+clc9aUCq9e/uSkOBGQIDMi48GWs7VWqXkzCnZqijM7Cyg9m/B+vhjd954w6BIyjuAB7h8+XrjptfDvff6cuiQG+7uMqWl169lZ2vAiiULZzNnTiV7We1YHZ09PrVkJauEwfhulE330zez7bso63CLTb+vjJrbfa1FlOq1BLnZGLreGufrDlQo1hj9JCQYGtFLlyReWnmT/WSwYmO1zfdykc3xrsjkyRXvm7xxpPLDD24cOmSoDzcqy5qANY5ATv5pm5tDZ69h6nXK+zCV8B092ub72fp8Zc1b2CxDRbi0wly0aBH169c3+bv5hs3RsiyzaNEiWrduTaNGjbj33ns5ccI0jFRxcTHTpk2jZcuWNGnShFGjRpGRkWGSJi8vz2gFfM3yNy/PRgVmBZ8eaU87L3VbISw6B0BSX9WcvI5irZGRYgb/UFoKBXLFhiySwvOpdeH5SLM91khWJYVZ/j05292Zq3Dja4qLs79LQmdhzeeUl29bk2t7+CsV9gQ3prci+tBrvFRVseyG3sHfkksrTICIiAiSkpKMfwcOHDBeW758OStXrmTJkiXs2rWLkJAQhg0bxuXL1/e1zZw5k6+//pp169axY8cOLl++zMiRI9Hd4C9t7NixJCYmsmXLFrZu3UpiYiLjx4932jMmZwehb9lCVVrDONIOlcIBOtPiGqbN3jsMGaSledK1qx95cn1V6W9k7YtnVN1Lo7WubC31tgWVc2O9cEUfA+WJjdWydau7Wefr4MGKV7Zu/F5dorbYuIYpW6jzs3mtqhJdz9vGAvol1bFLHS6vMN3c3AgNDTX+BQcHA4bR5apVq5g8eTJDhw6lbdu2rFq1ioKCArZu3QpAfn4+GzduZN68efTp04eoqCjeffddjh07xp49ewBISkrixx9/5K233qJbt2507dqVN998k5iYGJKTk53yjI/cehS5uY0bgWUZjaRuKtKhIxrFBVbbshz2RBN+/13L6tVNOX1aeSSSknJDVVZwHjv3w3a2CWGBKo0wnRRDsjpYtcqD22/3qzQoMYC+5LpFqasrzE2b3Bk0yI+xY30YONCvyg17tc8mSOpliI/X0qZbmNl5R+7DtFVhfnGsrX0EsYDLK8zU1FTatGlDx44defLJJ0lNTQUgLS2NzMxM+va9vlna29ubHj16EBcXB0BCQgKlpaUmacLCwoiMjDSmiY+Px8/Pj27duhnTdO/eHV9fX2MaR3NT0EXVDYbFbSXWOC5w5BqmUkR4vXWm7OVJ+suDxx7z4ccfG1hM07OnHykpGs6ckVi13zbvNdbgSIvBmsjMmd6cOKHls88qN9LRJF13uenqCvPZZ6+vxyYmatm7t2pTyErfwdmz1gRfsB213+LUqd5cLTJXEXo91MP+S1YdPE7a3DSNaOc4JyDg4layXbp04Z133iEiIoLs7Gyio6O5++67OXjwIJn/bB0ICTH1ExoSEsK5c4Z4f1lZWWi1WjMPRCEhIWRlZRnTBAUFmURakSSJ4OBgYxpLVH0EarotovDKFS5fMo8IYsi/i9n5s2fOgOlWfdLS0oAOqvI4c+YMxUXmexUt3U8JxbQynE5JAZqZnE5NTUWWQ1Xla4kzZyru2xUWSkycqOfkSV/y8zsqplHzfKWlJVa914LsMsBKX7V6vck9Sq2IHlGbyMq6QHKy5p//mwKNq1cgKvqmTevN/h/P0rTpJcVr5dHrdcZ8M7MkyltI9+ihPmKM3gqDNqX6XlZWxk2+p4A+CmlNSUxUfq6002mUb3/sgbd0hZwc2xRxZEAyycnqoyuVJyIiosLrLq0w77rrLpPjLl26EBUVxccff8xtt90GYBZSTJblSsOMlU+jlF5NPpUVrlp8fX2pV9/cek4pfxmJJk3MAxKHhzdXHGEq5dG0cRM8vcwrlTXPoyibJNGixb/MzjcPb44k2egjUgVxcRV7m3r77co/cg93d6vKIde3Ch+nRsOXX7Zn2zZ3unbVUaap2v6/mk5wcIixrPfuNY+1WR2offeNMjKIiFA3k6HRaIz5nmx4yex6Xp67avkkrfqRrdKzaLXuePqYfyfW1PmwsGYOsdbVSBINgoJtysPbx9du7bISLq0wy+Pn50fr1q1JSUnhvvvuAwwjxLCw6/Ps2dnZxlFnw4YN0el05OTkGNc+r6Xp0aOHMU12draJgpRlmZycHLPRqyOxeUrKGufrjjJNt3B7V9l6UT6sWlXR6+HgQS0NGsiE+Fr/bPFXOhL/j33EiRNaoIdd5Kpp3FgvUlNrlpXsgdNNWf9vP9zcVLg5vNHox2a3lrZ9u5+f+zeff2ObDPoyvUPaEEmSkVJOY8votVoDSLsaRUVFJCcnExoaSvPmzQkNDWX37t0m12NjY43rkVFRUbi7u5ukycjIICkpyZima9euFBQUEB8fb0wTHx9PYWGhybqmo7FqDdPGANIGN3r2RwbFyCSuojDtxZNPenPPPX706OHHx5+pn04TmOKK0UT0enVyfZ5wM3/8oTXuJa4IWZaQsrLQ/v47cpkV0cyV8pIc02RL6enqZdA7qtMtoT0Qa1MOjjaqcukR5uzZsxk4cCBhYWHGNcwrV64wevRoJEli4sSJvP7660RERNCqVSuWLVuGr68vI0aMAAw+cB999FHmzJlDSEgIgYGBzJo1i3bt2tG7d28AIiMj6d+/P1OmTGH58uXIssyUKVMYMGCAQ4f2N2LN6NLgl9VCPlaYBDizrXL2ZmtbUCrD0lI4cUJDWJhMbq7El18aRqp6vcTL80XQgariagZTy5Y1Y/PmekRE6Pjkkyu0amUfAaXCAvy79Ee6dAmP+uOAd6ucl97PH3LtIpYJUkEBMvDVV24kJmp58EHL3hj0ZXoK8Le/DMjI6RmVJ6wAR3fCXFphnj17lrFjxxqnVLt06cIPP/xAeLhh0fz555/n6tWrTJs2jby8PDp37sy2bdvw97/+MhcuXIhWq2XMmDEUFRXRq1cvVq9ejfaGtYC1a9cyY8YMhg8fDsCgQYNYunSp8x5UUr/VoGLn6yrRy2ReCag8XRWw5Eu2JinNP//UcPiwlrg4LTt3uhsNjoK8Chj3XDULV4twtZmHzZsNhmnJyVpee82T9evt48T/EgGMubScw9yKf17FsU8rQ64fCOoHg1ZkLLNliztPP22wBn7nHcseid58z7Z1RkscuNqJA2y0KQ9H1ymXVpjvv/9+hdclSWLmzJnMnDnTYhovLy+io6OJjrYciyAwMJA1a9ZUWU57UJmBkQnWdKMsTN+uP/Fv9XnYiivOvVngcO6/6Hm7F1dLzQ0xcor8WFTVkBYCM1y5VnzxhYfdFOYpIjlFpF3y0hw9Cgy0S143IsuY7J29etVye/Txdst+n6sbR3v6cWmFWZewbjpVwaoXqdr3YVYlWomrkZjforpFqDPILjYlW5c59pdP9TtVsAPC6KeuoHKEaXFqU5aVp3WVRpgOdE6edcHc2lGWce3hhKBaOH/RNkfkdRFHLW2U1DCH95Zw9Lq4UJgugaTa8CcD8z2YUJXwXvZHRiLnonmVMjhfrx0fpMB+bNln7nbNlZg2zYvbb/dj/nzXUezWBEgePbriaDE3UoNWTSpELrEidEwVEFOyLoJahVmKh8UpTkWFqTTCdPIUaU2akhU4Dz/PMlw5zuXatQZFadgr6xpY0/H87jv1DhFqS4dW88cx4A7H5e+wnAVWYavjAoOVrLliKihQTutUakv3VWBX7g/cXXkigQnp5dxO2ovaEnVHLnKsRzGhMF0BCRy0H5mdO817mXZRmBaUYGiw+ZSIIWnt+CAF9uOjg5HcfLM/4eGO2eJUG/mcEQ7Jt7Z8nY4eKYspWVfAitramhMWFd7fevPep9IiuKyXaeBZwMViG/x3WghirdWYn3/nwwacLAsyOy+o2/xc0BkUZkAE1UAtsJAF69Z4q4IYYboAEuo39rtRpnjeovcfBdt9WYbhLX9TLZ8Sl/7KVi3H2k+EshQIXJnasmri6BGmUJgugl6v7kVbVK4Warz2+DHze+lk3FQGm7bE6mf+VDx/8JCXTfkKBALn42puCquKo0eYYkrWJZDRyeYv+rffzK3zLG0dyc1XtuTT5pk7npT1MukF5sGYy5QHr4os/HWQ2bkcOYix09TnIRAIXANHe8hxFmINs46g5EugXz/zNUYJWXENs+eImxTzVbK+fXpBa5LTzfdo9eqm3gxdIBDUHgxhtdpXtxg2I6Zk6wIy6FSO7iRkZCsWHJQUppKyBDj+l/qNzgKBoPZQ/4LyEktNQ0zJ1gFW7u/ChUJfVWklZOtswEWXSCAQVMKYr0dWtwh2YRd9mevA/EVz6gKoVZbXsGYfpVVRUAQCQZ3kZF6T6hbBLhwhyqH5ixFmDeMKPixcrz4endCXAoFAYB+EwqxhJNGapP3q0ydn1XecMAKBQFCHkPLy8mrJltWaQ/369apbBIFAIKh1+HOJdAeqNLGGKRAIBIJaQR8c69BfKEyBQCAQ1Aq2M5TTpx2n1oTCdDLJyaLIBQKBwFHcequ/w/IWrbeTeXV6LXHaKBAIBHUMoTDL8d5779GxY0dCQ0O58847OXDggF3z/2a3MPgRCASCmohQmDewbds2XnzxRV544QV+/vlnunbtyoMPPkh6enp1iyYQCASCakZsK7mBfv360a5dO95++23juU6dOjF06FDmzrWPwyWxpUQgEAgcS15evkPyFSPMfygpKSEhIYG+ffuanO/bty9xcXF2u48/l+yWl0AgEAich/D08w85OTnodDpCQkJMzoeEhJCVlaX4m+TkZKvvc+ilb4hc+EpVRBQIBAJBJax86xjJyVer9NuIiIgKr4sRZjnKOyuXZdmiA/OIiAir/0KnT+HyDz/SkExnPE6FhHLeqfdrwWm759mMv+2eZ1W4kz0VXv+MB02OFzPD5Pg24k2OfSg0OZ7NfJPj/7DR9PcN/kSDzuL9R7DF5Ph1/s/4vy8FJtde6v69xXyG8BUvM8/i9d7lNo5PY6nJ8XqeMDmex8vG/+uRZ3LtK4aYHP9MT5PjGO62KIclRvKp6rQTWGV1/tdYwX8rvH5jvb2ZJJNrg7tmmByf8zdtxDtziAB3ZYWgJHMYyjYY5cuzIg7R2ezcc7zNCywzO1/Q+lbVefyf+9sKKaG5e4bieSV+euZj4/8vDTjII0+EValtrkxZgljDNFJSUkLjxo1Zt24d999/v/H81KlTOX78ODt27LDr/ZKTk4lo1IiytHN8vbcBkZE62vZrSJlOIjj4+jpn506lbPviCs2bm6597vk+h8gObjRubHo+6Wg2foFu5Ow9yXe/NwZfP56a5IabG0g5OZz/7TwT3mjPc88W0fc+TwByj51jwRtBZF/yYPEbOho30/LHEZlHHvIgLdOH27sW89U3RXh4wJqlV5m+sJHhGX7PIKSpO8WyB6/O0PPOBw0YcW8+b66S8Q+Q0CQlkXzag3c+a8KI+y5x+7Bgzp+X2PHhJVq30dFjSKBB6IyzSMXFyOHNwM0NKSsL6dIl9DfdBJKElJ0NV65w6soVIlq3huJi9Mmn0fyrGfj6IpeUcnjHBXz9JSL7NgJJ4tCeq5w9XUJEGy033+bHiZ0ZpKZp6fdEKN4+Eoe+y2XUxKbccUcp0W+WEaC5zLEvU2nTPxSfsEAy0nRs+0wioo2GgUO1IBmeR/b0RG7RAoDLCSkU5ZUQ0ru14TmuXIHCK9AgELRaKC1FOnsWuWlTcHNDf7mQnEN/E9A1Ak9fNygrQ8rMBC8v5KAgZL3MhYRz+N3cCB8/Q19WysxE9vEBf3+QZaQzZ5AbNgRPw7uTLlwAnQ65keGdUFAAGg34+HD4cAqNQ/9Foyb/dPj0ejRpaehbtDB45S8oQCoouP7bwkLw8AB3d5BluHwZvL0NxwBFReDlZbyPVFSEHGwIBCBdvIjs5gYBAYb7pKaib9LEkF6nQ5OUhD4y0lAuhYVosrLQ/+tf18utpATq1zc8Y1oacni44Tmu5V2/vuFYr0fKyDAc+/sTH6/lwQe8CPDT8/4HxdzWVQ8XczlzvIAGtzTBx1+LdP48mowMdFFRhvtfvWp4Jje362Xs60vyuXNEtGqFlJVlyN/TE0pKkHJykENCDOkLCgzP1ratQfZLlwx1NzTUkFdWFrKXl7EcpNxc5AYNDOWt1xuuh4Ya6vWFC8ju7obnBkPaevWMz01JiUFOSYLSUn77NpsCj0B6DvRCp4OQEHN7iLy8fBbPuMridw3vdPjQIt7/oJgzaXra3xJoTJd7MQ9JIyFdvMi8eZ68vqEJ/3vrMo8+oTe8g9xcUv52xyfQg9Dmnsb3v+uzfGL21+epcWXc3NmX5FOnaKWDzg91JCXdk5SUSzRoIENZGdlxqfR5qj2jH7zKS/MNdbDkcjGD+nvxW1IAi1/JZcJkjaGupZ9hTnQwvkGeTJ9reP7AQPPna9ZMT3q66TgvPT0ff+8yNOnp1+u2gxAK8wb69etH+/btWb58ufFc586dGTJkiN2Mfq6RnJysqkdzI0VFcPKkhvBw2VAp/yE/H/78U0v79rpr7WitpCplVpcR5WUdNa28Ll+GZs2uK5X586/y3HMlTpXBkWVW3kDS319m06ZChg71M5675RYde/cWlP+pwxBrmDfw3//+l/Hjx9O5c2e6devG+++/z/nz5xkzZkx1iwYYOutRUeaOD+rVg86dLU/HCQSC2oe/P/zyy2U2bPAgIkLP2LHOVZaOpvxoMjJSx5136tixo4AJE3zo3buMefOqtlZZVYTCvIHhw4dz8eJFoqOjyczMpE2bNnz22WeEh4dXt2gCgUBgRvv2epYtK6puMRzCihVXTEaTS5canrNHDx2JiZerRSahMMsxduxYxo4dW91iCAQCQZ3mzjt1rF9/hb17tdx9dxmdOlX/LJpQmAKBQCBwSYYNK2XYsNLqFsOI2FYiEAgEAoEKhMIUCAQCgUAFQmEKBAKBQKACoTAFAoFAIFCBUJgCgUAgEKhAKEyBQCAQCFQgXOMJBAKBQKACMcIUCAQCgUAFQmEKBAKBQKACoTAFAoFAIFCBUJgCgUAgEKhAKEyBQCAQCFQgFGY18N5779GxY0dCQ0O58847OXDgQHWL5HAWLVpE/fr1Tf5uvvlm43VZllm0aBGtW7emUaNG3HvvvZw4ccIkj+LiYqZNm0bLli1p0qQJo0aNIiMjwyRNXl4e48aNIzw8nPDwcMaNG0deXp5TntEW9u/fz6hRo2jTpg3169fno48+MrnuzPJJT09n5MiRNGnShJYtWzJ9+nRKSlwv1mJlZTZx4kSzOte/f3+TNHWpzN544w369OlDs2bNuOmmmxg5ciTHjx83SSPqWcUIhelktm3bxosvvsgLL7zAzz//TNeuXXnwwQdJT0+vbtEcTkREBElJSca/GzsKy5cvZ+XKlSxZsoRdu3YREhLCsGHDuHz5ety7mTNn8vXXX7Nu3Tp27NjB5cuXGTlyJDrd9bA/Y8eOJTExkS1btrB161YSExMZP368U5+zKhQWFtK2bVsWL16Mt7e32XVnlY9Op2PkyJEUFBSwY8cO1q1bx/bt25k1a5ZjC6AKVFZmAL179zapc1u2bDG5XpfK7JdffuGpp54iJiaG7du34+bmxv33309ubq4xjahnFSP2YTqZfv360a5dO95++23juU6dOjF06FDmzp1bjZI5lkWLFrF9+3ZiY2PNrsmyTOvWrXn66aeZOnUqAFevXiUiIoL58+czZswY8vPzadWqFStXruShhx4C4MyZM3To0IGtW7fSr18/kpKS6NatGzt37qR79+4AxMbGMmjQIH799VciIiKc98A20LRpU5YuXcojjzwCOLd8fvjhBx566CGOHj1KWFgYAJs3b2bSpEkkJycTEBBQDSVSOeXLDAwjzIsXL7J582bF39T1MisoKCA8PJyPPvqIQYMGiXqmAjHCdCIlJSUkJCTQt29fk/N9+/YlLi6umqRyHqmpqbRp04aOHTvy5JNPkpqaCkBaWhqZmZkm5eLt7U2PHj2M5ZKQkEBpaalJmrCwMCIjI41p4uPj8fPzo1u3bsY03bt3x9fXt0aXrzPLJz4+nsjISGMjBoZOXnFxMQkJCQ59TkcQGxtLq1at6Ny5M5MmTeLChQvGa3W9zAoKCtDr9dSvXx8Q9UwNIoC0E8nJyUGn0xESEmJyPiQkhKysrGqSyjl06dKFd955h4iICLKzs4mOjubuu+/m4MGDZGZmAiiWy7lz5wDIyspCq9USFBRkluZa2WVlZREUFIQkScbrkiQRHBxco8vXmeWTlZVldp+goCC0Wm2NK8P+/fszePBgmjdvzt9//82CBQsYMmQIe/bswdPTs86X2YsvvkiHDh3o2rUrIOqZGoTCrAZurEhgmHIrf662cdddd5kcd+nShaioKD7++GNuu+02oGrlUj6NUvraUr7OKh9Leda0MnzggQeM/7dr146oqCg6dOhATEwMQ4YMsfi7ulBmL730EgcPHmTnzp1otVqTa6KeWUZMyToRSz2o7Oxss95WbcfPz4/WrVuTkpJCaGgoQIXl0rBhQ3Q6HTk5ORWmyc7ORpavL8vLskxOTk6NLl9nlk/Dhg3N7mNpZqSm0bhxY5o0aUJKSgpQd8ts5syZfP7552zfvp0WLVoYz4t6VjlCYToRDw8PoqKi2L17t8n53bt3m8z31wWKiopITk4mNDSU5s2bExoaalIuRUVFxMbGGsslKioKd3d3kzQZGRlGAwOArl27UlBQQHx8vDFNfHw8hYWFNbp8nVk+Xbt2JSkpyWSbwO7du/H09CQqKsqhz+locnJyOHfunFEx1MUymzFjBlu3bmX79u0m27pA1DM1aF988cVXqluIuoS/vz+LFi2iUaNGeHl5ER0dzYEDB1ixYgX16tWrbvEcxuzZs/Hw8ECv1/Pnn38ybdo0UlJSePPNN2CGnbsAAAT/SURBVKlfvz46nY4333yTVq1aodPpmDVrFpmZmbz11lt4enri5eXF+fPnWbt2Le3btyc/P58pU6YQEBDAq6++ikajITg4mEOHDrF161Y6duxIRkYGU6ZMoVOnTi6/taSgoICTJ0+SmZnJxo0badu2LQEBAZSUlFCvXj2nlU+LFi34+uuv2bVrF+3atePkyZNMnTqVBx98kMGDB1dzKZlSUZlptVrmzZuHn58fZWVlHD16lOeeew6dTkd0dHSdLLOpU6fy6aefsmHDBsLCwigsLKSwsBAwdOYlSRL1rBLEtpJq4L333mP58uVkZmbSpk0bFi5cyB133FHdYjmUJ598kgMHDpCTk0NwcDBdunRh1qxZtG7dGjBM2SxevJgNGzaQl5dH586dWbZsGW3btjXmUVRUxMsvv8zWrVspKiqiV69evP766yaWdrm5ucyYMYPvvvsOgEGDBrF06VKjJaCrsm/fPsWGYvTo0axatcqp5ZOens7UqVP5+eef8fLyYsSIESxYsABPT08HloD1VFRmb7zxBo888giJiYnk5+cTGhpKz549mTVrlkl51KUys/QNzJgxg5kzZwLO/Q5rQpmVRyhMgUAgEAhUINYwBQKBQCBQgVCYAoFAIBCoQChMgUAgEAhUIBSmQCAQCAQqEApTIBAIBAIVCIUpEAgEAoEKhMIUCOoo14J6CwQCdQjn6wJBLUKtAly5cqWDJREIah/CcYFAUIsoHyx5w4YNHDp0iBUrVpic79atG2FhYZSVleHl5eVMEQWCGotQmAJBLWbixIls27bNGOtQIBBUHbGGKRDUUZTWMDt06MADDzxAbGws/fr1o1GjRnTv3t0YneLHH3+kV69ehIaG0qNHD+Li4szyPX/+PM8//zytW7emYcOGdOrUieXLl5uEexIIaiJCYQoEAhPS0tIYM2YMffv2Ze7cuRQWFjJ69Gi2bdvGpEmTGDJkCLNnzyYrK4tHH32U4uJi428vXLhA//79iYmJ4fHHH2fJkiV06dKFuXPnGh18CwQ1FWH0IxAITPjzzz/59ttvjRF0OnbsyL333sv48eM5cOAAERERAISFhTFmzBh27tzJ0KFDAViwYAHFxcXs37+fhg0bAjBmzBgaNWrEihUrmDhxIs2bN6+eBxMIbESMMAUCgQmtWrUyCTfXpUsXwBD095qyBOjcuTMAqampgCE01FdffcWAAQPQarXk5OQY//r164der2f//v3OexCBwM6IEaZAIDDhxriGAJ6ennh6etK0aVOT8wEBAQDk5eUBkJ2dTV5eHps2bWLTpk2KeWdnZztAYoHAOQiFKRAITNBqtVadv2bMo9frARgxYgT/+c9/FNO2bNnSDhIKBNWDUJgCgcAuBAcHExAQQFlZGb17965ucQQCuyPWMAUCgV3QarUMGTKEb775hoSEBLPr+fn5lJaWVoNkAoF9ECNMgUBgN1555RX279/PwIEDefTRR2nbti2XL1/m+PHjfP311/z++++EhoZWt5gCQZUQClMgENiN4OBgfvrpJ6Kjo/n222/ZsGED9erVo1WrVrz44osEBgZWt4gCQZURrvEEAoFAIFCBWMMUCAQCgUAFQmEKBAKBQKACoTAFAoFAIFCBUJgCgUAgEKhAKEyBQCAQCFQgFKZAIBAIBCoQClMgEAgEAhUIhSkQCAQCgQqEwhQIBAKBQAVCYQoEAoFAoIL/B4mQXG4GhS5HAAAAAElFTkSuQmCC\n", + "image/png": 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24BNVbQd84n5HRNoD5wMdgAHAkyJS7O5rKHAl0M59DXDLLwPWq2pb4BHgwTyej8VisVgM5E2RqMMW92tt96XAaUB04sOLwOnu59OAEapaqqqLgYVATxFpATRU1SnqhJi9lNAmuq83gL5Ra8VisVgslUNene0iUiwiM4BVwDhVnQrsrarLAdz3vdzq+wJLPc1L3LJ93c+J5XFtVDUEbASaGuS4UkSmici01atX5+r0coo3jfw555zDtm3bMt6XNy385Zdfzty5c5PWzTQDbuvWrVmzZo2vfMuWLVx11VWx+R+9e/dm6tSpcQkgE7nzzjv5+OOPKyxDKgYNGsTgwYPT1nvppZfo2LEjHTp0oH379oHaVJT77rsv5/u0WAqJvCoSVQ2ralegJY51Ye5JHEyWhKYoT9UmUY6nVbWHqvaI5n4qNLxp5HfbbTeeeuqpuO3hcDij/T777LO0b98+6fZcp1K//PLLadKkCQsWLGDOnDkMGzbMqHC8/POf/+T444/PmQxBGTNmDI8++ihjx45lzpw5fPPNN7FcYrnEKhJLTadSwn9VdQMwEce3sdIdrsJ9X+VWKwFaeZq1BJa55S0N5XFtRKQW0AhYl5eTqESOPvpoFi5cyMSJEzn22GP53e9+R6dOnQiHw9x8880cdthhdO7cmf/+97+Akzpl4MCBtG/fnpNOOolVq1bF9tWnTx+iEzA//PBDunXrRpcuXejbt68xlfrq1as566yzOOywwzjssMP4/PPPAVi7di39+vXj0EMP5aqrrjLmvFq0aBFTp07lnnvuoajIubUOOOAATjrpJMBRhldccQUdOnSgX79+bN++HYi3oEwp3wHWrVvH6aefTufOnTniiCOYNWtWynIvzzzzDL/97W9jx4ty//33M3jwYPbZZx8A6tatyxVXXAE4mY6POOIIOnfuzBlnnMH69et913PNmjW0bt0aSJ5m/7bbbosl5LzwwguD/PwWS7Ujb+G/ItIcKFPVDSJSDzgexxn+DnAx8ID7/rbb5B3gVRF5GNgHx6n+laqGRWSz66ifClwEPOZpczEwBTgbGK9ZTtWv4izyhEIhxowZw4ABTjzBV199xezZs2nTpg1PP/00jRo14uuvv6a0tJSjjjqKfv368e233zJ//ny+++47Vq5cSfv27bn00kvj9rt69WquuOIKJk+eTJs2bVi3bh1NmjTxpVL/3e9+x1/+8hd69erFzz//TP/+/Zk3bx533303vXr14s477+T999/n6aef9sk+Z84cunbtSnFxsW8bwIIFC3jttdd45plnOPfcc3nzzTf5/e9/76tnSvl+1113ceihhzJ69GjGjx/PRRddxIwZM5KWR3n88ccZO3Yso0ePjks6CanT1V900UU89thjHHPMMdx5553cfffdPJrmRzSl2X/ggQd4/PHHjYk3LZaaQj7nkbQAXnQjr4qAkar6nohMAUaKyGXAz8A5AKo6R0RGAnOBEHCdqkbHc64BhgH1gDHuC+A5YLiILMSxRM7P4/nklehTKzgWyWWXXcYXX3xBz549Yynix44dy6xZs2JP7xs3bmTBggVMnjw5llp9n3324bjjjvPt/8svv6R3796xfTVp0sQox8cffxznU9m0aRObN29m8uTJsfTrJ510Eo0bNza2T0WbNm1i59i9e3eWLFlirGdK+f7ZZ5/x5ptvAk5Sx7Vr17Jx48ak5QDDhw+nZcuWjB49mtq1aweWc+PGjWzYsCGWZPLiiy+OS+WfDFOa/VatWqVpZbFUf/KmSFR1FuDLnKeqa4G+SdrcC9xrKJ8G+PwrqroDVxHliirKIh/zkSSyxx57xD6rKo899hj9+/ePq/PBBx+QLlhNVdPWAScr8JQpU6hXr55vW7r2HTp0YObMmUQikdjQlpfENPSJQ02J9bwp302GpogkLQfo2LEjM2bMoKSkxLheSzS1vknxJsObrj5ZqvpE2S2Wmo5NkVKN6N+/P0OHDqWsrAyAH374ga1bt9K7d29GjBhBOBxm+fLlTJgwwdf2yCOPZNKkSSxevBhwfAvgT6Xer18/Hn/88dj3qHLr3bs3r7zyCuA4qaM+Ay8HHnggPXr04K677op18AsWLIhlLM4G7/EnTpxIs2bNaNiwYdJycLIo//e//+XUU09l2bJlvn3efvvt3HLLLaxYsQJwFg0bMmQIjRo1onHjxrGMwMOHD49ZJ61bt2b69OkAgdezr127duw3s1hqIjZFSjXi8ssvZ8mSJXTr1g1VpXnz5owePZozzjiD8ePH06lTJ379618bF4tq3rw5Tz/9NGeeeSaRSIS99tqLcePGccopp3D22Wfz9ttv89hjjzFkyBCuu+46OnfuTCgUonfv3jz11FPcddddXHDBBXTr1o1jjjmG/fbbzyjjs88+y0033UTbtm3Zfffdadq0Kf/+97+zPvdBgwbxxz/+kc6dO7P77rvH1mBJVh6lV69eDB48mJNOOolx48bFpdw/8cQTWblyJccff3zMYov6ll588UWuvvpqtm3bxgEHHMALL7wAwF//+lfOPfdchg8fHtiSufLKK+ncuTPdunWLKT2LpSZh08hjU3lbqg5771kyxpAz3qaRt1gsFku1xCoSi8VisWSFVSQuu9oQn6XqsfecpaZgFQnOjOa1a9faP7al0lBV1q5dS926dataFIsla2zUFtCyZUtKSkoo1ISOlppJ3bp1admyZfqKFkuBYxUJTpy/acKaxWKxWNJjh7YsFovFkhVWkVgsFoslK6wisVgsFktWWEVisVgslqywisRisVgsWWEVicVisViywioSi8VisWSFVSQWi8ViyQqrSCwWi8WSFVaRWCwWSzVkKFdzAIuqWgzApkixWCyWasm1DK1qEWJYi8RisVgsWZE3RSIirURkgojME5E5InKDWz5IRH4RkRnu60RPm9tFZKGIzBeR/p7y7iLynbttiIizoKSI1BGR193yqSLSOl/nY7FYLBYz+bRIQsBNqnoIcARwnYi0d7c9oqpd3dcHAO6284EOwADgSREpdusPBa4E2rmvAW75ZcB6VW0LPAI8mMfzsVgsFouBvCkSVV2uqt+4nzcD84B9UzQ5DRihqqWquhhYCPQUkRZAQ1Wdos7KUy8Bp3vavOh+fgPoG7VWLBaLxVI5VIqPxB1yOhSY6hYNFJFZIvK8iDR2y/YFlnqalbhl+7qfE8vj2qhqCNgINDUc/0oRmSYi0+ziVRaLxZJb8q5IRKQ+8CbwZ1XdhDNMdSDQFVgO/Cda1dBcU5SnahNfoPq0qvZQ1R7Nmzev4BlYLBaLJRV5VSQiUhtHibyiqqMAVHWlqoZVNQI8A/R0q5cArTzNWwLL3PKWhvK4NiJSC2gErMvP2VgsFovFRD6jtgR4Dpinqg97ylt4qp0BzHY/vwOc70ZitcFxqn+lqsuBzSJyhLvPi4C3PW0udj+fDYx3/SgWi8ViqSTyOSHxKOAPwHciMsMt+xtwgYh0xRmCWgJcBaCqc0RkJDAXJ+LrOlUNu+2uAYYB9YAx7gscRTVcRBbiWCLn5/F8LBaLxWJAdrUH+B49eui0adOqWgyLxWLJimh8qrcLN5Xl7ngyXVV7mLbZme0Wi8ViyQqrSCwWi8WSFVaRWCwWiyUrrCKxWCwWS1ZYRWKxWCyWrLCKxGKxWCxZYRWJxWKxWLLCKhKLxWKxZIVVJBaLZZdi1ixn4t6ECVUtSc3BKhKLxbJLEVUgo0dXrRw1CatILBaLxZIVVpFYLJZdC40475FI1cpRg7CKxGKx7Fp89bXz/s03VStHDSKtIhGRB4OUWSwWS7Vg21YAdNv2Khak5hDEIjnBUPbbXAtisVgslYGIaYVuSzYkVSQico2IfAccJCKzPK/FwKzKE7GGsGiRjTe0WAqKXWQtptmzYdSovB4i1QqJr+KsRHg/cJunfLOq2nXRK0rbts77LraQmMVSsOwqf8VOnZz3PPY9SRWJqm4ENuIsjVsM7O3Wry8i9VX157xJZbFYLPnCDm3lnLRrtovIQGAQsBKIxssp0Dl/YlksFku+2TVMkul0YwZduSyPx0irSIA/Awep6to8ymGxWCyWPNCD6QB5VSRBoraW4gxxWSwWS81h1zBIKoUgiuRHYKKI3C4iN0Zf6RqJSCsRmSAi80Rkjojc4JY3EZFxIrLAfW/saXO7iCwUkfki0t9T3l1EvnO3DRE3fk9E6ojI6275VBFpXdELYLFYLJbsCKJIfgbGAbsBDTyvdISAm1T1EOAI4DoRaY8TAfaJqrYDPnG/4247H+gADACedJ38AEOBK4F27muAW34ZsF5V2wKPAAU7UXIRB/AJx1W1GBaLxZJz0vpIVPXuTHasqsuB5e7nzSIyD9gXOA3o41Z7EZgI3OqWj1DVUmCxiCwEeorIEqChqk4BEJGXgNNxQpNPwwkEAHgDeFxERLXwYmzbsgiw1rTFUuXYqK2cEyRqawKG/k9VAz9eu0NOhwJTgb1dJYOqLheRvdxq+wJfepqVuGVl7ufE8mibpe6+QiKyEWgKrAkqm8Vi2TUpvMfN6kuQqK2/ej7XBc7CGbYKhIjUB94E/qyqm1KkJzBt0BTlqdokynAlztAY++23XzqRLRZLDcYaJLknyNDW9ISiz0VkUpCdi0htHCXyiqpG5+ivFJEWrjXSAljllpcArTzNWwLL3PKWhnJvmxIRqQU0Anyz7lX1aeBpgB49etjnEIvFYskhQbL/NvG8mrnRVL8K0E6A54B5qvqwZ9M7wMXu54uBtz3l57uRWG1wnOpfucNgm0XkCHefFyW0ie7rbGB8IfpHLBZLAWFNkpwTZGhrOuVDTCFgMcHmthwF/AH4TkRmuGV/Ax4ARorIZTgRYecAqOocERkJzHWPc52qht121wDDgHo4TvYxbvlzwHDXMb8OJ+rLYrFYAmCfOXNFkKGtNpnsWFU/w+zDAOibpM29wL2G8mlAR0P5DlxFZLFYLBVB1VomuSJI1FZtHIugt1s0EfivqpblUS6LxVKVTJoEtWvDb35T1ZLkEWuR5IogQ1tDgdrAk+73P7hll+dLKIvFUsX06eO8W5ejJQBBFMlhqtrF8328iMzMl0AWi8WST6yvPfcESZESFpEDo19E5AAgnKK+xWKxFDzWR5I7glgkNwMTRORHHOf5/sAf8yqVxWKx5AlrkeSeIFFbn4hIO+AgHEXyvZsPy2KxWKofriKx7p/ckVSRiMjvAVHV4a7imOWWXyEiW1X11coS0mKxWHJFijRNlgxJ5SO5CRhtKH/d3WaxWCzVlhpjkRTAiaRSJMWqujmxUFU34YQDWywWS7UjapBUffdbc0ilSGqLyB6JhSLSAGeRK4vFYql2iLgqxGqSnJFKkTwHvOFdvtb9PMLdZrFYaiil7EZpjX1edEwSTZrByVJRkjrbVXWwiGwBJrlriiiwFXhAVYdWloAWi6XyacRGigmztaoFsVQLUob/qupTwFOuIhGTz8RisdQ8Sqlb1SJYqhFBJiSiqlvyLYjFYrFUBuXRv9ZJkiuCpEixFCLvvAPDhlW1FBZLtaXGpEgxhP9GIpUrQkaKRETq5FoQSwU57TT4o81UY7FUlKQWyfjxld8D54nKnloSZKnd5xO+1wc+yJtE1Y1p0+D116taCovFEpRYihSPRfLOO9C3L/zf/1WNTDmm4BQJ8IuIDAUQkcbAWODlvEpVnTjsMDjfrvBrsVQXjANaJSXO+4IFlSlKjSGtIlHVO4BNIvIUjhL5j6q+kHfJqjMffgj/+ldVS2GxWExIdB5JzcBkfVS2RZIqaeOZnq9fAXe47yoiZ6rqqHwLV2357W+d9zvuqFo5LBaLD3FViNHZXgB5q6ojqcJ/T0n4/i1Ojq1TcJS5VSSFxt57Q9u28PnnVS2JxVKwiGkcRmpWbvmCsUhU1YYEVTdWrXJeFoslLXF9bXVOLW/QGgXnbBeRliLyloisEpGVIvKmiLQM0O55t81sT9kgEflFRGa4rxM9224XkYUiMl9E+nvKu4vId+62IeIuJiAidUTkdbd8qjcn2K7AubxOB2anr2ixWCx5JkjU1gvAO8A+wL7Au25ZOoYBAwzlj6hqV/f1AYCItAfOBzq4bZ4UkWK3/lDgSqCd+4ru8zJgvaq2BVx+XoUAACAASURBVB4BHgwgU43hf5zLXDpUtRgWS/XF9NRuh7YyIogiaa6qL6hqyH0NA5qna6Sqk4F1AeU4DRihqqWquhhYCPQUkRZAQ1WdoqoKvASc7mnzovv5DaBv1FqxWCyWZBi7iRrmI6lsgiiSNSLyexEpdl+/B9ZmccyBIjLLHfpq7JbtCyz11Clxy/Z1PyeWx7VR1RCwEWhqOqCIXCki00Rk2urVq7MQ3WKx1BTidEYNewYtRIvkUuBcYIX7Otsty4ShwIFAV2A58B+33PQraoryVG38hapPq2oPVe3RvHlaY8pisdRgUuqMmmKRVPJ5pM3+q6o/A6fm4mCqujL6WUSeAd5zv5YArTxVWwLL3PKWhnJvmxIRqQU0IvhQmsVi2VUxTUisxhbJxo2wZ8JYTMFZJJlGbSXZVwvP1zMgFnb0DnC+G4nVBsep/pWqLgc2i8gRrv/jIuBtT5uL3c9nA+NdP4olAzZtgm3bqloKiyX/RCckejXJz2v3oDvTWLWtftUIlQWbN1WD8F8yjNoSkdeAKcBBIlIiIpcBD7mhvLOAY4G/AKjqHGAkMBf4ELhOVcPurq4BnsVxwC8CxrjlzwFNRWQhcCNwW4BzsSShUSNo06aqpbBYKgHxL7X7f+M78Q3dGb7giKqSKrcU2tAWbtSW5/swEflzukaqeoGhOOla76p6L3CvoXwa0NFQvgM4J50cluDYuYyWXQERQyf7889AJ1i2zL+twDGNyhWiRZLrqK0azxNcS38+rGoxLBZLCuL62kULnfdfSkxVLWkIYpFcCjyOM+lPgS/IPGprl2AgT1TJcRdxALuxMy5qwWKxxBN9gK8pHlVZvw5a7xVXphGz3yRfMQWVGrVlyS9tWQTUnPTYFkteMPSmWp1XHS+AVR3TKhIRaQ5cAbT21ldVa5VYLJbqSw1+4kq2Rkm+LJIgavhtnDkaHwPve14WiyVLFi6Ea6+FcDh9XUtuqMZTRsyYsv8ahrbySRAfye6qemveJbFYdkHOOw+++QYuvRR69KhqaWoAb74Jv/41dOqUtmpNmZAY1NmTT59QEIvkPW+6d4vFknuqcz9WUJx9NnTuDH/9K4wZY6xizNlYw8a5CjH89wYcZbJdRDaJyGYR2ZRvwSyWXYLo5J2NG6tWjhpGs//cxr9O/CJlHe9Su2pM3Vc9hhyDPoRUqUWiqg1UtUhV66lqQ/d7w/yJZLHsQqxZ47zb2aA5ZS3NuJN/GbdJkdEk8fHBB1CrFsyYkWPhck1AH0mVKBIROdh972Z65U8kS0555BEYMaKqpbCko6ZMaig0nn8+6aZ0V/zdd533KVNyJ05lYbqdos8s+SCVs/1GnJUJ/2PYpsBxeZHIkltuvNF5P//8qpXDYsb6RvLLZZdB9+7QpUusyOwPqfk/RD6TsiZVJKp6pft+bP4Ob7FYoPLDNXcpduwwFnt9JKn0SKEbiybFaLqf8jlvMUj4LyLyG/wTEl/Kk0zVis/5DXNpzxVVLYilWlLTooUKkm3bnHGdZs2Ach+J2S7xlG7YAOwJW7YAhZtePug9lE+FGGRm+3CcVQ1nANEYhuj66bs8vfgcwCoSS5YUuEIZOxYOOwwaN05ft9A4zh2Fd3tS03okRpPk22+BY5FZM4Gj8ihgdhQXBXOsO1ZKfobwglgkPYD2dtEoiyUfVIOx+Y0boX9/6NULPv20qqVJyWbqU4fS1JUqOmmnwCf5GMUzRXJV8YTE2cCv8ieCxWIpaINk507n/fvvq1aOADRkM734LHWlFM6C+PkkhfyjlPPd/N18ZcktkvyQ1CIRkXdxrmQDYK6IfAXlql5VbUZgiyUT7rnH+affcUd5x2UN/pzxNT1TV3jnHaAXunx5eVkKqyPZZMVCpmAUCTA4b0e1WHZl7rgj/r26UE2V3XmMYCTnxeyLrSs3AxAJ+S2TOMd1dTndZKl+g5TliFSK5Bdgb1X93FsoIr3dbRaLJUtiy74Wcidd4D6CdIzkvLjvt+y8B4C3OS1Q+4I//eJiX5HpdoqE83ePpfKRPApsNpRvc7dZqgEbaMR69qxqMZIjAuecU9VSWFJRyEouAzbhZHgqw+9bqI5InWDnUVXO9taqOssvjE7DmVNiqQY0ZgNNWF/VYiTlZS7kyzeWVrUYlcoMujCdhCxDBdxZh8PQjem8X9avqkXJCUUYnO2pzI5CN0kC5toyhQnnilSKpG6KbfXS7VhEnheRVSIy21PWRETGicgC972xZ9vtIrJQROaLSH9PeXcR+c7dNkTE+VVFpI6IvO6WTxWR1ulkshQef+BljuTLqhajUjmUGfRgelWLEZh164Vv6cYlm4dUtSg5wahIUpBXHf/uu3DKKVntYt8WhvMxZJNusmf+pranUiRfi4hvnp2IXAaB/gXDgAEJZbcBn6hqO+AT9zsi0h44H+jgtnlSRKIDf0Nxcn61c1/RfV4GrFfVtsAjwIMBZLJYCowCf9qFwn8iryAmRaLmyRj5F+bUU+G997LaRZ3ahvPZ4Z9LU1VRW38G3hKRCylXHD2A3YAz0u1YVScbrITTgD7u5xeBicCtbvkIVS0FFovIQqCniCwBGqrqFAAReQk4HRjjthnk7usN4HERkUKaOJnPNZItNYuCzrXl/qWqYxjsL+zjKzNbJFUztDWdbrzJWdyXxT4KIGgrZdLGlcBvRORYoKNb/L6qjs/ieHur6nJ3/8tFZC+3fF+IG98occvK3M+J5dE2S919hURkI9AU8CVLFpErcawa9ttvvyzET45JaWg4gtRKYfRdein8+CNMnJgXmSyFTyxqq4Cpzg9DLQ0BpqbcVGJSLpXw0xzG1yhF3BOBoiDTww0YFYQhlU1VWSTOwVUnABPyJoFDMrsylb0Z2BZV1aeBpwF69OiRl6tpVCShMKRSJC+8kA9RLNWRwjGkd1GSa8t8KlJ1vQuStLsLspOK5NrKDxnqwIxZKSItANz36LJwJUArT72WwDK3vKWhPK6NiNQCGgHr8iZ5BuSsb7jnHjjzzBztzJINn34Kw4fnYceFrEgkmi23GpsmHoznYTw1d0ivEn6adJ18OAxr1yZpWwC3TmUrkneAi93PFwNve8rPdyOx2uA41b9yh8E2i8gRbrTWRQltovs6Gxhflf6RTI58E4M5mXfT1rvkjpZ0eMu8bGhGtGkDJ56Yu/3tQvTuDRddlLv9VYfOuToPbQVFU3WFBXAB/vY3Jwv+OsOjsnFZ3YBluSLQeiSZICKv4TjWm4lICXAX8AAw0o38+hk4B0BV54jISGAuEAKuU9VoyvprcCLA6uE42ce45c8Bw13H/DqcqK8qIxPn1sPcFGjfL3JJxQVKxZIlzsuSG+67D0aOzGpx7+qwLkl1UHpBCHytK/MnSdNZvPGG875uHTRpUqGm5fWqoyJR1QuSbOqbpP69wL2G8mmUO/u95TtwFVGhUggmp6US+Pvfs95FIXfSBfBAXiVUZiBEuk4+2pcEThlfyRZJZQ9t1Vgq+4ezVD2q8MgjsJK90ldOQqxfKOCnjgIWLXcYeui4pXjzjfciP/EEdOjgfI5E4N//Rt3U96bILuNoyLbtwSrmCKtIcoRRkRToH3AHddhJ7aoWo9rz3Xdw441wAa9VtSiWfFIJJllcXzFwIMyd63weNQpuuYXImnVJRTH2MwsWBKuXI6wiyRWVPQMoC+qxgzYsrmoxqj1lZc77Rhplv7MCvVdg1xraqjJfVbLff7tjWejWbQDIWt80ucAPsXZoqxpQjfQIAMti8zoDsH27u361xcQ3dOe/znzXChOpRn/BKvHjzJwJf/pTTv9MRmURXV+sqlZI9JzfR/TjLFzvekLotWzfZm67bh0M9iwhZRqqs4qk8KnRPpLLL4du3WDlyrwe5osvnPt/5sy8HqZihMPw8cdpq13NfzPa/TdbDwJg4rcGq2bixDxNWqkYVWqRHH88PPYYrPE/iWdKUKtDfB/yh7evGMBHjOIswmHPdlcIY56wiMIVV8DNN5eXKbB6Nbz0kvEYucYqklwRcHZpWh58EO68M3t5cohO+ZJt1IPNpuVpcsfb7gyhMWNS16tMfr59KCecoGx6c5xvWy472KWr/GtKDD/2OW65aLmh9q7DKzvOohZllO7w/5luuy23c3p8ZGEFlZXBihUVOJShky/xJIeKWq6iBkWiwIYN8WV16zHvxJu44uLShIr5wSqSHJEzi+S22+BfOZx8mAPu3Xgde7CNtetze7sk3td16jjvO3fm9DBZMWh0Fz7mBN54z7Cqwi+5WyjUFCF0EcP5N7fk7BjZUhVDW7dsu4swtVizzn/vPfhgZgab+TxSnVvFz/u666BFC9i6NVj9SJolUmJDW0GH21q04OxZd/As5QncrbO9GlBjhrEMvLzZWZJ01Zr83i67uQ/lOVEkY8c6PU2WRP/Lpj960pwVmVDADrVCcLbn/fJI9K38QNkc8q23nPegisTUfzTyjHamHdpKLLPO9mpKwElB1ZlcR7QkXrJa7vTYaDRUVvTv71h3WRLtRI0dWaoO9uGHncabNgU8Ts26V2oUGWhS032zc6fjtjARCSssWwZHH83ubPW1jcQld4xHI8HWq7AWSTUgV/NIXuUChnJ1DiSqvuTiCXgVzZlFp+xlSZm4L7mgvwx5kwe5BV0RLEChEJ76dxWCO9uz6HldX4b3d72wy2z2SjJ3VSPu7NbPPisv0/IdxIa2TD6SiPLDtn05iffiynzy51GT5C1Fyq5GrnwkF/Iq4CQYKxS0LATkvrNLdl/n4n4/hHmso2nWNlRqiyT5BTln5WNMoRunLlzMIb8OcJzMxKvxpFbkuT1STtmwAWiCbtkKe+0BwBvf+zI9xYiE1XeS3q/pfCR/mn8dH9HT3DhaZIe2Cp8qnUfy2GNwxx15233sJs5zbychd0zL6JCoGOtomvU+wKtI/D+mFCW/IJsiDQAIlQV8+q1qTbJhQ3yYkJdCSCMf5M9UGX+4V191rkca54eEnYcv0zitsa9Y6r/2JkVi9JGo/7ep7P7IKpIK8PjjUFyc5AepSk3ypz85a5bkmVw/0fguz/hPnPfZs3N6nGyI+i4MIwopH2KLxGmg4WBKsaqz/z607/9xcitz9uKqli0waf5vFf07miLpxtw8nkZsYMv8zCP2THJEXhweU9hxVphbljL8N6Ik3ow2aWMBc/31zsNy0IiIAg7EyQjTTRyY229P/9hd6oZrVXb8b1mZeaEHMrcUou0i4dxYJJGt25ny9/fydlPduu0u3ufkvOy70shGkaT6ATzb/rFiIJtoxPcLijMWKWn/EWRoK4kiCXJXWEVSYBgTp9Xkme1R0nVi9esnnwPzwAPpd+emNq3s6/b28Y/Rp+msJMfNbH5BdAgius8XXoDlKeYWRp9ClyyBHTv82wf3H8tv7juZ8Q98lUKePBEd2qrMbLiZkOb+rOiIqWkd9zoRJ0XJzklT0rRNLotJDm9ZtG0kAmUh4QYeZSN7AuahLTAPbVWms90qklyRq5nthUy6f+LWrUln5ZdRi3U0jitLvD4xBZ2HC/fyyzB1qnnbGZP/zCT6pLQejItvpniKLfJ0BitWwKWXwkknpZYxHHYWrzzfsETb7BLn2iVzY+STClllmzfDnDm5O3ZFnO1p7s/Ymh4VHarznP9uONZyaTjzOKVkFknJ5kZ0YQZbaBCrN3p6S4ZwQ7kokbCxrVHJJ1wPkx8mV1hFkgGBLZKapkgWLIDnn8+o6SUMoynm4aMY0cUWMrlwq1c7fqIkbf/wBzjiCHPT2BOgQZHEnO1GH0nyHjbqW4lEyv/PKS0SUSJlTifx/nuG4YsEWQuV5SdcxOiO2S/0lRGZWCTr10PnzrBxg3+bYXfFOL9RJKB1lq5fiFo9kQgMnXkks+gSVy8cie+iY078hGP4LJKIQii+rn4/P5DMmWDDf3NEYAd8NSR2k950I7DAebyuIK9yoX+/iZcnZaxtGv74R3j/fTjmGDj66Iq3x/ynT2UlpYraKu/wNTbRMux/mPTUB7ZtAxq4FROe8QrgVgoiQu+pD7GQdoTCTmBK7g6e/ujphkSjiiSu0333Xfjuu5QRaaan/aARbIFnnZvKQmH/cUw3kRp8JJU8QmItkkwIeCfUNB9JuqfhFezNdgw5qTwEcnhm4tSPJpRM1Vunwfh7GdOLB6G8frRDDYeSn9cBLban7sxiOTwq308hHnsoHT9yAJC7TqsiFljQ5Wq9LF5dH0F5h1NzLk8ymeItknLL1Vfvx8U+mef+6P9/KeJTdsYuKo/h21aRZEJAbV9ZiuQ5LuVf/KNSjpWKFqygPx8Frp/c2V7xY7+1uheCsnpTnYo3jspjskhiGzP8LbW87w9v2OLbfELjaQC0+VU6ReLKkyaVyoQJzvGSpeJIi2new/oNcTLokp/YeMVfjUo71tFWgTUe1CLxKoNpi535RotdBWjCa3nGfDYSrOs0hX8HjvqUIt89ES5LFrWVXpFYZ3s1oCqjti7nOe4k/xmDP6Ev1/F4yjqf0jvl9lT3cjbO9kd/ORuAOUv2qHDbRB9JXIh3CplKy9L/fbzNQuqvH1MM6u8M4vcTnWOQmujaRskCCzLim2/cYzuyPtL3PfZ8djA/j3KU4MEHOz6oXPHCC/D00/FlgeYjZmCR5FvhmWSKRHCGYUVSWyQm0QwVtSwUyNqoceG/IrJERL4TkRkiMs0tayIi40Rkgfve2FP/dhFZKCLzRaS/p7y7u5+FIjJEpHLs/nVrd82hrWt4iie5Lqt9xMXGJ/ORZLLfaJx9Cr9F2n24v1dxsdcNFA199dffvjO5E8BrOUT3myp9uTM8kV5G0yXaurV8OYqg8Qpbt8I//+nzxwYath29thcAi39x0jXPn+9ExcU1yeLev/RSuOoq53NFfs2gFklcm9R79JXELJI0ksWyRhsCOFTxhcOr+g9nmrNmitpi5Ej/MQy5tmqqj+RYVe2qqj3c77cBn6hqO+AT9zsi0h44H+gADACeFJHoP3gocCXQzn0NqAzBd5YGVBo1xNmeU6/pWWflbl8eope6KIs72jsMMWyY8x4bwTD8lo0bJfRMmzf7s/2qxtqaOh/vUJB6lIpPtvIGPg48EBq7j10ScsJTIyn8MQB33eW8Etfz2LTRf54hN3IoGpZa7M7aTx0unfLweSGtIgk4OdS3X1NhQA2X1EfiXqDovBCjwkF894Ix/HfzlmDO9q6HBhM6AwppaOs04EX384vA6Z7yEapaqqqLgYVATxFpATRU1SnqBPm/5GmTV5Kmck4sq4Z6pMMeS+i5Z/7CBLe9PTb2OZdJGyPusJEUZ35Lp5yQaHDc7Nkwoaxhw/hFJHD1iLtf4/rsEl/XKTLdS8mHtrwrIBd9N8s51oxZhprlbHHdNaWl8eVFBh/MZ/ObG+sYw6WjT+z5TqdjqpMmHU0Qmb7nYE8D//ZyiyQLH4lnFntsaMsUGaaGhwrT0FZQH0m9eoFkzoSqUiQKjBWR6SJypVu2t6ouB3DfowmX9wWWetqWuGX7up8Ty/OOcQSmhkxInLutNV9vPAiGDIlNfAgU7RHwZNuwuPzLFr/zOVNyYpGkitoy/NFTZtXwhP9G//xGS0NjB4idg9kiCZY4syjsWCS6M/WiLsl+LrMSSzjGemdBr0yf8AFWf7OUx/q9m1OFk06RROWNW7wqoUkz/GvDZzJi7p1HlIgqbC3bjVf4XXlZOEKi5vIYs+UkmXTpuz9V+Y7O8UU1zUcCHKWq3YDfAteJSCoPrbHbTlHu34HIlSIyTUSmrc44nMV7lIAWSTX2kexxw2X8r09qx3ocARXJWpqVNwllHqqbSCSaHTXHiqT8JjMPPXgZxRm8ygVxLVUltY/EE9UbzEeSulJsbN5wLjfc4FFE7uqOsj5hkmiAe/tjTnCOsTl5Btx09/7vj/2FP407hVlvLkhZryLkwiJpiHdoMsP/r8fxZLRIwhH+tOQv/J5X2IkTZRhRYef8JfH1DH4z44REg+VifmgJegIVp0oUiaouc99XAW8BPYGV7nAV7vsqt3oJ0MrTvCWwzC1vaSg3He9pVe2hqj2aN29uqlIhTOa/8YerxopkG3tw62LzAlu5in5JFkKZyWqB2QxtxXwTRoskNpEkLWcxKraeTPnONXAG4Mim5BZa4uV96SX48Ud/vaIU2YqHDPHsb+4858Os+CGwioSxb39ttK8s6NDWmrKGgMHZn2R/QVizNrXlEMSCCjzXoq55vtSOBUvpW3sSyyO/cvaX5AGzpDR+havPVrZlxMZ4F68irNu6W3xjQ8i1KWljZQ+1V7oiEZE9RKRB9DPQD5gNvANc7Fa7GHjb/fwOcL6I1BGRNjhO9a/c4a/NInKEG611kadNns/BUBhwkmIOltqocnRpCSxenFCYwyGKDJIDRo+elUVimr0ec5EY/phJFKF3xE6ElENbnoOjtWoHkNLZx8UXQ8+ehq1BkwNELaHE4RTTeSaRe+6O5HMv0h0/pvhzFGc57L+l7Ne1SepjBlEkBx/i+RJ9888jYYo5aeNX765kPH1THjMSVp/TfMwvXSiJe152ruHcknifW1AfidGynDjJKHMuqAqLZG/gMxGZCXwFvK+qHwIPACeIyALgBPc7qjoHGAnMBT4ErlPV6K9wDfAsjgN+ETCmUs4gi6Gt6mylRNH994cDyjuRd96BHxclOa9XX3VyGXlx11JPvBblnUomFonrQyjOJvzXXxY03NNLgwYwbZvTIaV1tkePreXDIMborqilocQ6E3d0KqGeu790cyqiPpcgiiTJvkJ166c4QLrjOwRVJLHdrV/vjxAAPhycfg2b6Hmcv/+XAPTZ42t/nf1bJ23/5puwynXd6jJz4rTEhwvj9QxH0AT/oMnyioQi/tIkKVICFMHPPxsKc0OlKxJV/VFVu7ivDqp6r1u+VlX7qmo7932dp829qnqgqh6kqmM85dNUtaO7baAaU7TmnmyitqqTAz7Zf/wExnEIc2PfTzsNDupYnrbtGbmCsklfAPDjhf/g6e8SsiU++KDzngcrJpupRKY1IWJdXgX3G8ETMh0k/FckkM9IRFOmgQm6Dko06ihIqvFkP9NT689Nvv90Fkni8R94ABYt8tWTiDP2Fctc26QJ/Pa3CZWEyMpVpCN6TTr0dCatHrL/Nn+diPl3Xl+ylbPPhm/plvIYQRVJItFkkIltE61z43okBh+J8XecNNkocy4opPDfakNQH0FNtUgmcBzfc0hcWShUfiNfyTM88k8n99VRfM5VJExTdkl6LZJdottug6FDUzYpCmKRqDrLXUbzc3nkiZPp9tth1Sp3/xk6L7V8n6aQ0dh+Pb4URQyKq9xXk+q4RV7LJZVYmMfAKjK2vnyjP4tAUB9JbOnYIogsX0m/27vx8dF3J2/gHUqaMCFu00/sR3izXyn4jqnE544xPBwYz1WEnas3xhWFkuS7TbxPjPNDIsomGsaVGZfQNS1YlcxHkvg8YLpf9/6VUeZcYBVJBgQexgpokRSslZIuYeFPP8H06cZNa7fvDsAaT5SWj8Tx3sQ/9kcfwaBBxOIgH3wQrr3WFUrh9ddjq0DFxtwDzGwv+2AcV19fi1+udDoub4qUuN/xgQeQpT+bZYNAEzW9Q1bmCsRkKFckmf8ti1KEncYd1u2Qikq3x5cb7uPx8zLrgDZvUkRg6P3+FO3RJ+2iIti0IcI4+nH2iseS78z1yj/HpUxPsApa8xOjSD/RNfLJBNhrL5g3r1yO7Qnnr4YvpaW+Ycm71gyMfV60qHwCa2K9ZH3FlxwZV9ZmD79F5cxOj2c3Ma8B70vaaDpuq/18ZbnCKpIMCPzUFtRKKVRFAtChQ/KhlNatoUcP87YAJJ2Q6L6HB5zIjrsfgAED/F70SZNYdf71hG+6Ja5NEIvkw8/q81+u5qoJ8StIJVokgvIof4l+8csZxLrcujXYfBnVuKGtPUjSRjXlcQMPbc3/wan/3ruJu/fx/qxW/kLgNwckX2BFI8qqz53Q3sF3+8OEIx4fTUxmQ3cUs3BeeRVUuZzn6IH54SUdkY/GOR+WLImVbd6UYJF5vqrrU5C3RvkmDM7a8evY5549nVUMTER/q6WemXCmB4ujmn7vbxuOkBjPUa/YmSf049ceB5kafCmmHzKLzNjpsIokEwKaFTXCbzJ3bvo6FeQe3IWPEk48Lux35EhOZzT12METY9tyHiPi6q5Zup29WcXtH/UByi2SIIpk8DvtAHh/ZbwS9A1tebclapKHHoLJ6cec9YYb0MMOS1sP4juYbSQMG3kCEcp2Jr9hysN/Azrb3ZQqsfJkSQYN/Hovw2JQnv3sVux0XDvDfsvNO7QVaBXEbdt8nX5F0dFuuPKW8iHNiCa3ILSWE3pbVHe3lIES61xvbiRiHtr6+mvYb7/4Mp9spn7BNGM9HOGVgVM4sGfTuLa+oS3T/nI4bysRq0gyIOh4ucn3X5E/aqEzknN4I8CQQiJ3cA+QprM77zze4xQABvIEIzmPliyln5umfs0WJ45/yOKTAZiz01EOS5btZthZPItXmTMEayTVnI+E3/zWW+GfKcb0A1PeiaYaAlOPjySVtRF7uk/nI2l3kFP/hBPiy0335zZ/lBSYHdNeH8lu7k+xU/1hzTHFX+SdquPfX7QDLyLCxAnZKZIIRYQTurzEOUvev2xk/zZOnX4n+BRJLfF3yuEwRmf73BEJc3UW+oMKVKEXn/ra+oasQmGmTtqRUA98Jsk2v8+oJs5sr9YED/U1tK1mFsloTuNHDjRuO89VJUn54YfUO09y4jvKahkj436hJePoB0DY7cRKI/GK4+vZAfIJJZkFl8oiMQ5tBQwJTlpPBNatdz7PmIEuX5FiL+VDQeXravhvsOgIYCSU+qaKLFjo1B/3YbyshvNft9OseMNJIpycHSm1Xf2xJeL8Jhs2ONG74BnaKi5CajuO61SpYRQxTgSuCJ/Ri1qEmcQxsbLEKL/oLfn0oxdOwgAAF2JJREFU07B4qxPqWyTqG9qq5YmyKnI/h8MQmRs/RBUJa/lJR4+xeIlPNj2qF/WI99dEwmaLxN9YWVzaIr7of/7/pbVICo0shraCmrWFwhn4Zy8HZVOnowiRfJKd//o4f5onv0myuLqH8A5zLilZHp/cwHdt77kH3ZiQodcjT7Kn/Wwc4KkVjvsE/9Zb6BlnJK3lVaxhV0mYIn2Ki8oDB4LIFGQeSWmSVS9TWdIaUX5a4aT/2KZO4EXjxk70LpT/LlJcFOscfcN5H30UswT0gAOzViTj3NQu0Xfwn39EhW3bnDT2H6/oGD0ZwglDYMUeiyQa6h0KgZb8ElcvXBbxjUx8+r0/u4bWrYfUjz//sVMa+GeshyP+YayIsi4UbOJivrCKJAOSZvT0FVZvH0lFlxX1ogi9d45LXSnhZq9IapQwSSKmNsSP2yeOH8+/Y3hyJ38FfCQf0zc+U2yWmFKGxxHdpGpMPhiluNgpixpd//iHs1BUItHw0485nuF3lue7Ml2bhmz0F2LOWOsd2tq8rbx76dQxQWFFI9R2lDJ1ujmUlgEDYrO9S9r3o3ht+rkiqTA68w3TL6IrQnplTTRiTUp87tdbfb9hqEx9IxNrNvkfrhTYc3e/v6prm/iHHmPfY7BkjQ8S1tleWAR2omcRJpwLnr7fMPU5gcWL4bEUUZfZMJOuKbf7rsX2HeaKBpLF8QOwovyPFQlrnL46mPn84k3R9vnn5cMnW7ehyWRI6HFO4GNO5v20cr7OeUykT9p6aRWJZ4gnlUVSK7o+vNuR3Huvd5GucsbgTOp7kUu46F/tPIIY7s8kch2013pfWZlrgWpEqbO4fDmC2XMSnNBR0UeNonRH+j/AC7MPo2hn8PvDxAg3oeYGYmvmGS0yCcVbuxqOpB7Gc1m/ZKPPRxIqM6Uv8bfVCBx/XPyGozptYv+9E/wh4Yh/uGuVX8EaRz4C5nzLBKtIMiHgupjBnfK5EMrPVX9rmrbOsUeX8ac/5eHgK5KHhkaJO+9XX4WJEwLt+quvYPGszcZtMuETaFE+Xqxr18HsFOkzevUqr9unD3qYIYEV7lPqE08Eks/LEG7gD7yctt6nHM1K9k6/Q9XYg2Wqoa1Q6izyHI55Ld7EyW3Xth3L5oTJc1EahDf48rSEXQX/9VeaMoIuNmRVVGw8j0TqFJdRpLl/ovZnzTXUCavPIoku7uVlyYwNfotkp79eMks/MdRXFTZvj39g0rBh2UQDxkwqW5Jna84Wq0gyICvrowJRW6q5tUZ//BFuuim+bO0yc0ROtvyypk7qCldeCZ06xr6uGP0lN/FwoH0ffjic99JJse8fevzFq2nOA9wa+x7pcxzaNblldBZvxP78W9mDHSv8T9kATJoIAweat1WE774zFj/IbfRgWvr2aYe2nPdwGh9Jb8yhy4n33NBF/ZLu49WPm/NSs78Yt6388FukZfLlgWJWoJqzaQNcQvmY3KHNluYlvNG0INTKVX4HdzgheKFveCyJrBnyis+XEirzx+b2/rV/KErV7/iPhJVrHovPIGFymJseWBPlBfhpaY4yZBqwiiQDkvlIlixWWu0bjivz1VMnBbj3nkn2gPHww1ArYQTnl1/gjjvSy/jVV/6ys07cxsMJfXW+rKGvd3RKXeGZZ+L+AINmnJbxsbypl55gILdTvh62zk+92uMozoo50tszj3bkbn0MI54Eln/jXtbimQ+Q5O+o4Uhsclw6iyR6v4RDaX7bHua5LRrRwA8v39KNi3nJuC28dj1F25M/AccsEoRWe+801nmRS2Kf2xxSLy9DM4nXKBKBqd/4rYBQOL4T7on/D9aDafy4KX4UIFTmT19izNUXjvj8NZGwsj1cx1cvUXH8Hzf49hdp7c/M/BjX+8pyhVUkmRBNwpdwP7Q5QChZ5k/WF9c0ovz79vjFhJL94V980V/2u/5ruOee9CIefri/bMeP/uGmVOPyiZO1KoLJEevlSa6huWc1unBpmoUpMmQs/ZhJl8D112NORf4SF2UVfBDFu4/7+RvTSZ8ZwPsUKuFQch/JyJEUhx0LMxRSwjvNGkGVWMitb9vHnxCe/HlamdIRfud95Jqrkm73rgHz7Zz0c38i+7aiqE6QNPsVwBTSrVC0Ln64TsMRn3L1Ws9tcBaG2bNtc+oWx9/HoTJl/bYEZdChI4lEwhFfsEmyPF2JA3Bvczq/rvtTXFm4nT8Q5M886ivLFVaRZICGIzz5ZHzWjork1Zq9LL6zShYpVLzO70TbsCi9Az2ZYkqcjAWpFcnispZJt6WVIc0ci+t4Mu77ZyWtMz5WKk7jnYxTaniZTRoLK49oKFyugMLh5IrkvPMo/tYZHguHIFKWQpEkGU6a+9fn+KHfdUllCZoE+Qqe5UmuTbp9hxtSXBoqZt68AGP+IWWPxukVTkXxD20JRT/EzwUJh9Q4VLR4Mey/PyzGefq/c/1fqF0U9rUN7YhXLuF3P/DtK7RTSdRskUmf+urp2HG+SYoAmyPxocPhkFJM/HHrJ0u7kwOsIskAjSjPDC3zlZnqQSyBbNp6idRa4x9LnbXjoLTyJRtKDmmKSKccU9Ehs82RFGtb7OJ4h3Q0Uh6FlqhIjmccU3FMUdFIcoskFEaS/EAn8DGHMiMzQRPGU5/liqRV17lDeo/O6MOJm0cC0Izky2BHwprXCXVRVP2rbIbD6hvaAvh45Nq4JT7Wlfnv4VCZUrQtvgN/dcYh/nohv5KOvOdXOJFV5mvUvX785N9IKBwLfIgSbp+wLlAOsYokE1TZbfY3vjJftYjyyiuw994pq8Xi1NesSSjPYKXAZMcACBmGqnIxXGMiVW4iE6Fk80IsjmXhrp4YadAwqUXyCcfH1lMvJpxUkaz+873IJvPckGy45vCKW35bajcmIs5vv4bmSS2ecEiJhHLtIzGkZIlA0d7xEwbDIb/z+mz+x/qJM+PKQpFiHpt1THxZmfqe7DbW3ZuWLE2o50/QaAwOTWLpJ/6Pw2X+/3X4uOONbXOBVSQZoBGlNgEsEoWJEyJp60XCytVXQ+Jy8tN3ZjackswiWRr2R9HkTZFUUAmuJH9rJVR3NBSGps5waESLYgpiLc2YPzvJDP8UFkmLoXfyrzln5lzOp7imwm0O+HUxocOPSlsvEk6VBy0YR/FZfIHhFi0LC+y5Z1xZOOyPnjycqezcFD/HI7RlOzPXxg8Hh0JQpPFDTOeeXsYA4lPTmMK1I4a+4rsW/Y3/2KKEcGSTfyVssKpyhVUkGaDhiF+RKLTjB1+ZzprtK/OGNUb3N2JE7jr0QkgCmXTmuaXCeH0dkcceJ3JZ+ZDRwZ3MDuiiZSVElv5i3FYIHFPf8eUc2noDZWnmvIA7sTRLiyTRgossWepLXxIKCXvUSfBphJTwgh/jysqoDQlDbWWGdEChEBxYf2X8ccvC7EF8RFtZmSH819A9tzrfrHQTVUS4zH+twm+9Y2ybC6wiyQBVjBZJU9b6Kj73dfy4ZCQCtRKcYBqOsHVr7p4WCiHlSrIxeEvFeeCc6Wzf6SjmixjOcfOfTNMCbp1/KYN6pUlRU4XUcRdoKi11h3/SEP52FvqT45BIdCIHpRnxY8ehzdt8GYzLQrBn/QRFEoZIUby/YQd1/UNRdXb3HTNUpmi9eEd4WWmE3bp2iK8XUt+wnulhbNuoMeYh703xqVRM84jCK7JLMZMKq0gyQf1DWwBLiV8AKNl6zdK2ra/MVC9TCsEiyfTPbvHzzy/78e76o2PfVwWZAY8zqz7K4fVmpqhZ+Yzd7KwQWLpTKCtN70SfND7EtHvGAM7s+aDRY6koo7bvP1o2/0ffsFA4DG27xjvSt1PP5x2vV1zKXg3i07eHQhBuET/cVVYaobh2ka9e4v6e4mqfzFvWlRmHjcv2iE/auHaTP8Itn6MEVpFkgH47gzrEzwgPh4nP4UQSx/riJcZEcYlEdho64oBP+RUxBlL5SKJrf2TCCg3W2Vkqh692BJ9Lk3c8ywuUlkJZafob9imu4UYeyeqwiR3pJhqyV534oIP3ONmnSL5ZsY/P37SdetTfLf5h8tojZ8DWBEVSsoJIrfhOvWzrTp/SCG3a7vsvfozfOb51c5ipK/b3lddtHL98wgdz/MvqWkVSAGz/cFLs85QvlDq7x/8oOwz55EzhipHd61OrOCFRXDhCR+JTZ4S2+y2eHduCmRrpUoh72UKDpNtSJkZMw07SpEix7LLIQeXL1JbuFHbuyH9YL/jv59Gcwc7/b+/Oo6Oq7gCOf38gy9GqlSWIQStErAUVWYrFhfaIytqDC1a0iscqnLZu0KqQosdYD1JQPAqExQpIWmsQkaWcmIiAVltlX0IgIQlECWAAWcMSEvLrH++RmTeTQMxMmEzm9zlnTt7ceW/mvl9e5pd33333BtxUf4BmQWcp8TtXVJpImjcLuIFw6TKGlk/zlJ3Kzg26UF867Z2gs4rSJcuDemS9wos8ykxPWXGbnwVNgHVJ02McLfUmqz5Xb+OWZlne+tXi133UJxIR6SMiOSKSJyKjautzRvb1NQ3EdbuCBud752jYkxvcnfL4/uNBZXcMuZQp2bd5ysrLyskPmDyqcFvwsBHHDgaXjR8XnDQWfBSccJZXbzxEj6OB80MYE2YjUzvz4MQe5+Sz0ugfVFbS/LKgssCuvuU04Imx3h6PM3icsh+38JRlch0tA+6FOdE8PujG0Nd4Lui+lLKOnQKv3SNdu9IK74X6o9d05dJO3h6Ov4zPJ73Q28Pz+J5ivtzvvQ5zoop5ZcJBKpsONlqISENgK3AHUAisAh5Q1SonGu/WrZuuXl2NwfGCPsu33KTRKUpKrVeSMfVNU45zAm8zUWt2sZvghBNOnVnLOrp4ytqwo2I+llDEUVRxXa2oCOLiavY+IrJGVSsd0yfaz0i6A3mquk1VTwKpQM1H/zuDDyf4xrKxJBI7royvRt9UU28EJhGg1pMIEJREgLAkkcvYyVa/e41SnloV8ntWJtoTSTx4bhEtdMs8RGSYiKwWkdV791Y9DMOZ3Punn6AKb8e/XFG2rucz7F7vPfVMveJ5nrrJe4dvBnfy7TzvL3A2Q4J6No3nOdbe6W2dW3bTC3y/w3sB78Sb08hI854H/75tOmOHe+vyNTfyRJ98T9mh62/l1aHbPWWb6Mg7ib71UiYeZMSgHVxNDs34nu35vqay11qMI2Xcbm64IJfnEj5iw+gPKibQeuuu5SxZUMzYW52hHdqyjXcHzqdJA6dJbjrDmJfi6z+/fNIm5r3qG503e+NJJg5z7rsZ0HoNc5L3cS2Z3HXRUlIHL2DDf4vp1CiLPzCFLwdPZuuUTwFI7TubkmOneHeoc8NZSss/kzH8Y0Z0+YzuLfJJf30TE9/ynXnPG7mSZWOd+Tj+duEYDmQX0b1VAQDHPlvJt5uL6dtqLTM7vM72wkZMHZjONWxh7subOXzQF4tTJ09RtNnX5fvT1H1c1XQHrS4+ztKMMjq1d35v/3shjcWznfVu7nqCI4edujRuVM6hQ5D0O98/KScOn+TFuOkkJ+2lpOggL7WZAcDtt8OCxBVcJruYnXyEnTMzuJcPGXjLPgrmr+NfPMDk+7/gswUHmTv0ExpIOVPH7GfzG+nMui+NB/sdICODijb3Uc8c56t3nDb0/AWZFGSf4Jp4pwvpkW/2c8zvkEtMhMVznWbaq+IOsWsXPN/P+T3NeqWQzDmbGX7+dAZdtZ4Zv8ngoYt89ytMuW4qY/gLzz5+kMf6f8d7PEgL9jL6oW/4+uFkLsfp0nv47VT++vNF3N9rHzN+u4yefE7PJl8z4aXDTLptPgBXJ5Sx/9O1DGM63TocJXuLMiB+HeDczJt8YwpPMonMdWW0a+fEuEnjciZPxuO74JGHOHaghOs7+L5w770+l8IdyrinCivKpvf6gDVfneTR3r7pnEcOyOLoUZgxxHcN9dlf5wQ1Jaf1mkDWv7dxT4Jv+JnC978gORkeu9s3iOvGh8azcCEk3u77vtg6dh6zZsErA3xzyGx5KZW8PO9n5CzMZlHArSIrFu3h4o6+TkBdetTOtctob9q6D+itqo+7zx8GuqtqleMl17RpKxxOz/UQODS8MebcUoW1a6FzZ+/gq7m5zvOEhKq3rUvefBNGjIC0NN90CosXO1MsP/20b3bMggKYP99Zt6bO1LQV7YmkB5Ckqr3d54kAqjq2qm0imUiMMSZa1edrJKuA9iLSVkQaA4OB2hsHwBhjTJCobmRR1TIReRLIABoCM1U16yybGWOMCaOoTiQAqpoGBA/cb4wx5pyI9qYtY4wxEWaJxBhjTEgskRhjjAmJJRJjjDEhsURijDEmJFF9Q2JNiMhe4Juzrli5FhAwzVpss3h4WTy8LB5e0R6Pn6hqy8peiLlEEgoRWV3VnZ2xyOLhZfHwsnh41ed4WNOWMcaYkFgiMcYYExJLJD/M25GuQB1j8fCyeHhZPLzqbTzsGokxxpiQ2BmJMcaYkFgiMcYYExJLJNUkIn1EJEdE8kRk1Nm3iE4iUiAimSKyXkRWu2XNRGSJiOS6Py/xWz/RjUmOiPT2K+/qvk+eiEwUEYnE/tSEiMwUkT0issmvLGwxEJEmIjLHLV8hIleey/37oaqIR5KI7HSPk/Ui0s/vtXobDxG5XESWi8gWEckSkWfc8pg9PgBQVXuc5YEz10k+0A5oDGwAOkS6XrW0rwVAi4Cy8cAod3kUMM5d7uDGognQ1o1RQ/e1lUAPQICPgb6R3rcfEIOeQBdgU23EAPgjMM1dHgzMifQ+1yAeScCzlaxbr+MBtAa6uMsXAlvdfY7Z40NV7YykmroDeaq6TVVPAqnAwAjX6VwaCMx2l2cDd/mVp6pqiapuB/KA7iLSGrhIVb9S568hxW+bOk9V/wPsDygOZwz83+tDoFddPmOrIh5VqdfxUNXdqrrWXT4CbAHiieHjA6xpq7rigR1+zwvdsvpIgU9EZI2IDHPLWqnqbnD+kIA4t7yquMS7y4Hl0SycMajYRlXLgENA81qree15UkQ2uk1fp5tyYiYebpNTZ2AFMX58WCKpnsr+G6iv/aZvVtUuQF/gCRHpeYZ1q4pLLMWrJjGoD/GZCiQANwC7gQlueUzEQ0R+BMwDhqvq4TOtWklZvYuHJZLqKQQu93veBtgVobrUKlXd5f7cA8zHadYrck/FcX/ucVevKi6F7nJgeTQLZwwqthGR84CLqX7TUZ2gqkWqekpVy4G/4xwnEAPxEJFGOEnkPVX9yC2O6ePDEkn1rALai0hbEWmMcwFsUYTrFHYicoGIXHh6GbgT2ISzr4+4qz0CLHSXFwGD3V4mbYH2wEr31P6IiPzCbdsd4rdNtApnDPzfaxCwzG0njxqnvzRdd+McJ1DP4+HWfQawRVXf8Hspto+PSF/tj5YH0A+nh0Y+MDrS9amlfWyH08NkA5B1ej9x2meXArnuz2Z+24x2Y5KDX88soBvOl0s+MBl3FIVoeADv4zTXlOL8d/hYOGMANAXm4lx4XQm0i/Q+1yAe/wAygY04X3ytYyEewC04zUwbgfXuo18sHx+qakOkGGOMCY01bRljjAmJJRJjjDEhsURijDEmJJZIjDHGhMQSiTHGmJBYIjGmlohIc7/Rcb/zGy23WESmRLp+xoSLdf815hwQkSSgWFVfj3RdjAk3OyMx5hwTkV+JyGJ3OUlEZovIJ+LMBXOPiIx356lId4fjOD13xefuYJoZAXeWGxNRlkiMibwEoD/O8OH/BJar6nXAcaC/m0wmAYNUtSswExgTqcoaE+i8SFfAGMPHqloqIpk4k6ilu+WZwJXAT4FrgSXutBQNcYYsMaZOsERiTOSVAKhquYiUqu/CZTnO36gAWaraI1IVNOZMrGnLmLovB2gpIj3AGcZcRDpGuE7GVLBEYkwdp870zoOAcSKyAWfE2ZsiWytjfKz7rzHGmJDYGYkxxpiQWCIxxhgTEkskxhhjQmKJxBhjTEgskRhjjAmJJRJjjDEhsURijDEmJP8H908f5LoCQDcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 478.7304444895119.\n" + "The root mean squared error is 454.5654304135976.\n" ] } ], @@ -401,24 +366,26 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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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 1417.8878610870483.\n" + "The root mean squared error is 1400.910400461662.\n" ] } ], @@ -429,17 +396,19 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 14, "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" } ], @@ -449,91 +418,24 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Count\n", - "0 461289\n", - "1 318556\n", - "2 361129\n", - "3 506459" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# RNN_test_year = day_to_year(RNN_test_preds)\n", - "# RNN_test_year" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 39, + "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 24270.60509134455.\n" - ] - } - ], - "source": [ - "# # test RMSE with baseline and RNN\n", - "# return_rmse(y_test_year, traditional)\n", - "# return_rmse(y_test_year, RNN_test_year)" - ] + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/master_data.csv b/master_data.csv index b3d4729..c594d77 100644 --- a/master_data.csv +++ b/master_data.csv @@ -1,18 +1,18 @@ date,king,upwelling,noi,npgo,pdo,oni -1950-01-31,0,-16,2.644,-2.19,-1.61,-1.4 +1950-01-31,0,-16,2.6439999999999997,-2.19,-1.61,-1.4 1950-02-28,0,-166,2.077,-1.45,-2.17,-1.2 1950-03-31,21,-49,3.091,-0.97,-1.89,-1.1 1950-04-30,6630,-4,1.923,-0.86,-1.99,-1.2 1950-05-31,50638,49,2.211,-0.63,-3.19,-1.1 1950-06-30,16667,32,1.014,-0.58,-2.52,-0.9 1950-07-31,32937,71,-0.336,-0.74,-3.65,-0.6 -1950-08-31,40462,15,-0.236,-0.55,-2.98,-0.6 +1950-08-31,40462,15,-0.23600000000000002,-0.55,-2.98,-0.6 1950-09-30,205521,14,0.17,-0.6,-2.4,-0.5 1950-10-31,4356,-54,-2.54,0.12,-1.92,-0.6 1950-11-30,143,-72,0.483,-0.26,-1.45,-0.7 1950-12-31,0,-168,-1.545,-0.49,-1.06,-0.8 1951-01-31,0,-65,2.702,-0.48,-1.19,-0.8 -1951-02-28,0,-39,1.878,-0.41,-1.52,-0.6 +1951-02-28,0,-39,1.8780000000000001,-0.41,-1.52,-0.6 1951-03-31,32,-2,3.103,-0.57,-1.72,-0.2 1951-04-30,28801,34,-3.88,-0.56,-1.35,0.2 1951-05-31,86055,16,-2.213,-1.07,-1.29,0.2 @@ -22,27 +22,27 @@ date,king,upwelling,noi,npgo,pdo,oni 1951-09-30,97131,6,-1.36,-1.42,-0.78,0.8 1951-10-31,6489,-16,0.21,-1.07,-0.09,0.9 1951-11-30,212,-138,-1.165,-1.01,-0.31,0.7 -1951-12-31,0,-19,-0.464,-1.22,-1.45,0.6 +1951-12-31,0,-19,-0.46399999999999997,-1.22,-1.45,0.6 1952-01-31,0,-135,-1.444,-1.34,-2.19,0.5 1952-02-29,0,-30,2.156,-1.09,-1.35,0.4 1952-03-31,255,1,1.739,-0.67,-1.67,0.4 1952-04-30,8130,-2,-1.652,-0.43,-1.92,0.4 1952-05-31,107807,18,0.535,-0.63,-2.07,0.4 1952-06-30,62491,31,0.015,-0.97,-2.13,0.2 -1952-07-31,21800,82,-0.538,-1.34,-2.05,0.0 +1952-07-31,21800,82,-0.5379999999999999,-1.34,-2.05,0.0 1952-08-31,82290,50,1.034,-0.9,-1.58,0.1 1952-09-30,135053,15,0.375,-0.58,-1.36,0.2 1952-10-31,2576,-1,0.623,-0.26,-0.91,0.2 1952-11-30,477,-64,1.179,-0.32,-0.53,0.2 -1952-12-31,0,-273,-2.297,0.85,-0.83,0.3 +1952-12-31,0,-273,-2.2969999999999997,0.85,-0.83,0.3 1953-01-31,0,-235,1.742,0.94,-0.16,0.5 1953-02-28,0,-1,7.614,0.75,-0.46,0.6 1953-03-31,1254,-17,2.595,1.22,-1.27,0.7 -1953-04-30,126450,-3,-0.952,1.25,-0.9,0.7 +1953-04-30,126450,-3,-0.9520000000000001,1.25,-0.9,0.7 1953-05-31,42583,2,-0.631,1.27,-0.55,0.7 1953-06-30,27228,24,1.682,1.06,-0.2,0.7 1953-07-31,30593,67,0.185,1.04,0.06,0.7 -1953-08-31,27398,10,0.197,0.62,-0.92,0.7 +1953-08-31,27398,10,0.19699999999999998,0.62,-0.92,0.7 1953-09-30,75145,18,-0.721,0.29,-1.11,0.8 1953-10-31,1495,-9,1.669,1.08,-1.48,0.8 1953-11-30,333,-147,-0.853,2.05,-0.51,0.8 @@ -50,22 +50,22 @@ date,king,upwelling,noi,npgo,pdo,oni 1954-01-31,0,-57,0.5,1.09,-1.13,0.7 1954-02-28,2,-101,1.101,1.16,-0.95,0.4 1954-03-31,657,2,-1.304,0.08,-1.15,0.0 -1954-04-30,82877,4,0.761,-0.37,-1.25,-0.4 +1954-04-30,82877,4,0.7609999999999999,-0.37,-1.25,-0.4 1954-05-31,51230,28,-1.173,-0.16,-0.76,-0.5 1954-06-30,46801,4,0.498,0.18,0.49,-0.5 -1954-07-31,32596,37,0.403,-0.04,-0.08,-0.5 +1954-07-31,32596,37,0.40299999999999997,-0.04,-0.08,-0.5 1954-08-31,24969,37,1.899,-0.13,-0.32,-0.7 1954-09-30,80426,7,1.242,-0.26,-1.05,-0.7 1954-10-31,1189,-47,0.073,0.18,0.02,-0.6 1954-11-30,200,-141,-0.26,0.35,0.85,-0.5 1954-12-31,0,-164,0.95,0.4,0.49,-0.5 -1955-01-31,0,-44,2.308,0.33,-0.23,-0.6 +1955-01-31,0,-44,2.3080000000000003,0.33,-0.23,-0.6 1955-02-28,0,-2,7.473,-0.25,-1.01,-0.6 1955-03-31,1391,1,4.596,-0.22,-1.24,-0.7 1955-04-30,84436,0,1.63,0.04,-2.21,-0.7 1955-05-31,85769,67,3.862,0.26,-1.89,-0.7 1955-06-30,45502,60,1.871,-0.02,-2.18,-0.6 -1955-07-31,37437,68,1.333,-0.09,-2.75,-0.6 +1955-07-31,37437,68,1.3330000000000002,-0.09,-2.75,-0.6 1955-08-31,32833,98,1.984,0.27,-2.66,-0.6 1955-09-30,70558,37,1.979,-0.11,-2.48,-1.0 1955-10-31,1803,-11,2.494,-0.52,-3.35,-1.4 @@ -74,37 +74,37 @@ date,king,upwelling,noi,npgo,pdo,oni 1956-01-31,0,-150,-2.127,0.12,-2.26,-0.9 1956-02-29,0,-15,5.06,-0.14,-2.28,-0.6 1956-03-31,161,-22,5.19,0.02,-2.93,-0.6 -1956-04-30,8850,57,0.577,-0.04,-2.55,-0.5 +1956-04-30,8850,57,0.5770000000000001,-0.04,-2.55,-0.5 1956-05-31,54438,57,1.273,0.14,-1.62,-0.5 1956-06-30,64262,19,2.335,0.47,-1.98,-0.4 -1956-07-31,36938,92,2.644,0.46,-1.48,-0.5 +1956-07-31,36938,92,2.6439999999999997,0.46,-1.48,-0.5 1956-08-31,42203,62,3.113,0.54,-1.0,-0.5 1956-09-30,92030,5,0.005,0.3,-0.28,-0.4 1956-10-31,1854,-5,3.178,-0.05,-1.66,-0.4 1956-11-30,179,-20,5.012,-0.23,-2.44,-0.5 1956-12-31,2,-60,6.564,-0.77,-1.27,-0.4 -1957-01-31,0,-16,0.984,-1.27,-1.31,-0.3 +1957-01-31,0,-16,0.9840000000000001,-1.27,-1.31,-0.3 1957-02-28,0,-12,-3.795,-1.96,-1.57,0.0 -1957-03-31,328,-28,-0.274,-1.34,-0.83,0.3 -1957-04-30,90984,13,-0.869,-1.26,-0.81,0.6 -1957-05-31,45128,10,-4.281,-1.04,-0.02,0.7 -1957-06-30,104243,35,-0.776,-1.17,1.53,0.9 +1957-03-31,328,-28,-0.27399999999999997,-1.34,-0.83,0.3 +1957-04-30,90984,13,-0.8690000000000001,-1.26,-0.81,0.6 +1957-05-31,45128,10,-4.281000000000001,-1.04,-0.02,0.7 +1957-06-30,104243,35,-0.7759999999999999,-1.17,1.53,0.9 1957-07-31,30790,66,0.644,-0.76,0.59,1.0 1957-08-31,34829,35,1.251,-0.91,0.34,1.2 1957-09-30,95457,5,-3.812,-0.92,1.84,1.1 -1957-10-31,1352,-1,-3.018,-1.03,1.72,1.2 +1957-10-31,1352,-1,-3.0180000000000002,-1.03,1.72,1.2 1957-11-30,175,-15,1.107,-1.41,1.54,1.3 1957-12-31,0,-125,-0.04,-0.72,0.46,1.6 -1958-01-31,0,-271,-2.567,0.12,0.83,1.7 +1958-01-31,0,-271,-2.5669999999999997,0.12,0.83,1.7 1958-02-28,0,-162,-10.78,0.15,0.94,1.5 1958-03-31,468,-4,-5.996,-1.15,0.62,1.2 1958-04-30,15361,-1,0.264,-0.72,0.56,0.8 -1958-05-31,59377,38,-4.307,-0.85,1.46,0.7 +1958-05-31,59377,38,-4.3069999999999995,-0.85,1.46,0.7 1958-06-30,74375,49,-0.981,-0.72,1.82,0.6 -1958-07-31,27524,124,-0.842,-0.87,0.61,0.5 +1958-07-31,27524,124,-0.8420000000000001,-0.87,0.61,0.5 1958-08-31,19735,63,1.02,-0.63,1.06,0.4 1958-09-30,217504,7,0.491,-0.2,0.77,0.4 -1958-10-31,1948,-8,-0.189,-0.51,0.28,0.5 +1958-10-31,1948,-8,-0.18899999999999997,-0.51,0.28,0.5 1958-11-30,127,-26,1.675,-0.77,0.28,0.6 1958-12-31,0,-120,-0.327,-0.41,1.14,0.6 1959-01-31,0,-85,-0.619,0.12,1.04,0.6 @@ -116,38 +116,38 @@ date,king,upwelling,noi,npgo,pdo,oni 1959-07-31,49371,102,-0.252,-0.4,-0.42,-0.3 1959-08-31,24945,90,-0.813,-0.5,-0.31,-0.3 1959-09-30,168187,3,2.204,0.07,-0.53,-0.1 -1959-10-31,1643,0,-0.219,0.13,0.96,-0.1 +1959-10-31,1643,0,-0.21899999999999997,0.13,0.96,-0.1 1959-11-30,168,-14,2.88,-0.05,1.38,-0.1 1959-12-31,0,-56,1.261,0.42,1.02,-0.1 -1960-01-31,0,-103,-0.874,1.02,0.24,-0.1 +1960-01-31,0,-103,-0.8740000000000001,1.02,0.24,-0.1 1960-02-29,0,-27,1.975,0.82,0.61,-0.2 1960-03-31,184,-16,1.287,0.86,0.23,-0.1 1960-04-30,37064,-25,1.317,0.79,0.21,0.0 1960-05-31,32349,-11,1.021,0.92,0.35,-0.1 1960-06-30,65247,65,-0.535,0.31,1.2,-0.2 1960-07-31,19923,56,0.222,0.71,-0.42,0.0 -1960-08-31,22719,41,1.076,0.44,-0.31,0.1 +1960-08-31,22719,41,1.0759999999999998,0.44,-0.31,0.1 1960-09-30,76327,26,2.479,0.52,-0.96,0.2 1960-10-31,2012,-39,-0.015,0.49,-0.34,0.1 1960-11-30,224,-87,-0.192,0.95,-0.34,0.0 1960-12-31,0,-87,1.776,1.14,0.0,0.0 -1961-01-31,0,-253,-1.773,1.1,0.89,0.0 +1961-01-31,0,-253,-1.7730000000000001,1.1,0.89,0.0 1961-02-28,0,-134,6.931,1.36,0.92,0.0 1961-03-31,476,-91,-0.312,1.75,0.64,-0.1 1961-04-30,72526,11,2.374,0.96,0.28,0.0 -1961-05-31,25693,1,1.822,1.4,-0.54,0.1 +1961-05-31,25693,1,1.8219999999999998,1.4,-0.54,0.1 1961-06-30,41302,21,0.045,1.2,-0.7,0.2 1961-07-31,25159,51,0.17,1.06,-1.23,0.1 1961-08-31,22675,33,1.561,1.07,-0.87,-0.1 1961-09-30,90688,42,1.284,0.77,-1.34,-0.3 1961-10-31,3108,2,2.588,0.35,-1.78,-0.3 1961-11-30,353,-54,-0.485,0.22,-1.43,-0.2 -1961-12-31,0,-39,3.942,0.4,-1.73,-0.2 +1961-12-31,0,-39,3.9419999999999997,0.4,-1.73,-0.2 1962-01-31,0,-21,6.315,0.17,-1.0,-0.2 1962-02-28,0,-20,-3.386,-0.15,-0.87,-0.2 -1962-03-31,1278,-6,-0.668,-0.61,-1.06,-0.2 -1962-04-30,56201,-7,2.794,-0.47,-0.95,-0.3 -1962-05-31,33637,42,2.789,-0.74,-1.3,-0.3 +1962-03-31,1278,-6,-0.6679999999999999,-0.61,-1.06,-0.2 +1962-04-30,56201,-7,2.7939999999999996,-0.47,-0.95,-0.3 +1962-05-31,33637,42,2.7889999999999997,-0.74,-1.3,-0.3 1962-06-30,52392,29,1.669,-0.3,-1.71,-0.2 1962-07-31,25093,107,2.331,-0.06,-1.83,-0.1 1962-08-31,23526,21,0.312,-0.37,-0.66,-0.2 @@ -156,9 +156,9 @@ date,king,upwelling,noi,npgo,pdo,oni 1962-11-30,419,-113,3.053,0.45,-0.38,-0.3 1962-12-31,0,-99,-0.706,0.47,-0.23,-0.4 1963-01-31,0,13,2.157,-0.81,0.12,-0.4 -1963-02-28,0,-121,-2.644,-0.28,0.15,-0.2 +1963-02-28,0,-121,-2.6439999999999997,-0.28,0.15,-0.2 1963-03-31,2802,-3,1.515,-1.1,-0.28,0.1 -1963-04-30,43859,-9,-0.751,-0.81,-0.27,0.2 +1963-04-30,43859,-9,-0.7509999999999999,-0.81,-0.27,0.2 1963-05-31,28810,8,0.777,-0.43,-1.26,0.2 1963-06-30,36836,72,0.473,-0.72,-0.6,0.4 1963-07-31,27178,38,-1.075,-0.37,-1.11,0.7 @@ -168,7 +168,7 @@ date,king,upwelling,noi,npgo,pdo,oni 1963-11-30,503,-55,-2.134,0.66,-0.5,1.2 1963-12-31,0,-92,-0.29,0.54,0.45,1.1 1964-01-31,0,-118,4.083,1.07,0.34,1.0 -1964-02-29,0,13,5.868,0.29,0.21,0.6 +1964-02-29,0,13,5.867999999999999,0.29,0.21,0.6 1964-03-31,2418,2,3.747,0.35,-0.71,0.1 1964-04-30,69031,57,4.372,0.25,-1.27,-0.3 1964-05-31,19979,45,2.444,0.27,-2.39,-0.6 @@ -186,11 +186,11 @@ date,king,upwelling,noi,npgo,pdo,oni 1965-05-31,35945,84,3.785,-1.16,-0.66,0.4 1965-06-30,38482,103,-0.584,-0.83,-0.76,0.7 1965-07-31,37515,99,-1.284,-0.69,-0.74,1.0 -1965-08-31,38727,48,-1.343,-0.44,0.32,1.3 +1965-08-31,38727,48,-1.3430000000000002,-0.44,0.32,1.3 1965-09-30,112664,75,-0.285,-0.86,0.83,1.6 1965-10-31,6074,-33,-0.635,-0.68,0.3,1.7 -1965-11-30,229,-122,-7.802,-0.92,0.47,1.8 -1965-12-31,0,-60,-1.142,-1.19,0.25,1.5 +1965-11-30,229,-122,-7.8020000000000005,-0.92,0.47,1.8 +1965-12-31,0,-60,-1.1420000000000001,-1.19,0.25,1.5 1966-01-31,0,-106,-0.281,-0.32,-0.67,1.3 1966-02-28,0,-27,0.768,-1.11,-0.43,1.0 1966-03-31,2168,-50,0.258,-0.6,-1.0,0.9 @@ -206,12 +206,12 @@ date,king,upwelling,noi,npgo,pdo,oni 1967-01-31,0,-96,2.648,-0.74,-0.24,-0.4 1967-02-28,0,-9,5.8,-0.61,-0.25,-0.5 1967-03-31,1620,-15,-0.511,-1.12,-0.8,-0.5 -1967-04-30,71957,11,-1.076,-0.89,-1.13,-0.5 -1967-05-31,11358,88,2.152,-1.0,-1.54,-0.2 +1967-04-30,71957,11,-1.0759999999999998,-0.89,-1.13,-0.5 +1967-05-31,11358,88,2.1519999999999997,-1.0,-1.54,-0.2 1967-06-30,41847,135,-0.708,-1.25,-1.2,0.0 1967-07-31,53812,134,1.402,-1.17,-1.21,0.0 -1967-08-31,25760,93,-0.236,-0.75,-1.55,-0.2 -1967-09-30,154239,15,-2.308,-0.05,-0.6,-0.3 +1967-08-31,25760,93,-0.23600000000000002,-0.75,-1.55,-0.2 +1967-09-30,154239,15,-2.3080000000000003,-0.05,-0.6,-0.3 1967-10-31,5405,-74,0.855,0.01,-0.06,-0.4 1967-11-30,155,-14,-3.208,-0.59,0.36,-0.4 1967-12-31,0,-37,1.122,-0.23,-0.68,-0.5 @@ -222,7 +222,7 @@ date,king,upwelling,noi,npgo,pdo,oni 1968-05-31,28425,33,0.848,-1.26,-0.79,-0.1 1968-06-30,46767,59,1.135,-1.04,0.14,0.2 1968-07-31,36126,104,1.551,-1.02,0.44,0.5 -1968-08-31,27779,21,0.229,-0.96,-0.01,0.4 +1968-08-31,27779,21,0.22899999999999998,-0.96,-0.01,0.4 1968-09-30,126994,18,0.184,-0.91,0.26,0.3 1968-10-31,4010,-39,-0.473,-0.74,-0.18,0.4 1968-11-30,265,-121,-0.465,-0.23,0.03,0.6 @@ -232,66 +232,66 @@ date,king,upwelling,noi,npgo,pdo,oni 1969-03-31,1910,-2,1.665,-0.73,-1.21,0.9 1969-04-30,65371,-21,-1.301,-0.18,-1.07,0.7 1969-05-31,106281,13,-2.049,-0.09,-1.06,0.6 -1969-06-30,59177,61,-2.288,-0.78,0.84,0.5 +1969-06-30,59177,61,-2.2880000000000003,-0.78,0.84,0.5 1969-07-31,43021,106,0.632,-0.73,-0.37,0.4 -1969-08-31,38456,46,-0.834,-0.23,-0.92,0.5 +1969-08-31,38456,46,-0.8340000000000001,-0.23,-0.92,0.5 1969-09-30,187502,6,-0.444,0.02,-1.07,0.8 1969-10-31,5511,-14,-1.483,0.29,0.95,0.8 1969-11-30,359,-53,-1.27,-0.04,0.58,0.8 -1969-12-31,0,-157,-2.027,1.13,1.13,0.7 +1969-12-31,0,-157,-2.0269999999999997,1.13,1.13,0.7 1970-01-31,0,-98,-4.9,-0.19,1.06,0.6 1970-02-28,0,-71,-3.812,-0.32,1.01,0.4 1970-03-31,2020,1,1.829,-0.41,1.2,0.4 1970-04-30,78605,25,4.095,-0.22,0.31,0.3 -1970-05-31,30351,33,2.671,0.48,-0.89,0.1 -1970-06-30,35091,46,-0.026,0.37,-0.12,-0.3 +1970-05-31,30351,33,2.6710000000000003,0.48,-0.89,0.1 +1970-06-30,35091,46,-0.026000000000000002,0.37,-0.12,-0.3 1970-07-31,29811,71,-0.264,0.27,-0.92,-0.6 -1970-08-31,44559,73,0.458,0.41,-1.79,-0.8 +1970-08-31,44559,73,0.45799999999999996,0.41,-1.79,-0.8 1970-09-30,159710,11,2.211,0.4,-1.68,-0.8 1970-10-31,4364,-7,-0.142,0.23,-1.2,-0.8 1970-11-30,269,-54,-3.005,-0.21,-0.92,-0.9 -1970-12-31,0,-106,1.076,-0.07,-0.91,-1.2 +1970-12-31,0,-106,1.0759999999999998,-0.07,-0.91,-1.2 1971-01-31,0,-32,5.065,-0.2,-1.34,-1.3 1971-02-28,0,-16,7.349,0.06,-1.45,-1.3 -1971-03-31,683,-49,3.831,0.45,-1.55,-1.1 +1971-03-31,683,-49,3.8310000000000004,0.45,-1.55,-1.1 1971-04-30,60849,-2,2.526,0.85,-1.7,-0.9 1971-05-31,63985,66,2.057,0.49,-1.75,-0.8 1971-06-30,37757,13,0.491,0.41,-1.86,-0.7 1971-07-31,40154,65,1.12,0.79,-2.25,-0.8 1971-08-31,26749,24,-0.426,0.71,-0.32,-0.7 1971-09-30,166346,8,0.79,0.53,-0.17,-0.8 -1971-10-31,8820,1,3.652,0.33,-0.35,-0.8 -1971-11-30,359,-40,3.086,0.18,-1.28,-0.9 -1971-12-31,0,-27,2.493,-0.03,-1.77,-0.8 +1971-10-31,8820,1,3.6519999999999997,0.33,-0.35,-0.8 +1971-11-30,359,-40,3.0860000000000003,0.18,-1.28,-0.9 +1971-12-31,0,-27,2.4930000000000003,-0.03,-1.77,-0.8 1972-01-31,0,-19,4.455,-0.53,-2.12,-0.7 1972-02-29,0,-103,1.071,-0.41,-1.95,-0.4 1972-03-31,426,-25,1.899,-0.69,-1.53,0.0 -1972-04-30,154443,-1,-0.093,-0.68,-1.7,0.3 +1972-04-30,154443,-1,-0.09300000000000001,-0.68,-1.7,0.3 1972-05-31,31271,34,-1.278,-0.75,-2.16,0.6 1972-06-30,36758,55,-1.258,-0.76,-1.84,0.8 -1972-07-31,34072,52,-1.687,-0.32,-1.48,1.1 +1972-07-31,34072,52,-1.6869999999999998,-0.32,-1.48,1.1 1972-08-31,23577,56,-1.633,-0.43,-0.11,1.3 1972-09-30,109820,19,-0.636,-0.93,-0.2,1.5 1972-10-31,3939,34,-2.359,-1.1,-0.22,1.8 1972-11-30,150,-64,-1.25,-0.56,-0.05,2.0 -1972-12-31,0,-68,-0.199,-0.76,-0.37,1.9 +1972-12-31,0,-68,-0.19899999999999998,-0.76,-0.37,1.9 1973-01-31,0,-111,-2.303,-0.05,-0.15,1.7 1973-02-28,0,-43,-8.273,-0.5,-0.55,1.2 -1973-03-31,1585,-1,2.683,-0.07,-0.88,0.6 -1973-04-30,128495,43,3.538,-0.05,-1.35,0.0 -1973-05-31,12068,25,0.334,0.25,-1.59,-0.4 -1973-06-30,24164,27,1.967,0.51,-1.44,-0.8 +1973-03-31,1585,-1,2.6830000000000003,-0.07,-0.88,0.6 +1973-04-30,128495,43,3.5380000000000003,-0.05,-1.35,0.0 +1973-05-31,12068,25,0.33399999999999996,0.25,-1.59,-0.4 +1973-06-30,24164,27,1.9669999999999999,0.51,-1.44,-0.8 1973-07-31,21196,76,0.96,0.32,-1.4,-1.0 1973-08-31,31513,103,2.283,0.17,-1.56,-1.2 -1973-09-30,170895,3,0.876,0.36,-1.05,-1.4 +1973-09-30,170895,3,0.8759999999999999,0.36,-1.05,-1.4 1973-10-31,8457,-3,1.554,0.35,-1.36,-1.7 1973-11-30,262,-46,0.726,-0.11,-1.42,-1.9 1973-12-31,0,-129,0.595,0.34,-0.89,-1.9 -1974-01-31,0,-36,0.559,-1.0,-0.86,-1.7 +1974-01-31,0,-36,0.5589999999999999,-1.0,-0.86,-1.7 1974-02-28,0,-51,5.932,-0.39,-1.38,-1.5 -1974-03-31,434,-37,-1.769,-0.03,-1.21,-1.2 -1974-04-30,57516,2,3.413,0.01,-0.62,-1.0 -1974-05-31,76585,26,2.223,-0.17,-0.72,-0.9 +1974-03-31,434,-37,-1.7690000000000001,-0.03,-1.21,-1.2 +1974-04-30,57516,2,3.4130000000000003,0.01,-0.62,-1.0 +1974-05-31,76585,26,2.2230000000000003,-0.17,-0.72,-0.9 1974-06-30,13424,80,0.209,0.22,-0.37,-0.8 1974-07-31,32472,36,0.3,0.38,0.02,-0.6 1974-08-31,15554,72,0.76,0.26,-0.27,-0.4 @@ -300,15 +300,15 @@ date,king,upwelling,noi,npgo,pdo,oni 1974-11-30,308,-46,1.337,0.04,0.61,-0.7 1974-12-31,0,-75,4.283,0.72,0.27,-0.6 1975-01-31,0,-33,4.746,0.64,-0.65,-0.5 -1975-02-28,0,-77,-0.692,0.98,-0.93,-0.5 +1975-02-28,0,-77,-0.6920000000000001,0.98,-0.93,-0.5 1975-03-31,162,-1,-0.405,0.72,-1.07,-0.6 1975-04-30,48295,35,3.509,0.65,-1.79,-0.6 -1975-05-31,55647,26,0.864,0.72,-2.08,-0.7 +1975-05-31,55647,26,0.8640000000000001,0.72,-2.08,-0.7 1975-06-30,16989,98,1.393,0.46,-1.61,-0.8 1975-07-31,27362,68,0.624,0.51,-0.84,-1.0 1975-08-31,37907,40,3.053,1.03,-1.56,-1.1 1975-09-30,234145,38,2.615,0.57,-1.6,-1.3 -1975-10-31,4847,-48,2.152,0.96,-1.59,-1.4 +1975-10-31,4847,-48,2.1519999999999997,0.96,-1.59,-1.4 1975-11-30,212,-79,3.264,1.27,-1.73,-1.5 1975-12-31,0,-44,4.765,1.08,-1.49,-1.6 1976-01-31,0,-41,5.938,1.08,-1.08,-1.5 @@ -326,22 +326,22 @@ date,king,upwelling,noi,npgo,pdo,oni 1977-01-31,0,-40,0.306,1.32,1.24,0.7 1977-02-28,0,-109,4.662,1.93,1.51,0.6 1977-03-31,3569,8,4.331,1.7,0.62,0.4 -1977-04-30,98710,1,0.704,1.4,-0.25,0.3 +1977-04-30,98710,1,0.7040000000000001,1.4,-0.25,0.3 1977-05-31,17229,9,-1.105,1.07,-0.47,0.3 -1977-06-30,21790,71,0.206,0.92,0.03,0.4 +1977-06-30,21790,71,0.20600000000000002,0.92,0.03,0.4 1977-07-31,19233,73,1.129,1.04,-0.06,0.4 -1977-08-31,21992,41,-2.196,1.28,-0.09,0.4 +1977-08-31,21992,41,-2.1959999999999997,1.28,-0.09,0.4 1977-09-30,171303,7,-1.922,0.77,-0.64,0.5 1977-10-31,11805,-7,-0.705,1.25,-0.84,0.6 1977-11-30,1026,-67,0.418,1.45,-0.45,0.8 1977-12-31,0,-100,-6.57,0.85,-0.06,0.8 -1978-01-31,0,-145,-7.837,1.21,0.58,0.7 +1978-01-31,0,-145,-7.837000000000001,1.21,0.58,0.7 1978-02-28,0,-88,-6.265,1.24,0.78,0.4 1978-03-31,4291,-1,-5.78,0.62,1.13,0.1 1978-04-30,100712,-1,-2.177,0.45,1.03,-0.2 -1978-05-31,44860,28,3.151,0.59,0.77,-0.3 +1978-05-31,44860,28,3.1510000000000002,0.59,0.77,-0.3 1978-06-30,27125,34,2.891,0.38,0.33,-0.3 -1978-07-31,17198,92,2.047,0.43,-1.62,-0.4 +1978-07-31,17198,92,2.0469999999999997,0.43,-1.62,-0.4 1978-08-31,23513,13,1.42,0.86,-0.91,-0.4 1978-09-30,165411,-5,1.318,1.29,-0.78,-0.4 1978-10-31,11104,5,0.83,0.35,0.07,-0.3 @@ -356,35 +356,35 @@ date,king,upwelling,noi,npgo,pdo,oni 1979-07-31,18293,30,-2.333,-1.15,0.3,0.1 1979-08-31,41273,31,-1.179,-1.25,0.05,0.2 1979-09-30,142241,0,-1.069,-0.78,0.88,0.3 -1979-10-31,6865,-8,-3.067,-0.61,1.1,0.5 +1979-10-31,6865,-8,-3.0669999999999997,-0.61,1.1,0.5 1979-11-30,248,-127,-1.94,-0.49,0.58,0.5 -1979-12-31,0,-157,-3.921,-0.54,0.06,0.6 -1980-01-31,0,-19,-5.111,-1.9,0.06,0.6 +1979-12-31,0,-157,-3.9210000000000003,-0.54,0.06,0.6 +1980-01-31,0,-19,-5.111000000000001,-1.9,0.06,0.6 1980-02-29,0,-155,-8.808,-1.72,0.58,0.5 -1980-03-31,529,1,1.918,-1.15,0.62,0.3 -1980-04-30,28721,-7,-0.811,-0.07,0.78,0.4 +1980-03-31,529,1,1.9180000000000001,-1.15,0.62,0.3 +1980-04-30,28721,-7,-0.8109999999999999,-0.07,0.78,0.4 1980-05-31,23850,52,0.384,-0.21,0.64,0.5 1980-06-30,13453,32,1.308,-0.68,-0.72,0.5 1980-07-31,13499,103,-0.727,-0.99,-0.21,0.3 1980-08-31,20860,96,-0.748,-1.08,0.08,0.2 1980-09-30,100439,9,-1.227,-0.7,-0.13,0.0 -1980-10-31,6181,-19,0.112,-0.07,0.99,0.1 -1980-11-30,238,-121,2.172,-0.13,0.71,0.1 +1980-10-31,6181,-19,0.11199999999999999,-0.07,0.99,0.1 +1980-11-30,238,-121,2.1719999999999997,-0.13,0.71,0.1 1980-12-31,0,-113,-1.494,-0.28,0.3,0.0 1981-01-31,0,-206,-5.093,-0.26,1.18,-0.2 1981-02-28,0,-68,-0.531,-1.06,1.24,-0.4 1981-03-31,1849,-14,-2.467,-0.69,1.22,-0.4 -1981-04-30,41508,0,1.342,-0.63,1.07,-0.3 +1981-04-30,41508,0,1.3419999999999999,-0.63,1.07,-0.3 1981-05-31,19470,12,0.728,0.2,1.29,-0.2 1981-06-30,11311,8,0.875,0.01,1.82,-0.3 1981-07-31,11052,107,1.091,-0.53,0.82,-0.3 1981-08-31,25730,40,-0.833,-0.7,-0.05,-0.3 1981-09-30,119217,-1,1.382,0.0,0.43,-0.2 -1981-10-31,2093,-5,-0.931,-0.29,-0.09,-0.1 +1981-10-31,2093,-5,-0.9309999999999999,-0.29,-0.09,-0.1 1981-11-30,69,-103,-2.93,0.76,0.54,-0.1 -1981-12-31,0,-106,-1.241,0.52,0.59,0.0 +1981-12-31,0,-106,-1.2409999999999999,0.52,0.59,0.0 1982-01-31,0,-31,1.537,-0.2,0.11,0.0 -1982-02-28,0,-72,-0.756,-0.87,-0.19,0.1 +1982-02-28,0,-72,-0.7559999999999999,-0.87,-0.19,0.1 1982-03-31,306,-5,-4.487,-0.62,-0.37,0.2 1982-04-30,29768,-2,-2.286,-1.07,-0.7,0.5 1982-05-31,39937,79,-0.294,-0.99,-1.21,0.6 @@ -396,11 +396,11 @@ date,king,upwelling,noi,npgo,pdo,oni 1982-11-30,158,-52,-5.642,-0.87,-0.6,2.1 1982-12-31,0,-98,-3.853,-0.23,0.16,2.1 1983-01-31,0,-202,-8.412,0.6,0.4,2.1 -1983-02-28,0,-216,-12.164,0.47,0.97,1.8 +1983-02-28,0,-216,-12.164000000000001,0.47,0.97,1.8 1983-03-31,141,-95,-11.386,0.43,1.73,1.5 1983-04-30,25232,3,-5.171,-0.72,1.26,1.2 -1983-05-31,29525,35,-0.328,-0.74,1.01,1.0 -1983-06-30,10969,19,0.351,-0.08,1.9,0.7 +1983-05-31,29525,35,-0.32799999999999996,-0.74,1.01,1.0 +1983-06-30,10969,19,0.35100000000000003,-0.08,1.9,0.7 1983-07-31,7077,18,0.501,-0.05,2.47,0.3 1983-08-31,21478,35,-0.987,-0.01,1.18,0.0 1983-09-30,79742,25,-0.596,-0.41,0.47,-0.3 @@ -408,9 +408,9 @@ date,king,upwelling,noi,npgo,pdo,oni 1983-11-30,335,-166,-3.758,0.56,1.19,-0.8 1983-12-31,0,-52,-4.06,-0.68,1.72,-0.8 1984-01-31,0,-29,4.257,-0.22,1.42,-0.5 -1984-02-29,0,-131,2.994,0.4,1.23,-0.3 +1984-02-29,0,-131,2.9939999999999998,0.4,1.23,-0.3 1984-03-31,344,-33,1.301,0.72,1.56,-0.3 -1984-04-30,26449,-8,1.688,1.16,1.0,-0.4 +1984-04-30,26449,-8,1.6880000000000002,1.16,1.0,-0.4 1984-05-31,20073,-2,1.318,0.98,0.87,-0.4 1984-06-30,10909,37,1.482,0.74,-0.13,-0.4 1984-07-31,11412,121,-1.165,0.42,-0.35,-0.3 @@ -428,7 +428,7 @@ date,king,upwelling,noi,npgo,pdo,oni 1985-07-31,9982,83,-1.102,0.18,0.52,-0.4 1985-08-31,42967,46,0.5,-0.23,0.19,-0.4 1985-09-30,133568,12,0.845,-0.94,0.13,-0.4 -1985-10-31,11908,-9,0.881,-1.18,0.14,-0.3 +1985-10-31,11908,-9,0.8809999999999999,-1.18,0.14,-0.3 1985-11-30,568,-3,-0.695,-1.79,-0.65,-0.2 1985-12-31,0,-122,-0.512,-1.68,0.33,-0.3 1986-01-31,0,-301,-3.013,-0.77,1.04,-0.4 @@ -438,63 +438,63 @@ date,king,upwelling,noi,npgo,pdo,oni 1986-05-31,34742,1,-1.489,-0.5,0.85,-0.1 1986-06-30,16163,25,-0.069,-0.67,0.62,0.0 1986-07-31,10058,66,2.059,-0.94,1.05,0.2 -1986-08-31,29552,84,0.026,-1.02,-0.19,0.4 +1986-08-31,29552,84,0.026000000000000002,-1.02,-0.19,0.4 1986-09-30,183720,10,0.221,-0.77,-0.03,0.7 1986-10-31,12801,-7,-1.131,-0.29,1.0,0.9 1986-11-30,353,-16,2.169,-0.5,1.68,1.0 1986-12-31,0,-149,-3.139,-0.46,1.71,1.1 -1987-01-31,0,-147,0.291,0.69,1.47,1.1 +1987-01-31,0,-147,0.29100000000000004,0.69,1.47,1.1 1987-02-28,0,-35,-1.99,0.77,1.46,1.2 1987-03-31,2720,-38,-5.518,0.29,1.35,1.1 1987-04-30,67596,-1,-2.083,0.47,1.38,1.0 -1987-05-31,28257,23,-1.592,0.27,0.82,0.9 +1987-05-31,28257,23,-1.5919999999999999,0.27,0.82,0.9 1987-06-30,20636,43,-1.093,0.39,0.05,1.1 -1987-07-31,12397,35,-3.163,0.43,1.38,1.4 +1987-07-31,12397,35,-3.1630000000000003,0.43,1.38,1.4 1987-08-31,72174,99,-2.171,0.34,1.74,1.6 1987-09-30,248222,17,0.139,0.17,1.44,1.6 1987-10-31,16005,4,-3.03,0.12,0.68,1.4 -1987-11-30,603,-90,0.566,0.52,0.93,1.2 +1987-11-30,603,-90,0.5660000000000001,0.52,0.93,1.2 1987-12-31,0,-70,-0.861,0.69,0.94,1.1 1988-01-31,0,-125,0.3,1.11,0.23,0.8 -1988-02-29,0,-6,2.771,0.8,0.47,0.5 -1988-03-31,2654,-9,7.241,0.97,0.49,0.1 -1988-04-30,68506,0,-3.226,1.57,0.34,-0.3 -1988-05-31,19372,-6,1.243,1.78,0.37,-0.8 +1988-02-29,0,-6,2.7710000000000004,0.8,0.47,0.5 +1988-03-31,2654,-9,7.2410000000000005,0.97,0.49,0.1 +1988-04-30,68506,0,-3.2260000000000004,1.57,0.34,-0.3 +1988-05-31,19372,-6,1.2429999999999999,1.78,0.37,-0.8 1988-06-30,16646,14,-0.313,1.74,0.21,-1.2 1988-07-31,14669,66,0.16,1.72,-0.15,-1.2 1988-08-31,100639,71,-1.83,1.39,-0.88,-1.1 -1988-09-30,183331,17,2.047,1.32,-0.98,-1.2 +1988-09-30,183331,17,2.0469999999999997,1.32,-0.98,-1.2 1988-10-31,5813,0,0.531,1.49,-0.36,-1.4 1988-11-30,266,-92,2.418,1.24,-0.06,-1.7 1988-12-31,0,-20,4.167,0.76,-0.14,-1.8 -1989-01-31,0,-16,8.678,1.31,-1.24,-1.6 +1989-01-31,0,-16,8.677999999999999,1.31,-1.24,-1.6 1989-02-28,0,-15,3.283,0.66,-1.45,-1.4 -1989-03-31,36,-59,-1.981,0.93,-1.24,-1.1 +1989-03-31,36,-59,-1.9809999999999999,0.93,-1.24,-1.1 1989-04-30,55469,1,-0.508,0.78,-0.32,-0.9 -1989-05-31,25762,26,2.382,0.58,-0.09,-0.6 +1989-05-31,25762,26,2.3819999999999997,0.58,-0.09,-0.6 1989-06-30,13595,43,-1.819,0.52,0.11,-0.4 -1989-07-31,15194,48,3.336,1.26,0.56,-0.3 +1989-07-31,15194,48,3.3360000000000003,1.26,0.56,-0.3 1989-08-31,97285,62,0.247,0.67,-0.44,-0.3 1989-09-30,158016,28,-0.731,0.65,-0.57,-0.3 -1989-10-31,7527,-1,0.908,0.44,-0.51,-0.3 -1989-11-30,321,-12,0.928,-0.3,-0.76,-0.2 +1989-10-31,7527,-1,0.9079999999999999,0.44,-0.51,-0.3 +1989-11-30,321,-12,0.9279999999999999,-0.3,-0.76,-0.2 1989-12-31,0,-22,0.474,-0.17,-0.1,-0.1 1990-01-31,0,-47,2.57,0.27,-0.42,0.1 -1990-02-28,0,-26,1.408,-0.28,-1.28,0.2 -1990-03-31,676,-4,0.761,0.65,-1.24,0.2 +1990-02-28,0,-26,1.4080000000000001,-0.28,-1.28,0.2 +1990-03-31,676,-4,0.7609999999999999,0.65,-1.24,0.2 1990-04-30,66865,11,-1.402,0.24,-0.15,0.2 1990-05-31,26617,9,-1.015,-0.1,-0.32,0.2 -1990-06-30,15835,15,0.407,-0.07,-0.25,0.3 -1990-07-31,9148,103,-1.779,-0.26,-0.31,0.3 -1990-08-31,53532,25,-1.279,-0.33,-0.43,0.3 +1990-06-30,15835,15,0.40700000000000003,-0.07,-0.25,0.3 +1990-07-31,9148,103,-1.7790000000000001,-0.26,-0.31,0.3 +1990-08-31,53532,25,-1.2790000000000001,-0.33,-0.43,0.3 1990-09-30,119346,34,-0.368,-0.16,-0.26,0.4 -1990-10-31,4378,-5,0.942,-0.12,-1.16,0.3 +1990-10-31,4378,-5,0.9420000000000001,-0.12,-1.16,0.3 1990-11-30,128,-38,3.355,-0.74,-1.88,0.4 1990-12-31,0,-9,3.352,-1.53,-2.22,0.4 1991-01-31,0,-34,1.973,-0.74,-1.8,0.4 1991-02-28,0,-81,-1.423,-0.3,-1.09,0.3 1991-03-31,594,1,-4.147,-0.81,-1.11,0.2 -1991-04-30,40595,7,1.941,-0.21,-1.63,0.2 +1991-04-30,40595,7,1.9409999999999998,-0.21,-1.63,0.2 1991-05-31,16150,51,1.095,-0.06,-1.65,0.4 1991-06-30,11079,59,-0.023,-0.39,-2.25,0.6 1991-07-31,7818,80,-0.747,-0.48,-1.57,0.7 @@ -503,22 +503,22 @@ date,king,upwelling,noi,npgo,pdo,oni 1991-10-31,3502,14,-0.972,-1.09,0.41,0.8 1991-11-30,154,-50,1.876,-0.17,0.56,1.2 1991-12-31,0,-56,-3.824,-0.21,-0.21,1.4 -1992-01-31,0,-170,-4.153,0.49,0.08,1.6 -1992-02-29,0,-128,-8.402,-0.46,0.17,1.5 +1992-01-31,0,-170,-4.1530000000000005,0.49,0.08,1.6 +1992-02-29,0,-128,-8.402000000000001,-0.46,0.17,1.5 1992-03-31,2182,4,-8.343,-0.82,0.12,1.4 -1992-04-30,67188,-11,-3.277,-1.43,0.55,1.2 +1992-04-30,67188,-11,-3.2769999999999997,-1.43,0.55,1.2 1992-05-31,19055,66,-2.12,-1.8,1.26,1.0 1992-06-30,10518,61,-2.053,-1.35,1.22,0.8 1992-07-31,4545,81,-2.161,-1.46,1.5,0.5 -1992-08-31,25304,66,0.417,-1.25,0.85,0.2 +1992-08-31,25304,66,0.41700000000000004,-1.25,0.85,0.2 1992-09-30,86565,13,0.281,-0.95,0.45,0.0 1992-10-31,4163,-4,-2.741,-0.97,1.28,-0.1 1992-11-30,168,-42,0.899,-1.28,1.31,-0.1 1992-12-31,0,-33,-0.86,-1.87,0.33,0.0 -1993-01-31,0,-55,-6.047,-2.82,-0.28,0.2 +1993-01-31,0,-55,-6.047000000000001,-2.82,-0.28,0.2 1993-02-28,0,-50,-7.894,-2.14,0.02,0.3 1993-03-31,226,-35,-2.469,-1.74,0.24,0.5 -1993-04-30,76821,-64,-0.714,-0.76,1.0,0.7 +1993-04-30,76821,-64,-0.7140000000000001,-0.76,1.0,0.7 1993-05-31,33773,-4,-7.225,-1.09,1.79,0.8 1993-06-30,15402,24,-1.215,-1.68,2.05,0.6 1993-07-31,6643,65,-0.212,-1.68,1.48,0.3 @@ -534,16 +534,16 @@ date,king,upwelling,noi,npgo,pdo,oni 1994-05-31,6252,22,-0.914,-0.3,0.55,0.4 1994-06-30,8617,28,1.0,-0.61,0.1,0.4 1994-07-31,9014,118,-0.782,-0.71,-0.91,0.4 -1994-08-31,43333,37,-0.926,-0.86,-0.88,0.4 -1994-09-30,120477,17,-1.407,-1.1,-1.51,0.4 +1994-08-31,43333,37,-0.9259999999999999,-0.86,-0.88,0.4 +1994-09-30,120477,17,-1.4069999999999998,-1.1,-1.51,0.4 1994-10-31,6358,1,-1.6,-1.11,-1.06,0.6 -1994-11-30,229,-36,1.356,-1.69,-1.93,0.9 +1994-11-30,229,-36,1.3559999999999999,-1.69,-1.93,0.9 1994-12-31,0,-149,-2.533,-1.02,-2.22,1.0 1995-01-31,0,-292,-11.749,-0.5,-0.86,0.9 1995-02-28,0,-12,-2.736,-1.56,0.02,0.7 1995-03-31,206,-37,-4.533,-2.05,0.46,0.5 1995-04-30,6473,2,-0.973,-1.82,0.49,0.3 -1995-05-31,3513,69,-1.434,-1.5,0.89,0.2 +1995-05-31,3513,69,-1.4340000000000002,-1.5,0.89,0.2 1995-06-30,7296,31,-0.292,-1.05,1.03,0.0 1995-07-31,7734,63,-1.075,-1.5,1.0,-0.2 1995-08-31,42143,32,-1.785,-1.01,-0.11,-0.5 @@ -552,19 +552,19 @@ date,king,upwelling,noi,npgo,pdo,oni 1995-11-30,233,-78,0.83,-0.28,0.05,-1.0 1995-12-31,0,-161,-1.399,0.22,0.44,-0.9 1996-01-31,0,-45,1.09,-0.64,1.01,-0.9 -1996-02-29,0,-80,-3.874,-1.05,1.01,-0.7 -1996-03-31,72,0,-1.118,-1.15,0.85,-0.6 +1996-02-29,0,-80,-3.8739999999999997,-1.05,1.01,-0.7 +1996-03-31,72,0,-1.1179999999999999,-1.15,0.85,-0.6 1996-04-30,19942,-53,0.299,-0.44,1.37,-0.4 -1996-05-31,31478,34,-1.566,-1.27,2.13,-0.2 -1996-06-30,9330,69,1.471,-1.06,1.1,-0.2 -1996-07-31,6704,148,0.022,-0.88,0.57,-0.2 -1996-08-31,55776,70,0.936,-0.75,-0.66,-0.3 +1996-05-31,31478,34,-1.5659999999999998,-1.27,2.13,-0.2 +1996-06-30,9330,69,1.4709999999999999,-1.06,1.1,-0.2 +1996-07-31,6704,148,0.022000000000000002,-0.88,0.57,-0.2 +1996-08-31,55776,70,0.9359999999999999,-0.75,-0.66,-0.3 1996-09-30,141102,33,1.729,-0.93,0.07,-0.3 1996-10-31,8211,-1,2.151,-1.04,0.4,-0.4 1996-11-30,269,-18,1.419,-1.1,0.38,-0.4 1996-12-31,0,-91,-1.733,-1.35,-0.03,-0.5 1997-01-31,0,-56,-2.977,-1.64,0.44,-0.5 -1997-02-28,0,-2,6.571,-1.11,0.29,-0.4 +1997-02-28,0,-2,6.571000000000001,-1.11,0.29,-0.4 1997-03-31,289,-42,2.004,-0.62,0.39,-0.2 1997-04-30,66691,-2,-0.95,-0.51,0.73,0.1 1997-05-31,47020,1,-4.599,-0.88,1.59,0.6 @@ -572,14 +572,14 @@ date,king,upwelling,noi,npgo,pdo,oni 1997-07-31,8997,58,-0.972,-0.77,1.49,1.4 1997-08-31,76738,5,-3.764,-0.79,2.27,1.7 1997-09-30,137220,-3,-4.213,-0.48,1.8,2.0 -1997-10-31,4530,-13,-1.741,-0.59,1.78,2.2 -1997-11-30,246,-98,-8.608,0.17,1.48,2.3 +1997-10-31,4530,-13,-1.7409999999999999,-0.59,1.78,2.2 +1997-11-30,246,-98,-8.607999999999999,0.17,1.48,2.3 1997-12-31,0,-58,-2.092,0.26,0.99,2.3 1998-01-31,0,-184,-8.112,0.53,1.05,2.1 -1998-02-28,0,-176,-10.934,1.9,1.52,1.8 -1998-03-31,336,-1,-3.578,0.89,1.29,1.4 -1998-04-30,24325,9,-0.944,0.52,0.03,1.0 -1998-05-31,13681,20,-2.547,0.28,-0.83,0.5 +1998-02-28,0,-176,-10.934000000000001,1.9,1.52,1.8 +1998-03-31,336,-1,-3.5780000000000003,0.89,1.29,1.4 +1998-04-30,24325,9,-0.9440000000000001,0.52,0.03,1.0 +1998-05-31,13681,20,-2.5469999999999997,0.28,-0.83,0.5 1998-06-30,12914,89,1.351,0.12,-0.78,-0.1 1998-07-31,8519,60,0.856,0.39,-1.16,-0.7 1998-08-31,52601,100,1.58,0.27,-0.87,-1.0 @@ -589,7 +589,7 @@ date,king,upwelling,noi,npgo,pdo,oni 1998-12-31,0,-53,8.101,0.77,-1.09,-1.4 1999-01-31,0,-61,4.302,1.08,-0.78,-1.4 1999-02-28,0,-330,3.174,2.3,-1.06,-1.2 -1999-03-31,149,-66,0.822,1.97,-1.18,-1.0 +1999-03-31,149,-66,0.8220000000000001,1.97,-1.18,-1.0 1999-04-30,17967,31,4.186,1.65,-1.85,-0.9 1999-05-31,20553,31,2.295,1.6,-2.32,-0.9 1999-06-30,12206,29,0.057,1.91,-2.34,-1.0 @@ -618,69 +618,69 @@ date,king,upwelling,noi,npgo,pdo,oni 2001-05-31,82619,45,-0.607,1.95,-1.12,-0.2 2001-06-30,44588,37,1.43,2.35,-1.22,-0.1 2001-07-31,31568,134,0.528,1.5,-2.21,-0.1 -2001-08-31,92440,20,-0.027,1.92,-1.68,-0.1 +2001-08-31,92440,20,-0.027000000000000003,1.92,-1.68,-0.1 2001-09-30,290970,28,-0.81,1.89,-1.98,-0.2 2001-10-31,15072,2,0.735,1.16,-1.91,-0.3 2001-11-30,1723,-119,-2.977,1.76,-1.25,-0.4 2001-12-31,0,-161,-0.805,2.02,-1.12,-0.3 -2002-01-31,0,-87,2.848,1.86,-0.42,-0.2 -2002-02-28,0,-29,2.942,2.18,-1.51,0.0 -2002-03-31,1875,-6,1.664,1.9,-1.27,0.1 +2002-01-31,0,-87,2.8480000000000003,1.86,-0.42,-0.2 +2002-02-28,0,-29,2.9419999999999997,2.18,-1.51,0.0 +2002-03-31,1875,-6,1.6640000000000001,1.9,-1.27,0.1 2002-04-30,148169,17,0.226,0.9,-1.23,0.2 2002-05-31,118769,14,-0.127,1.1,-1.3,0.4 2002-06-30,84711,30,0.495,1.61,-1.18,0.6 -2002-07-31,42725,103,-1.938,1.38,-0.71,0.8 +2002-07-31,42725,103,-1.9380000000000002,1.38,-0.71,0.8 2002-08-31,142114,91,-0.31,0.81,0.11,0.8 2002-09-30,315605,24,-2.718,0.82,-0.21,0.9 2002-10-31,14684,13,-0.016,0.82,-0.2,1.1 2002-11-30,1383,-115,-1.922,1.66,1.08,1.2 -2002-12-31,0,-221,-5.861,2.64,1.57,1.1 +2002-12-31,0,-221,-5.861000000000001,2.64,1.57,1.1 2003-01-31,6,-208,-2.517,1.97,1.45,0.9 2003-02-28,93,0,-0.073,0.99,1.23,0.7 2003-03-31,19207,-82,-0.57,1.49,1.01,0.4 2003-04-30,119022,-17,-2.628,1.1,0.36,0.0 2003-05-31,57442,27,-0.762,0.92,0.21,-0.2 -2003-06-30,73600,80,-2.216,0.78,-0.42,-0.1 -2003-07-31,41208,93,0.286,1.2,0.37,0.1 +2003-06-30,73600,80,-2.2159999999999997,0.78,-0.42,-0.1 +2003-07-31,41208,93,0.28600000000000003,1.2,0.37,0.1 2003-08-31,85083,75,0.091,0.78,0.63,0.2 2003-09-30,497605,28,-1.554,0.87,-0.17,0.2 2003-10-31,24982,-16,0.415,1.29,0.74,0.3 2003-11-30,2666,-34,-0.763,-0.11,-0.23,0.3 2003-12-31,400,-205,-1.635,0.22,-0.6,0.3 -2004-01-31,0,-156,-0.554,0.27,-0.55,0.3 -2004-02-29,17,-98,-0.219,0.67,-0.21,0.3 +2004-01-31,0,-156,-0.5539999999999999,0.27,-0.55,0.3 +2004-02-29,17,-98,-0.21899999999999997,0.67,-0.21,0.3 2004-03-31,593,-5,5.006,0.38,-0.15,0.2 2004-04-30,104240,1,0.078,0.13,0.0,0.1 -2004-05-31,65458,16,1.529,0.21,0.61,0.2 +2004-05-31,65458,16,1.5290000000000001,0.21,0.61,0.2 2004-06-30,61579,43,0.547,0.78,-0.11,0.3 2004-07-31,30564,53,-1.063,0.41,0.04,0.5 2004-08-31,104978,21,-0.765,0.69,0.24,0.6 2004-09-30,445026,17,0.674,0.38,0.02,0.7 2004-10-31,31706,2,-2.109,0.04,-0.72,0.7 2004-11-30,1783,0,4.444,-0.44,-1.22,0.6 -2004-12-31,82,-16,0.037,-0.99,-0.64,0.7 +2004-12-31,82,-16,0.037000000000000005,-0.99,-0.64,0.7 2005-01-31,-1,-75,-2.242,-1.66,-0.15,0.7 2005-02-28,2,4,-5.398,-1.49,-0.01,0.6 2005-03-31,43,-9,-0.003,-1.49,0.7,0.5 2005-04-30,31349,-10,0.287,-1.69,0.28,0.5 2005-05-31,42660,-2,-2.468,-0.79,1.36,0.3 -2005-06-30,50299,23,1.027,-1.28,0.85,0.2 +2005-06-30,50299,23,1.0270000000000001,-1.28,0.85,0.2 2005-07-31,28909,73,-0.195,-1.66,0.11,0.0 -2005-08-31,45300,93,-0.756,-1.13,-0.42,-0.1 +2005-08-31,45300,93,-0.7559999999999999,-1.13,-0.42,-0.1 2005-09-30,347792,54,3.341,-1.57,-0.99,0.0 -2005-10-31,22426,-24,2.172,-0.89,-2.0,-0.2 +2005-10-31,22426,-24,2.1719999999999997,-0.89,-2.0,-0.2 2005-11-30,1624,-12,3.332,-1.66,-1.89,-0.5 2005-12-31,10,-85,-1.886,-1.25,-0.1,-0.7 2006-01-31,0,-157,4.121,-0.65,0.54,-0.7 -2006-02-28,1,0,2.257,-1.07,0.38,-0.6 -2006-03-31,35,-68,-0.108,-0.71,-0.74,-0.4 -2006-04-30,6992,1,-0.886,-0.57,-0.54,-0.2 +2006-02-28,1,0,2.2569999999999997,-1.07,0.38,-0.6 +2006-03-31,35,-68,-0.10800000000000001,-0.71,-0.74,-0.4 +2006-04-30,6992,1,-0.8859999999999999,-0.57,-0.54,-0.2 2006-05-31,89430,29,-0.66,-0.12,-0.37,0.0 2006-06-30,69176,62,-0.154,-0.13,0.21,0.0 -2006-07-31,28343,101,-0.913,-0.5,0.39,0.1 -2006-08-31,51212,114,-0.472,-0.37,-0.85,0.3 +2006-07-31,28343,101,-0.9129999999999999,-0.5,0.39,0.1 +2006-08-31,51212,114,-0.47200000000000003,-0.37,-0.85,0.3 2006-09-30,218249,37,0.579,-0.34,-1.54,0.5 -2006-10-31,25811,39,-1.142,-1.05,-0.46,0.7 +2006-10-31,25811,39,-1.1420000000000001,-1.05,-0.46,0.7 2006-11-30,4365,-118,0.905,-1.0,-0.81,0.9 2006-12-31,94,-69,2.745,-0.16,-0.42,0.9 2007-01-31,1,-1,8.058,-0.41,-0.69,0.7 @@ -689,22 +689,22 @@ date,king,upwelling,noi,npgo,pdo,oni 2007-04-30,30615,0,1.956,0.26,-0.46,-0.1 2007-05-31,35954,53,2.032,0.69,-0.29,-0.2 2007-06-30,31263,13,3.25,1.23,-0.07,-0.3 -2007-07-31,16619,14,-1.612,1.23,0.43,-0.4 +2007-07-31,16619,14,-1.6119999999999999,1.23,0.43,-0.4 2007-08-31,47217,32,-1.554,1.44,0.09,-0.6 2007-09-30,96567,25,1.379,0.91,-0.97,-0.9 2007-10-31,16778,-9,2.134,1.27,-2.33,-1.1 2007-11-30,829,-1,3.971,0.73,-1.46,-1.3 2007-12-31,35,-61,7.032,0.46,-0.87,-1.3 -2008-01-31,0,-25,1.343,0.56,-1.5,-1.4 +2008-01-31,0,-25,1.3430000000000002,0.56,-1.5,-1.4 2008-02-29,3,-11,5.692,1.14,-1.46,-1.3 2008-03-31,170,-9,8.118,1.37,-1.26,-1.1 2008-04-30,40190,9,4.419,1.4,-1.79,-0.9 2008-05-31,85222,34,0.569,1.74,-1.62,-0.7 2008-06-30,58034,45,1.47,1.28,-1.85,-0.5 2008-07-31,20237,49,-1.584,1.43,-1.96,-0.4 -2008-08-31,105202,20,-1.437,2.14,-1.85,-0.3 +2008-08-31,105202,20,-1.4369999999999998,2.14,-1.85,-0.3 2008-09-30,195828,31,-0.095,2.23,-1.87,-0.3 -2008-10-31,13410,0,2.203,1.77,-1.9,-0.4 +2008-10-31,13410,0,2.2030000000000003,1.77,-1.9,-0.4 2008-11-30,640,-38,2.515,1.97,-1.59,-0.6 2008-12-31,6,-19,4.223,0.45,-1.31,-0.7 2009-01-31,-4,-3,6.754,0.72,-1.81,-0.7 @@ -716,16 +716,16 @@ date,king,upwelling,noi,npgo,pdo,oni 2009-07-31,21397,81,0.195,0.56,-0.71,0.5 2009-08-31,111691,26,-0.257,0.72,-0.49,0.5 2009-09-30,159088,2,1.42,0.98,0.33,0.6 -2009-10-31,12401,-7,-0.416,1.15,-0.13,0.9 +2009-10-31,12401,-7,-0.41600000000000004,1.15,-0.13,0.9 2009-11-30,607,-114,1.02,1.05,-0.94,1.1 -2009-12-31,17,-64,-3.436,1.06,-0.51,1.3 +2009-12-31,17,-64,-3.4360000000000004,1.06,-0.51,1.3 2010-01-31,3,-257,-8.052,2.06,0.05,1.3 2010-02-28,2,-92,-6.329,1.84,0.25,1.2 -2010-03-31,468,-29,-0.178,1.65,-0.16,0.9 +2010-03-31,468,-29,-0.17800000000000002,1.65,-0.16,0.9 2010-04-30,133491,0,-1.751,1.18,-0.04,0.5 2010-05-31,110460,9,3.498,1.87,-0.18,0.0 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+2011-12-31,42,2,7.8870000000000005,0.13,-2.4,-0.9 2012-01-31,1,-62,4.865,0.83,-1.85,-0.7 -2012-02-29,0,-44,3.723,0.86,-1.35,-0.5 +2012-02-29,0,-44,3.7230000000000003,0.86,-1.35,-0.5 2012-03-31,38,-75,0.165,1.12,-1.66,-0.4 2012-04-30,23463,-16,-1.349,1.93,-1.01,-0.4 2012-05-31,134587,25,2.668,1.63,-2.12,-0.3 2012-06-30,55856,7,0.168,1.94,-1.63,-0.1 2012-07-31,25807,96,1.868,1.86,-2.4,0.1 -2012-08-31,70881,59,-1.316,1.73,-2.6,0.3 +2012-08-31,70881,59,-1.3159999999999998,1.73,-2.6,0.3 2012-09-30,260426,67,2.833,1.36,-2.99,0.3 2012-10-31,18041,-10,-0.19,1.87,-1.22,0.3 2012-11-30,814,-92,-2.34,1.4,-0.66,0.1 @@ -759,13 +759,13 @@ date,king,upwelling,noi,npgo,pdo,oni 2013-02-28,6,-4,8.002,1.2,-1.42,-0.4 2013-03-31,280,-3,2.063,0.64,-1.48,-0.3 2013-04-30,28808,16,2.727,0.48,-0.72,-0.2 -2013-05-31,54251,6,1.356,0.85,-0.4,-0.2 +2013-05-31,54251,6,1.3559999999999999,0.85,-0.4,-0.2 2013-06-30,61021,14,1.163,0.73,-1.19,-0.2 2013-07-31,32076,186,0.47,0.45,-1.34,-0.3 -2013-08-31,207289,17,-1.299,0.56,-1.56,-0.3 +2013-08-31,207289,17,-1.2990000000000002,0.56,-1.56,-0.3 2013-09-30,678435,-1,-0.257,0.39,-1.0,-0.2 2013-10-31,64363,13,0.932,-0.15,-1.65,-0.3 -2013-11-30,3103,-10,0.144,-0.81,-1.09,-0.3 +2013-11-30,3103,-10,0.14400000000000002,-0.81,-1.09,-0.3 2013-12-31,32,4,4.973,-1.45,-1.04,-0.3 2014-01-31,4,-17,3.977,-0.39,-0.56,-0.5 2014-02-28,0,-62,-0.953,-0.27,-0.42,-0.5 @@ -775,61 +775,61 @@ date,king,upwelling,noi,npgo,pdo,oni 2014-06-30,72245,60,0.565,-0.42,-0.29,0.0 2014-07-31,37489,100,-0.953,-0.13,0.24,-0.1 2014-08-31,83044,81,-1.605,-0.54,0.33,0.0 -2014-09-30,717915,5,-3.836,-0.78,0.75,0.1 -2014-10-31,52174,-36,-3.229,0.43,1.42,0.4 +2014-09-30,717915,5,-3.8360000000000003,-0.78,0.75,0.1 +2014-10-31,52174,-36,-3.2289999999999996,0.43,1.42,0.4 2014-11-30,1653,-54,-1.818,0.31,1.35,0.5 2014-12-31,39,-85,-2.97,-0.35,1.85,0.6 2015-01-31,0,-32,2.083,-0.55,1.51,0.6 2015-02-28,9,-20,-1.67,-1.27,1.52,0.5 -2015-03-31,1675,-7,0.931,-1.39,1.34,0.6 +2015-03-31,1675,-7,0.9309999999999999,-1.39,1.34,0.6 2015-04-30,137458,7,0.631,-1.38,0.9,0.7 2015-05-31,81338,86,-2.497,-0.72,0.33,0.8 2015-06-30,101296,121,-1.473,-1.25,0.82,1.0 2015-07-31,60439,74,-4.051,-1.47,1.41,1.2 -2015-08-31,143350,45,-3.224,-1.93,0.98,1.4 +2015-08-31,143350,45,-3.2239999999999998,-1.93,0.98,1.4 2015-09-30,709378,21,-2.707,-2.1,0.96,1.7 2015-10-31,97522,-3,-4.079,-1.31,0.83,2.0 2015-11-30,4487,-10,2.093,-2.25,0.16,2.2 -2015-12-31,147,-161,1.549,-1.11,0.28,2.3 +2015-12-31,147,-161,1.5490000000000002,-1.11,0.28,2.3 2016-01-31,6,-261,-6.935,0.94,0.74,2.2 2016-02-29,5,-112,0.825,0.23,1.3,2.0 2016-03-31,226,-85,-2.065,0.47,1.58,1.6 2016-04-30,34303,2,-1.135,-0.4,1.73,1.1 2016-05-31,102675,69,0.272,-0.68,1.59,0.6 -2016-06-30,77465,57,1.559,-0.96,0.85,0.1 -2016-07-31,42126,68,1.753,-0.49,0.56,-0.3 -2016-08-31,126230,90,0.404,-0.54,-0.61,-0.6 +2016-06-30,77465,57,1.5590000000000002,-0.96,0.85,0.1 +2016-07-31,42126,68,1.7530000000000001,-0.49,0.56,-0.3 +2016-08-31,126230,90,0.40399999999999997,-0.54,-0.61,-0.6 2016-09-30,294611,28,3.795,-0.77,-0.8,-0.8 -2016-10-31,18809,-119,-1.928,0.35,-0.43,-0.8 -2016-11-30,1503,-156,-0.487,1.16,0.96,-0.8 +2016-10-31,18809,-119,-1.9280000000000002,0.35,-0.43,-0.8 +2016-11-30,1503,-156,-0.48700000000000004,1.16,0.96,-0.8 2016-12-31,28,-8,2.524,-1.54,0.6,-0.7 2017-01-31,0,-83,-1.597,-0.27,-0.05,-0.4 2017-02-28,1,-44,-4.747,-0.67,-0.01,-0.1 2017-03-31,15,-63,2.897,-0.89,0.1,0.2 -2017-04-30,3331,-31,-0.849,-0.11,0.57,0.4 +2017-04-30,3331,-31,-0.8490000000000001,-0.11,0.57,0.4 2017-05-31,80277,29,0.58,-0.46,0.45,0.4 2017-06-30,59636,30,-1.26,-0.29,0.35,0.2 -2017-07-31,28408,106,1.417,-0.44,-0.43,-0.1 +2017-07-31,28408,106,1.4169999999999998,-0.44,-0.43,-0.1 2017-08-31,50690,87,-0.59,-0.53,-0.62,-0.5 -2017-09-30,235285,23,-0.104,-0.53,-0.18,-0.38 -2017-10-31,27977,2,3.587,-1.41,-0.68,-0.65 +2017-09-30,235285,23,-0.10400000000000001,-0.53,-0.18,-0.38 +2017-10-31,27977,2,3.5869999999999997,-1.41,-0.68,-0.65 2017-11-30,3295,-128,-1.166,-2.06,-0.59,-0.84 2017-12-31,66,-21,3.408,-2.69,-0.03,-0.97 -2018-01-31,2,-161,2.703,-1.49,0.4,-0.92 +2018-01-31,2,-161,2.7030000000000003,-1.49,0.4,-0.92 2018-02-28,0,16,6.37,-2.23,-0.08,-0.85 2018-03-31,20,-8,-0.816,-2.04,-0.71,-0.7 2018-04-30,6232,-8,1.112,-2.06,-0.81,-0.5 -2018-05-31,81640,59,-0.186,-1.92,-0.45,-0.22 +2018-05-31,81640,59,-0.18600000000000003,-1.92,-0.45,-0.22 2018-06-30,44306,34,0.753,-1.93,-0.65,-0.01 -2018-07-31,16751,91,1.283,-1.57,-0.01,0.09 +2018-07-31,16751,91,1.2830000000000001,-1.57,-0.01,0.09 2018-08-31,49960,68,0.564,-2.12,-0.21,0.23 2018-09-30,120497,12,0.625,-2.33,-0.35,0.49 2018-10-31,15063,0,-0.584,-2.34,-0.57,0.76 -2018-11-30,1494,-21,0.013,-1.75,-0.7,0.9 +2018-11-30,1494,-21,0.013000000000000001,-1.75,-0.7,0.9 2018-12-31,65,-113,2.574,-1.08,-0.21,0.81 2019-01-31,0,-92,-2.657,-0.86,-0.33,0.75 2019-02-28,0,-1,-4.261,-2.57,-0.72,0.72 -2019-03-31,44,-12,-1.888,-1.99,-0.33,0.71 +2019-03-31,44,-12,-1.8880000000000001,-1.99,-0.33,0.71 2019-04-30,4661,6,1.068,-2.15,0.1,0.66 2019-05-31,49952,51,-2.324,-2.12,0.27,0.54 2019-06-30,34452,76,0.534,-1.99,-0.05,0.45 @@ -842,12 +842,12 @@ date,king,upwelling,noi,npgo,pdo,oni 2020-01-31,5,-187,4.917,-1.71,-1.41,0.5 2020-02-29,1,8,6.851,-2.04,-1.47,0.48 2020-03-31,51,6,-0.6,-1.77,-1.75,0.4 -2020-04-30,10328,16,1.467,-1.89,-1.31,0.19 +2020-04-30,10328,16,1.4669999999999999,-1.89,-1.31,0.19 2020-05-31,45211,1,-1.671,-1.05,-0.51,-0.08 -2020-06-30,58336,29,0.942,-1.45,-0.76,-0.3 -2020-07-31,29846,73,0.577,-1.25,-0.9,-0.41 -2020-08-31,105269,43,-0.463,-1.422,-1.32,-0.57 +2020-06-30,58336,29,0.9420000000000001,-1.45,-0.76,-0.3 +2020-07-31,29846,73,0.5770000000000001,-1.25,-0.9,-0.41 +2020-08-31,105269,43,-0.46299999999999997,-1.422,-1.32,-0.57 2020-09-30,254930,-1,-0.276,-1.161,-1.03,-0.89 -2020-10-31,30917,10,1.612,-1.476,-0.62,-1.17 -2020-11-30,843,-43,1.998,-1.71,-1.58,-1.27 +2020-10-31,30917,10,1.6119999999999999,-1.476,-0.62,-1.17 +2020-11-30,843,-43,1.9980000000000002,-1.71,-1.58,-1.27 2020-12-31,9,-97,5.098,-1.87,-0.98,-1.19 diff --git a/monthly_nn.ipynb b/monthly_nn.ipynb new file mode 100644 index 0000000..2478a55 --- /dev/null +++ b/monthly_nn.ipynb @@ -0,0 +1,6671 @@ +{ + "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", + "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": 2, + "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['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": 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", + "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(chris_path)\n", + " print(king_all_copy)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "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" + ] + } + ], + "source": [ + "print(data_copy)\n", + "data_copy.shape\n", + "forecast_set = data_copy" + ] + }, + { + "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": 77, + "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", + " \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", + " # create lists to be filled \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": 78, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm = create_train_test(data_copy)\n", + "\n", + "# Make arrays for train and test \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])).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)\n", + "\n", + "# Make arrays for y train and test for testing of normalization \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": 79, + "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": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def create_nn_model(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " create nn 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(Dense(32, input_dim = (x_train.shape[1]), activation = 'relu'))\n", + " model.add(Dense(16, activation = 'relu'))\n", + " model.add(Dense(8, activation = 'relu'))\n", + " model.add(Dense(1))\n", + " model.compile(loss='mean_squared_error', optimizer = 'adam')\n", + " \n", + " history = model.fit(x_train, y_train, epochs = 3000, batch_size = 100)\n", + " \n", + " # Predictions\n", + " nn_train_predict = model.predict(x_train)\n", + " nn_test_predict = model.predict(x_test)\n", + " \n", + " # Descale \n", + " nn_train_predict = scaler.inverse_transform(nn_train_predict)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " nn_test_predict = scaler.inverse_transform(nn_test_predict)\n", + " nn_test_predict = nn_test_predict.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", + " \n", + " return model, nn_train_predict, nn_test_predict, history, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(918, 6)\n", + "Epoch 1/3000\n", + "10/10 [==============================] - 0s 980us/step - loss: 991432680.7273\n", + "Epoch 2/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1120946321.4545\n", + "Epoch 3/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 956586932.3636\n", + "Epoch 4/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 980322176.0000\n", + "Epoch 5/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 734022644.3636\n", + "Epoch 6/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 870754330.1818\n", + "Epoch 7/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1026949143.2727\n", + "Epoch 8/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 964317201.4545\n", + "Epoch 9/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1026203031.2727\n", + "Epoch 10/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 831096314.1818\n", + "Epoch 11/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 905880186.1818\n", + "Epoch 12/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1010551424.0000\n", + "Epoch 13/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 864442644.3636\n", + "Epoch 14/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 851433117.0909\n", + "Epoch 15/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1153174312.7273\n", + "Epoch 16/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1181130711.2727\n", + "Epoch 17/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 937398516.3636\n", + "Epoch 18/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1151585931.6364\n", + "Epoch 19/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 949975674.1818\n", + "Epoch 20/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 959467589.8182\n", + "Epoch 21/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 873340334.5455\n", + "Epoch 22/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1083537303.2727\n", + "Epoch 23/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1003179095.2727\n", + "Epoch 24/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 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[==============================] - 0s 2ms/step - loss: 844480663.2727\n", + "Epoch 54/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 940181474.9091\n", + "Epoch 55/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 898198528.0000\n", + "Epoch 56/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1048184296.7273\n", + "Epoch 57/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 1012406341.8182\n", + "Epoch 58/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 832115642.1818\n", + "Epoch 59/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 987409216.0000\n", + "Epoch 60/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1007196386.9091\n", + "Epoch 61/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 1186307991.2727\n", + "Epoch 62/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 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[==============================] - 0s 1ms/step - loss: 638182318.5455\n", + "Epoch 73/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 780913786.1818\n", + "Epoch 74/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 898920174.5455\n", + "Epoch 75/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 732504898.9091\n", + "Epoch 76/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 825095901.0909\n", + "Epoch 77/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 767312593.4545\n", + "Epoch 78/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 794200599.2727\n", + "Epoch 79/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 806559680.0000\n", + "Epoch 80/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 703321604.3636\n", + "Epoch 81/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 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[==============================] - 0s 2ms/step - loss: 741833402.1818\n", + "Epoch 92/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 936409163.6364\n", + "Epoch 93/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 907270900.3636\n", + "Epoch 94/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 767485457.4545\n", + "Epoch 95/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 831787520.0000\n", + "Epoch 96/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 931542312.7273\n", + "Epoch 97/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 726857344.0000\n", + "Epoch 98/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 677206178.9091\n", + "Epoch 99/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 825223394.9091\n", + "Epoch 100/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 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- loss: 348004082.9091\n", + "Epoch 1892/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 580551214.5455\n", + "Epoch 1893/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 394470308.3636\n", + "Epoch 1894/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 453243354.1818\n", + "Epoch 1895/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 529167563.6364\n", + "Epoch 1896/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 544175671.2727\n", + "Epoch 1897/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 444362225.4545\n", + "Epoch 1898/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 366737872.0000\n", + "Epoch 1899/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 416103160.7273\n", + "Epoch 1900/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 387223815.2727\n", + "Epoch 1901/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 469108061.0909\n", + "Epoch 1902/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 374722640.0000\n", + "Epoch 1903/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 536165486.5455\n", + "Epoch 1904/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 462099287.2727\n", + "Epoch 1905/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 306793115.6364\n", + "Epoch 1906/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 393602021.8182\n", + "Epoch 1907/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 477932234.1818\n", + "Epoch 1908/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 436427447.2727\n", + "Epoch 1909/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 463162900.3636\n", + "Epoch 1910/3000\n", + "10/10 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- loss: 553305233.4545\n", + "Epoch 1920/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 394641776.0000\n", + "Epoch 1921/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 312934116.3636\n", + "Epoch 1922/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 466073280.0000\n", + "Epoch 1923/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 476629597.0909\n", + "Epoch 1924/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 521837134.5455\n", + "Epoch 1925/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 511495409.4545\n", + "Epoch 1926/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 451761957.8182\n", + "Epoch 1927/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 359663362.9091\n", + "Epoch 1928/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 517339072.0000\n", + "Epoch 1929/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 457950693.8182\n", + "Epoch 1930/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 349302160.0000\n", + "Epoch 1931/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 490964485.8182\n", + "Epoch 1932/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 491756526.5455\n", + "Epoch 1933/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 411368443.6364\n", + "Epoch 1934/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 444513856.0000\n", + "Epoch 1935/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 509740817.4545\n", + "Epoch 1936/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 552491473.4545\n", + "Epoch 1937/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 461731559.2727\n", + "Epoch 1938/3000\n", + "10/10 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910us/step - loss: 374511313.4545\n", + "Epoch 1948/3000\n", + "10/10 [==============================] - 0s 906us/step - loss: 359153714.9091\n", + "Epoch 1949/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 467826126.5455\n", + "Epoch 1950/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 448868574.5455\n", + "Epoch 1951/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 542286429.0909\n", + "Epoch 1952/3000\n", + "10/10 [==============================] - 0s 954us/step - loss: 351281227.6364\n", + "Epoch 1953/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 465742557.0909\n", + "Epoch 1954/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 378662132.3636\n", + "Epoch 1955/3000\n", + "10/10 [==============================] - 0s 927us/step - loss: 422439013.8182\n", + "Epoch 1956/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 444774941.0909\n", + "Epoch 1957/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 461452162.9091\n", + "Epoch 1958/3000\n", + "10/10 [==============================] - 0s 961us/step - loss: 513802010.1818\n", + "Epoch 1959/3000\n", + "10/10 [==============================] - 0s 987us/step - loss: 380492436.3636\n", + "Epoch 1960/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 373810034.9091\n", + "Epoch 1961/3000\n", + "10/10 [==============================] - 0s 894us/step - loss: 461479581.0909\n", + "Epoch 1962/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 355729141.8182\n", + "Epoch 1963/3000\n", + "10/10 [==============================] - 0s 965us/step - loss: 409598808.7273\n", + "Epoch 1964/3000\n", + "10/10 [==============================] - 0s 932us/step - loss: 313137701.8182\n", + "Epoch 1965/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 431911237.8182\n", + "Epoch 1966/3000\n", + "10/10 [==============================] - 0s 944us/step - loss: 411091537.4545\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1967/3000\n", + "10/10 [==============================] - 0s 942us/step - loss: 429936721.4545\n", + "Epoch 1968/3000\n", + "10/10 [==============================] - 0s 965us/step - loss: 419120503.2727\n", + "Epoch 1969/3000\n", + "10/10 [==============================] - 0s 949us/step - loss: 388783640.7273\n", + "Epoch 1970/3000\n", + "10/10 [==============================] - 0s 935us/step - loss: 454318270.5455\n", + "Epoch 1971/3000\n", + "10/10 [==============================] - 0s 930us/step - loss: 370506493.0909\n", + "Epoch 1972/3000\n", + "10/10 [==============================] - 0s 888us/step - loss: 435339749.8182\n", + "Epoch 1973/3000\n", + "10/10 [==============================] - 0s 939us/step - loss: 460076334.5455\n", + "Epoch 1974/3000\n", + "10/10 [==============================] - 0s 923us/step - loss: 483646891.6364\n", + "Epoch 1975/3000\n", + "10/10 [==============================] - 0s 890us/step - loss: 437257082.1818\n", + "Epoch 1976/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 499003677.0909\n", + "Epoch 1977/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 433880946.9091\n", + "Epoch 1978/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 427801557.8182\n", + "Epoch 1979/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 328972298.1818\n", + "Epoch 1980/3000\n", + "10/10 [==============================] - 0s 922us/step - loss: 392676853.8182\n", + "Epoch 1981/3000\n", + "10/10 [==============================] - 0s 872us/step - loss: 519588648.7273\n", + "Epoch 1982/3000\n", + "10/10 [==============================] - 0s 905us/step - loss: 443628753.4545\n", + "Epoch 1983/3000\n", + "10/10 [==============================] - 0s 908us/step - loss: 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"stream", + "text": [ + "10/10 [==============================] - 0s 1ms/step - loss: 382561792.0000\n", + "Epoch 2587/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 383714024.7273\n", + "Epoch 2588/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 341995266.9091\n", + "Epoch 2589/3000\n", + "10/10 [==============================] - 0s 3ms/step - loss: 403137626.1818\n", + "Epoch 2590/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 338628049.4545\n", + "Epoch 2591/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 403274893.0909\n", + "Epoch 2592/3000\n", + "10/10 [==============================] - 0s 944us/step - loss: 375757986.9091\n", + "Epoch 2593/3000\n", + "10/10 [==============================] - 0s 898us/step - loss: 440722804.3636\n", + "Epoch 2594/3000\n", + "10/10 [==============================] - 0s 913us/step - loss: 489417853.0909\n", + "Epoch 2595/3000\n", + "10/10 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"stream", + "text": [ + "Epoch 2761/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 341636135.2727\n", + "Epoch 2762/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 377531825.4545\n", + "Epoch 2763/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 385356349.0909\n", + "Epoch 2764/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 481805015.2727\n", + "Epoch 2765/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 368764256.0000\n", + "Epoch 2766/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 294939844.3636\n", + "Epoch 2767/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 296057818.1818\n", + "Epoch 2768/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 427876634.1818\n", + "Epoch 2769/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 389751115.6364\n", + "Epoch 2770/3000\n", + 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414829757.0909\n", + "Epoch 2910/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 317251516.3636\n", + "Epoch 2911/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 327235684.3636\n", + "Epoch 2912/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 274291697.4545\n", + "Epoch 2913/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 426120194.9091\n", + "Epoch 2914/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 320020247.2727\n", + "Epoch 2915/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 314426277.8182\n", + "Epoch 2916/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 396820192.0000\n", + "Epoch 2917/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 342402946.9091\n", + "Epoch 2918/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 375880939.6364\n", + "Epoch 2919/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 335807611.6364\n", + "Epoch 2920/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 356718109.0909\n", + "Epoch 2921/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 302283051.6364\n", + "Epoch 2922/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 448345437.0909\n", + "Epoch 2923/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 312370779.6364\n", + "Epoch 2924/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 457482187.6364\n", + "Epoch 2925/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 467051130.1818\n", + "Epoch 2926/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 377873652.3636\n", + "Epoch 2927/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 404772069.8182\n", + "Epoch 2928/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 373026999.2727\n", + "Epoch 2929/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 409139733.8182\n", + "Epoch 2930/3000\n", + "10/10 [==============================] - 0s 965us/step - loss: 476271360.0000\n", + "Epoch 2931/3000\n", + "10/10 [==============================] - 0s 962us/step - loss: 450123514.1818\n", + "Epoch 2932/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 335306373.8182\n", + "Epoch 2933/3000\n", + "10/10 [==============================] - 0s 934us/step - loss: 376473024.0000\n", + "Epoch 2934/3000\n", + "10/10 [==============================] - 0s 941us/step - loss: 313878594.9091\n", + "Epoch 2935/3000\n", + "10/10 [==============================] - 0s 949us/step - loss: 410917029.8182\n", + "Epoch 2936/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 391376637.0909\n", + "Epoch 2937/3000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10/10 [==============================] - 0s 893us/step - loss: 349614248.7273\n", + "Epoch 2938/3000\n", + "10/10 [==============================] - 0s 983us/step - loss: 475278490.1818\n", + "Epoch 2939/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 393699752.7273\n", + "Epoch 2940/3000\n", + "10/10 [==============================] - 0s 946us/step - loss: 404036852.3636\n", + "Epoch 2941/3000\n", + "10/10 [==============================] - 0s 939us/step - loss: 310075509.8182\n", + "Epoch 2942/3000\n", + "10/10 [==============================] - 0s 957us/step - loss: 310417889.4545\n", + "Epoch 2943/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414358536.7273\n", + "Epoch 2944/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 344871547.6364\n", + "Epoch 2945/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 356274475.6364\n", + "Epoch 2946/3000\n", + "10/10 [==============================] - 0s 979us/step - loss: 357627610.1818\n", + "Epoch 2947/3000\n", + "10/10 [==============================] - 0s 954us/step - loss: 308476590.5455\n", + "Epoch 2948/3000\n", + "10/10 [==============================] - 0s 948us/step - loss: 344861485.0909\n", + "Epoch 2949/3000\n", + "10/10 [==============================] - 0s 967us/step - loss: 367587838.5455\n", + "Epoch 2950/3000\n", + "10/10 [==============================] - 0s 918us/step - loss: 366758368.0000\n", + "Epoch 2951/3000\n", + "10/10 [==============================] - 0s 930us/step - loss: 350354638.5455\n", + "Epoch 2952/3000\n", + "10/10 [==============================] - 0s 970us/step - loss: 363771441.4545\n", + "Epoch 2953/3000\n", + "10/10 [==============================] - 0s 957us/step - loss: 385292110.5455\n", + "Epoch 2954/3000\n", + "10/10 [==============================] - 0s 947us/step - loss: 407589152.0000\n", + "Epoch 2955/3000\n", + "10/10 [==============================] - 0s 908us/step - loss: 342974936.7273\n", + "Epoch 2956/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 416040910.5455\n", + "Epoch 2957/3000\n", + "10/10 [==============================] - 0s 989us/step - loss: 341894946.9091\n", + "Epoch 2958/3000\n", + "10/10 [==============================] - 0s 903us/step - loss: 355141790.5455\n", + "Epoch 2959/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 329033154.9091\n", + "Epoch 2960/3000\n", + "10/10 [==============================] - 0s 945us/step - loss: 417409678.5455\n", + "Epoch 2961/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 379748576.0000\n", + "Epoch 2962/3000\n", + "10/10 [==============================] - 0s 897us/step - loss: 356126507.6364\n", + "Epoch 2963/3000\n", + "10/10 [==============================] - 0s 937us/step - loss: 410452302.5455\n", + "Epoch 2964/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 294462520.7273\n", + "Epoch 2965/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 326169524.3636\n", + "Epoch 2966/3000\n", + "10/10 [==============================] - 0s 973us/step - loss: 330078720.0000\n", + "Epoch 2967/3000\n", + "10/10 [==============================] - 0s 991us/step - loss: 332932516.3636\n", + "Epoch 2968/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 493803624.7273\n", + "Epoch 2969/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 472380657.4545\n", + "Epoch 2970/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 464569550.5455\n", + "Epoch 2971/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 419182048.0000\n", + "Epoch 2972/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 431501954.9091\n", + "Epoch 2973/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 421149061.8182\n", + "Epoch 2974/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 340897744.0000\n", + "Epoch 2975/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 347571086.5455\n", + "Epoch 2976/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 416178693.8182\n", + "Epoch 2977/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 297465639.2727\n", + "Epoch 2978/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 358959688.7273\n", + "Epoch 2979/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 390126804.3636\n", + "Epoch 2980/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 326467739.6364\n", + "Epoch 2981/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 305814135.2727\n", + "Epoch 2982/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 424695109.8182\n", + "Epoch 2983/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 301992577.4545\n", + "Epoch 2984/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 339711704.7273\n", + "Epoch 2985/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 464452765.0909\n", + "Epoch 2986/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 299091185.4545\n", + "Epoch 2987/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 331288641.4545\n", + "Epoch 2988/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 433518656.0000\n", + "Epoch 2989/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 345901474.9091\n", + "Epoch 2990/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 414442202.1818\n", + "Epoch 2991/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 335004619.6364\n", + "Epoch 2992/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 330958909.0909\n", + "Epoch 2993/3000\n", + "10/10 [==============================] - 0s 2ms/step - loss: 411063717.8182\n", + "Epoch 2994/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 477360256.0000\n", + "Epoch 2995/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 313871268.3636\n", + "Epoch 2996/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 365627371.6364\n", + "Epoch 2997/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 319629053.0909\n", + "Epoch 2998/3000\n", + "10/10 [==============================] - 0s 938us/step - loss: 454736247.2727\n", + "Epoch 2999/3000\n", + "10/10 [==============================] - 0s 925us/step - loss: 372288258.9091\n", + "Epoch 3000/3000\n", + "10/10 [==============================] - 0s 1ms/step - loss: 338687688.7273\n" + ] + } + ], + "source": [ + "# train nn model \n", + "print(x_train.shape)\n", + "model, nn_train_preds, nn_test_preds, history_nn, y_train, y_test = create_nn_model(x_train, y_train, x_test, y_test, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "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 7141.462663113151.\n" + ] + } + ], + "source": [ + "# plot train of nn_model\n", + "# plot results \n", + "plot_predictions(y_train, nn_train_preds)\n", + "return_rmse(y_train, nn_train_preds)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "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 36717.236100895294.\n" + ] + } + ], + "source": [ + "# plot test nn \n", + "# get results \n", + "plot_predictions(y_test, nn_test_preds)\n", + "return_rmse(y_test, nn_test_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot loss\n", + "plot_loss(history_nn)" + ] + } + ], + "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/multivar_gru.ipynb b/multivar_gru.ipynb index 5143a22..ea84e1f 100644 --- a/multivar_gru.ipynb +++ b/multivar_gru.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 81, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -94,13 +94,13 @@ " 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(chris_path)\n", " print(king_all_copy)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 84, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "(984, 1)" ] }, - "execution_count": 85, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -391,7 +391,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 87, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -508,7 +508,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 88, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -635,7 +635,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 90, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -699,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -716,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 96, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -902,13 +902,13 @@ "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(chris_path_cov)\n", "cov_data" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1026,7 +1026,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 97, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1169,7 +1169,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 98, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1182,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1324,7 +1324,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 99, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1337,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1491,7 +1491,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 100, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1504,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1670,7 +1670,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 101, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1684,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1850,7 +1850,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 102, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1869,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1880,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1905,7 +1905,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1915,7 +1915,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1924,7 +1924,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1943,7 +1943,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1959,7 +1959,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1974,7 +1974,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1983,7 +1983,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1993,7 +1993,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2007,7 +2007,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2171,7 +2171,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 114, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2184,7 +2184,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -2200,7 +2200,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -2252,12 +2252,12 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2293,7 +2293,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2379,46 +2379,38 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(792, 6, 6) (792,) (54, 6, 6) (54,)\n" - ] - } - ], + "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", + "n_train_months = 66 * 12 # MENTAL NOTE: IF ERROR IN MONTH 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": 120, + "execution_count": 59, "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": 121, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2435,9 +2427,9 @@ } ], "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)" @@ -5686,7 +5678,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -5715,9 +5707,16 @@ "\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))" + " print(\"The root mean squared error is {}.\".format(rmse))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 127, @@ -6031,7 +6030,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.3" } }, "nbformat": 4, diff --git a/other_regression_methods_day.ipynb b/other_regression_methods_day.ipynb index 5b2e6ca..d6faef6 100644 --- a/other_regression_methods_day.ipynb +++ b/other_regression_methods_day.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 187, + "execution_count": 32, "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", @@ -32,13 +31,10 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 33, "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", @@ -46,7 +42,6 @@ " 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 +55,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -108,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -126,20 +121,14 @@ " \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", + " # Create list\n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -147,7 +136,6 @@ " 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", @@ -156,7 +144,7 @@ " 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", + " # make y_test_not_norm for testing \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", @@ -167,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -180,7 +168,7 @@ "max val king_test:\n", "32446\n", "(1824, 1)\n", - "(22365, 180, 1)\n" + "(22365, 180)\n" ] } ], @@ -188,40 +176,16 @@ "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", + "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1])).astype(np.float32)\n", + "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1]))\n", "y_train = np.array(y_train)\n", "y_test = np.array(y_test)\n", + "\n", + "# create non-norm for testing \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)" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(22365, 180)\n", - "(1644, 180)\n" - ] - } - ], - "source": [ - "x_train_overall = x_train.reshape((x_train.shape[0], x_train.shape[1]))\n", - "x_test_overall = x_test.reshape((x_test.shape[0], x_test.shape[1]))\n", - "print(x_train_overall.shape)\n", - "print(x_test_overall.shape)" + "y_train_not_norm = y_train_not_norm.reshape((y_train_not_norm.shape[0], 1))\n" ] }, { @@ -233,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -256,20 +220,12 @@ "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": 210, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -332,7 +288,7 @@ } ], "source": [ - "lr_train, lr_test, y_train, y_test = create_linear_model(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "lr_train, lr_test, y_train, y_test = create_linear_model(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, lr_train)\n", "plot_predictions(y_test, lr_test)\n", @@ -342,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +306,7 @@ " '''\n", " creating a basic Ridge Regression model (L2)\n", " '''\n", - " rr = Ridge(alpha=0.01)\n", + " rr = Ridge(alpha=0.1)\n", " rr.fit(x_train, y_train)\n", " train_preds_rr = rr.predict(x_train)\n", " test_preds_rr = rr.predict(x_test)\n", @@ -369,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -413,7 +369,7 @@ } ], "source": [ - "rr_train, rr_test, y_train, y_test = create_RR_model(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "rr_train, rr_test, y_train, y_test = create_RR_model(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, rr_train)\n", "plot_predictions(y_test, rr_test)\n", @@ -423,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -431,23 +387,42 @@ " '''\n", " creating lasso regression (L1)\n", " '''\n", - " lasso = Lasso(alpha=0.01)\n", + " lasso = Lasso(alpha=0.0001)\n", " lasso.fit(x_train, y_train)\n", " train_preds_lasso = lasso.predict(x_train)\n", + " print(train_preds_lasso)\n", " test_preds_lasso = lasso.predict(x_test)\n", + " print(train_preds_lasso.shape)\n", + " train_preds_lasso = train_preds_lasso.reshape(train_preds_lasso.shape[0], 1)\n", + " test_preds_lasso = test_preds_lasso.reshape(test_preds_lasso.shape[0], 1)\n", + " #Descale \n", + " \n", + " train_preds_lasso = scaler.inverse_transform(train_preds_lasso)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " test_preds_lasso = scaler.inverse_transform(test_preds_lasso)\n", + " test_preds_lasso = test_preds_lasso.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", "\n", " \n", - " return train_preds_lasso, test_preds_lasso " + " return train_preds_lasso, test_preds_lasso, y_train, y_test " ] }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 82, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00835204 0.00929928 0.00851214 ... 0.00212162 0.00212162 0.00213496]\n", + "(22365,)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -473,13 +448,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 0.04915541842199333.\n", - "The root mean squared error is 0.09078446501653406.\n" + "The root mean squared error is 538.4020947589681.\n", + "The root mean squared error is 1356.7620677164293.\n" ] } ], "source": [ - "lasso_train, lasso_test = create_lasso(x_train_overall, y_train, x_test_overall, y_test, scaler)\n", + "lasso_train, lasso_test, y_train, y_test = create_lasso(x_train, y_train, x_test, y_test, scaler)\n", "\n", "plot_predictions(y_train, lasso_train)\n", "plot_predictions(y_test, lasso_test)\n", diff --git a/other_regression_methods_month.ipynb b/other_regression_methods_month.ipynb index 134b7fc..921ad69 100644 --- a/other_regression_methods_month.ipynb +++ b/other_regression_methods_month.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,13 +33,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "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", @@ -47,7 +44,6 @@ " 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", @@ -61,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -109,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -208,7 +204,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 80, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -257,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -297,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -315,24 +311,14 @@ " \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", - "# 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", + " # Create empty lists \n", " x_train = []\n", " y_train = []\n", " x_test = []\n", @@ -341,7 +327,6 @@ " y_train_not_norm = []\n", " x_train_not_norm = []\n", " x_test_not_norm = []\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", @@ -350,7 +335,7 @@ " 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", + " # Making the non-norm for testing \n", " for i in range(6, 60):\n", " x_test_not_norm.append(king_test['king'][i-6:i])\n", " y_test_not_norm.append(king_test['king'][i])\n", @@ -370,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -385,7 +370,6 @@ } ], "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, y_train, x_test, y_test, scaler, y_test_not_norm, y_train_not_norm, x_test_not_norm, x_train_not_norm = create_train_test(data_copy)\n", "x_train = np.array(x_train)\n", "x_test = np.array(x_test)\n", @@ -393,20 +377,18 @@ "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", + "\n", + "# Getting non-normalized data for testing\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", "x_test_not_norm = np.array(x_test_not_norm)\n", "x_test_not_norm = np.reshape(x_test_not_norm, (x_test_not_norm.shape[0],x_test_not_norm.shape[1],1))\n", "x_train_not_norm = np.array(x_train_not_norm)\n", "x_train_not_norm = np.reshape(x_train_not_norm, (x_train_not_norm.shape[0],x_train_not_norm.shape[1],1)).astype(np.float32)\n", "\n", + "# Shape checks \n", "print(x_train.shape)\n", "print(x_test.shape)\n", "\n", @@ -416,31 +398,7 @@ }, { "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2],\n", - " [3, 4]])" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = [[1, 2], [3, 4]]\n", - "a = np.array(a)\n", - "a.reshape((-1, 1))\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 99, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -455,6 +413,7 @@ } ], "source": [ + "# Create copies for models \n", "x_train_lr = x_train.reshape((x_train.shape[0], x_train.shape[1]))\n", "print(x_train_lr.shape)\n", "x_test_lr = x_test.reshape((x_test.shape[0], x_test.shape[1]))\n", @@ -480,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -502,16 +461,7 @@ "\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" + " print(\"The root mean squared error is {}.\".format(rmse))" ] }, { @@ -589,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -597,7 +547,7 @@ " '''\n", " creating a basic Ridge Regression model (L2)\n", " '''\n", - " rr = Ridge(alpha=0.01)\n", + " rr = Ridge(alpha=0.1)\n", " rr.fit(x_train, y_train)\n", " train_preds_rr = rr.predict(x_train)\n", " test_preds_rr = rr.predict(x_test)\n", @@ -615,12 +565,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 34, "metadata": {}, "outputs": [ { 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -646,8 +596,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 27009.22197757247.\n", - "The root mean squared error is 68350.03056015464.\n" + "The root mean squared error is 27009.219641017073.\n", + "The root mean squared error is 68350.22719746032.\n" ] } ], @@ -662,77 +612,43 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "# def create_lasso_copy(x_train, y_train, x_test, y_test, scaler): \n", - "# '''\n", - "# creating a basic Ridge Regression model (L2)\n", - "# '''\n", - "# transformer_x = RobustScaler().fit(x_train)\n", - "# transformer_y = RobustScaler().fit(y_train) \n", - "# x_rtrain = transformer_x.transform(x_train)\n", - "# y_rtrain = transformer_y.transform(y_train)\n", - "# x_rtest = transformer_x.transform(x_test)\n", - "# y_rtest = transformer_y.transform(y_test)\n", - "\n", - "# #Fit Train Model\n", - "# lasso = Lasso()\n", - "# lasso_alg = lasso.fit(x_rtrain,y_rtrain)\n", - "\n", - " \n", - "# # train_score = lasso_alg.score(x_rtrain,y_rtrain)\n", - "# # test_score = lasso_alg.score(x_rtest,y_rtest)\n", - "\n", - "# # print (\"training score:\", train_score)\n", - "# # print (\"test score:\", test_score)\n", - "\n", - "# # example = [[10,100]]\n", - "# train_preds_rr = transformer_y.inverse_transform(lasso.predict(x_rtrain).reshape(-1, 1))\n", - "# test_preds_rr = transformer_y.inverse_transform(lasso.predict(x_rtest).reshape(-1, 1))\n", - "# # train_preds_rr = scaler.inverse_transform(train_preds_rr)\n", - "# y_train = scaler.inverse_transform(y_train)\n", - "# # test_preds_rr = scaler.inverse_transform(test_preds_rr)\n", - "# test_preds_rr = test_preds_rr.astype(np.int64)\n", - "# y_test = scaler.inverse_transform(y_test)\n", + "def create_lasso(x_train, y_train, x_test, y_test, scaler):\n", + " '''\n", + " creating a basic lasso regression model (L1)\n", + " '''\n", + " # Fit and predict \n", + " lasso = Lasso(alpha = 0.0001)\n", + " lasso.fit(x_train, y_train)\n", + " train_preds_lasso = lasso.predict(x_train)\n", + " test_preds_lasso = lasso.predict(x_test)\n", " \n", - "# return train_preds_rr, test_preds_rr, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "def create_lasso(x_train, y_train, x_test, y_test):\n", - " lassoModel = Lasso(alpha = 0.1)\n", - " lassoModel.fit(x_train, y_train)\n", + " # Reshape\n", + " train_preds_lasso = train_preds_lasso.reshape(train_preds_lasso.shape[0], 1)\n", + " test_preds_lasso = test_preds_lasso.reshape(test_preds_lasso.shape[0], 1)\n", " \n", - " train_preds = lassoModel.predict(x_train)\n", - " test_preds = lassoModel.predict(x_test)\n", + " # Descale \n", + " train_preds_lasso = scaler.inverse_transform(train_preds_lasso)\n", + " y_train = scaler.inverse_transform(y_train)\n", + " test_preds_lasso = scaler.inverse_transform(test_preds_lasso)\n", + " test_preds_lasso = test_preds_lasso.astype(np.int64)\n", + " y_test = scaler.inverse_transform(y_test)\n", " \n", - " return train_preds, test_preds\n", + " return train_preds_lasso, test_preds_lasso, y_train, y_test\n", " " ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 30, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chrisshell/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1983623331840.0, tolerance: 434337824.0\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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K119/HVDQtG3bNujYULB6K1asSG5ubt5v//kpbtvddlssNqhmLFAtqNFYYkKtWrUYN24cY8eOpWrVqrRo0YL33nsPMC/ZpUuXApCRkUGTJmae76RJk8LWe9ddd/HII4/keW7l5uby1FNPFbu93bt3Z/bs2ezevZucnBzeffddTj/99KDpYOaPvPbaa6xatYoxY8YUqvOss84iKyuLl19+OS9t4cKFzJ49m549ezJlyhRycnLYtWsXc+bMoVu3bhx99NGsWLGCrKwsMjIymDFjRti216hRg/379xf7GljKJlbQxIJDh8zYDBiNxhIzTjzxRDp27MjkyZN5++23efXVV+nYsSNt27blk0+M9/zo0aMZNGgQp512GvXr1w9bZ4cOHXjmmWcYMmQIJ5xwAu3atWP79u3FbmujRo149NFHOfPMM+nYsSOdO3dmwIABQdNdkpKSmDx5MrNmzeKFF14oUKeI8NFHHzF9+nRatWpF27ZtGT16NI0bN+bCCy+kQ4cOdOzYkbPOOovHH3+chg0b0qxZMy699FI6dOjAFVdcwYknnhi27f379+ejjz6yzgAJioRSuxORLl26aNQXPtu+HRo3Nt9//BG6do3u8eKIlStXcsIJJ8S6GZYExN570UVEFqtql0D7rEYTC3wnCFrTmcViKedYQRML3PEZsKYzi8VS7rGCJhb4Chqr0VgslnKOFTSxwNd0ZjUai8VSzrGCJhZYjcZisSQQVtDEAqvRWCyWBMIKmlhgNZqY4rtMwKBBgzhYDGHvG/b/+uuvZ8WKFUHzFjWCcfPmzdm9e3eh9AMHDnDDDTfkzX/p2bMnCxYsKBCg05/777+fb7/9NuI2hGL06NGMHTs2bL433niDdu3a0bZtW9q0aeOpTKQ88sgjJV6npfhYQRML9u6FOnXMd6vRlDq+ywRUqlSJCRMmFNifk5MTpGRoXnnlFdq0aRN0f0mHyr/++uupW7cua9asYfny5UycODGgQPLlwQcf5Oyzzy6xNnhl2rRpPPPMM3zzzTcsX76cn376KS+WW0liBU18YgVNLMjIMIKmShWr0cSY0047jbVr1/Ldd99x5plncvnll9O+fXtycnK466676Nq1Kx06dODFF18ETGiakSNH0qZNG8477zx27tyZV9cZZ5yBO9n3q6++onPnznTs2JFevXoFDJW/a9cuLr74Yrp27UrXrl35/vvvAdizZw99+vThxBNP5IYbbggYc2zdunUsWLCAhx56iAoVzGPcsmVLzjvvPMAIy+HDh9O2bVv69OnDIec+89XAAoX0B0hLS2PgwIF06NCBk046iV9++SVkui8vv/wy55xzTt7xXB599FHGjh1LY2eicpUqVRg+fDhgIlWfdNJJdOjQgQsvvJD09PRC13P37t00b94cCL6Mwr333psXMPWKK67w8u+3lBI2qGYs2LsXatc2nwksaGK8SgDZ2dlMmzaNfv36AfDjjz+ybNkyWrRowUsvvUStWrVYuHAhWVlZnHLKKfTp04eff/6Z1atX8+uvv7Jjxw7atGnDtddeW6DeXbt2MXz4cObMmUOLFi1IS0ujbt26hULlX3755fz973/n1FNPZfPmzfTt25eVK1fywAMPcOqpp3L//ffzxRdf8NJLLxVq+/Lly+nUqRNJSUkBz23NmjW8++67vPzyy1x66aV88MEHXHnllYXyBQrpP2rUKE488UQ+/vhjZs6cydChQ1myZEnQdJfnnnuOb775ho8//rhAUFAIvRzB0KFDefbZZzn99NO5//77eeCBB3gmzD8x0DIKY8aM4bnnngsYGNUSW6ygiQUZGSZic9Wq1nQWA9xeLxiN5rrrrmPevHl069YtbwmAb775hl9++SWv95+RkcGaNWuYM2dOXuj8xo0bc9ZZZxWq/4cffqBnz555ddWtWzdgO7799tsCYzr79u1j//79zJkzJy+8/nnnnUcd18waAS1atMg7x9TUVDZu3BgwX6CQ/nPnzuWDDz4ATNDNPXv2kJGRETQd4M0336Rp06Z8/PHHJCcne25nRkYGe/fuzQsCOmzYsAJLNQQj0DIKzZo183xcS+liBU0s2LsXWreGatUSWqOJ0SoBeWM0/lSvXj3vu6ry7LPP0rdv3wJ5vvzyS0QkZP2qGjYPmKjO8+fPp2rVqoX2hSvftm1bli5dSm5ubp7pzBf/ZQb8TVn++XxD+gcy1YlI0HSAdu3asWTJErZu3RpwvR536YRAgjkYvssRBFuKwL/tlvjEjtHEAqvRxD19+/Zl/PjxHHaibP/222/8+eef9OzZk8mTJ5OTk8P27duZNWtWobI9evRg9uzZbNiwATBjG1A4VH6fPn147rnn8n67wq9nz568/fbbgBlEd8csfGnVqhVdunRh1KhReQJgzZo1eRGni4Pv8b/77jvq169PzZo1g6aDiYL94osvcsEFF7Bt27ZCdd53333cfffd/PHHH4BZVG7cuHHUqlWLOnXq5EV0fvPNN/O0m+bNm7N48WKAoGvm+JOcnJz3P0so4jw4ctQFjYg0E5FZIrJSRJaLyG1O+mgR+V1EljjbuT5l7hORtSKyWkT6+qSnisivzr5x4nSnRKSyiExx0heISHOfMsNEZI2zDYv2+XrCHaNJcI0mnrn++utp06YNnTt3pl27dtxwww1kZ2dz4YUX0rp1a9q3b89NN90UcDGxBg0a8NJLL3HRRRfRsWNHLrvsMqBwqPxx48axaNEiOnToQJs2bfK830aNGsWcOXPo3Lkz33zzDUcddVTANr7yyiv88ccfHHPMMbRv357hw4fnDbYXh9GjR+e16957781bgydYusupp57K2LFjOe+88wp5v5177rnccsstnH322bRt25bU1NQ8LWTSpEncdddddOjQgSVLlnD//fcDcOeddzJ+/HhOPvnksN50LiNGjMhbviChaNsWnn461q0IStSXCRCRRkAjVf1JRGoAi4GBwKXAAVUd65e/DfAu0A1oDHwLHKuqOSLyI3Ab8APwJTBOVaeJyM1AB1W9UUQGAxeq6mUiUhdYBHQB1Dl2qqoW7iI6RH2ZgJwcqFgRRo2COXPMujQJtD6HDdVuiRXl9t7LzobkZLjhBvBz1S9NYrpMgKpuV9WfnO/7gZVAkxBFBgCTVTVLVTcAa4FujsCqqarz1UjHNzACyy3jdq/eB3o52k5fYLqqpjnCZTrQr4RPMTJc00nt2sZ0ZjUai8VSHNxIIwcOxLYdISjVMRrHpHUisMBJGikiv4jIayLiutY0Abb4FNvqpDVxvvunFyijqtlABlAvRF3+7RohIotEZNGuXbuKfH6ecG+KWrWM6cyO0VgsluLgjuH9+Wds2xGCUhM0IpICfADcrqr7gPFAK6ATsB140s0aoLiGSC9qmfwE1ZdUtYuqdmnQoEHI8yg2bvgZ1xkgATUau6qrpbQp1/ec42yS8BqNiCRjhMzbqvohgKruUNUcVc0FXsaMyYDROnwd4psC25z0pgHSC5QRkYpALSAtRF2xw9VoEtQZoEqVKuzZs6d8P/iWuEJV2bNnD1WqVIl1U6JDGdBooj6PxhkreRVYqapP+aQ3UtXtzs8LgWXO90+Bd0TkKYwzQGvgR8cZYL+InIQxvQ0FnvUpMwyYD1wCzFRVFZGvgUd8zHJ9gPuida6e8NdoEsx01rRpU7Zu3UrUTZQWiw9VqlShadOm4TOWRVxBE8caTWlM2DwFuAr4VUTcWXL/BIaISCeMKWsjcAOAqi4XkanACiAbuEVV3SiHNwETgarANGcDI8jeFJG1GE1msFNXmoj8F1jo5HtQVdOidJ7eSHCNJjk5OeCEPovFUkTKgOks6oJGVecSeKzkyxBlHgYeDpC+CCgU/1xVM4GAcStU9TXgNa/tjTr+Gk12tnFxjiBsh8ViseRRBkxnNjJAaePrdeaGHkkwrcZisZQgZUCjsYKmtMnIMCaz5GTzCQk3TmOxWEoQV6M5eBCc2HDxhhU0pY0bfgasRuPPqFFw9dWxboXFUrbwjYUXp51WG725tHEDaoLVaPyZNw+ChLO3WCxBSPPxbzpwAFJSYteWIFiNprSxGk1w9u2LazuzxRKX+Go0ceoQYAVNaRNIo7GCxrBvX34sOIvF4o20tPx3Spx21KygKW0yMgprNNZ0Zti3z/TI4nRA02KJS9LTwV1Kwmo0FsCYzqxGE5h9+8xnnD4sFkvckZlp3h/uMtZWo7EAVqMJRk5O/kMSpw+LxRJ3uOMzVtBY8sjMhKysfI3GOgPk4/uA2HEai8Ub/oImTq0BVtCUJr7hZ8C6N/vims0gbntlFkvcYTUaSyF8A2qC1Wh8cYUwWI3GYvGKO4fGChpLHv4ajRU0+fhqNFbQWCzecDWaJs7CwdZ0Zimk0VSoAJUrW9MZWNOZxVIUXI2mfn1jio/TZ8cKmtLEX6OBhFyTJiBWo7FYIsfVaGrVMqFnrEZjKaTRQEKushkQq9FYLJGTnm6ETFISVK8et8+OFTSlidVogmM1GoslctLSoG5d8z0lxQoaC0ajqVChYHRVq9EYXEFTqVLcPiwWS9yRng516pjvcWw6s8sElCZuQE3xWdm6alWr0YARNCkp5npYjcZi8YavRmNNZxagYPgZl2rVrEYDRtDUrBnX6r/FEneUEY0m6oJGRJqJyCwRWSkiy0XkNie9rohMF5E1zmcdnzL3ichaEVktIn190lNF5Fdn3zgRoxqISGURmeKkLxCR5j5lhjnHWCMiw6J9viHxDajpYjUagytoatSwGo3F4hVfQZPgGk028A9VPQE4CbhFRNoA9wIzVLU1MMP5jbNvMNAW6Ae8ICJJTl3jgRFAa2fr56RfB6Sr6jHA08BjTl11gVFAd6AbMMpXoJU6wTQaK2jMtbGCxmLxjqp1BnBR1e2q+pPzfT+wEmgCDAAmOdkmAQOd7wOAyaqapaobgLVANxFpBNRU1fmqqsAbfmXcut4HejnaTl9guqqmqWo6MJ184VT6BNNorOnMaDTuXIA4fVgslrjizz8hO9uazvxxTFonAguAI1V1OxhhBBzhZGsCbPEpttVJa+J8908vUEZVs4EMoF6IuvzbNUJEFonIol27dhX9BMNhNZrgWNOZxStXXglPPhnrVsQeNyqArzNAnC4cWGqCRkRSgA+A21V1X6isAdI0RHpRy+QnqL6kql1UtUuDBg1CNK2YWI0mONYZwOKV6dNh7txYtyL2uFEBfDUaiMv3SakIGhFJxgiZt1X1Qyd5h2MOw/nc6aRvBZr5FG8KbHPSmwZIL1BGRCoCtYC0EHWVPrm5+eYhX6xGY7AajcUL7riEb7TvRMVf0FSvbj7j0HxWGl5nArwKrFTVp3x2fQq4XmDDgE980gc7nmQtMIP+Pzrmtf0icpJT51C/Mm5dlwAznXGcr4E+IlLHcQLo46SVPvv3m4fE33RWtSocPmxsrYlKbq65Pr4ajRZSPC0Wc29kZ1tBA4VNZ65GE4cWgdKYsHkKcBXwq4gscdL+CYwBporIdcBmYBCAqi4XkanACozH2i2qmuOUuwmYCFQFpjkbGEH2poisxWgyg5260kTkv8BCJ9+DqpoWrRMNSaDwM1BwqYAaNUq3TfHCn38awVKzZr7QzcqCKlVi3TJLvOH24q2gCW46i0ONJuqCRlXnEnisBKBXkDIPAw8HSF8EtAuQnokjqALsew14zWt7o0aggJpQcJXNRBU0bviZmjWNgAHTK7OCxuKP24u3gib/WvibzuJQo7GRAUoLLxpNouIraFxha8dpLIHwFTSJbl5NTzdRm91nJo5NZ1bQlBbhNBoraPLHaMAKGktgXHPR4cOQmRnbtsQaNyqAGzsxkU1nFodwGk0cuiSWGu61qVnT9NAgLntlljggzWeINSMj//lJRHyjAoA1nVmwGk0orOnM4hVfQbMv1HS8BMA3zhnEtUYTVtCIyGNe0ixhsBpNcNwXhhuCBuKyV2aJA1zTGViHgLS0goKmjGs0vQOknVPSDSn3uGp+pUoF060zgNVoLN7xN50lMunpBU1nrnUkDgVN0DEaEbkJuBloKSK/+OyqAXwf7YaVOwKFn4GC7s2JiitoatTIn7gahw+LJQ5ISzOD36pW0PibzpKSzPskDk1noZwB3sFMiHwUJzY4BmwAACAASURBVIS/w/6YTXosywQKqAlWowEjaKpVg4oVrUZjCU16OjRpAlu3Jragyc01nVdfjQbidk2aoKYzVc1Q1Y2qOgQTM+wwJiBliogcVVoNLDdYjSY4bpwzgMqVTc8sDh8WSxyQlgbNm5vviSxo3HlEvhoNxO1SAV6cAUYCOzBruXzhbJ9HuV3lD6vRBMdX0IjYwJqW4FhBY/CPCuASpxqNl3k0twPHqeqeaDemXLN3Lxx9dOF0K2gKChowvTIraCyBSE+H+vXNPZLIgsb1vvM3ncXpMhtevM62YBYSsxSHjIzAprOkJOOJZk1n+b9r1IjLh8USYw4fNh2QunXNs2QFTZkxnXnRaNYD34nIF0CWm+gX8t8Sjr17A5vOwK5Jk5EBxxyT/9uaziyB8O3FJ7qg8V8iwKV6ddi5s3D+GONF0Gx2tkrOZomUrCwTlymQRgN2lc1ApjOr0Vj88e3F16qV2JEBQmk0cfjshBU0qvpAaTSkXOP2vIJpNFWrJrZG47/yaI0asHt37NpjiU98e/G1asGeBB42DuYMUFZNZyIyC+PWXABVPSsqLSqPBAs/41KtWuJqNKpWo7F4w1/QrF8f2/bEkvR0s16Tf1DRMux1dqfP9yrAxZiVLy1eCRZQ0yWRNZqDB83kM39nADtGY/HH33SWyGM0/lEBXFJS8p+pCvETM9mL6WyxX9L3IjI7Su0pn1iNJji+cc5crEZjCYSvRlOzZmILGv8lAlyqVzdWgkOH8oNsxgFeTGe+Z1MBSAUaRq1F5REvYzSJam8OJGhq1DAPSna2CUtjsUC+oKld23TaMjPhr78KB6pNBEJpNGA6anEkaLzoVouBRc7nfOAfwHVeDyAir4nIThFZ5pM2WkR+F5Elznauz777RGStiKwWkb4+6aki8quzb5yIWVZORCqLyBQnfYGINPcpM0xE1jjbMK9tLnFc01kojSZRTWfBNBqIy0FNSwxJTzfPUFJS/rOUqFqN/xIBLnH67IQVNKraQlVbOp+tVbWPqs6N4BgTgX4B0p9W1U7O9iWAiLQBBgNtnTIviIiz5CLjgRFAa2dz67wOSFfVY4CngcecuuoCo4DuQDdglIgE+M+UAl40Gms6y0+zgTUtgfA1FyW6oPFfIsAlTtek8RLrLFlEbhWR951tpIgkez2Aqs4BvEZ7HgBMVtUsVd0ArAW6iUgjoKaqzldVBd4ABvqUmeR8fx/o5Wg7fYHpqpqmqumYWG2BBF702bvXxPByexv+WI0msEZjBY3FFyto8glnOitrGg1Gk0gFXnC2VCetuIwUkV8c05p7xZpgQt64bHXSmjjf/dMLlFHVbEy4nHoh6iqEiIwQkUUismjXrl3FO6tAuOFngnmBJLJG474oAmk0cdYrs8QY35erK2gScdLm4cPm2ShPGg3QVVWHqepMZ7sG6FrM444HWgGdgO3Ak066BMirIdKLWqZgoupLqtpFVbs0aNAgVLuLRrAlAlwS2b3Zms4sXrEajSFYVACI26XQvQiaHBFp5f4QkZZATnEOqqo7VDVHVXOBlzFjKGC0jmY+WZsC25z0pgHSC5QRkYpALYypLlhdpU+wgJou1aoZ75mcYl3Wskko01mcPSyWGGMFjSFYVAAo06azu4BZIvKdM39mJsbzrMg4Yy4uFwKuR9qnwGDHk6wFZtD/R1XdDuwXkZOc8ZehwCc+ZVyPskuAmc44ztdAHxGp45jm+jhppU+ogJqQ2EsF7NtnZjj7uqhajcbij2pg01kiCppgSwRA3JrOvEzYnCEirYHjMOaoVaqaFaZYHiLyLnAGUF9EtmI8wc4QkU4YU9ZG4AbnWMtFZCqwAhN94BZVdbv5N2E82Kpilpie5qS/CrwpImsxmsxgp640EfkvsNDJ92DMlqDOyIBmzYLv911lM5jDQHnFP/wMWI3GUpgDB8y8Kvfl6t4ziSxoypDpLKigEZErAVHVNx3B8ouTPlxE/lTVd7wcwFkK2p9XQ+R/GHg4QPoioF2A9ExgUJC6XgNe89LOqLJ3L7RvH3x/oms0/oLGajQWf/zD4icnm+cmEQVNsCUCIL/TWoZMZ/8APg6QPoVims4SDi9jNGAFjUucqv+WGBKoF5+o8c5CaTRJSUYAx9mzE0rQJKlqoS6lqu4DPM+jSXhUzcPgZYwmEV2cAwmaChWMsLEajcUlUC8+UQWNbyieQMThUgGhBE2yiBQKliMiNbALoHnnwAETSTWcezNYjcaXlBQraCz5WEGTT3q6MS8nB+nvx+FSAaEEzavA+36xw5oDkwkxxmLxI1z4GSjoDJBoBBM0NWrE3cNiiSHBTGeJOGEzWFQAlziMfh7UGUBVx4rIAWC2iKRgPMT+BMaoaklEBkgMwgXUhMTWaDIyggsaq9FYXIJpNFu3Bs5fngm2RIBLHJrOQro3q+oEYIIjaCTQmI0lDFajCU6g1TVd4rBXZokhaWlQuXLBFSUT2XQWSqMpY6azPFT1gBUyRcRqNMHJzDRzIwJdG6vRWHxxX67iE1kqUQVNsCUCXOJQo4mftT7LK+6CZqFU3UR1bw4UfsbFajQWXwKZi2rWNC/U7ARbWT7YEgEuZVWj8UdEKpd0Q8otmzaZz6ZNg+dJVPfmUILGajQWXwL14hMxgrN/KJ5AxGEnzct6NK/5/U4Bvoxai8obmzZBw4YmnlcwEtV0ZjWa8Bw8aEyMiU6gXnwixjs7dAiyssqcM4AXjeZ3ERkP4ASn/AZ4K6qtKk9s2gRHHx06T8WKxifeajT5uO7NGnBlh8Rh0CC4+upYtyL2BDKdJaKgCRUVwKV6dSNo4ujZ8bKU83+AfSIyASNknlTV16PesvKCF0EDibkmTTiNJjc38YSvPz/+CCtWxLoVsSeU6cwKmoKkpBghE0fvk6CCRkQucjfgR+Ak4GdAnTRLOHJzYfNmOOqo8HmrVUu8l2o4jQYS23y2dy/s3g3bt8e6JbEl2IqSiShoQgXUdInDCM6h5tH09/v9MybGWX/M5M0Po9WocsPOncaeajWawHgRNPv3w5FHll6b4ok1a8zn7t1mYbxKCRr5Kdj6K4noDODVdAZG0BxxRPTb5IFQkQGuKc2GlEtcjzMvgiYRNRq3JxrMdAZx1SsrdVxBA6bTEspzsTwTbEVJq9EEJg5X2fTiddZURD4SkZ0iskNEPhCRBL3jIyQSQZOoGk1yspnx7Y9dkwbWrs3/nsjms2Av10QUNJFqNHGCF6+z1zHLJTcGmgCfOWmWcESq0SSioKlVq+Bsbxer0RTUaBJZ0AQznVWubLZEEzQiga0ALnH47HgRNA1U9XVVzXa2iUCDKLerfLBpk3mRhgo/41K1auKZzoLFOQOr0YARNMcdZ77/8Uds2xJLgpnOwNw/iSRoXO+7CiFe3WXRdAbsFpErRSTJ2a4E9kS7YeUCr67NkLgaTTBBE4e9slJnzRo45RTzPZE1mlDjEokW7yxcVAAos6aza4FLgT+c7RInzRMi8pozvrPMJ62uiEwXkTXOZx2fffeJyFoRWS0ifX3SU0XkV2ffOBFjbxGRyiIyxUlf4Ld+zjDnGGtEZJjXNpcYkQgaq9EUJNE1mrQ0s7VpA/XrJ7agcU1ngSKgJ5qg+eOP8J5kZVGjUdXNqnqBqjZwtoGquimCY0wE+vml3QvMUNXWwAznNyLSBhgMtHXKvCAiSU6Z8cAIoLWzuXVeB6Sr6jHA08BjTl11gVFAd6AbMMpXoJUKmzdHJmisRpNPoms07vjMMcdAo0aJLWjS0oxASUoqvC/RBM26ddCyZeg8ZVGjKa7XmarOAdL8kgcAk5zvk4CBPumTVTVLVTcAa4FuItIIqKmq81VVgTf8yrh1vQ/0crSdvsB0VU1T1XRgOoUFXvTIyDBbJKYzq9HkU6mS2RJVo3EFTevWRtAk+hhNMHfeRBI0f/0FW7ZAq1ah85VFQUN0vM6OVNXtAM6nqws2Abb45NvqpDVxvvunFyijqtlABlAvRF2FEJERIrJIRBbt2rWrGKflQyQeZ2A1mkCkpCS2oBExvdeGDRNbowkVFj+RlnPeuNGElgmn0SQlmfdJWTKdUbpeZwH8XNEQ6UUtUzBR9SVV7aKqXRo0KKFTi1TQVKtmogjk5JTM8csC4QSNG1gzEVmzxoQuqlIlX6OJoyCJpUqohb4SSaNZv958htNoIO7WpImV19kOxxyG87nTSd8KNPPJ1xTY5qQ3DZBeoIyIVARqYUx1weoqHYqi0UDihITPyjJbOEGTyBpN69bme6NGJt5Xmr8FOkEIZzrbv9/EFSzvrFtnPsNpNBB3SwVE6nW2nQi9zoLwKeB6gQ0DPvFJH+x4krXADPr/6JjX9ovISc74y1C/Mm5dlwAznXGcr4E+IlLHcQLo46SVDps2md6o11hDibbKZqg4Zy6JuiaNakFB07Ch+UxU81k405lqYnRI1q/P13DDEWfPTqigmoDxOgMuKOoBRORd4AygvohsxXiCjQGmish1wGZgkHOs5SIyFVgBZAO3qKprS7oJ48FWFZjmbACvAm+KyFqMJjPYqStNRP4LLHTyPaiqpdcl3LTJmD4CzXoPRKKtsulF0CSqRrNnjzEH+Wo0YARNu3axa1csUA1tOnPvn4wMbxOjyzKux5mXd0qcmc7CChoRaQAMB5r75ldVT1qNqg4JsqtXkPwPAw8HSF8EFHrKVDUTR1AF2Pca8FqgfVHHFTReSVSNJtTLISUlMXvxvh5nkC9oEtHzbP9+M24ZSqOBxBinWb/e2/gMxJ3pLKygwZio/g/4FkigkepismkTnH++9/xWoylMomo0wQRNIgrdYHHOXBJF0KgaQXPWWd7yV69ulpeIE7wImmqqek/UW1KeyMyEHTu8OwJAvqBJNI3GjtEUZs0aE8uqRQvzOyXFvDgSUdCEinMGiSNodu40GkokGk0cPTtenAE+F5Fzo96S8sTmzeYzEkHjms6sRpNPIms0zZsXXOgsUSdthlt/JVEETSQeZxB3pjMvguY2jLA5JCL7RGS/iCTIDKkiEqlrM1iNJhApKWY29F9/lU6bikpGhll2uaTw9ThzSdQwNF5NZ+V90mYkc2gg7pwBvMQ6q6GqFVS1qqrWdH6HeDtYiiRoyopG89tv8P33xa/Hq0YDcfXABOTKK+HCC0umLn/XZpdEjQ5gTWeGdeuMt1nz5t7yuxpNnEzyDSpoROR457NzoK30mlgG2bTJ2NibBIx4E5iyotHccw/06QPFDdWzb19+qIxglJUIzkuXwpw5JaPV7NxpzveYYwqmJ6pGE850VrUqVKxY/gXN+vXmfVKlirf81asbIRMn75NQGs0dzueTAbaxUW5X2WbTJnNTJCd7L1NW3JvXrjVa19NPF6+ejAyjzYSaE1AWIjhnZcHWrWZm+syZxa/P3+PMpVEjI4DiyO5eKqSnm1U0g3VIRBIjDI2XqM2+xNmzE1TQqOoI5/PMAJtHH7sEJZJ1aFzKgnuz62IJ8OyzxQuJEi7OGZQNjcYNdAjwzTfFry+YoHGjAySaQ4A7WTNUhyQRVtmMZA4NxN2aNF6cARCRk0XkchEZ6m7RbliZpjiCJp41ml27jCAcMcL0lJ55puh1eRE0cdYrC4greBs1KhlBs3atMSn62+ITdS5NqDhnLuVdozl40PzfI9Fo4mypAC/r0byJMZWdCnR1ti5RblfZJTvbmFIiFTTJycbWHM8ajftS7d8fLr4Y/ve/oo9L7NsXPmRIWdBoXLfT666DDRvyfxeVNWvM/Bl/s2uiRgcIFefMpbwLmg0bzGc512i6AKeo6s2q+jdnuzXaDSuzbNtmQmZEKmgg/tekcQVNy5bw738bYfG//xWtrvKk0VSrBlddZX4XV6sJ5HEGiRtYM1ScM5fyLmginUMDcffseBE0y4CG0W5IuaEors0u8b7KpitomjeHTp1gwABjPivKQ15exmjcQdrWrc11KY6gCebaDFC/vtF4E1HQJLpGE+kcGig7pjMR+UxEPgXqAytE5GsR+dTdSq+JZYziCJqyoNE0apTvIXf//cZ09txzkdcViUYTz4LGHaQVMW7fM2ca82lR+OMPY+oIJGgqVIAjj7Sms0CUd0Gzbp3pdNWr571MnJnOQsU6sy7MRcEVNJFEbnaJd41mw4aC6nvnziZw6FNPwa235msgXvAiaKpVMy/YOOmVFcL1wuvd2/zu3Rteegl+/BFOPjny+oJ5nLkk2lyav/4y/3svprN9+8z/w+uyHGUJ386MV8qKRgP8DmSr6mzfDbMc8tbSaV4ZZNMmY+Zwe/2RUBY0Gn878X/+Y8wbL7zgvZ7Dh815hhM0IqZnFq8azR9/mI6Ba9I46ywjGItqPgsnaBItOkC48DMutWqZeUxx0nsvcSKdQwNxp9GEEjTPAIGe8IPOPksgiuLa7FKtWvwKmr/+gi1b8iMKu3TrBv36wdix3m9qV3CEEzQQd1FoC+DrHAHmhdi1a/EETXJycG040QJrRiJooHyaz3JzjSUhkvEZKFMaTXNV/cU/0VmArHnUWlTWKY6gqVo1fk1nmzYZ00SgntX995u1L1580Vtd7gvBi6CJ5wjOrjeQ70ugd29jOiuK2/eaNeb6Vgxi0W7UyISoKeoYUFkjXJwzF99VNssiubnB9/3+u+nkRarRJCWZcDVlQNCECqoTIkBVAqNqlggojqCJV43Gv/fuS48e0L07vPWWt7q8BNR0iXeNRqTg/7tPH+PePmtW5PUF8zhzadjQ3GM7d0Zed1kkXJwzl7Ks0cyebZw8vvsu8P6ieJy5xNFSAaEEzUIRGe6fKCLXAYuj16QyzO7dRlAUx3QWrxpNKEEDMGgQ/Pxzfr5QRCJo4l2jadbMxOJyOekk84BHaj5TNVEB/INp+pJokzbLu+ls2za47DLz3hg/PnCeQFqzV+JoqYBQguZ24BoR+U5EnnS22cD1mDVqio2IbBSRX0VkiYgsctLqish0EVnjfNbxyX+fiKwVkdUi0tcnPdWpZ62IjBMx7hkiUllEpjjpC0SkeUm0OyjFcW2G+NZoNmwwL1T3ZefPRReZzw8+CF+XK2jCRQaA+Ndo/AVvcjKceSZMnx5ZXdu2mf99KI0m0cLQeDWdlUVBc/gwXHqp6UT16weffhp4TZ31640ZrFmzyI8RR89OqKCaO1T1ZOABYKOzPaCqPVS1JLtUZ6pqJ1V1w9rcC8xQ1dbADOc3ItIGGAy0BfoBL4hIklNmPDACaO1s/Zz064B0VT0GeBp4rATbXZjiCpp412iaNzdeVYFo0cK4O0ciaDxqNL/tqRef75B16wL3NPv0MfsiCUcTzuMMEi86gCtoatcOna8sCpp77jHrOr36qhnjzMyEDz8snG/dOvM+iSQSvEsZMZ0BoKqzVPVZZyuBOOhhGQBMcr5PAgb6pE9W1SxV3QCsBbqJSCOgpqrOV1UF3vAr49b1PtDL1XaiQnnWaAL13v255BJYsMB4p4XCHSj3IGgOVK5H6paPePBBj+0sLf78E3bsCHxN+vQxn5FoNeVN0KxdW/x2pqUZIZOUFDpfWRM0U6eaZTZuvRUGDzbm1pYt4e23C+f18twFo4yYzkoDBb4RkcUiMsJJO1JVtwM4n0c46U0A3zfYVietCQXn9bjpBcqoajaQARSaXisiI0RkkYgs2lWcBb02bTK9iHCqfjCqVWN2Zjd+WxXCCyUWqHrz5b/4YvMZqGfmy4cfGjPQEUeEzgd8sasbBzSFX3/12NbSItQgbevWprMRyTjNkiVGow1lIqlc2YxXlIUxmnPOMfOKMjOLXseCBXD88eHzpaQYTbssLOe8cqUJwNqjBzzxhEkTgSuugBkzjAnVl2BaswcOVqnLI+svi4vLEmtBc4qqdgbOAW4RkZ4h8gbSRDREeqgyBRNUX1LVLqrapUGDBuHaHBzXtbmIStPyfc3owzf88744EzTp6eYhDidojj0W2rULbT77+Wf49lu47bbgbrw+TF2XCsDq1fGxJG0eoZwjRIybs9dwNKrw2WemTLjee1mYtLl9u9FoVq2iyKroH3/AwoUm8kQ4RMrGmjQHDpjOWNWqRqupVCl/3xVXmPtg8uT8tIwM2LOnyBrNo5su51/b/8bHHxez3SVATAWNqm5zPncCHwHdgB2OOQzn0/Xl3Ar4dveaAtuc9KYB0guUEZGKQC2gGKt1haEYc2iys+GaD87nLyqzdm0Jt6u4hPM48+WSS2Du3OC97rFjTQ/0hhvCVrV/P3y5tjWVyWTzZokvq2I4b6A+fcyLYuHC8HUtWWLMjRdcED5vWQhDM3+++ezSBR5/HBYXwUn1yy/NpxdBA2TXrMuT83qwY0fkhyo1broJVq82wqRp04L7jjvOXC/fKQLFcG3etAnGrjoPIC6sATETNCJSXURquN+BPphI0Z8Cw5xsw4BPnO+fAoMdT7IWmEH/Hx3z2n4ROckZfxnqV8at6xJgpjOOEx2KIWieeQYWbj6S1vzG+o0ViGIrI8ddD8OLoLn4YtMz++ijwvs2b4YpU8zCaeEGeIHPP4fM7GSG8zKQP4wRF6xfb8YGgplJe/Uy5pwvvghf16efml65l5dqWYgOMH++6a1//rkxj157rZl0GAmffWbMiB06eMr+Ppdw5+IhTJwYeXNLhd27zRjMHXcYk2IgrrjCaPwrV5rfkXTw/LjnHnNLHSWbE1vQAEcCc0VkKfAj8IWqfgWMAXqLyBqgt/MbVV0OTAVWAF8Bt6hqjlPXTcArGAeBdcA0J/1VoJ6IrAXuwPFgiwr79xsTUxEEzW+/mZBhA1O3cBPj2X+gAnv2RKGNRcW94f3DzwSibVvTOwtkPnvmGXP33367p8NOnQqN6xzkGl4HzHWKG1zbeTAzad26cPbZ5uUSauY3GEHTo4enMas8jSaueiJ+zJsHqalmIuKECfDLLzBmjPfymZnGkeL88z2ZoVXhiT3XAuY9HZd89ZVp6GWXBc8zeLDpnLhOAUVZhwbjzDZlCtzV43vO0Fn8+msc3CuqajefLTU1VYvE7t2qt9yiOnt2RMVyclRPOUW1dm3Vba98oZ/QX0F1wYKiNSMqjBihWr++9/z//KdqUpLqrl35aenpqikpqlde6amKfftUK1dWvfX8dXqAagqqDz0UYbujybHHql5ySeg8b72lCqrffRc8z+bNJs9jj3k77pNPmvxpad7bWppkZZl/3B135KddfrlqcrLqL794q+Orr8w5fvGFp+zffmuyp1Q4oMccU4Q2lwaDB6seeaR54EPRp49q8+aqubmqN9ygWq9eRIfJyVFNTVVt0kT1wINP6hP8Q8G8nqINsEiDvFdj7QxQfqhXz6zL0jOUP0NhnnvO9ECeeQYaNalAS4z24GWCfakRqYvlxRebMCyffJKfNmGCGQy9805PVXz2GWRlwaW906nOQZo2yGT16gjbHS1ycrwFOrzwQjMe9cYbwfN89pn59DI+A/EfHeDnn80/zneZhP/9z5hKr73Wm3PEZ58ZD7xgJiY/Hn8cGlZJ5+81X2Pt2jj0CcjOhq+/Np54weahuVx5JWzcaLTCInicvfmmGRIbMwaq161Me4zdLNbmMytoYsj69XDffeb+GzoUqFaNFmzI2xc3RCpoTjzRmNlc81lWFowbZ7yqOnb0VMXUqdCkCfTobsxOxzY6ED+ms99/NzO7w70EqlUzoXneey/4RNxPPjHu0Mcd5+3Y8R4dYN4889mjR35a/fqmR7VokVm7KBSqZmzn7LNNUMgwLF1qvMhv6zibHjlzAeNbEVf88IMxq593Xvi8Awcar7S33or4uTtwwLxPuneHyy8Hqle3gibRUYXrrzferC++6Jiiq1alOgc5snZm/Aia7GwziB+JoBExWs2335rJme+8Y16Md93lqfi+fTBtmnlHV6hp1tU47oh0Vq+Ok6GJSGznQ4ea8btAPqb79pngmwMGeHeJj/dJm/Pnm3HKxo0Lpg8aZDS8++8npGq6fLlxqvHobfbEE0ZpvLHHUjofNIKmKE5uUeWLL4wrv7tAXihq1DDCZupUcx0i0GjGjDG3xTPPOIpTSgqN2E7dWtlW0CQqL79s3jFjx/rM0XMWS2t5xIH4ETRbtxphE6nny8UXm17/J5+Yk+zUyfRSPfDpp8ZJ6dJLyVu189i6u9m71zjvxJxI3E579jQv3kmTCu/7+mtzjbyazSBPo1n28+GIHblKhXnzCmozLiJmcbwqVeDvfw9e/vPPzaeH3v+mTcZTeMQIqH1kZY7M2UaTxspPPxWx7dHiyy/h1FO9xfYD432WlmZMtB6fu40bzWN2xRUm0AAA1asjQPuWB62gSUR27YJ774UzzoDhvvGxq5rVF1rWy4gfQROJx5kv3bqZuQL//CesWGHGZjz22qdONcK3e3fyVgo8rqbpwcfFOM26daaH6j8XIhAVKsBVVxnt7vffC+775BMzthfoxRyMmjV5JvlO2j95NY9FN3Jf5GzZYs4x2DLWDRsajWbaNOOFFYjPPjMea/4aUQAKODE6L/HO7bLiS9Bs2WK87ryYzVz69DHmRvCs0dx9t7nVCjj3Oc9Oh6P3smxZeOfHaGIFTQy4915jTXn+eb93ryto6qSzZUvkUw+iggdf/v37Tcf9llt8xnorVDARnbdtM1Lj0ks9HS4jw3T0Bw3KV/8Bjq1uXtJxIWjcAKMhIhvs2GHm3z35JMZ8lptrTIguhw8bk8r553uKkOAy5jHh74efQMhlxoyin0JUCDQ+48/IkWYphDvuKOwYsHu3Mb15MJulpRmrwJAhjkXAFTStD7BqVdzEksyfeHruud7LJCfnu0F7EDTvvWe2++7z6/s4ppL2FVdx4EB+KMZYYAVNKTNvHrz2mnnO2rTx2+mazmrsJjfXDI3EnPXrQ/beVeGaa0wwgBdeMGb4vHFvV7jcfrvn6LOu2WzQICehYkWoWpXmueupVClO5tKEifuWnW3eE4sXG0Xu3UWtzct30qT8QabvtEtQcwAAIABJREFUvzfjVx7NZqowerR5mQyp/w0jm3zMggXGzyJumD/fdJZCOXxUqmRsPCtXFl6Rddo0c6IeBM348UaY5A37OYImtUUaqnHkEPDll6ZTcsIJkZW7/37zogijNW/ZYkyH3bubDmwBmjeHzp1pv2IKYBSrmBHM7zlRtyLPo/HA4cOqHTuqNm2qun9/gAxZWaqgs6+bpKD69ddRa4p3LrtMtVWroLsff9zMYXj8cdUXXlAVUe3Rw8dvf+5c1exsz4c7/3zVo44y0wjy6N1btVkzbdMmVwcMKNpplCh166redFPQ3XfcYa7JK6+o9uxpppXMvfMjk7h4scl0++1mR8AboSC5uar33GOKX321avZFg/SjpiMVzOWNG7p2NSccgtxc589ZZ5nr6DsfaNAg1UaNws41OXRI9YgjVPv180mcM0cVdOs7sxVUx40r+mmUGIcOqVarpnrzzVGpPjtb9YwzzPS0tWuDZHrsMd1HioLqf/8blWbkQYh5NDF/scfbFk1BM26cueLvvRckQ26ualKSbhk5RkF1/PioNcU73bqZF30AZsxQrVDBzFt0BcMHH5j35/HHq27cGNmh0tPNvL5//MNvx4cfqoIO7L5NTzgh8lMoUdLTzT/xiScC7p482eweOdL83r1btXVr1fr1cnRtpRNUb73VXKwWLVTPPTfs4XJzVW+7zdR5443OO/iWW3RXzZYKqo88UoLnVhwOHlStWFH13nuDZnnzTdVatYw82fTVCnPz/P3vZmdWlmrNmqrXXx/2UC++aK7HzJk+iUuXqoLmTn1PjzjCCOSY42Hi6eHDqp9/rvrJJ+Z8Fi5UXbVK9fffjZwKxWOPmepffz1EpvXrVUFb1E3XSy8t0ll4xgqaUhI0n35qnrdAbN9unqPevf166/6kpGjO7XdopUqqd91V5KaUHPXrm8gAfmzebHadcIKZxe/L7NnmhdK4sffJ4KqqEydq4KgIhw+rNmmi97SYrMnJ5mfMWLTINPLDDwvt+vVX1erVVU8+2bw3XX77zXTej6uxVdPqtlL96SdTx4QJIQ918KDqNdeYrLfd5nPfPPSQKmjbNjkFe/WxxNEo9JNPCu3680/Va681uzt0UK1SRbVqVdX/pn6kh5Kqq65ebXotoPrxxyEP8+mn5r5LTfV7jrZtM+VHjdJ+/cxxYs6tt5qTDfJSmDVLtV070+xAW/Xqpj/z11+Fyy5aZDplvp28oHTrpgNqzYp6J80KmlIQNKtXm6vZtKkxmfi/DK+6ytwYq1eHqeiII1RvuEGPO0714ouL1JSSIyPDnNSYMQWSMzONolOjhurKlYGL/vKLETS1aqk++2xwAaxqXspPP21exi1bBnlwHnhAX+UaBdU1a4p+SsVm6lRzTZYsKZC8d6/RXBo2NL1Rf+bMUa1UMVvPYKZmndTT1BEoo8Py5fkvof/8x++avPqqKuiNV2RojRoRWSajh9u93rmzQPKyZapt2hiT6r/+ZZ6LjRvNCxJUW8h6/bjrQ5p7m2NKPHAgYPXp6arDhuULqxUrAmTq31+1Vi391x0HNSkp9D0XdXJzjck5gNa6ZYuJSAOqRx+tOmWKERyzZhlB+vbbpg/Sv7/J07Gj6g8/5Jc/cED1uONMmJk9ezy0ZexY/TcPalJSrmZmltQJFsYKmlIQNKpG9e3e3VzV444zJrLcXNPDBxMCLCxHH616+eV6zjmqJ55Y5KaUDEuWmIZPnVogecQIk/zBB6GLb9qkeuqpJu+RR5p3UUZG/v7cXPOQtWxp8vTubV6wAfn9d51b4bRIQmBFh0cfNY31UeNyclQvuMBYjv7v/4IXffP1wwqqw3hdD6WeEjBPbq7pqFStqtqggeq0aQEyffmlKug7o1cXGPaJKQMGqG+gsdxc1ddeM+dxxBGq33xTuMi336q2OWKXgmqvpJk6vu2zumpV4Y7GtGnmpZqUZISur7ZYgF9+URXRDwZOin28wFWrzH3y/PN5SZmZ5vapXt0oOqNGhRaGublGcW7SxAjqm282HZobbjC/C5gOQ7F5s05hkILqzz8X66xCYgVNKQkaVXNzfPSRMSmBapcuRugcdZQxIYRl0CDV6tX1lmH7tXbtYjWl+DhjI7pokaqacetRo0zSPfd4qyI318SU7N3blKtdW/X++435+qSTTFr79uZ3OHb2v1ZB9anHgr1pSoHhw40EcMjOVr3zTvU8AD2q+zQF1WrJWTpggBEq27aZfRkZ+T3dXr3y0wvhmN62vPSlgtEGY0purpEmQ4dqbq7pmQ8caM7jzDNDnIeq/rU/U/9X7wFtwpY8k1HDhuY6vPiiGbIBoxUtXOihLVdeqRsqHRv7Mc6nnjIN37BBVY1QOPZYkzRwoBk68cq+fcZ0WqGCMRuC6t13R9acFZ2GKKhOmhRZuUiwgqYUBY1LdrYZpDvqKHOVP/rIY8F161QrV9YnO78V+yC9Y8eqgu5em66jRxvTFqhedFHRxkl+/DH/BQTGtPbqq95NP7nfztA67NEbz1wV+cFLirPOMhJSjafPySebcxkxwoOtXFVzV67Sr5uP0Juv3KvNmuVfiy5djH9AUpLqww+HuSbbt+dJtpYtVS+8sGROrcisXau7qKdjL/4+72Vau7YZSvL0v/38c81t2Eh/m7NdX3xRdcgQI2zAvFzvuSf8wHge69ZpblJFrVt5vw4fXpyTKia9eqm2aaO7duWb/Fq2NMpoUVm40Dj2+Y8BeuHwk//TyhzSO6/1YmsrGlbQxEDQuBw6VAR19d//1o8Y4KtMxIQtw/6lf6/8nFavbu6U/v1Vv/+++PX++qvqG28ENccHJzdXT6rys55Z00vXNko0b665Qy7Xl14yJpBatYxN3YuQ8Sc31zhLPfywkV3t23t0V87JMW+to4/WYUMytX79oh2/JNiwQXVIj/VaiUwFs+TFpEnFHx/JzTXjf0Uaj7vlFj1bpmvntl6lUwmzb5/mVkzWiedM1nr1jEn1vvtKbsyoSP/rrVu1Ez9p32OiN8BpBU0MBU2ROHBAlx5xthkemRybkd7vv1dNlr80icN61VVGOMQDQ7su1yZsMeaj0iYrS7dLIz2/9ao889bmzaXfDFU1AxDJyfpqp3EKQQbHQ3DokLH1jxihunVr0ZvRr59q9YqZ+rfk8frrknjwSlDV7dv17opPanKFwxH3/HfuLL4VYcOEr/RMZigY7SNunp0jpmnjin9ErX4raMqaoFHVfa+/bxy+Lpwfk+P/61+qSRzW9f2CT0yMBQ//+6CC6v6rR5b6sRd/vFnrs1OrJB/W//0v/BpWUefpp3UNrbx4Shdgzx7V004zT39yshmw//e/C7uph2PjRjMo/Z8jX1Q9++zICkeZKQPfUVD96V1vZtbsbONcWamSuSYXXGDmRHkaV/Xl8GHt32C+prBPJzyfHft7xIcnBs5VUN3zfRBX0WISStDYEDRxSo1hF9EgOZ31X642gZ1Kmd9WKy3YSIv2KaV+7FAc29HEg/tt8k8RrXC1fj28/roJXfL00/DoozBqlIn56TWszcsvK5lU4adXl3DrreHXsIo6t91Gqwva0ZDtzPnIW1jrjRvhlFNgwQJ4910TO27AAHjoIROCbMIEb2uTAbz6KoBy3c5HIwsMWgp0/o8JYvnTI0GCd/qwerUJrnzvvSb6zW23mfBBgweb1bWvusrE3wtLdjY5Vwxl9q4TGNJjIzfcnBT7e8SH9peaMDi/vjiv9A8eTAIl6hYvGo2qavf2B/RsppslokuZDidk6bl8HifhCfL55RfTE3+XyzzHGfn5ZzM4HWxi3HXXeTt218Zb9UxmhJz/Uuqkpeml1T7Tpkm/a+6e0DafxYvNIHvt2oVXl16wIF/LOf54M/8lFM4cWu3XdbcpFNAPO3bk5KjWrJypN/G8mcQUgOxs4xxWpYpqnTqq77yTP/6Rk2O854YPN/tCRvRwK7v8cv2ZjgpmDDLe+P13cx7jjvhvVAb1SHTTGdAPWA2sBe4NlTeeBM2QIaota+40rjdLl3oud9ttJuSUZ08dP3JyVKtWzta/82ScBFzL5+BBY64Z3eRF40Me5oFZvty4hDZtaoZ1tm83k/8OHTLn2bevh1nkGzdq1vkXaSUy9a5Gb8aBzawgz925wXjS9h4e9Hp8+aVxXjjqqOBzlXJzzcT8+vXN/RPq0n72mXl7fNDjCY29e2RgTj8tW7snLzLB9/zsguvX58/x6t8/tAt2Zqa5JlddFSRDdrbZCTru/K99vZrjitxc1brVD+lwXozofeKVhBY0QBKwDmgJVIL/b+/cw6OqrgX+W0BQRCwYnoI8AoQWBFFCDKASDVdofZUWrNa2trbFR9FW6efVqzx81Kt8Vumt2mIpStE2V3tr7ZsixZpWBYOKgBBEBESDoGgFK+GRdf9Y55gJmcQ8ZjJnZtbv+853ztnnzM5eZyZn7b322muxGhha3/1RUjQ33qjatm21Hji2u+r48Y3qhTz4oH7cU68TM6wxbN2qW2ctsHUIXJbiZfjx6ddP9ctFm/TjVZ7z56vuqDvJuXGj9eB79rTjeMycaXo8rgdcVZWtsOvQQV84coyCaunDqYx/E59wlLeIr6rOm1fnemmpuU2PHNm4wdh992nDC2PXrdNz+76oPajU/eSoXnVVywRIEtdeq3pkzgE9QFvTspdeqvrPf+rm16q1d2/zGFy0qHGd+6lTzR2/zr0HD9b4L996q06dap2aVHkBfhLF46q0iGcauXq8aTSkaBqfCCN9KQQ2qepmABEpBc4HXklpqxpBXh4cOiS8ce09DLjpYsjPt23w4JotzIsiwrqNOVx5xXGMP2U/Q/IOcPfdR3PO6J0UjwliyZvirXu8d6+FaP/1r2HlSjZSAnyTIdOKm5RKtrXIz4eKdwfAjTfaRMNll8Hll9vkw+TJUFLC1t2dKLn4eA5WteHvpZUMzjkIW6VO8rXR/Y6kuro7L/6pklNHV9U8l40bLRPk+vUweTLlp/wMroeCouj9ywwbBl26KE93+ipfu3YSLFhgEy6DBvFi+1P4+l2TGTOyij898Cad/i3wmtgEU5s2cZPRffss5e5+x3H9tcrEwdtp2ybou2zZAvPm8eYfX+SPbOO6k54k59FX7G9FkJNPhn0H2rF+8SqGP/VjKC1l+8IlnJnzHB/l5FK2eDvDh1XDpphnUc8zKRl5NI89lsvGv21nSP+qGsvrHXdY+oc5c9Abb6LsODjjjMZn5m5thp/cngefO5HqB6fSJicHunWrvfXoYftEU58GypQNmAIsiDn/KnBvffdHaUSzfLn9mp9cctAWT06ZYoGPjjqqZtgSbHs5SoeyVruzQ9+ip+7lKB3ERu3LFn2fY+rcH3cbNUr19tv13lk79BNCcaWU6dMtNPrHIedXr1adM8dsYKDbOU7z2KSd2a0vMLJBmSvpoaD6Q66pe33AAAutq+YK3LlzdHuq556rmj/ooC3YOO881aFD9Z32vbQ/m7UP2/RtujXuNxBspVxgcw18pfa1rl311jPMdbfe0PQR4ZVXrMkPPWTnOzbt0fwe72mntnv1eUY16XmE3n33cUXd67NmqaoN/sHSZUSVBx6wNm7ufFJ8WQsKml03WT6iide30Fo3iEwDpgH07du3NdrUKMLcWpu3tqVkxoyaC6pQWQmvvmqZjw4dYvqCcaz/50CWXPtXeg27HVRZ/NoGxv332Xx3TDkPfbPMPhvb1QqP27Y1t5sgXXPF1ZbYMkhPHzmGDLFB2I4d0KuXwIgRts2ezc6VW5gwuQu7dh/F0v98kpPyrrHnVV1dexQX0BPoe91eVg6cDleMsEIRy21/7rkfJ6MrL7cMw1HtqZ5+Ovz+923ZcfXt9Oxp6eYvmqS89TSU3bOK7l1+VPMMwv2hQ/XWN1WFube+w8y9P+GCOydyRHuFjh2pnvhZFgzrQElJJAe7tcjPh44d4YUX7Kv8j8lHs30PLHkKCnIXW3a08BVbXV2zxWFgtdL3P/eyrP91XHl1kf0QRCw9dUkJAGXBv9hpp7WSgM1g+HDbr1n0AgM+ewDefddyy+/aBe+8gx7ZIe4Ls8XUp4EyZQPGAEtizm8Abqjv/iiNaA4eNJ/+BlJ8qGrNvMzMmXWv3XSTXYsT1b5eJk5UPfnkJjW1VVmyxGRavrx2eVWVrUzv0KFeR6O4fPGLttC+Pvbts++hqfGlWpMVK+yZhPFPw0RpCxY0v86lS62O2FhqYYqV0tKWtbe1GDfOgtOOHm1rZJYubX5d3/iGeaDVF1YnvB4xX5FafPCBhtNJtXjtNct3FCcjSKMhy50B2gGbgQHUOAMMq+/+KCkaVQs931DCorVr7cVaXBz/H2D/frOIde1qHleNoX9/C2oYVbZssV/u/Pm1y6+80sp/9aum1RdGuN+1K/71MAXNYUGsI8X+/WZRnT7d3HDBzH0tZcIE1dxcixqsako5N1eTGm4+kVx1lT2Ldu0sBH9LePhhq6u+sFCDBpkJM+rk5dW8U15+WfXLXzaHmPbtbSVFc83DDSmaCC0nSg6qehCYDiwB1gOPquq61Laq8eTl2WLDeHz4IVxwAXTqBL/8pVnADicnBxYvNlPTt78d13pUi337YOtWM09FleOPN8tWRUVN2cKFcP/98P3v20K7plBYaPvnn49/fdUq248a1fS2thY5OTB2LDzxBHz961BUBD/6UcvrveMOs67cdRe8/XZN/Ucc0fK6W4PTTrP5/UceMfNZSzjzTNsvW1b32o4dsGlTtM1mIcOHwzPP2PMYMcK+02uugddfh3vvTZJ5uD4NlK1b1EY0V1xhUZPjMXOm9bAaYw6YN8/ubTDtq1pcJrBAkVFm+HDVc86x4xUrrDc2YULzokp/8IGtzbn55vjXw0V7UXUECLnlFvvuevRoWfyyw/nSl2y09L3vWf31JbuLItXViV3iM3So6lln1S0P8+E9m5qIUU1i1ixra26u/eYblTytEZDNI5p0Jy/PItC8/37t8l27LJTK1KkwYcIn13PVVeYG+8gjDd8XhmOJ8ogGAhfnCutlf+ELcNxxUFpqnt5NpVMn+MxnYOXK+NdXrYq2I0DIeefZc3jsMejdO3H13nYb7N8P8+ZZj/3Tn05c3clGBLp0SVx9JSU26V9VVbu8rAw6dDCX6qhz9dVmAdm6FWbNgmOPTf7fdEUTcULPs9dfr10+dy78+99w882Nq6dNGxg/3mJcNeBs9LE5Kj+/6W1tTYYMMZPilCmmiB9/HHJzm19fYaGZzg43LVZVwZo1UFDQsva2BieeCG++mXjzzaBBMG2aHYf7bKWkBD76CJ57rnZ5WZmZK9u3T027mkJuLlx0kXnktRauaCLOxy7OMfM0lZVmS734YuuJN5YxY2DPHnilgaWqFRXm1typU/Pa21rk55vC/Mc/LLjjyJEtq2/0aNi5E7Ztq12+Zg0cOBDt+ZnW4LbbbJ7mggtS3ZLUMn68ddpi52n+9S9YvTo95mdShSuaiBMsbamlaG6/3SLszp7dtLqKimz/7LP137NxY/TNZmCTmAAzZljvrKWEDgGHm8/Ky22fDiOaZNKliz3rdOixJ5POne23EKtonnnGRsKnn566dkUdVzQR51OfsqFuqGi2boX58+HSS5u+YG7gQOjatWFFU1ERfbMZwEkn2dzJ3LmJqW/ECHuJHu55tmqV2bD79UvM33HSn5IS65Ds2WPnZWU2Nxh25Jy6uKJJA2JdnG+7zSY4b7qp6fWImPmsPkXz7rs235EOIxqwiddE5fto397Mb/FGNAUF0XcEcFqPkhKzKDz9tJ2XldlvsTXnPNINVzRpQKhoXn3VknddfrmtJWkOY8bYqCVeLrXQESBdFE2iKSw0xRI6S+zbB2vX+vyMU5uxY20d0bJl9htZudLnZz4JVzRpQF6eBc6dNct63jfc0Py6wkSIK1bUvRa6NqeD6SwZFBbaItgNG+z85Zet55rt8zNObTp0sEDhy5aZqXX/flc0n4QrmjQgL89eeKWlth6mZ8/m1zV6tEUQiGc+q6gwW3PogJBtjB5t+9B8lg4RAZzUUFJiHZHf/MbOTz01te2JOq5o0oDQxblTJ7juupbV1bGjTXzXp2gGDmzeosdMID8fjjmmRtGUl5vzRIQCejsRIQjYzPz5MHRoy9ZwZQOuaNKAIUNsMnrGjMT8oIuK4i/cTBfX5mTRpo2NakLPs3SJCOC0PqNGWafko4/cbNYYXNGkAb172zC9OZ5m8Yi3cPPQIQsKmM2KBkzRrF4N771njgA+P+PEo107KC62Y1c0n4wrmjThhBPiR2duDqFDQKz5bNs2C7eSrY4AIYWFNh/2i1+Y8vX5Gac+zj7bFM748aluSfRxRZOFxFu4me2uzSGhQ8BPf2p7H9E49fGtb5m5uU+fVLck+riiyULiLdzMdtfmkN69Ldbbhg3QrZu/RJz6adMmez00m4ormizl8IWbFRUW7qZ799S2K9WI1MQ984gAjpMYXNFkKYcv3KyoqPFuy3ZC85nPzzhOYnBFk6UUFNjQPzSfbdzoZrOQsWNt70ESHScxZOnSPOfoo2sWbn74IbzxhjsChBQXw/Ll7k3kOInCRzRZzJgxZjpLl6yarYWIKRs3IzpOYkiJohGROSLypoi8FGyfi7l2g4hsEpEKEZkYUz5KRNYE1/5HxF4DInKEiPxvUL5CRPrHfOYSEXk12C5pTRnTgXDh5m9/a+c+onEcJxmkckRzj6qODLY/AYjIUOBCYBgwCbhfRMJlij8BpgGDg21SUP5N4D1VHQTcA9wZ1HUsMBs4BSgEZotIl1aRLE0IHQIWLbL9oEGpa4vjOJlL1Exn5wOlqlqlqq8Dm4BCEekFHKOqz6qqAr8APh/zmeBVya+BkmC0MxFYqqq7VfU9YCk1ysmhZuHmtm2W38YTNzmOkwxSqWimi8jLIrIwZqTRG3gj5p7tQVnv4Pjw8lqfUdWDwL+A3AbqqoOITBORchEp37VrV8ukSiNEajyr3GzmOE6ySJqiEZEnRWRtnO18zAw2EBgJVAI/DD8WpyptoLy5n6ldqPqAqhaoakG3bt0akCrzCM1n7gjgOE6ySJp7s6pOaMx9IvIz4A/B6XYgNklxH+CtoLxPnPLYz2wXkXbAp4DdQXnxYZ95qikyZAOhovERjeM4ySJVXme9Yk4nA2uD498BFwaeZAOwSf+VqloJ7BGRomD+5WvAEzGfCT3KpgB/C+ZxlgBniUiXwDR3VlDmxHDqqZZMberUVLfEcZxMJVULNueKyEjMlLUFuAxAVdeJyKPAK8BB4DuqGqbnugJ4COgA/DnYAH4OLBaRTdhI5sKgrt0icisQpLHiFlXdnWS50o6cHLjzzlS3wnGcTEas8++EFBQUaHl5eaqb4TiOk1aIyCpVjZtYI2ruzY7jOE6G4YrGcRzHSSquaBzHcZyk4orGcRzHSSquaBzHcZyk4orGcRzHSSquaBzHcZyk4utoDkNEdgFbW1BFV+CdBDUnqmSDjJAdcrqMmUOq5eynqnGDRbqiSTAiUl7foqVMIRtkhOyQ02XMHKIsp5vOHMdxnKTiisZxHMdJKq5oEs8DqW5AK5ANMkJ2yOkyZg6RldPnaBzHcZyk4iMax3EcJ6m4onEcx3GSiiuaBCEik0SkQkQ2icj1qW5PohCRhSKyU0TWxpQdKyJLReTVYN8llW1sKSJyvIgsF5H1IrJORL4blGeMnCJypIisFJHVgYw3B+UZI2OIiLQVkRdF5A/BeSbKuEVE1ojISyJSHpRFVk5XNAlARNoC9wGfBYYCF4nI0NS2KmE8BEw6rOx6YJmqDgaWBefpzEFghqp+BigCvhN8f5kkZxVwpqqeCIwEJolIEZklY8h3gfUx55koI8AZqjoyZu1MZOV0RZMYCoFNqrpZVfcDpcD5KW5TQlDVp7EU2bGcDywKjhcBn2/VRiUYVa1U1ReC4z3YS6o3GSSnGnuD05xgUzJIRgAR6QOcDSyIKc4oGRsgsnK6okkMvYE3Ys63B2WZSg9VrQR7SQPdU9yehCEi/YGTgBVkmJyBSeklYCewVFUzTkZgHnAdUB1TlmkygnUS/ioiq0RkWlAWWTnbpboBGYLEKXO/8TRDRI4G/g/4nqp+IBLva01fVPUQMFJEOgOPi8gJqW5TIhGRc4CdqrpKRIpT3Z4kM05V3xKR7sBSEdmQ6gY1hI9oEsN24PiY8z7AWylqS2vwtoj0Agj2O1PcnhYjIjmYknlEVX8TFGecnACq+j7wFDb3lkkyjgPOE5EtmPn6TBF5mMySEQBVfSvY7wQex8z3kZXTFU1ieB4YLCIDRKQ9cCHwuxS3KZn8DrgkOL4EeCKFbWkxYkOXnwPrVfXumEsZI6eIdAtGMohIB2ACsIEMklFVb1DVPqraH/sf/JuqfoUMkhFARDqKSKfwGDgLWEuE5fTIAAlCRD6H2YfbAgtV9QcpblJCEJFfAcVYCPK3gdnAb4FHgb7ANmCqqh7uMJA2iMipQBmwhhrb/n9h8zQZIaeIjMAmiNtiHcxHVfUWEcklQ2SMJTCdfV9Vz8k0GUUkDxvFgE1//FJVfxBlOV3ROI7jOEnFTWeO4zhOUnFF4ziO4yQVVzSO4zhOUnFF4ziO4yQVVzSO4zhOUnFF4zgpRERygwi8L4nIDhF5MzjeKyL3p7p9jpMI3L3ZcSKCiMwB9qrqXalui+MkEh/ROE4EEZHimHwqc0RkkYj8NchD8gURmRvkI/lLED4HERklIn8PAi0uCcOROE6qcUXjOOnBQCz8/fnAw8ByVR0OfAScHSibHwNTVHUUsBDIiOgUTvrj0ZsdJz34s6oeEJE1WBiZvwTla4D+wBDgBCySL8E9lSlop+PUwRWN46QHVQCqWi0iB7RmcrUa+z8WYJ2qjklVAx2nPtx05jiZQQXQTUTGgKU9EJFhKW6T4wCuaBwnIwhSiE8B7hSR1cBLwNjUtspxDHdvdhzHcZLRmbMQAAAAO0lEQVSKj2gcx3GcpOKKxnEcx0kqrmgcx3GcpOKKxnEcx0kqrmgcx3GcpOKKxnEcx0kqrmgcx3GcpPL/w6HcKLipF78AAAAASUVORK5CYII=\n", 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" ] @@ -758,14 +674,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 65816.70042312301.\n", - "The root mean squared error is 61579.33041874606.\n" + "The root mean squared error is 70010.61354592441.\n", + "The root mean squared error is 68346.74540545818.\n" ] } ], "source": [ - "# lasso_train, lasso_test, y_train, y_test = create_lasso_copy(x_train_rr, y_train, x_test_rr, y_test, scaler)\n", - "lasso_train, lasso_test = create_lasso(x_train_not_norm, y_train_not_norm, x_test_not_norm, y_test_not_norm)\n", + "lasso_train, lasso_test, y_train, y_test = create_lasso(x_train_lasso, y_train, x_test_lasso, y_test, scaler)\n", + "# lasso_train, lasso_test = create_lasso(x_train_not_norm, y_train_not_norm, x_test_not_norm, y_test_not_norm)\n", "#lasso_train, lasso_test = create_lasso(x_train_lasso, y_train, x_test_lasso, y_test, scaler)\n", "\n", "plot_predictions(y_train_not_norm, lasso_train)\n",