From f48989aaf9128aff487c285efdf39fb2834e5ca2 Mon Sep 17 00:00:00 2001 From: Ismael Castro Date: Thu, 3 Jun 2021 15:37:52 -0700 Subject: [PATCH] final code --- daily_robust_rnn.ipynb | 74 +- monthly_robust_rnn.ipynb | 694 +- monthly_simple_lstm.ipynb | 6634 +++++++++++--- multivar_simple_gru.ipynb | 16776 +++++++++++++++++++++++++++++++----- 4 files changed, 20443 insertions(+), 3735 deletions(-) diff --git a/daily_robust_rnn.ipynb b/daily_robust_rnn.ipynb index 79ef2d4..be67394 100644 --- a/daily_robust_rnn.ipynb +++ b/daily_robust_rnn.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -238,11 +238,11 @@ " create single layer rnn model trained on x_train and y_train\n", " and make predictions on the x_test data\n", " '''\n", - " # create a model\n", " model = Sequential()\n", - " model.add(SimpleRNN(32))\n", - "# model.add(SimpleRNN(32, return_sequences=True))\n", - "# model.add(SimpleRNN(16))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(1))\n", " model.add(Dense(1))\n", "\n", " model.compile(optimizer='adam', loss='mean_squared_error')\n", @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -278,11 +278,11 @@ "output_type": "stream", "text": [ "Epoch 1/3\n", - "350/350 [==============================] - 8s 21ms/step - loss: 0.0030\n", + "350/350 [==============================] - 37s 98ms/step - loss: 0.0049\n", "Epoch 2/3\n", - "350/350 [==============================] - 7s 20ms/step - loss: 5.5065e-04\n", + "350/350 [==============================] - 37s 106ms/step - loss: 3.2152e-04\n", "Epoch 3/3\n", - "350/350 [==============================] - 6s 18ms/step - loss: 3.9362e-04\n", + "350/350 [==============================] - 41s 116ms/step - loss: 3.2219e-04\n", "predicting\n" ] } @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -319,12 +319,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -336,7 +336,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 570.6108536758172.\n", + "The root mean squared error is 543.7638003235986.\n", "(22365, 1)\n" ] } @@ -350,12 +350,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -367,7 +367,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 1367.212325791287.\n" + "The root mean squared error is 1459.2136695683014.\n" ] } ], @@ -378,12 +378,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -428,19 +428,19 @@ " \n", " \n", " 0\n", - " 479454\n", + " 445740\n", " \n", " \n", " 1\n", - " 343823\n", + " 297384\n", " \n", " \n", " 2\n", - " 384598\n", + " 343807\n", " \n", " \n", " 3\n", - " 524886\n", + " 484961\n", " \n", " \n", "\n", @@ -448,13 +448,13 @@ ], "text/plain": [ " Count\n", - "0 479454\n", - "1 343823\n", - "2 384598\n", - "3 524886" + "0 445740\n", + "1 297384\n", + "2 343807\n", + "3 484961" ] }, - "execution_count": 54, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -474,7 +474,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115830.72196205116.\n", - "The root mean squared error is 8328.45420831501.\n" + "The root mean squared error is 42965.42172200804.\n" ] } ], diff --git a/monthly_robust_rnn.ipynb b/monthly_robust_rnn.ipynb index bd9f074..9a7bbcf 100644 --- a/monthly_robust_rnn.ipynb +++ b/monthly_robust_rnn.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -191,7 +191,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 43, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ "(984, 1)" ] }, - "execution_count": 44, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -450,12 +450,12 @@ " '''\n", " # create a model\n", " model = Sequential()\n", - " model.add(SimpleRNN(32))\n", - " #model.add(SimpleRNN(32, return_sequences=True))\n", - " #model.add(SimpleRNN(32, return_sequences=True))\n", - " #model.add(SimpleRNN(1))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(50, return_sequences=True))\n", + " model.add(SimpleRNN(1))\n", " model.add(Dense(1))\n", - "\n", + " \n", " model.compile(optimizer='adam', loss='mean_squared_error')\n", "\n", " # fit the RNN model\n", @@ -478,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -486,207 +486,207 @@ "output_type": "stream", "text": [ "Epoch 1/300\n", - "15/15 [==============================] - 1s 2ms/step - loss: 0.0102\n", + "15/15 [==============================] - 2s 6ms/step - loss: 0.0087\n", "Epoch 2/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0065\n", "Epoch 3/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0097\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0073\n", "Epoch 4/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0064\n", "Epoch 5/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0074\n", "Epoch 6/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0077\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0070\n", "Epoch 7/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0060\n", "Epoch 8/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0087\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0057\n", "Epoch 9/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0113\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0078\n", "Epoch 10/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0121\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0066\n", "Epoch 11/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0046\n", "Epoch 12/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0059\n", "Epoch 13/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0046\n", "Epoch 14/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0077\n", "Epoch 15/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0058\n", "Epoch 16/300\n", - "15/15 [==============================] - 0s 3ms/step - loss: 0.0068\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0057\n", "Epoch 17/300\n", - "15/15 [==============================] - 0s 3ms/step - loss: 0.0100\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0052\n", "Epoch 18/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0125\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0066\n", "Epoch 19/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0060\n", "Epoch 20/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0084\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0034\n", "Epoch 21/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0107\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0050\n", "Epoch 22/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0099\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0068\n", "Epoch 23/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0078\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0047\n", "Epoch 24/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0071\n", "Epoch 25/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0075\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0052\n", "Epoch 26/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0087\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0064\n", "Epoch 27/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0093\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0041\n", "Epoch 28/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0059\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0041\n", "Epoch 29/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0068\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0036\n", "Epoch 30/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0083\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0047\n", "Epoch 31/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0033\n", "Epoch 32/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0101\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0056\n", "Epoch 33/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0044\n", "Epoch 34/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0080\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0043\n", "Epoch 35/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0058\n", "Epoch 36/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0038\n", "Epoch 37/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0051\n", "Epoch 38/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0084\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0059\n", "Epoch 39/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0091\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0037\n", "Epoch 40/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0106\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0046\n", "Epoch 41/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0074\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0034\n", "Epoch 42/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0097\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0035\n", "Epoch 43/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0079\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0051\n", "Epoch 44/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0067\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0037\n", "Epoch 45/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0029\n", "Epoch 46/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0032\n", "Epoch 47/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0030\n", "Epoch 48/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0091\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0045\n", "Epoch 49/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0094\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0045\n", "Epoch 50/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0076\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0033\n", "Epoch 51/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0029\n", "Epoch 52/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0071\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0037\n", "Epoch 53/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0073\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0040\n", "Epoch 54/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0034\n", "Epoch 55/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0075\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0033\n", "Epoch 56/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0095\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0030\n", "Epoch 57/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0053\n", "Epoch 58/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0090\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0033\n", "Epoch 59/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0070\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0032\n", "Epoch 60/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0065\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0030\n", "Epoch 61/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0053\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0041\n", "Epoch 62/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0053\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0042\n", "Epoch 63/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0064\n", + "15/15 [==============================] - 0s 5ms/step - loss: 0.0036\n", "Epoch 64/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 5ms/step - loss: 0.0040\n", "Epoch 65/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0050\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0037\n", "Epoch 66/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0063\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0047\n", "Epoch 67/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0056\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0031\n", "Epoch 68/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0048\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0034\n", "Epoch 69/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0064\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0037\n", "Epoch 70/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0031\n", "Epoch 71/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0063\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0043\n", "Epoch 72/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0033\n", "Epoch 73/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 74/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0062\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0030\n", "Epoch 75/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0052\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 76/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0064\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0031\n", "Epoch 77/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0086\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 78/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0075\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0027\n", "Epoch 79/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0074\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0021\n", "Epoch 80/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0059\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0023\n", "Epoch 81/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0050\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0028\n", "Epoch 82/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0071\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 83/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0061\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0021\n", "Epoch 84/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0031\n", "Epoch 85/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0052\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0029\n", "Epoch 86/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0052\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0027\n", "Epoch 87/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0059\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0026\n", "Epoch 88/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0057\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0021\n", "Epoch 89/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0053\n", + "15/15 [==============================] - 0s 6ms/step - loss: 0.0034\n", "Epoch 90/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0058\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 91/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0054\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0030\n", "Epoch 92/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0047\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0027\n", "Epoch 93/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0026\n", "Epoch 94/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0050\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0038\n", "Epoch 95/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0056\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0020\n", "Epoch 96/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0049\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0021\n", "Epoch 97/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0062\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0022\n", "Epoch 98/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0054\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0032\n", "Epoch 99/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0069\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.0022\n", "Epoch 100/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0022\n", "Epoch 101/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 7ms/step - loss: 0.0025\n", "Epoch 102/300\n" ] }, @@ -694,409 +694,415 @@ "name": "stdout", "output_type": "stream", "text": [ - "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "15/15 [==============================] - 0s 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231/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 5ms/step - loss: 0.0012\n", "Epoch 232/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0041\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0014\n", "Epoch 233/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.1471e-04\n", "Epoch 234/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 235/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 236/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 237/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0029\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.3257e-04\n", "Epoch 238/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0053\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 239/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0047\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 240/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 241/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0040\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0013\n", "Epoch 242/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.5184e-04\n", "Epoch 243/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 5ms/step - loss: 0.0010\n", "Epoch 244/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0031\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 245/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 246/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0040\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 247/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0031\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 248/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 249/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.7671e-04\n", "Epoch 250/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0045\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.6993e-04\n", "Epoch 251/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 252/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0029\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 253/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 254/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 255/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.1898e-04\n", "Epoch 256/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.7241e-04\n", "Epoch 257/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0024\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.0241e-04\n", "Epoch 258/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.7702e-04\n", "Epoch 259/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.6940e-04\n", "Epoch 260/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.4033e-04\n", "Epoch 261/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0031\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.3492e-04\n", "Epoch 262/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.2241e-04\n", "Epoch 263/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0032\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 264/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.0973e-04\n", "Epoch 265/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 5ms/step - loss: 7.2411e-04\n", "Epoch 266/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.6420e-04\n", "Epoch 267/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0029\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.0289e-04\n", "Epoch 268/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0014\n", "Epoch 269/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.1272e-04\n", "Epoch 270/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0035\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 271/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.7306e-04\n", "Epoch 272/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.3846e-04\n", "Epoch 273/300\n", - "15/15 [==============================] - 0s 3ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.1848e-04\n", "Epoch 274/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0042\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.6656e-04\n", "Epoch 275/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0038\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.6239e-04\n", "Epoch 276/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0034\n", + "15/15 [==============================] - 0s 5ms/step - loss: 8.8095e-04\n", "Epoch 277/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.5426e-04\n", "Epoch 278/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0030\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.6447e-04\n", "Epoch 279/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0046\n", + "15/15 [==============================] - 0s 4ms/step - loss: 9.7429e-04\n", "Epoch 280/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0033\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 281/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0040\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 282/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0025\n", + "15/15 [==============================] - 0s 5ms/step - loss: 9.5202e-04\n", "Epoch 283/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0033\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 284/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0039\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 285/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0027\n", + "15/15 [==============================] - 0s 5ms/step - loss: 9.1126e-04\n", "Epoch 286/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.7324e-04\n", "Epoch 287/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.3310e-04\n", "Epoch 288/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0014\n", "Epoch 289/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0034\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 290/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0034\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0011\n", "Epoch 291/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0031\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0010\n", "Epoch 292/300\n", - "15/15 [==============================] - 0s 2ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.9255e-04\n", "Epoch 293/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.9852e-04\n", "Epoch 294/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0029\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 295/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 4ms/step - loss: 7.9417e-04\n", "Epoch 296/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0051\n", + "15/15 [==============================] - 0s 4ms/step - loss: 8.9771e-04\n", "Epoch 297/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0036\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0012\n", "Epoch 298/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0037\n", + "15/15 [==============================] - 0s 5ms/step - loss: 0.0017\n", "Epoch 299/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0025\n", - "Epoch 300/300\n", - "15/15 [==============================] - 0s 1ms/step - loss: 0.0043\n", + "15/15 [==============================] - 0s 4ms/step - loss: 0.0015\n", + "Epoch 300/300\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15/15 [==============================] - 0s 4ms/step - loss: 0.0018\n", "predicting\n" ] } @@ -1107,12 +1113,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1124,7 +1130,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 17289.742043500715.\n", + "The root mean squared error is 10405.464624348822.\n", "(918, 1)\n" ] } @@ -1138,12 +1144,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 72, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1155,7 +1161,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 50445.582783350335.\n" + "The root mean squared error is 54480.461813965594.\n" ] } ], @@ -1166,12 +1172,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1186,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -1202,7 +1208,7 @@ "4" ] }, - "execution_count": 55, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1216,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1251,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -1288,19 +1294,19 @@ " \n", " \n", " 0\n", - " 440603\n", + " 458287\n", " \n", " \n", " 1\n", - " 310447\n", + " 382317\n", " \n", " \n", " 2\n", - " 271963\n", + " 287356\n", " \n", " \n", " 3\n", - " 372677\n", + " 471529\n", " \n", " \n", "\n", @@ -1308,13 +1314,13 @@ ], "text/plain": [ " Count\n", - "0 440603\n", - "1 310447\n", - "2 271963\n", - "3 372677" + "0 458287\n", + "1 382317\n", + "2 287356\n", + "3 471529" ] }, - "execution_count": 57, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1326,7 +1332,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1334,7 +1340,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 102053.96230548817.\n" + "The root mean squared error is 63496.75312958923.\n" ] } ], diff --git a/monthly_simple_lstm.ipynb b/monthly_simple_lstm.ipynb index d2f3e09..8e1389a 100644 --- a/monthly_simple_lstm.ipynb +++ b/monthly_simple_lstm.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -188,7 +188,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 44, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -234,7 +234,7 @@ "(984, 1)" ] }, - "execution_count": 45, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -400,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -413,7 +413,7 @@ " LSTM_model.add(LSTM(5, input_shape=(x_train.shape[1],1)))\n", " LSTM_model.add(Dense(1))\n", " LSTM_model.compile(loss='mean_squared_error', optimizer='adam')\n", - " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=1000, batch_size=150, verbose=2)\n", + " history_LSTM = LSTM_model.fit(x_train, y_train, epochs=3000, batch_size=150, verbose=2)\n", " \n", " train_preds = LSTM_model.predict(x_train)\n", " test_preds = LSTM_model.predict(x_test)\n", @@ -427,2037 +427,6097 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/1000\n", - "7/7 - 1s - loss: 0.0110\n", - "Epoch 2/1000\n", - "7/7 - 0s - loss: 0.0098\n", - "Epoch 3/1000\n", + "Epoch 1/3000\n", + "7/7 - 2s - loss: 0.0131\n", + "Epoch 2/3000\n", + "7/7 - 0s - loss: 0.0111\n", + "Epoch 3/3000\n", + "7/7 - 0s - loss: 0.0099\n", + "Epoch 4/3000\n", + "7/7 - 0s - loss: 0.0095\n", + "Epoch 5/3000\n", + "7/7 - 0s - loss: 0.0094\n", + "Epoch 6/3000\n", + "7/7 - 0s - loss: 0.0094\n", + "Epoch 7/3000\n", + "7/7 - 0s - loss: 0.0094\n", + "Epoch 8/3000\n", + "7/7 - 0s - loss: 0.0094\n", + "Epoch 9/3000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 10/3000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 11/3000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 12/3000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 13/3000\n", + "7/7 - 0s - loss: 0.0093\n", + "Epoch 14/3000\n", "7/7 - 0s - loss: 0.0092\n", - "Epoch 4/1000\n", - "7/7 - 0s - loss: 0.0090\n", - "Epoch 5/1000\n", + "Epoch 15/3000\n", + "7/7 - 0s - loss: 0.0092\n", + "Epoch 16/3000\n", + "7/7 - 0s - 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loss: 0.0034\n", + "Epoch 2874/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2875/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2876/3000\n", + "7/7 - 0s - loss: 0.0040\n", + "Epoch 2877/3000\n", + "7/7 - 0s - loss: 0.0037\n", + "Epoch 2878/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2879/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2880/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2881/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2882/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2883/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2884/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2885/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2886/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2887/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2888/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2889/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2890/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2891/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2892/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2893/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2894/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2895/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2896/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2897/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2898/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2899/3000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2900/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2901/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2902/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2903/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2904/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2905/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2906/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2907/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2908/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2909/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2910/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2911/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2912/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2913/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2914/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2915/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2916/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2917/3000\n", + "7/7 - 0s - loss: 0.0037\n", + "Epoch 2918/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2919/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2920/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2921/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2922/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2923/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2924/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2925/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2926/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2927/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2928/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2929/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2930/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2931/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2932/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2933/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2934/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2935/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2936/3000\n", + "7/7 - 0s - loss: 0.0044\n", + "Epoch 2937/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2938/3000\n", + "7/7 - 0s - loss: 0.0037\n", + "Epoch 2939/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2940/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2941/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2942/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2943/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2944/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2945/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2946/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2947/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2948/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2949/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2950/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2951/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2952/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2953/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2954/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2955/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2956/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2957/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2958/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2959/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2960/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2961/3000\n", + "7/7 - 0s - loss: 0.0043\n", + "Epoch 2962/3000\n", + "7/7 - 0s - loss: 0.0037\n", + "Epoch 2963/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2964/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2965/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2966/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2967/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2968/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2969/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2970/3000\n", + "7/7 - 0s - loss: 0.0045\n", + "Epoch 2971/3000\n", + "7/7 - 0s - loss: 0.0038\n", + "Epoch 2972/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2973/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2974/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2975/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2976/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2977/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2978/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2979/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2980/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2981/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2982/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2983/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2984/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2985/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2986/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2987/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2988/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2989/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2990/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 2991/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2992/3000\n", + "7/7 - 0s - loss: 0.0034\n", + "Epoch 2993/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2994/3000\n", + "7/7 - 0s - loss: 0.0033\n", + "Epoch 2995/3000\n", + "7/7 - 0s - loss: 0.0044\n", + "Epoch 2996/3000\n", + "7/7 - 0s - loss: 0.0036\n", + "Epoch 2997/3000\n", + "7/7 - 0s - loss: 0.0037\n", + "Epoch 2998/3000\n", + "7/7 - 0s - loss: 0.0038\n", + "Epoch 2999/3000\n", + "7/7 - 0s - loss: 0.0035\n", + "Epoch 3000/3000\n", + "7/7 - 0s - loss: 0.0034\n" ] } ], @@ -2468,12 +6528,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2487,7 +6547,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 24708.75384775695.\n" + "The root mean squared error is 17317.243859199847.\n" ] } ], @@ -2498,12 +6558,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -2517,7 +6577,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The root mean squared error is 55689.75246047483.\n" + "The root mean squared error is 66531.72241765632.\n" ] } ], @@ -2528,12 +6588,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ipLyysV9DRGS3oqCSoubNsimvdMorFFhERCIUVFLUvFnwo1NtRUSkioJKilrkZANQrH4VEZEoBZUUqaYiIlJdvYKKmV1nZm3DHRofN7NPzeyMTL/c7ixSU3l0SsY2lxQRaXLqW1P5obtvIdgbPo9gh8Y76spkZsPNbJGZ5ZvZmCTXzczuC6/PNbMhdeU1s4Fm9rGZfWZmr5hZ25hrN4XpF5nZmfUsW0rWbSsB4MmPlrN5R1kmHyUi0mTUN6hY+H0k8A93nxNzLnmGYL/4B4ERwACCbYAHJCQbQbCXfD9gNPBwPfI+Boxx98OBl4EbwzwDCPayPxQYDjwU2bM+Ew7t3i56/NC7+Zl6jIhIk1LfoDLTzN4kCCqTzKwNUFdnwjAg392XunspMB4YlZBmFPC0B6YC7c2sWx15DwKmhMdvAefG3Gu8u5e4+zKCfe+H1bN8DXbsAZ1YdNtwLhjai0feW8qYl+ZSUq5OexHZu9V3j/ofAYOApe6+3cw6EjSB1aYHsCLmcwFwdD3S9Kgj7zzgO8B/gPOAXjH3mprkXnHMbDRBrYjevXvXUYTaNW+WzZ++ezhllZWMn74CM/jzd4/YqXuKiDRl9a2pHAsscvdNZnYJcDOwuY48yZrHEhfLqilNbXl/CFxtZjOBNkBpA56Huz/q7kPdfWheXl7SF2+I7CzjrvMGcuFRvRg3bQXjpn210/cUEWmq6htUHga2m9lA4FfAl8DTdeQpoKoWAdATWFXPNDXmdffP3f0Mdz8SGAcsacDzMsLMuO3swzi6b0d+N2E+H+av2xWPFRHZ7dQ3qJS7uxP0W9zr7vcS1BJqMx3oZ2Z9zSyXoBN9QkKaCcCl4SiwY4DN7r66trxm1iX8nkVQYxobc68Lzay5mfUl6PyfVs/y7bRm2VmMveRIurVrwaVPTOPjJet31aNFRHYb9Q0qW83sJuAHwGvhqKqc2jK4ezlwDTAJWAg87+7zzewqM7sqTDYRWErQqf534Ke15Q3zXGRmXwCfE9RE/hHmmQ88DywA3gCudvdd2nPeoXUu4648hmwzrnhymmbbi8hex7weG02Z2b7AxcB0d3/fzHoDJ7l7XU1gu7WhQ4f6jBkz0n7fJz5Yxq2vLuC6U/txw+n9035/EZHGZGYz3X1osmv1qqm4+9fAM0A7M/sWUNzUA0omXX5cH447oBP3Tl7MTf/6THvZi8heo77LtJxP0D9xHnA+8ImZfS+TL9aUZWUZT1x+FId2b8u4aV9x/iMf8+b8rznnoQ+ZsXxDY7+eiEjG1Lf5aw5wuruvDT/nAW+7+8AMv19GZar5K8LduXPSIh5+d0n0XJc2zZn229My9kwRkUzb6eYvICsSUELrG5B3r2Vm/Hr4wfzjiqOi59ZuLeGchz5ka3Hy9cKWrSvip8/MVCe/iDRJ9Q0Mb5jZJDO73MwuB14jGLkl9XDyQV3Iv30Ez40+BoBZX21i0K1vcfHfp/LGvK/5KH9ddImXm//9GRM/+5rX561u8HPGT/uKpz5ans5XFxFpkHo1fwGY2bnA8QQz16e4+8uZfLFdIdPNX8l8ub6If89axd/e/iLp9UO7t2X+qi0ALL/jrAbdu8+Y11LKJyLSELU1f9V37S/c/SXgpbS91V5qv06tue60flx7yoE8PyNY1mVOQdWKN5GAAjDrq410at2cnh1a8kH+OrYWl3NC/860bVHrFCEqK52srFoXkRYRyYhag4qZbSXJ+lkEtRV397ZJrkk9ZGUZFw7rzYXDepO/dhs/+b+ZLF67LS7NOQ99VC3fyQfl8Y8ral98eX1RKXltmqf1fUVE6qPWPhV3b+PubZN8tVFASZ8Du+zDWz//Jp//cTgzbq4aGTb6xP2rpf3vokJmfrmBtxes4Vv3v8+qTTuqpdGmYSLSWOrd/CWZ1yInmxY52XF9Ij8/vT8XPjqV2Ss20bF1LhuKSjn34Y+j14+74x3+dM7htGtZ1SS2eUewcHNZRbDlTU727jtQ76Z/zeWTZRt45xcnNfariEga1Lujfk/UGB31qfr86y3079KGSfO/5sF385m3ckut6e+9cBD3Tl5MsyzjzRu+CQRB5oF38rnyxP3Zp3ntf08sLdzGZys3M2pQtS1p0kqDC0SannTMU5FGdvC+bcnKMkYc3o1Xrz2B5398LAA/TtJEBnDd+NksLSziizXbosOVJ8xexb2TF3PPW8lHnsU65a73uG78bPbmPzpEpOEUVJqoYX07svyOs7hp5CF8OOYUJl1/IgCd92lOy5zsuLQH3/IGnyxdzy9emAMEfS7PfPIl42M2FNtYVMr3H5vKfxetjctbUl7XrtEiIlXUp7IH6NG+JQD5t4+gWXYWlZXOS58WsGpTMZPmf82C1Vu44NGqnZZfmFnACzMLADhnSA9ysrJ47bPVfJi/ng/z17Pw1uHRtNtKymmREKRERGqioLIHaRZ2yGdlGecNDTbB/OnJB/Dplxt55pOvmDCn+kaYB938BmcP6s6/Z1ddu+G52dHjlz9dyZU1NLGJiCTKaPOXmQ03s0Vmlm9mY5JcNzO7L7w+18yG1JXXzAaZ2VQzm21mM8xsWHi+j5ntCM/PNrOxic/bG+VkZ3H0/p2476LB3HnuEQC8dcOJHNGzXTRNbEABeGP+19Hj2ycuZOJn9VsyRv0vIpKxmkq4O+SDwOkE+8dPN7MJ7r4gJtkIgm1/+wFHAw8DR9eR907gD+7+upmNDD+fFN5vibsPylSZmrrzj+rF+UcFNZgJ13wDgDkrNjF9+QaWFG5jv06tueP1z6vl++kzn7L8jrNYUriN1+au5vLj+1Sb1V9cVsHBt7zBzWcdwv+cENRs/j5lKS1zs7nkmP2Svs+2kvLocUWlk61VAESavEw2fw0D8t19KYCZjSfY4z42qIwCnvbgT9ypZtbezLoBfWrJ60Bk4mU7gi2FJUUDe7VnYK/20c+9O7bi2U++YsHqLWwoKo2ejwz9Bbj7rS84pFtbDshrzYn98jj/qF58tjJYaua21xZGg8rtExcC1BhUfvzPquHcZRWVZGcFfTf/mb2SE/vl0aF1bnoKKSK7TCaDSg9gRcznAoLaSF1petSR93pgkpn9laD57riYdH3NbBawBbjZ3d/fyTLsdUYe3o2Rh3eLfv5yfRHnPPRRXIABWLh6CwtXb+HVuasxC4JJxH2TF3PO4Kr5LUUl5bROmBfzYf46PsxfH/1cWlFJi5xsVmzYznXjZ/PN/nk89cPal6MRkd1PJoNKsraMxEb3mtLUlvcnwA3u/lK4I+XjwGnAaqC3u683syOBf5vZoe4eN0vQzEYDowF69+5d78Lsrfbr1JppvzmVDdtLwWHFxu1xM/oBbnxxbtznu9/6grtj5sLc+soC/vd7R8Sl+cn/zYz7XF4R/OeNLDGzpDB+HTQRaRoyGVQKgF4xn3tSvamqpjS5teS9DLguPH4BeAzA3UuAkvB4ppktAfoDcVPm3f1R4FEIZtSnUK69TrPsLLq0aQFAl7YtePeXJ7Fy0w62lZQzf+VmnGBHy6F9OjLi3uqVw+dmrGD1lmIO6daGy4/rQ3mFs6W4PC7NFU9O5w/fOTS6OVnBxh3sKK2gZW78cOaKSmf9thK6tG2RmcKKyE7J2DItZtYM+AI4FVgJTAcudvf5MWnOAq4BRhI0b93n7sNqy2tmC4GfuPu7ZnYqcKe7HxlucbzB3SvMbH/gfeBwd69xU/imtExLU/HRknX8YcICFq3ZCsBV3zyAse8tqSNXlZ+cdEB0++UzBnTl0UvjV4K46V9zGTdtBQtvHV4t4Lz+WdAUN/ywbohI5qRlP5WGcvdyM7sGmARkA0+EQeGq8PpYgt0jRwL5wHbgitryhre+Erg3DDzFhE1ZwInArWZWDlQAV9UWUCQzjjugMxOuPZ6S8sroCLHTB3Sp1mQW0aFVDhu3V62qHAkoAG8uWEOfMa/x9A+HcWL/PADGTQu62tZtK6FXx1Zx9/rJM58Cta8jNmfFJrq1a6GajkiGaEFJ1VR2mU3bS7n+udm8u6gweu7Va7/Bt+7/oM685wzuwV/PG8gBvwl2sX75p8cxuHeHuDSJi1P+8MnptGuZw98uGBSXpl3LHOb87oydLY7IXqtRaioiidq3yuXJcIOxNVuKyTIjr01zlt9xFpu2lzLo1rdqzPvyrJW8PGtl9POcFZuqBZWI974o5Jv983jn82Ads0hQifwBpf1mRDJHQUUaRdeE5qf2rXJZfsdZVFQ6T3ywjCmLCzmoaxse+2BZ0vy/f2UBv38lmPJ05/eO4N2YhTAve2IaS/40Mvr5izVb2VBUyqNTlkbPzVu5mcN6VK0qUB9rtxTTrlUOzZtpLTSRmqj5S81fu7UtxWU8P30Fs77axIaiUr4zqDuFW0vihiwnc+e5R/Crl+bWeD22nwZg4B/e5KCubXj+qmOTpq+odA74zUS+PbA79180OO5a/tqtdGiVS6d9tIWz7B3U/CVNVtsWOdEZ+hEVlc7xB3bmzflfM6dgEwUbd1CwMX5b5doCCsClT0zjgYsH06tDKwb2as/mHWVMWx5s03zagK58VrCZDq1z6NkhGAywblsJEIwwIyaovDBjBTe+OJdOrXOZecvp6SiySJOmoCJNTnaWceR+HThyv6o+lc07ythWUk7LnGzOfvBDvtqwnX3btuDnp/fnlv/MS7ovzDXPzgLg7vMHRs89+G4+w/bvyLcfCAYPRDr912wpBqBVwjDmyMTP9UWlrN68g27tWqaxpOm1dmsxo5+eySM/OLJa82N9Lfp6K307tya3mbZikuT0L0P2CO1a5tCjfUs6ts5lyq9OZvkdZzH1N6cG65L9/kwm/+KbNeb9+fNzosezvtrEEb9/M/p5wpxVFJdV8NaCNQAUl1XyYrgXTaLXP/s66fndxbhPVjB7xSb+b+qXKeVfuWkHZ94zhT++uqDuxLLXUk1F9ni5zbI4IG8fFt8+gmZZRt+bJtK/6z4ctG9bzhjQlYmfreb1eckDws/GzYr7XFpRyS9fmMOQ3u05/5H4uTe3vrqAW19dwKxbTmfGlxu55d/z+O8vT6o2SbOp2hiu/zZ9uaZ/Sc0UVGSvkRNuYpY4OfLbA7sDsKGolNteXcC05Ruq9dEkuvSJaazbVpr02uA/Vg2N/nJDEQfv2zZpuvpYs6WYo/80mfsuGsx3wvdsLKadCaQeFFREQh1b53J3OKdlR2kFhVtLeG9xIV3bNOdvby/m4H3bROfK1BV0IiZ+9nVcUPn7lKV8smwDj12WdOBMNQtWB+uhvjizIG1BJdXYYCnnTK8lhdto3zJHo+12UwoqIkm0zM2md6dW/KBTsBfMGYfuC8B5Q3tSVuG8OLOAFs2yeKGG/pWI+yYv5r7JiwG4/rR+3PN2cFxeUUmz7CwefncJxx3QKW5Pm1iR1ZtzdoMNzCI1lcaehXDqXe9pVYTdmIKKSAMcd0BnAL4ZznE5oX9etN/ltEO68PbCtTXmjQQUgMN+P4nismBEWnaWxU3WjFUajlprlt34QaWxg0ksrYqw+1JQEdkJ3xnYneMP6MTKTTs4tHs7thaX8d9Faznl4K60a5nDig3byW2WxW2vLeSVOVU7P0QCCgTzbl6cWcC5Q3oweeFatpWUM6xvR/LaNGf+qmBHzUnz17C1uIw2LXIor6gkO8t4de5qrn9uNvN+f2Z0MEDBxu0sXruNkw/qUu1dvdp2Rg1TUelpuY/s2RRURHZSp32aR9v327fK5ZzBPaPXIisp33fhIL5/dG8O7LIP05dt4G9vf8EXa6o2IvvlC3P45Qtz4u7bpU1z1m4tiX4efs/7fPDrk+l38+scf0BnPshfB8Ab81dz9qAefJi/nsv+MY2KSq91peZUlVUGgbCyEWPK3rwCSFOhoCKyC5gZx+zfCYARh3djxOHdmFuwibkFm+nftQ33v7OY9xevi8sTG1AgmCfy7Qc+wJ1oQAG44bk53PBcfEAqLa+sdYJiWUVldDRcrO2l5bRolk1Wkj6cisaMJqFkk1hl96LJjyKN5Iie7bnkmP0Y1rcj//zR0Sy/4yyW33EWE392Aj856QB6dmjJq9d+g+MP7BTNM2/lllruWGVrcfU+h8gf+U9+tJx+v32dlxIGGZRXVDLg/03i1homN0YGDSTWFmZ+uWGX1SAiO4PK7ktBRWQ3M6B7W349/GA++PUpHNajHU//8Gim/fZUBnSr/3yXRWu2UlJewfJ1RUz5opDX5q7m3nAUWmQr518kNLdFzj877auk9yyvrF5LeHfRWs59+GOe+mh5vd9tZ8T2RcnuKaPNX2Y2HLiXYPfGx9z9joTrFl4fSbDz4+Xu/mltec1sEDAWaAGUAz9192nhtZuAHxHs/Pgzd5+UyfKJ7ArZWUaXNi2YeN0JuDtXPj2TtxeuqXWr5ov//kmDnxMZUVXTOLPysPmr0qGkvILmzbKjeT5ZtoHLj+/b4Gc21A7VVHZ7GQsqZpYNPAicDhQA081sgrvH1q1HAP3Cr6OBh4Gj68h7J/AHd3/dzEaGn08yswHAhcChQHfgbTPr7+76Vyh7DDPj4UuGUFxWQZsWOYw8fF8GdGvLmwvW8NNwO+WG2FZSzj7Ng18D0aBSQ1SpCJu/lq0r4qCb32D5HWfRtmWwZXRhQv9Ppqj5a/eXyZrKMCDf3ZcCmNl4YBQQG1RGAU970CA71czam1k3oE8teR2ItAO0A1bF3Gu8u5cAy8wsP3yH5JujizRROdlZ0U72I3q2B2Dk4d34cMwpZJvReZ9c3lywhkp3Hngnn8+/3lrjvS589GMeu/QosrOM88Z+VOtzkzV/lYUd50WlwS/7yKTOTCmrUPPX7i6TfSo9gBUxnwvCc/VJU1ve64G/mNkK4K/ATQ14HmY22sxmmNmMwsLCxMsiTVaP9i3Zt10LmmVnMfLwbnzriO489cNhHJDXmrMHdWfKjSfTLqxZRMxbuYVj/jyZkfe9T1lYEykuq+S56dX7VcoTRn+5O6XhL/myikqWFG7jwN++zsTPVmeohAoqDfHyrAKmLl2/y5+byaCSrBKdOESkpjS15f0JcIO79wJuAB5vwPNw90fdfai7D83Ly0uSRWTP0bVtCyb/4iTuuXAwvTu1Ys7vzuCBiwdz7pCecekSm69+/dJnbN4eP4IscUhxaUVldMZ/aXkl81cFI9Nem5veoLK1uIwbnpvNpu2l0cAndbvhuTlc+OjUXf7cTAaVAqBXzOeeVDVV1ZWmtryXAf8Kj18gaOKq7/NE9nrfOqI7d50/kGV/HsnVJx/Afp1aJU332AdLmfjZaopKypm/anO1/WIee39ZNKiUVVQSmdpSmebhxU9//CUvz1rJ399fGh3WLLuvTPapTAf6mVlfYCVBJ/rFCWkmANeEfSZHA5vdfbWZFdaSdxXwTeBd4BRgccy9njWzuwk66vsB0zJUNpEmz8y48cyDufHMg9laXMY7n69l1lebmL0i+Lr/nXwAurdrwarNxdXy/2XSIs4fGtR4SssryQp7+N2r5rJYGtfLd6+a1S+7r4wFFXcvN7NrgEkEw4KfcPf5ZnZVeH0sMJFgOHE+wZDiK2rLG976SuBeM2sGFAOjwzzzzex5gs78cuBqjfwSqZ82LXIYNagHowYF3ZDPTf+K215byNbi8qQBJSKy1Mz6olK2h531O8oq6HvTRH5xen+uPbVfWt9TNZXdX0bnqbj7RILAEXtubMyxA1fXN294/gPgyBry3A7cvhOvLCLABUf15oKjerOhqJQ7Xl/IpPlraNuyGY9fdhRn/G1KNN3sFZuix9OXBTtCRoYmPzplaVqDihOMLpPdm9b+EpEadWydy53fG8id36s6d/C+bZIOU162vgioCjSZaKoqVVDZ7WmZFhFpkBtO7w9A63C5/Yhpy+L3ri+rcBZ9vZVJ8+M7+Bsqdl2x2Oav3WGBS6lONRURaZAzD92X9391Mr06tmJrcRlfrt/Ot+7/oFq6ikrnzHuCprJkS/GXlFdQXFZZbe5Mosh6XxWVHjcBs6yikuys7JqySSNRUBGRBovsE9OmRQ6H9WjHlBtPZvn6IlZs3M5vX55XLX1JeQVfby6md8dWlFc6/+8/85i9YjMLV29h2Z9H1jpKbH1RKRAsyx87T6W0opIWOQoquxsFFRHZab07taJ3ON8lWVA56OY3ALjxzIMYul8Hxk2rWvwif+02+nVtU+O9l6wNRphtL62I66gvS9hbZWNRKdnZRtsWtdd8JLPUpyIiaTX9t6dx2bH7Jb32l0mL+NPEhXHn/jO79jnKSwqDoLKjtCKuppI4u37wH99i2O1v1+sdt5WU88WamtdEk9QpqIhIWuW1ac4fRh3G8jvO4q7zBtInrMFEOvbnFGyOS//Af/OjgSPRxqLSmOavirgRZWUVlRRuLeG7D33I6s07gPrvt3Lx36dyxt+maHviDFDzl4hkzLlH9uTcI4NZ93NWbGLUgx8mTVewcQfTlm3g8B7tuP652ezXsRWPX35U3JbKO0or4kZ/lVZUMmHOKj79ahNPf/xlg95rbhjYdpRV0Cq34b8G75u8mOXri7j7/EENzjvzy40c0q1NSs9tCvbMUonIbmdgr/Y8/+NjKSop54onpwPQvFkWJeWVXPZE/IpK+Wu34e4UlQa7UWZnGdvLyqOfIaipRNYey8mymLxbObBLzX00sbYVlyf95f715mLWbClmYK/2SfPd/dYXwfcGBpUtxWWc+/BHnHxQHv+4YljdGTJgyheFDOrdPmN9T2r+EpFdZljfjpx8cBeW/Xkki24bzqLbRpCdlXzk10PvLmFdWFPpvE8u20sr4lZOXr+tNLoUfklMB/5pd0+hvraWlCc9f+pd79ZYq9oZkcEFHy3Z9UvSQ7Aa9aVPTOPaZ2dl7BkKKiKyy5kZzZsFfSzP/M/RSdP8ZdIiRv9zJgDd27dkaWER05ZXTbD82bhZlIS/pL+uZX2y2mwtTh5UIpuOpVtkcEFJeeOsDBBZkSCTgxQUVESkUR2zfyc+veV0TujXucY0B+TtA8DSwiKG9G4PBPNXXp0bjBxL9S//30+Yz9yCTTVeLymvPbg0dFZ/Y28ylqntCeKekbE7i4jUU8fWufzzR0cz6foTuXXUoRzRs13c9e8dWbWpWE52FtecfCAAa7YEzWOJm4y9WcvSMLEjvmaHgwd2lFZw5xufsyOhhrKthppMxPbSmq9/+tXGaqPaGnvtssj2BJlc4UZBRUR2Gwft24ZLj+3DhGu+wSM/qFqMfGDP9jx+2VAgGLHVoXVurff5xfNzgGCk1YszC+KuFWzcEffZHZ755EseencJf39/ady1bTX0uURsr6WZ7LsPfcSpd70Xd66xayoRlRmMKgoqIrJbOvPQfVl023DeuP4EWuZmM6R3BwCuPvlAenVoGU331A+rj6KKdMCf+/BH/PKFOXHXTrjzv9XSRwYLJNZ4aupziVi7pYRrx81iYziXpi5l5Y07LybS7JXJ5i8NKRaR3VbzZtkcvG9bADq0zo0uTFlZ6dx9/kCO3r8TPdq35Lwje/JCQo2kz5jXoseVlU5WlrF+W3zQiIgMr43sBRNRV1B5+uPlvDJnFd3ateA3Iw+pMd2KDdv5eOn6aN9QY4n0ATXZ5i8zG25mi8ws38zGJLluZnZfeH2umQ2pK6+ZPWdms8Ov5WY2Ozzfx8x2xFwbm/g8EdkzZGUZ3x3Skx7tgxrLX84byOvXnVBj+khwWL5+e9Lrkb/ctxQnBpWyZMmjmmUHv0KL6mgmu/LpGfzqxbmsqyGo7SqRCkomm78yVlMxs2zgQeB0oACYbmYT3H1BTLIRBHvJ9yPYo/5h4Oja8rr7BTHPuAuIXfNhibsPylSZRGT3dUi3tjVeG3jrm/z4xP05uFswKfKVa77Btx+oWq6/OBziuyWhppKsTyW2XyQ3O2g2q61vBaqGEOevTb4cza4SCZ4VTbT5axiQ7+5LAcxsPDCKYA/5iFHA0+G2wlPNrL2ZdQP61JXXgrWyzwdOyWAZRKQJGXvJEN5dVMifv3s464tKKdxawoh73wfgkSlVnfD9uu7D5cf14cmPlgNQUhYEhUjzV3aWUVHpSYNKcVlVAIks2V9XTaVj61yWrSviqxpqSulW05pmkQpKUx1S3ANYEfO5IDxXnzT1yXsCsMbdF8ec62tms8zsPTNLWhc2s9FmNsPMZhQWFta/NCKy2xt+WDfuOPcIzIzO+zSvsfbSIiebXw8/mFbhIpeRmsaSwiKe/HBZNF2yPpXYRSsjQ5DrqqlEFNUyBDmdamrdivapZHAQWiaDSrK1FxKLWlOa+uS9CBgX83k10NvdBwM/B541s2r/otz9UXcf6u5D8/Lyanx5EdkzPHDxYG4/5zBeuOpYzODOc48AoGVuNpcf1weoWssL4PevLIj+8l0S01z1x1cX8I3/fYc/xyzdvy0MEnUFi/LwfonB5/Ovt/Djf86IrmGWLjXVVLyJj/4qAHrFfO4JJG6cUFOa3Nrymlkz4LtAdCC7u5cAJeHxTDNbAvQHZuxsQUSk6frWEd2jx8v+HL+t8fEHduahd5fUmHf2ik3R48c/CGowBRtXRs9Fmr02by+jpLwiuvRMooqwapDY8X/jC3P5bGWwA2ZNi1emoqaaSuR8eROdpzId6Gdmfc0sF7gQmJCQZgJwaTgK7Bhgs7uvrkfe04DP3T06htDM8sIOfsxsf4LO//iZTCIiMY4/sDN/PW8gZw/qXm2ZmM775LJ0XRErNmxnxYbkfSHbS4Kax9J1RRz+uzej5xOXb4n07a/fVr/5LDvLqzXsBGJrKLWtBrAzMlZTcfdyM7sGmARkA0+4+3wzuyq8PhaYCIwE8oHtwBW15Y25/YXEN30BnAjcamblQAVwlbtvQESkFt87smd0GZiBf3gz2lk/dL+OvDH/66STJSNiF7iMXYKlPKHTIlJTKQyHFOc2i/973pIv1Jyymlq34oNKanvJ1CWjkx/dfSJB4Ig9Nzbm2IGr65s35trlSc69BLy0E68rInu5T35zKu6wblsJO8oqeKOWNcSS+dWLc3h+RgHH7N8x7nykuSnS8V9aXom711ij2Fk19ZnExrrEdc7SRcu0iIiEWuRk0zI3m14dW9G/axuW/mkkD39/CN8ZGPTLtGle+9/hz88IWuSnLo1vJEm25tfzM1bEXN+54OLu3PP2F6zdWhx+Tp4uNtjUtQJzqhRURERqkJVljDi8G/ddNJhnrzyaX404GID9O7du0H1KyqoHldfnVdWCdnahyfmrtnDP24u5btxsoJaaSsz5HaWZGVesoCIiUg/HHdCZswd154wBXXnqh8M4Zv+OXDSsNx3rWDH52nGzWLu1+vIsOdlZ0RpF4pDiOSs2sWZL/Tcei/TRfLw02FemrtFfEKz2nAlaUFJEpJ7atMjh0UuDJfjHjz4WCDrZn/3kqxrzvDIncSZFoG2LHFYRLMOfGFRGPfghbVs0Y+7vz6zXe8Xmd/fqs/pCcTWVDAUV1VRERHbClSfsz36dWkUXt6zLBUODKXgvfVrA/FVbAJjx5Ub6jHmNBau2RCcobqljheSIikqPm09TVuFxwePzr7dEj2MXksxUR71qKiIiO6Fv59a8d+PJAKzdUsy7XxQyeeEaJs1fkzT9vu1aVDsXqc2MvO99Ztx8WvT89OUbyM3OqnVi5I0vzOFfs6omZJZVVMYFleH3vF+1ZUBMDaZYNRURkd1bl7YtOH9oLx75wVDeuuHEpGnat8rhtrMPizsXGwT+M7uquey8sR8z6sEPa31m4rDn0vLKGgcqxy7fkqldKFVTERHJgH5d20RrCFC1adiIw7pVqyXE/oJfmbDdcV2yE2ZOfrlhO51qGDwQu+R9ppZqUVAREdkFrju1H/27tok2f/Vo35KVm4IAsi5m+ZYXZqxImr8mibPxz37wQw7rkXx15tg4kqmaipq/RER2gRtO789ZR3SLfp78i29y81nVtyDemmRvlvy12+J2pdy8vYzfvPwZy9YVRXefjDVv5ZZq5yC+mW1nJ1zWRDUVEZFG0CInm/85YX8G925PUUkFB3bZh+PueCdp2tPufo+ubZvz8ZhTycoyXvy0gGc/+YoOrXLIasDCYbuiT0U1FRGRRnTkfh05sX8e3du35OObTuHmsw5h6k2nVku3ZksJ5zz8Ee7OxqKguWxrcTlJKio1io0j5QoqIiJ7tm7tWvI/J+zPvu1acP9Fg/nlGf3jrs9ZsYk/v/45D/w3Hwg69Ztl1f/X+K5o/lJQERHZDX17YHeuOaUfV57QF4AffSP4/uiUqm2iJn++NtrZf87gxB3Xq1Pzl4jIXu63Zw1g+R1nccu3BsSdf+j7Q+I+X3JM7zrvFTv6K1NDijMaVMxsuJktMrN8MxuT5LqZ2X3h9blmNqSuvGb2nJnNDr+Wm9nsmGs3hekXmVn9Fs0REWkiXr32Gzx48RD+cflRjDhsX3Kyqzrp92meU2O+yDL7sTtSJq43li4ZG/0Vbu37IHA6wV70081sgrsviEk2gmDb337A0cDDwNG15XX3C2KecRewOTweQLAj5KFAd+BtM+vv7plZi0BEZBc7rEc7DuvRLvr501tO57yxH/OzU/uxX6dWNeb71YtzOX9or7g+lcTdKdMlk0OKhwH57r4UwMzGA6OA2KAyCng63AFyqpm1N7NuQJ+68pqZAecDp8Tca7y7lwDLzCw/fIePM1dEEZHG06ZFDm9cX7UczOE92jG4d3sqKp1nElZOHvXAB8wp2Bz9XN4E56n0AGKnhhYQ1EbqStOjnnlPANa4++KYe01Ncq84ZjYaGA3Qu3fdbZAiIk3FK9d+Awg64Xt2aMUD7yymKFyNODagAJQ2wY76ZDNyEkNjTWnqk/ciYFwDn4e7P+ruQ919aF5eXpIsIiJNW052Fj856YC4Wkys7u1aNMmaSgHQK+ZzTyBxt5qa0uTWltfMmgHfBY5s4PNERPYavTq24q0bTmT0P2eybF0RAH8cdSjbSyvo2rb6EvzpkMmgMh3oZ2Z9gZUEnegXJ6SZAFwT9pkcDWx299VmVlhH3tOAz929IOFez5rZ3QQd9f2AaRkol4hIk9Gvaxv++8uT2FJcxv2TF3Pe0F60yMnO2PMyFlTcvdzMrgEmAdnAE+4+38yuCq+PBSYCI4F8YDtwRW15Y25/IfFNX4T3fp6gM78cuFojv0REAm1b5PDbswbUnXAnWewMy73N0KFDfcaMGY39GiIiTYqZzXT3ocmuaUa9iIikjYKKiIikjYKKiIikjYKKiIikjYKKiIikjYKKiIikjYKKiIikzV49TyWcuf/lTtyiM7AuTa/TFOxt5QWVeW+hMjfMfu6edPHEvTqo7Cwzm1HTBKA90d5WXlCZ9xYqc/qo+UtERNJGQUVERNJGQWXnPNrYL7CL7W3lBZV5b6Eyp4n6VEREJG1UUxERkbRRUBERkbRRUEmBmQ03s0Vmlm9mYxr7fdLFzHqZ2X/NbKGZzTez68LzHc3sLTNbHH7vEJPnpvDnsMjMzmy8t0+dmWWb2SwzezX8vEeXF8DM2pvZi2b2efjf+9g9udxmdkP4b3qemY0zsxZ7YnnN7AkzW2tm82LONbicZnakmX0WXrvPzKzeL+Hu+mrAF8FOlEuA/YFcYA4woLHfK01l6wYMCY/bAF8AA4A7gTHh+THA/4bHA8LyNwf6hj+X7MYuRwrl/jnwLPBq+HmPLm9YlqeA/wmPc4H2e2q5gR7AMqBl+Pl54PI9sbzAicAQYF7MuQaXk2Ar9mMBA14HRtT3HVRTabhhQL67L3X3UmA8MKqR3ykt3H21u38aHm8FFhL8DzmK4JcQ4fezw+NRwHh3L3H3ZQTbQg/bpS+9k8ysJ3AW8FjM6T22vABm1pbgl8/jAO5e6u6b2LPL3QxoaWbNgFbAKvbA8rr7FGBDwukGldPMugFt3f1jDyLM0zF56qSg0nA9gBUxnwvCc3sUM+sDDAY+Abq6+2oIAg/QJUy2J/ws7gF+BVTGnNuTywtBLbsQ+EfY7PeYmbVmDy23u68E/gp8BawGNrv7m+yh5U2ioeXsER4nnq8XBZWGS9a2uEeNyzazfYCXgOvdfUttSZOcazI/CzP7FrDW3WfWN0uSc02mvDGaETSRPOzug4EigmaRmjTpcod9CKMImni6A63N7JLasiQ512TK2wA1lXOnyq+g0nAFQK+Yzz0JqtJ7BDPLIQgoz7j7v8LTa8IqMeH3teH5pv6zOB74jpktJ2jGPMXM/o89t7wRBUCBu38Sfn6RIMjsqeU+DVjm7oXuXgb8CziOPbe8iRpazoLwOPF8vSioNNx0oJ+Z9TWzXOBCYEIjv1NahCM8HgcWuvvdMZcmAJeFx5cB/4k5f6GZNTezvkA/gg6+JsHdb3L3nu7eh+C/4zvufgl7aHkj3P1rYIWZHRSeOhVYwJ5b7q+AY8ysVfhv/FSC/sI9tbyJGlTOsIlsq5kdE/68Lo3JU7fGHq3QFL+AkQQjo5YAv23s90ljub5BUM2dC8wOv0YCnYDJwOLwe8eYPL8Nfw6LaMAIkd3tCziJqtFfe0N5BwEzwv/W/wY67MnlBv4AfA7MA/5JMOJpjysvMI6g36iMoMbxo1TKCQwNf1ZLgAcIV1+pz5eWaRERkbRR85eIiKSNgoqIiKSNgoqIiKSNgoqIiKSNgoqIiKSNgopIE2VmJ0VWVhbZXSioiIhI2iioiGSYmV1iZtPMbLaZPRLu37LNzO4ys0/NbLKZ5YVpB5nZVDOba2YvR/a+MLMDzextM5sT5jkgvP0+MfuiPNOgfS9EMkBBRSSDzOwQ4ALgeHcfBFQA3wdaA5+6+xDgPeB3YZangV+7+xHAZzHnnwEedPeBBOtWrQ7PDwauJ9gbY3+C9cxEGk2zxn4BkT3cqcCRwPSwEtGSYEG/SuC5MM3/Af8ys3ZAe3d/Lzz/FPCCmbUBerj7ywDuXgwQ3m+auxeEn2cDfYAPMl4qkRooqIhklgFPuftNcSfNbklIV9t6SbU1aZXEHFeg/6elkan5SySzJgPfM7MuEN0vfD+C//e+F6a5GPjA3TcDG83shPD8D4D3PNjTpsDMzg7v0dzMWu3KQojUl/6qEckgd19gZjcDb5pZFsHqsVcTbIx1qJnNBDYT9LtAsDT52DBoLAWuCM//AHjEzG4N73HeLiyGSL1plWKRRmBm29x9n8Z+D5F0U/OXiIikjWoqIiKSNqqpiIhI2iioiIhI2iioiIhI2iioiIhI2iioiIhI2vx/ucmuz5bb+1cAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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" ] @@ -2550,7 +6610,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -2568,7 +6628,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -2591,7 +6651,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -2610,7 +6670,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -2640,19 +6700,19 @@ " \n", " \n", " 0\n", - " 225430\n", + " 606305\n", " \n", " \n", " 1\n", - " 248594\n", + " 457613\n", " \n", " \n", " 2\n", - " 230231\n", + " 366556\n", " \n", " \n", " 3\n", - " 243977\n", + " 642477\n", " \n", " \n", "\n", @@ -2660,13 +6720,13 @@ ], "text/plain": [ " Count\n", - "0 225430\n", - "1 248594\n", - "2 230231\n", - "3 243977" + "0 606305\n", + "1 457613\n", + "2 366556\n", + "3 642477" ] }, - "execution_count": 59, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -2677,7 +6737,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -2685,7 +6745,7 @@ "output_type": "stream", "text": [ "The root mean squared error is 115854.5707848853.\n", - "The root mean squared error is 215181.95938960588.\n" + "The root mean squared error is 100206.57175554904.\n" ] } ], diff --git a/multivar_simple_gru.ipynb b/multivar_simple_gru.ipynb index 5060529..7eb6aa7 100644 --- a/multivar_simple_gru.ipynb +++ b/multivar_simple_gru.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 112, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -213,7 +213,7 @@ "[984 rows x 1 columns]" ] }, - "execution_count": 115, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -259,7 +259,7 @@ "(984, 1)" ] }, - "execution_count": 116, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -387,7 +387,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 118, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -504,7 +504,7 @@ "[984 rows x 2 columns]" ] }, - "execution_count": 119, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -516,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 162, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -631,7 +631,7 @@ "[852 rows x 2 columns]" ] }, - "execution_count": 121, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -647,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 164, "metadata": {}, "outputs": [], "source": [ @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -702,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ @@ -713,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -879,7 +879,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 126, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -894,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 169, "metadata": {}, "outputs": [ { @@ -1012,7 +1012,7 @@ "[852 rows x 3 columns]" ] }, - "execution_count": 127, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -1025,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -1155,7 +1155,7 @@ "[852 rows x 4 columns]" ] }, - "execution_count": 128, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -1168,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -1310,7 +1310,7 @@ "[852 rows x 5 columns]" ] }, - "execution_count": 129, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -1323,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -1477,7 +1477,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 130, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -1490,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -1656,7 +1656,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 131, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -1670,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -1836,7 +1836,7 @@ "[852 rows x 7 columns]" ] }, - "execution_count": 132, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -1855,7 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 175, "metadata": {}, "outputs": [ { @@ -2019,7 +2019,7 @@ "[852 rows x 6 columns]" ] }, - "execution_count": 133, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -2032,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -2048,7 +2048,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -2100,7 +2100,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -2141,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -2228,7 +2228,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -2256,7 +2256,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 181, "metadata": {}, "outputs": [], "source": [ @@ -2265,7 +2265,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -2292,129 +2292,129 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/1000\n", - "1/1 - 6s - loss: 0.1902 - root_mean_squared_error: 0.4361 - val_loss: 0.1280 - val_root_mean_squared_error: 0.3578\n", + "Epoch 1/5000\n", + "1/1 - 5s - loss: 0.0206 - root_mean_squared_error: 0.1437 - 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", - "1/1 - 0s - loss: 0.1436 - root_mean_squared_error: 0.3789 - val_loss: 0.0922 - val_root_mean_squared_error: 0.3036\n", + "Epoch 2/5000\n", + "1/1 - 0s - loss: 0.0115 - root_mean_squared_error: 0.1072 - val_loss: 0.0453 - val_root_mean_squared_error: 0.2129\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 3/1000\n", - "1/1 - 0s - loss: 0.1013 - root_mean_squared_error: 0.3182 - val_loss: 0.0638 - val_root_mean_squared_error: 0.2526\n", + "Epoch 3/5000\n", + "1/1 - 0s - loss: 0.0128 - root_mean_squared_error: 0.1132 - val_loss: 0.0458 - val_root_mean_squared_error: 0.2140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 4/1000\n", - "1/1 - 0s - loss: 0.0653 - root_mean_squared_error: 0.2555 - val_loss: 0.0448 - val_root_mean_squared_error: 0.2117\n", + "Epoch 4/5000\n", + "1/1 - 0s - loss: 0.0153 - root_mean_squared_error: 0.1237 - val_loss: 0.0452 - val_root_mean_squared_error: 0.2126\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 5/1000\n", - "1/1 - 0s - loss: 0.0377 - root_mean_squared_error: 0.1941 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 5/5000\n", + "1/1 - 0s - loss: 0.0145 - root_mean_squared_error: 0.1206 - val_loss: 0.0444 - val_root_mean_squared_error: 0.2108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 6/1000\n", - "1/1 - 0s - loss: 0.0199 - root_mean_squared_error: 0.1409 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "Epoch 6/5000\n", + "1/1 - 0s - loss: 0.0123 - root_mean_squared_error: 0.1111 - val_loss: 0.0449 - val_root_mean_squared_error: 0.2119\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 7/1000\n", - "1/1 - 0s - loss: 0.0121 - root_mean_squared_error: 0.1100 - val_loss: 0.0482 - val_root_mean_squared_error: 0.2197\n", + "Epoch 7/5000\n", + "1/1 - 0s - loss: 0.0108 - root_mean_squared_error: 0.1039 - val_loss: 0.0469 - val_root_mean_squared_error: 0.2167\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 8/1000\n", - "1/1 - 0s - loss: 0.0130 - root_mean_squared_error: 0.1142 - val_loss: 0.0625 - val_root_mean_squared_error: 0.2499\n", + "Epoch 8/5000\n", + "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1037 - val_loss: 0.0494 - val_root_mean_squared_error: 0.2222\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 9/1000\n", - "1/1 - 0s - loss: 0.0195 - root_mean_squared_error: 0.1396 - val_loss: 0.0761 - val_root_mean_squared_error: 0.2759\n", + "Epoch 9/5000\n", + "1/1 - 0s - loss: 0.0116 - root_mean_squared_error: 0.1078 - val_loss: 0.0506 - val_root_mean_squared_error: 0.2250\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 10/1000\n", - "1/1 - 0s - loss: 0.0274 - root_mean_squared_error: 0.1656 - val_loss: 0.0857 - val_root_mean_squared_error: 0.2928\n", + "Epoch 10/5000\n", + "1/1 - 0s - loss: 0.0122 - root_mean_squared_error: 0.1106 - val_loss: 0.0501 - val_root_mean_squared_error: 0.2237\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 11/1000\n", - "1/1 - 0s - loss: 0.0335 - root_mean_squared_error: 0.1832 - val_loss: 0.0900 - val_root_mean_squared_error: 0.2999\n", + "Epoch 11/5000\n", + "1/1 - 0s - loss: 0.0120 - root_mean_squared_error: 0.1093 - val_loss: 0.0481 - val_root_mean_squared_error: 0.2194\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 12/1000\n", - "1/1 - 0s - loss: 0.0364 - root_mean_squared_error: 0.1907 - val_loss: 0.0891 - val_root_mean_squared_error: 0.2984\n", + "Epoch 12/5000\n", + "1/1 - 0s - loss: 0.0111 - root_mean_squared_error: 0.1053 - val_loss: 0.0458 - val_root_mean_squared_error: 0.2140\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 13/1000\n", - "1/1 - 0s - loss: 0.0358 - root_mean_squared_error: 0.1892 - val_loss: 0.0842 - val_root_mean_squared_error: 0.2902\n", + "Epoch 13/5000\n", + "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1013 - val_loss: 0.0438 - val_root_mean_squared_error: 0.2093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 14/1000\n", - "1/1 - 0s - loss: 0.0326 - root_mean_squared_error: 0.1806 - val_loss: 0.0768 - val_root_mean_squared_error: 0.2772\n", + "Epoch 14/5000\n", + "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0997 - val_loss: 0.0425 - val_root_mean_squared_error: 0.2062\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 15/1000\n", - "1/1 - 0s - loss: 0.0279 - root_mean_squared_error: 0.1669 - val_loss: 0.0683 - val_root_mean_squared_error: 0.2613\n", + "Epoch 15/5000\n", + "1/1 - 0s - loss: 0.0101 - root_mean_squared_error: 0.1006 - 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 16/1000\n", - "1/1 - 0s - loss: 0.0226 - root_mean_squared_error: 0.1505 - val_loss: 0.0598 - val_root_mean_squared_error: 0.2445\n", + "Epoch 16/5000\n", + "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1023 - 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 17/1000\n", - "1/1 - 0s - loss: 0.0178 - root_mean_squared_error: 0.1335 - val_loss: 0.0521 - val_root_mean_squared_error: 0.2283\n", + "Epoch 17/5000\n", + "1/1 - 0s - loss: 0.0106 - root_mean_squared_error: 0.1029 - 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 18/1000\n", - "1/1 - 0s - loss: 0.0140 - root_mean_squared_error: 0.1184 - val_loss: 0.0459 - val_root_mean_squared_error: 0.2143\n", + "Epoch 18/5000\n", + "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1018 - 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 19/1000\n", - "1/1 - 0s - loss: 0.0116 - root_mean_squared_error: 0.1075 - val_loss: 0.0413 - val_root_mean_squared_error: 0.2033\n", + "Epoch 19/5000\n", + "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0996 - 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 20/1000\n", - "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1022 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 20/5000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0976 - val_loss: 0.0426 - val_root_mean_squared_error: 0.2065\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 21/1000\n", - "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1023 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 21/5000\n", + "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0968 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2087\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 22/1000\n", - "1/1 - 0s - loss: 0.0113 - root_mean_squared_error: 0.1062 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "Epoch 22/5000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0973 - val_loss: 0.0442 - val_root_mean_squared_error: 0.2103\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 23/1000\n", - "1/1 - 0s - loss: 0.0125 - root_mean_squared_error: 0.1118 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 23/5000\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0980 - val_loss: 0.0443 - val_root_mean_squared_error: 0.2105\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 24/1000\n", - "1/1 - 0s - loss: 0.0137 - root_mean_squared_error: 0.1172 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 24/5000\n", + "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0981 - val_loss: 0.0438 - val_root_mean_squared_error: 0.2093\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 25/1000\n", - "1/1 - 0s - loss: 0.0147 - root_mean_squared_error: 0.1212 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 25/5000\n", + "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0973 - val_loss: 0.0428 - val_root_mean_squared_error: 0.2070\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 26/1000\n", - "1/1 - 0s - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 26/5000\n", + "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0960 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2043\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 27/1000\n", - "1/1 - 0s - loss: 0.0152 - root_mean_squared_error: 0.1233 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 27/5000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - 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 28/1000\n", - "1/1 - 0s - loss: 0.0148 - root_mean_squared_error: 0.1215 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 28/5000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0946 - val_loss: 0.0400 - val_root_mean_squared_error: 0.2000\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 29/1000\n", - "1/1 - 0s - loss: 0.0139 - root_mean_squared_error: 0.1181 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 29/5000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1989\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 30/1000\n", - "1/1 - 0s - loss: 0.0129 - root_mean_squared_error: 0.1138 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 30/5000\n", + "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0951 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1984\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 31/1000\n", - "1/1 - 0s - loss: 0.0119 - root_mean_squared_error: 0.1091 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 31/5000\n", + "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0950 - 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 32/1000\n", - "1/1 - 0s - loss: 0.0110 - root_mean_squared_error: 0.1048 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "Epoch 32/5000\n", + "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 33/1000\n", - "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1013 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "Epoch 33/5000\n", + "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - 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 34/1000\n", - "1/1 - 0s - loss: 0.0098 - root_mean_squared_error: 0.0992 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1987\n", + "Epoch 34/5000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - 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 35/1000\n", - "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0984 - val_loss: 0.0409 - val_root_mean_squared_error: 0.2023\n", + "Epoch 35/5000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - 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 36/1000\n", - "1/1 - 0s - loss: 0.0097 - root_mean_squared_error: 0.0987 - val_loss: 0.0424 - val_root_mean_squared_error: 0.2058\n", + "Epoch 36/5000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - 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 37/1000\n", - "1/1 - 0s - loss: 0.0100 - root_mean_squared_error: 0.0999 - val_loss: 0.0436 - val_root_mean_squared_error: 0.2088\n", + "Epoch 37/5000\n", + "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - 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 38/1000\n", - "1/1 - 0s - loss: 0.0103 - root_mean_squared_error: 0.1013 - val_loss: 0.0445 - val_root_mean_squared_error: 0.2111\n", + "Epoch 38/5000\n", + "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - 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 39/1000\n", - "1/1 - 0s - loss: 0.0105 - root_mean_squared_error: 0.1026 - val_loss: 0.0451 - val_root_mean_squared_error: 0.2124\n", + "Epoch 39/5000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2422,122 +2422,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 40/1000\n", - "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1034 - val_loss: 0.0453 - val_root_mean_squared_error: 0.2128\n", + "Epoch 40/5000\n", + "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0919 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 41/1000\n", - "1/1 - 0s - loss: 0.0107 - root_mean_squared_error: 0.1036 - val_loss: 0.0450 - val_root_mean_squared_error: 0.2122\n", + "Epoch 41/5000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 42/1000\n", - "1/1 - 0s - loss: 0.0106 - root_mean_squared_error: 0.1031 - val_loss: 0.0445 - val_root_mean_squared_error: 0.2109\n", + "Epoch 42/5000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 43/1000\n", - "1/1 - 0s - loss: 0.0104 - root_mean_squared_error: 0.1021 - val_loss: 0.0437 - val_root_mean_squared_error: 0.2090\n", + "Epoch 43/5000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 44/1000\n", - "1/1 - 0s - loss: 0.0102 - root_mean_squared_error: 0.1008 - val_loss: 0.0427 - val_root_mean_squared_error: 0.2066\n", + "Epoch 44/5000\n", + "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 45/1000\n", - "1/1 - 0s - loss: 0.0099 - root_mean_squared_error: 0.0994 - val_loss: 0.0417 - val_root_mean_squared_error: 0.2041\n", + "Epoch 45/5000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 46/1000\n", - "1/1 - 0s - loss: 0.0096 - root_mean_squared_error: 0.0982 - val_loss: 0.0407 - val_root_mean_squared_error: 0.2017\n", + "Epoch 46/5000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 47/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1994\n", + "Epoch 47/5000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - 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 48/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1975\n", + "Epoch 48/5000\n", + "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0908 - 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 49/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0965 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "Epoch 49/5000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 50/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0966 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "Epoch 50/5000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 51/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0969 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "Epoch 51/5000\n", + "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 52/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0972 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 52/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0903 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 53/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "Epoch 53/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1943\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 54/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0975 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "Epoch 54/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 55/1000\n", - "1/1 - 0s - loss: 0.0095 - root_mean_squared_error: 0.0974 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 55/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 56/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0971 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "Epoch 56/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - 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 57/1000\n", - "1/1 - 0s - loss: 0.0094 - root_mean_squared_error: 0.0967 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1943\n", + "Epoch 57/5000\n", + "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 58/1000\n", - "1/1 - 0s - loss: 0.0093 - root_mean_squared_error: 0.0963 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "Epoch 58/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1948\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 59/1000\n", - "1/1 - 0s - loss: 0.0092 - root_mean_squared_error: 0.0958 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 59/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1951\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 60/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0955 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "Epoch 60/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - 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 61/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0953 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1979\n", + "Epoch 61/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - 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 62/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0951 - val_loss: 0.0395 - val_root_mean_squared_error: 0.1988\n", + "Epoch 62/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 63/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0398 - val_root_mean_squared_error: 0.1996\n", + "Epoch 63/5000\n", + "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1933\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 64/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2002\n", + "Epoch 64/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 65/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "Epoch 65/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 66/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0403 - val_root_mean_squared_error: 0.2007\n", + "Epoch 66/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 67/1000\n", - "1/1 - 0s - loss: 0.0091 - root_mean_squared_error: 0.0952 - val_loss: 0.0402 - val_root_mean_squared_error: 0.2006\n", + "Epoch 67/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - 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 68/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0951 - val_loss: 0.0401 - val_root_mean_squared_error: 0.2003\n", + "Epoch 68/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 69/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0949 - val_loss: 0.0399 - val_root_mean_squared_error: 0.1998\n", + "Epoch 69/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - 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 70/1000\n", - "1/1 - 0s - loss: 0.0090 - root_mean_squared_error: 0.0947 - val_loss: 0.0397 - val_root_mean_squared_error: 0.1992\n", + "Epoch 70/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 71/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0946 - val_loss: 0.0394 - val_root_mean_squared_error: 0.1986\n", + "Epoch 71/5000\n", + "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 72/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0944 - val_loss: 0.0392 - val_root_mean_squared_error: 0.1980\n", + "Epoch 72/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 73/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0943 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1974\n", + "Epoch 73/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - 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 74/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0942 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "Epoch 74/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 75/1000\n", - "1/1 - 0s - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n", + "Epoch 75/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 76/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0941 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 76/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 77/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "Epoch 77/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 78/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0940 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 78/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, @@ -2545,122 +2545,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 79/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 79/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 80/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0939 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "Epoch 80/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 81/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0938 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 81/5000\n", + "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 82/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0937 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "Epoch 82/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 83/1000\n", - "1/1 - 0s - loss: 0.0088 - root_mean_squared_error: 0.0936 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 83/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 84/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0935 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "Epoch 84/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 85/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0934 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1971\n", + "Epoch 85/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 86/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "Epoch 86/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 87/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0933 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1976\n", + "Epoch 87/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 88/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "Epoch 88/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - 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 89/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0932 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "Epoch 89/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 90/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1978\n", + "Epoch 90/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 91/1000\n", - "1/1 - 0s - loss: 0.0087 - root_mean_squared_error: 0.0931 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1977\n", + "Epoch 91/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 92/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0930 - val_loss: 0.0391 - val_root_mean_squared_error: 0.1976\n", + "Epoch 92/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 93/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0390 - val_root_mean_squared_error: 0.1974\n", + "Epoch 93/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - 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 94/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0929 - val_loss: 0.0389 - val_root_mean_squared_error: 0.1972\n", + "Epoch 94/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 95/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0928 - val_loss: 0.0388 - val_root_mean_squared_error: 0.1970\n", + "Epoch 95/5000\n", + "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - 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 96/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1968\n", + "Epoch 96/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 97/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0927 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "Epoch 97/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 98/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 98/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - 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 99/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0926 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1963\n", + "Epoch 99/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - 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 100/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "Epoch 100/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 101/1000\n", - "1/1 - 0s - loss: 0.0086 - root_mean_squared_error: 0.0925 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "Epoch 101/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 102/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "Epoch 102/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - 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 103/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0924 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "Epoch 103/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - 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 104/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "Epoch 104/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - 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 105/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0923 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n", + "Epoch 105/5000\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 106/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 106/5000\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 107/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0922 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 107/5000\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 108/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "Epoch 108/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 109/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0921 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "Epoch 109/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 110/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "Epoch 110/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 111/1000\n", - "1/1 - 0s - loss: 0.0085 - root_mean_squared_error: 0.0920 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "Epoch 111/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 112/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "Epoch 112/5000\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 113/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0919 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1967\n", + "Epoch 113/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 114/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0387 - val_root_mean_squared_error: 0.1966\n", + "Epoch 114/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 115/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0918 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 115/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 116/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1965\n", + "Epoch 116/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 117/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0917 - val_loss: 0.0386 - val_root_mean_squared_error: 0.1964\n" + "Epoch 117/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n" ] }, { @@ -2668,122 +2668,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 118/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1963\n", + "Epoch 118/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 119/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1962\n", + "Epoch 119/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - 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 120/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0916 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "Epoch 120/5000\n", + "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - 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 121/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0385 - val_root_mean_squared_error: 0.1961\n", + "Epoch 121/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - 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 122/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0915 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 122/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - 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 123/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 123/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - 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 124/1000\n", - "1/1 - 0s - loss: 0.0084 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 124/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - 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 125/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0914 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 125/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 126/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 126/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 127/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0913 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 127/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 128/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 128/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 129/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 129/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 130/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0912 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 130/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - 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 131/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 131/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - 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 132/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1961\n", + "Epoch 132/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - 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 133/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0911 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 133/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 134/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 134/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 135/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1960\n", + "Epoch 135/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 136/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0910 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "Epoch 136/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 137/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0384 - val_root_mean_squared_error: 0.1959\n", + "Epoch 137/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - 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 138/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "Epoch 138/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - 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 139/1000\n", - "1/1 - 0s - loss: 0.0083 - root_mean_squared_error: 0.0909 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1958\n", + "Epoch 139/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 140/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 140/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 141/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 141/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 142/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0908 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 142/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 143/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1957\n", + "Epoch 143/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 144/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 144/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 145/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0907 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 145/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 146/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 146/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 147/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 147/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 148/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0906 - val_loss: 0.0383 - val_root_mean_squared_error: 0.1956\n", + "Epoch 148/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - 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 149/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1956\n", + "Epoch 149/5000\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 150/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1956\n", + "Epoch 150/5000\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 151/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "Epoch 151/5000\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 152/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0905 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "Epoch 152/5000\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 153/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "Epoch 153/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 154/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1955\n", + "Epoch 154/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 155/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0904 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "Epoch 155/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 156/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n" + "Epoch 156/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n" ] }, { @@ -2791,122 +2791,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 157/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1954\n", + "Epoch 157/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 158/1000\n", - "1/1 - 0s - loss: 0.0082 - root_mean_squared_error: 0.0903 - val_loss: 0.0382 - val_root_mean_squared_error: 0.1953\n", + "Epoch 158/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 159/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0903 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "Epoch 159/5000\n", + "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 160/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1953\n", + "Epoch 160/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 161/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "Epoch 161/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 162/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "Epoch 162/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 163/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0902 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "Epoch 163/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 164/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1952\n", + "Epoch 164/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 165/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "Epoch 165/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 166/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "Epoch 166/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 167/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0901 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "Epoch 167/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 168/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0381 - val_root_mean_squared_error: 0.1951\n", + "Epoch 168/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 169/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1951\n", + "Epoch 169/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 170/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "Epoch 170/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 171/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0900 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "Epoch 171/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 172/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1950\n", + "Epoch 172/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 173/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "Epoch 173/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 174/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "Epoch 174/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - 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 175/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0899 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "Epoch 175/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - 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 176/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1949\n", + "Epoch 176/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 177/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0380 - val_root_mean_squared_error: 0.1948\n", + "Epoch 177/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 178/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1948\n", + "Epoch 178/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 179/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1948\n", + "Epoch 179/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 180/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0898 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "Epoch 180/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 181/1000\n", - "1/1 - 0s - loss: 0.0081 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "Epoch 181/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 182/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "Epoch 182/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 183/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1947\n", + "Epoch 183/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 184/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "Epoch 184/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 185/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0897 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "Epoch 185/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 186/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "Epoch 186/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 187/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0379 - val_root_mean_squared_error: 0.1946\n", + "Epoch 187/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 188/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "Epoch 188/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 189/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "Epoch 189/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 190/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0896 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "Epoch 190/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 191/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n", + "Epoch 191/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 192/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "Epoch 192/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 193/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "Epoch 193/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 194/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n", + "Epoch 194/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 195/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0895 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1944\n" + "Epoch 195/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n" ] }, { @@ -2914,122 +2914,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 196/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "Epoch 196/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 197/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1943\n", + "Epoch 197/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 198/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1943\n", + "Epoch 198/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 199/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "Epoch 199/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 200/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "Epoch 200/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 201/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0894 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "Epoch 201/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 202/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1942\n", + "Epoch 202/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 203/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "Epoch 203/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 204/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "Epoch 204/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 205/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "Epoch 205/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 206/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1941\n", + "Epoch 206/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 207/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0893 - val_loss: 0.0377 - val_root_mean_squared_error: 0.1940\n", + "Epoch 207/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 208/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "Epoch 208/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 209/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "Epoch 209/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 210/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1940\n", + "Epoch 210/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 211/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "Epoch 211/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - 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 212/1000\n", - "1/1 - 0s - loss: 0.0080 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "Epoch 212/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - 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 213/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0892 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "Epoch 213/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - 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 214/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1939\n", + "Epoch 214/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - 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 215/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "Epoch 215/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 216/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0376 - val_root_mean_squared_error: 0.1938\n", + "Epoch 216/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 217/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "Epoch 217/5000\n", + "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 218/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1938\n", + "Epoch 218/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 219/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "Epoch 219/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 220/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0891 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "Epoch 220/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 221/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1937\n", + "Epoch 221/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 222/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "Epoch 222/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 223/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "Epoch 223/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 224/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "Epoch 224/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 225/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1936\n", + "Epoch 225/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 226/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0375 - val_root_mean_squared_error: 0.1935\n", + "Epoch 226/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 227/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0890 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "Epoch 227/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 228/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "Epoch 228/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 229/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1935\n", + "Epoch 229/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 230/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "Epoch 230/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 231/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "Epoch 231/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 232/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "Epoch 232/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 233/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1934\n", + "Epoch 233/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - 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 234/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0889 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n" + "Epoch 234/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n" ] }, { @@ -3037,122 +3037,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 235/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "Epoch 235/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 236/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "Epoch 236/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 237/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0374 - val_root_mean_squared_error: 0.1933\n", + "Epoch 237/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 238/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 238/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 239/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 239/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 240/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 240/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 241/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1932\n", + "Epoch 241/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 242/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0888 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "Epoch 242/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - 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 243/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "Epoch 243/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - 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 244/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "Epoch 244/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - 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 245/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1931\n", + "Epoch 245/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 246/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "Epoch 246/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 247/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0373 - val_root_mean_squared_error: 0.1930\n", + "Epoch 247/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 248/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "Epoch 248/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 249/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1930\n", + "Epoch 249/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 250/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "Epoch 250/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 251/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0887 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "Epoch 251/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 252/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "Epoch 252/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 253/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1929\n", + "Epoch 253/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 254/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "Epoch 254/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 255/1000\n", - "1/1 - 0s - loss: 0.0079 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "Epoch 255/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 256/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "Epoch 256/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 257/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0372 - val_root_mean_squared_error: 0.1928\n", + "Epoch 257/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 258/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "Epoch 258/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 259/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0886 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "Epoch 259/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 260/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "Epoch 260/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 261/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1927\n", + "Epoch 261/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 262/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "Epoch 262/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 263/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "Epoch 263/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - 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 264/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "Epoch 264/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 265/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1926\n", + "Epoch 265/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 266/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "Epoch 266/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 267/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "Epoch 267/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 268/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0371 - val_root_mean_squared_error: 0.1925\n", + "Epoch 268/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 269/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0885 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "Epoch 269/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 270/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1925\n", + "Epoch 270/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 271/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "Epoch 271/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 272/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "Epoch 272/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 273/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n" + "Epoch 273/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n" ] }, { @@ -3160,122 +3160,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 274/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1924\n", + "Epoch 274/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 275/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "Epoch 275/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - 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 276/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "Epoch 276/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - 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 277/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "Epoch 277/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - 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 278/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1923\n", + "Epoch 278/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - 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 279/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0884 - val_loss: 0.0370 - val_root_mean_squared_error: 0.1922\n", + "Epoch 279/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - 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 280/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "Epoch 280/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 281/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "Epoch 281/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 282/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1922\n", + "Epoch 282/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 283/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "Epoch 283/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 284/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "Epoch 284/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 285/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "Epoch 285/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - 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 286/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "Epoch 286/5000\n", + "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0851 - 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 287/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1921\n", + "Epoch 287/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - 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 288/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "Epoch 288/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - 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 289/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0883 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "Epoch 289/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 290/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0369 - val_root_mean_squared_error: 0.1920\n", + "Epoch 290/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 291/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1920\n", + "Epoch 291/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 292/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "Epoch 292/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 293/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "Epoch 293/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 294/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "Epoch 294/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - 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 295/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1919\n", + "Epoch 295/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - 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 296/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "Epoch 296/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - 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 297/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "Epoch 297/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 298/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "Epoch 298/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 299/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "Epoch 299/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 300/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1918\n", + "Epoch 300/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 301/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1917\n", + "Epoch 301/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 302/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0368 - val_root_mean_squared_error: 0.1917\n", + "Epoch 302/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 303/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "Epoch 303/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 304/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1917\n", + "Epoch 304/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 305/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "Epoch 305/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - 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 306/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "Epoch 306/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 307/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "Epoch 307/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 308/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "Epoch 308/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 309/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1916\n", + "Epoch 309/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 310/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "Epoch 310/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 311/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0881 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "Epoch 311/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 312/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n" + "Epoch 312/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n" ] }, { @@ -3283,122 +3283,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 313/1000\n", - "1/1 - 0s - loss: 0.0078 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "Epoch 313/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 314/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0367 - val_root_mean_squared_error: 0.1915\n", + "Epoch 314/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - 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 315/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "Epoch 315/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 316/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "Epoch 316/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 317/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "Epoch 317/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 318/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1914\n", + "Epoch 318/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 319/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "Epoch 319/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 320/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "Epoch 320/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 321/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "Epoch 321/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 322/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0880 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "Epoch 322/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 323/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1913\n", + "Epoch 323/5000\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 324/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "Epoch 324/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - 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 325/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "Epoch 325/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - 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 326/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0366 - val_root_mean_squared_error: 0.1912\n", + "Epoch 326/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - 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 327/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1912\n", + "Epoch 327/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 328/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1912\n", + "Epoch 328/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 329/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "Epoch 329/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 330/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "Epoch 330/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - 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 331/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "Epoch 331/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - 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 332/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1911\n", + "Epoch 332/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - 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 333/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 333/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 334/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 334/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 335/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0879 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 335/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 336/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 336/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 337/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1910\n", + "Epoch 337/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - 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 338/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1909\n", + "Epoch 338/5000\n", + "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - 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 339/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0365 - val_root_mean_squared_error: 0.1909\n", + "Epoch 339/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - 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 340/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "Epoch 340/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - 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 341/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "Epoch 341/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - 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 342/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1909\n", + "Epoch 342/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 343/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "Epoch 343/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 344/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "Epoch 344/5000\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 345/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "Epoch 345/5000\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 346/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "Epoch 346/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - 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 347/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0878 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1908\n", + "Epoch 347/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 348/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "Epoch 348/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 349/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "Epoch 349/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 350/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "Epoch 350/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 351/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n" + "Epoch 351/5000\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" ] }, { @@ -3406,122 +3406,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 352/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0364 - val_root_mean_squared_error: 0.1907\n", + "Epoch 352/5000\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 353/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 353/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 354/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 354/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 355/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 355/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 356/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 356/5000\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 357/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1906\n", + "Epoch 357/5000\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 358/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "Epoch 358/5000\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 359/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "Epoch 359/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 360/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0877 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "Epoch 360/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 361/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "Epoch 361/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 362/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1905\n", + "Epoch 362/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - 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 363/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "Epoch 363/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - 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 364/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "Epoch 364/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - 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 365/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0363 - val_root_mean_squared_error: 0.1904\n", + "Epoch 365/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 366/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "Epoch 366/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1829\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 367/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1904\n", + "Epoch 367/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - 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 368/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 368/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - 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 369/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 369/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - 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 370/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 370/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 371/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 371/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 372/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 372/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 373/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0876 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1903\n", + "Epoch 373/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 374/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 374/5000\n", + "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 375/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 375/5000\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 376/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 376/5000\n", + "1/1 - 0s - loss: 0.0070 - 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 377/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 377/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - 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 378/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1902\n", + "Epoch 378/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 379/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0362 - val_root_mean_squared_error: 0.1901\n", + "Epoch 379/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 380/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "Epoch 380/5000\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 381/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "Epoch 381/5000\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 382/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "Epoch 382/5000\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 383/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1901\n", + "Epoch 383/5000\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 384/1000\n", - "1/1 - 0s - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 384/5000\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 385/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 385/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 386/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0875 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 386/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 387/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 387/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - 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 388/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 388/5000\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 389/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1900\n", + "Epoch 389/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 390/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n" + "Epoch 390/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n" ] }, { @@ -3529,122 +3529,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 391/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "Epoch 391/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 392/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "Epoch 392/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1818\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 393/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0361 - val_root_mean_squared_error: 0.1899\n", + "Epoch 393/5000\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 394/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1899\n", + "Epoch 394/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 395/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 395/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 396/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 396/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 397/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 397/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 398/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 398/5000\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 399/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0874 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 399/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 400/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1898\n", + "Epoch 400/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 401/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "Epoch 401/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 402/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "Epoch 402/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 403/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "Epoch 403/5000\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 404/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "Epoch 404/5000\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 405/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1897\n", + "Epoch 405/5000\n", + "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 406/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "Epoch 406/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 407/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "Epoch 407/5000\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 408/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0360 - val_root_mean_squared_error: 0.1896\n", + "Epoch 408/5000\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 409/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "Epoch 409/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 410/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "Epoch 410/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1810\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 411/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1896\n", + "Epoch 411/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - 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 412/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 412/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - 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 413/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 413/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 414/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 414/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 415/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 415/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - 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 416/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 416/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1807\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 417/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1895\n", + "Epoch 417/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 418/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "Epoch 418/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - 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 419/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "Epoch 419/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - 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 420/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "Epoch 420/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 421/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "Epoch 421/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 422/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1894\n", + "Epoch 422/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 423/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0359 - val_root_mean_squared_error: 0.1893\n", + "Epoch 423/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 424/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "Epoch 424/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - 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 425/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "Epoch 425/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - 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 426/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "Epoch 426/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 427/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "Epoch 427/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 428/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1893\n", + "Epoch 428/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - 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 429/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n" + "Epoch 429/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n" ] }, { @@ -3652,122 +3652,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 430/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "Epoch 430/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 431/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "Epoch 431/5000\n", + "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - 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 432/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "Epoch 432/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0828 - 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 433/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "Epoch 433/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 434/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1892\n", + "Epoch 434/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 435/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "Epoch 435/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 436/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "Epoch 436/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 437/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "Epoch 437/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 438/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0358 - val_root_mean_squared_error: 0.1891\n", + "Epoch 438/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 439/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "Epoch 439/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 440/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1891\n", + "Epoch 440/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - 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 441/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 441/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 442/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0871 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 442/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 443/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 443/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - 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 444/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 444/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - 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 445/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 445/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 446/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1890\n", + "Epoch 446/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 447/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 447/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 448/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 448/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - 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 449/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 449/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - 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 450/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 450/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 451/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 451/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 452/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1889\n", + "Epoch 452/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 453/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", + "Epoch 453/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - 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 454/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0357 - val_root_mean_squared_error: 0.1888\n", + "Epoch 454/5000\n", + "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 455/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "Epoch 455/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 456/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0870 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "Epoch 456/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 457/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "Epoch 457/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - 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 458/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1888\n", + "Epoch 458/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1782\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 459/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 459/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 460/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 460/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 461/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 461/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - 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 462/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 462/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 463/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 463/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 464/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1887\n", + "Epoch 464/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 465/1000\n", - "1/1 - 0s - loss: 0.0076 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "Epoch 465/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 466/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "Epoch 466/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 467/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "Epoch 467/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 468/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n" + "Epoch 468/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n" ] }, { @@ -3775,122 +3775,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 469/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "Epoch 469/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 470/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0356 - val_root_mean_squared_error: 0.1886\n", + "Epoch 470/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - 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 471/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0869 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 471/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 472/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 472/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 473/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 473/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1771\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 474/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 474/5000\n", + "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - 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 475/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 475/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 476/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1885\n", + "Epoch 476/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 477/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 477/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - 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 478/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 478/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - 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 479/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 479/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 480/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 480/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1765\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 481/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 481/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - 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 482/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1884\n", + "Epoch 482/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 483/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "Epoch 483/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 484/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "Epoch 484/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1762\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 485/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "Epoch 485/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 486/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0868 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "Epoch 486/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - 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 487/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0355 - val_root_mean_squared_error: 0.1883\n", + "Epoch 487/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0811 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 488/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1883\n", + "Epoch 488/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 489/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1883\n", + "Epoch 489/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 490/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 490/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - 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 491/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 491/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 492/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 492/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0809 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 493/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 493/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 494/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 494/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 495/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1882\n", + "Epoch 495/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0808 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1751\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 496/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 496/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1750\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 497/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 497/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0807 - 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 498/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 498/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1748\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 499/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 499/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 500/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0867 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 500/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1746\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 501/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1881\n", + "Epoch 501/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - 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 502/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "Epoch 502/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 503/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0354 - val_root_mean_squared_error: 0.1880\n", + "Epoch 503/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0804 - 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 504/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 504/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0803 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1742\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 505/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 505/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1741\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 506/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 506/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 507/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n" + "Epoch 507/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0802 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1739\n" ] }, { @@ -3898,122 +3898,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 508/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1880\n", + "Epoch 508/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 509/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 509/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1738\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 510/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 510/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - 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 511/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 511/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0800 - 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 512/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 512/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0297 - val_root_mean_squared_error: 0.1723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 513/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 513/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 514/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1879\n", + "Epoch 514/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0801 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 515/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0866 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 515/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0805 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 516/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 516/5000\n", + "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0810 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 517/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 517/5000\n", + "1/1 - 0s - loss: 0.0065 - root_mean_squared_error: 0.0806 - val_loss: 0.0303 - val_root_mean_squared_error: 0.1740\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 518/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 518/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0797 - val_loss: 0.0305 - val_root_mean_squared_error: 0.1747\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 519/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 519/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1697\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 520/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0353 - val_root_mean_squared_error: 0.1878\n", + "Epoch 520/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0803 - val_loss: 0.0306 - val_root_mean_squared_error: 0.1751\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 521/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 521/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0799 - val_loss: 0.0299 - val_root_mean_squared_error: 0.1728\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 522/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 522/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0289 - val_root_mean_squared_error: 0.1701\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 523/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 523/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - 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 524/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 524/5000\n", + "1/1 - 0s - loss: 0.0064 - root_mean_squared_error: 0.0798 - val_loss: 0.0295 - val_root_mean_squared_error: 0.1717\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 525/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 525/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1704\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 526/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 526/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0304 - val_root_mean_squared_error: 0.1744\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 527/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1877\n", + "Epoch 527/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0796 - 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 528/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 528/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - 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 529/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 529/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0792 - val_loss: 0.0302 - val_root_mean_squared_error: 0.1737\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 530/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0865 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 530/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0793 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1703\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 531/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 531/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1707\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 532/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 532/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - 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 533/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1876\n", + "Epoch 533/5000\n", + "1/1 - 0s - loss: 0.0063 - root_mean_squared_error: 0.0791 - val_loss: 0.0288 - val_root_mean_squared_error: 0.1698\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 534/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "Epoch 534/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0790 - 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 535/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "Epoch 535/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0296 - val_root_mean_squared_error: 0.1721\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 536/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "Epoch 536/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0789 - 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 537/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "Epoch 537/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0788 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 538/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0352 - val_root_mean_squared_error: 0.1875\n", + "Epoch 538/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1712\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 539/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", + "Epoch 539/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0786 - 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 540/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1875\n", + "Epoch 540/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0787 - val_loss: 0.0293 - val_root_mean_squared_error: 0.1710\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 541/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "Epoch 541/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0785 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1702\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 542/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "Epoch 542/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0784 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1690\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 543/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "Epoch 543/5000\n", + "1/1 - 0s - loss: 0.0062 - root_mean_squared_error: 0.0784 - val_loss: 0.0292 - val_root_mean_squared_error: 0.1708\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 544/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "Epoch 544/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0784 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1692\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 545/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0864 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n", + "Epoch 545/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0783 - 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 546/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1874\n" + "Epoch 546/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0290 - val_root_mean_squared_error: 0.1703\n" ] }, { @@ -4021,122 +4021,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 547/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 547/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 548/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 548/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0781 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 549/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 549/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0780 - val_loss: 0.0286 - val_root_mean_squared_error: 0.1693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 550/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 550/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0780 - val_loss: 0.0282 - val_root_mean_squared_error: 0.1680\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 551/1000\n", - "1/1 - 0s - loss: 0.0075 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 551/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0780 - val_loss: 0.0287 - val_root_mean_squared_error: 0.1693\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 552/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 552/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0779 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1681\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 553/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1873\n", + "Epoch 553/5000\n", + "1/1 - 0s - loss: 0.0061 - root_mean_squared_error: 0.0778 - val_loss: 0.0283 - val_root_mean_squared_error: 0.1682\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 554/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", + "Epoch 554/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1687\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 555/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0351 - val_root_mean_squared_error: 0.1872\n", + "Epoch 555/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0280 - val_root_mean_squared_error: 0.1673\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 556/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "Epoch 556/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0776 - val_loss: 0.0284 - val_root_mean_squared_error: 0.1684\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 557/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "Epoch 557/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0776 - 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 558/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "Epoch 558/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0775 - 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 559/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1872\n", + "Epoch 559/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 560/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0863 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 560/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0774 - 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 561/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 561/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - 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 562/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 562/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - 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 563/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 563/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0772 - val_loss: 0.0279 - val_root_mean_squared_error: 0.1669\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 564/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 564/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0771 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 565/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 565/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - 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 566/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1871\n", + "Epoch 566/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0770 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1666\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 567/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 567/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 568/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 568/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0769 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 569/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 569/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0768 - val_loss: 0.0273 - val_root_mean_squared_error: 0.1651\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 570/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 570/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0276 - val_root_mean_squared_error: 0.1661\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 571/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 571/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0271 - val_root_mean_squared_error: 0.1648\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 572/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 572/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0766 - val_loss: 0.0275 - val_root_mean_squared_error: 0.1658\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 573/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0350 - val_root_mean_squared_error: 0.1870\n", + "Epoch 573/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 574/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 574/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0765 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 575/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0862 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 575/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1637\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 576/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 576/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0274 - val_root_mean_squared_error: 0.1656\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 577/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 577/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - val_loss: 0.0265 - val_root_mean_squared_error: 0.1628\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 578/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 578/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - 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 579/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1869\n", + "Epoch 579/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0764 - val_loss: 0.0260 - val_root_mean_squared_error: 0.1614\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 580/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 580/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0765 - val_loss: 0.0281 - val_root_mean_squared_error: 0.1678\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 581/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 581/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0768 - 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 582/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 582/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0771 - val_loss: 0.0285 - val_root_mean_squared_error: 0.1688\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 583/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 583/5000\n", + "1/1 - 0s - loss: 0.0060 - root_mean_squared_error: 0.0773 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1601\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 584/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 584/5000\n", + "1/1 - 0s - loss: 0.0059 - root_mean_squared_error: 0.0767 - val_loss: 0.0272 - val_root_mean_squared_error: 0.1650\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 585/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n" + "Epoch 585/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0759 - val_loss: 0.0268 - val_root_mean_squared_error: 0.1638\n" ] }, { @@ -4144,122 +4144,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 586/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1868\n", + "Epoch 586/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 587/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "Epoch 587/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0760 - val_loss: 0.0277 - val_root_mean_squared_error: 0.1664\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 588/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "Epoch 588/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0763 - 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 589/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0861 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "Epoch 589/5000\n", + "1/1 - 0s - loss: 0.0058 - root_mean_squared_error: 0.0759 - 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 590/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0349 - val_root_mean_squared_error: 0.1867\n", + "Epoch 590/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0267 - val_root_mean_squared_error: 0.1635\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 591/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", + "Epoch 591/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0754 - val_loss: 0.0254 - val_root_mean_squared_error: 0.1595\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 592/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1867\n", + "Epoch 592/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0270 - val_root_mean_squared_error: 0.1643\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 593/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 593/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0756 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1605\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 594/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 594/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0752 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 595/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 595/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0751 - 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 596/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 596/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0752 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 597/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 597/5000\n", + "1/1 - 0s - loss: 0.0057 - root_mean_squared_error: 0.0752 - val_loss: 0.0262 - val_root_mean_squared_error: 0.1620\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 598/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 598/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0259 - val_root_mean_squared_error: 0.1609\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 599/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1866\n", + "Epoch 599/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1591\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 600/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 600/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0263 - val_root_mean_squared_error: 0.1621\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 601/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 601/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1589\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 602/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 602/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0747 - val_loss: 0.0256 - val_root_mean_squared_error: 0.1599\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 603/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 603/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0258 - val_root_mean_squared_error: 0.1606\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 604/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0860 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 604/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 605/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1865\n", + "Epoch 605/5000\n", + "1/1 - 0s - loss: 0.0056 - root_mean_squared_error: 0.0745 - val_loss: 0.0257 - val_root_mean_squared_error: 0.1604\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 606/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "Epoch 606/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0744 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 607/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "Epoch 607/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - 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 608/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0348 - val_root_mean_squared_error: 0.1864\n", + "Epoch 608/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0255 - val_root_mean_squared_error: 0.1597\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 609/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "Epoch 609/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0742 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 610/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "Epoch 610/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0741 - val_loss: 0.0253 - val_root_mean_squared_error: 0.1590\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 611/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "Epoch 611/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0740 - val_loss: 0.0250 - val_root_mean_squared_error: 0.1581\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 612/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1864\n", + "Epoch 612/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0739 - val_loss: 0.0247 - val_root_mean_squared_error: 0.1573\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 613/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 613/5000\n", + "1/1 - 0s - loss: 0.0055 - root_mean_squared_error: 0.0738 - val_loss: 0.0252 - val_root_mean_squared_error: 0.1586\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 614/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 614/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0738 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 615/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 615/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0737 - val_loss: 0.0249 - val_root_mean_squared_error: 0.1577\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 616/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 616/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0736 - 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 617/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 617/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 618/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0859 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1863\n", + "Epoch 618/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0735 - val_loss: 0.0248 - val_root_mean_squared_error: 0.1574\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 619/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 619/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - 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 620/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 620/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0734 - val_loss: 0.0245 - val_root_mean_squared_error: 0.1567\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 621/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 621/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0733 - 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 622/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 622/5000\n", + "1/1 - 0s - loss: 0.0054 - root_mean_squared_error: 0.0732 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1555\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 623/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 623/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0244 - val_root_mean_squared_error: 0.1563\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 624/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n" + "Epoch 624/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0731 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1549\n" ] }, { @@ -4267,122 +4267,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 625/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0347 - val_root_mean_squared_error: 0.1862\n", + "Epoch 625/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0730 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1557\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 626/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 626/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0729 - val_loss: 0.0240 - val_root_mean_squared_error: 0.1549\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 627/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 627/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0728 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1547\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 628/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 628/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - 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 629/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 629/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0727 - 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 630/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 630/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0726 - val_loss: 0.0239 - val_root_mean_squared_error: 0.1546\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 631/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0858 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1861\n", + "Epoch 631/5000\n", + "1/1 - 0s - loss: 0.0053 - root_mean_squared_error: 0.0725 - 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 632/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 632/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0724 - val_loss: 0.0237 - val_root_mean_squared_error: 0.1539\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 633/1000\n", - "1/1 - 0s - loss: 0.0074 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 633/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1536\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 634/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 634/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0234 - val_root_mean_squared_error: 0.1531\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 635/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 635/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0722 - val_loss: 0.0236 - val_root_mean_squared_error: 0.1535\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 636/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 636/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0721 - val_loss: 0.0233 - val_root_mean_squared_error: 0.1526\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 637/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 637/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - 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 638/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1860\n", + "Epoch 638/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0720 - 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 639/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "Epoch 639/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0719 - 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 640/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "Epoch 640/5000\n", + "1/1 - 0s - loss: 0.0052 - root_mean_squared_error: 0.0718 - 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 641/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "Epoch 641/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0717 - 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 642/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0346 - val_root_mean_squared_error: 0.1859\n", + "Epoch 642/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0716 - val_loss: 0.0230 - val_root_mean_squared_error: 0.1518\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 643/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", + "Epoch 643/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0228 - val_root_mean_squared_error: 0.1511\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 644/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0857 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1859\n", + "Epoch 644/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0715 - val_loss: 0.0229 - val_root_mean_squared_error: 0.1514\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 645/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 645/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0714 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1507\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 646/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 646/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0713 - val_loss: 0.0227 - val_root_mean_squared_error: 0.1508\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 647/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 647/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0712 - 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 648/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 648/5000\n", + "1/1 - 0s - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0225 - val_root_mean_squared_error: 0.1502\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 649/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 649/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0710 - 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 650/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1858\n", + "Epoch 650/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0709 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1496\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 651/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 651/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0224 - val_root_mean_squared_error: 0.1497\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 652/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 652/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0708 - val_loss: 0.0222 - val_root_mean_squared_error: 0.1491\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 653/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 653/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0707 - val_loss: 0.0223 - val_root_mean_squared_error: 0.1492\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 654/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 654/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0706 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1486\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 655/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 655/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0705 - val_loss: 0.0221 - val_root_mean_squared_error: 0.1486\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 656/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1857\n", + "Epoch 656/5000\n", + "1/1 - 0s - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0220 - val_root_mean_squared_error: 0.1482\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 657/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0856 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "Epoch 657/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0703 - val_loss: 0.0219 - val_root_mean_squared_error: 0.1480\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 658/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "Epoch 658/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0702 - val_loss: 0.0218 - val_root_mean_squared_error: 0.1478\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 659/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0345 - val_root_mean_squared_error: 0.1856\n", + "Epoch 659/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0701 - val_loss: 0.0217 - val_root_mean_squared_error: 0.1475\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 660/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "Epoch 660/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0700 - 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 661/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "Epoch 661/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0699 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 662/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1856\n", + "Epoch 662/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0698 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1469\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 663/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n" + "Epoch 663/5000\n", + "1/1 - 0s - loss: 0.0049 - root_mean_squared_error: 0.0697 - val_loss: 0.0214 - val_root_mean_squared_error: 0.1464\n" ] }, { @@ -4390,122 +4390,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 664/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "Epoch 664/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0696 - 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 665/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "Epoch 665/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0695 - val_loss: 0.0213 - val_root_mean_squared_error: 0.1459\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 666/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "Epoch 666/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0694 - val_loss: 0.0212 - val_root_mean_squared_error: 0.1458\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 667/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "Epoch 667/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0693 - val_loss: 0.0211 - val_root_mean_squared_error: 0.1454\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 668/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1855\n", + "Epoch 668/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0692 - 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 669/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0855 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 669/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0691 - 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 670/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 670/5000\n", + "1/1 - 0s - loss: 0.0048 - root_mean_squared_error: 0.0690 - val_loss: 0.0209 - val_root_mean_squared_error: 0.1446\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 671/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 671/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0689 - val_loss: 0.0208 - val_root_mean_squared_error: 0.1443\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 672/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 672/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0688 - val_loss: 0.0207 - val_root_mean_squared_error: 0.1440\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 673/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 673/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0686 - 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 674/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1854\n", + "Epoch 674/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0685 - 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 675/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0344 - val_root_mean_squared_error: 0.1853\n", + "Epoch 675/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0684 - val_loss: 0.0205 - val_root_mean_squared_error: 0.1433\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 676/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "Epoch 676/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0683 - 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 677/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "Epoch 677/5000\n", + "1/1 - 0s - loss: 0.0047 - root_mean_squared_error: 0.0682 - val_loss: 0.0204 - val_root_mean_squared_error: 0.1427\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 678/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "Epoch 678/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0681 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1423\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 679/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "Epoch 679/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0680 - val_loss: 0.0202 - val_root_mean_squared_error: 0.1421\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 680/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1853\n", + "Epoch 680/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0679 - val_loss: 0.0201 - val_root_mean_squared_error: 0.1417\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 681/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0854 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "Epoch 681/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0677 - val_loss: 0.0200 - val_root_mean_squared_error: 0.1416\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 682/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "Epoch 682/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0676 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1411\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 683/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "Epoch 683/5000\n", + "1/1 - 0s - loss: 0.0046 - root_mean_squared_error: 0.0675 - val_loss: 0.0199 - val_root_mean_squared_error: 0.1410\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 684/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "Epoch 684/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0674 - 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 685/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1852\n", + "Epoch 685/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0673 - val_loss: 0.0197 - val_root_mean_squared_error: 0.1404\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 686/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "Epoch 686/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0671 - val_loss: 0.0196 - val_root_mean_squared_error: 0.1399\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 687/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "Epoch 687/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0670 - val_loss: 0.0195 - val_root_mean_squared_error: 0.1398\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 688/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "Epoch 688/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0669 - val_loss: 0.0194 - val_root_mean_squared_error: 0.1393\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 689/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "Epoch 689/5000\n", + "1/1 - 0s - loss: 0.0045 - root_mean_squared_error: 0.0668 - 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 690/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0343 - val_root_mean_squared_error: 0.1851\n", + "Epoch 690/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0666 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1387\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 691/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1851\n", + "Epoch 691/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0665 - val_loss: 0.0192 - val_root_mean_squared_error: 0.1386\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 692/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0853 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "Epoch 692/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0664 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 693/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "Epoch 693/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0663 - val_loss: 0.0190 - val_root_mean_squared_error: 0.1380\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 694/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "Epoch 694/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0661 - val_loss: 0.0189 - val_root_mean_squared_error: 0.1373\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 695/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "Epoch 695/5000\n", + "1/1 - 0s - loss: 0.0044 - root_mean_squared_error: 0.0660 - 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 696/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1850\n", + "Epoch 696/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0659 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1367\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 697/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "Epoch 697/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0657 - val_loss: 0.0187 - val_root_mean_squared_error: 0.1368\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 698/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "Epoch 698/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0656 - val_loss: 0.0185 - val_root_mean_squared_error: 0.1359\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 699/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "Epoch 699/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0655 - val_loss: 0.0186 - val_root_mean_squared_error: 0.1363\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 700/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "Epoch 700/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0654 - val_loss: 0.0183 - val_root_mean_squared_error: 0.1352\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 701/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1849\n", + "Epoch 701/5000\n", + "1/1 - 0s - loss: 0.0043 - root_mean_squared_error: 0.0652 - val_loss: 0.0184 - val_root_mean_squared_error: 0.1358\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 702/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n" + "Epoch 702/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0651 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1344\n" ] }, { @@ -4513,122 +4513,368 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 703/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0852 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "Epoch 703/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0650 - 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 704/1000\n", - "1/1 - 0s - loss: 0.0073 - root_mean_squared_error: 0.0851 - val_loss: 0.0342 - val_root_mean_squared_error: 0.1848\n", + "Epoch 704/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0649 - 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 705/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "Epoch 705/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0647 - 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 706/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "Epoch 706/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0646 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1325\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 707/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1848\n", + "Epoch 707/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - val_loss: 0.0182 - val_root_mean_squared_error: 0.1348\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 708/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "Epoch 708/5000\n", + "1/1 - 0s - loss: 0.0042 - root_mean_squared_error: 0.0645 - 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 709/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "Epoch 709/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0644 - val_loss: 0.0181 - val_root_mean_squared_error: 0.1346\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 710/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "Epoch 710/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0643 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 711/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "Epoch 711/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0642 - val_loss: 0.0179 - val_root_mean_squared_error: 0.1340\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 712/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1847\n", + "Epoch 712/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0641 - val_loss: 0.0170 - val_root_mean_squared_error: 0.1302\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 713/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "Epoch 713/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0639 - val_loss: 0.0176 - val_root_mean_squared_error: 0.1326\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 714/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0851 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "Epoch 714/5000\n", + "1/1 - 0s - loss: 0.0041 - root_mean_squared_error: 0.0637 - 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 715/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "Epoch 715/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0634 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1308\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 716/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "Epoch 716/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0633 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1306\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 717/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1846\n", + "Epoch 717/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0167 - val_root_mean_squared_error: 0.1293\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 718/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0341 - val_root_mean_squared_error: 0.1845\n", + "Epoch 718/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0631 - val_loss: 0.0171 - val_root_mean_squared_error: 0.1307\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 719/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "Epoch 719/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0630 - val_loss: 0.0165 - val_root_mean_squared_error: 0.1283\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 720/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "Epoch 720/5000\n", + "1/1 - 0s - loss: 0.0040 - root_mean_squared_error: 0.0629 - 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 721/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1845\n", + "Epoch 721/5000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0627 - 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 722/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "Epoch 722/5000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0626 - 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 723/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "Epoch 723/5000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0624 - val_loss: 0.0164 - val_root_mean_squared_error: 0.1280\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 724/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0850 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "Epoch 724/5000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0623 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 725/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "Epoch 725/5000\n", + "1/1 - 0s - loss: 0.0039 - root_mean_squared_error: 0.0621 - 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 726/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1844\n", + "Epoch 726/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0620 - 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 727/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "Epoch 727/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0619 - val_loss: 0.0162 - val_root_mean_squared_error: 0.1272\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 728/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "Epoch 728/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0618 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1257\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 729/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "Epoch 729/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0617 - val_loss: 0.0159 - val_root_mean_squared_error: 0.1261\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 730/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0340 - val_root_mean_squared_error: 0.1843\n", + "Epoch 730/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0615 - val_loss: 0.0158 - val_root_mean_squared_error: 0.1256\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 731/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1843\n", + "Epoch 731/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0614 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1249\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 732/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "Epoch 732/5000\n", + "1/1 - 0s - loss: 0.0038 - root_mean_squared_error: 0.0613 - val_loss: 0.0157 - val_root_mean_squared_error: 0.1253\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 733/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "Epoch 733/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0612 - 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 734/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0849 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "Epoch 734/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0611 - 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 735/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1842\n", + "Epoch 735/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0609 - 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 736/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0608 - val_loss: 0.0153 - val_root_mean_squared_error: 0.1236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 737/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0607 - val_loss: 0.0152 - val_root_mean_squared_error: 0.1232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 738/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0605 - val_loss: 0.0150 - val_root_mean_squared_error: 0.1225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 739/5000\n", + "1/1 - 0s - loss: 0.0037 - root_mean_squared_error: 0.0604 - val_loss: 0.0151 - val_root_mean_squared_error: 0.1228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 740/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0603 - val_loss: 0.0148 - val_root_mean_squared_error: 0.1218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 741/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0602 - val_loss: 0.0149 - val_root_mean_squared_error: 0.1221\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 736/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "Epoch 742/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0601 - 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 737/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "Epoch 743/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0599 - 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 738/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "Epoch 744/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0598 - val_loss: 0.0146 - val_root_mean_squared_error: 0.1208\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 739/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "Epoch 745/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0597 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1202\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 740/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1841\n", + "Epoch 746/5000\n", + "1/1 - 0s - loss: 0.0036 - root_mean_squared_error: 0.0596 - val_loss: 0.0145 - val_root_mean_squared_error: 0.1204\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 741/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n" + "Epoch 747/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0595 - val_loss: 0.0143 - val_root_mean_squared_error: 0.1195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 748/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0594 - 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 749/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0593 - val_loss: 0.0142 - val_root_mean_squared_error: 0.1190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 750/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0591 - 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 751/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0590 - val_loss: 0.0141 - val_root_mean_squared_error: 0.1185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 752/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0589 - 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 753/5000\n", + "1/1 - 0s - loss: 0.0035 - root_mean_squared_error: 0.0588 - 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 754/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0587 - 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 755/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0586 - 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 756/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0585 - 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 757/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0584 - val_loss: 0.0136 - val_root_mean_squared_error: 0.1167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 758/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0583 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 759/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0582 - 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 760/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0581 - 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 761/5000\n", + "1/1 - 0s - loss: 0.0034 - root_mean_squared_error: 0.0580 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 762/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0579 - val_loss: 0.0133 - val_root_mean_squared_error: 0.1153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 763/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - 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 764/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0577 - 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 765/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0576 - 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 766/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0575 - val_loss: 0.0130 - val_root_mean_squared_error: 0.1141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 767/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0574 - val_loss: 0.0129 - val_root_mean_squared_error: 0.1137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 768/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0573 - 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 769/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0572 - val_loss: 0.0128 - val_root_mean_squared_error: 0.1132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 770/5000\n", + "1/1 - 0s - loss: 0.0033 - root_mean_squared_error: 0.0571 - val_loss: 0.0127 - val_root_mean_squared_error: 0.1129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 771/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0570 - 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 772/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0569 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 773/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0568 - val_loss: 0.0126 - val_root_mean_squared_error: 0.1122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 774/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0567 - val_loss: 0.0125 - val_root_mean_squared_error: 0.1117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 775/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0566 - 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 776/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0565 - 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 777/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0564 - 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 778/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - 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 779/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0563 - val_loss: 0.0123 - val_root_mean_squared_error: 0.1107\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 780/5000\n", + "1/1 - 0s - loss: 0.0032 - root_mean_squared_error: 0.0562 - val_loss: 0.0121 - val_root_mean_squared_error: 0.1100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 781/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0561 - 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 782/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0560 - 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 783/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0559 - 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 784/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - 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 785/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0558 - 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 786/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0557 - val_loss: 0.0116 - val_root_mean_squared_error: 0.1078\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 787/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - 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 788/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - 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 789/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - 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 790/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - val_loss: 0.0112 - val_root_mean_squared_error: 0.1059\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 791/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - 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 792/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0556 - 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 793/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0555 - 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 794/5000\n", + "1/1 - 0s - loss: 0.0031 - root_mean_squared_error: 0.0554 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 795/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0551 - 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 796/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - 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 797/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - 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 798/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - 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 799/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0548 - 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 800/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0547 - 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 801/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0546 - 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 802/5000\n", + "1/1 - 0s - loss: 0.0030 - root_mean_squared_error: 0.0544 - 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 803/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1055\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 804/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0543 - 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 805/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - val_loss: 0.0111 - val_root_mean_squared_error: 0.1054\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 806/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0542 - 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 807/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0540 - 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 808/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - 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 809/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0539 - val_loss: 0.0106 - val_root_mean_squared_error: 0.1027\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 810/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - 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 811/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0538 - 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 812/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0537 - 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 813/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0536 - val_loss: 0.0105 - val_root_mean_squared_error: 0.1026\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 814/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0535 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1019\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 815/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - 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 816/5000\n", + "1/1 - 0s - loss: 0.0029 - root_mean_squared_error: 0.0534 - val_loss: 0.0103 - val_root_mean_squared_error: 0.1014\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 817/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0533 - val_loss: 0.0104 - val_root_mean_squared_error: 0.1022\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 818/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - 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 819/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0532 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1011\n" ] }, { @@ -4636,122 +4882,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 742/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0339 - val_root_mean_squared_error: 0.1840\n", + "Epoch 820/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0531 - 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 743/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0848 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "Epoch 821/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - 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 744/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1840\n", + "Epoch 822/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0530 - val_loss: 0.0102 - val_root_mean_squared_error: 0.1012\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 745/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "Epoch 823/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0529 - 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 746/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "Epoch 824/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - 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 747/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "Epoch 825/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0528 - 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 748/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1839\n", + "Epoch 826/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - val_loss: 0.0099 - val_root_mean_squared_error: 0.0996\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 749/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "Epoch 827/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0527 - 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 750/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "Epoch 828/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0526 - 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 751/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "Epoch 829/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - 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 752/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0847 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1838\n", + "Epoch 830/5000\n", + "1/1 - 0s - loss: 0.0028 - root_mean_squared_error: 0.0525 - 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 753/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", + "Epoch 831/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - 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 754/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0338 - val_root_mean_squared_error: 0.1837\n", + "Epoch 832/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0524 - 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 755/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "Epoch 833/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0523 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0984\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 756/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1837\n", + "Epoch 834/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - 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 757/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "Epoch 835/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0522 - val_loss: 0.0097 - val_root_mean_squared_error: 0.0983\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 758/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "Epoch 836/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - val_loss: 0.0096 - val_root_mean_squared_error: 0.0980\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 759/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "Epoch 837/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0521 - 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 760/1000\n", - "1/1 - 0s - loss: 0.0072 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1836\n", + "Epoch 838/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - val_loss: 0.0095 - val_root_mean_squared_error: 0.0976\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 761/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0846 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "Epoch 839/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0520 - 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 762/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "Epoch 840/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0519 - 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 763/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "Epoch 841/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - 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 764/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1835\n", + "Epoch 842/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0518 - 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 765/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0337 - val_root_mean_squared_error: 0.1834\n", + "Epoch 843/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0968\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 766/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "Epoch 844/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0517 - val_loss: 0.0094 - val_root_mean_squared_error: 0.0969\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 767/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "Epoch 845/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0965\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 768/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1834\n", + "Epoch 846/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0516 - val_loss: 0.0093 - val_root_mean_squared_error: 0.0966\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 769/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "Epoch 847/5000\n", + "1/1 - 0s - loss: 0.0027 - root_mean_squared_error: 0.0515 - 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 770/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0845 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "Epoch 848/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0515 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 771/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "Epoch 849/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0961\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 772/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1833\n", + "Epoch 850/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0514 - 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 773/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "Epoch 851/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0092 - val_root_mean_squared_error: 0.0958\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 774/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "Epoch 852/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0513 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 775/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0336 - val_root_mean_squared_error: 0.1832\n", + "Epoch 853/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - val_loss: 0.0091 - val_root_mean_squared_error: 0.0954\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 776/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1832\n", + "Epoch 854/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0512 - 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 777/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "Epoch 855/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - 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 778/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0844 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "Epoch 856/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0511 - 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 779/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1831\n", + "Epoch 857/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0947\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 780/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n" + "Epoch 858/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0510 - val_loss: 0.0090 - val_root_mean_squared_error: 0.0947\n" ] }, { @@ -4759,122 +5005,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 781/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "Epoch 859/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 782/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "Epoch 860/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0509 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0944\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 783/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1830\n", + "Epoch 861/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0089 - val_root_mean_squared_error: 0.0942\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 784/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", + "Epoch 862/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0508 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0940\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 785/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0335 - val_root_mean_squared_error: 0.1829\n", + "Epoch 863/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0939\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 786/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0843 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1829\n", + "Epoch 864/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0507 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 787/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "Epoch 865/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - val_loss: 0.0088 - val_root_mean_squared_error: 0.0937\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 788/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "Epoch 866/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0506 - 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 789/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "Epoch 867/5000\n", + "1/1 - 0s - loss: 0.0026 - root_mean_squared_error: 0.0505 - 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 790/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1828\n", + "Epoch 868/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0505 - 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 791/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "Epoch 869/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0087 - val_root_mean_squared_error: 0.0930\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 792/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "Epoch 870/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0930\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 793/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1827\n", + "Epoch 871/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0504 - 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 794/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0842 - val_loss: 0.0334 - val_root_mean_squared_error: 0.1826\n", + "Epoch 872/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - 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 795/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "Epoch 873/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0503 - val_loss: 0.0086 - val_root_mean_squared_error: 0.0925\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 796/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "Epoch 874/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - val_loss: 0.0085 - val_root_mean_squared_error: 0.0924\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 797/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1826\n", + "Epoch 875/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0502 - 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 798/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "Epoch 876/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - 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 799/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "Epoch 877/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - 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 800/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1825\n", + "Epoch 878/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0501 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 801/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "Epoch 879/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0918\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 802/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0841 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "Epoch 880/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0500 - val_loss: 0.0084 - val_root_mean_squared_error: 0.0916\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 803/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "Epoch 881/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - 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 804/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0333 - val_root_mean_squared_error: 0.1824\n", + "Epoch 882/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0499 - 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 805/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "Epoch 883/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - 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 806/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "Epoch 884/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - 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 807/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1823\n", + "Epoch 885/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0498 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0910\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 808/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "Epoch 886/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0083 - val_root_mean_squared_error: 0.0908\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 809/1000\n", - "1/1 - 0s - loss: 0.0071 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "Epoch 887/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0497 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 810/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0840 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1822\n", + "Epoch 888/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - 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 811/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "Epoch 889/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0496 - 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 812/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "Epoch 890/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - 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 813/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0332 - val_root_mean_squared_error: 0.1821\n", + "Epoch 891/5000\n", + "1/1 - 0s - loss: 0.0025 - root_mean_squared_error: 0.0495 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0902\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 814/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "Epoch 892/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0495 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0901\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 815/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "Epoch 893/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - 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 816/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "Epoch 894/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0494 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0899\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 817/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0839 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1820\n", + "Epoch 895/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0081 - val_root_mean_squared_error: 0.0897\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 818/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "Epoch 896/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - 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 819/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n" + "Epoch 897/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0493 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0895\n" ] }, { @@ -4882,122 +5128,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 820/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1819\n", + "Epoch 898/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0894\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 821/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0331 - val_root_mean_squared_error: 0.1818\n", + "Epoch 899/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0492 - val_loss: 0.0080 - val_root_mean_squared_error: 0.0893\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 822/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1818\n", + "Epoch 900/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - 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 823/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1818\n", + "Epoch 901/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - 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 824/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "Epoch 902/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0491 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0889\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 825/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0838 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "Epoch 903/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - 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 826/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1817\n", + "Epoch 904/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - 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 827/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "Epoch 905/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0490 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0886\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 828/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "Epoch 906/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - 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 829/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1816\n", + "Epoch 907/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0489 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0884\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 830/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0330 - val_root_mean_squared_error: 0.1815\n", + "Epoch 908/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - 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 831/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "Epoch 909/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - 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 832/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0837 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1815\n", + "Epoch 910/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0488 - val_loss: 0.0078 - val_root_mean_squared_error: 0.0880\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 833/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "Epoch 911/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0879\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 834/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "Epoch 912/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0487 - 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 835/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1814\n", + "Epoch 913/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0877\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 836/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "Epoch 914/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0876\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 837/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "Epoch 915/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0486 - val_loss: 0.0077 - val_root_mean_squared_error: 0.0875\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 838/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0329 - val_root_mean_squared_error: 0.1813\n", + "Epoch 916/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0874\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 839/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "Epoch 917/5000\n", + "1/1 - 0s - loss: 0.0024 - root_mean_squared_error: 0.0485 - 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 840/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0836 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "Epoch 918/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0485 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0872\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 841/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1812\n", + "Epoch 919/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 842/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "Epoch 920/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0484 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0870\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 843/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "Epoch 921/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0869\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 844/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1811\n", + "Epoch 922/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0867\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 845/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "Epoch 923/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0483 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0866\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 846/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "Epoch 924/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0865\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 847/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0835 - val_loss: 0.0328 - val_root_mean_squared_error: 0.1810\n", + "Epoch 925/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0864\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 848/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "Epoch 926/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0482 - val_loss: 0.0075 - val_root_mean_squared_error: 0.0863\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 849/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "Epoch 927/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0862\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 850/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1809\n", + "Epoch 928/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - 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 851/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "Epoch 929/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0481 - 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 852/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "Epoch 930/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0859\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 853/1000\n", - "1/1 - 0s - loss: 0.0070 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1808\n", + "Epoch 931/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0480 - val_loss: 0.0074 - val_root_mean_squared_error: 0.0858\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 854/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "Epoch 932/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0857\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 855/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0834 - val_loss: 0.0327 - val_root_mean_squared_error: 0.1807\n", + "Epoch 933/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - 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 856/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1807\n", + "Epoch 934/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0479 - 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 857/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "Epoch 935/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - 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 858/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n" + "Epoch 936/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0853\n" ] }, { @@ -5005,122 +5251,122 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 859/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1806\n", + "Epoch 937/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0478 - val_loss: 0.0073 - val_root_mean_squared_error: 0.0852\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 860/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "Epoch 938/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - 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 861/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "Epoch 939/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0850\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 862/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0833 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1805\n", + "Epoch 940/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0477 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 863/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0326 - val_root_mean_squared_error: 0.1804\n", + "Epoch 941/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 864/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "Epoch 942/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0476 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 865/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1804\n", + "Epoch 943/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0847\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 866/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "Epoch 944/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 867/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "Epoch 945/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0475 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0845\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 868/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1803\n", + "Epoch 946/5000\n", + "1/1 - 0s - loss: 0.0023 - root_mean_squared_error: 0.0474 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 869/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0832 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "Epoch 947/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 870/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "Epoch 948/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0474 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 871/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0325 - val_root_mean_squared_error: 0.1802\n", + "Epoch 949/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0841\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 872/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "Epoch 950/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 873/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "Epoch 951/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0473 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0839\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 874/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1801\n", + "Epoch 952/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0837\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 875/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "Epoch 953/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0472 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 876/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "Epoch 954/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 877/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0831 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1800\n", + "Epoch 955/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - 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 878/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", + "Epoch 956/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0471 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0833\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 879/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0324 - val_root_mean_squared_error: 0.1799\n", + "Epoch 957/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 880/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1799\n", + "Epoch 958/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0830\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 881/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "Epoch 959/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 882/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "Epoch 960/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0827\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 883/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1798\n", + "Epoch 961/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0834\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 884/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0830 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "Epoch 962/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - 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 885/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "Epoch 963/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0835\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 886/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1797\n", + "Epoch 964/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - 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 887/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0323 - val_root_mean_squared_error: 0.1796\n", + "Epoch 965/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0070 - val_root_mean_squared_error: 0.0838\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 888/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1796\n", + "Epoch 966/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0468 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0812\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 889/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "Epoch 967/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 890/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "Epoch 968/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 891/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0829 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1795\n", + "Epoch 969/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0848\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 892/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "Epoch 970/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 893/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "Epoch 971/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0470 - val_loss: 0.0071 - val_root_mean_squared_error: 0.0843\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 894/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1794\n", + "Epoch 972/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0469 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 895/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0322 - val_root_mean_squared_error: 0.1793\n", + "Epoch 973/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0466 - 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 896/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n", + "Epoch 974/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - 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 897/1000\n", - "1/1 - 0s - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1793\n" + "Epoch 975/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0809\n" ] }, { @@ -5128,332 +5374,12728 @@ "output_type": "stream", "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 898/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0828 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "Epoch 976/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 899/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "Epoch 977/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 900/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1792\n", + "Epoch 978/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0465 - 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 901/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "Epoch 979/5000\n", + "1/1 - 0s - loss: 0.0022 - root_mean_squared_error: 0.0464 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 902/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "Epoch 980/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0463 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 903/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0321 - val_root_mean_squared_error: 0.1791\n", + "Epoch 981/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0067 - val_root_mean_squared_error: 0.0818\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 904/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "Epoch 982/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 905/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "Epoch 983/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0068 - val_root_mean_squared_error: 0.0823\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 906/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1790\n", + "Epoch 984/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0802\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 907/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "Epoch 985/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0462 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0815\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 908/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1789\n", + "Epoch 986/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0461 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 909/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "Epoch 987/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - 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 910/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "Epoch 988/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0814\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 911/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0320 - val_root_mean_squared_error: 0.1788\n", + "Epoch 989/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - 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 912/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "Epoch 990/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0460 - val_loss: 0.0066 - val_root_mean_squared_error: 0.0813\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 913/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0826 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "Epoch 991/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - 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 914/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1787\n", + "Epoch 992/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0459 - 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 915/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "Epoch 993/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0806\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 916/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "Epoch 994/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - 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 917/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1786\n", + "Epoch 995/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0808\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 918/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "Epoch 996/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0458 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 919/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n", + "Epoch 997/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - val_loss: 0.0065 - val_root_mean_squared_error: 0.0804\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 920/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0825 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1785\n", + "Epoch 998/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0457 - 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 921/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", + "Epoch 999/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0797\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 922/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1784\n", + "Epoch 1000/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0802\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 923/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "Epoch 1001/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 924/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "Epoch 1002/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0456 - 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 925/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1783\n", + "Epoch 1003/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - 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 926/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", + "Epoch 1004/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0455 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 927/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0824 - val_loss: 0.0318 - val_root_mean_squared_error: 0.1782\n", + "Epoch 1005/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - 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 928/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1782\n", + "Epoch 1006/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - 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 929/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "Epoch 1007/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0454 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0796\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 930/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1781\n", + "Epoch 1008/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - 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 931/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "Epoch 1009/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - val_loss: 0.0063 - val_root_mean_squared_error: 0.0794\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 932/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "Epoch 1010/5000\n", + "1/1 - 0s - loss: 0.0021 - root_mean_squared_error: 0.0453 - 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 933/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1780\n", + "Epoch 1011/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - 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 934/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0823 - val_loss: 0.0317 - val_root_mean_squared_error: 0.1779\n", + "Epoch 1012/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - 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 935/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n", + "Epoch 1013/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0452 - 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 936/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1779\n" + "Epoch 1014/5000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - 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 1015/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - 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 1016/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - 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 1017/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0451 - 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 1018/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - 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 1019/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - 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 1020/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0450 - 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 1021/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0062 - val_root_mean_squared_error: 0.0784\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1022/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1023/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0449 - 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 1024/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1025/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0781\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1026/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0448 - 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 1027/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - 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 1028/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - 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 1029/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0447 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1030/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0778\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1031/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - 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 1032/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0446 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0776\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 937/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "Epoch 1033/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - 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 938/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "Epoch 1034/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - 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 939/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1778\n", + "Epoch 1035/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0445 - 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 940/1000\n", - "1/1 - 0s - loss: 0.0068 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", + "Epoch 1036/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 941/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0822 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1777\n", + "Epoch 1037/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - val_loss: 0.0060 - val_root_mean_squared_error: 0.0772\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 942/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0316 - val_root_mean_squared_error: 0.1776\n", + "Epoch 1038/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0444 - 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 943/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", + "Epoch 1039/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - 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 944/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1776\n", + "Epoch 1040/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 945/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "Epoch 1041/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0443 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 946/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1775\n", + "Epoch 1042/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - 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 947/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "Epoch 1043/5000\n", + "1/1 - 0s - loss: 0.0020 - root_mean_squared_error: 0.0442 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0768\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 948/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0821 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "Epoch 1044/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0442 - 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 949/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0315 - val_root_mean_squared_error: 0.1774\n", + "Epoch 1045/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - 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 950/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "Epoch 1046/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - 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 951/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "Epoch 1047/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0441 - 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 952/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1773\n", + "Epoch 1048/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - 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 953/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", + "Epoch 1049/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0763\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 954/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0820 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1772\n", + "Epoch 1050/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - 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 955/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", + "Epoch 1051/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - 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 956/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0314 - val_root_mean_squared_error: 0.1771\n", + "Epoch 1052/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0761\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1053/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - 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 1054/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - 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 1055/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0438 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1056/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - val_loss: 0.0058 - val_root_mean_squared_error: 0.0759\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1057/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - 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 1058/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - 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 1059/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1060/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - 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 1061/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0436 - 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 1062/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 957/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1771\n", + "Epoch 1063/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - 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 958/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "Epoch 1064/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - 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 959/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1770\n", + "Epoch 1065/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - 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 960/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "Epoch 1066/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - 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 961/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0819 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "Epoch 1067/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - 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 962/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1769\n", + "Epoch 1068/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 963/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0313 - val_root_mean_squared_error: 0.1768\n", + "Epoch 1069/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 964/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1768\n", + "Epoch 1070/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 965/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "Epoch 1071/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 966/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1767\n", + "Epoch 1072/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 967/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "Epoch 1073/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 968/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "Epoch 1074/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0769\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 969/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1766\n", + "Epoch 1075/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0435 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 970/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0312 - val_root_mean_squared_error: 0.1765\n", + "Epoch 1076/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0437 - 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 971/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1765\n", + "Epoch 1077/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - 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 972/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "Epoch 1078/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0440 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0780\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 973/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0817 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "Epoch 1079/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0439 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 974/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1764\n", + "Epoch 1080/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0434 - 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 975/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n" + "Epoch 1081/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0430 - 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 1082/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1083/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0431 - 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 1084/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0433 - 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 1085/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0432 - val_loss: 0.0056 - val_root_mean_squared_error: 0.0751\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1086/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0736\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1087/5000\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 1088/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0428 - val_loss: 0.0057 - val_root_mean_squared_error: 0.0755\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1089/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1090/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0429 - 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Epoch 1091/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0427 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0732\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1092/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0727\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1093/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - 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 1094/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - 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 1095/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0426 - 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 1096/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - 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 1097/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - 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 976/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1763\n", + "Epoch 1098/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - 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 977/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0311 - val_root_mean_squared_error: 0.1762\n", + "Epoch 1099/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - 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 978/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1762\n", + "Epoch 1100/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0424 - 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 979/1000\n", - "1/1 - 0s - loss: 0.0067 - root_mean_squared_error: 0.0816 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "Epoch 1101/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0423 - val_loss: 0.0053 - val_root_mean_squared_error: 0.0725\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 980/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1761\n", + "Epoch 1102/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - 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 981/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "Epoch 1103/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - 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 982/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "Epoch 1104/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1105/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0422 - 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 1106/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1107/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1108/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - 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 1109/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - 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 1110/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - 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 1111/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0420 - 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 1112/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1113/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0419 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0722\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1114/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - 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 1115/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - 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 1116/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0418 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0714\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1117/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - 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 1118/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0417 - 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 1119/5000\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 1120/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - 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 1121/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - 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 1122/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0718\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1123/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - 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 1124/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - 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 1125/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0415 - 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 1126/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - 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 1127/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - 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 1128/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - 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 1129/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0712\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1130/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - 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 1131/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0413 - 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 1132/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - 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 1133/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - 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 1134/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - 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 1135/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0704\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1136/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - 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 1137/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0411 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1138/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0706\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1139/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1140/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0703\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1141/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1142/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1143/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0702\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1144/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - 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 1145/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0701\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1146/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1147/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0700\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1148/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1149/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - 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 1150/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - 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 1151/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - 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 1152/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0693\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1153/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0049 - val_root_mean_squared_error: 0.0697\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1154/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - 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 1155/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0696\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1156/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0689\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1157/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - 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 1158/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1159/5000\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 1160/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0683\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1161/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - 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 1162/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0403 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1163/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0404 - 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 1164/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - 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 1165/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0406 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0721\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1166/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0410 - 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 1167/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0412 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1168/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0416 - 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 1169/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0414 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0719\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1170/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0409 - 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 1171/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0681\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1172/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0399 - 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 1173/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - 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 1174/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0405 - 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 1175/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0406 - 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 1176/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0402 - 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 1177/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - 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 1178/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - 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 1179/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - 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 1180/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0401 - 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 1181/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1182/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - 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 1183/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - 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 1184/5000\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 1185/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0398 - 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 1186/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0397 - 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 1187/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0396 - 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 1188/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - 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 1189/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0686\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1190/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - 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 1191/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0395 - 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 1192/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0394 - 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 1193/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - 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 1194/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - 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 1195/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1196/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - 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 1197/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - 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 1198/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - 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 1199/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - 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 1200/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1201/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1202/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0391 - 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 1203/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - 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 1204/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0390 - 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 1205/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - 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 1206/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0668\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1207/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - 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 1208/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - 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 1209/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - 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 1210/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - 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 1211/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0388 - 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 1212/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - 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 1213/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - 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 1214/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - 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 1215/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0387 - 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 1216/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - 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 1217/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - 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 1218/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0654\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1219/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0656\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1220/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - val_loss: 0.0043 - val_root_mean_squared_error: 0.0656\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1221/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - 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 1222/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0385 - 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 1223/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - 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 1224/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - 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 1225/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - 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 1226/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - 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 1227/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - 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 1228/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - 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 1229/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - 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 1230/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - 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 1231/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - 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 1232/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - 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 1233/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - 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 1234/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - 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 1235/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1236/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0646\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1237/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1238/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1239/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0644\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1240/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0644\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1241/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0643\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1242/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1243/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1244/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0379 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0641\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1245/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0641\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1246/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0640\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1247/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1248/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1249/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1250/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1251/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1252/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1253/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0635\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1254/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0636\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1255/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0633\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1256/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0637\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1257/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0630\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1258/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1259/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0375 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0624\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1260/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - 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 1261/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0612\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1262/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0381 - 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 1263/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0392 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1264/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0408 - 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 1265/5000\n", + "1/1 - 0s - loss: 0.0019 - root_mean_squared_error: 0.0442 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1266/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0425 - 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 1267/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0394 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0631\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1268/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1269/5000\n", + "1/1 - 0s - loss: 0.0016 - root_mean_squared_error: 0.0400 - 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 1270/5000\n", + "1/1 - 0s - loss: 0.0018 - root_mean_squared_error: 0.0421 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1271/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0384 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1272/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0380 - 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 1273/5000\n", + "1/1 - 0s - loss: 0.0017 - root_mean_squared_error: 0.0407 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1274/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0386 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1275/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0374 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0674\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1276/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0389 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0600\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1277/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0382 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1278/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - 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 1279/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0381 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0609\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1280/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0378 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0624\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1281/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0645\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1282/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1283/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0376 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0627\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1284/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0642\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1285/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1286/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0373 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1287/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0040 - val_root_mean_squared_error: 0.0632\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1288/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1289/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0372 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0624\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1290/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1291/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1292/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0371 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0622\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1293/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0625\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1294/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1295/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0370 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1296/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0619\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1297/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1298/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0369 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0621\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1299/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1300/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1301/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0368 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0618\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1302/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0614\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1303/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1304/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1305/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0367 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0612\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1306/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1307/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1308/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1309/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1310/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0614\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1311/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1312/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0607\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1313/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0612\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1314/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1315/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0365 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1316/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0610\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1317/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1318/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1319/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1320/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0602\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1321/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0605\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1322/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0606\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1323/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1324/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1325/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0603\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1326/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1327/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0363 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1328/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1329/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0600\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1330/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0603\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1331/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1332/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1333/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0601\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1334/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1335/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1336/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1337/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0596\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1338/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0361 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0598\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1339/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0597\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1340/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0596\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1341/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0597\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1342/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1343/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1344/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0360 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0595\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1345/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1346/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1347/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0594\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1348/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1349/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0593\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1350/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0359 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1351/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1352/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0592\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1353/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1354/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0591\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1355/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1356/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1357/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0590\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1358/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0589\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1359/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0588\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1360/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0588\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1361/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0587\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1362/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0357 - val_loss: 0.0035 - val_root_mean_squared_error: 0.0587\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1363/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0587\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1364/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1365/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1366/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0586\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1367/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1368/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0585\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1369/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1370/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1371/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0584\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1372/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1373/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0583\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1374/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0355 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1375/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1376/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0582\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1377/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1378/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0581\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1379/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1380/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0354 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1381/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0580\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1382/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0579\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1383/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0579\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1384/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0579\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1385/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1386/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0578\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1387/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0353 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1388/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1389/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0577\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1390/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1391/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1392/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0576\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1393/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1394/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1395/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0575\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1396/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1397/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1398/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1399/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0351 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1400/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0573\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1401/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1402/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1403/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0572\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1404/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1405/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0350 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1406/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0570\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1407/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0570\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1408/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0570\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1409/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1410/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1411/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0569\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1412/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1413/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1414/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0568\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1415/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1416/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1417/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1418/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0348 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1419/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1420/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1421/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1422/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0565\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1423/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1424/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1425/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0564\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1426/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1427/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1428/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1429/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1430/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1431/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0562\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1432/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0561\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1433/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1434/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1435/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1436/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0345 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1437/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0560\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1438/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1439/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1440/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0559\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1441/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1442/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0558\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1443/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1444/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1445/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1446/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1447/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1448/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0556\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1449/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1450/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1451/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0555\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1452/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1453/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1454/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1455/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1456/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1457/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1458/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0552\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1459/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0552\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1460/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0552\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1461/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1462/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1463/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1464/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0550\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1465/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0550\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1466/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0550\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1467/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1468/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1469/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1470/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1471/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1472/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1473/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0339 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0547\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1474/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1475/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0545\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1476/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0549\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1477/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1478/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0551\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1479/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0539\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1480/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0557\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1481/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0339 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0533\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1482/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0574\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1483/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1484/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0617\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1485/5000\n", + "1/1 - 0s - loss: 0.0014 - root_mean_squared_error: 0.0377 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1486/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0383 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0638\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1487/5000\n", + "1/1 - 0s - loss: 0.0015 - root_mean_squared_error: 0.0393 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1488/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0356 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1489/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0034 - val_root_mean_squared_error: 0.0579\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1490/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1491/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0364 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0604\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1492/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0366 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1493/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0340 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1494/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0036 - val_root_mean_squared_error: 0.0599\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1495/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0362 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1496/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0352 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1497/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0561\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1498/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0341 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1499/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0349 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0567\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1500/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0343 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1501/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1502/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0033 - val_root_mean_squared_error: 0.0571\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1503/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1504/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1505/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0566\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1506/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0342 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1507/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0338 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0538\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1508/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0333 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0554\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1509/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1510/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0337 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1511/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0543\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1512/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1513/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0336 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1514/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1515/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1516/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0548\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1517/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0532\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1518/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1519/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0544\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1520/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0334 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1521/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0534\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1522/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0542\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1523/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1524/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0332 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1525/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0535\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1526/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1527/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1528/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1529/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1530/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0536\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1531/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0331 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1532/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0529\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1533/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0534\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1534/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1535/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0330 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1536/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1537/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1538/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0531\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1539/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1540/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0329 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1541/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0530\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1542/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1543/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1544/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0528\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1545/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1546/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0328 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0527\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1547/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1548/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1549/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0526\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1550/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1551/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1552/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0028 - val_root_mean_squared_error: 0.0525\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1553/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1554/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0524\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1555/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1556/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1557/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0523\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1558/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0326 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1559/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1560/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0522\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1561/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1562/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0521\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1563/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1564/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1565/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0520\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1566/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1567/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0519\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1568/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1569/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1570/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0518\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1571/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1572/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1573/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0517\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1574/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1575/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1576/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1577/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1578/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0515\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1579/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1580/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1581/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0514\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1582/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1583/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0322 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1584/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1585/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0513\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1586/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1587/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0512\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1588/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1589/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1590/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0511\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1591/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1592/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1593/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0510\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1594/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1595/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1596/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1597/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1598/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0508\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1599/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1600/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0319 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1601/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1602/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0507\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1603/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1604/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0506\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1605/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0505\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1606/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0318 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0505\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1607/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0505\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1608/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1609/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1610/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1611/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1612/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1613/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1614/5000\n", + "1/1 - 0s - loss: 9.9996e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1615/5000\n", + "1/1 - 0s - loss: 9.9886e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1616/5000\n", + "1/1 - 0s - loss: 9.9776e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0502\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1617/5000\n", + "1/1 - 0s - loss: 9.9666e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1618/5000\n", + "1/1 - 0s - loss: 9.9555e-04 - root_mean_squared_error: 0.0316 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0501\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1619/5000\n", + "1/1 - 0s - loss: 9.9445e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1620/5000\n", + "1/1 - 0s - loss: 9.9335e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1621/5000\n", + "1/1 - 0s - loss: 9.9224e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1622/5000\n", + "1/1 - 0s - loss: 9.9113e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1623/5000\n", + "1/1 - 0s - loss: 9.9003e-04 - root_mean_squared_error: 0.0315 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1624/5000\n", + "1/1 - 0s - loss: 9.8892e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0499\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1625/5000\n", + "1/1 - 0s - loss: 9.8781e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1626/5000\n", + "1/1 - 0s - loss: 9.8670e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1627/5000\n", + "1/1 - 0s - loss: 9.8560e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1628/5000\n", + "1/1 - 0s - loss: 9.8449e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1629/5000\n", + "1/1 - 0s - loss: 9.8338e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1630/5000\n", + "1/1 - 0s - loss: 9.8227e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1631/5000\n", + "1/1 - 0s - loss: 9.8116e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1632/5000\n", + "1/1 - 0s - loss: 9.8005e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0496\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1633/5000\n", + "1/1 - 0s - loss: 9.7893e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0495\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1634/5000\n", + "1/1 - 0s - loss: 9.7782e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1635/5000\n", + "1/1 - 0s - loss: 9.7671e-04 - root_mean_squared_error: 0.0313 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0495\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1636/5000\n", + "1/1 - 0s - loss: 9.7559e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1637/5000\n", + "1/1 - 0s - loss: 9.7448e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0494\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1638/5000\n", + "1/1 - 0s - loss: 9.7337e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1639/5000\n", + "1/1 - 0s - loss: 9.7225e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1640/5000\n", + "1/1 - 0s - loss: 9.7114e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1641/5000\n", + "1/1 - 0s - loss: 9.7002e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1642/5000\n", + "1/1 - 0s - loss: 9.6891e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1643/5000\n", + "1/1 - 0s - loss: 9.6779e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0492\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1644/5000\n", + "1/1 - 0s - loss: 9.6668e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1645/5000\n", + "1/1 - 0s - loss: 9.6556e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1646/5000\n", + "1/1 - 0s - loss: 9.6444e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1647/5000\n", + "1/1 - 0s - loss: 9.6333e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1648/5000\n", + "1/1 - 0s - loss: 9.6221e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0490\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1649/5000\n", + "1/1 - 0s - loss: 9.6109e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1650/5000\n", + "1/1 - 0s - loss: 9.5998e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1651/5000\n", + "1/1 - 0s - loss: 9.5886e-04 - root_mean_squared_error: 0.0310 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1652/5000\n", + "1/1 - 0s - loss: 9.5774e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0488\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1653/5000\n", + "1/1 - 0s - loss: 9.5662e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0488\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1654/5000\n", + "1/1 - 0s - loss: 9.5551e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1655/5000\n", + "1/1 - 0s - loss: 9.5439e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1656/5000\n", + "1/1 - 0s - loss: 9.5327e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1657/5000\n", + "1/1 - 0s - loss: 9.5215e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1658/5000\n", + "1/1 - 0s - loss: 9.5103e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1659/5000\n", + "1/1 - 0s - loss: 9.4992e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0486\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1660/5000\n", + "1/1 - 0s - loss: 9.4880e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1661/5000\n", + "1/1 - 0s - loss: 9.4769e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1662/5000\n", + "1/1 - 0s - loss: 9.4658e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0484\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1663/5000\n", + "1/1 - 0s - loss: 9.4548e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1664/5000\n", + "1/1 - 0s - loss: 9.4440e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0483\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1665/5000\n", + "1/1 - 0s - loss: 9.4335e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1666/5000\n", + "1/1 - 0s - loss: 9.4236e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1667/5000\n", + "1/1 - 0s - loss: 9.4149e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0485\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1668/5000\n", + "1/1 - 0s - loss: 9.4087e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1669/5000\n", + "1/1 - 0s - loss: 9.4071e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0487\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1670/5000\n", + "1/1 - 0s - loss: 9.4165e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1671/5000\n", + "1/1 - 0s - loss: 9.4444e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0493\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1672/5000\n", + "1/1 - 0s - loss: 9.5227e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1673/5000\n", + "1/1 - 0s - loss: 9.6688e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1674/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0468\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1675/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0325 - val_loss: 0.0030 - val_root_mean_squared_error: 0.0546\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1676/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0344 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1677/5000\n", + "1/1 - 0s - loss: 0.0012 - root_mean_squared_error: 0.0347 - val_loss: 0.0032 - val_root_mean_squared_error: 0.0563\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1678/5000\n", + "1/1 - 0s - loss: 0.0013 - root_mean_squared_error: 0.0358 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1679/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0327 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0491\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1680/5000\n", + "1/1 - 0s - loss: 9.4227e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0498\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1681/5000\n", + "1/1 - 0s - loss: 9.5416e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1682/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0324 - val_loss: 0.0029 - val_root_mean_squared_error: 0.0537\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1683/5000\n", + "1/1 - 0s - loss: 0.0011 - root_mean_squared_error: 0.0335 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1684/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0317 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1685/5000\n", + "1/1 - 0s - loss: 9.2667e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0504\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1686/5000\n", + "1/1 - 0s - loss: 9.7389e-04 - root_mean_squared_error: 0.0312 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0472\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1687/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0321 - val_loss: 0.0027 - val_root_mean_squared_error: 0.0516\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1688/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0320 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1689/5000\n", + "1/1 - 0s - loss: 9.3844e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1690/5000\n", + "1/1 - 0s - loss: 9.3782e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0026 - val_root_mean_squared_error: 0.0509\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1691/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0316 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0471\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1692/5000\n", + "1/1 - 0s - loss: 9.8754e-04 - root_mean_squared_error: 0.0314 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1693/5000\n", + "1/1 - 0s - loss: 9.3431e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0477\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1694/5000\n", + "1/1 - 0s - loss: 9.2146e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1695/5000\n", + "1/1 - 0s - loss: 9.5381e-04 - root_mean_squared_error: 0.0309 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0500\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1696/5000\n", + "1/1 - 0s - loss: 9.6799e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1697/5000\n", + "1/1 - 0s - loss: 9.2698e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0474\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1698/5000\n", + "1/1 - 0s - loss: 9.1488e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1699/5000\n", + "1/1 - 0s - loss: 9.3686e-04 - root_mean_squared_error: 0.0306 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1700/5000\n", + "1/1 - 0s - loss: 9.4073e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1701/5000\n", + "1/1 - 0s - loss: 9.1896e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0476\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1702/5000\n", + "1/1 - 0s - loss: 9.0889e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1703/5000\n", + "1/1 - 0s - loss: 9.2294e-04 - root_mean_squared_error: 0.0304 - val_loss: 0.0024 - val_root_mean_squared_error: 0.0489\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1704/5000\n", + "1/1 - 0s - loss: 9.2978e-04 - root_mean_squared_error: 0.0305 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1705/5000\n", + "1/1 - 0s - loss: 9.1222e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0468\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1706/5000\n", + "1/1 - 0s - loss: 9.0521e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0481\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1707/5000\n", + "1/1 - 0s - loss: 9.1423e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1708/5000\n", + "1/1 - 0s - loss: 9.1719e-04 - root_mean_squared_error: 0.0303 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1709/5000\n", + "1/1 - 0s - loss: 9.0695e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1710/5000\n", + "1/1 - 0s - loss: 9.0029e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1711/5000\n", + "1/1 - 0s - loss: 9.0547e-04 - root_mean_squared_error: 0.0301 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0479\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1712/5000\n", + "1/1 - 0s - loss: 9.0929e-04 - root_mean_squared_error: 0.0302 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1713/5000\n", + "1/1 - 0s - loss: 9.0209e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1714/5000\n", + "1/1 - 0s - loss: 8.9638e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1715/5000\n", + "1/1 - 0s - loss: 8.9851e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1716/5000\n", + "1/1 - 0s - loss: 9.0132e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1717/5000\n", + "1/1 - 0s - loss: 8.9798e-04 - root_mean_squared_error: 0.0300 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1718/5000\n", + "1/1 - 0s - loss: 8.9282e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1719/5000\n", + "1/1 - 0s - loss: 8.9252e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0473\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1720/5000\n", + "1/1 - 0s - loss: 8.9496e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1721/5000\n", + "1/1 - 0s - loss: 8.9345e-04 - root_mean_squared_error: 0.0299 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0468\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1722/5000\n", + "1/1 - 0s - loss: 8.8962e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1723/5000\n", + "1/1 - 0s - loss: 8.8765e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1724/5000\n", + "1/1 - 0s - loss: 8.8855e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0470\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1725/5000\n", + "1/1 - 0s - loss: 8.8878e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1726/5000\n", + "1/1 - 0s - loss: 8.8633e-04 - root_mean_squared_error: 0.0298 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0465\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1727/5000\n", + "1/1 - 0s - loss: 8.8380e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0466\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1728/5000\n", + "1/1 - 0s - loss: 8.8328e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1729/5000\n", + "1/1 - 0s - loss: 8.8346e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1730/5000\n", + "1/1 - 0s - loss: 8.8262e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1731/5000\n", + "1/1 - 0s - loss: 8.8047e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0463\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1732/5000\n", + "1/1 - 0s - loss: 8.7897e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1733/5000\n", + "1/1 - 0s - loss: 8.7853e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1734/5000\n", + "1/1 - 0s - loss: 8.7822e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1735/5000\n", + "1/1 - 0s - loss: 8.7706e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1736/5000\n", + "1/1 - 0s - loss: 8.7544e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0461\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1737/5000\n", + "1/1 - 0s - loss: 8.7422e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1738/5000\n", + "1/1 - 0s - loss: 8.7367e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1739/5000\n", + "1/1 - 0s - loss: 8.7306e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0462\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1740/5000\n", + "1/1 - 0s - loss: 8.7199e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1741/5000\n", + "1/1 - 0s - loss: 8.7063e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0459\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1742/5000\n", + "1/1 - 0s - loss: 8.6954e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1743/5000\n", + "1/1 - 0s - loss: 8.6879e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1744/5000\n", + "1/1 - 0s - loss: 8.6809e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0460\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1745/5000\n", + "1/1 - 0s - loss: 8.6711e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1746/5000\n", + "1/1 - 0s - loss: 8.6595e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1747/5000\n", + "1/1 - 0s - loss: 8.6486e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1748/5000\n", + "1/1 - 0s - loss: 8.6399e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1749/5000\n", + "1/1 - 0s - loss: 8.6321e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1750/5000\n", + "1/1 - 0s - loss: 8.6233e-04 - root_mean_squared_error: 0.0294 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1751/5000\n", + "1/1 - 0s - loss: 8.6131e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0457\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1752/5000\n", + "1/1 - 0s - loss: 8.6025e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1753/5000\n", + "1/1 - 0s - loss: 8.5929e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1754/5000\n", + "1/1 - 0s - loss: 8.5843e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0456\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1755/5000\n", + "1/1 - 0s - loss: 8.5759e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1756/5000\n", + "1/1 - 0s - loss: 8.5666e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0455\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1757/5000\n", + "1/1 - 0s - loss: 8.5568e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1758/5000\n", + "1/1 - 0s - loss: 8.5469e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1759/5000\n", + "1/1 - 0s - loss: 8.5376e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1760/5000\n", + "1/1 - 0s - loss: 8.5287e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1761/5000\n", + "1/1 - 0s - loss: 8.5199e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1762/5000\n", + "1/1 - 0s - loss: 8.5108e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1763/5000\n", + "1/1 - 0s - loss: 8.5014e-04 - root_mean_squared_error: 0.0292 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0453\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1764/5000\n", + "1/1 - 0s - loss: 8.4919e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1765/5000\n", + "1/1 - 0s - loss: 8.4825e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1766/5000\n", + "1/1 - 0s - loss: 8.4735e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0452\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1767/5000\n", + "1/1 - 0s - loss: 8.4646e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1768/5000\n", + "1/1 - 0s - loss: 8.4556e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1769/5000\n", + "1/1 - 0s - loss: 8.4465e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1770/5000\n", + "1/1 - 0s - loss: 8.4373e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1771/5000\n", + "1/1 - 0s - loss: 8.4280e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0450\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1772/5000\n", + "1/1 - 0s - loss: 8.4189e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1773/5000\n", + "1/1 - 0s - loss: 8.4098e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1774/5000\n", + "1/1 - 0s - loss: 8.4009e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1775/5000\n", + "1/1 - 0s - loss: 8.3919e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0449\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1776/5000\n", + "1/1 - 0s - loss: 8.3829e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1777/5000\n", + "1/1 - 0s - loss: 8.3739e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1778/5000\n", + "1/1 - 0s - loss: 8.3648e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0448\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1779/5000\n", + "1/1 - 0s - loss: 8.3557e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1780/5000\n", + "1/1 - 0s - loss: 8.3467e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1781/5000\n", + "1/1 - 0s - loss: 8.3377e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1782/5000\n", + "1/1 - 0s - loss: 8.3288e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1783/5000\n", + "1/1 - 0s - loss: 8.3198e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0446\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1784/5000\n", + "1/1 - 0s - loss: 8.3109e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0446\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1785/5000\n", + "1/1 - 0s - loss: 8.3020e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1786/5000\n", + "1/1 - 0s - loss: 8.2931e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0446\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1787/5000\n", + "1/1 - 0s - loss: 8.2841e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1788/5000\n", + "1/1 - 0s - loss: 8.2752e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1789/5000\n", + "1/1 - 0s - loss: 8.2663e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1790/5000\n", + "1/1 - 0s - loss: 8.2574e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1791/5000\n", + "1/1 - 0s - loss: 8.2485e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1792/5000\n", + "1/1 - 0s - loss: 8.2397e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1793/5000\n", + "1/1 - 0s - loss: 8.2308e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1794/5000\n", + "1/1 - 0s - loss: 8.2220e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1795/5000\n", + "1/1 - 0s - loss: 8.2132e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0443\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1796/5000\n", + "1/1 - 0s - loss: 8.2044e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1797/5000\n", + "1/1 - 0s - loss: 8.1956e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1798/5000\n", + "1/1 - 0s - loss: 8.1868e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1799/5000\n", + "1/1 - 0s - loss: 8.1780e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1800/5000\n", + "1/1 - 0s - loss: 8.1692e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1801/5000\n", + "1/1 - 0s - loss: 8.1605e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1802/5000\n", + "1/1 - 0s - loss: 8.1517e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1803/5000\n", + "1/1 - 0s - loss: 8.1430e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1804/5000\n", + "1/1 - 0s - loss: 8.1343e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1805/5000\n", + "1/1 - 0s - loss: 8.1256e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1806/5000\n", + "1/1 - 0s - loss: 8.1169e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1807/5000\n", + "1/1 - 0s - loss: 8.1082e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1808/5000\n", + "1/1 - 0s - loss: 8.0996e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1809/5000\n", + "1/1 - 0s - loss: 8.0910e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1810/5000\n", + "1/1 - 0s - loss: 8.0824e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0438\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1811/5000\n", + "1/1 - 0s - loss: 8.0739e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1812/5000\n", + "1/1 - 0s - loss: 8.0656e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1813/5000\n", + "1/1 - 0s - loss: 8.0574e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1814/5000\n", + "1/1 - 0s - loss: 8.0494e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1815/5000\n", + "1/1 - 0s - loss: 8.0420e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1816/5000\n", + "1/1 - 0s - loss: 8.0354e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1817/5000\n", + "1/1 - 0s - loss: 8.0302e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0440\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1818/5000\n", + "1/1 - 0s - loss: 8.0279e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1819/5000\n", + "1/1 - 0s - loss: 8.0297e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1820/5000\n", + "1/1 - 0s - loss: 8.0415e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1821/5000\n", + "1/1 - 0s - loss: 8.0664e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1822/5000\n", + "1/1 - 0s - loss: 8.1282e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1823/5000\n", + "1/1 - 0s - loss: 8.2255e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1824/5000\n", + "1/1 - 0s - loss: 8.4596e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1825/5000\n", + "1/1 - 0s - loss: 8.7300e-04 - root_mean_squared_error: 0.0295 - val_loss: 0.0023 - val_root_mean_squared_error: 0.0482\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1826/5000\n", + "1/1 - 0s - loss: 9.4114e-04 - root_mean_squared_error: 0.0307 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1827/5000\n", + "1/1 - 0s - loss: 9.6588e-04 - root_mean_squared_error: 0.0311 - val_loss: 0.0025 - val_root_mean_squared_error: 0.0503\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1828/5000\n", + "1/1 - 0s - loss: 0.0010 - root_mean_squared_error: 0.0323 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1829/5000\n", + "1/1 - 0s - loss: 9.4898e-04 - root_mean_squared_error: 0.0308 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0469\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1830/5000\n", + "1/1 - 0s - loss: 8.7639e-04 - root_mean_squared_error: 0.0296 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1831/5000\n", + "1/1 - 0s - loss: 8.0184e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1832/5000\n", + "1/1 - 0s - loss: 8.0247e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1833/5000\n", + "1/1 - 0s - loss: 8.5614e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1834/5000\n", + "1/1 - 0s - loss: 8.8202e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0467\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1835/5000\n", + "1/1 - 0s - loss: 8.8019e-04 - root_mean_squared_error: 0.0297 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1836/5000\n", + "1/1 - 0s - loss: 8.1695e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1837/5000\n", + "1/1 - 0s - loss: 7.8772e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0447\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1838/5000\n", + "1/1 - 0s - loss: 8.0492e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1839/5000\n", + "1/1 - 0s - loss: 8.3379e-04 - root_mean_squared_error: 0.0289 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0458\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1840/5000\n", + "1/1 - 0s - loss: 8.4824e-04 - root_mean_squared_error: 0.0291 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1841/5000\n", + "1/1 - 0s - loss: 8.1331e-04 - root_mean_squared_error: 0.0285 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1842/5000\n", + "1/1 - 0s - loss: 7.8601e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1843/5000\n", + "1/1 - 0s - loss: 7.8856e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1844/5000\n", + "1/1 - 0s - loss: 8.0775e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0451\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1845/5000\n", + "1/1 - 0s - loss: 8.2140e-04 - root_mean_squared_error: 0.0287 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1846/5000\n", + "1/1 - 0s - loss: 8.0470e-04 - root_mean_squared_error: 0.0284 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0437\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1847/5000\n", + "1/1 - 0s - loss: 7.8442e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1848/5000\n", + "1/1 - 0s - loss: 7.8016e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1849/5000\n", + "1/1 - 0s - loss: 7.9089e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0445\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1850/5000\n", + "1/1 - 0s - loss: 8.0146e-04 - root_mean_squared_error: 0.0283 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1851/5000\n", + "1/1 - 0s - loss: 7.9526e-04 - root_mean_squared_error: 0.0282 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1852/5000\n", + "1/1 - 0s - loss: 7.8195e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1853/5000\n", + "1/1 - 0s - loss: 7.7525e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1854/5000\n", + "1/1 - 0s - loss: 7.8017e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0441\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1855/5000\n", + "1/1 - 0s - loss: 7.8743e-04 - root_mean_squared_error: 0.0281 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1856/5000\n", + "1/1 - 0s - loss: 7.8639e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0435\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1857/5000\n", + "1/1 - 0s - loss: 7.7883e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1858/5000\n", + "1/1 - 0s - loss: 7.7178e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1859/5000\n", + "1/1 - 0s - loss: 7.7223e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0436\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1860/5000\n", + "1/1 - 0s - loss: 7.7636e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1861/5000\n", + "1/1 - 0s - loss: 7.7811e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0433\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1862/5000\n", + "1/1 - 0s - loss: 7.7534e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1863/5000\n", + "1/1 - 0s - loss: 7.6952e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1864/5000\n", + "1/1 - 0s - loss: 7.6690e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1865/5000\n", + "1/1 - 0s - loss: 7.6772e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1866/5000\n", + "1/1 - 0s - loss: 7.6976e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0431\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1867/5000\n", + "1/1 - 0s - loss: 7.7024e-04 - root_mean_squared_error: 0.0278 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1868/5000\n", + "1/1 - 0s - loss: 7.6735e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0429\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1869/5000\n", + "1/1 - 0s - loss: 7.6419e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1870/5000\n", + "1/1 - 0s - loss: 7.6230e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1871/5000\n", + "1/1 - 0s - loss: 7.6229e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1872/5000\n", + "1/1 - 0s - loss: 7.6317e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1873/5000\n", + "1/1 - 0s - loss: 7.6295e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1874/5000\n", + "1/1 - 0s - loss: 7.6163e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1875/5000\n", + "1/1 - 0s - loss: 7.5957e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0424\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1876/5000\n", + "1/1 - 0s - loss: 7.5790e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1877/5000\n", + "1/1 - 0s - loss: 7.5724e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1878/5000\n", + "1/1 - 0s - loss: 7.5714e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0426\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1879/5000\n", + "1/1 - 0s - loss: 7.5707e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1880/5000\n", + "1/1 - 0s - loss: 7.5642e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1881/5000\n", + "1/1 - 0s - loss: 7.5525e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1882/5000\n", + "1/1 - 0s - loss: 7.5390e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1883/5000\n", + "1/1 - 0s - loss: 7.5273e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1884/5000\n", + "1/1 - 0s - loss: 7.5198e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1885/5000\n", + "1/1 - 0s - loss: 7.5155e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0423\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1886/5000\n", + "1/1 - 0s - loss: 7.5115e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1887/5000\n", + "1/1 - 0s - loss: 7.5060e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0422\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1888/5000\n", + "1/1 - 0s - loss: 7.4978e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1889/5000\n", + "1/1 - 0s - loss: 7.4878e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1890/5000\n", + "1/1 - 0s - loss: 7.4778e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1891/5000\n", + "1/1 - 0s - loss: 7.4688e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1892/5000\n", + "1/1 - 0s - loss: 7.4615e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1893/5000\n", + "1/1 - 0s - loss: 7.4553e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1894/5000\n", + "1/1 - 0s - loss: 7.4495e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0420\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1895/5000\n", + "1/1 - 0s - loss: 7.4435e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1896/5000\n", + "1/1 - 0s - loss: 7.4365e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1897/5000\n", + "1/1 - 0s - loss: 7.4289e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1898/5000\n", + "1/1 - 0s - loss: 7.4207e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1899/5000\n", + "1/1 - 0s - loss: 7.4124e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1900/5000\n", + "1/1 - 0s - loss: 7.4043e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1901/5000\n", + "1/1 - 0s - loss: 7.3967e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1902/5000\n", + "1/1 - 0s - loss: 7.3895e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1903/5000\n", + "1/1 - 0s - loss: 7.3826e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1904/5000\n", + "1/1 - 0s - loss: 7.3759e-04 - root_mean_squared_error: 0.0272 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1905/5000\n", + "1/1 - 0s - loss: 7.3693e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1906/5000\n", + "1/1 - 0s - loss: 7.3626e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1907/5000\n", + "1/1 - 0s - loss: 7.3557e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1908/5000\n", + "1/1 - 0s - loss: 7.3487e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1909/5000\n", + "1/1 - 0s - loss: 7.3416e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1910/5000\n", + "1/1 - 0s - loss: 7.3344e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1911/5000\n", + "1/1 - 0s - loss: 7.3271e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1912/5000\n", + "1/1 - 0s - loss: 7.3198e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1913/5000\n", + "1/1 - 0s - loss: 7.3125e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1914/5000\n", + "1/1 - 0s - loss: 7.3053e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1915/5000\n", + "1/1 - 0s - loss: 7.2981e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1916/5000\n", + "1/1 - 0s - loss: 7.2910e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1917/5000\n", + "1/1 - 0s - loss: 7.2839e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1918/5000\n", + "1/1 - 0s - loss: 7.2768e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1919/5000\n", + "1/1 - 0s - loss: 7.2697e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1920/5000\n", + "1/1 - 0s - loss: 7.2628e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1921/5000\n", + "1/1 - 0s - loss: 7.2558e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1922/5000\n", + "1/1 - 0s - loss: 7.2490e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1923/5000\n", + "1/1 - 0s - loss: 7.2422e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1924/5000\n", + "1/1 - 0s - loss: 7.2356e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0410\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1925/5000\n", + "1/1 - 0s - loss: 7.2291e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0412\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1926/5000\n", + "1/1 - 0s - loss: 7.2230e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0409\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1927/5000\n", + "1/1 - 0s - loss: 7.2174e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1928/5000\n", + "1/1 - 0s - loss: 7.2126e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1929/5000\n", + "1/1 - 0s - loss: 7.2090e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1930/5000\n", + "1/1 - 0s - loss: 7.2080e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0406\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1931/5000\n", + "1/1 - 0s - loss: 7.2102e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1932/5000\n", + "1/1 - 0s - loss: 7.2203e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1933/5000\n", + "1/1 - 0s - loss: 7.2390e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0419\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1934/5000\n", + "1/1 - 0s - loss: 7.2834e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1935/5000\n", + "1/1 - 0s - loss: 7.3481e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0428\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1936/5000\n", + "1/1 - 0s - loss: 7.4977e-04 - root_mean_squared_error: 0.0274 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1937/5000\n", + "1/1 - 0s - loss: 7.6659e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0444\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1938/5000\n", + "1/1 - 0s - loss: 8.0778e-04 - root_mean_squared_error: 0.0284 - 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 1939/5000\n", + "1/1 - 0s - loss: 8.2992e-04 - root_mean_squared_error: 0.0288 - val_loss: 0.0022 - val_root_mean_squared_error: 0.0464\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1940/5000\n", + "1/1 - 0s - loss: 8.9350e-04 - root_mean_squared_error: 0.0299 - 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 1941/5000\n", + "1/1 - 0s - loss: 8.5872e-04 - root_mean_squared_error: 0.0293 - val_loss: 0.0021 - val_root_mean_squared_error: 0.0454\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1942/5000\n", + "1/1 - 0s - loss: 8.4389e-04 - root_mean_squared_error: 0.0290 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1943/5000\n", + "1/1 - 0s - loss: 7.6124e-04 - root_mean_squared_error: 0.0276 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1944/5000\n", + "1/1 - 0s - loss: 7.1702e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0416\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1945/5000\n", + "1/1 - 0s - loss: 7.1617e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1946/5000\n", + "1/1 - 0s - loss: 7.4656e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0439\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1947/5000\n", + "1/1 - 0s - loss: 7.8612e-04 - root_mean_squared_error: 0.0280 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1948/5000\n", + "1/1 - 0s - loss: 7.7588e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1949/5000\n", + "1/1 - 0s - loss: 7.5441e-04 - root_mean_squared_error: 0.0275 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1950/5000\n", + "1/1 - 0s - loss: 7.1849e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1951/5000\n", + "1/1 - 0s - loss: 7.0654e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1952/5000\n", + "1/1 - 0s - loss: 7.1844e-04 - root_mean_squared_error: 0.0268 - 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 1953/5000\n", + "1/1 - 0s - loss: 7.3542e-04 - root_mean_squared_error: 0.0271 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1954/5000\n", + "1/1 - 0s - loss: 7.4505e-04 - root_mean_squared_error: 0.0273 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1955/5000\n", + "1/1 - 0s - loss: 7.2864e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0415\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1956/5000\n", + "1/1 - 0s - loss: 7.1156e-04 - root_mean_squared_error: 0.0267 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1957/5000\n", + "1/1 - 0s - loss: 7.0262e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1958/5000\n", + "1/1 - 0s - loss: 7.0696e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0417\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1959/5000\n", + "1/1 - 0s - loss: 7.1744e-04 - root_mean_squared_error: 0.0268 - 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 1960/5000\n", + "1/1 - 0s - loss: 7.2047e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1961/5000\n", + "1/1 - 0s - loss: 7.1698e-04 - root_mean_squared_error: 0.0268 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1962/5000\n", + "1/1 - 0s - loss: 7.0623e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1963/5000\n", + "1/1 - 0s - loss: 6.9925e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1964/5000\n", + "1/1 - 0s - loss: 6.9904e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1965/5000\n", + "1/1 - 0s - loss: 7.0333e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0414\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1966/5000\n", + "1/1 - 0s - loss: 7.0739e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1967/5000\n", + "1/1 - 0s - loss: 7.0590e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0411\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1968/5000\n", + "1/1 - 0s - loss: 7.0163e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0400\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1969/5000\n", + "1/1 - 0s - loss: 6.9646e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1970/5000\n", + "1/1 - 0s - loss: 6.9416e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0406\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1971/5000\n", + "1/1 - 0s - loss: 6.9501e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1972/5000\n", + "1/1 - 0s - loss: 6.9702e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0409\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1973/5000\n", + "1/1 - 0s - loss: 6.9824e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1974/5000\n", + "1/1 - 0s - loss: 6.9684e-04 - root_mean_squared_error: 0.0264 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0407\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1975/5000\n", + "1/1 - 0s - loss: 6.9427e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1976/5000\n", + "1/1 - 0s - loss: 6.9141e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0402\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1977/5000\n", + "1/1 - 0s - loss: 6.8974e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0402\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1978/5000\n", + "1/1 - 0s - loss: 6.8947e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1979/5000\n", + "1/1 - 0s - loss: 6.9001e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0405\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1980/5000\n", + "1/1 - 0s - loss: 6.9053e-04 - root_mean_squared_error: 0.0263 - 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 1981/5000\n", + "1/1 - 0s - loss: 6.9017e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1982/5000\n", + "1/1 - 0s - loss: 6.8913e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1983/5000\n", + "1/1 - 0s - loss: 6.8745e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1984/5000\n", + "1/1 - 0s - loss: 6.8588e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1985/5000\n", + "1/1 - 0s - loss: 6.8474e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1986/5000\n", + "1/1 - 0s - loss: 6.8415e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1987/5000\n", + "1/1 - 0s - loss: 6.8396e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0396\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1988/5000\n", + "1/1 - 0s - loss: 6.8385e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1989/5000\n", + "1/1 - 0s - loss: 6.8360e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1990/5000\n", + "1/1 - 0s - loss: 6.8300e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1991/5000\n", + "1/1 - 0s - loss: 6.8219e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1992/5000\n", + "1/1 - 0s - loss: 6.8117e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1993/5000\n", + "1/1 - 0s - loss: 6.8016e-04 - root_mean_squared_error: 0.0261 - 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 1994/5000\n", + "1/1 - 0s - loss: 6.7924e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1995/5000\n", + "1/1 - 0s - loss: 6.7848e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1996/5000\n", + "1/1 - 0s - loss: 6.7787e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1997/5000\n", + "1/1 - 0s - loss: 6.7737e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1998/5000\n", + "1/1 - 0s - loss: 6.7692e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 1999/5000\n", + "1/1 - 0s - loss: 6.7647e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2000/5000\n", + "1/1 - 0s - loss: 6.7599e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2001/5000\n", + "1/1 - 0s - loss: 6.7544e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2002/5000\n", + "1/1 - 0s - loss: 6.7486e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2003/5000\n", + "1/1 - 0s - loss: 6.7421e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2004/5000\n", + "1/1 - 0s - loss: 6.7355e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2005/5000\n", + "1/1 - 0s - loss: 6.7285e-04 - root_mean_squared_error: 0.0259 - 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 2006/5000\n", + "1/1 - 0s - loss: 6.7216e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2007/5000\n", + "1/1 - 0s - loss: 6.7147e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2008/5000\n", + "1/1 - 0s - loss: 6.7079e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2009/5000\n", + "1/1 - 0s - loss: 6.7012e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2010/5000\n", + "1/1 - 0s - loss: 6.6946e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2011/5000\n", + "1/1 - 0s - loss: 6.6881e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2012/5000\n", + "1/1 - 0s - loss: 6.6818e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0392\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2013/5000\n", + "1/1 - 0s - loss: 6.6756e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2014/5000\n", + "1/1 - 0s - loss: 6.6695e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2015/5000\n", + "1/1 - 0s - loss: 6.6634e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2016/5000\n", + "1/1 - 0s - loss: 6.6575e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2017/5000\n", + "1/1 - 0s - loss: 6.6518e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2018/5000\n", + "1/1 - 0s - loss: 6.6463e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2019/5000\n", + "1/1 - 0s - loss: 6.6411e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2020/5000\n", + "1/1 - 0s - loss: 6.6365e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2021/5000\n", + "1/1 - 0s - loss: 6.6326e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0394\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2022/5000\n", + "1/1 - 0s - loss: 6.6302e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2023/5000\n", + "1/1 - 0s - loss: 6.6295e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2024/5000\n", + "1/1 - 0s - loss: 6.6331e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2025/5000\n", + "1/1 - 0s - loss: 6.6411e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2026/5000\n", + "1/1 - 0s - loss: 6.6618e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2027/5000\n", + "1/1 - 0s - loss: 6.6931e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0403\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2028/5000\n", + "1/1 - 0s - loss: 6.7635e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2029/5000\n", + "1/1 - 0s - loss: 6.8519e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0413\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2030/5000\n", + "1/1 - 0s - loss: 7.0574e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2031/5000\n", + "1/1 - 0s - loss: 7.2380e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0019 - val_root_mean_squared_error: 0.0430\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2032/5000\n", + "1/1 - 0s - loss: 7.6981e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2033/5000\n", + "1/1 - 0s - loss: 7.7858e-04 - root_mean_squared_error: 0.0279 - val_loss: 0.0020 - val_root_mean_squared_error: 0.0442\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2034/5000\n", + "1/1 - 0s - loss: 8.2028e-04 - root_mean_squared_error: 0.0286 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2035/5000\n", + "1/1 - 0s - loss: 7.6658e-04 - root_mean_squared_error: 0.0277 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0421\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2036/5000\n", + "1/1 - 0s - loss: 7.2986e-04 - root_mean_squared_error: 0.0270 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2037/5000\n", + "1/1 - 0s - loss: 6.7403e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2038/5000\n", + "1/1 - 0s - loss: 6.5393e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0401\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2039/5000\n", + "1/1 - 0s - loss: 6.6730e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2040/5000\n", + "1/1 - 0s - loss: 6.9401e-04 - root_mean_squared_error: 0.0263 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0418\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2041/5000\n", + "1/1 - 0s - loss: 7.2220e-04 - root_mean_squared_error: 0.0269 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2042/5000\n", + "1/1 - 0s - loss: 7.0766e-04 - root_mean_squared_error: 0.0266 - val_loss: 0.0017 - val_root_mean_squared_error: 0.0408\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2043/5000\n", + "1/1 - 0s - loss: 6.8861e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2044/5000\n", + "1/1 - 0s - loss: 6.6025e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2045/5000\n", + "1/1 - 0s - loss: 6.4940e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2046/5000\n", + "1/1 - 0s - loss: 6.5644e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2047/5000\n", + "1/1 - 0s - loss: 6.6985e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0406\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2048/5000\n", + "1/1 - 0s - loss: 6.8041e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2049/5000\n", + "1/1 - 0s - loss: 6.7219e-04 - root_mean_squared_error: 0.0259 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2050/5000\n", + "1/1 - 0s - loss: 6.6039e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2051/5000\n", + "1/1 - 0s - loss: 6.4855e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2052/5000\n", + "1/1 - 0s - loss: 6.4570e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2053/5000\n", + "1/1 - 0s - loss: 6.5073e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2054/5000\n", + "1/1 - 0s - loss: 6.5702e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2055/5000\n", + "1/1 - 0s - loss: 6.6078e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2056/5000\n", + "1/1 - 0s - loss: 6.5606e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2057/5000\n", + "1/1 - 0s - loss: 6.4952e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2058/5000\n", + "1/1 - 0s - loss: 6.4338e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2059/5000\n", + "1/1 - 0s - loss: 6.4153e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2060/5000\n", + "1/1 - 0s - loss: 6.4352e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2061/5000\n", + "1/1 - 0s - loss: 6.4646e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2062/5000\n", + "1/1 - 0s - loss: 6.4823e-04 - root_mean_squared_error: 0.0255 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2063/5000\n", + "1/1 - 0s - loss: 6.4639e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0390\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2064/5000\n", + "1/1 - 0s - loss: 6.4330e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2065/5000\n", + "1/1 - 0s - loss: 6.3971e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2066/5000\n", + "1/1 - 0s - loss: 6.3759e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0384\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2067/5000\n", + "1/1 - 0s - loss: 6.3724e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2068/5000\n", + "1/1 - 0s - loss: 6.3806e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2069/5000\n", + "1/1 - 0s - loss: 6.3915e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2070/5000\n", + "1/1 - 0s - loss: 6.3936e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0388\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2071/5000\n", + "1/1 - 0s - loss: 6.3881e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2072/5000\n", + "1/1 - 0s - loss: 6.3714e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0385\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2073/5000\n", + "1/1 - 0s - loss: 6.3531e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2074/5000\n", + "1/1 - 0s - loss: 6.3361e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2075/5000\n", + "1/1 - 0s - loss: 6.3250e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2076/5000\n", + "1/1 - 0s - loss: 6.3200e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2077/5000\n", + "1/1 - 0s - loss: 6.3193e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2078/5000\n", + "1/1 - 0s - loss: 6.3202e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2079/5000\n", + "1/1 - 0s - loss: 6.3194e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2080/5000\n", + "1/1 - 0s - loss: 6.3166e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2081/5000\n", + "1/1 - 0s - loss: 6.3099e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2082/5000\n", + "1/1 - 0s - loss: 6.3017e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2083/5000\n", + "1/1 - 0s - loss: 6.2917e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2084/5000\n", + "1/1 - 0s - loss: 6.2820e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2085/5000\n", + "1/1 - 0s - loss: 6.2729e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2086/5000\n", + "1/1 - 0s - loss: 6.2651e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2087/5000\n", + "1/1 - 0s - loss: 6.2587e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2088/5000\n", + "1/1 - 0s - loss: 6.2534e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2089/5000\n", + "1/1 - 0s - loss: 6.2490e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2090/5000\n", + "1/1 - 0s - loss: 6.2450e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2091/5000\n", + "1/1 - 0s - loss: 6.2413e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2092/5000\n", + "1/1 - 0s - loss: 6.2375e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2093/5000\n", + "1/1 - 0s - loss: 6.2339e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2094/5000\n", + "1/1 - 0s - loss: 6.2299e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2095/5000\n", + "1/1 - 0s - loss: 6.2263e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2096/5000\n", + "1/1 - 0s - loss: 6.2223e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2097/5000\n", + "1/1 - 0s - loss: 6.2190e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2098/5000\n", + "1/1 - 0s - loss: 6.2155e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2099/5000\n", + "1/1 - 0s - loss: 6.2132e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2100/5000\n", + "1/1 - 0s - loss: 6.2108e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2101/5000\n", + "1/1 - 0s - loss: 6.2109e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2102/5000\n", + "1/1 - 0s - loss: 6.2112e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2103/5000\n", + "1/1 - 0s - loss: 6.2164e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2104/5000\n", + "1/1 - 0s - loss: 6.2222e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0382\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2105/5000\n", + "1/1 - 0s - loss: 6.2382e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0367\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2106/5000\n", + "1/1 - 0s - loss: 6.2549e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0386\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2107/5000\n", + "1/1 - 0s - loss: 6.2934e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2108/5000\n", + "1/1 - 0s - loss: 6.3289e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2109/5000\n", + "1/1 - 0s - loss: 6.4113e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2110/5000\n", + "1/1 - 0s - loss: 6.4696e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0398\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2111/5000\n", + "1/1 - 0s - loss: 6.6177e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2112/5000\n", + "1/1 - 0s - loss: 6.6675e-04 - root_mean_squared_error: 0.0258 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2113/5000\n", + "1/1 - 0s - loss: 6.8469e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2114/5000\n", + "1/1 - 0s - loss: 6.7823e-04 - root_mean_squared_error: 0.0260 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0404\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2115/5000\n", + "1/1 - 0s - loss: 6.8291e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2116/5000\n", + "1/1 - 0s - loss: 6.5966e-04 - root_mean_squared_error: 0.0257 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0393\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2117/5000\n", + "1/1 - 0s - loss: 6.4404e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2118/5000\n", + "1/1 - 0s - loss: 6.2325e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2119/5000\n", + "1/1 - 0s - loss: 6.1180e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2120/5000\n", + "1/1 - 0s - loss: 6.0902e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0367\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2121/5000\n", + "1/1 - 0s - loss: 6.1355e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2122/5000\n", + "1/1 - 0s - loss: 6.2244e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2123/5000\n", + "1/1 - 0s - loss: 6.2934e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0389\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2124/5000\n", + "1/1 - 0s - loss: 6.3675e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2125/5000\n", + "1/1 - 0s - loss: 6.3392e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2126/5000\n", + "1/1 - 0s - loss: 6.3128e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2127/5000\n", + "1/1 - 0s - loss: 6.2118e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0380\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2128/5000\n", + "1/1 - 0s - loss: 6.1312e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2129/5000\n", + "1/1 - 0s - loss: 6.0649e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2130/5000\n", + "1/1 - 0s - loss: 6.0376e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2131/5000\n", + "1/1 - 0s - loss: 6.0452e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2132/5000\n", + "1/1 - 0s - loss: 6.0736e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2133/5000\n", + "1/1 - 0s - loss: 6.1113e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2134/5000\n", + "1/1 - 0s - loss: 6.1318e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0381\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2135/5000\n", + "1/1 - 0s - loss: 6.1494e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2136/5000\n", + "1/1 - 0s - loss: 6.1312e-04 - root_mean_squared_error: 0.0248 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2137/5000\n", + "1/1 - 0s - loss: 6.1121e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2138/5000\n", + "1/1 - 0s - loss: 6.0713e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2139/5000\n", + "1/1 - 0s - loss: 6.0370e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2140/5000\n", + "1/1 - 0s - loss: 6.0056e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2141/5000\n", + "1/1 - 0s - loss: 5.9860e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2142/5000\n", + "1/1 - 0s - loss: 5.9776e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0366\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2143/5000\n", + "1/1 - 0s - loss: 5.9783e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0371\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2144/5000\n", + "1/1 - 0s - loss: 5.9851e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2145/5000\n", + "1/1 - 0s - loss: 5.9934e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2146/5000\n", + "1/1 - 0s - loss: 6.0036e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2147/5000\n", + "1/1 - 0s - loss: 6.0088e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2148/5000\n", + "1/1 - 0s - loss: 6.0159e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2149/5000\n", + "1/1 - 0s - loss: 6.0141e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0375\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2150/5000\n", + "1/1 - 0s - loss: 6.0157e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2151/5000\n", + "1/1 - 0s - loss: 6.0070e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2152/5000\n", + "1/1 - 0s - loss: 6.0025e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2153/5000\n", + "1/1 - 0s - loss: 5.9891e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2154/5000\n", + "1/1 - 0s - loss: 5.9804e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2155/5000\n", + "1/1 - 0s - loss: 5.9662e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2156/5000\n", + "1/1 - 0s - loss: 5.9567e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2157/5000\n", + "1/1 - 0s - loss: 5.9444e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2158/5000\n", + "1/1 - 0s - loss: 5.9361e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2159/5000\n", + "1/1 - 0s - loss: 5.9264e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2160/5000\n", + "1/1 - 0s - loss: 5.9199e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2161/5000\n", + "1/1 - 0s - loss: 5.9127e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2162/5000\n", + "1/1 - 0s - loss: 5.9083e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2163/5000\n", + "1/1 - 0s - loss: 5.9034e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2164/5000\n", + "1/1 - 0s - loss: 5.9015e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2165/5000\n", + "1/1 - 0s - loss: 5.8993e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2166/5000\n", + "1/1 - 0s - loss: 5.9014e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2167/5000\n", + "1/1 - 0s - loss: 5.9036e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2168/5000\n", + "1/1 - 0s - loss: 5.9136e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2169/5000\n", + "1/1 - 0s - loss: 5.9242e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2170/5000\n", + "1/1 - 0s - loss: 5.9508e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2171/5000\n", + "1/1 - 0s - loss: 5.9770e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0377\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2172/5000\n", + "1/1 - 0s - loss: 6.0378e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2173/5000\n", + "1/1 - 0s - loss: 6.0878e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0383\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2174/5000\n", + "1/1 - 0s - loss: 6.2082e-04 - root_mean_squared_error: 0.0249 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2175/5000\n", + "1/1 - 0s - loss: 6.2707e-04 - root_mean_squared_error: 0.0250 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0391\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2176/5000\n", + "1/1 - 0s - loss: 6.4501e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2177/5000\n", + "1/1 - 0s - loss: 6.4480e-04 - root_mean_squared_error: 0.0254 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0395\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2178/5000\n", + "1/1 - 0s - loss: 6.5730e-04 - root_mean_squared_error: 0.0256 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2179/5000\n", + "1/1 - 0s - loss: 6.4004e-04 - root_mean_squared_error: 0.0253 - val_loss: 0.0015 - val_root_mean_squared_error: 0.0387\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2180/5000\n", + "1/1 - 0s - loss: 6.3140e-04 - root_mean_squared_error: 0.0251 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2181/5000\n", + "1/1 - 0s - loss: 6.0684e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2182/5000\n", + "1/1 - 0s - loss: 5.9065e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2183/5000\n", + "1/1 - 0s - loss: 5.8017e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2184/5000\n", + "1/1 - 0s - loss: 5.7854e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2185/5000\n", + "1/1 - 0s - loss: 5.8368e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2186/5000\n", + "1/1 - 0s - loss: 5.9146e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2187/5000\n", + "1/1 - 0s - loss: 6.0100e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2188/5000\n", + "1/1 - 0s - loss: 6.0349e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0378\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2189/5000\n", + "1/1 - 0s - loss: 6.0631e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2190/5000\n", + "1/1 - 0s - loss: 5.9881e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0373\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2191/5000\n", + "1/1 - 0s - loss: 5.9228e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2192/5000\n", + "1/1 - 0s - loss: 5.8280e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2193/5000\n", + "1/1 - 0s - loss: 5.7649e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2194/5000\n", + "1/1 - 0s - loss: 5.7352e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2195/5000\n", + "1/1 - 0s - loss: 5.7401e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2196/5000\n", + "1/1 - 0s - loss: 5.7682e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2197/5000\n", + "1/1 - 0s - loss: 5.8002e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2198/5000\n", + "1/1 - 0s - loss: 5.8341e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2199/5000\n", + "1/1 - 0s - loss: 5.8400e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0369\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2200/5000\n", + "1/1 - 0s - loss: 5.8437e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2201/5000\n", + "1/1 - 0s - loss: 5.8140e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0367\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2202/5000\n", + "1/1 - 0s - loss: 5.7863e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2203/5000\n", + "1/1 - 0s - loss: 5.7464e-04 - root_mean_squared_error: 0.0240 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2204/5000\n", + "1/1 - 0s - loss: 5.7154e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2205/5000\n", + "1/1 - 0s - loss: 5.6923e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2206/5000\n", + "1/1 - 0s - loss: 5.6806e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2207/5000\n", + "1/1 - 0s - loss: 5.6788e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2208/5000\n", + "1/1 - 0s - loss: 5.6838e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2209/5000\n", + "1/1 - 0s - loss: 5.6928e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0352\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2210/5000\n", + "1/1 - 0s - loss: 5.7015e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2211/5000\n", + "1/1 - 0s - loss: 5.7121e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0350\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2212/5000\n", + "1/1 - 0s - loss: 5.7167e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2213/5000\n", + "1/1 - 0s - loss: 5.7243e-04 - root_mean_squared_error: 0.0239 - 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 2214/5000\n", + "1/1 - 0s - loss: 5.7221e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2215/5000\n", + "1/1 - 0s - loss: 5.7239e-04 - root_mean_squared_error: 0.0239 - 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 2216/5000\n", + "1/1 - 0s - loss: 5.7145e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2217/5000\n", + "1/1 - 0s - loss: 5.7101e-04 - root_mean_squared_error: 0.0239 - 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 2218/5000\n", + "1/1 - 0s - loss: 5.6968e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2219/5000\n", + "1/1 - 0s - loss: 5.6891e-04 - root_mean_squared_error: 0.0239 - 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 2220/5000\n", + "1/1 - 0s - loss: 5.6758e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2221/5000\n", + "1/1 - 0s - loss: 5.6680e-04 - root_mean_squared_error: 0.0238 - 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 2222/5000\n", + "1/1 - 0s - loss: 5.6567e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2223/5000\n", + "1/1 - 0s - loss: 5.6501e-04 - root_mean_squared_error: 0.0238 - 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 2224/5000\n", + "1/1 - 0s - loss: 5.6414e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2225/5000\n", + "1/1 - 0s - loss: 5.6366e-04 - root_mean_squared_error: 0.0237 - 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 2226/5000\n", + "1/1 - 0s - loss: 5.6304e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2227/5000\n", + "1/1 - 0s - loss: 5.6279e-04 - root_mean_squared_error: 0.0237 - 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 2228/5000\n", + "1/1 - 0s - loss: 5.6240e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2229/5000\n", + "1/1 - 0s - loss: 5.6246e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2230/5000\n", + "1/1 - 0s - loss: 5.6241e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0360\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2231/5000\n", + "1/1 - 0s - loss: 5.6304e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2232/5000\n", + "1/1 - 0s - loss: 5.6356e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2233/5000\n", + "1/1 - 0s - loss: 5.6526e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2234/5000\n", + "1/1 - 0s - loss: 5.6676e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2235/5000\n", + "1/1 - 0s - loss: 5.7051e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2236/5000\n", + "1/1 - 0s - loss: 5.7351e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0368\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2237/5000\n", + "1/1 - 0s - loss: 5.8080e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2238/5000\n", + "1/1 - 0s - loss: 5.8512e-04 - root_mean_squared_error: 0.0242 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0374\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2239/5000\n", + "1/1 - 0s - loss: 5.9682e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2240/5000\n", + "1/1 - 0s - loss: 5.9954e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2241/5000\n", + "1/1 - 0s - loss: 6.1210e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2242/5000\n", + "1/1 - 0s - loss: 6.0638e-04 - root_mean_squared_error: 0.0246 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0379\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2243/5000\n", + "1/1 - 0s - loss: 6.0945e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2244/5000\n", + "1/1 - 0s - loss: 5.9291e-04 - root_mean_squared_error: 0.0243 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0370\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2245/5000\n", + "1/1 - 0s - loss: 5.8253e-04 - root_mean_squared_error: 0.0241 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2246/5000\n", + "1/1 - 0s - loss: 5.6633e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2247/5000\n", + "1/1 - 0s - loss: 5.5614e-04 - root_mean_squared_error: 0.0236 - 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 2248/5000\n", + "1/1 - 0s - loss: 5.5066e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2249/5000\n", + "1/1 - 0s - loss: 5.5046e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2250/5000\n", + "1/1 - 0s - loss: 5.5410e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2251/5000\n", + "1/1 - 0s - loss: 5.5920e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2252/5000\n", + "1/1 - 0s - loss: 5.6562e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2253/5000\n", + "1/1 - 0s - loss: 5.6830e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0365\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2254/5000\n", + "1/1 - 0s - loss: 5.7180e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2255/5000\n", + "1/1 - 0s - loss: 5.6853e-04 - root_mean_squared_error: 0.0238 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0363\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2256/5000\n", + "1/1 - 0s - loss: 5.6615e-04 - root_mean_squared_error: 0.0238 - 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 2257/5000\n", + "1/1 - 0s - loss: 5.5933e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2258/5000\n", + "1/1 - 0s - loss: 5.5400e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2259/5000\n", + "1/1 - 0s - loss: 5.4892e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2260/5000\n", + "1/1 - 0s - loss: 5.4592e-04 - root_mean_squared_error: 0.0234 - 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 2261/5000\n", + "1/1 - 0s - loss: 5.4480e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2262/5000\n", + "1/1 - 0s - loss: 5.4522e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2263/5000\n", + "1/1 - 0s - loss: 5.4666e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2264/5000\n", + "1/1 - 0s - loss: 5.4835e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2265/5000\n", + "1/1 - 0s - loss: 5.5039e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2266/5000\n", + "1/1 - 0s - loss: 5.5139e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2267/5000\n", + "1/1 - 0s - loss: 5.5269e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2268/5000\n", + "1/1 - 0s - loss: 5.5214e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0357\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2269/5000\n", + "1/1 - 0s - loss: 5.5200e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2270/5000\n", + "1/1 - 0s - loss: 5.5013e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2271/5000\n", + "1/1 - 0s - loss: 5.4884e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2272/5000\n", + "1/1 - 0s - loss: 5.4661e-04 - root_mean_squared_error: 0.0234 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0353\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2273/5000\n", + "1/1 - 0s - loss: 5.4501e-04 - root_mean_squared_error: 0.0233 - 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 2274/5000\n", + "1/1 - 0s - loss: 5.4319e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2275/5000\n", + "1/1 - 0s - loss: 5.4184e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2276/5000\n", + "1/1 - 0s - loss: 5.4056e-04 - root_mean_squared_error: 0.0232 - 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 2277/5000\n", + "1/1 - 0s - loss: 5.3957e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2278/5000\n", + "1/1 - 0s - loss: 5.3870e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2279/5000\n", + "1/1 - 0s - loss: 5.3800e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2280/5000\n", + "1/1 - 0s - loss: 5.3738e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2281/5000\n", + "1/1 - 0s - loss: 5.3684e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2282/5000\n", + "1/1 - 0s - loss: 5.3634e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2283/5000\n", + "1/1 - 0s - loss: 5.3589e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2284/5000\n", + "1/1 - 0s - loss: 5.3546e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2285/5000\n", + "1/1 - 0s - loss: 5.3504e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2286/5000\n", + "1/1 - 0s - loss: 5.3464e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2287/5000\n", + "1/1 - 0s - loss: 5.3424e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2288/5000\n", + "1/1 - 0s - loss: 5.3385e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2289/5000\n", + "1/1 - 0s - loss: 5.3347e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2290/5000\n", + "1/1 - 0s - loss: 5.3309e-04 - root_mean_squared_error: 0.0231 - 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 2291/5000\n", + "1/1 - 0s - loss: 5.3274e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2292/5000\n", + "1/1 - 0s - loss: 5.3240e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2293/5000\n", + "1/1 - 0s - loss: 5.3210e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2294/5000\n", + "1/1 - 0s - loss: 5.3187e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2295/5000\n", + "1/1 - 0s - loss: 5.3173e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2296/5000\n", + "1/1 - 0s - loss: 5.3179e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2297/5000\n", + "1/1 - 0s - loss: 5.3211e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2298/5000\n", + "1/1 - 0s - loss: 5.3307e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2299/5000\n", + "1/1 - 0s - loss: 5.3476e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0351\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2300/5000\n", + "1/1 - 0s - loss: 5.3856e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2301/5000\n", + "1/1 - 0s - loss: 5.4421e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0359\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2302/5000\n", + "1/1 - 0s - loss: 5.5699e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2303/5000\n", + "1/1 - 0s - loss: 5.7221e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0376\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2304/5000\n", + "1/1 - 0s - loss: 6.0938e-04 - root_mean_squared_error: 0.0247 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2305/5000\n", + "1/1 - 0s - loss: 6.3354e-04 - root_mean_squared_error: 0.0252 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0399\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2306/5000\n", + "1/1 - 0s - loss: 7.0200e-04 - root_mean_squared_error: 0.0265 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2307/5000\n", + "1/1 - 0s - loss: 6.7999e-04 - root_mean_squared_error: 0.0261 - val_loss: 0.0016 - val_root_mean_squared_error: 0.0397\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2308/5000\n", + "1/1 - 0s - loss: 6.8713e-04 - root_mean_squared_error: 0.0262 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2309/5000\n", + "1/1 - 0s - loss: 5.9818e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2310/5000\n", + "1/1 - 0s - loss: 5.4432e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2311/5000\n", + "1/1 - 0s - loss: 5.2741e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2312/5000\n", + "1/1 - 0s - loss: 5.5065e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2313/5000\n", + "1/1 - 0s - loss: 5.9381e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2314/5000\n", + "1/1 - 0s - loss: 5.9883e-04 - root_mean_squared_error: 0.0245 - val_loss: 0.0014 - val_root_mean_squared_error: 0.0372\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2315/5000\n", + "1/1 - 0s - loss: 5.9367e-04 - root_mean_squared_error: 0.0244 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2316/5000\n", + "1/1 - 0s - loss: 5.5216e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2317/5000\n", + "1/1 - 0s - loss: 5.2745e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0345\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2318/5000\n", + "1/1 - 0s - loss: 5.2560e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2319/5000\n", + "1/1 - 0s - loss: 5.4119e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0362\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2320/5000\n", + "1/1 - 0s - loss: 5.5956e-04 - root_mean_squared_error: 0.0237 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2321/5000\n", + "1/1 - 0s - loss: 5.5538e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2322/5000\n", + "1/1 - 0s - loss: 5.4377e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2323/5000\n", + "1/1 - 0s - loss: 5.2678e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2324/5000\n", + "1/1 - 0s - loss: 5.2105e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2325/5000\n", + "1/1 - 0s - loss: 5.2686e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2326/5000\n", + "1/1 - 0s - loss: 5.3570e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2327/5000\n", + "1/1 - 0s - loss: 5.4175e-04 - root_mean_squared_error: 0.0233 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2328/5000\n", + "1/1 - 0s - loss: 5.3569e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2329/5000\n", + "1/1 - 0s - loss: 5.2730e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2330/5000\n", + "1/1 - 0s - loss: 5.1994e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2331/5000\n", + "1/1 - 0s - loss: 5.1876e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2332/5000\n", + "1/1 - 0s - loss: 5.2254e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2333/5000\n", + "1/1 - 0s - loss: 5.2639e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0348\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2334/5000\n", + "1/1 - 0s - loss: 5.2792e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2335/5000\n", + "1/1 - 0s - loss: 5.2440e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2336/5000\n", + "1/1 - 0s - loss: 5.2007e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2337/5000\n", + "1/1 - 0s - loss: 5.1660e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2338/5000\n", + "1/1 - 0s - loss: 5.1578e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2339/5000\n", + "1/1 - 0s - loss: 5.1714e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2340/5000\n", + "1/1 - 0s - loss: 5.1897e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2341/5000\n", + "1/1 - 0s - loss: 5.2014e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2342/5000\n", + "1/1 - 0s - loss: 5.1916e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2343/5000\n", + "1/1 - 0s - loss: 5.1730e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2344/5000\n", + "1/1 - 0s - loss: 5.1491e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2345/5000\n", + "1/1 - 0s - loss: 5.1326e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2346/5000\n", + "1/1 - 0s - loss: 5.1268e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2347/5000\n", + "1/1 - 0s - loss: 5.1299e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2348/5000\n", + "1/1 - 0s - loss: 5.1366e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2349/5000\n", + "1/1 - 0s - loss: 5.1399e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0339\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2350/5000\n", + "1/1 - 0s - loss: 5.1387e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2351/5000\n", + "1/1 - 0s - loss: 5.1300e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2352/5000\n", + "1/1 - 0s - loss: 5.1191e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2353/5000\n", + "1/1 - 0s - loss: 5.1075e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2354/5000\n", + "1/1 - 0s - loss: 5.0986e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2355/5000\n", + "1/1 - 0s - loss: 5.0934e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2356/5000\n", + "1/1 - 0s - loss: 5.0913e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2357/5000\n", + "1/1 - 0s - loss: 5.0911e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2358/5000\n", + "1/1 - 0s - loss: 5.0909e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2359/5000\n", + "1/1 - 0s - loss: 5.0901e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2360/5000\n", + "1/1 - 0s - loss: 5.0872e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2361/5000\n", + "1/1 - 0s - loss: 5.0831e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2362/5000\n", + "1/1 - 0s - loss: 5.0775e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2363/5000\n", + "1/1 - 0s - loss: 5.0715e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2364/5000\n", + "1/1 - 0s - loss: 5.0653e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2365/5000\n", + "1/1 - 0s - loss: 5.0596e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2366/5000\n", + "1/1 - 0s - loss: 5.0545e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2367/5000\n", + "1/1 - 0s - loss: 5.0501e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2368/5000\n", + "1/1 - 0s - loss: 5.0463e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2369/5000\n", + "1/1 - 0s - loss: 5.0430e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2370/5000\n", + "1/1 - 0s - loss: 5.0401e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2371/5000\n", + "1/1 - 0s - loss: 5.0373e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2372/5000\n", + "1/1 - 0s - loss: 5.0346e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2373/5000\n", + "1/1 - 0s - loss: 5.0318e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2374/5000\n", + "1/1 - 0s - loss: 5.0291e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2375/5000\n", + "1/1 - 0s - loss: 5.0264e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2376/5000\n", + "1/1 - 0s - loss: 5.0238e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2377/5000\n", + "1/1 - 0s - loss: 5.0211e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2378/5000\n", + "1/1 - 0s - loss: 5.0187e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2379/5000\n", + "1/1 - 0s - loss: 5.0163e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2380/5000\n", + "1/1 - 0s - loss: 5.0146e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2381/5000\n", + "1/1 - 0s - loss: 5.0130e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2382/5000\n", + "1/1 - 0s - loss: 5.0127e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2383/5000\n", + "1/1 - 0s - loss: 5.0128e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2384/5000\n", + "1/1 - 0s - loss: 5.0155e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2385/5000\n", + "1/1 - 0s - loss: 5.0190e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2386/5000\n", + "1/1 - 0s - loss: 5.0280e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2387/5000\n", + "1/1 - 0s - loss: 5.0383e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2388/5000\n", + "1/1 - 0s - loss: 5.0606e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2389/5000\n", + "1/1 - 0s - loss: 5.0839e-04 - root_mean_squared_error: 0.0225 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0341\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2390/5000\n", + "1/1 - 0s - loss: 5.1337e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2391/5000\n", + "1/1 - 0s - loss: 5.1780e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2392/5000\n", + "1/1 - 0s - loss: 5.2792e-04 - root_mean_squared_error: 0.0230 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2393/5000\n", + "1/1 - 0s - loss: 5.3430e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0355\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2394/5000\n", + "1/1 - 0s - loss: 5.5112e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2395/5000\n", + "1/1 - 0s - loss: 5.5432e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0361\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2396/5000\n", + "1/1 - 0s - loss: 5.7087e-04 - root_mean_squared_error: 0.0239 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2397/5000\n", + "1/1 - 0s - loss: 5.5925e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0358\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2398/5000\n", + "1/1 - 0s - loss: 5.5767e-04 - root_mean_squared_error: 0.0236 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2399/5000\n", + "1/1 - 0s - loss: 5.3272e-04 - root_mean_squared_error: 0.0231 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0344\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2400/5000\n", + "1/1 - 0s - loss: 5.1574e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2401/5000\n", + "1/1 - 0s - loss: 4.9984e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2402/5000\n", + "1/1 - 0s - loss: 4.9331e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0332\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2403/5000\n", + "1/1 - 0s - loss: 4.9498e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2404/5000\n", + "1/1 - 0s - loss: 5.0187e-04 - root_mean_squared_error: 0.0224 - 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 2405/5000\n", + "1/1 - 0s - loss: 5.1175e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2406/5000\n", + "1/1 - 0s - loss: 5.1713e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0346\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2407/5000\n", + "1/1 - 0s - loss: 5.2322e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2408/5000\n", + "1/1 - 0s - loss: 5.1834e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0343\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2409/5000\n", + "1/1 - 0s - loss: 5.1418e-04 - root_mean_squared_error: 0.0227 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2410/5000\n", + "1/1 - 0s - loss: 5.0389e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0334\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2411/5000\n", + "1/1 - 0s - loss: 4.9621e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0324\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2412/5000\n", + "1/1 - 0s - loss: 4.9086e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2413/5000\n", + "1/1 - 0s - loss: 4.8943e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2414/5000\n", + "1/1 - 0s - loss: 4.9119e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2415/5000\n", + "1/1 - 0s - loss: 4.9454e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2416/5000\n", + "1/1 - 0s - loss: 4.9859e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2417/5000\n", + "1/1 - 0s - loss: 5.0050e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2418/5000\n", + "1/1 - 0s - loss: 5.0228e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2419/5000\n", + "1/1 - 0s - loss: 5.0029e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0335\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2420/5000\n", + "1/1 - 0s - loss: 4.9839e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2421/5000\n", + "1/1 - 0s - loss: 4.9426e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2422/5000\n", + "1/1 - 0s - loss: 4.9084e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2423/5000\n", + "1/1 - 0s - loss: 4.8777e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2424/5000\n", + "1/1 - 0s - loss: 4.8596e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2425/5000\n", + "1/1 - 0s - loss: 4.8532e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2426/5000\n", + "1/1 - 0s - loss: 4.8562e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2427/5000\n", + "1/1 - 0s - loss: 4.8653e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2428/5000\n", + "1/1 - 0s - loss: 4.8760e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2429/5000\n", + "1/1 - 0s - loss: 4.8890e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2430/5000\n", + "1/1 - 0s - loss: 4.8968e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2431/5000\n", + "1/1 - 0s - loss: 4.9071e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2432/5000\n", + "1/1 - 0s - loss: 4.9069e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2433/5000\n", + "1/1 - 0s - loss: 4.9100e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2434/5000\n", + "1/1 - 0s - loss: 4.9006e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2435/5000\n", + "1/1 - 0s - loss: 4.8954e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2436/5000\n", + "1/1 - 0s - loss: 4.8810e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2437/5000\n", + "1/1 - 0s - loss: 4.8717e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2438/5000\n", + "1/1 - 0s - loss: 4.8577e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2439/5000\n", + "1/1 - 0s - loss: 4.8481e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2440/5000\n", + "1/1 - 0s - loss: 4.8367e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2441/5000\n", + "1/1 - 0s - loss: 4.8286e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2442/5000\n", + "1/1 - 0s - loss: 4.8201e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2443/5000\n", + "1/1 - 0s - loss: 4.8139e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2444/5000\n", + "1/1 - 0s - loss: 4.8076e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2445/5000\n", + "1/1 - 0s - loss: 4.8028e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2446/5000\n", + "1/1 - 0s - loss: 4.7981e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0324\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2447/5000\n", + "1/1 - 0s - loss: 4.7948e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2448/5000\n", + "1/1 - 0s - loss: 4.7916e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0324\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2449/5000\n", + "1/1 - 0s - loss: 4.7901e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2450/5000\n", + "1/1 - 0s - loss: 4.7889e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0324\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2451/5000\n", + "1/1 - 0s - loss: 4.7902e-04 - root_mean_squared_error: 0.0219 - val_loss: 9.9937e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2452/5000\n", + "1/1 - 0s - loss: 4.7925e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2453/5000\n", + "1/1 - 0s - loss: 4.7996e-04 - root_mean_squared_error: 0.0219 - val_loss: 9.9005e-04 - val_root_mean_squared_error: 0.0315\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2454/5000\n", + "1/1 - 0s - loss: 4.8085e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2455/5000\n", + "1/1 - 0s - loss: 4.8282e-04 - root_mean_squared_error: 0.0220 - val_loss: 9.7822e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2456/5000\n", + "1/1 - 0s - loss: 4.8505e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2457/5000\n", + "1/1 - 0s - loss: 4.8978e-04 - root_mean_squared_error: 0.0221 - val_loss: 9.6496e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2458/5000\n", + "1/1 - 0s - loss: 4.9444e-04 - root_mean_squared_error: 0.0222 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0338\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2459/5000\n", + "1/1 - 0s - loss: 5.0491e-04 - root_mean_squared_error: 0.0225 - val_loss: 9.5311e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2460/5000\n", + "1/1 - 0s - loss: 5.1268e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2461/5000\n", + "1/1 - 0s - loss: 5.3227e-04 - root_mean_squared_error: 0.0231 - val_loss: 9.4666e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2462/5000\n", + "1/1 - 0s - loss: 5.3848e-04 - root_mean_squared_error: 0.0232 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0356\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2463/5000\n", + "1/1 - 0s - loss: 5.6160e-04 - root_mean_squared_error: 0.0237 - val_loss: 9.4405e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2464/5000\n", + "1/1 - 0s - loss: 5.5035e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2465/5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 - 0s - loss: 5.5217e-04 - root_mean_squared_error: 0.0235 - val_loss: 9.4600e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2466/5000\n", + "1/1 - 0s - loss: 5.2071e-04 - root_mean_squared_error: 0.0228 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0337\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2467/5000\n", + "1/1 - 0s - loss: 4.9936e-04 - root_mean_squared_error: 0.0223 - val_loss: 9.7955e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2468/5000\n", + "1/1 - 0s - loss: 4.7903e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2469/5000\n", + "1/1 - 0s - loss: 4.7158e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2470/5000\n", + "1/1 - 0s - loss: 4.7535e-04 - root_mean_squared_error: 0.0218 - val_loss: 9.6803e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2471/5000\n", + "1/1 - 0s - loss: 4.8549e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0336\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2472/5000\n", + "1/1 - 0s - loss: 4.9912e-04 - root_mean_squared_error: 0.0223 - val_loss: 9.5482e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2473/5000\n", + "1/1 - 0s - loss: 5.0393e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0340\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2474/5000\n", + "1/1 - 0s - loss: 5.0915e-04 - root_mean_squared_error: 0.0226 - val_loss: 9.5347e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2475/5000\n", + "1/1 - 0s - loss: 4.9910e-04 - root_mean_squared_error: 0.0223 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0333\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2476/5000\n", + "1/1 - 0s - loss: 4.9032e-04 - root_mean_squared_error: 0.0221 - val_loss: 9.7122e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2477/5000\n", + "1/1 - 0s - loss: 4.7787e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2478/5000\n", + "1/1 - 0s - loss: 4.7046e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2479/5000\n", + "1/1 - 0s - loss: 4.6853e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.8056e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2480/5000\n", + "1/1 - 0s - loss: 4.7140e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0326\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2481/5000\n", + "1/1 - 0s - loss: 4.7691e-04 - root_mean_squared_error: 0.0218 - val_loss: 9.5925e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2482/5000\n", + "1/1 - 0s - loss: 4.8105e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2483/5000\n", + "1/1 - 0s - loss: 4.8472e-04 - root_mean_squared_error: 0.0220 - val_loss: 9.5380e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2484/5000\n", + "1/1 - 0s - loss: 4.8283e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2485/5000\n", + "1/1 - 0s - loss: 4.8056e-04 - root_mean_squared_error: 0.0219 - val_loss: 9.6012e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2486/5000\n", + "1/1 - 0s - loss: 4.7498e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2487/5000\n", + "1/1 - 0s - loss: 4.7045e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.7989e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2488/5000\n", + "1/1 - 0s - loss: 4.6684e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.9797e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2489/5000\n", + "1/1 - 0s - loss: 4.6525e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2490/5000\n", + "1/1 - 0s - loss: 4.6549e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.7212e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2491/5000\n", + "1/1 - 0s - loss: 4.6690e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2492/5000\n", + "1/1 - 0s - loss: 4.6886e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.5860e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2493/5000\n", + "1/1 - 0s - loss: 4.7021e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2494/5000\n", + "1/1 - 0s - loss: 4.7146e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.5307e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2495/5000\n", + "1/1 - 0s - loss: 4.7115e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2496/5000\n", + "1/1 - 0s - loss: 4.7084e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.5267e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2497/5000\n", + "1/1 - 0s - loss: 4.6918e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2498/5000\n", + "1/1 - 0s - loss: 4.6772e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.5818e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2499/5000\n", + "1/1 - 0s - loss: 4.6569e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2500/5000\n", + "1/1 - 0s - loss: 4.6402e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.6767e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2501/5000\n", + "1/1 - 0s - loss: 4.6254e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.9234e-04 - val_root_mean_squared_error: 0.0315\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2502/5000\n", + "1/1 - 0s - loss: 4.6151e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.7878e-04 - val_root_mean_squared_error: 0.0313\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2503/5000\n", + "1/1 - 0s - loss: 4.6089e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.7762e-04 - val_root_mean_squared_error: 0.0313\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2504/5000\n", + "1/1 - 0s - loss: 4.6060e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.8855e-04 - val_root_mean_squared_error: 0.0314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2505/5000\n", + "1/1 - 0s - loss: 4.6055e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.6605e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2506/5000\n", + "1/1 - 0s - loss: 4.6065e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.9612e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2507/5000\n", + "1/1 - 0s - loss: 4.6086e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.5712e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2508/5000\n", + "1/1 - 0s - loss: 4.6108e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2509/5000\n", + "1/1 - 0s - loss: 4.6141e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.4994e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2510/5000\n", + "1/1 - 0s - loss: 4.6163e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2511/5000\n", + "1/1 - 0s - loss: 4.6204e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.4434e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2512/5000\n", + "1/1 - 0s - loss: 4.6223e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2513/5000\n", + "1/1 - 0s - loss: 4.6277e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.3905e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2514/5000\n", + "1/1 - 0s - loss: 4.6305e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2515/5000\n", + "1/1 - 0s - loss: 4.6396e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.3322e-04 - val_root_mean_squared_error: 0.0305\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2516/5000\n", + "1/1 - 0s - loss: 4.6453e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2517/5000\n", + "1/1 - 0s - loss: 4.6616e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.2599e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2518/5000\n", + "1/1 - 0s - loss: 4.6721e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0323\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2519/5000\n", + "1/1 - 0s - loss: 4.7002e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.1820e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2520/5000\n", + "1/1 - 0s - loss: 4.7160e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0325\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2521/5000\n", + "1/1 - 0s - loss: 4.7598e-04 - root_mean_squared_error: 0.0218 - val_loss: 9.1093e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2522/5000\n", + "1/1 - 0s - loss: 4.7770e-04 - root_mean_squared_error: 0.0219 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2523/5000\n", + "1/1 - 0s - loss: 4.8351e-04 - root_mean_squared_error: 0.0220 - val_loss: 9.0554e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2524/5000\n", + "1/1 - 0s - loss: 4.8416e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2525/5000\n", + "1/1 - 0s - loss: 4.9004e-04 - root_mean_squared_error: 0.0221 - val_loss: 9.0263e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2526/5000\n", + "1/1 - 0s - loss: 4.8774e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2527/5000\n", + "1/1 - 0s - loss: 4.9079e-04 - root_mean_squared_error: 0.0222 - val_loss: 9.0278e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2528/5000\n", + "1/1 - 0s - loss: 4.8438e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0328\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2529/5000\n", + "1/1 - 0s - loss: 4.8226e-04 - root_mean_squared_error: 0.0220 - val_loss: 9.0819e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2530/5000\n", + "1/1 - 0s - loss: 4.7358e-04 - root_mean_squared_error: 0.0218 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0322\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2531/5000\n", + "1/1 - 0s - loss: 4.6799e-04 - root_mean_squared_error: 0.0216 - val_loss: 9.2145e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2532/5000\n", + "1/1 - 0s - loss: 4.6094e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.9567e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2533/5000\n", + "1/1 - 0s - loss: 4.5629e-04 - root_mean_squared_error: 0.0214 - val_loss: 9.4182e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2534/5000\n", + "1/1 - 0s - loss: 4.5303e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.6175e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2535/5000\n", + "1/1 - 0s - loss: 4.5155e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.6469e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2536/5000\n", + "1/1 - 0s - loss: 4.5144e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.3849e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2537/5000\n", + "1/1 - 0s - loss: 4.5232e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.8601e-04 - val_root_mean_squared_error: 0.0314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2538/5000\n", + "1/1 - 0s - loss: 4.5392e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.2283e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2539/5000\n", + "1/1 - 0s - loss: 4.5567e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2540/5000\n", + "1/1 - 0s - loss: 4.5804e-04 - root_mean_squared_error: 0.0214 - val_loss: 9.1187e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2541/5000\n", + "1/1 - 0s - loss: 4.5964e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2542/5000\n", + "1/1 - 0s - loss: 4.6223e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.0426e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2543/5000\n", + "1/1 - 0s - loss: 4.6302e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2544/5000\n", + "1/1 - 0s - loss: 4.6530e-04 - root_mean_squared_error: 0.0216 - val_loss: 8.9950e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2545/5000\n", + "1/1 - 0s - loss: 4.6489e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0321\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2546/5000\n", + "1/1 - 0s - loss: 4.6634e-04 - root_mean_squared_error: 0.0216 - val_loss: 8.9734e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2547/5000\n", + "1/1 - 0s - loss: 4.6470e-04 - root_mean_squared_error: 0.0216 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2548/5000\n", + "1/1 - 0s - loss: 4.6498e-04 - root_mean_squared_error: 0.0216 - val_loss: 8.9750e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2549/5000\n", + "1/1 - 0s - loss: 4.6240e-04 - root_mean_squared_error: 0.0215 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0319\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2550/5000\n", + "1/1 - 0s - loss: 4.6154e-04 - root_mean_squared_error: 0.0215 - val_loss: 8.9995e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2551/5000\n", + "1/1 - 0s - loss: 4.5859e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0317\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2552/5000\n", + "1/1 - 0s - loss: 4.5700e-04 - root_mean_squared_error: 0.0214 - val_loss: 9.0429e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2553/5000\n", + "1/1 - 0s - loss: 4.5430e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.8645e-04 - val_root_mean_squared_error: 0.0314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2554/5000\n", + "1/1 - 0s - loss: 4.5259e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.0974e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2555/5000\n", + "1/1 - 0s - loss: 4.5054e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.7188e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2556/5000\n", + "1/1 - 0s - loss: 4.4915e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.1497e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2557/5000\n", + "1/1 - 0s - loss: 4.4778e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.6003e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2558/5000\n", + "1/1 - 0s - loss: 4.4681e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.1889e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2559/5000\n", + "1/1 - 0s - loss: 4.4596e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.5110e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2560/5000\n", + "1/1 - 0s - loss: 4.4532e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.2097e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2561/5000\n", + "1/1 - 0s - loss: 4.4476e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.4470e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2562/5000\n", + "1/1 - 0s - loss: 4.4431e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.2130e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2563/5000\n", + "1/1 - 0s - loss: 4.4391e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.4045e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2564/5000\n", + "1/1 - 0s - loss: 4.4357e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.2014e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2565/5000\n", + "1/1 - 0s - loss: 4.4325e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.3805e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2566/5000\n", + "1/1 - 0s - loss: 4.4297e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.1767e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2567/5000\n", + "1/1 - 0s - loss: 4.4272e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.3746e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2568/5000\n", + "1/1 - 0s - loss: 4.4251e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.1379e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2569/5000\n", + "1/1 - 0s - loss: 4.4235e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.3899e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2570/5000\n", + "1/1 - 0s - loss: 4.4227e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.0814e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2571/5000\n", + "1/1 - 0s - loss: 4.4229e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.4367e-04 - val_root_mean_squared_error: 0.0307\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2572/5000\n", + "1/1 - 0s - loss: 4.4253e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.0014e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2573/5000\n", + "1/1 - 0s - loss: 4.4298e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.5378e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2574/5000\n", + "1/1 - 0s - loss: 4.4402e-04 - root_mean_squared_error: 0.0211 - val_loss: 8.8910e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2575/5000\n", + "1/1 - 0s - loss: 4.4556e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.7403e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2576/5000\n", + "1/1 - 0s - loss: 4.4877e-04 - root_mean_squared_error: 0.0212 - val_loss: 8.7504e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2577/5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 - 0s - loss: 4.5298e-04 - root_mean_squared_error: 0.0213 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2578/5000\n", + "1/1 - 0s - loss: 4.6205e-04 - root_mean_squared_error: 0.0215 - val_loss: 8.6091e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2579/5000\n", + "1/1 - 0s - loss: 4.7192e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2580/5000\n", + "1/1 - 0s - loss: 4.9497e-04 - root_mean_squared_error: 0.0222 - val_loss: 8.5492e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2581/5000\n", + "1/1 - 0s - loss: 5.1072e-04 - root_mean_squared_error: 0.0226 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0347\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2582/5000\n", + "1/1 - 0s - loss: 5.5320e-04 - root_mean_squared_error: 0.0235 - val_loss: 8.5794e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2583/5000\n", + "1/1 - 0s - loss: 5.5157e-04 - root_mean_squared_error: 0.0235 - val_loss: 0.0012 - val_root_mean_squared_error: 0.0354\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2584/5000\n", + "1/1 - 0s - loss: 5.7566e-04 - root_mean_squared_error: 0.0240 - val_loss: 8.5002e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2585/5000\n", + "1/1 - 0s - loss: 5.2476e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2586/5000\n", + "1/1 - 0s - loss: 4.9026e-04 - root_mean_squared_error: 0.0221 - val_loss: 8.7582e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2587/5000\n", + "1/1 - 0s - loss: 4.5042e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.1725e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2588/5000\n", + "1/1 - 0s - loss: 4.3780e-04 - root_mean_squared_error: 0.0209 - val_loss: 9.8677e-04 - val_root_mean_squared_error: 0.0314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2589/5000\n", + "1/1 - 0s - loss: 4.4917e-04 - root_mean_squared_error: 0.0212 - val_loss: 8.6267e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2590/5000\n", + "1/1 - 0s - loss: 4.7025e-04 - root_mean_squared_error: 0.0217 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2591/5000\n", + "1/1 - 0s - loss: 4.9501e-04 - root_mean_squared_error: 0.0222 - val_loss: 8.6228e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2592/5000\n", + "1/1 - 0s - loss: 4.9006e-04 - root_mean_squared_error: 0.0221 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0327\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2593/5000\n", + "1/1 - 0s - loss: 4.8335e-04 - root_mean_squared_error: 0.0220 - val_loss: 8.6752e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2594/5000\n", + "1/1 - 0s - loss: 4.5767e-04 - root_mean_squared_error: 0.0214 - val_loss: 9.5713e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2595/5000\n", + "1/1 - 0s - loss: 4.4087e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.2004e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2596/5000\n", + "1/1 - 0s - loss: 4.3540e-04 - root_mean_squared_error: 0.0209 - val_loss: 8.8174e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2597/5000\n", + "1/1 - 0s - loss: 4.4152e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.9671e-04 - val_root_mean_squared_error: 0.0316\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2598/5000\n", + "1/1 - 0s - loss: 4.5310e-04 - root_mean_squared_error: 0.0213 - val_loss: 8.6096e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2599/5000\n", + "1/1 - 0s - loss: 4.5826e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0318\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2600/5000\n", + "1/1 - 0s - loss: 4.5963e-04 - root_mean_squared_error: 0.0214 - val_loss: 8.6533e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2601/5000\n", + "1/1 - 0s - loss: 4.5004e-04 - root_mean_squared_error: 0.0212 - val_loss: 9.5752e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2602/5000\n", + "1/1 - 0s - loss: 4.4128e-04 - root_mean_squared_error: 0.0210 - val_loss: 8.8982e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2603/5000\n", + "1/1 - 0s - loss: 4.3447e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.9874e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2604/5000\n", + "1/1 - 0s - loss: 4.3301e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.3429e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2605/5000\n", + "1/1 - 0s - loss: 4.3593e-04 - root_mean_squared_error: 0.0209 - val_loss: 8.6715e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2606/5000\n", + "1/1 - 0s - loss: 4.4010e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.6270e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2607/5000\n", + "1/1 - 0s - loss: 4.4356e-04 - root_mean_squared_error: 0.0211 - val_loss: 8.5982e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2608/5000\n", + "1/1 - 0s - loss: 4.4253e-04 - root_mean_squared_error: 0.0210 - val_loss: 9.5093e-04 - val_root_mean_squared_error: 0.0308\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2609/5000\n", + "1/1 - 0s - loss: 4.3997e-04 - root_mean_squared_error: 0.0210 - val_loss: 8.7060e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2610/5000\n", + "1/1 - 0s - loss: 4.3547e-04 - root_mean_squared_error: 0.0209 - val_loss: 9.1415e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2611/5000\n", + "1/1 - 0s - loss: 4.3216e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.9337e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2612/5000\n", + "1/1 - 0s - loss: 4.3062e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.8374e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2613/5000\n", + "1/1 - 0s - loss: 4.3095e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.1879e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2614/5000\n", + "1/1 - 0s - loss: 4.3245e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.6532e-04 - val_root_mean_squared_error: 0.0294\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2615/5000\n", + "1/1 - 0s - loss: 4.3398e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.3176e-04 - val_root_mean_squared_error: 0.0305\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2616/5000\n", + "1/1 - 0s - loss: 4.3520e-04 - root_mean_squared_error: 0.0209 - val_loss: 8.5950e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2617/5000\n", + "1/1 - 0s - loss: 4.3493e-04 - root_mean_squared_error: 0.0209 - val_loss: 9.2783e-04 - val_root_mean_squared_error: 0.0305\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2618/5000\n", + "1/1 - 0s - loss: 4.3414e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.6236e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2619/5000\n", + "1/1 - 0s - loss: 4.3231e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.0979e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2620/5000\n", + "1/1 - 0s - loss: 4.3057e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.7269e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2621/5000\n", + "1/1 - 0s - loss: 4.2900e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.9022e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2622/5000\n", + "1/1 - 0s - loss: 4.2803e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.8636e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2623/5000\n", + "1/1 - 0s - loss: 4.2766e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.7421e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2624/5000\n", + "1/1 - 0s - loss: 4.2776e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.9768e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2625/5000\n", + "1/1 - 0s - loss: 4.2811e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.6413e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2626/5000\n", + "1/1 - 0s - loss: 4.2847e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.0422e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2627/5000\n", + "1/1 - 0s - loss: 4.2877e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.5824e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2628/5000\n", + "1/1 - 0s - loss: 4.2874e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.0410e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2629/5000\n", + "1/1 - 0s - loss: 4.2860e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.5688e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2630/5000\n", + "1/1 - 0s - loss: 4.2811e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.9953e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2631/5000\n", + "1/1 - 0s - loss: 4.2758e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.5826e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2632/5000\n", + "1/1 - 0s - loss: 4.2687e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.9216e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2633/5000\n", + "1/1 - 0s - loss: 4.2621e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6139e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2634/5000\n", + "1/1 - 0s - loss: 4.2556e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.8470e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2635/5000\n", + "1/1 - 0s - loss: 4.2500e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6464e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2636/5000\n", + "1/1 - 0s - loss: 4.2450e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.7717e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2637/5000\n", + "1/1 - 0s - loss: 4.2408e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6735e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2638/5000\n", + "1/1 - 0s - loss: 4.2373e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.7082e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2639/5000\n", + "1/1 - 0s - loss: 4.2342e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6928e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2640/5000\n", + "1/1 - 0s - loss: 4.2315e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6528e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2641/5000\n", + "1/1 - 0s - loss: 4.2292e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.7053e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2642/5000\n", + "1/1 - 0s - loss: 4.2270e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6080e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2643/5000\n", + "1/1 - 0s - loss: 4.2250e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.7189e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2644/5000\n", + "1/1 - 0s - loss: 4.2231e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.5649e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2645/5000\n", + "1/1 - 0s - loss: 4.2214e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.7335e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2646/5000\n", + "1/1 - 0s - loss: 4.2201e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.5206e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2647/5000\n", + "1/1 - 0s - loss: 4.2190e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.7564e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2648/5000\n", + "1/1 - 0s - loss: 4.2186e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.4673e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2649/5000\n", + "1/1 - 0s - loss: 4.2188e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.7936e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2650/5000\n", + "1/1 - 0s - loss: 4.2206e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.4014e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2651/5000\n", + "1/1 - 0s - loss: 4.2235e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.8653e-04 - val_root_mean_squared_error: 0.0298\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2652/5000\n", + "1/1 - 0s - loss: 4.2302e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.3177e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2653/5000\n", + "1/1 - 0s - loss: 4.2391e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.9937e-04 - val_root_mean_squared_error: 0.0300\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2654/5000\n", + "1/1 - 0s - loss: 4.2572e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.2148e-04 - val_root_mean_squared_error: 0.0287\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2655/5000\n", + "1/1 - 0s - loss: 4.2792e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.2247e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2656/5000\n", + "1/1 - 0s - loss: 4.3241e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.0994e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2657/5000\n", + "1/1 - 0s - loss: 4.3720e-04 - root_mean_squared_error: 0.0209 - val_loss: 9.6280e-04 - val_root_mean_squared_error: 0.0310\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2658/5000\n", + "1/1 - 0s - loss: 4.4761e-04 - root_mean_squared_error: 0.0212 - val_loss: 8.0021e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2659/5000\n", + "1/1 - 0s - loss: 4.5611e-04 - root_mean_squared_error: 0.0214 - val_loss: 0.0010 - val_root_mean_squared_error: 0.0320\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2660/5000\n", + "1/1 - 0s - loss: 4.7662e-04 - root_mean_squared_error: 0.0218 - val_loss: 7.9665e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2661/5000\n", + "1/1 - 0s - loss: 4.8452e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0331\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2662/5000\n", + "1/1 - 0s - loss: 5.1043e-04 - root_mean_squared_error: 0.0226 - val_loss: 7.9559e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2663/5000\n", + "1/1 - 0s - loss: 5.0010e-04 - root_mean_squared_error: 0.0224 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0329\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2664/5000\n", + "1/1 - 0s - loss: 5.0392e-04 - root_mean_squared_error: 0.0224 - val_loss: 7.9341e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2665/5000\n", + "1/1 - 0s - loss: 4.7015e-04 - root_mean_squared_error: 0.0217 - val_loss: 9.6903e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2666/5000\n", + "1/1 - 0s - loss: 4.4727e-04 - root_mean_squared_error: 0.0211 - val_loss: 8.2034e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2667/5000\n", + "1/1 - 0s - loss: 4.2485e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.5817e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2668/5000\n", + "1/1 - 0s - loss: 4.1672e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.9239e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2669/5000\n", + "1/1 - 0s - loss: 4.2106e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.1264e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2670/5000\n", + "1/1 - 0s - loss: 4.3238e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.6697e-04 - val_root_mean_squared_error: 0.0311\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2671/5000\n", + "1/1 - 0s - loss: 4.4748e-04 - root_mean_squared_error: 0.0212 - val_loss: 8.0375e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2672/5000\n", + "1/1 - 0s - loss: 4.5219e-04 - root_mean_squared_error: 0.0213 - val_loss: 9.8771e-04 - val_root_mean_squared_error: 0.0314\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2673/5000\n", + "1/1 - 0s - loss: 4.5731e-04 - root_mean_squared_error: 0.0214 - val_loss: 8.0173e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2674/5000\n", + "1/1 - 0s - loss: 4.4565e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.3722e-04 - val_root_mean_squared_error: 0.0306\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2675/5000\n", + "1/1 - 0s - loss: 4.3565e-04 - root_mean_squared_error: 0.0209 - val_loss: 8.1614e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2676/5000\n", + "1/1 - 0s - loss: 4.2266e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.6399e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2677/5000\n", + "1/1 - 0s - loss: 4.1552e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.5779e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2678/5000\n", + "1/1 - 0s - loss: 4.1455e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.1947e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2679/5000\n", + "1/1 - 0s - loss: 4.1847e-04 - root_mean_squared_error: 0.0205 - val_loss: 9.0519e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2680/5000\n", + "1/1 - 0s - loss: 4.2483e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.0344e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2681/5000\n", + "1/1 - 0s - loss: 4.2886e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.2415e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2682/5000\n", + "1/1 - 0s - loss: 4.3216e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.0009e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2683/5000\n", + "1/1 - 0s - loss: 4.2916e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.0633e-04 - val_root_mean_squared_error: 0.0301\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2684/5000\n", + "1/1 - 0s - loss: 4.2590e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.0649e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2685/5000\n", + "1/1 - 0s - loss: 4.1986e-04 - root_mean_squared_error: 0.0205 - val_loss: 8.6640e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2686/5000\n", + "1/1 - 0s - loss: 4.1528e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.2639e-04 - val_root_mean_squared_error: 0.0287\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2687/5000\n", + "1/1 - 0s - loss: 4.1233e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.3073e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2688/5000\n", + "1/1 - 0s - loss: 4.1165e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.5439e-04 - val_root_mean_squared_error: 0.0292\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2689/5000\n", + "1/1 - 0s - loss: 4.1277e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.1021e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2690/5000\n", + "1/1 - 0s - loss: 4.1472e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.7502e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2691/5000\n", + "1/1 - 0s - loss: 4.1695e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.0131e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2692/5000\n", + "1/1 - 0s - loss: 4.1799e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.8181e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2693/5000\n", + "1/1 - 0s - loss: 4.1882e-04 - root_mean_squared_error: 0.0205 - val_loss: 7.9836e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2694/5000\n", + "1/1 - 0s - loss: 4.1784e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.7448e-04 - val_root_mean_squared_error: 0.0296\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2695/5000\n", + "1/1 - 0s - loss: 4.1687e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.0023e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2696/5000\n", + "1/1 - 0s - loss: 4.1477e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.5784e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2697/5000\n", + "1/1 - 0s - loss: 4.1296e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.0764e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2698/5000\n", + "1/1 - 0s - loss: 4.1105e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.3888e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2699/5000\n", + "1/1 - 0s - loss: 4.0965e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.1861e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2700/5000\n", + "1/1 - 0s - loss: 4.0871e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.2339e-04 - val_root_mean_squared_error: 0.0287\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2701/5000\n", + "1/1 - 0s - loss: 4.0829e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.2980e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2702/5000\n", + "1/1 - 0s - loss: 4.0826e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.1167e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2703/5000\n", + "1/1 - 0s - loss: 4.0848e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.3857e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2704/5000\n", + "1/1 - 0s - loss: 4.0885e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.0299e-04 - val_root_mean_squared_error: 0.0283\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2705/5000\n", + "1/1 - 0s - loss: 4.0921e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.4456e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2706/5000\n", + "1/1 - 0s - loss: 4.0964e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.9656e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2707/5000\n", + "1/1 - 0s - loss: 4.0988e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.4767e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2708/5000\n", + "1/1 - 0s - loss: 4.1022e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.9236e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2709/5000\n", + "1/1 - 0s - loss: 4.1023e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.4916e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2710/5000\n", + "1/1 - 0s - loss: 4.1042e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.8973e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2711/5000\n", + "1/1 - 0s - loss: 4.1024e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.4991e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2712/5000\n", + "1/1 - 0s - loss: 4.1038e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.8758e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2713/5000\n", + "1/1 - 0s - loss: 4.1019e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.5077e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2714/5000\n", + "1/1 - 0s - loss: 4.1042e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.8483e-04 - val_root_mean_squared_error: 0.0280\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2715/5000\n", + "1/1 - 0s - loss: 4.1033e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.5232e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2716/5000\n", + "1/1 - 0s - loss: 4.1081e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.8100e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2717/5000\n", + "1/1 - 0s - loss: 4.1092e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.5598e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2718/5000\n", + "1/1 - 0s - loss: 4.1181e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.7649e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2719/5000\n", + "1/1 - 0s - loss: 4.1220e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.6246e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2720/5000\n", + "1/1 - 0s - loss: 4.1368e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.7162e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2721/5000\n", + "1/1 - 0s - loss: 4.1439e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.7215e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2722/5000\n", + "1/1 - 0s - loss: 4.1673e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.6663e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2723/5000\n", + "1/1 - 0s - loss: 4.1783e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.8522e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2724/5000\n", + "1/1 - 0s - loss: 4.2141e-04 - root_mean_squared_error: 0.0205 - val_loss: 7.6157e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2725/5000\n", + "1/1 - 0s - loss: 4.2275e-04 - root_mean_squared_error: 0.0206 - val_loss: 9.0137e-04 - val_root_mean_squared_error: 0.0300\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2726/5000\n", + "1/1 - 0s - loss: 4.2772e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.5719e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2727/5000\n", + "1/1 - 0s - loss: 4.2859e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.1675e-04 - val_root_mean_squared_error: 0.0303\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2728/5000\n", + "1/1 - 0s - loss: 4.3413e-04 - root_mean_squared_error: 0.0208 - val_loss: 7.5412e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2729/5000\n", + "1/1 - 0s - loss: 4.3290e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.2264e-04 - val_root_mean_squared_error: 0.0304\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2730/5000\n", + "1/1 - 0s - loss: 4.3660e-04 - root_mean_squared_error: 0.0209 - val_loss: 7.5327e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2731/5000\n", + "1/1 - 0s - loss: 4.3170e-04 - root_mean_squared_error: 0.0208 - val_loss: 9.1009e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2732/5000\n", + "1/1 - 0s - loss: 4.3097e-04 - root_mean_squared_error: 0.0208 - val_loss: 7.5599e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2733/5000\n", + "1/1 - 0s - loss: 4.2328e-04 - root_mean_squared_error: 0.0206 - val_loss: 8.7992e-04 - val_root_mean_squared_error: 0.0297\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2734/5000\n", + "1/1 - 0s - loss: 4.1868e-04 - root_mean_squared_error: 0.0205 - val_loss: 7.6462e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2735/5000\n", + "1/1 - 0s - loss: 4.1157e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.4367e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2736/5000\n", + "1/1 - 0s - loss: 4.0680e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.7915e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2737/5000\n", + "1/1 - 0s - loss: 4.0277e-04 - root_mean_squared_error: 0.0201 - val_loss: 8.1264e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2738/5000\n", + "1/1 - 0s - loss: 4.0044e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.9679e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2739/5000\n", + "1/1 - 0s - loss: 3.9943e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.9038e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2740/5000\n", + "1/1 - 0s - loss: 3.9948e-04 - root_mean_squared_error: 0.0200 - val_loss: 8.1394e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2741/5000\n", + "1/1 - 0s - loss: 4.0030e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.7513e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2742/5000\n", + "1/1 - 0s - loss: 4.0157e-04 - root_mean_squared_error: 0.0200 - val_loss: 8.2925e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2743/5000\n", + "1/1 - 0s - loss: 4.0333e-04 - root_mean_squared_error: 0.0201 - val_loss: 7.6433e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2744/5000\n", + "1/1 - 0s - loss: 4.0490e-04 - root_mean_squared_error: 0.0201 - val_loss: 8.4280e-04 - val_root_mean_squared_error: 0.0290\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2745/5000\n", + "1/1 - 0s - loss: 4.0719e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.5632e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2746/5000\n", + "1/1 - 0s - loss: 4.0857e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.5481e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2747/5000\n", + "1/1 - 0s - loss: 4.1121e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.5035e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2748/5000\n", + "1/1 - 0s - loss: 4.1207e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.6454e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2749/5000\n", + "1/1 - 0s - loss: 4.1481e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.4604e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2750/5000\n", + "1/1 - 0s - loss: 4.1480e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.7042e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2751/5000\n", + "1/1 - 0s - loss: 4.1712e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.4346e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2752/5000\n", + "1/1 - 0s - loss: 4.1586e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.7020e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2753/5000\n", + "1/1 - 0s - loss: 4.1706e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.4280e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2754/5000\n", + "1/1 - 0s - loss: 4.1445e-04 - root_mean_squared_error: 0.0204 - val_loss: 8.6254e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2755/5000\n", + "1/1 - 0s - loss: 4.1408e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.4440e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2756/5000\n", + "1/1 - 0s - loss: 4.1062e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.4855e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2757/5000\n", + "1/1 - 0s - loss: 4.0899e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.4818e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2758/5000\n", + "1/1 - 0s - loss: 4.0557e-04 - root_mean_squared_error: 0.0201 - val_loss: 8.3175e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2759/5000\n", + "1/1 - 0s - loss: 4.0352e-04 - root_mean_squared_error: 0.0201 - val_loss: 7.5348e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2760/5000\n", + "1/1 - 0s - loss: 4.0086e-04 - root_mean_squared_error: 0.0200 - val_loss: 8.1564e-04 - val_root_mean_squared_error: 0.0286\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2761/5000\n", + "1/1 - 0s - loss: 3.9912e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.5900e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2762/5000\n", + "1/1 - 0s - loss: 3.9737e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.0211e-04 - val_root_mean_squared_error: 0.0283\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2763/5000\n", + "1/1 - 0s - loss: 3.9618e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.6373e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2764/5000\n", + "1/1 - 0s - loss: 3.9515e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.9154e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2765/5000\n", + "1/1 - 0s - loss: 3.9441e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.6712e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2766/5000\n", + "1/1 - 0s - loss: 3.9381e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.8354e-04 - val_root_mean_squared_error: 0.0280\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2767/5000\n", + "1/1 - 0s - loss: 3.9334e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.6924e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2768/5000\n", + "1/1 - 0s - loss: 3.9296e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7748e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2769/5000\n", + "1/1 - 0s - loss: 3.9265e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7030e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2770/5000\n", + "1/1 - 0s - loss: 3.9237e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7272e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2771/5000\n", + "1/1 - 0s - loss: 3.9212e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7067e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2772/5000\n", + "1/1 - 0s - loss: 3.9188e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.6874e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2773/5000\n", + "1/1 - 0s - loss: 3.9166e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7069e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2774/5000\n", + "1/1 - 0s - loss: 3.9144e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.6506e-04 - val_root_mean_squared_error: 0.0277\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2775/5000\n", + "1/1 - 0s - loss: 3.9124e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7086e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2776/5000\n", + "1/1 - 0s - loss: 3.9105e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.6130e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2777/5000\n", + "1/1 - 0s - loss: 3.9087e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7170e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2778/5000\n", + "1/1 - 0s - loss: 3.9073e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.5696e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2779/5000\n", + "1/1 - 0s - loss: 3.9062e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7401e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2780/5000\n", + "1/1 - 0s - loss: 3.9059e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.5141e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2781/5000\n", + "1/1 - 0s - loss: 3.9066e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.7909e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2782/5000\n", + "1/1 - 0s - loss: 3.9095e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.4376e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2783/5000\n", + "1/1 - 0s - loss: 3.9151e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.8968e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2784/5000\n", + "1/1 - 0s - loss: 3.9274e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.3328e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2785/5000\n", + "1/1 - 0s - loss: 3.9467e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.1170e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2786/5000\n", + "1/1 - 0s - loss: 3.9869e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.2055e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2787/5000\n", + "1/1 - 0s - loss: 4.0429e-04 - root_mean_squared_error: 0.0201 - val_loss: 8.5823e-04 - val_root_mean_squared_error: 0.0293\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2788/5000\n", + "1/1 - 0s - loss: 4.1652e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.1109e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2789/5000\n", + "1/1 - 0s - loss: 4.3023e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.5235e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2790/5000\n", + "1/1 - 0s - loss: 4.6295e-04 - root_mean_squared_error: 0.0215 - val_loss: 7.1627e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2791/5000\n", + "1/1 - 0s - loss: 4.8316e-04 - root_mean_squared_error: 0.0220 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2792/5000\n", + "1/1 - 0s - loss: 5.3976e-04 - root_mean_squared_error: 0.0232 - val_loss: 7.2427e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2793/5000\n", + "1/1 - 0s - loss: 5.2439e-04 - root_mean_squared_error: 0.0229 - val_loss: 0.0011 - val_root_mean_squared_error: 0.0330\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2794/5000\n", + "1/1 - 0s - loss: 5.3475e-04 - root_mean_squared_error: 0.0231 - val_loss: 7.0436e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2795/5000\n", + "1/1 - 0s - loss: 4.6236e-04 - root_mean_squared_error: 0.0215 - val_loss: 8.6472e-04 - val_root_mean_squared_error: 0.0294\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2796/5000\n", + "1/1 - 0s - loss: 4.1524e-04 - root_mean_squared_error: 0.0204 - val_loss: 7.5317e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2797/5000\n", + "1/1 - 0s - loss: 3.8911e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.2941e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2798/5000\n", + "1/1 - 0s - loss: 3.9771e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.9597e-04 - val_root_mean_squared_error: 0.0299\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2799/5000\n", + "1/1 - 0s - loss: 4.2867e-04 - root_mean_squared_error: 0.0207 - val_loss: 7.1567e-04 - val_root_mean_squared_error: 0.0268\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2800/5000\n", + "1/1 - 0s - loss: 4.4706e-04 - root_mean_squared_error: 0.0211 - val_loss: 9.5565e-04 - val_root_mean_squared_error: 0.0309\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2801/5000\n", + "1/1 - 0s - loss: 4.6214e-04 - root_mean_squared_error: 0.0215 - val_loss: 7.2098e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2802/5000\n", + "1/1 - 0s - loss: 4.3375e-04 - root_mean_squared_error: 0.0208 - val_loss: 8.5129e-04 - val_root_mean_squared_error: 0.0292\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2803/5000\n", + "1/1 - 0s - loss: 4.0970e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.3887e-04 - val_root_mean_squared_error: 0.0272\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2804/5000\n", + "1/1 - 0s - loss: 3.8938e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.4755e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2805/5000\n", + "1/1 - 0s - loss: 3.8714e-04 - root_mean_squared_error: 0.0197 - val_loss: 8.2185e-04 - val_root_mean_squared_error: 0.0287\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2806/5000\n", + "1/1 - 0s - loss: 3.9893e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.1420e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2807/5000\n", + "1/1 - 0s - loss: 4.1081e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.6784e-04 - val_root_mean_squared_error: 0.0295\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2808/5000\n", + "1/1 - 0s - loss: 4.1868e-04 - root_mean_squared_error: 0.0205 - val_loss: 7.1405e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2809/5000\n", + "1/1 - 0s - loss: 4.0858e-04 - root_mean_squared_error: 0.0202 - val_loss: 8.1482e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2810/5000\n", + "1/1 - 0s - loss: 3.9726e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.3452e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2811/5000\n", + "1/1 - 0s - loss: 3.8673e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.4583e-04 - val_root_mean_squared_error: 0.0273\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2812/5000\n", + "1/1 - 0s - loss: 3.8400e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.8142e-04 - val_root_mean_squared_error: 0.0280\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2813/5000\n", + "1/1 - 0s - loss: 3.8812e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.1561e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2814/5000\n", + "1/1 - 0s - loss: 3.9416e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.1417e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2815/5000\n", + "1/1 - 0s - loss: 3.9903e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.0798e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2816/5000\n", + "1/1 - 0s - loss: 3.9677e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.9403e-04 - val_root_mean_squared_error: 0.0282\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2817/5000\n", + "1/1 - 0s - loss: 3.9229e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.2095e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2818/5000\n", + "1/1 - 0s - loss: 3.8607e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.4966e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2819/5000\n", + "1/1 - 0s - loss: 3.8259e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.5057e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2820/5000\n", + "1/1 - 0s - loss: 3.8253e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.1946e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2821/5000\n", + "1/1 - 0s - loss: 3.8489e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.7938e-04 - val_root_mean_squared_error: 0.0279\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2822/5000\n", + "1/1 - 0s - loss: 3.8794e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.0965e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2823/5000\n", + "1/1 - 0s - loss: 3.8920e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.8226e-04 - val_root_mean_squared_error: 0.0280\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2824/5000\n", + "1/1 - 0s - loss: 3.8923e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.0952e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2825/5000\n", + "1/1 - 0s - loss: 3.8679e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.6276e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2826/5000\n", + "1/1 - 0s - loss: 3.8417e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.2073e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2827/5000\n", + "1/1 - 0s - loss: 3.8168e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.3668e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2828/5000\n", + "1/1 - 0s - loss: 3.8035e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.3732e-04 - val_root_mean_squared_error: 0.0272\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2829/5000\n", + "1/1 - 0s - loss: 3.8023e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.1838e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2830/5000\n", + "1/1 - 0s - loss: 3.8095e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.5319e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2831/5000\n", + "1/1 - 0s - loss: 3.8197e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.1007e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2832/5000\n", + "1/1 - 0s - loss: 3.8255e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.5664e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2833/5000\n", + "1/1 - 0s - loss: 3.8270e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.0899e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2834/5000\n", + "1/1 - 0s - loss: 3.8198e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.4942e-04 - val_root_mean_squared_error: 0.0274\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2835/5000\n", + "1/1 - 0s - loss: 3.8104e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.1289e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2836/5000\n", + "1/1 - 0s - loss: 3.7985e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.3564e-04 - val_root_mean_squared_error: 0.0271\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2837/5000\n", + "1/1 - 0s - loss: 3.7889e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.2028e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2838/5000\n", + "1/1 - 0s - loss: 3.7821e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.2325e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2839/5000\n", + "1/1 - 0s - loss: 3.7789e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.2894e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2840/5000\n", + "1/1 - 0s - loss: 3.7785e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.1358e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2841/5000\n", + "1/1 - 0s - loss: 3.7797e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.3510e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2842/5000\n", + "1/1 - 0s - loss: 3.7815e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0831e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2843/5000\n", + "1/1 - 0s - loss: 3.7824e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.3743e-04 - val_root_mean_squared_error: 0.0272\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2844/5000\n", + "1/1 - 0s - loss: 3.7826e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0527e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2845/5000\n", + "1/1 - 0s - loss: 3.7809e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.3571e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2846/5000\n", + "1/1 - 0s - loss: 3.7787e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0453e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2847/5000\n", + "1/1 - 0s - loss: 3.7749e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.3183e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2848/5000\n", + "1/1 - 0s - loss: 3.7710e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0474e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2849/5000\n", + "1/1 - 0s - loss: 3.7665e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.2669e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2850/5000\n", + "1/1 - 0s - loss: 3.7624e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0621e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2851/5000\n", + "1/1 - 0s - loss: 3.7582e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.2181e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2852/5000\n", + "1/1 - 0s - loss: 3.7545e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0773e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2853/5000\n", + "1/1 - 0s - loss: 3.7512e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.1710e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2854/5000\n", + "1/1 - 0s - loss: 3.7482e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0900e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2855/5000\n", + "1/1 - 0s - loss: 3.7455e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.1321e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2856/5000\n", + "1/1 - 0s - loss: 3.7430e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0954e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2857/5000\n", + "1/1 - 0s - loss: 3.7408e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0955e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2858/5000\n", + "1/1 - 0s - loss: 3.7386e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0982e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2859/5000\n", + "1/1 - 0s - loss: 3.7366e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0658e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2860/5000\n", + "1/1 - 0s - loss: 3.7346e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0991e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2861/5000\n", + "1/1 - 0s - loss: 3.7327e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0371e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2862/5000\n", + "1/1 - 0s - loss: 3.7308e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1011e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2863/5000\n", + "1/1 - 0s - loss: 3.7290e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.0108e-04 - val_root_mean_squared_error: 0.0265\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2864/5000\n", + "1/1 - 0s - loss: 3.7273e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1053e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2865/5000\n", + "1/1 - 0s - loss: 3.7258e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.9793e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2866/5000\n", + "1/1 - 0s - loss: 3.7244e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1159e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2867/5000\n", + "1/1 - 0s - loss: 3.7234e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.9424e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2868/5000\n", + "1/1 - 0s - loss: 3.7228e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1376e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2869/5000\n", + "1/1 - 0s - loss: 3.7229e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.8941e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2870/5000\n", + "1/1 - 0s - loss: 3.7239e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1810e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2871/5000\n", + "1/1 - 0s - loss: 3.7268e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.8338e-04 - val_root_mean_squared_error: 0.0261\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2872/5000\n", + "1/1 - 0s - loss: 3.7315e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.2639e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2873/5000\n", + "1/1 - 0s - loss: 3.7413e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.7541e-04 - val_root_mean_squared_error: 0.0260\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2874/5000\n", + "1/1 - 0s - loss: 3.7550e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.4211e-04 - val_root_mean_squared_error: 0.0272\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2875/5000\n", + "1/1 - 0s - loss: 3.7824e-04 - root_mean_squared_error: 0.0194 - val_loss: 6.6589e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2876/5000\n", + "1/1 - 0s - loss: 3.8170e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.7210e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2877/5000\n", + "1/1 - 0s - loss: 3.8890e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.5690e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2878/5000\n", + "1/1 - 0s - loss: 3.9664e-04 - root_mean_squared_error: 0.0199 - val_loss: 8.2758e-04 - val_root_mean_squared_error: 0.0288\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2879/5000\n", + "1/1 - 0s - loss: 4.1401e-04 - root_mean_squared_error: 0.0203 - val_loss: 6.5458e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2880/5000\n", + "1/1 - 0s - loss: 4.2672e-04 - root_mean_squared_error: 0.0207 - val_loss: 9.1331e-04 - val_root_mean_squared_error: 0.0302\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2881/5000\n", + "1/1 - 0s - loss: 4.5925e-04 - root_mean_squared_error: 0.0214 - val_loss: 6.6000e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2882/5000\n", + "1/1 - 0s - loss: 4.6358e-04 - root_mean_squared_error: 0.0215 - val_loss: 9.7270e-04 - val_root_mean_squared_error: 0.0312\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2883/5000\n", + "1/1 - 0s - loss: 4.9087e-04 - root_mean_squared_error: 0.0222 - val_loss: 6.5492e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2884/5000\n", + "1/1 - 0s - loss: 4.5872e-04 - root_mean_squared_error: 0.0214 - val_loss: 8.8787e-04 - val_root_mean_squared_error: 0.0298\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2885/5000\n", + "1/1 - 0s - loss: 4.4020e-04 - root_mean_squared_error: 0.0210 - val_loss: 6.5273e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2886/5000\n", + "1/1 - 0s - loss: 3.9795e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.3616e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2887/5000\n", + "1/1 - 0s - loss: 3.7450e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.1142e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2888/5000\n", + "1/1 - 0s - loss: 3.6921e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.6787e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2889/5000\n", + "1/1 - 0s - loss: 3.8015e-04 - root_mean_squared_error: 0.0195 - val_loss: 8.0571e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2890/5000\n", + "1/1 - 0s - loss: 4.0033e-04 - root_mean_squared_error: 0.0200 - val_loss: 6.6064e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2891/5000\n", + "1/1 - 0s - loss: 4.1105e-04 - root_mean_squared_error: 0.0203 - val_loss: 8.4559e-04 - val_root_mean_squared_error: 0.0291\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2892/5000\n", + "1/1 - 0s - loss: 4.2174e-04 - root_mean_squared_error: 0.0205 - val_loss: 6.5928e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2893/5000\n", + "1/1 - 0s - loss: 4.0733e-04 - root_mean_squared_error: 0.0202 - val_loss: 7.9104e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2894/5000\n", + "1/1 - 0s - loss: 3.9465e-04 - root_mean_squared_error: 0.0199 - val_loss: 6.6565e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2895/5000\n", + "1/1 - 0s - loss: 3.7694e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.0720e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2896/5000\n", + "1/1 - 0s - loss: 3.6786e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.0971e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2897/5000\n", + "1/1 - 0s - loss: 3.6800e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.6419e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2898/5000\n", + "1/1 - 0s - loss: 3.7462e-04 - root_mean_squared_error: 0.0194 - val_loss: 7.6275e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2899/5000\n", + "1/1 - 0s - loss: 3.8362e-04 - root_mean_squared_error: 0.0196 - val_loss: 6.5436e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2900/5000\n", + "1/1 - 0s - loss: 3.8686e-04 - root_mean_squared_error: 0.0197 - val_loss: 7.7092e-04 - val_root_mean_squared_error: 0.0278\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2901/5000\n", + "1/1 - 0s - loss: 3.8808e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.5498e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2902/5000\n", + "1/1 - 0s - loss: 3.8098e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.3388e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2903/5000\n", + "1/1 - 0s - loss: 3.7432e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.6701e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2904/5000\n", + "1/1 - 0s - loss: 3.6789e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.8827e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2905/5000\n", + "1/1 - 0s - loss: 3.6498e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9500e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2906/5000\n", + "1/1 - 0s - loss: 3.6554e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.6073e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2907/5000\n", + "1/1 - 0s - loss: 3.6824e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.2260e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2908/5000\n", + "1/1 - 0s - loss: 3.7154e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.5188e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2909/5000\n", + "1/1 - 0s - loss: 3.7293e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.2829e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2910/5000\n", + "1/1 - 0s - loss: 3.7338e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.5252e-04 - val_root_mean_squared_error: 0.0255\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2911/5000\n", + "1/1 - 0s - loss: 3.7119e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1270e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2912/5000\n", + "1/1 - 0s - loss: 3.6885e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.5939e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2913/5000\n", + "1/1 - 0s - loss: 3.6600e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.8974e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2914/5000\n", + "1/1 - 0s - loss: 3.6398e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.7133e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2915/5000\n", + "1/1 - 0s - loss: 3.6287e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.6983e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2916/5000\n", + "1/1 - 0s - loss: 3.6271e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.8529e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2917/5000\n", + "1/1 - 0s - loss: 3.6322e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.5759e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2918/5000\n", + "1/1 - 0s - loss: 3.6400e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9530e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2919/5000\n", + "1/1 - 0s - loss: 3.6480e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.5216e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2920/5000\n", + "1/1 - 0s - loss: 3.6512e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9752e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2921/5000\n", + "1/1 - 0s - loss: 3.6523e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.5106e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2922/5000\n", + "1/1 - 0s - loss: 3.6469e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9262e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2923/5000\n", + "1/1 - 0s - loss: 3.6407e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.5255e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2924/5000\n", + "1/1 - 0s - loss: 3.6312e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.8385e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2925/5000\n", + "1/1 - 0s - loss: 3.6226e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.5612e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2926/5000\n", + "1/1 - 0s - loss: 3.6144e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7413e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2927/5000\n", + "1/1 - 0s - loss: 3.6080e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.6096e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2928/5000\n", + "1/1 - 0s - loss: 3.6032e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.6562e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2929/5000\n", + "1/1 - 0s - loss: 3.6000e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.6592e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2930/5000\n", + "1/1 - 0s - loss: 3.5982e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.5900e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2931/5000\n", + "1/1 - 0s - loss: 3.5973e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.6979e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2932/5000\n", + "1/1 - 0s - loss: 3.5971e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.5378e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2933/5000\n", + "1/1 - 0s - loss: 3.5972e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7246e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2934/5000\n", + "1/1 - 0s - loss: 3.5976e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.4932e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2935/5000\n", + "1/1 - 0s - loss: 3.5979e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7439e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2936/5000\n", + "1/1 - 0s - loss: 3.5986e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.4541e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2937/5000\n", + "1/1 - 0s - loss: 3.5991e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7630e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2938/5000\n", + "1/1 - 0s - loss: 3.6004e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.4183e-04 - val_root_mean_squared_error: 0.0253\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2939/5000\n", + "1/1 - 0s - loss: 3.6012e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7871e-04 - val_root_mean_squared_error: 0.0261\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2940/5000\n", + "1/1 - 0s - loss: 3.6037e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.3822e-04 - val_root_mean_squared_error: 0.0253\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2941/5000\n", + "1/1 - 0s - loss: 3.6058e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.8245e-04 - val_root_mean_squared_error: 0.0261\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2942/5000\n", + "1/1 - 0s - loss: 3.6109e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.3404e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2943/5000\n", + "1/1 - 0s - loss: 3.6154e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.8845e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2944/5000\n", + "1/1 - 0s - loss: 3.6255e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.2904e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2945/5000\n", + "1/1 - 0s - loss: 3.6346e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9801e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2946/5000\n", + "1/1 - 0s - loss: 3.6541e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.2340e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2947/5000\n", + "1/1 - 0s - loss: 3.6706e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.1285e-04 - val_root_mean_squared_error: 0.0267\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2948/5000\n", + "1/1 - 0s - loss: 3.7068e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.1789e-04 - val_root_mean_squared_error: 0.0249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2949/5000\n", + "1/1 - 0s - loss: 3.7336e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.3476e-04 - val_root_mean_squared_error: 0.0271\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2950/5000\n", + "1/1 - 0s - loss: 3.7961e-04 - root_mean_squared_error: 0.0195 - val_loss: 6.1367e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2951/5000\n", + "1/1 - 0s - loss: 3.8306e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.6295e-04 - val_root_mean_squared_error: 0.0276\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2952/5000\n", + "1/1 - 0s - loss: 3.9237e-04 - root_mean_squared_error: 0.0198 - val_loss: 6.1151e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2953/5000\n", + "1/1 - 0s - loss: 3.9455e-04 - root_mean_squared_error: 0.0199 - val_loss: 7.8820e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2954/5000\n", + "1/1 - 0s - loss: 4.0449e-04 - root_mean_squared_error: 0.0201 - val_loss: 6.1040e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2955/5000\n", + "1/1 - 0s - loss: 4.0091e-04 - root_mean_squared_error: 0.0200 - val_loss: 7.8978e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2956/5000\n", + "1/1 - 0s - loss: 4.0463e-04 - root_mean_squared_error: 0.0201 - val_loss: 6.0941e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2957/5000\n", + "1/1 - 0s - loss: 3.9296e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.5378e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2958/5000\n", + "1/1 - 0s - loss: 3.8656e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.1403e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2959/5000\n", + "1/1 - 0s - loss: 3.7308e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.9891e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2960/5000\n", + "1/1 - 0s - loss: 3.6408e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.3178e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2961/5000\n", + "1/1 - 0s - loss: 3.5704e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.5507e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2962/5000\n", + "1/1 - 0s - loss: 3.5402e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.5994e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2963/5000\n", + "1/1 - 0s - loss: 3.5431e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.3043e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2964/5000\n", + "1/1 - 0s - loss: 3.5690e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.8882e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2965/5000\n", + "1/1 - 0s - loss: 3.6107e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.1809e-04 - val_root_mean_squared_error: 0.0249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2966/5000\n", + "1/1 - 0s - loss: 3.6486e-04 - root_mean_squared_error: 0.0191 - val_loss: 7.1110e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2967/5000\n", + "1/1 - 0s - loss: 3.6974e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.1134e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2968/5000\n", + "1/1 - 0s - loss: 3.7129e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.2065e-04 - val_root_mean_squared_error: 0.0268\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2969/5000\n", + "1/1 - 0s - loss: 3.7416e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.0780e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2970/5000\n", + "1/1 - 0s - loss: 3.7191e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.1287e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2971/5000\n", + "1/1 - 0s - loss: 3.7103e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.0833e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2972/5000\n", + "1/1 - 0s - loss: 3.6617e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9114e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2973/5000\n", + "1/1 - 0s - loss: 3.6270e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.1451e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2974/5000\n", + "1/1 - 0s - loss: 3.5817e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.6547e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2975/5000\n", + "1/1 - 0s - loss: 3.5500e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.2541e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2976/5000\n", + "1/1 - 0s - loss: 3.5258e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.4369e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2977/5000\n", + "1/1 - 0s - loss: 3.5126e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.3812e-04 - val_root_mean_squared_error: 0.0253\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2978/5000\n", + "1/1 - 0s - loss: 3.5083e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.2805e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2979/5000\n", + "1/1 - 0s - loss: 3.5107e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.5024e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2980/5000\n", + "1/1 - 0s - loss: 3.5181e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.1756e-04 - val_root_mean_squared_error: 0.0249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2981/5000\n", + "1/1 - 0s - loss: 3.5278e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.6081e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2982/5000\n", + "1/1 - 0s - loss: 3.5405e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.1046e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2983/5000\n", + "1/1 - 0s - loss: 3.5512e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.6996e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2984/5000\n", + "1/1 - 0s - loss: 3.5666e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.0525e-04 - val_root_mean_squared_error: 0.0246\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2985/5000\n", + "1/1 - 0s - loss: 3.5760e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.7825e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2986/5000\n", + "1/1 - 0s - loss: 3.5941e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.0105e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2987/5000\n", + "1/1 - 0s - loss: 3.6018e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.8588e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2988/5000\n", + "1/1 - 0s - loss: 3.6227e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.9756e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2989/5000\n", + "1/1 - 0s - loss: 3.6272e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.9226e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2990/5000\n", + "1/1 - 0s - loss: 3.6490e-04 - root_mean_squared_error: 0.0191 - val_loss: 5.9498e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2991/5000\n", + "1/1 - 0s - loss: 3.6468e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9583e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2992/5000\n", + "1/1 - 0s - loss: 3.6642e-04 - root_mean_squared_error: 0.0191 - val_loss: 5.9361e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2993/5000\n", + "1/1 - 0s - loss: 3.6515e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.9475e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2994/5000\n", + "1/1 - 0s - loss: 3.6588e-04 - root_mean_squared_error: 0.0191 - val_loss: 5.9350e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2995/5000\n", + "1/1 - 0s - loss: 3.6361e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.8839e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2996/5000\n", + "1/1 - 0s - loss: 3.6319e-04 - root_mean_squared_error: 0.0191 - val_loss: 5.9454e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2997/5000\n", + "1/1 - 0s - loss: 3.6044e-04 - root_mean_squared_error: 0.0190 - val_loss: 6.7822e-04 - val_root_mean_squared_error: 0.0260\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2998/5000\n", + "1/1 - 0s - loss: 3.5926e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.9650e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 2999/5000\n", + "1/1 - 0s - loss: 3.5668e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.6676e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3000/5000\n", + "1/1 - 0s - loss: 3.5530e-04 - root_mean_squared_error: 0.0188 - val_loss: 5.9894e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3001/5000\n", + "1/1 - 0s - loss: 3.5326e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.5607e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3002/5000\n", + "1/1 - 0s - loss: 3.5206e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.0125e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3003/5000\n", + "1/1 - 0s - loss: 3.5062e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.4721e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3004/5000\n", + "1/1 - 0s - loss: 3.4973e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.0291e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3005/5000\n", + "1/1 - 0s - loss: 3.4879e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.4050e-04 - val_root_mean_squared_error: 0.0253\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3006/5000\n", + "1/1 - 0s - loss: 3.4819e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.0359e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3007/5000\n", + "1/1 - 0s - loss: 3.4759e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.3592e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3008/5000\n", + "1/1 - 0s - loss: 3.4722e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.0308e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3009/5000\n", + "1/1 - 0s - loss: 3.4686e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.3338e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3010/5000\n", + "1/1 - 0s - loss: 3.4667e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.0133e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3011/5000\n", + "1/1 - 0s - loss: 3.4651e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.3298e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3012/5000\n", + "1/1 - 0s - loss: 3.4653e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.9834e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3013/5000\n", + "1/1 - 0s - loss: 3.4659e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.3513e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3014/5000\n", + "1/1 - 0s - loss: 3.4693e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.9407e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3015/5000\n", + "1/1 - 0s - loss: 3.4734e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.4085e-04 - val_root_mean_squared_error: 0.0253\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3016/5000\n", + "1/1 - 0s - loss: 3.4830e-04 - root_mean_squared_error: 0.0187 - val_loss: 5.8851e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3017/5000\n", + "1/1 - 0s - loss: 3.4940e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.5211e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3018/5000\n", + "1/1 - 0s - loss: 3.5167e-04 - root_mean_squared_error: 0.0188 - val_loss: 5.8201e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3019/5000\n", + "1/1 - 0s - loss: 3.5411e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.7246e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3020/5000\n", + "1/1 - 0s - loss: 3.5919e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.7595e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3021/5000\n", + "1/1 - 0s - loss: 3.6397e-04 - root_mean_squared_error: 0.0191 - val_loss: 7.0709e-04 - val_root_mean_squared_error: 0.0266\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3022/5000\n", + "1/1 - 0s - loss: 3.7457e-04 - root_mean_squared_error: 0.0194 - val_loss: 5.7316e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3023/5000\n", + "1/1 - 0s - loss: 3.8191e-04 - root_mean_squared_error: 0.0195 - val_loss: 7.5785e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3024/5000\n", + "1/1 - 0s - loss: 4.0032e-04 - root_mean_squared_error: 0.0200 - val_loss: 5.7508e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3025/5000\n", + "1/1 - 0s - loss: 4.0514e-04 - root_mean_squared_error: 0.0201 - val_loss: 8.0373e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3026/5000\n", + "1/1 - 0s - loss: 4.2472e-04 - root_mean_squared_error: 0.0206 - val_loss: 5.7493e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3027/5000\n", + "1/1 - 0s - loss: 4.1365e-04 - root_mean_squared_error: 0.0203 - val_loss: 7.8780e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3028/5000\n", + "1/1 - 0s - loss: 4.1385e-04 - root_mean_squared_error: 0.0203 - val_loss: 5.6934e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3029/5000\n", + "1/1 - 0s - loss: 3.8644e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.9855e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3030/5000\n", + "1/1 - 0s - loss: 3.6790e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.8310e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3031/5000\n", + "1/1 - 0s - loss: 3.4953e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.1718e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3032/5000\n", + "1/1 - 0s - loss: 3.4176e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.3018e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3033/5000\n", + "1/1 - 0s - loss: 3.4335e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.8390e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3034/5000\n", + "1/1 - 0s - loss: 3.5112e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.8462e-04 - val_root_mean_squared_error: 0.0262\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3035/5000\n", + "1/1 - 0s - loss: 3.6257e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.7715e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3036/5000\n", + "1/1 - 0s - loss: 3.6915e-04 - root_mean_squared_error: 0.0192 - val_loss: 7.1279e-04 - val_root_mean_squared_error: 0.0267\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3037/5000\n", + "1/1 - 0s - loss: 3.7701e-04 - root_mean_squared_error: 0.0194 - val_loss: 5.7346e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3038/5000\n", + "1/1 - 0s - loss: 3.7206e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.9453e-04 - val_root_mean_squared_error: 0.0264\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3039/5000\n", + "1/1 - 0s - loss: 3.6827e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.7234e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3040/5000\n", + "1/1 - 0s - loss: 3.5655e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.4315e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3041/5000\n", + "1/1 - 0s - loss: 3.4778e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.8780e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3042/5000\n", + "1/1 - 0s - loss: 3.4127e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.9941e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3043/5000\n", + "1/1 - 0s - loss: 3.3931e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.1968e-04 - val_root_mean_squared_error: 0.0249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3044/5000\n", + "1/1 - 0s - loss: 3.4116e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.7808e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3045/5000\n", + "1/1 - 0s - loss: 3.4505e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.4817e-04 - val_root_mean_squared_error: 0.0255\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3046/5000\n", + "1/1 - 0s - loss: 3.4986e-04 - root_mean_squared_error: 0.0187 - val_loss: 5.6988e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3047/5000\n", + "1/1 - 0s - loss: 3.5226e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.5868e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3048/5000\n", + "1/1 - 0s - loss: 3.5447e-04 - root_mean_squared_error: 0.0188 - val_loss: 5.6631e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3049/5000\n", + "1/1 - 0s - loss: 3.5238e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.4709e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3050/5000\n", + "1/1 - 0s - loss: 3.5035e-04 - root_mean_squared_error: 0.0187 - val_loss: 5.6835e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3051/5000\n", + "1/1 - 0s - loss: 3.4588e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.2179e-04 - val_root_mean_squared_error: 0.0249\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3052/5000\n", + "1/1 - 0s - loss: 3.4228e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.7869e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3053/5000\n", + "1/1 - 0s - loss: 3.3919e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.9776e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3054/5000\n", + "1/1 - 0s - loss: 3.3752e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.9473e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3055/5000\n", + "1/1 - 0s - loss: 3.3715e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.8178e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3056/5000\n", + "1/1 - 0s - loss: 3.3777e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.0965e-04 - val_root_mean_squared_error: 0.0247\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3057/5000\n", + "1/1 - 0s - loss: 3.3901e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.7164e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3058/5000\n", + "1/1 - 0s - loss: 3.4034e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.2025e-04 - val_root_mean_squared_error: 0.0249\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3059/5000\n", + "1/1 - 0s - loss: 3.4185e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.6526e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3060/5000\n", + "1/1 - 0s - loss: 3.4266e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.2501e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3061/5000\n", + "1/1 - 0s - loss: 3.4358e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.6222e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3062/5000\n", + "1/1 - 0s - loss: 3.4327e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.2326e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3063/5000\n", + "1/1 - 0s - loss: 3.4311e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.6247e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3064/5000\n", + "1/1 - 0s - loss: 3.4184e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.1644e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3065/5000\n", + "1/1 - 0s - loss: 3.4088e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.6505e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3066/5000\n", + "1/1 - 0s - loss: 3.3938e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.0761e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3067/5000\n", + "1/1 - 0s - loss: 3.3825e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.6845e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3068/5000\n", + "1/1 - 0s - loss: 3.3706e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.9847e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3069/5000\n", + "1/1 - 0s - loss: 3.3618e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7149e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3070/5000\n", + "1/1 - 0s - loss: 3.3540e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.9020e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3071/5000\n", + "1/1 - 0s - loss: 3.3481e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7421e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3072/5000\n", + "1/1 - 0s - loss: 3.3435e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.8364e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3073/5000\n", + "1/1 - 0s - loss: 3.3400e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7658e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3074/5000\n", + "1/1 - 0s - loss: 3.3372e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7892e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3075/5000\n", + "1/1 - 0s - loss: 3.3350e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7857e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3076/5000\n", + "1/1 - 0s - loss: 3.3332e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.7509e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3077/5000\n", + "1/1 - 0s - loss: 3.3318e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.8008e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3078/5000\n", + "1/1 - 0s - loss: 3.3306e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.7138e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3079/5000\n", + "1/1 - 0s - loss: 3.3296e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.8144e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3080/5000\n", + "1/1 - 0s - loss: 3.3289e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.6740e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3081/5000\n", + "1/1 - 0s - loss: 3.3286e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.8325e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3082/5000\n", + "1/1 - 0s - loss: 3.3290e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.6316e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3083/5000\n", + "1/1 - 0s - loss: 3.3299e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.8655e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3084/5000\n", + "1/1 - 0s - loss: 3.3324e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.5836e-04 - val_root_mean_squared_error: 0.0236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3085/5000\n", + "1/1 - 0s - loss: 3.3361e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.9273e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3086/5000\n", + "1/1 - 0s - loss: 3.3434e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.5268e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3087/5000\n", + "1/1 - 0s - loss: 3.3533e-04 - root_mean_squared_error: 0.0183 - val_loss: 6.0398e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3088/5000\n", + "1/1 - 0s - loss: 3.3724e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.4593e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3089/5000\n", + "1/1 - 0s - loss: 3.3964e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.2452e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3090/5000\n", + "1/1 - 0s - loss: 3.4442e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.3942e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3091/5000\n", + "1/1 - 0s - loss: 3.4975e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.6223e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3092/5000\n", + "1/1 - 0s - loss: 3.6113e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.3712e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3093/5000\n", + "1/1 - 0s - loss: 3.7091e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.2549e-04 - val_root_mean_squared_error: 0.0269\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3094/5000\n", + "1/1 - 0s - loss: 3.9406e-04 - root_mean_squared_error: 0.0199 - val_loss: 5.4294e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3095/5000\n", + "1/1 - 0s - loss: 4.0360e-04 - root_mean_squared_error: 0.0201 - val_loss: 7.9620e-04 - val_root_mean_squared_error: 0.0282\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3096/5000\n", + "1/1 - 0s - loss: 4.3339e-04 - root_mean_squared_error: 0.0208 - val_loss: 5.4611e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3097/5000\n", + "1/1 - 0s - loss: 4.2176e-04 - root_mean_squared_error: 0.0205 - val_loss: 7.8822e-04 - val_root_mean_squared_error: 0.0281\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3098/5000\n", + "1/1 - 0s - loss: 4.2538e-04 - root_mean_squared_error: 0.0206 - val_loss: 5.3404e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3099/5000\n", + "1/1 - 0s - loss: 3.8662e-04 - root_mean_squared_error: 0.0197 - val_loss: 6.6831e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3100/5000\n", + "1/1 - 0s - loss: 3.6025e-04 - root_mean_squared_error: 0.0190 - val_loss: 5.4766e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3101/5000\n", + "1/1 - 0s - loss: 3.3655e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.6921e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3102/5000\n", + "1/1 - 0s - loss: 3.2970e-04 - root_mean_squared_error: 0.0182 - val_loss: 6.1352e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3103/5000\n", + "1/1 - 0s - loss: 3.3706e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.4301e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3104/5000\n", + "1/1 - 0s - loss: 3.5097e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.8150e-04 - val_root_mean_squared_error: 0.0261\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3105/5000\n", + "1/1 - 0s - loss: 3.6856e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.4356e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3106/5000\n", + "1/1 - 0s - loss: 3.7127e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.9077e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3107/5000\n", + "1/1 - 0s - loss: 3.7425e-04 - root_mean_squared_error: 0.0193 - val_loss: 5.3858e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3108/5000\n", + "1/1 - 0s - loss: 3.5884e-04 - root_mean_squared_error: 0.0189 - val_loss: 6.3328e-04 - val_root_mean_squared_error: 0.0252\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3109/5000\n", + "1/1 - 0s - loss: 3.4619e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.4583e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3110/5000\n", + "1/1 - 0s - loss: 3.3335e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.6842e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3111/5000\n", + "1/1 - 0s - loss: 3.2799e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.8392e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3112/5000\n", + "1/1 - 0s - loss: 3.2983e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.4055e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3113/5000\n", + "1/1 - 0s - loss: 3.3588e-04 - root_mean_squared_error: 0.0183 - val_loss: 6.2511e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3114/5000\n", + "1/1 - 0s - loss: 3.4337e-04 - root_mean_squared_error: 0.0185 - val_loss: 5.3412e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3115/5000\n", + "1/1 - 0s - loss: 3.4598e-04 - root_mean_squared_error: 0.0186 - val_loss: 6.3127e-04 - val_root_mean_squared_error: 0.0251\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3116/5000\n", + "1/1 - 0s - loss: 3.4724e-04 - root_mean_squared_error: 0.0186 - val_loss: 5.3294e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3117/5000\n", + "1/1 - 0s - loss: 3.4163e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.0338e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3118/5000\n", + "1/1 - 0s - loss: 3.3628e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.3903e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3119/5000\n", + "1/1 - 0s - loss: 3.3030e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.6583e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3120/5000\n", + "1/1 - 0s - loss: 3.2686e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.5943e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3121/5000\n", + "1/1 - 0s - loss: 3.2613e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.4194e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3122/5000\n", + "1/1 - 0s - loss: 3.2763e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.8381e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3123/5000\n", + "1/1 - 0s - loss: 3.3032e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.3269e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3124/5000\n", + "1/1 - 0s - loss: 3.3250e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.9660e-04 - val_root_mean_squared_error: 0.0244\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3125/5000\n", + "1/1 - 0s - loss: 3.3437e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.3017e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3126/5000\n", + "1/1 - 0s - loss: 3.3404e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.9246e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3127/5000\n", + "1/1 - 0s - loss: 3.3341e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.3030e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3128/5000\n", + "1/1 - 0s - loss: 3.3108e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.7759e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3129/5000\n", + "1/1 - 0s - loss: 3.2897e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.3517e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3130/5000\n", + "1/1 - 0s - loss: 3.2668e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.5947e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3131/5000\n", + "1/1 - 0s - loss: 3.2507e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.4495e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3132/5000\n", + "1/1 - 0s - loss: 3.2414e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.4539e-04 - val_root_mean_squared_error: 0.0234\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3133/5000\n", + "1/1 - 0s - loss: 3.2393e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.5660e-04 - val_root_mean_squared_error: 0.0236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3134/5000\n", + "1/1 - 0s - loss: 3.2426e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.3656e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3135/5000\n", + "1/1 - 0s - loss: 3.2486e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.6520e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3136/5000\n", + "1/1 - 0s - loss: 3.2557e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.3126e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3137/5000\n", + "1/1 - 0s - loss: 3.2605e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.6890e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3138/5000\n", + "1/1 - 0s - loss: 3.2648e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.2779e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3139/5000\n", + "1/1 - 0s - loss: 3.2646e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.6833e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3140/5000\n", + "1/1 - 0s - loss: 3.2643e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.2652e-04 - val_root_mean_squared_error: 0.0229\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3141/5000\n", + "1/1 - 0s - loss: 3.2594e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.6503e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3142/5000\n", + "1/1 - 0s - loss: 3.2552e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.2713e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3143/5000\n", + "1/1 - 0s - loss: 3.2482e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.6018e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3144/5000\n", + "1/1 - 0s - loss: 3.2424e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.2892e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3145/5000\n", + "1/1 - 0s - loss: 3.2358e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.5510e-04 - val_root_mean_squared_error: 0.0236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3146/5000\n", + "1/1 - 0s - loss: 3.2306e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.3043e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3147/5000\n", + "1/1 - 0s - loss: 3.2253e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.5019e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3148/5000\n", + "1/1 - 0s - loss: 3.2212e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3131e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3149/5000\n", + "1/1 - 0s - loss: 3.2173e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4600e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3150/5000\n", + "1/1 - 0s - loss: 3.2142e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3161e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3151/5000\n", + "1/1 - 0s - loss: 3.2113e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4277e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3152/5000\n", + "1/1 - 0s - loss: 3.2088e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3182e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3153/5000\n", + "1/1 - 0s - loss: 3.2065e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4064e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3154/5000\n", + "1/1 - 0s - loss: 3.2045e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3161e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3155/5000\n", + "1/1 - 0s - loss: 3.2025e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3927e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3156/5000\n", + "1/1 - 0s - loss: 3.2007e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3083e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3157/5000\n", + "1/1 - 0s - loss: 3.1990e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3833e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3158/5000\n", + "1/1 - 0s - loss: 3.1974e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.2921e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3159/5000\n", + "1/1 - 0s - loss: 3.1959e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3791e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3160/5000\n", + "1/1 - 0s - loss: 3.1946e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.2700e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3161/5000\n", + "1/1 - 0s - loss: 3.1935e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3840e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3162/5000\n", + "1/1 - 0s - loss: 3.1928e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.2419e-04 - val_root_mean_squared_error: 0.0229\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3163/5000\n", + "1/1 - 0s - loss: 3.1924e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4041e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3164/5000\n", + "1/1 - 0s - loss: 3.1929e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.2061e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3165/5000\n", + "1/1 - 0s - loss: 3.1943e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4464e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3166/5000\n", + "1/1 - 0s - loss: 3.1978e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.1572e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3167/5000\n", + "1/1 - 0s - loss: 3.2035e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.5275e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3168/5000\n", + "1/1 - 0s - loss: 3.2148e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.0932e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3169/5000\n", + "1/1 - 0s - loss: 3.2312e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.6856e-04 - val_root_mean_squared_error: 0.0238\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3170/5000\n", + "1/1 - 0s - loss: 3.2635e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.0229e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3171/5000\n", + "1/1 - 0s - loss: 3.3056e-04 - root_mean_squared_error: 0.0182 - val_loss: 6.0018e-04 - val_root_mean_squared_error: 0.0245\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3172/5000\n", + "1/1 - 0s - loss: 3.3931e-04 - root_mean_squared_error: 0.0184 - val_loss: 4.9867e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3173/5000\n", + "1/1 - 0s - loss: 3.4887e-04 - root_mean_squared_error: 0.0187 - val_loss: 6.6173e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3174/5000\n", + "1/1 - 0s - loss: 3.7048e-04 - root_mean_squared_error: 0.0192 - val_loss: 5.0576e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3175/5000\n", + "1/1 - 0s - loss: 3.8588e-04 - root_mean_squared_error: 0.0196 - val_loss: 7.5805e-04 - val_root_mean_squared_error: 0.0275\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3176/5000\n", + "1/1 - 0s - loss: 4.2563e-04 - root_mean_squared_error: 0.0206 - val_loss: 5.1916e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3177/5000\n", + "1/1 - 0s - loss: 4.2733e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.1059e-04 - val_root_mean_squared_error: 0.0285\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3178/5000\n", + "1/1 - 0s - loss: 4.5378e-04 - root_mean_squared_error: 0.0213 - val_loss: 5.0631e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3179/5000\n", + "1/1 - 0s - loss: 4.0954e-04 - root_mean_squared_error: 0.0202 - val_loss: 6.9064e-04 - val_root_mean_squared_error: 0.0263\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3180/5000\n", + "1/1 - 0s - loss: 3.7990e-04 - root_mean_squared_error: 0.0195 - val_loss: 4.9774e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3181/5000\n", + "1/1 - 0s - loss: 3.3651e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.4179e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3182/5000\n", + "1/1 - 0s - loss: 3.1779e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.6766e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3183/5000\n", + "1/1 - 0s - loss: 3.2234e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.0404e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3184/5000\n", + "1/1 - 0s - loss: 3.4109e-04 - root_mean_squared_error: 0.0185 - val_loss: 6.6051e-04 - val_root_mean_squared_error: 0.0257\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3185/5000\n", + "1/1 - 0s - loss: 3.6663e-04 - root_mean_squared_error: 0.0191 - val_loss: 5.0948e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3186/5000\n", + "1/1 - 0s - loss: 3.6991e-04 - root_mean_squared_error: 0.0192 - val_loss: 6.6695e-04 - val_root_mean_squared_error: 0.0258\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3187/5000\n", + "1/1 - 0s - loss: 3.7164e-04 - root_mean_squared_error: 0.0193 - val_loss: 5.0343e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3188/5000\n", + "1/1 - 0s - loss: 3.4818e-04 - root_mean_squared_error: 0.0187 - val_loss: 5.8507e-04 - val_root_mean_squared_error: 0.0242\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3189/5000\n", + "1/1 - 0s - loss: 3.3013e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.1443e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3190/5000\n", + "1/1 - 0s - loss: 3.1718e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.1580e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3191/5000\n", + "1/1 - 0s - loss: 3.1621e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.6868e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3192/5000\n", + "1/1 - 0s - loss: 3.2435e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.9784e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3193/5000\n", + "1/1 - 0s - loss: 3.3335e-04 - root_mean_squared_error: 0.0183 - val_loss: 6.0734e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3194/5000\n", + "1/1 - 0s - loss: 3.4078e-04 - root_mean_squared_error: 0.0185 - val_loss: 4.9621e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3195/5000\n", + "1/1 - 0s - loss: 3.3674e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.8247e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3196/5000\n", + "1/1 - 0s - loss: 3.3091e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.0010e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3197/5000\n", + "1/1 - 0s - loss: 3.2112e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.3359e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3198/5000\n", + "1/1 - 0s - loss: 3.1502e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2146e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3199/5000\n", + "1/1 - 0s - loss: 3.1357e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.0148e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3200/5000\n", + "1/1 - 0s - loss: 3.1616e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.5195e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3201/5000\n", + "1/1 - 0s - loss: 3.2053e-04 - root_mean_squared_error: 0.0179 - val_loss: 4.9156e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3202/5000\n", + "1/1 - 0s - loss: 3.2313e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.6241e-04 - val_root_mean_squared_error: 0.0237\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3203/5000\n", + "1/1 - 0s - loss: 3.2432e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.9207e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3204/5000\n", + "1/1 - 0s - loss: 3.2166e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.4702e-04 - val_root_mean_squared_error: 0.0234\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3205/5000\n", + "1/1 - 0s - loss: 3.1858e-04 - root_mean_squared_error: 0.0178 - val_loss: 4.9884e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3206/5000\n", + "1/1 - 0s - loss: 3.1498e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2145e-04 - val_root_mean_squared_error: 0.0228\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3207/5000\n", + "1/1 - 0s - loss: 3.1268e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.1239e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3208/5000\n", + "1/1 - 0s - loss: 3.1187e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.0283e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3209/5000\n", + "1/1 - 0s - loss: 3.1238e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2695e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3210/5000\n", + "1/1 - 0s - loss: 3.1365e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.9343e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3211/5000\n", + "1/1 - 0s - loss: 3.1483e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.3484e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3212/5000\n", + "1/1 - 0s - loss: 3.1570e-04 - root_mean_squared_error: 0.0178 - val_loss: 4.9120e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3213/5000\n", + "1/1 - 0s - loss: 3.1546e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.3202e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3214/5000\n", + "1/1 - 0s - loss: 3.1482e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.9338e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3215/5000\n", + "1/1 - 0s - loss: 3.1346e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2203e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3216/5000\n", + "1/1 - 0s - loss: 3.1219e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.9873e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3217/5000\n", + "1/1 - 0s - loss: 3.1106e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0993e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3218/5000\n", + "1/1 - 0s - loss: 3.1035e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0599e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3219/5000\n", + "1/1 - 0s - loss: 3.1005e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0022e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3220/5000\n", + "1/1 - 0s - loss: 3.1009e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.1272e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3221/5000\n", + "1/1 - 0s - loss: 3.1033e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.9407e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3222/5000\n", + "1/1 - 0s - loss: 3.1061e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.1700e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3223/5000\n", + "1/1 - 0s - loss: 3.1089e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.9075e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3224/5000\n", + "1/1 - 0s - loss: 3.1099e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.1824e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3225/5000\n", + "1/1 - 0s - loss: 3.1103e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.8933e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3226/5000\n", + "1/1 - 0s - loss: 3.1083e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.1679e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3227/5000\n", + "1/1 - 0s - loss: 3.1061e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.8894e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3228/5000\n", + "1/1 - 0s - loss: 3.1023e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.1367e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3229/5000\n", + "1/1 - 0s - loss: 3.0987e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.8931e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3230/5000\n", + "1/1 - 0s - loss: 3.0943e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0969e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3231/5000\n", + "1/1 - 0s - loss: 3.0905e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.9020e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3232/5000\n", + "1/1 - 0s - loss: 3.0867e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0609e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3233/5000\n", + "1/1 - 0s - loss: 3.0833e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.9112e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3234/5000\n", + "1/1 - 0s - loss: 3.0802e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0293e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3235/5000\n", + "1/1 - 0s - loss: 3.0775e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9184e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3236/5000\n", + "1/1 - 0s - loss: 3.0750e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.0032e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3237/5000\n", + "1/1 - 0s - loss: 3.0728e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9203e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3238/5000\n", + "1/1 - 0s - loss: 3.0707e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9813e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3239/5000\n", + "1/1 - 0s - loss: 3.0688e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9190e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3240/5000\n", + "1/1 - 0s - loss: 3.0669e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9642e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3241/5000\n", + "1/1 - 0s - loss: 3.0652e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9137e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3242/5000\n", + "1/1 - 0s - loss: 3.0635e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9525e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3243/5000\n", + "1/1 - 0s - loss: 3.0618e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9059e-04 - val_root_mean_squared_error: 0.0221\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3244/5000\n", + "1/1 - 0s - loss: 3.0602e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9443e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3245/5000\n", + "1/1 - 0s - loss: 3.0586e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8952e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3246/5000\n", + "1/1 - 0s - loss: 3.0571e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9393e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3247/5000\n", + "1/1 - 0s - loss: 3.0556e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8809e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3248/5000\n", + "1/1 - 0s - loss: 3.0541e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9381e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3249/5000\n", + "1/1 - 0s - loss: 3.0528e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8623e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3250/5000\n", + "1/1 - 0s - loss: 3.0516e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9433e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3251/5000\n", + "1/1 - 0s - loss: 3.0506e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8376e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3252/5000\n", + "1/1 - 0s - loss: 3.0499e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9593e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3253/5000\n", + "1/1 - 0s - loss: 3.0498e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.8044e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3254/5000\n", + "1/1 - 0s - loss: 3.0505e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9934e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3255/5000\n", + "1/1 - 0s - loss: 3.0529e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.7588e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3256/5000\n", + "1/1 - 0s - loss: 3.0572e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.0632e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3257/5000\n", + "1/1 - 0s - loss: 3.0661e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.6977e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3258/5000\n", + "1/1 - 0s - loss: 3.0801e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.2066e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3259/5000\n", + "1/1 - 0s - loss: 3.1080e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.6296e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3260/5000\n", + "1/1 - 0s - loss: 3.1475e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.5091e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3261/5000\n", + "1/1 - 0s - loss: 3.2296e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.5963e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3262/5000\n", + "1/1 - 0s - loss: 3.3291e-04 - root_mean_squared_error: 0.0182 - val_loss: 6.1433e-04 - val_root_mean_squared_error: 0.0248\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3263/5000\n", + "1/1 - 0s - loss: 3.5526e-04 - root_mean_squared_error: 0.0188 - val_loss: 4.6953e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3264/5000\n", + "1/1 - 0s - loss: 3.7396e-04 - root_mean_squared_error: 0.0193 - val_loss: 7.2632e-04 - val_root_mean_squared_error: 0.0270\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3265/5000\n", + "1/1 - 0s - loss: 4.2085e-04 - root_mean_squared_error: 0.0205 - val_loss: 4.9090e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3266/5000\n", + "1/1 - 0s - loss: 4.2832e-04 - root_mean_squared_error: 0.0207 - val_loss: 8.0766e-04 - val_root_mean_squared_error: 0.0284\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3267/5000\n", + "1/1 - 0s - loss: 4.6709e-04 - root_mean_squared_error: 0.0216 - val_loss: 4.7707e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3268/5000\n", + "1/1 - 0s - loss: 4.1531e-04 - root_mean_squared_error: 0.0204 - val_loss: 6.7291e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3269/5000\n", + "1/1 - 0s - loss: 3.8016e-04 - root_mean_squared_error: 0.0195 - val_loss: 4.5730e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3270/5000\n", + "1/1 - 0s - loss: 3.2573e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.9666e-04 - val_root_mean_squared_error: 0.0223\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3271/5000\n", + "1/1 - 0s - loss: 3.0403e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.4038e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3272/5000\n", + "1/1 - 0s - loss: 3.1305e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.6586e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3273/5000\n", + "1/1 - 0s - loss: 3.3807e-04 - root_mean_squared_error: 0.0184 - val_loss: 6.4528e-04 - val_root_mean_squared_error: 0.0254\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3274/5000\n", + "1/1 - 0s - loss: 3.6959e-04 - root_mean_squared_error: 0.0192 - val_loss: 4.7447e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3275/5000\n", + "1/1 - 0s - loss: 3.6528e-04 - root_mean_squared_error: 0.0191 - val_loss: 6.2282e-04 - val_root_mean_squared_error: 0.0250\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3276/5000\n", + "1/1 - 0s - loss: 3.5763e-04 - root_mean_squared_error: 0.0189 - val_loss: 4.6569e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3277/5000\n", + "1/1 - 0s - loss: 3.2686e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.1858e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3278/5000\n", + "1/1 - 0s - loss: 3.0747e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9082e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3279/5000\n", + "1/1 - 0s - loss: 3.0197e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.6440e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3280/5000\n", + "1/1 - 0s - loss: 3.1018e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.5812e-04 - val_root_mean_squared_error: 0.0236\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3281/5000\n", + "1/1 - 0s - loss: 3.2458e-04 - root_mean_squared_error: 0.0180 - val_loss: 4.5894e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3282/5000\n", + "1/1 - 0s - loss: 3.2971e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.7049e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3283/5000\n", + "1/1 - 0s - loss: 3.2969e-04 - root_mean_squared_error: 0.0182 - val_loss: 4.5888e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3284/5000\n", + "1/1 - 0s - loss: 3.1719e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.1308e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3285/5000\n", + "1/1 - 0s - loss: 3.0670e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.7492e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3286/5000\n", + "1/1 - 0s - loss: 3.0053e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.6826e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3287/5000\n", + "1/1 - 0s - loss: 3.0145e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.1122e-04 - val_root_mean_squared_error: 0.0226\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3288/5000\n", + "1/1 - 0s - loss: 3.0695e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.5258e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3289/5000\n", + "1/1 - 0s - loss: 3.1167e-04 - root_mean_squared_error: 0.0177 - val_loss: 5.2862e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3290/5000\n", + "1/1 - 0s - loss: 3.1414e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.5154e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3291/5000\n", + "1/1 - 0s - loss: 3.1055e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0676e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3292/5000\n", + "1/1 - 0s - loss: 3.0601e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.6093e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3293/5000\n", + "1/1 - 0s - loss: 3.0115e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.7712e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3294/5000\n", + "1/1 - 0s - loss: 2.9888e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.8075e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3295/5000\n", + "1/1 - 0s - loss: 2.9923e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5809e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3296/5000\n", + "1/1 - 0s - loss: 3.0126e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.0050e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3297/5000\n", + "1/1 - 0s - loss: 3.0373e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.5168e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3298/5000\n", + "1/1 - 0s - loss: 3.0479e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.0197e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3299/5000\n", + "1/1 - 0s - loss: 3.0485e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.5162e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3300/5000\n", + "1/1 - 0s - loss: 3.0287e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.8842e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3301/5000\n", + "1/1 - 0s - loss: 3.0069e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5847e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3302/5000\n", + "1/1 - 0s - loss: 2.9858e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.7056e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3303/5000\n", + "1/1 - 0s - loss: 2.9742e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.7121e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3304/5000\n", + "1/1 - 0s - loss: 2.9727e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.5904e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3305/5000\n", + "1/1 - 0s - loss: 2.9787e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.8179e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3306/5000\n", + "1/1 - 0s - loss: 2.9878e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5300e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3307/5000\n", + "1/1 - 0s - loss: 2.9938e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.8618e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3308/5000\n", + "1/1 - 0s - loss: 2.9966e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5113e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3309/5000\n", + "1/1 - 0s - loss: 2.9926e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.8168e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3310/5000\n", + "1/1 - 0s - loss: 2.9862e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.5287e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3311/5000\n", + "1/1 - 0s - loss: 2.9764e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.7308e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3312/5000\n", + "1/1 - 0s - loss: 2.9676e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.5730e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3313/5000\n", + "1/1 - 0s - loss: 2.9605e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6425e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3314/5000\n", + "1/1 - 0s - loss: 2.9561e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6336e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3315/5000\n", + "1/1 - 0s - loss: 2.9543e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.5745e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3316/5000\n", + "1/1 - 0s - loss: 2.9545e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6797e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3317/5000\n", + "1/1 - 0s - loss: 2.9557e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.5286e-04 - val_root_mean_squared_error: 0.0213\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3318/5000\n", + "1/1 - 0s - loss: 2.9571e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.7063e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3319/5000\n", + "1/1 - 0s - loss: 2.9583e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4973e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3320/5000\n", + "1/1 - 0s - loss: 2.9584e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.7126e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3321/5000\n", + "1/1 - 0s - loss: 2.9581e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4844e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3322/5000\n", + "1/1 - 0s - loss: 2.9563e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.7001e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3323/5000\n", + "1/1 - 0s - loss: 2.9544e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4828e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3324/5000\n", + "1/1 - 0s - loss: 2.9514e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6798e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3325/5000\n", + "1/1 - 0s - loss: 2.9487e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4876e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3326/5000\n", + "1/1 - 0s - loss: 2.9455e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6522e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3327/5000\n", + "1/1 - 0s - loss: 2.9426e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4935e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3328/5000\n", + "1/1 - 0s - loss: 2.9397e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.6273e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3329/5000\n", + "1/1 - 0s - loss: 2.9372e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4950e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3330/5000\n", + "1/1 - 0s - loss: 2.9347e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.6041e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3331/5000\n", + "1/1 - 0s - loss: 2.9325e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4968e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3332/5000\n", + "1/1 - 0s - loss: 2.9304e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5854e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3333/5000\n", + "1/1 - 0s - loss: 2.9285e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4952e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3334/5000\n", + "1/1 - 0s - loss: 2.9267e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5732e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3335/5000\n", + "1/1 - 0s - loss: 2.9249e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4918e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3336/5000\n", + "1/1 - 0s - loss: 2.9233e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5639e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3337/5000\n", + "1/1 - 0s - loss: 2.9218e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4831e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3338/5000\n", + "1/1 - 0s - loss: 2.9203e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5599e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3339/5000\n", + "1/1 - 0s - loss: 2.9190e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4691e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3340/5000\n", + "1/1 - 0s - loss: 2.9177e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5591e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3341/5000\n", + "1/1 - 0s - loss: 2.9167e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4511e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3342/5000\n", + "1/1 - 0s - loss: 2.9158e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5665e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3343/5000\n", + "1/1 - 0s - loss: 2.9154e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4263e-04 - val_root_mean_squared_error: 0.0210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3344/5000\n", + "1/1 - 0s - loss: 2.9153e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5864e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3345/5000\n", + "1/1 - 0s - loss: 2.9162e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.3942e-04 - val_root_mean_squared_error: 0.0210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3346/5000\n", + "1/1 - 0s - loss: 2.9180e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.6264e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3347/5000\n", + "1/1 - 0s - loss: 2.9221e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.3511e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3348/5000\n", + "1/1 - 0s - loss: 2.9283e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.7040e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3349/5000\n", + "1/1 - 0s - loss: 2.9403e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.2977e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3350/5000\n", + "1/1 - 0s - loss: 2.9571e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.8525e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3351/5000\n", + "1/1 - 0s - loss: 2.9897e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.2457e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3352/5000\n", + "1/1 - 0s - loss: 3.0308e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.1461e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3353/5000\n", + "1/1 - 0s - loss: 3.1153e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.2330e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3354/5000\n", + "1/1 - 0s - loss: 3.2054e-04 - root_mean_squared_error: 0.0179 - val_loss: 5.7072e-04 - val_root_mean_squared_error: 0.0239\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3355/5000\n", + "1/1 - 0s - loss: 3.4056e-04 - root_mean_squared_error: 0.0185 - val_loss: 4.3240e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3356/5000\n", + "1/1 - 0s - loss: 3.5476e-04 - root_mean_squared_error: 0.0188 - val_loss: 6.5736e-04 - val_root_mean_squared_error: 0.0256\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3357/5000\n", + "1/1 - 0s - loss: 3.9061e-04 - root_mean_squared_error: 0.0198 - val_loss: 4.4683e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3358/5000\n", + "1/1 - 0s - loss: 3.9325e-04 - root_mean_squared_error: 0.0198 - val_loss: 7.0797e-04 - val_root_mean_squared_error: 0.0266\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3359/5000\n", + "1/1 - 0s - loss: 4.1813e-04 - root_mean_squared_error: 0.0204 - val_loss: 4.3529e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3360/5000\n", + "1/1 - 0s - loss: 3.8013e-04 - root_mean_squared_error: 0.0195 - val_loss: 6.0742e-04 - val_root_mean_squared_error: 0.0246\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3361/5000\n", + "1/1 - 0s - loss: 3.5489e-04 - root_mean_squared_error: 0.0188 - val_loss: 4.1971e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3362/5000\n", + "1/1 - 0s - loss: 3.1258e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.6787e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3363/5000\n", + "1/1 - 0s - loss: 2.9174e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.7315e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3364/5000\n", + "1/1 - 0s - loss: 2.9180e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.2820e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3365/5000\n", + "1/1 - 0s - loss: 3.0710e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.5771e-04 - val_root_mean_squared_error: 0.0236\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3366/5000\n", + "1/1 - 0s - loss: 3.3024e-04 - root_mean_squared_error: 0.0182 - val_loss: 4.3354e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3367/5000\n", + "1/1 - 0s - loss: 3.3791e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.7821e-04 - val_root_mean_squared_error: 0.0240\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3368/5000\n", + "1/1 - 0s - loss: 3.4455e-04 - root_mean_squared_error: 0.0186 - val_loss: 4.2804e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3369/5000\n", + "1/1 - 0s - loss: 3.2546e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.1419e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3370/5000\n", + "1/1 - 0s - loss: 3.0942e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.2793e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3371/5000\n", + "1/1 - 0s - loss: 2.9334e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.4463e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3372/5000\n", + "1/1 - 0s - loss: 2.8767e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.6897e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3373/5000\n", + "1/1 - 0s - loss: 2.9176e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.2152e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3374/5000\n", + "1/1 - 0s - loss: 3.0040e-04 - root_mean_squared_error: 0.0173 - val_loss: 5.1431e-04 - val_root_mean_squared_error: 0.0227\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3375/5000\n", + "1/1 - 0s - loss: 3.0956e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.2055e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3376/5000\n", + "1/1 - 0s - loss: 3.0982e-04 - root_mean_squared_error: 0.0176 - val_loss: 5.0764e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3377/5000\n", + "1/1 - 0s - loss: 3.0756e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.1921e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3378/5000\n", + "1/1 - 0s - loss: 2.9845e-04 - root_mean_squared_error: 0.0173 - val_loss: 4.6477e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3379/5000\n", + "1/1 - 0s - loss: 2.9116e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.3104e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3380/5000\n", + "1/1 - 0s - loss: 2.8670e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2986e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3381/5000\n", + "1/1 - 0s - loss: 2.8656e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.5782e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3382/5000\n", + "1/1 - 0s - loss: 2.8954e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.1655e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3383/5000\n", + "1/1 - 0s - loss: 2.9309e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.7702e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3384/5000\n", + "1/1 - 0s - loss: 2.9613e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.1512e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3385/5000\n", + "1/1 - 0s - loss: 2.9585e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.7404e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3386/5000\n", + "1/1 - 0s - loss: 2.9464e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.1707e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3387/5000\n", + "1/1 - 0s - loss: 2.9124e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5441e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3388/5000\n", + "1/1 - 0s - loss: 2.8823e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.2381e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3389/5000\n", + "1/1 - 0s - loss: 2.8577e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3344e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3390/5000\n", + "1/1 - 0s - loss: 2.8463e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3610e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3391/5000\n", + "1/1 - 0s - loss: 2.8472e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2106e-04 - val_root_mean_squared_error: 0.0205\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3392/5000\n", + "1/1 - 0s - loss: 2.8561e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4819e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3393/5000\n", + "1/1 - 0s - loss: 2.8677e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.1623e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3394/5000\n", + "1/1 - 0s - loss: 2.8753e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.5283e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3395/5000\n", + "1/1 - 0s - loss: 2.8795e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.1565e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3396/5000\n", + "1/1 - 0s - loss: 2.8748e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.4915e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3397/5000\n", + "1/1 - 0s - loss: 2.8677e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.1699e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3398/5000\n", + "1/1 - 0s - loss: 2.8562e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4038e-04 - val_root_mean_squared_error: 0.0210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3399/5000\n", + "1/1 - 0s - loss: 2.8460e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2056e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3400/5000\n", + "1/1 - 0s - loss: 2.8371e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3067e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3401/5000\n", + "1/1 - 0s - loss: 2.8311e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.2584e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3402/5000\n", + "1/1 - 0s - loss: 2.8279e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.2328e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3403/5000\n", + "1/1 - 0s - loss: 2.8272e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3140e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3404/5000\n", + "1/1 - 0s - loss: 2.8282e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1848e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3405/5000\n", + "1/1 - 0s - loss: 2.8300e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3530e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3406/5000\n", + "1/1 - 0s - loss: 2.8320e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1533e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3407/5000\n", + "1/1 - 0s - loss: 2.8334e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3699e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3408/5000\n", + "1/1 - 0s - loss: 2.8345e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1302e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3409/5000\n", + "1/1 - 0s - loss: 2.8343e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3714e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3410/5000\n", + "1/1 - 0s - loss: 2.8341e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1163e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3411/5000\n", + "1/1 - 0s - loss: 2.8326e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3648e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3412/5000\n", + "1/1 - 0s - loss: 2.8313e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1109e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3413/5000\n", + "1/1 - 0s - loss: 2.8290e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3541e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3414/5000\n", + "1/1 - 0s - loss: 2.8272e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1091e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3415/5000\n", + "1/1 - 0s - loss: 2.8248e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3433e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3416/5000\n", + "1/1 - 0s - loss: 2.8233e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.1046e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3417/5000\n", + "1/1 - 0s - loss: 2.8213e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3351e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3418/5000\n", + "1/1 - 0s - loss: 2.8203e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0947e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3419/5000\n", + "1/1 - 0s - loss: 2.8190e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3346e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3420/5000\n", + "1/1 - 0s - loss: 2.8189e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0799e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3421/5000\n", + "1/1 - 0s - loss: 2.8187e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3451e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3422/5000\n", + "1/1 - 0s - loss: 2.8201e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0616e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3423/5000\n", + "1/1 - 0s - loss: 2.8214e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3714e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3424/5000\n", + "1/1 - 0s - loss: 2.8254e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0377e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3425/5000\n", + "1/1 - 0s - loss: 2.8294e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.4200e-04 - val_root_mean_squared_error: 0.0210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3426/5000\n", + "1/1 - 0s - loss: 2.8381e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.0078e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3427/5000\n", + "1/1 - 0s - loss: 2.8471e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.5027e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3428/5000\n", + "1/1 - 0s - loss: 2.8651e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.9743e-04 - val_root_mean_squared_error: 0.0199\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3429/5000\n", + "1/1 - 0s - loss: 2.8829e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.6406e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3430/5000\n", + "1/1 - 0s - loss: 2.9188e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.9477e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3431/5000\n", + "1/1 - 0s - loss: 2.9507e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.8600e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3432/5000\n", + "1/1 - 0s - loss: 3.0183e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.9432e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3433/5000\n", + "1/1 - 0s - loss: 3.0663e-04 - root_mean_squared_error: 0.0175 - val_loss: 5.1774e-04 - val_root_mean_squared_error: 0.0228\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3434/5000\n", + "1/1 - 0s - loss: 3.1792e-04 - root_mean_squared_error: 0.0178 - val_loss: 3.9704e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3435/5000\n", + "1/1 - 0s - loss: 3.2254e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.5254e-04 - val_root_mean_squared_error: 0.0235\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3436/5000\n", + "1/1 - 0s - loss: 3.3662e-04 - root_mean_squared_error: 0.0183 - val_loss: 3.9975e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3437/5000\n", + "1/1 - 0s - loss: 3.3490e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.6512e-04 - val_root_mean_squared_error: 0.0238\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3438/5000\n", + "1/1 - 0s - loss: 3.4256e-04 - root_mean_squared_error: 0.0185 - val_loss: 3.9602e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3439/5000\n", + "1/1 - 0s - loss: 3.2857e-04 - root_mean_squared_error: 0.0181 - val_loss: 5.2857e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3440/5000\n", + "1/1 - 0s - loss: 3.2111e-04 - root_mean_squared_error: 0.0179 - val_loss: 3.9115e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3441/5000\n", + "1/1 - 0s - loss: 3.0235e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.6383e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3442/5000\n", + "1/1 - 0s - loss: 2.8971e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.0279e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3443/5000\n", + "1/1 - 0s - loss: 2.8025e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.1742e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3444/5000\n", + "1/1 - 0s - loss: 2.7702e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.3228e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3445/5000\n", + "1/1 - 0s - loss: 2.7890e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.9900e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3446/5000\n", + "1/1 - 0s - loss: 2.8395e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.6445e-04 - val_root_mean_squared_error: 0.0216\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3447/5000\n", + "1/1 - 0s - loss: 2.9110e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.9405e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3448/5000\n", + "1/1 - 0s - loss: 2.9608e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.8597e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3449/5000\n", + "1/1 - 0s - loss: 3.0227e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.9119e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3450/5000\n", + "1/1 - 0s - loss: 3.0159e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.8576e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3451/5000\n", + "1/1 - 0s - loss: 3.0197e-04 - root_mean_squared_error: 0.0174 - val_loss: 3.8801e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3452/5000\n", + "1/1 - 0s - loss: 2.9548e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.6125e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3453/5000\n", + "1/1 - 0s - loss: 2.9030e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.9027e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3454/5000\n", + "1/1 - 0s - loss: 2.8335e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.2923e-04 - val_root_mean_squared_error: 0.0207\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3455/5000\n", + "1/1 - 0s - loss: 2.7865e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.0255e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3456/5000\n", + "1/1 - 0s - loss: 2.7584e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0616e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3457/5000\n", + "1/1 - 0s - loss: 2.7522e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.1980e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3458/5000\n", + "1/1 - 0s - loss: 2.7627e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.9359e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3459/5000\n", + "1/1 - 0s - loss: 2.7827e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3507e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3460/5000\n", + "1/1 - 0s - loss: 2.8087e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.8744e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3461/5000\n", + "1/1 - 0s - loss: 2.8282e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.4525e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3462/5000\n", + "1/1 - 0s - loss: 2.8504e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.8483e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3463/5000\n", + "1/1 - 0s - loss: 2.8544e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4852e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3464/5000\n", + "1/1 - 0s - loss: 2.8625e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.8403e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3465/5000\n", + "1/1 - 0s - loss: 2.8499e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4422e-04 - val_root_mean_squared_error: 0.0211\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3466/5000\n", + "1/1 - 0s - loss: 2.8433e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.8459e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3467/5000\n", + "1/1 - 0s - loss: 2.8220e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.3467e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3468/5000\n", + "1/1 - 0s - loss: 2.8070e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.8651e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3469/5000\n", + "1/1 - 0s - loss: 2.7858e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.2331e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3470/5000\n", + "1/1 - 0s - loss: 2.7699e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.8976e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3471/5000\n", + "1/1 - 0s - loss: 2.7542e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.1266e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3472/5000\n", + "1/1 - 0s - loss: 2.7427e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.9396e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3473/5000\n", + "1/1 - 0s - loss: 2.7340e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.0415e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3474/5000\n", + "1/1 - 0s - loss: 2.7284e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.9857e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3475/5000\n", + "1/1 - 0s - loss: 2.7252e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.9781e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3476/5000\n", + "1/1 - 0s - loss: 2.7239e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.0290e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3477/5000\n", + "1/1 - 0s - loss: 2.7240e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.9297e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3478/5000\n", + "1/1 - 0s - loss: 2.7251e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.0666e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3479/5000\n", + "1/1 - 0s - loss: 2.7271e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.8876e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3480/5000\n", + "1/1 - 0s - loss: 2.7297e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.1034e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3481/5000\n", + "1/1 - 0s - loss: 2.7337e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.8488e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3482/5000\n", + "1/1 - 0s - loss: 2.7382e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.1503e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3483/5000\n", + "1/1 - 0s - loss: 2.7454e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.8131e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3484/5000\n", + "1/1 - 0s - loss: 2.7529e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.2197e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3485/5000\n", + "1/1 - 0s - loss: 2.7660e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.7812e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3486/5000\n", + "1/1 - 0s - loss: 2.7789e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3246e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3487/5000\n", + "1/1 - 0s - loss: 2.8030e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.7532e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3488/5000\n", + "1/1 - 0s - loss: 2.8251e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.4846e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3489/5000\n", + "1/1 - 0s - loss: 2.8696e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.7362e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3490/5000\n", + "1/1 - 0s - loss: 2.9053e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.7238e-04 - val_root_mean_squared_error: 0.0217\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3491/5000\n", + "1/1 - 0s - loss: 2.9837e-04 - root_mean_squared_error: 0.0173 - val_loss: 3.7440e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3492/5000\n", + "1/1 - 0s - loss: 3.0306e-04 - root_mean_squared_error: 0.0174 - val_loss: 5.0400e-04 - val_root_mean_squared_error: 0.0225\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3493/5000\n", + "1/1 - 0s - loss: 3.1489e-04 - root_mean_squared_error: 0.0177 - val_loss: 3.7776e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3494/5000\n", + "1/1 - 0s - loss: 3.1782e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.3194e-04 - val_root_mean_squared_error: 0.0231\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3495/5000\n", + "1/1 - 0s - loss: 3.2988e-04 - root_mean_squared_error: 0.0182 - val_loss: 3.7907e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3496/5000\n", + "1/1 - 0s - loss: 3.2460e-04 - root_mean_squared_error: 0.0180 - val_loss: 5.2913e-04 - val_root_mean_squared_error: 0.0230\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3497/5000\n", + "1/1 - 0s - loss: 3.2706e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.7392e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3498/5000\n", + "1/1 - 0s - loss: 3.1127e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.8233e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3499/5000\n", + "1/1 - 0s - loss: 3.0099e-04 - root_mean_squared_error: 0.0173 - val_loss: 3.7230e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3500/5000\n", + "1/1 - 0s - loss: 2.8521e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2414e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3501/5000\n", + "1/1 - 0s - loss: 2.7521e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.8830e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3502/5000\n", + "1/1 - 0s - loss: 2.6971e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.8905e-04 - val_root_mean_squared_error: 0.0197\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3503/5000\n", + "1/1 - 0s - loss: 2.6938e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.1730e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3504/5000\n", + "1/1 - 0s - loss: 2.7289e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.7632e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3505/5000\n", + "1/1 - 0s - loss: 2.7815e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.4518e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3506/5000\n", + "1/1 - 0s - loss: 2.8488e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.7262e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3507/5000\n", + "1/1 - 0s - loss: 2.8858e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.6149e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3508/5000\n", + "1/1 - 0s - loss: 2.9335e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.6992e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3509/5000\n", + "1/1 - 0s - loss: 2.9165e-04 - root_mean_squared_error: 0.0171 - val_loss: 4.5739e-04 - val_root_mean_squared_error: 0.0214\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3510/5000\n", + "1/1 - 0s - loss: 2.9096e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.6755e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3511/5000\n", + "1/1 - 0s - loss: 2.8476e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.3352e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3512/5000\n", + "1/1 - 0s - loss: 2.7989e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.7055e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3513/5000\n", + "1/1 - 0s - loss: 2.7397e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0525e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3514/5000\n", + "1/1 - 0s - loss: 2.7001e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.8162e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3515/5000\n", + "1/1 - 0s - loss: 2.6767e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.8492e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3516/5000\n", + "1/1 - 0s - loss: 2.6709e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9629e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3517/5000\n", + "1/1 - 0s - loss: 2.6789e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.7337e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3518/5000\n", + "1/1 - 0s - loss: 2.6950e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.0966e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3519/5000\n", + "1/1 - 0s - loss: 2.7166e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.6755e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3520/5000\n", + "1/1 - 0s - loss: 2.7346e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.1985e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3521/5000\n", + "1/1 - 0s - loss: 2.7559e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.6495e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3522/5000\n", + "1/1 - 0s - loss: 2.7640e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.2553e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3523/5000\n", + "1/1 - 0s - loss: 2.7773e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.6386e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3524/5000\n", + "1/1 - 0s - loss: 2.7730e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.2552e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3525/5000\n", + "1/1 - 0s - loss: 2.7757e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.6345e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3526/5000\n", + "1/1 - 0s - loss: 2.7614e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.2025e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3527/5000\n", + "1/1 - 0s - loss: 2.7544e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.6379e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3528/5000\n", + "1/1 - 0s - loss: 2.7351e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.1160e-04 - val_root_mean_squared_error: 0.0203\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3529/5000\n", + "1/1 - 0s - loss: 2.7219e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.6533e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3530/5000\n", + "1/1 - 0s - loss: 2.7028e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.0192e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3531/5000\n", + "1/1 - 0s - loss: 2.6889e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.6803e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3532/5000\n", + "1/1 - 0s - loss: 2.6744e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.9314e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3533/5000\n", + "1/1 - 0s - loss: 2.6640e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7126e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3534/5000\n", + "1/1 - 0s - loss: 2.6553e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.8603e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3535/5000\n", + "1/1 - 0s - loss: 2.6492e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7420e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3536/5000\n", + "1/1 - 0s - loss: 2.6447e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.8052e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3537/5000\n", + "1/1 - 0s - loss: 2.6416e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7642e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3538/5000\n", + "1/1 - 0s - loss: 2.6395e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.7618e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3539/5000\n", + "1/1 - 0s - loss: 2.6380e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.7809e-04 - val_root_mean_squared_error: 0.0194\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3540/5000\n", + "1/1 - 0s - loss: 2.6370e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.7267e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3541/5000\n", + "1/1 - 0s - loss: 2.6363e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.7974e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3542/5000\n", + "1/1 - 0s - loss: 2.6361e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.6966e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3543/5000\n", + "1/1 - 0s - loss: 2.6363e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.8192e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3544/5000\n", + "1/1 - 0s - loss: 2.6371e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.6674e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3545/5000\n", + "1/1 - 0s - loss: 2.6384e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.8511e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3546/5000\n", + "1/1 - 0s - loss: 2.6411e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.6347e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3547/5000\n", + "1/1 - 0s - loss: 2.6448e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9023e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3548/5000\n", + "1/1 - 0s - loss: 2.6518e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.5962e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3549/5000\n", + "1/1 - 0s - loss: 2.6611e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9920e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3550/5000\n", + "1/1 - 0s - loss: 2.6784e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.5556e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3551/5000\n", + "1/1 - 0s - loss: 2.7002e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.1584e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3552/5000\n", + "1/1 - 0s - loss: 2.7422e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.5295e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3553/5000\n", + "1/1 - 0s - loss: 2.7905e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.4714e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3554/5000\n", + "1/1 - 0s - loss: 2.8894e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.5571e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3555/5000\n", + "1/1 - 0s - loss: 2.9820e-04 - root_mean_squared_error: 0.0173 - val_loss: 5.0205e-04 - val_root_mean_squared_error: 0.0224\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3556/5000\n", + "1/1 - 0s - loss: 3.1877e-04 - root_mean_squared_error: 0.0179 - val_loss: 3.6747e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3557/5000\n", + "1/1 - 0s - loss: 3.3014e-04 - root_mean_squared_error: 0.0182 - val_loss: 5.7267e-04 - val_root_mean_squared_error: 0.0239\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3558/5000\n", + "1/1 - 0s - loss: 3.5985e-04 - root_mean_squared_error: 0.0190 - val_loss: 3.7715e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3559/5000\n", + "1/1 - 0s - loss: 3.5580e-04 - root_mean_squared_error: 0.0189 - val_loss: 5.8944e-04 - val_root_mean_squared_error: 0.0243\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3560/5000\n", + "1/1 - 0s - loss: 3.6682e-04 - root_mean_squared_error: 0.0192 - val_loss: 3.6135e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3561/5000\n", + "1/1 - 0s - loss: 3.3177e-04 - root_mean_squared_error: 0.0182 - val_loss: 4.9006e-04 - val_root_mean_squared_error: 0.0221\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3562/5000\n", + "1/1 - 0s - loss: 3.0687e-04 - root_mean_squared_error: 0.0175 - val_loss: 3.5166e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3563/5000\n", + "1/1 - 0s - loss: 2.7615e-04 - root_mean_squared_error: 0.0166 - val_loss: 3.8483e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3564/5000\n", + "1/1 - 0s - loss: 2.6230e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.9768e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3565/5000\n", + "1/1 - 0s - loss: 2.6420e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.5749e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3566/5000\n", + "1/1 - 0s - loss: 2.7669e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.6273e-04 - val_root_mean_squared_error: 0.0215\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3567/5000\n", + "1/1 - 0s - loss: 2.9484e-04 - root_mean_squared_error: 0.0172 - val_loss: 3.6140e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3568/5000\n", + "1/1 - 0s - loss: 3.0243e-04 - root_mean_squared_error: 0.0174 - val_loss: 4.8420e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3569/5000\n", + "1/1 - 0s - loss: 3.1001e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.5622e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3570/5000\n", + "1/1 - 0s - loss: 2.9713e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.4221e-04 - val_root_mean_squared_error: 0.0210\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3571/5000\n", + "1/1 - 0s - loss: 2.8565e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.5129e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3572/5000\n", + "1/1 - 0s - loss: 2.6997e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.8172e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3573/5000\n", + "1/1 - 0s - loss: 2.6123e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.7600e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3574/5000\n", + "1/1 - 0s - loss: 2.6015e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.5322e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3575/5000\n", + "1/1 - 0s - loss: 2.6520e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.1713e-04 - val_root_mean_squared_error: 0.0204\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3576/5000\n", + "1/1 - 0s - loss: 2.7308e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.5017e-04 - val_root_mean_squared_error: 0.0187\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3577/5000\n", + "1/1 - 0s - loss: 2.7761e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3152e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3578/5000\n", + "1/1 - 0s - loss: 2.8047e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.4732e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3579/5000\n", + "1/1 - 0s - loss: 2.7593e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0831e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3580/5000\n", + "1/1 - 0s - loss: 2.7096e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.4796e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3581/5000\n", + "1/1 - 0s - loss: 2.6425e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7450e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3582/5000\n", + "1/1 - 0s - loss: 2.5990e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.6274e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3583/5000\n", + "1/1 - 0s - loss: 2.5829e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.5392e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3584/5000\n", + "1/1 - 0s - loss: 2.5933e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.8436e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3585/5000\n", + "1/1 - 0s - loss: 2.6201e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.4727e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3586/5000\n", + "1/1 - 0s - loss: 2.6465e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9810e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3587/5000\n", + "1/1 - 0s - loss: 2.6705e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.4509e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3588/5000\n", + "1/1 - 0s - loss: 2.6715e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9710e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3589/5000\n", + "1/1 - 0s - loss: 2.6676e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.4423e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3590/5000\n", + "1/1 - 0s - loss: 2.6438e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.8349e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3591/5000\n", + "1/1 - 0s - loss: 2.6205e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.4755e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3592/5000\n", + "1/1 - 0s - loss: 2.5946e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.6695e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3593/5000\n", + "1/1 - 0s - loss: 2.5765e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.5683e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3594/5000\n", + "1/1 - 0s - loss: 2.5675e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.5530e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3595/5000\n", + "1/1 - 0s - loss: 2.5675e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6813e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3596/5000\n", + "1/1 - 0s - loss: 2.5737e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.4889e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3597/5000\n", + "1/1 - 0s - loss: 2.5823e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.7580e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3598/5000\n", + "1/1 - 0s - loss: 2.5917e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.4472e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3599/5000\n", + "1/1 - 0s - loss: 2.5974e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.7849e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3600/5000\n", + "1/1 - 0s - loss: 2.6022e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.4235e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3601/5000\n", + "1/1 - 0s - loss: 2.6007e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.7713e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3602/5000\n", + "1/1 - 0s - loss: 2.5986e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.4228e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3603/5000\n", + "1/1 - 0s - loss: 2.5912e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.7322e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3604/5000\n", + "1/1 - 0s - loss: 2.5845e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.4403e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3605/5000\n", + "1/1 - 0s - loss: 2.5757e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6800e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3606/5000\n", + "1/1 - 0s - loss: 2.5685e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.4631e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3607/5000\n", + "1/1 - 0s - loss: 2.5613e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6238e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3608/5000\n", + "1/1 - 0s - loss: 2.5557e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.4824e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3609/5000\n", + "1/1 - 0s - loss: 2.5509e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.5722e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3610/5000\n", + "1/1 - 0s - loss: 2.5473e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.4992e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3611/5000\n", + "1/1 - 0s - loss: 2.5446e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.5324e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3612/5000\n", + "1/1 - 0s - loss: 2.5426e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5172e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3613/5000\n", + "1/1 - 0s - loss: 2.5410e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5045e-04 - val_root_mean_squared_error: 0.0187\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3614/5000\n", + "1/1 - 0s - loss: 2.5398e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5355e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3615/5000\n", + "1/1 - 0s - loss: 2.5388e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.4832e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3616/5000\n", + "1/1 - 0s - loss: 2.5382e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5513e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3617/5000\n", + "1/1 - 0s - loss: 2.5377e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.4605e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3618/5000\n", + "1/1 - 0s - loss: 2.5374e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5657e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3619/5000\n", + "1/1 - 0s - loss: 2.5375e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.4350e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3620/5000\n", + "1/1 - 0s - loss: 2.5380e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5847e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3621/5000\n", + "1/1 - 0s - loss: 2.5392e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.4081e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3622/5000\n", + "1/1 - 0s - loss: 2.5410e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6178e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3623/5000\n", + "1/1 - 0s - loss: 2.5445e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.3800e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3624/5000\n", + "1/1 - 0s - loss: 2.5491e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6740e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3625/5000\n", + "1/1 - 0s - loss: 2.5575e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.3486e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3626/5000\n", + "1/1 - 0s - loss: 2.5682e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.7708e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3627/5000\n", + "1/1 - 0s - loss: 2.5880e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.3154e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3628/5000\n", + "1/1 - 0s - loss: 2.6117e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.9434e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3629/5000\n", + "1/1 - 0s - loss: 2.6573e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.2979e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3630/5000\n", + "1/1 - 0s - loss: 2.7067e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.2543e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3631/5000\n", + "1/1 - 0s - loss: 2.8075e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.3322e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3632/5000\n", + "1/1 - 0s - loss: 2.8947e-04 - root_mean_squared_error: 0.0170 - val_loss: 4.7673e-04 - val_root_mean_squared_error: 0.0218\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3633/5000\n", + "1/1 - 0s - loss: 3.0887e-04 - root_mean_squared_error: 0.0176 - val_loss: 3.4412e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3634/5000\n", + "1/1 - 0s - loss: 3.1823e-04 - root_mean_squared_error: 0.0178 - val_loss: 5.3678e-04 - val_root_mean_squared_error: 0.0232\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3635/5000\n", + "1/1 - 0s - loss: 3.4363e-04 - root_mean_squared_error: 0.0185 - val_loss: 3.5140e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3636/5000\n", + "1/1 - 0s - loss: 3.3822e-04 - root_mean_squared_error: 0.0184 - val_loss: 5.4477e-04 - val_root_mean_squared_error: 0.0233\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3637/5000\n", + "1/1 - 0s - loss: 3.4560e-04 - root_mean_squared_error: 0.0186 - val_loss: 3.3676e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3638/5000\n", + "1/1 - 0s - loss: 3.1468e-04 - root_mean_squared_error: 0.0177 - val_loss: 4.5580e-04 - val_root_mean_squared_error: 0.0213\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3639/5000\n", + "1/1 - 0s - loss: 2.9264e-04 - root_mean_squared_error: 0.0171 - val_loss: 3.2786e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3640/5000\n", + "1/1 - 0s - loss: 2.6560e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.6174e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3641/5000\n", + "1/1 - 0s - loss: 2.5258e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6575e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3642/5000\n", + "1/1 - 0s - loss: 2.5272e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.3348e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3643/5000\n", + "1/1 - 0s - loss: 2.6242e-04 - root_mean_squared_error: 0.0162 - val_loss: 4.2260e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3644/5000\n", + "1/1 - 0s - loss: 2.7744e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.3547e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3645/5000\n", + "1/1 - 0s - loss: 2.8597e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4862e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3646/5000\n", + "1/1 - 0s - loss: 2.9498e-04 - root_mean_squared_error: 0.0172 - val_loss: 3.3204e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3647/5000\n", + "1/1 - 0s - loss: 2.8681e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.2191e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3648/5000\n", + "1/1 - 0s - loss: 2.7924e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.2565e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3649/5000\n", + "1/1 - 0s - loss: 2.6444e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.6807e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3650/5000\n", + "1/1 - 0s - loss: 2.5435e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.4058e-04 - val_root_mean_squared_error: 0.0185\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3651/5000\n", + "1/1 - 0s - loss: 2.4970e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.3374e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3652/5000\n", + "1/1 - 0s - loss: 2.5121e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.7533e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3653/5000\n", + "1/1 - 0s - loss: 2.5673e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.2626e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3654/5000\n", + "1/1 - 0s - loss: 2.6222e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.9860e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3655/5000\n", + "1/1 - 0s - loss: 2.6716e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.2401e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3656/5000\n", + "1/1 - 0s - loss: 2.6641e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9109e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3657/5000\n", + "1/1 - 0s - loss: 2.6466e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.2178e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3658/5000\n", + "1/1 - 0s - loss: 2.5895e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.6408e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3659/5000\n", + "1/1 - 0s - loss: 2.5408e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.2834e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3660/5000\n", + "1/1 - 0s - loss: 2.5003e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.3991e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3661/5000\n", + "1/1 - 0s - loss: 2.4821e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.4467e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3662/5000\n", + "1/1 - 0s - loss: 2.4849e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.2775e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3663/5000\n", + "1/1 - 0s - loss: 2.5019e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.6093e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3664/5000\n", + "1/1 - 0s - loss: 2.5255e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.2280e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3665/5000\n", + "1/1 - 0s - loss: 2.5431e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6903e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3666/5000\n", + "1/1 - 0s - loss: 2.5581e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.2023e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3667/5000\n", + "1/1 - 0s - loss: 2.5554e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6631e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3668/5000\n", + "1/1 - 0s - loss: 2.5496e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.2022e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3669/5000\n", + "1/1 - 0s - loss: 2.5296e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.5554e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3670/5000\n", + "1/1 - 0s - loss: 2.5104e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.2406e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3671/5000\n", + "1/1 - 0s - loss: 2.4898e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.4302e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3672/5000\n", + "1/1 - 0s - loss: 2.4752e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.3138e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3673/5000\n", + "1/1 - 0s - loss: 2.4667e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.3320e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3674/5000\n", + "1/1 - 0s - loss: 2.4641e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.3899e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3675/5000\n", + "1/1 - 0s - loss: 2.4659e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.2619e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3676/5000\n", + "1/1 - 0s - loss: 2.4704e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.4487e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3677/5000\n", + "1/1 - 0s - loss: 2.4767e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.2139e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3678/5000\n", + "1/1 - 0s - loss: 2.4824e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.4891e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3679/5000\n", + "1/1 - 0s - loss: 2.4885e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1891e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3680/5000\n", + "1/1 - 0s - loss: 2.4915e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5148e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3681/5000\n", + "1/1 - 0s - loss: 2.4949e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1809e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3682/5000\n", + "1/1 - 0s - loss: 2.4946e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5260e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3683/5000\n", + "1/1 - 0s - loss: 2.4957e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1774e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3684/5000\n", + "1/1 - 0s - loss: 2.4932e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5224e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3685/5000\n", + "1/1 - 0s - loss: 2.4929e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1702e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3686/5000\n", + "1/1 - 0s - loss: 2.4897e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5106e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3687/5000\n", + "1/1 - 0s - loss: 2.4891e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1616e-04 - val_root_mean_squared_error: 0.0178\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3688/5000\n", + "1/1 - 0s - loss: 2.4861e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5012e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3689/5000\n", + "1/1 - 0s - loss: 2.4860e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1560e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3690/5000\n", + "1/1 - 0s - loss: 2.4839e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5023e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3691/5000\n", + "1/1 - 0s - loss: 2.4851e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1516e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3692/5000\n", + "1/1 - 0s - loss: 2.4846e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5176e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3693/5000\n", + "1/1 - 0s - loss: 2.4883e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1422e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3694/5000\n", + "1/1 - 0s - loss: 2.4905e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5495e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3695/5000\n", + "1/1 - 0s - loss: 2.4984e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1254e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3696/5000\n", + "1/1 - 0s - loss: 2.5044e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.6046e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3697/5000\n", + "1/1 - 0s - loss: 2.5191e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.1058e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3698/5000\n", + "1/1 - 0s - loss: 2.5304e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6932e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3699/5000\n", + "1/1 - 0s - loss: 2.5557e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.0923e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3700/5000\n", + "1/1 - 0s - loss: 2.5738e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.8245e-04 - val_root_mean_squared_error: 0.0196\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3701/5000\n", + "1/1 - 0s - loss: 2.6147e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.0918e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3702/5000\n", + "1/1 - 0s - loss: 2.6392e-04 - root_mean_squared_error: 0.0162 - val_loss: 4.0002e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3703/5000\n", + "1/1 - 0s - loss: 2.6997e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.1053e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3704/5000\n", + "1/1 - 0s - loss: 2.7242e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.1937e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3705/5000\n", + "1/1 - 0s - loss: 2.7989e-04 - root_mean_squared_error: 0.0167 - val_loss: 3.1218e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3706/5000\n", + "1/1 - 0s - loss: 2.8032e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3195e-04 - val_root_mean_squared_error: 0.0208\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3707/5000\n", + "1/1 - 0s - loss: 2.8636e-04 - root_mean_squared_error: 0.0169 - val_loss: 3.1159e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3708/5000\n", + "1/1 - 0s - loss: 2.8189e-04 - root_mean_squared_error: 0.0168 - val_loss: 4.2518e-04 - val_root_mean_squared_error: 0.0206\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3709/5000\n", + "1/1 - 0s - loss: 2.8218e-04 - root_mean_squared_error: 0.0168 - val_loss: 3.0811e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3710/5000\n", + "1/1 - 0s - loss: 2.7263e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.9604e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3711/5000\n", + "1/1 - 0s - loss: 2.6666e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.0676e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3712/5000\n", + "1/1 - 0s - loss: 2.5677e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.5939e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3713/5000\n", + "1/1 - 0s - loss: 2.5001e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.1318e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3714/5000\n", + "1/1 - 0s - loss: 2.4455e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.3154e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3715/5000\n", + "1/1 - 0s - loss: 2.4176e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.2635e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3716/5000\n", + "1/1 - 0s - loss: 2.4114e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.1581e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3717/5000\n", + "1/1 - 0s - loss: 2.4218e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.4176e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3718/5000\n", + "1/1 - 0s - loss: 2.4440e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.0807e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3719/5000\n", + "1/1 - 0s - loss: 2.4698e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.5662e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3720/5000\n", + "1/1 - 0s - loss: 2.5026e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.0453e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3721/5000\n", + "1/1 - 0s - loss: 2.5252e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.6889e-04 - val_root_mean_squared_error: 0.0192\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3722/5000\n", + "1/1 - 0s - loss: 2.5569e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.0295e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3723/5000\n", + "1/1 - 0s - loss: 2.5650e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.7544e-04 - val_root_mean_squared_error: 0.0194\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3724/5000\n", + "1/1 - 0s - loss: 2.5850e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.0188e-04 - val_root_mean_squared_error: 0.0174\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3725/5000\n", + "1/1 - 0s - loss: 2.5735e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.7360e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3726/5000\n", + "1/1 - 0s - loss: 2.5747e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.0103e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3727/5000\n", + "1/1 - 0s - loss: 2.5469e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.6397e-04 - val_root_mean_squared_error: 0.0191\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3728/5000\n", + "1/1 - 0s - loss: 2.5311e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.0124e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3729/5000\n", + "1/1 - 0s - loss: 2.4981e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5053e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3730/5000\n", + "1/1 - 0s - loss: 2.4756e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.0321e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3731/5000\n", + "1/1 - 0s - loss: 2.4484e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.3766e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3732/5000\n", + "1/1 - 0s - loss: 2.4295e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.0651e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3733/5000\n", + "1/1 - 0s - loss: 2.4126e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.2748e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3734/5000\n", + "1/1 - 0s - loss: 2.4012e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.1013e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3735/5000\n", + "1/1 - 0s - loss: 2.3929e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.1996e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3736/5000\n", + "1/1 - 0s - loss: 2.3875e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.1343e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3737/5000\n", + "1/1 - 0s - loss: 2.3841e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1439e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3738/5000\n", + "1/1 - 0s - loss: 2.3824e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1638e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3739/5000\n", + "1/1 - 0s - loss: 2.3817e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1021e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3740/5000\n", + "1/1 - 0s - loss: 2.3819e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1925e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3741/5000\n", + "1/1 - 0s - loss: 2.3829e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.0689e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3742/5000\n", + "1/1 - 0s - loss: 2.3844e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.2242e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3743/5000\n", + "1/1 - 0s - loss: 2.3869e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.0390e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3744/5000\n", + "1/1 - 0s - loss: 2.3901e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.2646e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3745/5000\n", + "1/1 - 0s - loss: 2.3953e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.0085e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3746/5000\n", + "1/1 - 0s - loss: 2.4017e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.3259e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3747/5000\n", + "1/1 - 0s - loss: 2.4128e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.9769e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3748/5000\n", + "1/1 - 0s - loss: 2.4260e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.4301e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3749/5000\n", + "1/1 - 0s - loss: 2.4502e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.9513e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3750/5000\n", + "1/1 - 0s - loss: 2.4776e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.6164e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3751/5000\n", + "1/1 - 0s - loss: 2.5307e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.9509e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3752/5000\n", + "1/1 - 0s - loss: 2.5846e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.9430e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3753/5000\n", + "1/1 - 0s - loss: 2.6954e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.0075e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3754/5000\n", + "1/1 - 0s - loss: 2.7824e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.4478e-04 - val_root_mean_squared_error: 0.0211\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3755/5000\n", + "1/1 - 0s - loss: 2.9781e-04 - root_mean_squared_error: 0.0173 - val_loss: 3.1210e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3756/5000\n", + "1/1 - 0s - loss: 3.0512e-04 - root_mean_squared_error: 0.0175 - val_loss: 4.9487e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3757/5000\n", + "1/1 - 0s - loss: 3.2665e-04 - root_mean_squared_error: 0.0181 - val_loss: 3.1558e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3758/5000\n", + "1/1 - 0s - loss: 3.1735e-04 - root_mean_squared_error: 0.0178 - val_loss: 4.8472e-04 - val_root_mean_squared_error: 0.0220\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3759/5000\n", + "1/1 - 0s - loss: 3.1781e-04 - root_mean_squared_error: 0.0178 - val_loss: 2.9890e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3760/5000\n", + "1/1 - 0s - loss: 2.8789e-04 - root_mean_squared_error: 0.0170 - val_loss: 3.9512e-04 - val_root_mean_squared_error: 0.0199\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3761/5000\n", + "1/1 - 0s - loss: 2.6624e-04 - root_mean_squared_error: 0.0163 - val_loss: 2.9490e-04 - val_root_mean_squared_error: 0.0172\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3762/5000\n", + "1/1 - 0s - loss: 2.4489e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.1771e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3763/5000\n", + "1/1 - 0s - loss: 2.3614e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.3279e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3764/5000\n", + "1/1 - 0s - loss: 2.3860e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.9793e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3765/5000\n", + "1/1 - 0s - loss: 2.4819e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.8195e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3766/5000\n", + "1/1 - 0s - loss: 2.6174e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.9962e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3767/5000\n", + "1/1 - 0s - loss: 2.6883e-04 - root_mean_squared_error: 0.0164 - val_loss: 4.0272e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3768/5000\n", + "1/1 - 0s - loss: 2.7640e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.9551e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3769/5000\n", + "1/1 - 0s - loss: 2.6899e-04 - root_mean_squared_error: 0.0164 - val_loss: 3.7887e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3770/5000\n", + "1/1 - 0s - loss: 2.6207e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.9000e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3771/5000\n", + "1/1 - 0s - loss: 2.4868e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.3158e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3772/5000\n", + "1/1 - 0s - loss: 2.3937e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.0273e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3773/5000\n", + "1/1 - 0s - loss: 2.3462e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.9976e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3774/5000\n", + "1/1 - 0s - loss: 2.3526e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.3226e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3775/5000\n", + "1/1 - 0s - loss: 2.3961e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.9103e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3776/5000\n", + "1/1 - 0s - loss: 2.4460e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.5518e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3777/5000\n", + "1/1 - 0s - loss: 2.4954e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.8908e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3778/5000\n", + "1/1 - 0s - loss: 2.5017e-04 - root_mean_squared_error: 0.0158 - val_loss: 3.5473e-04 - val_root_mean_squared_error: 0.0188\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3779/5000\n", + "1/1 - 0s - loss: 2.4996e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.8692e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3780/5000\n", + "1/1 - 0s - loss: 2.4557e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.3458e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3781/5000\n", + "1/1 - 0s - loss: 2.4144e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.8987e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3782/5000\n", + "1/1 - 0s - loss: 2.3692e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1169e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3783/5000\n", + "1/1 - 0s - loss: 2.3402e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.0124e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3784/5000\n", + "1/1 - 0s - loss: 2.3280e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.9733e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3785/5000\n", + "1/1 - 0s - loss: 2.3312e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.1521e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3786/5000\n", + "1/1 - 0s - loss: 2.3446e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.9005e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3787/5000\n", + "1/1 - 0s - loss: 2.3611e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.2513e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3788/5000\n", + "1/1 - 0s - loss: 2.3787e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.8625e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3789/5000\n", + "1/1 - 0s - loss: 2.3870e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.2811e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3790/5000\n", + "1/1 - 0s - loss: 2.3928e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.8528e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3791/5000\n", + "1/1 - 0s - loss: 2.3850e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.2431e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3792/5000\n", + "1/1 - 0s - loss: 2.3761e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.8685e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3793/5000\n", + "1/1 - 0s - loss: 2.3597e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1634e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3794/5000\n", + "1/1 - 0s - loss: 2.3457e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.9018e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3795/5000\n", + "1/1 - 0s - loss: 2.3318e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.0734e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3796/5000\n", + "1/1 - 0s - loss: 2.3215e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.9397e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3797/5000\n", + "1/1 - 0s - loss: 2.3142e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.9937e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3798/5000\n", + "1/1 - 0s - loss: 2.3100e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.9782e-04 - val_root_mean_squared_error: 0.0173\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3799/5000\n", + "1/1 - 0s - loss: 2.3084e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.9342e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3800/5000\n", + "1/1 - 0s - loss: 2.3087e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.0173e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3801/5000\n", + "1/1 - 0s - loss: 2.3101e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.8968e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3802/5000\n", + "1/1 - 0s - loss: 2.3122e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.0553e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3803/5000\n", + "1/1 - 0s - loss: 2.3150e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.8730e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3804/5000\n", + "1/1 - 0s - loss: 2.3178e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.0901e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3805/5000\n", + "1/1 - 0s - loss: 2.3215e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.8522e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3806/5000\n", + "1/1 - 0s - loss: 2.3248e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1233e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3807/5000\n", + "1/1 - 0s - loss: 2.3298e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.8298e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3808/5000\n", + "1/1 - 0s - loss: 2.3342e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.1634e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3809/5000\n", + "1/1 - 0s - loss: 2.3420e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.8094e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3810/5000\n", + "1/1 - 0s - loss: 2.3488e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.2207e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3811/5000\n", + "1/1 - 0s - loss: 2.3617e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.7944e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3812/5000\n", + "1/1 - 0s - loss: 2.3726e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.3055e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3813/5000\n", + "1/1 - 0s - loss: 2.3942e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.7854e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3814/5000\n", + "1/1 - 0s - loss: 2.4119e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.4299e-04 - val_root_mean_squared_error: 0.0185\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3815/5000\n", + "1/1 - 0s - loss: 2.4487e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.7839e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3816/5000\n", + "1/1 - 0s - loss: 2.4752e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.6055e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3817/5000\n", + "1/1 - 0s - loss: 2.5337e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.7958e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3818/5000\n", + "1/1 - 0s - loss: 2.5657e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.8216e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3819/5000\n", + "1/1 - 0s - loss: 2.6453e-04 - root_mean_squared_error: 0.0163 - val_loss: 2.8198e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3820/5000\n", + "1/1 - 0s - loss: 2.6641e-04 - root_mean_squared_error: 0.0163 - val_loss: 4.0031e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3821/5000\n", + "1/1 - 0s - loss: 2.7403e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.8294e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3822/5000\n", + "1/1 - 0s - loss: 2.7113e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.0047e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3823/5000\n", + "1/1 - 0s - loss: 2.7339e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.7963e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3824/5000\n", + "1/1 - 0s - loss: 2.6409e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7414e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3825/5000\n", + "1/1 - 0s - loss: 2.5862e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.7577e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3826/5000\n", + "1/1 - 0s - loss: 2.4724e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.3417e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3827/5000\n", + "1/1 - 0s - loss: 2.3928e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.7971e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3828/5000\n", + "1/1 - 0s - loss: 2.3221e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.0191e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3829/5000\n", + "1/1 - 0s - loss: 2.2836e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.9304e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3830/5000\n", + "1/1 - 0s - loss: 2.2718e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.8446e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3831/5000\n", + "1/1 - 0s - loss: 2.2818e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.1018e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3832/5000\n", + "1/1 - 0s - loss: 2.3070e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.7685e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3833/5000\n", + "1/1 - 0s - loss: 2.3376e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.2656e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3834/5000\n", + "1/1 - 0s - loss: 2.3763e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.7374e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3835/5000\n", + "1/1 - 0s - loss: 2.4022e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.3948e-04 - val_root_mean_squared_error: 0.0184\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3836/5000\n", + "1/1 - 0s - loss: 2.4373e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.7244e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3837/5000\n", + "1/1 - 0s - loss: 2.4430e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.4513e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3838/5000\n", + "1/1 - 0s - loss: 2.4599e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.7156e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3839/5000\n", + "1/1 - 0s - loss: 2.4404e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.4026e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3840/5000\n", + "1/1 - 0s - loss: 2.4320e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.7090e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3841/5000\n", + "1/1 - 0s - loss: 2.3956e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.2659e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3842/5000\n", + "1/1 - 0s - loss: 2.3699e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.7169e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3843/5000\n", + "1/1 - 0s - loss: 2.3335e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.1032e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3844/5000\n", + "1/1 - 0s - loss: 2.3074e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.7487e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3845/5000\n", + "1/1 - 0s - loss: 2.2833e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.9673e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3846/5000\n", + "1/1 - 0s - loss: 2.2671e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.7973e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3847/5000\n", + "1/1 - 0s - loss: 2.2562e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8721e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3848/5000\n", + "1/1 - 0s - loss: 2.2503e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8491e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3849/5000\n", + "1/1 - 0s - loss: 2.2481e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8072e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3850/5000\n", + "1/1 - 0s - loss: 2.2487e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8989e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3851/5000\n", + "1/1 - 0s - loss: 2.2513e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.7605e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3852/5000\n", + "1/1 - 0s - loss: 2.2554e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.9493e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3853/5000\n", + "1/1 - 0s - loss: 2.2611e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.7258e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3854/5000\n", + "1/1 - 0s - loss: 2.2673e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.0057e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3855/5000\n", + "1/1 - 0s - loss: 2.2762e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.6996e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3856/5000\n", + "1/1 - 0s - loss: 2.2849e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.0743e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3857/5000\n", + "1/1 - 0s - loss: 2.2986e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.6793e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3858/5000\n", + "1/1 - 0s - loss: 2.3112e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1647e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3859/5000\n", + "1/1 - 0s - loss: 2.3336e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.6649e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3860/5000\n", + "1/1 - 0s - loss: 2.3530e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.2938e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3861/5000\n", + "1/1 - 0s - loss: 2.3912e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.6621e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3862/5000\n", + "1/1 - 0s - loss: 2.4213e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.4836e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3863/5000\n", + "1/1 - 0s - loss: 2.4858e-04 - root_mean_squared_error: 0.0158 - val_loss: 2.6814e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3864/5000\n", + "1/1 - 0s - loss: 2.5262e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.7402e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3865/5000\n", + "1/1 - 0s - loss: 2.6228e-04 - root_mean_squared_error: 0.0162 - val_loss: 2.7234e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3866/5000\n", + "1/1 - 0s - loss: 2.6559e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.9951e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3867/5000\n", + "1/1 - 0s - loss: 2.7616e-04 - root_mean_squared_error: 0.0166 - val_loss: 2.7504e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3868/5000\n", + "1/1 - 0s - loss: 2.7395e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0508e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3869/5000\n", + "1/1 - 0s - loss: 2.7821e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.7067e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3870/5000\n", + "1/1 - 0s - loss: 2.6668e-04 - root_mean_squared_error: 0.0163 - val_loss: 3.7361e-04 - val_root_mean_squared_error: 0.0193\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3871/5000\n", + "1/1 - 0s - loss: 2.5961e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.6384e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3872/5000\n", + "1/1 - 0s - loss: 2.4456e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.2218e-04 - val_root_mean_squared_error: 0.0179\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3873/5000\n", + "1/1 - 0s - loss: 2.3405e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.6902e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3874/5000\n", + "1/1 - 0s - loss: 2.2574e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8463e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3875/5000\n", + "1/1 - 0s - loss: 2.2218e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8872e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3876/5000\n", + "1/1 - 0s - loss: 2.2257e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.6891e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3877/5000\n", + "1/1 - 0s - loss: 2.2573e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.1227e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3878/5000\n", + "1/1 - 0s - loss: 2.3071e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.6431e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3879/5000\n", + "1/1 - 0s - loss: 2.3524e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.3103e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3880/5000\n", + "1/1 - 0s - loss: 2.4070e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.6253e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3881/5000\n", + "1/1 - 0s - loss: 2.4224e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.3891e-04 - val_root_mean_squared_error: 0.0184\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3882/5000\n", + "1/1 - 0s - loss: 2.4466e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.6080e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3883/5000\n", + "1/1 - 0s - loss: 2.4163e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.3094e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3884/5000\n", + "1/1 - 0s - loss: 2.3952e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.6004e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3885/5000\n", + "1/1 - 0s - loss: 2.3398e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.1036e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3886/5000\n", + "1/1 - 0s - loss: 2.2966e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.6295e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3887/5000\n", + "1/1 - 0s - loss: 2.2517e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8826e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3888/5000\n", + "1/1 - 0s - loss: 2.2217e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.7059e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3889/5000\n", + "1/1 - 0s - loss: 2.2047e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.7275e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3890/5000\n", + "1/1 - 0s - loss: 2.2006e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.8114e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3891/5000\n", + "1/1 - 0s - loss: 2.2063e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.6448e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3892/5000\n", + "1/1 - 0s - loss: 2.2182e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9189e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3893/5000\n", + "1/1 - 0s - loss: 2.2346e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.6051e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3894/5000\n", + "1/1 - 0s - loss: 2.2504e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.0133e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3895/5000\n", + "1/1 - 0s - loss: 2.2700e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.5836e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3896/5000\n", + "1/1 - 0s - loss: 2.2830e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.0896e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3897/5000\n", + "1/1 - 0s - loss: 2.3022e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.5701e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3898/5000\n", + "1/1 - 0s - loss: 2.3087e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1395e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3899/5000\n", + "1/1 - 0s - loss: 2.3234e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.5629e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3900/5000\n", + "1/1 - 0s - loss: 2.3204e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1513e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3901/5000\n", + "1/1 - 0s - loss: 2.3265e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.5601e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3902/5000\n", + "1/1 - 0s - loss: 2.3139e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.1214e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3903/5000\n", + "1/1 - 0s - loss: 2.3107e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.5606e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3904/5000\n", + "1/1 - 0s - loss: 2.2929e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.0630e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3905/5000\n", + "1/1 - 0s - loss: 2.2841e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.5633e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3906/5000\n", + "1/1 - 0s - loss: 2.2665e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.9973e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3907/5000\n", + "1/1 - 0s - loss: 2.2570e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.5666e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3908/5000\n", + "1/1 - 0s - loss: 2.2430e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.9399e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3909/5000\n", + "1/1 - 0s - loss: 2.2354e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.5688e-04 - val_root_mean_squared_error: 0.0160\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3910/5000\n", + "1/1 - 0s - loss: 2.2257e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8982e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3911/5000\n", + "1/1 - 0s - loss: 2.2208e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.5687e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3912/5000\n", + "1/1 - 0s - loss: 2.2146e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8738e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3913/5000\n", + "1/1 - 0s - loss: 2.2122e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.5658e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3914/5000\n", + "1/1 - 0s - loss: 2.2089e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8663e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3915/5000\n", + "1/1 - 0s - loss: 2.2089e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.5593e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3916/5000\n", + "1/1 - 0s - loss: 2.2082e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.8762e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3917/5000\n", + "1/1 - 0s - loss: 2.2112e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.5480e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3918/5000\n", + "1/1 - 0s - loss: 2.2137e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9071e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3919/5000\n", + "1/1 - 0s - loss: 2.2215e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.5327e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3920/5000\n", + "1/1 - 0s - loss: 2.2289e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9692e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3921/5000\n", + "1/1 - 0s - loss: 2.2453e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.5176e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3922/5000\n", + "1/1 - 0s - loss: 2.2611e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.0810e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3923/5000\n", + "1/1 - 0s - loss: 2.2937e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.5125e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3924/5000\n", + "1/1 - 0s - loss: 2.3235e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.2696e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3925/5000\n", + "1/1 - 0s - loss: 2.3851e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.5326e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3926/5000\n", + "1/1 - 0s - loss: 2.4327e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.5562e-04 - val_root_mean_squared_error: 0.0189\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3927/5000\n", + "1/1 - 0s - loss: 2.5368e-04 - root_mean_squared_error: 0.0159 - val_loss: 2.5876e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3928/5000\n", + "1/1 - 0s - loss: 2.5904e-04 - root_mean_squared_error: 0.0161 - val_loss: 3.8961e-04 - val_root_mean_squared_error: 0.0197\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3929/5000\n", + "1/1 - 0s - loss: 2.7257e-04 - root_mean_squared_error: 0.0165 - val_loss: 2.6434e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3930/5000\n", + "1/1 - 0s - loss: 2.7318e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.0753e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3931/5000\n", + "1/1 - 0s - loss: 2.8193e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.6126e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3932/5000\n", + "1/1 - 0s - loss: 2.7075e-04 - root_mean_squared_error: 0.0165 - val_loss: 3.8058e-04 - val_root_mean_squared_error: 0.0195\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3933/5000\n", + "1/1 - 0s - loss: 2.6502e-04 - root_mean_squared_error: 0.0163 - val_loss: 2.5054e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3934/5000\n", + "1/1 - 0s - loss: 2.4617e-04 - root_mean_squared_error: 0.0157 - val_loss: 3.1965e-04 - val_root_mean_squared_error: 0.0179\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3935/5000\n", + "1/1 - 0s - loss: 2.3274e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.5196e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3936/5000\n", + "1/1 - 0s - loss: 2.2089e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.7192e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3937/5000\n", + "1/1 - 0s - loss: 2.1547e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7417e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3938/5000\n", + "1/1 - 0s - loss: 2.1554e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.5390e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3939/5000\n", + "1/1 - 0s - loss: 2.1956e-04 - root_mean_squared_error: 0.0148 - val_loss: 3.0305e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3940/5000\n", + "1/1 - 0s - loss: 2.2606e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.5042e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3941/5000\n", + "1/1 - 0s - loss: 2.3176e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.2419e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3942/5000\n", + "1/1 - 0s - loss: 2.3851e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.4874e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3943/5000\n", + "1/1 - 0s - loss: 2.3947e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.2859e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3944/5000\n", + "1/1 - 0s - loss: 2.4120e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.4585e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3945/5000\n", + "1/1 - 0s - loss: 2.3581e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.1242e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3946/5000\n", + "1/1 - 0s - loss: 2.3139e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.4551e-04 - val_root_mean_squared_error: 0.0157\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3947/5000\n", + "1/1 - 0s - loss: 2.2417e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.8530e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3948/5000\n", + "1/1 - 0s - loss: 2.1887e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.5223e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3949/5000\n", + "1/1 - 0s - loss: 2.1501e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.6254e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3950/5000\n", + "1/1 - 0s - loss: 2.1334e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.6491e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3951/5000\n", + "1/1 - 0s - loss: 2.1354e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.5010e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3952/5000\n", + "1/1 - 0s - loss: 2.1504e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7921e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3953/5000\n", + "1/1 - 0s - loss: 2.1739e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.4544e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3954/5000\n", + "1/1 - 0s - loss: 2.1964e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.9134e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3955/5000\n", + "1/1 - 0s - loss: 2.2224e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.4426e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3956/5000\n", + "1/1 - 0s - loss: 2.2353e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.9847e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3957/5000\n", + "1/1 - 0s - loss: 2.2521e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.4363e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3958/5000\n", + "1/1 - 0s - loss: 2.2491e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.9882e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3959/5000\n", + "1/1 - 0s - loss: 2.2512e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.4283e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3960/5000\n", + "1/1 - 0s - loss: 2.2338e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9237e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3961/5000\n", + "1/1 - 0s - loss: 2.2218e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.4277e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3962/5000\n", + "1/1 - 0s - loss: 2.1975e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.8170e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3963/5000\n", + "1/1 - 0s - loss: 2.1787e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.4443e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3964/5000\n", + "1/1 - 0s - loss: 2.1568e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7063e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3965/5000\n", + "1/1 - 0s - loss: 2.1405e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.4780e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3966/5000\n", + "1/1 - 0s - loss: 2.1269e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.6171e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3967/5000\n", + "1/1 - 0s - loss: 2.1178e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.5182e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3968/5000\n", + "1/1 - 0s - loss: 2.1121e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.5531e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3969/5000\n", + "1/1 - 0s - loss: 2.1092e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.5558e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3970/5000\n", + "1/1 - 0s - loss: 2.1083e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.5056e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3971/5000\n", + "1/1 - 0s - loss: 2.1088e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.5910e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3972/5000\n", + "1/1 - 0s - loss: 2.1105e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.4688e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3973/5000\n", + "1/1 - 0s - loss: 2.1131e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.6291e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3974/5000\n", + "1/1 - 0s - loss: 2.1169e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.4402e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3975/5000\n", + "1/1 - 0s - loss: 2.1214e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.6768e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3976/5000\n", + "1/1 - 0s - loss: 2.1281e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.4171e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3977/5000\n", + "1/1 - 0s - loss: 2.1355e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7414e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3978/5000\n", + "1/1 - 0s - loss: 2.1475e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.3974e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3979/5000\n", + "1/1 - 0s - loss: 2.1604e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8367e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3980/5000\n", + "1/1 - 0s - loss: 2.1830e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.3836e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3981/5000\n", + "1/1 - 0s - loss: 2.2064e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9891e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3982/5000\n", + "1/1 - 0s - loss: 2.2505e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.3888e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3983/5000\n", + "1/1 - 0s - loss: 2.2923e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.2372e-04 - val_root_mean_squared_error: 0.0180\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3984/5000\n", + "1/1 - 0s - loss: 2.3761e-04 - root_mean_squared_error: 0.0154 - val_loss: 2.4340e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3985/5000\n", + "1/1 - 0s - loss: 2.4403e-04 - root_mean_squared_error: 0.0156 - val_loss: 3.6052e-04 - val_root_mean_squared_error: 0.0190\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3986/5000\n", + "1/1 - 0s - loss: 2.5801e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.5213e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3987/5000\n", + "1/1 - 0s - loss: 2.6404e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.9926e-04 - val_root_mean_squared_error: 0.0200\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3988/5000\n", + "1/1 - 0s - loss: 2.8011e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.5704e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3989/5000\n", + "1/1 - 0s - loss: 2.7671e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.0406e-04 - val_root_mean_squared_error: 0.0201\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3990/5000\n", + "1/1 - 0s - loss: 2.8113e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.4680e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3991/5000\n", + "1/1 - 0s - loss: 2.6179e-04 - root_mean_squared_error: 0.0162 - val_loss: 3.4823e-04 - val_root_mean_squared_error: 0.0187\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3992/5000\n", + "1/1 - 0s - loss: 2.4770e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.3538e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3993/5000\n", + "1/1 - 0s - loss: 2.2689e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.7722e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3994/5000\n", + "1/1 - 0s - loss: 2.1414e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.5120e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3995/5000\n", + "1/1 - 0s - loss: 2.0876e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.4420e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3996/5000\n", + "1/1 - 0s - loss: 2.1074e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.8730e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3997/5000\n", + "1/1 - 0s - loss: 2.1760e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.3977e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3998/5000\n", + "1/1 - 0s - loss: 2.2535e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.1678e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 3999/5000\n", + "1/1 - 0s - loss: 2.3442e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.3895e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4000/5000\n", + "1/1 - 0s - loss: 2.3689e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.2292e-04 - val_root_mean_squared_error: 0.0180\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4001/5000\n", + "1/1 - 0s - loss: 2.3995e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.3417e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4002/5000\n", + "1/1 - 0s - loss: 2.3339e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.0171e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4003/5000\n", + "1/1 - 0s - loss: 2.2765e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.3243e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4004/5000\n", + "1/1 - 0s - loss: 2.1823e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.6858e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4005/5000\n", + "1/1 - 0s - loss: 2.1158e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.4248e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4006/5000\n", + "1/1 - 0s - loss: 2.0766e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.4469e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4007/5000\n", + "1/1 - 0s - loss: 2.0714e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.6071e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4008/5000\n", + "1/1 - 0s - loss: 2.0917e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.3424e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4009/5000\n", + "1/1 - 0s - loss: 2.1233e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7747e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4010/5000\n", + "1/1 - 0s - loss: 2.1607e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.3139e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4011/5000\n", + "1/1 - 0s - loss: 2.1817e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.8660e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4012/5000\n", + "1/1 - 0s - loss: 2.2026e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.3119e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4013/5000\n", + "1/1 - 0s - loss: 2.1951e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.8540e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4014/5000\n", + "1/1 - 0s - loss: 2.1892e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.3160e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4015/5000\n", + "1/1 - 0s - loss: 2.1617e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7490e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4016/5000\n", + "1/1 - 0s - loss: 2.1383e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.3298e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4017/5000\n", + "1/1 - 0s - loss: 2.1084e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.6027e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4018/5000\n", + "1/1 - 0s - loss: 2.0851e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.3673e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4019/5000\n", + "1/1 - 0s - loss: 2.0666e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.4734e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4020/5000\n", + "1/1 - 0s - loss: 2.0559e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.4312e-04 - val_root_mean_squared_error: 0.0156\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4021/5000\n", + "1/1 - 0s - loss: 2.0521e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.3901e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4022/5000\n", + "1/1 - 0s - loss: 2.0538e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.5039e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4023/5000\n", + "1/1 - 0s - loss: 2.0591e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.3459e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4024/5000\n", + "1/1 - 0s - loss: 2.0660e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.5679e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4025/5000\n", + "1/1 - 0s - loss: 2.0741e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.3203e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4026/5000\n", + "1/1 - 0s - loss: 2.0810e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.6157e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4027/5000\n", + "1/1 - 0s - loss: 2.0894e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.3003e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4028/5000\n", + "1/1 - 0s - loss: 2.0949e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.6527e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4029/5000\n", + "1/1 - 0s - loss: 2.1034e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.2833e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4030/5000\n", + "1/1 - 0s - loss: 2.1073e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.6849e-04 - val_root_mean_squared_error: 0.0164\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4031/5000\n", + "1/1 - 0s - loss: 2.1157e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.2737e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4032/5000\n", + "1/1 - 0s - loss: 2.1180e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7150e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4033/5000\n", + "1/1 - 0s - loss: 2.1261e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.2707e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4034/5000\n", + "1/1 - 0s - loss: 2.1272e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7457e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4035/5000\n", + "1/1 - 0s - loss: 2.1361e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.2703e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4036/5000\n", + "1/1 - 0s - loss: 2.1374e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7794e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4037/5000\n", + "1/1 - 0s - loss: 2.1485e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.2686e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4038/5000\n", + "1/1 - 0s - loss: 2.1511e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8211e-04 - val_root_mean_squared_error: 0.0168\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4039/5000\n", + "1/1 - 0s - loss: 2.1663e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.2649e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4040/5000\n", + "1/1 - 0s - loss: 2.1708e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8748e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4041/5000\n", + "1/1 - 0s - loss: 2.1914e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.2633e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4042/5000\n", + "1/1 - 0s - loss: 2.1971e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.9385e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4043/5000\n", + "1/1 - 0s - loss: 2.2229e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.2638e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4044/5000\n", + "1/1 - 0s - loss: 2.2265e-04 - root_mean_squared_error: 0.0149 - val_loss: 3.0001e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4045/5000\n", + "1/1 - 0s - loss: 2.2537e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.2640e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4046/5000\n", + "1/1 - 0s - loss: 2.2494e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.0341e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4047/5000\n", + "1/1 - 0s - loss: 2.2696e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.2592e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4048/5000\n", + "1/1 - 0s - loss: 2.2513e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.0128e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4049/5000\n", + "1/1 - 0s - loss: 2.2558e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.2489e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4050/5000\n", + "1/1 - 0s - loss: 2.2231e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9261e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4051/5000\n", + "1/1 - 0s - loss: 2.2097e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.2398e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4052/5000\n", + "1/1 - 0s - loss: 2.1703e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7944e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4053/5000\n", + "1/1 - 0s - loss: 2.1464e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.2404e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4054/5000\n", + "1/1 - 0s - loss: 2.1110e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.6551e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4055/5000\n", + "1/1 - 0s - loss: 2.0873e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.2541e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4056/5000\n", + "1/1 - 0s - loss: 2.0623e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.5362e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4057/5000\n", + "1/1 - 0s - loss: 2.0452e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.2773e-04 - val_root_mean_squared_error: 0.0151\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4058/5000\n", + "1/1 - 0s - loss: 2.0307e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.4469e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4059/5000\n", + "1/1 - 0s - loss: 2.0210e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3044e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4060/5000\n", + "1/1 - 0s - loss: 2.0139e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3839e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4061/5000\n", + "1/1 - 0s - loss: 2.0094e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3311e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4062/5000\n", + "1/1 - 0s - loss: 2.0066e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3396e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4063/5000\n", + "1/1 - 0s - loss: 2.0051e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3559e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4064/5000\n", + "1/1 - 0s - loss: 2.0045e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3063e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4065/5000\n", + "1/1 - 0s - loss: 2.0046e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3800e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4066/5000\n", + "1/1 - 0s - loss: 2.0053e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.2782e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4067/5000\n", + "1/1 - 0s - loss: 2.0067e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.4079e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4068/5000\n", + "1/1 - 0s - loss: 2.0090e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.2517e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4069/5000\n", + "1/1 - 0s - loss: 2.0122e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.4481e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4070/5000\n", + "1/1 - 0s - loss: 2.0177e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.2255e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4071/5000\n", + "1/1 - 0s - loss: 2.0250e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.5150e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4072/5000\n", + "1/1 - 0s - loss: 2.0376e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.2016e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4073/5000\n", + "1/1 - 0s - loss: 2.0539e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.6350e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4074/5000\n", + "1/1 - 0s - loss: 2.0833e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.1909e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4075/5000\n", + "1/1 - 0s - loss: 2.1191e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.8580e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4076/5000\n", + "1/1 - 0s - loss: 2.1869e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.2236e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4077/5000\n", + "1/1 - 0s - loss: 2.2600e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.2665e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4078/5000\n", + "1/1 - 0s - loss: 2.4069e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.3478e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4079/5000\n", + "1/1 - 0s - loss: 2.5266e-04 - root_mean_squared_error: 0.0159 - val_loss: 3.9091e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4080/5000\n", + "1/1 - 0s - loss: 2.7854e-04 - root_mean_squared_error: 0.0167 - val_loss: 2.5409e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4081/5000\n", + "1/1 - 0s - loss: 2.8728e-04 - root_mean_squared_error: 0.0169 - val_loss: 4.4750e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4082/5000\n", + "1/1 - 0s - loss: 3.1186e-04 - root_mean_squared_error: 0.0177 - val_loss: 2.5409e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4083/5000\n", + "1/1 - 0s - loss: 2.9454e-04 - root_mean_squared_error: 0.0172 - val_loss: 4.0996e-04 - val_root_mean_squared_error: 0.0202\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4084/5000\n", + "1/1 - 0s - loss: 2.8502e-04 - root_mean_squared_error: 0.0169 - val_loss: 2.2312e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4085/5000\n", + "1/1 - 0s - loss: 2.4524e-04 - root_mean_squared_error: 0.0157 - val_loss: 2.8962e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4086/5000\n", + "1/1 - 0s - loss: 2.1767e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.2645e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4087/5000\n", + "1/1 - 0s - loss: 2.0129e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.2634e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4088/5000\n", + "1/1 - 0s - loss: 2.0218e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.8650e-04 - val_root_mean_squared_error: 0.0169\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4089/5000\n", + "1/1 - 0s - loss: 2.1471e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.2876e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4090/5000\n", + "1/1 - 0s - loss: 2.2847e-04 - root_mean_squared_error: 0.0151 - val_loss: 3.3104e-04 - val_root_mean_squared_error: 0.0182\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4091/5000\n", + "1/1 - 0s - loss: 2.4274e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.2750e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4092/5000\n", + "1/1 - 0s - loss: 2.4104e-04 - root_mean_squared_error: 0.0155 - val_loss: 3.1483e-04 - val_root_mean_squared_error: 0.0177\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4093/5000\n", + "1/1 - 0s - loss: 2.3915e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.1523e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4094/5000\n", + "1/1 - 0s - loss: 2.2361e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.6266e-04 - val_root_mean_squared_error: 0.0162\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4095/5000\n", + "1/1 - 0s - loss: 2.1149e-04 - root_mean_squared_error: 0.0145 - val_loss: 2.2245e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4096/5000\n", + "1/1 - 0s - loss: 2.0139e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.2596e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4097/5000\n", + "1/1 - 0s - loss: 1.9856e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.5241e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4098/5000\n", + "1/1 - 0s - loss: 2.0215e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.1555e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4099/5000\n", + "1/1 - 0s - loss: 2.0869e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.7512e-04 - val_root_mean_squared_error: 0.0166\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4100/5000\n", + "1/1 - 0s - loss: 2.1579e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.1336e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4101/5000\n", + "1/1 - 0s - loss: 2.1640e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.7149e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4102/5000\n", + "1/1 - 0s - loss: 2.1498e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.1236e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4103/5000\n", + "1/1 - 0s - loss: 2.0804e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.4891e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4104/5000\n", + "1/1 - 0s - loss: 2.0224e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.1844e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4105/5000\n", + "1/1 - 0s - loss: 1.9812e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.2782e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4106/5000\n", + "1/1 - 0s - loss: 1.9693e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3245e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4107/5000\n", + "1/1 - 0s - loss: 1.9792e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1674e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4108/5000\n", + "1/1 - 0s - loss: 1.9975e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.4757e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4109/5000\n", + "1/1 - 0s - loss: 2.0186e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.1356e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4110/5000\n", + "1/1 - 0s - loss: 2.0305e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.5505e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4111/5000\n", + "1/1 - 0s - loss: 2.0404e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.1363e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4112/5000\n", + "1/1 - 0s - loss: 2.0339e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.4995e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4113/5000\n", + "1/1 - 0s - loss: 2.0215e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.1435e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4114/5000\n", + "1/1 - 0s - loss: 1.9973e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.3647e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4115/5000\n", + "1/1 - 0s - loss: 1.9745e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1856e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4116/5000\n", + "1/1 - 0s - loss: 1.9575e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.2500e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4117/5000\n", + "1/1 - 0s - loss: 1.9506e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.2689e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4118/5000\n", + "1/1 - 0s - loss: 1.9522e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1881e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4119/5000\n", + "1/1 - 0s - loss: 1.9581e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3445e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4120/5000\n", + "1/1 - 0s - loss: 1.9653e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1465e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4121/5000\n", + "1/1 - 0s - loss: 1.9712e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3843e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4122/5000\n", + "1/1 - 0s - loss: 1.9769e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1184e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4123/5000\n", + "1/1 - 0s - loss: 1.9796e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.3929e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4124/5000\n", + "1/1 - 0s - loss: 1.9820e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1128e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4125/5000\n", + "1/1 - 0s - loss: 1.9794e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.3808e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4126/5000\n", + "1/1 - 0s - loss: 1.9764e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.1219e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4127/5000\n", + "1/1 - 0s - loss: 1.9694e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3554e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4128/5000\n", + "1/1 - 0s - loss: 1.9633e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1340e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4129/5000\n", + "1/1 - 0s - loss: 1.9564e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.3194e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4130/5000\n", + "1/1 - 0s - loss: 1.9513e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1449e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4131/5000\n", + "1/1 - 0s - loss: 1.9466e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.2827e-04 - val_root_mean_squared_error: 0.0151\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4132/5000\n", + "1/1 - 0s - loss: 1.9427e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1542e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4133/5000\n", + "1/1 - 0s - loss: 1.9389e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2535e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4134/5000\n", + "1/1 - 0s - loss: 1.9356e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1660e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4135/5000\n", + "1/1 - 0s - loss: 1.9327e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2336e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4136/5000\n", + "1/1 - 0s - loss: 1.9306e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1759e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4137/5000\n", + "1/1 - 0s - loss: 1.9291e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2200e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4138/5000\n", + "1/1 - 0s - loss: 1.9278e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1794e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4139/5000\n", + "1/1 - 0s - loss: 1.9264e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2070e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4140/5000\n", + "1/1 - 0s - loss: 1.9250e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1794e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4141/5000\n", + "1/1 - 0s - loss: 1.9236e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1972e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4142/5000\n", + "1/1 - 0s - loss: 1.9223e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1784e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4143/5000\n", + "1/1 - 0s - loss: 1.9211e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1924e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4144/5000\n", + "1/1 - 0s - loss: 1.9201e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1781e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4145/5000\n", + "1/1 - 0s - loss: 1.9191e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1892e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4146/5000\n", + "1/1 - 0s - loss: 1.9180e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1755e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4147/5000\n", + "1/1 - 0s - loss: 1.9169e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1861e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4148/5000\n", + "1/1 - 0s - loss: 1.9157e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1687e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4149/5000\n", + "1/1 - 0s - loss: 1.9145e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1828e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4150/5000\n", + "1/1 - 0s - loss: 1.9134e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1604e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4151/5000\n", + "1/1 - 0s - loss: 1.9124e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1823e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4152/5000\n", + "1/1 - 0s - loss: 1.9115e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1511e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4153/5000\n", + "1/1 - 0s - loss: 1.9106e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1867e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4154/5000\n", + "1/1 - 0s - loss: 1.9098e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1395e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4155/5000\n", + "1/1 - 0s - loss: 1.9092e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1969e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4156/5000\n", + "1/1 - 0s - loss: 1.9088e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1223e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4157/5000\n", + "1/1 - 0s - loss: 1.9090e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2184e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4158/5000\n", + "1/1 - 0s - loss: 1.9102e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.0965e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4159/5000\n", + "1/1 - 0s - loss: 1.9131e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2652e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4160/5000\n", + "1/1 - 0s - loss: 1.9191e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.0632e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4161/5000\n", + "1/1 - 0s - loss: 1.9300e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.3713e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4162/5000\n", + "1/1 - 0s - loss: 1.9518e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.0358e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4163/5000\n", + "1/1 - 0s - loss: 1.9879e-04 - root_mean_squared_error: 0.0141 - val_loss: 2.6259e-04 - val_root_mean_squared_error: 0.0162\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4164/5000\n", + "1/1 - 0s - loss: 2.0621e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.0786e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4165/5000\n", + "1/1 - 0s - loss: 2.1747e-04 - root_mean_squared_error: 0.0147 - val_loss: 3.2725e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4166/5000\n", + "1/1 - 0s - loss: 2.4189e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.3878e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4167/5000\n", + "1/1 - 0s - loss: 2.7132e-04 - root_mean_squared_error: 0.0165 - val_loss: 4.7966e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4168/5000\n", + "1/1 - 0s - loss: 3.3814e-04 - root_mean_squared_error: 0.0184 - val_loss: 3.1033e-04 - val_root_mean_squared_error: 0.0176\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4169/5000\n", + "1/1 - 0s - loss: 3.7435e-04 - root_mean_squared_error: 0.0193 - val_loss: 6.6901e-04 - val_root_mean_squared_error: 0.0259\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4170/5000\n", + "1/1 - 0s - loss: 4.5831e-04 - root_mean_squared_error: 0.0214 - val_loss: 3.0489e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4171/5000\n", + "1/1 - 0s - loss: 3.9020e-04 - root_mean_squared_error: 0.0198 - val_loss: 4.9170e-04 - val_root_mean_squared_error: 0.0222\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4172/5000\n", + "1/1 - 0s - loss: 3.2748e-04 - root_mean_squared_error: 0.0181 - val_loss: 2.0117e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4173/5000\n", + "1/1 - 0s - loss: 2.2446e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.1563e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4174/5000\n", + "1/1 - 0s - loss: 1.9650e-04 - root_mean_squared_error: 0.0140 - val_loss: 3.4451e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4175/5000\n", + "1/1 - 0s - loss: 2.3520e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.4798e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4176/5000\n", + "1/1 - 0s - loss: 2.7858e-04 - root_mean_squared_error: 0.0167 - val_loss: 4.3646e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4177/5000\n", + "1/1 - 0s - loss: 3.0703e-04 - root_mean_squared_error: 0.0175 - val_loss: 2.2155e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4178/5000\n", + "1/1 - 0s - loss: 2.5633e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.7194e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4179/5000\n", + "1/1 - 0s - loss: 2.1932e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.3315e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4180/5000\n", + "1/1 - 0s - loss: 2.0083e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.0660e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4181/5000\n", + "1/1 - 0s - loss: 2.0927e-04 - root_mean_squared_error: 0.0145 - val_loss: 3.0863e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4182/5000\n", + "1/1 - 0s - loss: 2.2921e-04 - root_mean_squared_error: 0.0151 - val_loss: 2.1232e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4183/5000\n", + "1/1 - 0s - loss: 2.3526e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.7986e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4184/5000\n", + "1/1 - 0s - loss: 2.3350e-04 - root_mean_squared_error: 0.0153 - val_loss: 2.0385e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4185/5000\n", + "1/1 - 0s - loss: 2.0545e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.1935e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4186/5000\n", + "1/1 - 0s - loss: 1.9033e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.4253e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4187/5000\n", + "1/1 - 0s - loss: 1.9773e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.9858e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4188/5000\n", + "1/1 - 0s - loss: 2.1379e-04 - root_mean_squared_error: 0.0146 - val_loss: 2.7190e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4189/5000\n", + "1/1 - 0s - loss: 2.2016e-04 - root_mean_squared_error: 0.0148 - val_loss: 1.9976e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4190/5000\n", + "1/1 - 0s - loss: 2.0566e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.3506e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4191/5000\n", + "1/1 - 0s - loss: 1.9433e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.1033e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4192/5000\n", + "1/1 - 0s - loss: 1.9161e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.0368e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4193/5000\n", + "1/1 - 0s - loss: 1.9485e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.4153e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4194/5000\n", + "1/1 - 0s - loss: 1.9975e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.9994e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4195/5000\n", + "1/1 - 0s - loss: 2.0179e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.5521e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4196/5000\n", + "1/1 - 0s - loss: 2.0169e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.0330e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4197/5000\n", + "1/1 - 0s - loss: 1.9548e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.1763e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4198/5000\n", + "1/1 - 0s - loss: 1.8896e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.1417e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4199/5000\n", + "1/1 - 0s - loss: 1.8761e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0160e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4200/5000\n", + "1/1 - 0s - loss: 1.9168e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.3664e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4201/5000\n", + "1/1 - 0s - loss: 1.9533e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.9869e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4202/5000\n", + "1/1 - 0s - loss: 1.9423e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2844e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4203/5000\n", + "1/1 - 0s - loss: 1.9142e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.0160e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4204/5000\n", + "1/1 - 0s - loss: 1.8890e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0899e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4205/5000\n", + "1/1 - 0s - loss: 1.8775e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.1637e-04 - val_root_mean_squared_error: 0.0147\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4206/5000\n", + "1/1 - 0s - loss: 1.8755e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0107e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4207/5000\n", + "1/1 - 0s - loss: 1.8871e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.2278e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4208/5000\n", + "1/1 - 0s - loss: 1.9048e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.9863e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4209/5000\n", + "1/1 - 0s - loss: 1.9037e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.1988e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4210/5000\n", + "1/1 - 0s - loss: 1.8874e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0082e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4211/5000\n", + "1/1 - 0s - loss: 1.8674e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0846e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4212/5000\n", + "1/1 - 0s - loss: 1.8603e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.1004e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4213/5000\n", + "1/1 - 0s - loss: 1.8630e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0123e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4214/5000\n", + "1/1 - 0s - loss: 1.8670e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.1618e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4215/5000\n", + "1/1 - 0s - loss: 1.8709e-04 - root_mean_squared_error: 0.0137 - val_loss: 1.9974e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4216/5000\n", + "1/1 - 0s - loss: 1.8727e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.1808e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4217/5000\n", + "1/1 - 0s - loss: 1.8710e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.0020e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4218/5000\n", + "1/1 - 0s - loss: 1.8627e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.1025e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4219/5000\n", + "1/1 - 0s - loss: 1.8533e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0544e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4220/5000\n", + "1/1 - 0s - loss: 1.8491e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0351e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4221/5000\n", + "1/1 - 0s - loss: 1.8501e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.1037e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4222/5000\n", + "1/1 - 0s - loss: 1.8523e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0106e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4223/5000\n", + "1/1 - 0s - loss: 1.8532e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.1319e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4224/5000\n", + "1/1 - 0s - loss: 1.8535e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.9972e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4225/5000\n", + "1/1 - 0s - loss: 1.8528e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.1146e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4226/5000\n", + "1/1 - 0s - loss: 1.8502e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0194e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4227/5000\n", + "1/1 - 0s - loss: 1.8456e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0744e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4228/5000\n", + "1/1 - 0s - loss: 1.8415e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0401e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4229/5000\n", + "1/1 - 0s - loss: 1.8395e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0433e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4230/5000\n", + "1/1 - 0s - loss: 1.8391e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0706e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4231/5000\n", + "1/1 - 0s - loss: 1.8389e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0112e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4232/5000\n", + "1/1 - 0s - loss: 1.8386e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0921e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4233/5000\n", + "1/1 - 0s - loss: 1.8386e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0072e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4234/5000\n", + "1/1 - 0s - loss: 1.8383e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0880e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4235/5000\n", + "1/1 - 0s - loss: 1.8371e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0068e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4236/5000\n", + "1/1 - 0s - loss: 1.8349e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0723e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4237/5000\n", + "1/1 - 0s - loss: 1.8327e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0148e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4238/5000\n", + "1/1 - 0s - loss: 1.8309e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0439e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4239/5000\n", + "1/1 - 0s - loss: 1.8295e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0304e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4240/5000\n", + "1/1 - 0s - loss: 1.8280e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0248e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4241/5000\n", + "1/1 - 0s - loss: 1.8268e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0388e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4242/5000\n", + "1/1 - 0s - loss: 1.8260e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0101e-04 - val_root_mean_squared_error: 0.0142\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4243/5000\n", + "1/1 - 0s - loss: 1.8255e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0485e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4244/5000\n", + "1/1 - 0s - loss: 1.8249e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9982e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4245/5000\n", + "1/1 - 0s - loss: 1.8241e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0495e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4246/5000\n", + "1/1 - 0s - loss: 1.8232e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9940e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4247/5000\n", + "1/1 - 0s - loss: 1.8222e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0457e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4248/5000\n", + "1/1 - 0s - loss: 1.8213e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9891e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4249/5000\n", + "1/1 - 0s - loss: 1.8201e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0395e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4250/5000\n", + "1/1 - 0s - loss: 1.8189e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9890e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4251/5000\n", + "1/1 - 0s - loss: 1.8176e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0296e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4252/5000\n", + "1/1 - 0s - loss: 1.8164e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9905e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4253/5000\n", + "1/1 - 0s - loss: 1.8153e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0229e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4254/5000\n", + "1/1 - 0s - loss: 1.8141e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9902e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4255/5000\n", + "1/1 - 0s - loss: 1.8129e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0158e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4256/5000\n", + "1/1 - 0s - loss: 1.8118e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9912e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4257/5000\n", + "1/1 - 0s - loss: 1.8107e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0088e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4258/5000\n", + "1/1 - 0s - loss: 1.8096e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.9898e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4259/5000\n", + "1/1 - 0s - loss: 1.8086e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.0041e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4260/5000\n", + "1/1 - 0s - loss: 1.8075e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9883e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4261/5000\n", + "1/1 - 0s - loss: 1.8065e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9986e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4262/5000\n", + "1/1 - 0s - loss: 1.8054e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9870e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4263/5000\n", + "1/1 - 0s - loss: 1.8044e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9952e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4264/5000\n", + "1/1 - 0s - loss: 1.8034e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9829e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4265/5000\n", + "1/1 - 0s - loss: 1.8024e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9920e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4266/5000\n", + "1/1 - 0s - loss: 1.8014e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9795e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4267/5000\n", + "1/1 - 0s - loss: 1.8004e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9886e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4268/5000\n", + "1/1 - 0s - loss: 1.7994e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9746e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4269/5000\n", + "1/1 - 0s - loss: 1.7984e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9876e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4270/5000\n", + "1/1 - 0s - loss: 1.7974e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9689e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4271/5000\n", + "1/1 - 0s - loss: 1.7964e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9868e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4272/5000\n", + "1/1 - 0s - loss: 1.7955e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9629e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4273/5000\n", + "1/1 - 0s - loss: 1.7946e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9888e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4274/5000\n", + "1/1 - 0s - loss: 1.7937e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9534e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4275/5000\n", + "1/1 - 0s - loss: 1.7929e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9943e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4276/5000\n", + "1/1 - 0s - loss: 1.7923e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9416e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4277/5000\n", + "1/1 - 0s - loss: 1.7919e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.0053e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4278/5000\n", + "1/1 - 0s - loss: 1.7919e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9245e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4279/5000\n", + "1/1 - 0s - loss: 1.7925e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.0292e-04 - val_root_mean_squared_error: 0.0142\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4280/5000\n", + "1/1 - 0s - loss: 1.7942e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9007e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4281/5000\n", + "1/1 - 0s - loss: 1.7975e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.0769e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4282/5000\n", + "1/1 - 0s - loss: 1.8043e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.8714e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4283/5000\n", + "1/1 - 0s - loss: 1.8157e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.1795e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4284/5000\n", + "1/1 - 0s - loss: 1.8378e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8481e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4285/5000\n", + "1/1 - 0s - loss: 1.8726e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.4143e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4286/5000\n", + "1/1 - 0s - loss: 1.9423e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.8873e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4287/5000\n", + "1/1 - 0s - loss: 2.0426e-04 - root_mean_squared_error: 0.0143 - val_loss: 2.9751e-04 - val_root_mean_squared_error: 0.0172\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4288/5000\n", + "1/1 - 0s - loss: 2.2544e-04 - root_mean_squared_error: 0.0150 - val_loss: 2.1403e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4289/5000\n", + "1/1 - 0s - loss: 2.4984e-04 - root_mean_squared_error: 0.0158 - val_loss: 4.2166e-04 - val_root_mean_squared_error: 0.0205\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4290/5000\n", + "1/1 - 0s - loss: 3.0413e-04 - root_mean_squared_error: 0.0174 - val_loss: 2.7180e-04 - val_root_mean_squared_error: 0.0165\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4291/5000\n", + "1/1 - 0s - loss: 3.3488e-04 - root_mean_squared_error: 0.0183 - val_loss: 5.8072e-04 - val_root_mean_squared_error: 0.0241\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4292/5000\n", + "1/1 - 0s - loss: 4.0683e-04 - root_mean_squared_error: 0.0202 - val_loss: 2.8044e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4293/5000\n", + "1/1 - 0s - loss: 3.6312e-04 - root_mean_squared_error: 0.0191 - val_loss: 4.7988e-04 - val_root_mean_squared_error: 0.0219\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4294/5000\n", + "1/1 - 0s - loss: 3.2710e-04 - root_mean_squared_error: 0.0181 - val_loss: 1.8875e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4295/5000\n", + "1/1 - 0s - loss: 2.2984e-04 - root_mean_squared_error: 0.0152 - val_loss: 2.1772e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4296/5000\n", + "1/1 - 0s - loss: 1.8349e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.6054e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4297/5000\n", + "1/1 - 0s - loss: 1.9518e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.0663e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4298/5000\n", + "1/1 - 0s - loss: 2.3730e-04 - root_mean_squared_error: 0.0154 - val_loss: 3.9384e-04 - val_root_mean_squared_error: 0.0198\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4299/5000\n", + "1/1 - 0s - loss: 2.8210e-04 - root_mean_squared_error: 0.0168 - val_loss: 2.1634e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4300/5000\n", + "1/1 - 0s - loss: 2.5931e-04 - root_mean_squared_error: 0.0161 - val_loss: 2.9797e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4301/5000\n", + "1/1 - 0s - loss: 2.2962e-04 - root_mean_squared_error: 0.0152 - val_loss: 1.8840e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4302/5000\n", + "1/1 - 0s - loss: 1.9002e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.8985e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4303/5000\n", + "1/1 - 0s - loss: 1.7975e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.4795e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4304/5000\n", + "1/1 - 0s - loss: 1.9623e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.9306e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4305/5000\n", + "1/1 - 0s - loss: 2.1644e-04 - root_mean_squared_error: 0.0147 - val_loss: 2.8872e-04 - val_root_mean_squared_error: 0.0170\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4306/5000\n", + "1/1 - 0s - loss: 2.2809e-04 - root_mean_squared_error: 0.0151 - val_loss: 1.8422e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4307/5000\n", + "1/1 - 0s - loss: 2.0641e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.2954e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4308/5000\n", + "1/1 - 0s - loss: 1.8604e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.9650e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4309/5000\n", + "1/1 - 0s - loss: 1.7772e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.8078e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4310/5000\n", + "1/1 - 0s - loss: 1.8494e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.4603e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4311/5000\n", + "1/1 - 0s - loss: 1.9744e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.8583e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4312/5000\n", + "1/1 - 0s - loss: 2.0044e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.4533e-04 - val_root_mean_squared_error: 0.0157\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4313/5000\n", + "1/1 - 0s - loss: 1.9667e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.8105e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4314/5000\n", + "1/1 - 0s - loss: 1.8494e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0094e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4315/5000\n", + "1/1 - 0s - loss: 1.7750e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.0082e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4316/5000\n", + "1/1 - 0s - loss: 1.7770e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.8045e-04 - val_root_mean_squared_error: 0.0134\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4317/5000\n", + "1/1 - 0s - loss: 1.8352e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.3327e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4318/5000\n", + "1/1 - 0s - loss: 1.8939e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.8350e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4319/5000\n", + "1/1 - 0s - loss: 1.8894e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.2048e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4320/5000\n", + "1/1 - 0s - loss: 1.8480e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8137e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4321/5000\n", + "1/1 - 0s - loss: 1.7867e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.9287e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4322/5000\n", + "1/1 - 0s - loss: 1.7577e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.9777e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4323/5000\n", + "1/1 - 0s - loss: 1.7669e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.8020e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4324/5000\n", + "1/1 - 0s - loss: 1.7952e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.1446e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4325/5000\n", + "1/1 - 0s - loss: 1.8188e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.8026e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4326/5000\n", + "1/1 - 0s - loss: 1.8145e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.0636e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4327/5000\n", + "1/1 - 0s - loss: 1.7942e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.8398e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4328/5000\n", + "1/1 - 0s - loss: 1.7648e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.9210e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4329/5000\n", + "1/1 - 0s - loss: 1.7486e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9309e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4330/5000\n", + "1/1 - 0s - loss: 1.7507e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8229e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4331/5000\n", + "1/1 - 0s - loss: 1.7635e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.0344e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4332/5000\n", + "1/1 - 0s - loss: 1.7762e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.7983e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4333/5000\n", + "1/1 - 0s - loss: 1.7777e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.0219e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4334/5000\n", + "1/1 - 0s - loss: 1.7707e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.8246e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4335/5000\n", + "1/1 - 0s - loss: 1.7573e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.9369e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4336/5000\n", + "1/1 - 0s - loss: 1.7456e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8746e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4337/5000\n", + "1/1 - 0s - loss: 1.7397e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8575e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4338/5000\n", + "1/1 - 0s - loss: 1.7404e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9539e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4339/5000\n", + "1/1 - 0s - loss: 1.7456e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8156e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4340/5000\n", + "1/1 - 0s - loss: 1.7506e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9852e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4341/5000\n", + "1/1 - 0s - loss: 1.7523e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8159e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4342/5000\n", + "1/1 - 0s - loss: 1.7493e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9537e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4343/5000\n", + "1/1 - 0s - loss: 1.7439e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8321e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4344/5000\n", + "1/1 - 0s - loss: 1.7377e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9056e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4345/5000\n", + "1/1 - 0s - loss: 1.7330e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8737e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4346/5000\n", + "1/1 - 0s - loss: 1.7304e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8554e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4347/5000\n", + "1/1 - 0s - loss: 1.7301e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9161e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4348/5000\n", + "1/1 - 0s - loss: 1.7313e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8322e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4349/5000\n", + "1/1 - 0s - loss: 1.7327e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9312e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4350/5000\n", + "1/1 - 0s - loss: 1.7334e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8227e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4351/5000\n", + "1/1 - 0s - loss: 1.7324e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9306e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4352/5000\n", + "1/1 - 0s - loss: 1.7305e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.8270e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4353/5000\n", + "1/1 - 0s - loss: 1.7278e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.9034e-04 - val_root_mean_squared_error: 0.0138\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4354/5000\n", + "1/1 - 0s - loss: 1.7251e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8481e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4355/5000\n", + "1/1 - 0s - loss: 1.7226e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8746e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4356/5000\n", + "1/1 - 0s - loss: 1.7207e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8650e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4357/5000\n", + "1/1 - 0s - loss: 1.7195e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8509e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4358/5000\n", + "1/1 - 0s - loss: 1.7190e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8847e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4359/5000\n", + "1/1 - 0s - loss: 1.7187e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8301e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4360/5000\n", + "1/1 - 0s - loss: 1.7185e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8925e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4361/5000\n", + "1/1 - 0s - loss: 1.7181e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8235e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4362/5000\n", + "1/1 - 0s - loss: 1.7175e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8913e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4363/5000\n", + "1/1 - 0s - loss: 1.7167e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8186e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4364/5000\n", + "1/1 - 0s - loss: 1.7156e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8875e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4365/5000\n", + "1/1 - 0s - loss: 1.7143e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8206e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4366/5000\n", + "1/1 - 0s - loss: 1.7129e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8753e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4367/5000\n", + "1/1 - 0s - loss: 1.7115e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8247e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4368/5000\n", + "1/1 - 0s - loss: 1.7101e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8647e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4369/5000\n", + "1/1 - 0s - loss: 1.7088e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8260e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4370/5000\n", + "1/1 - 0s - loss: 1.7075e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8524e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4371/5000\n", + "1/1 - 0s - loss: 1.7063e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8304e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4372/5000\n", + "1/1 - 0s - loss: 1.7052e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8415e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4373/5000\n", + "1/1 - 0s - loss: 1.7041e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8324e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4374/5000\n", + "1/1 - 0s - loss: 1.7031e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8348e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4375/5000\n", + "1/1 - 0s - loss: 1.7022e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8347e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4376/5000\n", + "1/1 - 0s - loss: 1.7012e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8269e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4377/5000\n", + "1/1 - 0s - loss: 1.7003e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8367e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4378/5000\n", + "1/1 - 0s - loss: 1.6994e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8209e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4379/5000\n", + "1/1 - 0s - loss: 1.6985e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8366e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4380/5000\n", + "1/1 - 0s - loss: 1.6976e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8147e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4381/5000\n", + "1/1 - 0s - loss: 1.6967e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8383e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4382/5000\n", + "1/1 - 0s - loss: 1.6959e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8075e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4383/5000\n", + "1/1 - 0s - loss: 1.6950e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8405e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4384/5000\n", + "1/1 - 0s - loss: 1.6943e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8006e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4385/5000\n", + "1/1 - 0s - loss: 1.6936e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8445e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4386/5000\n", + "1/1 - 0s - loss: 1.6930e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7907e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4387/5000\n", + "1/1 - 0s - loss: 1.6925e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8525e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4388/5000\n", + "1/1 - 0s - loss: 1.6923e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7779e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4389/5000\n", + "1/1 - 0s - loss: 1.6923e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8662e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4390/5000\n", + "1/1 - 0s - loss: 1.6930e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7619e-04 - val_root_mean_squared_error: 0.0133\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4391/5000\n", + "1/1 - 0s - loss: 1.6943e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8927e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4392/5000\n", + "1/1 - 0s - loss: 1.6971e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7409e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4393/5000\n", + "1/1 - 0s - loss: 1.7015e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.9428e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4394/5000\n", + "1/1 - 0s - loss: 1.7095e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.7180e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4395/5000\n", + "1/1 - 0s - loss: 1.7215e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.0396e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4396/5000\n", + "1/1 - 0s - loss: 1.7435e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.7032e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4397/5000\n", + "1/1 - 0s - loss: 1.7745e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.2387e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4398/5000\n", + "1/1 - 0s - loss: 1.8332e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.7332e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4399/5000\n", + "1/1 - 0s - loss: 1.9091e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.6564e-04 - val_root_mean_squared_error: 0.0163\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4400/5000\n", + "1/1 - 0s - loss: 2.0606e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.8949e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4401/5000\n", + "1/1 - 0s - loss: 2.2205e-04 - root_mean_squared_error: 0.0149 - val_loss: 3.4689e-04 - val_root_mean_squared_error: 0.0186\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4402/5000\n", + "1/1 - 0s - loss: 2.5581e-04 - root_mean_squared_error: 0.0160 - val_loss: 2.2453e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4403/5000\n", + "1/1 - 0s - loss: 2.7601e-04 - root_mean_squared_error: 0.0166 - val_loss: 4.5137e-04 - val_root_mean_squared_error: 0.0212\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4404/5000\n", + "1/1 - 0s - loss: 3.2190e-04 - root_mean_squared_error: 0.0179 - val_loss: 2.4162e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4405/5000\n", + "1/1 - 0s - loss: 3.0825e-04 - root_mean_squared_error: 0.0176 - val_loss: 4.3602e-04 - val_root_mean_squared_error: 0.0209\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4406/5000\n", + "1/1 - 0s - loss: 3.0500e-04 - root_mean_squared_error: 0.0175 - val_loss: 1.8905e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4407/5000\n", + "1/1 - 0s - loss: 2.4079e-04 - root_mean_squared_error: 0.0155 - val_loss: 2.5541e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4408/5000\n", + "1/1 - 0s - loss: 1.9445e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.8115e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4409/5000\n", + "1/1 - 0s - loss: 1.7009e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7249e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4410/5000\n", + "1/1 - 0s - loss: 1.7873e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.7861e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4411/5000\n", + "1/1 - 0s - loss: 2.0698e-04 - root_mean_squared_error: 0.0144 - val_loss: 1.9472e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4412/5000\n", + "1/1 - 0s - loss: 2.2622e-04 - root_mean_squared_error: 0.0150 - val_loss: 3.1664e-04 - val_root_mean_squared_error: 0.0178\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4413/5000\n", + "1/1 - 0s - loss: 2.4093e-04 - root_mean_squared_error: 0.0155 - val_loss: 1.8309e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4414/5000\n", + "1/1 - 0s - loss: 2.1795e-04 - root_mean_squared_error: 0.0148 - val_loss: 2.4277e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4415/5000\n", + "1/1 - 0s - loss: 1.9557e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.7044e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4416/5000\n", + "1/1 - 0s - loss: 1.7337e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.7584e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4417/5000\n", + "1/1 - 0s - loss: 1.6783e-04 - root_mean_squared_error: 0.0130 - val_loss: 2.1263e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4418/5000\n", + "1/1 - 0s - loss: 1.7713e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.6987e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4419/5000\n", + "1/1 - 0s - loss: 1.8988e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.4928e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4420/5000\n", + "1/1 - 0s - loss: 1.9951e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.7057e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4421/5000\n", + "1/1 - 0s - loss: 1.9215e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.2377e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4422/5000\n", + "1/1 - 0s - loss: 1.8175e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6979e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4423/5000\n", + "1/1 - 0s - loss: 1.7078e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.7851e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4424/5000\n", + "1/1 - 0s - loss: 1.6681e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.9228e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4425/5000\n", + "1/1 - 0s - loss: 1.6967e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.6618e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4426/5000\n", + "1/1 - 0s - loss: 1.7552e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.1779e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4427/5000\n", + "1/1 - 0s - loss: 1.8098e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6833e-04 - val_root_mean_squared_error: 0.0130\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4428/5000\n", + "1/1 - 0s - loss: 1.8054e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.1315e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4429/5000\n", + "1/1 - 0s - loss: 1.7737e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.6750e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4430/5000\n", + "1/1 - 0s - loss: 1.7138e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8549e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4431/5000\n", + "1/1 - 0s - loss: 1.6697e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.7738e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4432/5000\n", + "1/1 - 0s - loss: 1.6556e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.6909e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4433/5000\n", + "1/1 - 0s - loss: 1.6703e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.9371e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4434/5000\n", + "1/1 - 0s - loss: 1.6978e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.6610e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4435/5000\n", + "1/1 - 0s - loss: 1.7155e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.9912e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4436/5000\n", + "1/1 - 0s - loss: 1.7195e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.6549e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4437/5000\n", + "1/1 - 0s - loss: 1.7023e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.8894e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4438/5000\n", + "1/1 - 0s - loss: 1.6802e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.7032e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4439/5000\n", + "1/1 - 0s - loss: 1.6590e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.7620e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4440/5000\n", + "1/1 - 0s - loss: 1.6483e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7827e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4441/5000\n", + "1/1 - 0s - loss: 1.6491e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6949e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4442/5000\n", + "1/1 - 0s - loss: 1.6577e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.8607e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4443/5000\n", + "1/1 - 0s - loss: 1.6683e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.6599e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4444/5000\n", + "1/1 - 0s - loss: 1.6738e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.8871e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4445/5000\n", + "1/1 - 0s - loss: 1.6742e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.6651e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4446/5000\n", + "1/1 - 0s - loss: 1.6674e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.8421e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4447/5000\n", + "1/1 - 0s - loss: 1.6586e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.6907e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4448/5000\n", + "1/1 - 0s - loss: 1.6489e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7789e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4449/5000\n", + "1/1 - 0s - loss: 1.6418e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7300e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4450/5000\n", + "1/1 - 0s - loss: 1.6381e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7194e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4451/5000\n", + "1/1 - 0s - loss: 1.6378e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7826e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4452/5000\n", + "1/1 - 0s - loss: 1.6399e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6861e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4453/5000\n", + "1/1 - 0s - loss: 1.6428e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8129e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4454/5000\n", + "1/1 - 0s - loss: 1.6454e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6758e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4455/5000\n", + "1/1 - 0s - loss: 1.6462e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8220e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4456/5000\n", + "1/1 - 0s - loss: 1.6459e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6722e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4457/5000\n", + "1/1 - 0s - loss: 1.6437e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8090e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4458/5000\n", + "1/1 - 0s - loss: 1.6409e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6833e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4459/5000\n", + "1/1 - 0s - loss: 1.6373e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7807e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4460/5000\n", + "1/1 - 0s - loss: 1.6338e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6973e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4461/5000\n", + "1/1 - 0s - loss: 1.6306e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7541e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4462/5000\n", + "1/1 - 0s - loss: 1.6280e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7139e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4463/5000\n", + "1/1 - 0s - loss: 1.6262e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7270e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4464/5000\n", + "1/1 - 0s - loss: 1.6249e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7334e-04 - val_root_mean_squared_error: 0.0132\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4465/5000\n", + "1/1 - 0s - loss: 1.6241e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7074e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4466/5000\n", + "1/1 - 0s - loss: 1.6237e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7468e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4467/5000\n", + "1/1 - 0s - loss: 1.6234e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6943e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4468/5000\n", + "1/1 - 0s - loss: 1.6233e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7581e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4469/5000\n", + "1/1 - 0s - loss: 1.6232e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6822e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4470/5000\n", + "1/1 - 0s - loss: 1.6230e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7653e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4471/5000\n", + "1/1 - 0s - loss: 1.6229e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6742e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4472/5000\n", + "1/1 - 0s - loss: 1.6226e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7692e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4473/5000\n", + "1/1 - 0s - loss: 1.6225e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6663e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4474/5000\n", + "1/1 - 0s - loss: 1.6222e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7755e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4475/5000\n", + "1/1 - 0s - loss: 1.6222e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6584e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4476/5000\n", + "1/1 - 0s - loss: 1.6222e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7830e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4477/5000\n", + "1/1 - 0s - loss: 1.6227e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6508e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4478/5000\n", + "1/1 - 0s - loss: 1.6232e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.7960e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4479/5000\n", + "1/1 - 0s - loss: 1.6246e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6392e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4480/5000\n", + "1/1 - 0s - loss: 1.6264e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8187e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4481/5000\n", + "1/1 - 0s - loss: 1.6297e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6256e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4482/5000\n", + "1/1 - 0s - loss: 1.6337e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8557e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4483/5000\n", + "1/1 - 0s - loss: 1.6408e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6112e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4484/5000\n", + "1/1 - 0s - loss: 1.6494e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.9209e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4485/5000\n", + "1/1 - 0s - loss: 1.6643e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.5998e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4486/5000\n", + "1/1 - 0s - loss: 1.6817e-04 - root_mean_squared_error: 0.0130 - val_loss: 2.0339e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4487/5000\n", + "1/1 - 0s - loss: 1.7127e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.6049e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4488/5000\n", + "1/1 - 0s - loss: 1.7466e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.2294e-04 - val_root_mean_squared_error: 0.0149\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4489/5000\n", + "1/1 - 0s - loss: 1.8098e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6474e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4490/5000\n", + "1/1 - 0s - loss: 1.8702e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.5532e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4491/5000\n", + "1/1 - 0s - loss: 1.9888e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.7509e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4492/5000\n", + "1/1 - 0s - loss: 2.0738e-04 - root_mean_squared_error: 0.0144 - val_loss: 2.9992e-04 - val_root_mean_squared_error: 0.0173\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4493/5000\n", + "1/1 - 0s - loss: 2.2536e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.8820e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4494/5000\n", + "1/1 - 0s - loss: 2.3038e-04 - root_mean_squared_error: 0.0152 - val_loss: 3.3414e-04 - val_root_mean_squared_error: 0.0183\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4495/5000\n", + "1/1 - 0s - loss: 2.4547e-04 - root_mean_squared_error: 0.0157 - val_loss: 1.8779e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4496/5000\n", + "1/1 - 0s - loss: 2.3423e-04 - root_mean_squared_error: 0.0153 - val_loss: 3.1055e-04 - val_root_mean_squared_error: 0.0176\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4497/5000\n", + "1/1 - 0s - loss: 2.2859e-04 - root_mean_squared_error: 0.0151 - val_loss: 1.6698e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4498/5000\n", + "1/1 - 0s - loss: 2.0212e-04 - root_mean_squared_error: 0.0142 - val_loss: 2.3119e-04 - val_root_mean_squared_error: 0.0152\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4499/5000\n", + "1/1 - 0s - loss: 1.8231e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5985e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4500/5000\n", + "1/1 - 0s - loss: 1.6574e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.7232e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4501/5000\n", + "1/1 - 0s - loss: 1.5990e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.8934e-04 - val_root_mean_squared_error: 0.0138\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4502/5000\n", + "1/1 - 0s - loss: 1.6327e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6153e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4503/5000\n", + "1/1 - 0s - loss: 1.7203e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.2824e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4504/5000\n", + "1/1 - 0s - loss: 1.8350e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6548e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4505/5000\n", + "1/1 - 0s - loss: 1.8954e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.4474e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4506/5000\n", + "1/1 - 0s - loss: 1.9532e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.6240e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4507/5000\n", + "1/1 - 0s - loss: 1.8938e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.2634e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4508/5000\n", + "1/1 - 0s - loss: 1.8329e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5669e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4509/5000\n", + "1/1 - 0s - loss: 1.7201e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.8919e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4510/5000\n", + "1/1 - 0s - loss: 1.6384e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.6400e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4511/5000\n", + "1/1 - 0s - loss: 1.5932e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.6372e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4512/5000\n", + "1/1 - 0s - loss: 1.5929e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.8503e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4513/5000\n", + "1/1 - 0s - loss: 1.6253e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5733e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4514/5000\n", + "1/1 - 0s - loss: 1.6677e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.0387e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4515/5000\n", + "1/1 - 0s - loss: 1.7118e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.5759e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4516/5000\n", + "1/1 - 0s - loss: 1.7265e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.0780e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4517/5000\n", + "1/1 - 0s - loss: 1.7331e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.5662e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4518/5000\n", + "1/1 - 0s - loss: 1.7045e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.9615e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4519/5000\n", + "1/1 - 0s - loss: 1.6741e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.5640e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4520/5000\n", + "1/1 - 0s - loss: 1.6332e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7864e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4521/5000\n", + "1/1 - 0s - loss: 1.6023e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.6140e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4522/5000\n", + "1/1 - 0s - loss: 1.5823e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.6527e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4523/5000\n", + "1/1 - 0s - loss: 1.5760e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7080e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4524/5000\n", + "1/1 - 0s - loss: 1.5806e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.5844e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4525/5000\n", + "1/1 - 0s - loss: 1.5917e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7959e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4526/5000\n", + "1/1 - 0s - loss: 1.6057e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5595e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4527/5000\n", + "1/1 - 0s - loss: 1.6164e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8441e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4528/5000\n", + "1/1 - 0s - loss: 1.6254e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5558e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4529/5000\n", + "1/1 - 0s - loss: 1.6259e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8481e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4530/5000\n", + "1/1 - 0s - loss: 1.6251e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5597e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4531/5000\n", + "1/1 - 0s - loss: 1.6170e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8174e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4532/5000\n", + "1/1 - 0s - loss: 1.6094e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5650e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4533/5000\n", + "1/1 - 0s - loss: 1.5986e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7654e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4534/5000\n", + "1/1 - 0s - loss: 1.5895e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.5759e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4535/5000\n", + "1/1 - 0s - loss: 1.5805e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7092e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4536/5000\n", + "1/1 - 0s - loss: 1.5735e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5957e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4537/5000\n", + "1/1 - 0s - loss: 1.5678e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6633e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4538/5000\n", + "1/1 - 0s - loss: 1.5639e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6220e-04 - val_root_mean_squared_error: 0.0127\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4539/5000\n", + "1/1 - 0s - loss: 1.5615e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6317e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4540/5000\n", + "1/1 - 0s - loss: 1.5603e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6483e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4541/5000\n", + "1/1 - 0s - loss: 1.5600e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6090e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4542/5000\n", + "1/1 - 0s - loss: 1.5602e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6703e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4543/5000\n", + "1/1 - 0s - loss: 1.5609e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5895e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4544/5000\n", + "1/1 - 0s - loss: 1.5619e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6908e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4545/5000\n", + "1/1 - 0s - loss: 1.5634e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5731e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4546/5000\n", + "1/1 - 0s - loss: 1.5652e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7146e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4547/5000\n", + "1/1 - 0s - loss: 1.5678e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5601e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4548/5000\n", + "1/1 - 0s - loss: 1.5707e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7460e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4549/5000\n", + "1/1 - 0s - loss: 1.5751e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.5486e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4550/5000\n", + "1/1 - 0s - loss: 1.5802e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7901e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4551/5000\n", + "1/1 - 0s - loss: 1.5884e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.5373e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4552/5000\n", + "1/1 - 0s - loss: 1.5976e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.8566e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4553/5000\n", + "1/1 - 0s - loss: 1.6132e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.5291e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4554/5000\n", + "1/1 - 0s - loss: 1.6302e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.9639e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4555/5000\n", + "1/1 - 0s - loss: 1.6604e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.5343e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4556/5000\n", + "1/1 - 0s - loss: 1.6914e-04 - root_mean_squared_error: 0.0130 - val_loss: 2.1406e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4557/5000\n", + "1/1 - 0s - loss: 1.7490e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.5714e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4558/5000\n", + "1/1 - 0s - loss: 1.8011e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.4191e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4559/5000\n", + "1/1 - 0s - loss: 1.9039e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.6565e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4560/5000\n", + "1/1 - 0s - loss: 1.9735e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.7901e-04 - val_root_mean_squared_error: 0.0167\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4561/5000\n", + "1/1 - 0s - loss: 2.1229e-04 - root_mean_squared_error: 0.0146 - val_loss: 1.7619e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4562/5000\n", + "1/1 - 0s - loss: 2.1637e-04 - root_mean_squared_error: 0.0147 - val_loss: 3.0773e-04 - val_root_mean_squared_error: 0.0175\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4563/5000\n", + "1/1 - 0s - loss: 2.2896e-04 - root_mean_squared_error: 0.0151 - val_loss: 1.7680e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4564/5000\n", + "1/1 - 0s - loss: 2.2054e-04 - root_mean_squared_error: 0.0149 - val_loss: 2.9195e-04 - val_root_mean_squared_error: 0.0171\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4565/5000\n", + "1/1 - 0s - loss: 2.1732e-04 - root_mean_squared_error: 0.0147 - val_loss: 1.6078e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4566/5000\n", + "1/1 - 0s - loss: 1.9597e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.2821e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4567/5000\n", + "1/1 - 0s - loss: 1.7969e-04 - root_mean_squared_error: 0.0134 - val_loss: 1.5128e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4568/5000\n", + "1/1 - 0s - loss: 1.6342e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.7189e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4569/5000\n", + "1/1 - 0s - loss: 1.5530e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6999e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4570/5000\n", + "1/1 - 0s - loss: 1.5463e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.5415e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4571/5000\n", + "1/1 - 0s - loss: 1.5957e-04 - root_mean_squared_error: 0.0126 - val_loss: 2.0223e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4572/5000\n", + "1/1 - 0s - loss: 1.6773e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.5517e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4573/5000\n", + "1/1 - 0s - loss: 1.7462e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.2442e-04 - val_root_mean_squared_error: 0.0150\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4574/5000\n", + "1/1 - 0s - loss: 1.8201e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5492e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4575/5000\n", + "1/1 - 0s - loss: 1.8171e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.2281e-04 - val_root_mean_squared_error: 0.0149\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4576/5000\n", + "1/1 - 0s - loss: 1.8125e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.5044e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4577/5000\n", + "1/1 - 0s - loss: 1.7323e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.9805e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4578/5000\n", + "1/1 - 0s - loss: 1.6622e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.5027e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4579/5000\n", + "1/1 - 0s - loss: 1.5869e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.6945e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4580/5000\n", + "1/1 - 0s - loss: 1.5425e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.6102e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4581/5000\n", + "1/1 - 0s - loss: 1.5294e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.5357e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4582/5000\n", + "1/1 - 0s - loss: 1.5430e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.7752e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4583/5000\n", + "1/1 - 0s - loss: 1.5731e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4953e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4584/5000\n", + "1/1 - 0s - loss: 1.6043e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.9116e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4585/5000\n", + "1/1 - 0s - loss: 1.6366e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.4966e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4586/5000\n", + "1/1 - 0s - loss: 1.6480e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.9588e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4587/5000\n", + "1/1 - 0s - loss: 1.6567e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.4947e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4588/5000\n", + "1/1 - 0s - loss: 1.6397e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8979e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4589/5000\n", + "1/1 - 0s - loss: 1.6222e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.4887e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4590/5000\n", + "1/1 - 0s - loss: 1.5912e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.7677e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4591/5000\n", + "1/1 - 0s - loss: 1.5648e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5073e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4592/5000\n", + "1/1 - 0s - loss: 1.5406e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.6380e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4593/5000\n", + "1/1 - 0s - loss: 1.5250e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.5614e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4594/5000\n", + "1/1 - 0s - loss: 1.5175e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.5535e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4595/5000\n", + "1/1 - 0s - loss: 1.5172e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.6315e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4596/5000\n", + "1/1 - 0s - loss: 1.5222e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.5128e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4597/5000\n", + "1/1 - 0s - loss: 1.5300e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.6965e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4598/5000\n", + "1/1 - 0s - loss: 1.5396e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4946e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4599/5000\n", + "1/1 - 0s - loss: 1.5480e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.7466e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4600/5000\n", + "1/1 - 0s - loss: 1.5571e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4829e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4601/5000\n", + "1/1 - 0s - loss: 1.5623e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7795e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4602/5000\n", + "1/1 - 0s - loss: 1.5688e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4746e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4603/5000\n", + "1/1 - 0s - loss: 1.5698e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7951e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4604/5000\n", + "1/1 - 0s - loss: 1.5729e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4721e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4605/5000\n", + "1/1 - 0s - loss: 1.5704e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7945e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4606/5000\n", + "1/1 - 0s - loss: 1.5705e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4744e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4607/5000\n", + "1/1 - 0s - loss: 1.5659e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7837e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4608/5000\n", + "1/1 - 0s - loss: 1.5645e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4769e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4609/5000\n", + "1/1 - 0s - loss: 1.5597e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7716e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4610/5000\n", + "1/1 - 0s - loss: 1.5586e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4761e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4611/5000\n", + "1/1 - 0s - loss: 1.5551e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7652e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4612/5000\n", + "1/1 - 0s - loss: 1.5555e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4722e-04 - val_root_mean_squared_error: 0.0121\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4613/5000\n", + "1/1 - 0s - loss: 1.5540e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7700e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4614/5000\n", + "1/1 - 0s - loss: 1.5567e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4675e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4615/5000\n", + "1/1 - 0s - loss: 1.5575e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7888e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4616/5000\n", + "1/1 - 0s - loss: 1.5633e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4641e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4617/5000\n", + "1/1 - 0s - loss: 1.5667e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.8233e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4618/5000\n", + "1/1 - 0s - loss: 1.5768e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4627e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4619/5000\n", + "1/1 - 0s - loss: 1.5837e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.8766e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4620/5000\n", + "1/1 - 0s - loss: 1.5998e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4648e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4621/5000\n", + "1/1 - 0s - loss: 1.6113e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.9540e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4622/5000\n", + "1/1 - 0s - loss: 1.6365e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.4735e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4623/5000\n", + "1/1 - 0s - loss: 1.6535e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.0618e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4624/5000\n", + "1/1 - 0s - loss: 1.6915e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.4923e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4625/5000\n", + "1/1 - 0s - loss: 1.7137e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.1997e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4626/5000\n", + "1/1 - 0s - loss: 1.7661e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.5206e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4627/5000\n", + "1/1 - 0s - loss: 1.7871e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.3435e-04 - val_root_mean_squared_error: 0.0153\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4628/5000\n", + "1/1 - 0s - loss: 1.8466e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.5450e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4629/5000\n", + "1/1 - 0s - loss: 1.8491e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.4261e-04 - val_root_mean_squared_error: 0.0156\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4630/5000\n", + "1/1 - 0s - loss: 1.8912e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.5394e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4631/5000\n", + "1/1 - 0s - loss: 1.8531e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.3584e-04 - val_root_mean_squared_error: 0.0154\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4632/5000\n", + "1/1 - 0s - loss: 1.8466e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.4937e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4633/5000\n", + "1/1 - 0s - loss: 1.7675e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.1245e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4634/5000\n", + "1/1 - 0s - loss: 1.7129e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.4484e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4635/5000\n", + "1/1 - 0s - loss: 1.6290e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.8348e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4636/5000\n", + "1/1 - 0s - loss: 1.5693e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.4608e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4637/5000\n", + "1/1 - 0s - loss: 1.5193e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.6163e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4638/5000\n", + "1/1 - 0s - loss: 1.4913e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.5383e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4639/5000\n", + "1/1 - 0s - loss: 1.4810e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4997e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4640/5000\n", + "1/1 - 0s - loss: 1.4852e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6477e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4641/5000\n", + "1/1 - 0s - loss: 1.4997e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4506e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4642/5000\n", + "1/1 - 0s - loss: 1.5193e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.7595e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4643/5000\n", + "1/1 - 0s - loss: 1.5441e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4342e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4644/5000\n", + "1/1 - 0s - loss: 1.5644e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.8558e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4645/5000\n", + "1/1 - 0s - loss: 1.5896e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4319e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4646/5000\n", + "1/1 - 0s - loss: 1.6011e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.9199e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4647/5000\n", + "1/1 - 0s - loss: 1.6194e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.4336e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4648/5000\n", + "1/1 - 0s - loss: 1.6180e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.9385e-04 - val_root_mean_squared_error: 0.0139\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4649/5000\n", + "1/1 - 0s - loss: 1.6250e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.4337e-04 - val_root_mean_squared_error: 0.0120\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4650/5000\n", + "1/1 - 0s - loss: 1.6123e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.9108e-04 - val_root_mean_squared_error: 0.0138\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4651/5000\n", + "1/1 - 0s - loss: 1.6084e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.4308e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4652/5000\n", + "1/1 - 0s - loss: 1.5892e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.8506e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4653/5000\n", + "1/1 - 0s - loss: 1.5782e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4274e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4654/5000\n", + "1/1 - 0s - loss: 1.5580e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.7775e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4655/5000\n", + "1/1 - 0s - loss: 1.5449e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4271e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4656/5000\n", + "1/1 - 0s - loss: 1.5277e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.7081e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4657/5000\n", + "1/1 - 0s - loss: 1.5160e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.4314e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4658/5000\n", + "1/1 - 0s - loss: 1.5033e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.6508e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4659/5000\n", + "1/1 - 0s - loss: 1.4946e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4395e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4660/5000\n", + "1/1 - 0s - loss: 1.4862e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6080e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4661/5000\n", + "1/1 - 0s - loss: 1.4803e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4492e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4662/5000\n", + "1/1 - 0s - loss: 1.4750e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5787e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4663/5000\n", + "1/1 - 0s - loss: 1.4712e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4576e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4664/5000\n", + "1/1 - 0s - loss: 1.4680e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5604e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4665/5000\n", + "1/1 - 0s - loss: 1.4657e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4625e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4666/5000\n", + "1/1 - 0s - loss: 1.4639e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5509e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4667/5000\n", + "1/1 - 0s - loss: 1.4625e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4625e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4668/5000\n", + "1/1 - 0s - loss: 1.4615e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5488e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4669/5000\n", + "1/1 - 0s - loss: 1.4609e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4576e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4670/5000\n", + "1/1 - 0s - loss: 1.4606e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5548e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4671/5000\n", + "1/1 - 0s - loss: 1.4609e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4481e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4672/5000\n", + "1/1 - 0s - loss: 1.4616e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5713e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4673/5000\n", + "1/1 - 0s - loss: 1.4634e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4346e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4674/5000\n", + "1/1 - 0s - loss: 1.4663e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.6041e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4675/5000\n", + "1/1 - 0s - loss: 1.4715e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4183e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4676/5000\n", + "1/1 - 0s - loss: 1.4790e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6662e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4677/5000\n", + "1/1 - 0s - loss: 1.4923e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4050e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4678/5000\n", + "1/1 - 0s - loss: 1.5104e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.7865e-04 - val_root_mean_squared_error: 0.0134\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4679/5000\n", + "1/1 - 0s - loss: 1.5433e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.4131e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4680/5000\n", + "1/1 - 0s - loss: 1.5857e-04 - root_mean_squared_error: 0.0126 - val_loss: 2.0268e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4681/5000\n", + "1/1 - 0s - loss: 1.6659e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.4884e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4682/5000\n", + "1/1 - 0s - loss: 1.7580e-04 - root_mean_squared_error: 0.0133 - val_loss: 2.4987e-04 - val_root_mean_squared_error: 0.0158\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4683/5000\n", + "1/1 - 0s - loss: 1.9408e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.7014e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4684/5000\n", + "1/1 - 0s - loss: 2.1003e-04 - root_mean_squared_error: 0.0145 - val_loss: 3.2819e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4685/5000\n", + "1/1 - 0s - loss: 2.4315e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.0041e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4686/5000\n", + "1/1 - 0s - loss: 2.5464e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.9369e-04 - val_root_mean_squared_error: 0.0198\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4687/5000\n", + "1/1 - 0s - loss: 2.8298e-04 - root_mean_squared_error: 0.0168 - val_loss: 1.9573e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4688/5000\n", + "1/1 - 0s - loss: 2.5674e-04 - root_mean_squared_error: 0.0160 - val_loss: 3.2822e-04 - val_root_mean_squared_error: 0.0181\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4689/5000\n", + "1/1 - 0s - loss: 2.3521e-04 - root_mean_squared_error: 0.0153 - val_loss: 1.4611e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4690/5000\n", + "1/1 - 0s - loss: 1.8512e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.8412e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4691/5000\n", + "1/1 - 0s - loss: 1.5434e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.6069e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4692/5000\n", + "1/1 - 0s - loss: 1.4649e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.4585e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4693/5000\n", + "1/1 - 0s - loss: 1.5976e-04 - root_mean_squared_error: 0.0126 - val_loss: 2.3974e-04 - val_root_mean_squared_error: 0.0155\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4694/5000\n", + "1/1 - 0s - loss: 1.8233e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.6467e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4695/5000\n", + "1/1 - 0s - loss: 1.9398e-04 - root_mean_squared_error: 0.0139 - val_loss: 2.5944e-04 - val_root_mean_squared_error: 0.0161\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4696/5000\n", + "1/1 - 0s - loss: 2.0171e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.4865e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4697/5000\n", + "1/1 - 0s - loss: 1.8596e-04 - root_mean_squared_error: 0.0136 - val_loss: 2.0249e-04 - val_root_mean_squared_error: 0.0142\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4698/5000\n", + "1/1 - 0s - loss: 1.7250e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.3919e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4699/5000\n", + "1/1 - 0s - loss: 1.5613e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.5200e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4700/5000\n", + "1/1 - 0s - loss: 1.4904e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6930e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4701/5000\n", + "1/1 - 0s - loss: 1.4945e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4059e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4702/5000\n", + "1/1 - 0s - loss: 1.5515e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.9496e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4703/5000\n", + "1/1 - 0s - loss: 1.6305e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.3838e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4704/5000\n", + "1/1 - 0s - loss: 1.6515e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.8715e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4705/5000\n", + "1/1 - 0s - loss: 1.6495e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.3671e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4706/5000\n", + "1/1 - 0s - loss: 1.5628e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.6034e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4707/5000\n", + "1/1 - 0s - loss: 1.4840e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.4446e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4708/5000\n", + "1/1 - 0s - loss: 1.4346e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.4180e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4709/5000\n", + "1/1 - 0s - loss: 1.4397e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.6053e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4710/5000\n", + "1/1 - 0s - loss: 1.4817e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.3743e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4711/5000\n", + "1/1 - 0s - loss: 1.5181e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.7116e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4712/5000\n", + "1/1 - 0s - loss: 1.5376e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.3653e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4713/5000\n", + "1/1 - 0s - loss: 1.5147e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.6807e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4714/5000\n", + "1/1 - 0s - loss: 1.4885e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.3827e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4715/5000\n", + "1/1 - 0s - loss: 1.4617e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5414e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4716/5000\n", + "1/1 - 0s - loss: 1.4456e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.4434e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4717/5000\n", + "1/1 - 0s - loss: 1.4365e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.4199e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4718/5000\n", + "1/1 - 0s - loss: 1.4328e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.5295e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4719/5000\n", + "1/1 - 0s - loss: 1.4372e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3896e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4720/5000\n", + "1/1 - 0s - loss: 1.4476e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.6168e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4721/5000\n", + "1/1 - 0s - loss: 1.4606e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.3950e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4722/5000\n", + "1/1 - 0s - loss: 1.4654e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.6166e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4723/5000\n", + "1/1 - 0s - loss: 1.4611e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.3906e-04 - val_root_mean_squared_error: 0.0118\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4724/5000\n", + "1/1 - 0s - loss: 1.4470e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.5387e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4725/5000\n", + "1/1 - 0s - loss: 1.4324e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3974e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4726/5000\n", + "1/1 - 0s - loss: 1.4217e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4670e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4727/5000\n", + "1/1 - 0s - loss: 1.4172e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4471e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4728/5000\n", + "1/1 - 0s - loss: 1.4166e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4209e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4729/5000\n", + "1/1 - 0s - loss: 1.4174e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5016e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4730/5000\n", + "1/1 - 0s - loss: 1.4187e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3962e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4731/5000\n", + "1/1 - 0s - loss: 1.4207e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5258e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4732/5000\n", + "1/1 - 0s - loss: 1.4237e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3820e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4733/5000\n", + "1/1 - 0s - loss: 1.4259e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5306e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4734/5000\n", + "1/1 - 0s - loss: 1.4268e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3762e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4735/5000\n", + "1/1 - 0s - loss: 1.4243e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5189e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4736/5000\n", + "1/1 - 0s - loss: 1.4206e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3875e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4737/5000\n", + "1/1 - 0s - loss: 1.4159e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4988e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4738/5000\n", + "1/1 - 0s - loss: 1.4123e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4027e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4739/5000\n", + "1/1 - 0s - loss: 1.4094e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4775e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4740/5000\n", + "1/1 - 0s - loss: 1.4071e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4161e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4741/5000\n", + "1/1 - 0s - loss: 1.4047e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.4507e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4742/5000\n", + "1/1 - 0s - loss: 1.4024e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4291e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4743/5000\n", + "1/1 - 0s - loss: 1.4005e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4298e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4744/5000\n", + "1/1 - 0s - loss: 1.3995e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4409e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4745/5000\n", + "1/1 - 0s - loss: 1.3992e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4175e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4746/5000\n", + "1/1 - 0s - loss: 1.3991e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4544e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4747/5000\n", + "1/1 - 0s - loss: 1.3990e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4064e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4748/5000\n", + "1/1 - 0s - loss: 1.3987e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4643e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4749/5000\n", + "1/1 - 0s - loss: 1.3984e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3966e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4750/5000\n", + "1/1 - 0s - loss: 1.3982e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4704e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4751/5000\n", + "1/1 - 0s - loss: 1.3984e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3857e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4752/5000\n", + "1/1 - 0s - loss: 1.3987e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4792e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4753/5000\n", + "1/1 - 0s - loss: 1.3993e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3754e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4754/5000\n", + "1/1 - 0s - loss: 1.3999e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4919e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4755/5000\n", + "1/1 - 0s - loss: 1.4009e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3673e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4756/5000\n", + "1/1 - 0s - loss: 1.4022e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5116e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4757/5000\n", + "1/1 - 0s - loss: 1.4047e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3574e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4758/5000\n", + "1/1 - 0s - loss: 1.4080e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5445e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4759/5000\n", + "1/1 - 0s - loss: 1.4137e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3455e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4760/5000\n", + "1/1 - 0s - loss: 1.4208e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5981e-04 - val_root_mean_squared_error: 0.0126\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4761/5000\n", + "1/1 - 0s - loss: 1.4326e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3353e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4762/5000\n", + "1/1 - 0s - loss: 1.4471e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.6915e-04 - val_root_mean_squared_error: 0.0130\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4763/5000\n", + "1/1 - 0s - loss: 1.4722e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.3365e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4764/5000\n", + "1/1 - 0s - loss: 1.5013e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.8556e-04 - val_root_mean_squared_error: 0.0136\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4765/5000\n", + "1/1 - 0s - loss: 1.5536e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.3720e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4766/5000\n", + "1/1 - 0s - loss: 1.6082e-04 - root_mean_squared_error: 0.0127 - val_loss: 2.1379e-04 - val_root_mean_squared_error: 0.0146\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4767/5000\n", + "1/1 - 0s - loss: 1.7118e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.4729e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4768/5000\n", + "1/1 - 0s - loss: 1.7986e-04 - root_mean_squared_error: 0.0134 - val_loss: 2.5741e-04 - val_root_mean_squared_error: 0.0160\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4769/5000\n", + "1/1 - 0s - loss: 1.9737e-04 - root_mean_squared_error: 0.0140 - val_loss: 1.6344e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4770/5000\n", + "1/1 - 0s - loss: 2.0575e-04 - root_mean_squared_error: 0.0143 - val_loss: 3.0299e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4771/5000\n", + "1/1 - 0s - loss: 2.2503e-04 - root_mean_squared_error: 0.0150 - val_loss: 1.7141e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4772/5000\n", + "1/1 - 0s - loss: 2.2023e-04 - root_mean_squared_error: 0.0148 - val_loss: 3.0220e-04 - val_root_mean_squared_error: 0.0174\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4773/5000\n", + "1/1 - 0s - loss: 2.2188e-04 - root_mean_squared_error: 0.0149 - val_loss: 1.5225e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4774/5000\n", + "1/1 - 0s - loss: 1.9596e-04 - root_mean_squared_error: 0.0140 - val_loss: 2.2783e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4775/5000\n", + "1/1 - 0s - loss: 1.7506e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.3188e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4776/5000\n", + "1/1 - 0s - loss: 1.5159e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.5319e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4777/5000\n", + "1/1 - 0s - loss: 1.3987e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5318e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4778/5000\n", + "1/1 - 0s - loss: 1.3946e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3562e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4779/5000\n", + "1/1 - 0s - loss: 1.4731e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.9583e-04 - val_root_mean_squared_error: 0.0140\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4780/5000\n", + "1/1 - 0s - loss: 1.5920e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.4169e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4781/5000\n", + "1/1 - 0s - loss: 1.6728e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.1669e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4782/5000\n", + "1/1 - 0s - loss: 1.7493e-04 - root_mean_squared_error: 0.0132 - val_loss: 1.3808e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4783/5000\n", + "1/1 - 0s - loss: 1.7040e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.9923e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4784/5000\n", + "1/1 - 0s - loss: 1.6534e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.3005e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4785/5000\n", + "1/1 - 0s - loss: 1.5300e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.6139e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4786/5000\n", + "1/1 - 0s - loss: 1.4366e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3635e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4787/5000\n", + "1/1 - 0s - loss: 1.3793e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3642e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4788/5000\n", + "1/1 - 0s - loss: 1.3762e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5735e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4789/5000\n", + "1/1 - 0s - loss: 1.4139e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3063e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4790/5000\n", + "1/1 - 0s - loss: 1.4634e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.7492e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4791/5000\n", + "1/1 - 0s - loss: 1.5128e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.3090e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4792/5000\n", + "1/1 - 0s - loss: 1.5216e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.7683e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4793/5000\n", + "1/1 - 0s - loss: 1.5201e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.3038e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4794/5000\n", + "1/1 - 0s - loss: 1.4807e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.6400e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4795/5000\n", + "1/1 - 0s - loss: 1.4429e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.3136e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4796/5000\n", + "1/1 - 0s - loss: 1.4023e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4664e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4797/5000\n", + "1/1 - 0s - loss: 1.3755e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3750e-04 - val_root_mean_squared_error: 0.0117\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4798/5000\n", + "1/1 - 0s - loss: 1.3637e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3469e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4799/5000\n", + "1/1 - 0s - loss: 1.3661e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4782e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4800/5000\n", + "1/1 - 0s - loss: 1.3786e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3050e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4801/5000\n", + "1/1 - 0s - loss: 1.3941e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5620e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4802/5000\n", + "1/1 - 0s - loss: 1.4094e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3034e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4803/5000\n", + "1/1 - 0s - loss: 1.4160e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5824e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4804/5000\n", + "1/1 - 0s - loss: 1.4180e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.3058e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4805/5000\n", + "1/1 - 0s - loss: 1.4097e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5438e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4806/5000\n", + "1/1 - 0s - loss: 1.3996e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.3096e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4807/5000\n", + "1/1 - 0s - loss: 1.3856e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.4770e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4808/5000\n", + "1/1 - 0s - loss: 1.3736e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3248e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4809/5000\n", + "1/1 - 0s - loss: 1.3631e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4111e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4810/5000\n", + "1/1 - 0s - loss: 1.3560e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3550e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4811/5000\n", + "1/1 - 0s - loss: 1.3519e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3610e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4812/5000\n", + "1/1 - 0s - loss: 1.3505e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3932e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4813/5000\n", + "1/1 - 0s - loss: 1.3512e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3305e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4814/5000\n", + "1/1 - 0s - loss: 1.3533e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.4286e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4815/5000\n", + "1/1 - 0s - loss: 1.3564e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3154e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4816/5000\n", + "1/1 - 0s - loss: 1.3595e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4561e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4817/5000\n", + "1/1 - 0s - loss: 1.3628e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.3059e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4818/5000\n", + "1/1 - 0s - loss: 1.3650e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4755e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4819/5000\n", + "1/1 - 0s - loss: 1.3675e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2983e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4820/5000\n", + "1/1 - 0s - loss: 1.3688e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4895e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4821/5000\n", + "1/1 - 0s - loss: 1.3708e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2943e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4822/5000\n", + "1/1 - 0s - loss: 1.3717e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5021e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4823/5000\n", + "1/1 - 0s - loss: 1.3737e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2927e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4824/5000\n", + "1/1 - 0s - loss: 1.3745e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5155e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4825/5000\n", + "1/1 - 0s - loss: 1.3769e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2905e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4826/5000\n", + "1/1 - 0s - loss: 1.3783e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5316e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4827/5000\n", + "1/1 - 0s - loss: 1.3821e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2856e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4828/5000\n", + "1/1 - 0s - loss: 1.3848e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5553e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4829/5000\n", + "1/1 - 0s - loss: 1.3912e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2794e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4830/5000\n", + "1/1 - 0s - loss: 1.3963e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5931e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4831/5000\n", + "1/1 - 0s - loss: 1.4070e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.2763e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4832/5000\n", + "1/1 - 0s - loss: 1.4159e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.6527e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4833/5000\n", + "1/1 - 0s - loss: 1.4337e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.2804e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4834/5000\n", + "1/1 - 0s - loss: 1.4483e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.7428e-04 - val_root_mean_squared_error: 0.0132\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4835/5000\n", + "1/1 - 0s - loss: 1.4772e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.2960e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4836/5000\n", + "1/1 - 0s - loss: 1.4994e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.8710e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4837/5000\n", + "1/1 - 0s - loss: 1.5440e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.3273e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4838/5000\n", + "1/1 - 0s - loss: 1.5728e-04 - root_mean_squared_error: 0.0125 - val_loss: 2.0341e-04 - val_root_mean_squared_error: 0.0143\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4839/5000\n", + "1/1 - 0s - loss: 1.6340e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.3705e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4840/5000\n", + "1/1 - 0s - loss: 1.6600e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.1963e-04 - val_root_mean_squared_error: 0.0148\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4841/5000\n", + "1/1 - 0s - loss: 1.7263e-04 - root_mean_squared_error: 0.0131 - val_loss: 1.4026e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4842/5000\n", + "1/1 - 0s - loss: 1.7269e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.2704e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4843/5000\n", + "1/1 - 0s - loss: 1.7667e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.3861e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4844/5000\n", + "1/1 - 0s - loss: 1.7164e-04 - root_mean_squared_error: 0.0131 - val_loss: 2.1581e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4845/5000\n", + "1/1 - 0s - loss: 1.6959e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.3167e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4846/5000\n", + "1/1 - 0s - loss: 1.6012e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.8696e-04 - val_root_mean_squared_error: 0.0137\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4847/5000\n", + "1/1 - 0s - loss: 1.5315e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.2617e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4848/5000\n", + "1/1 - 0s - loss: 1.4425e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.5594e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4849/5000\n", + "1/1 - 0s - loss: 1.3816e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2922e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4850/5000\n", + "1/1 - 0s - loss: 1.3403e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.3623e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4851/5000\n", + "1/1 - 0s - loss: 1.3242e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3993e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4852/5000\n", + "1/1 - 0s - loss: 1.3281e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2811e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4853/5000\n", + "1/1 - 0s - loss: 1.3459e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.5303e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4854/5000\n", + "1/1 - 0s - loss: 1.3728e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2588e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4855/5000\n", + "1/1 - 0s - loss: 1.3994e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.6450e-04 - val_root_mean_squared_error: 0.0128\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4856/5000\n", + "1/1 - 0s - loss: 1.4304e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.2560e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4857/5000\n", + "1/1 - 0s - loss: 1.4466e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.7148e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4858/5000\n", + "1/1 - 0s - loss: 1.4669e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.2552e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4859/5000\n", + "1/1 - 0s - loss: 1.4628e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.7173e-04 - val_root_mean_squared_error: 0.0131\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4860/5000\n", + "1/1 - 0s - loss: 1.4637e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.2521e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4861/5000\n", + "1/1 - 0s - loss: 1.4423e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.6545e-04 - val_root_mean_squared_error: 0.0129\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4862/5000\n", + "1/1 - 0s - loss: 1.4270e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.2499e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4863/5000\n", + "1/1 - 0s - loss: 1.4001e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5573e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4864/5000\n", + "1/1 - 0s - loss: 1.3799e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2543e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4865/5000\n", + "1/1 - 0s - loss: 1.3578e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4608e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4866/5000\n", + "1/1 - 0s - loss: 1.3414e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2692e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4867/5000\n", + "1/1 - 0s - loss: 1.3277e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3848e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4868/5000\n", + "1/1 - 0s - loss: 1.3184e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2932e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4869/5000\n", + "1/1 - 0s - loss: 1.3122e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3324e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4870/5000\n", + "1/1 - 0s - loss: 1.3089e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3221e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4871/5000\n", + "1/1 - 0s - loss: 1.3077e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2985e-04 - val_root_mean_squared_error: 0.0114\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4872/5000\n", + "1/1 - 0s - loss: 1.3082e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3525e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4873/5000\n", + "1/1 - 0s - loss: 1.3097e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2768e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4874/5000\n", + "1/1 - 0s - loss: 1.3121e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3831e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4875/5000\n", + "1/1 - 0s - loss: 1.3152e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2622e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4876/5000\n", + "1/1 - 0s - loss: 1.3187e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.4159e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4877/5000\n", + "1/1 - 0s - loss: 1.3236e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2509e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4878/5000\n", + "1/1 - 0s - loss: 1.3290e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.4569e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4879/5000\n", + "1/1 - 0s - loss: 1.3370e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2413e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4880/5000\n", + "1/1 - 0s - loss: 1.3459e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.5160e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4881/5000\n", + "1/1 - 0s - loss: 1.3602e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2360e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4882/5000\n", + "1/1 - 0s - loss: 1.3755e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.6081e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4883/5000\n", + "1/1 - 0s - loss: 1.4017e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2436e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4884/5000\n", + "1/1 - 0s - loss: 1.4279e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.7541e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4885/5000\n", + "1/1 - 0s - loss: 1.4753e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.2785e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4886/5000\n", + "1/1 - 0s - loss: 1.5177e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.9760e-04 - val_root_mean_squared_error: 0.0141\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4887/5000\n", + "1/1 - 0s - loss: 1.5983e-04 - root_mean_squared_error: 0.0126 - val_loss: 1.3543e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4888/5000\n", + "1/1 - 0s - loss: 1.6555e-04 - root_mean_squared_error: 0.0129 - val_loss: 2.2707e-04 - val_root_mean_squared_error: 0.0151\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4889/5000\n", + "1/1 - 0s - loss: 1.7720e-04 - root_mean_squared_error: 0.0133 - val_loss: 1.4545e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4890/5000\n", + "1/1 - 0s - loss: 1.8165e-04 - root_mean_squared_error: 0.0135 - val_loss: 2.5345e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4891/5000\n", + "1/1 - 0s - loss: 1.9289e-04 - root_mean_squared_error: 0.0139 - val_loss: 1.4942e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4892/5000\n", + "1/1 - 0s - loss: 1.8934e-04 - root_mean_squared_error: 0.0138 - val_loss: 2.5175e-04 - val_root_mean_squared_error: 0.0159\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4893/5000\n", + "1/1 - 0s - loss: 1.9051e-04 - root_mean_squared_error: 0.0138 - val_loss: 1.3841e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4894/5000\n", + "1/1 - 0s - loss: 1.7525e-04 - root_mean_squared_error: 0.0132 - val_loss: 2.0821e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4895/5000\n", + "1/1 - 0s - loss: 1.6340e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.2370e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4896/5000\n", + "1/1 - 0s - loss: 1.4669e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.5468e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4897/5000\n", + "1/1 - 0s - loss: 1.3586e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2843e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4898/5000\n", + "1/1 - 0s - loss: 1.3023e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2849e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4899/5000\n", + "1/1 - 0s - loss: 1.3034e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.5160e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4900/5000\n", + "1/1 - 0s - loss: 1.3438e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.2553e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4901/5000\n", + "1/1 - 0s - loss: 1.3992e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.7428e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4902/5000\n", + "1/1 - 0s - loss: 1.4621e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.2633e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4903/5000\n", + "1/1 - 0s - loss: 1.4942e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.8299e-04 - val_root_mean_squared_error: 0.0135\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4904/5000\n", + "1/1 - 0s - loss: 1.5272e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.2347e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4905/5000\n", + "1/1 - 0s - loss: 1.5026e-04 - root_mean_squared_error: 0.0123 - val_loss: 1.7346e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4906/5000\n", + "1/1 - 0s - loss: 1.4809e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.2008e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4907/5000\n", + "1/1 - 0s - loss: 1.4178e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.5271e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4908/5000\n", + "1/1 - 0s - loss: 1.3666e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2236e-04 - val_root_mean_squared_error: 0.0111\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4909/5000\n", + "1/1 - 0s - loss: 1.3174e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.3412e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4910/5000\n", + "1/1 - 0s - loss: 1.2895e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3091e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4911/5000\n", + "1/1 - 0s - loss: 1.2820e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2424e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4912/5000\n", + "1/1 - 0s - loss: 1.2918e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.4143e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4913/5000\n", + "1/1 - 0s - loss: 1.3127e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2080e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4914/5000\n", + "1/1 - 0s - loss: 1.3352e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.5043e-04 - val_root_mean_squared_error: 0.0123\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4915/5000\n", + "1/1 - 0s - loss: 1.3593e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2002e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4916/5000\n", + "1/1 - 0s - loss: 1.3699e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5537e-04 - val_root_mean_squared_error: 0.0125\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4917/5000\n", + "1/1 - 0s - loss: 1.3801e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2000e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4918/5000\n", + "1/1 - 0s - loss: 1.3723e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5452e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4919/5000\n", + "1/1 - 0s - loss: 1.3659e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.2037e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4920/5000\n", + "1/1 - 0s - loss: 1.3479e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.4844e-04 - val_root_mean_squared_error: 0.0122\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4921/5000\n", + "1/1 - 0s - loss: 1.3329e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2145e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4922/5000\n", + "1/1 - 0s - loss: 1.3150e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.4007e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4923/5000\n", + "1/1 - 0s - loss: 1.3006e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2337e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4924/5000\n", + "1/1 - 0s - loss: 1.2881e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3274e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4925/5000\n", + "1/1 - 0s - loss: 1.2791e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2595e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4926/5000\n", + "1/1 - 0s - loss: 1.2731e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2779e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4927/5000\n", + "1/1 - 0s - loss: 1.2700e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2907e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4928/5000\n", + "1/1 - 0s - loss: 1.2695e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2467e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4929/5000\n", + "1/1 - 0s - loss: 1.2711e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3254e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4930/5000\n", + "1/1 - 0s - loss: 1.2742e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2267e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4931/5000\n", + "1/1 - 0s - loss: 1.2781e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3600e-04 - val_root_mean_squared_error: 0.0117\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4932/5000\n", + "1/1 - 0s - loss: 1.2826e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2144e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4933/5000\n", + "1/1 - 0s - loss: 1.2870e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.3930e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4934/5000\n", + "1/1 - 0s - loss: 1.2921e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2065e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4935/5000\n", + "1/1 - 0s - loss: 1.2968e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.4270e-04 - val_root_mean_squared_error: 0.0119\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4936/5000\n", + "1/1 - 0s - loss: 1.3036e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.2010e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4937/5000\n", + "1/1 - 0s - loss: 1.3101e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.4690e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4938/5000\n", + "1/1 - 0s - loss: 1.3209e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.1972e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4939/5000\n", + "1/1 - 0s - loss: 1.3310e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.5301e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4940/5000\n", + "1/1 - 0s - loss: 1.3489e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.1980e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4941/5000\n", + "1/1 - 0s - loss: 1.3647e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.6218e-04 - val_root_mean_squared_error: 0.0127\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4942/5000\n", + "1/1 - 0s - loss: 1.3938e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.2112e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4943/5000\n", + "1/1 - 0s - loss: 1.4170e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.7531e-04 - val_root_mean_squared_error: 0.0132\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4944/5000\n", + "1/1 - 0s - loss: 1.4619e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.2441e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4945/5000\n", + "1/1 - 0s - loss: 1.4918e-04 - root_mean_squared_error: 0.0122 - val_loss: 1.9207e-04 - val_root_mean_squared_error: 0.0139\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4946/5000\n", + "1/1 - 0s - loss: 1.5535e-04 - root_mean_squared_error: 0.0125 - val_loss: 1.2944e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4947/5000\n", + "1/1 - 0s - loss: 1.5812e-04 - root_mean_squared_error: 0.0126 - val_loss: 2.0886e-04 - val_root_mean_squared_error: 0.0145\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4948/5000\n", + "1/1 - 0s - loss: 1.6478e-04 - root_mean_squared_error: 0.0128 - val_loss: 1.3373e-04 - val_root_mean_squared_error: 0.0116\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4949/5000\n", + "1/1 - 0s - loss: 1.6509e-04 - root_mean_squared_error: 0.0128 - val_loss: 2.1701e-04 - val_root_mean_squared_error: 0.0147\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4950/5000\n", + "1/1 - 0s - loss: 1.6907e-04 - root_mean_squared_error: 0.0130 - val_loss: 1.3279e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4951/5000\n", + "1/1 - 0s - loss: 1.6432e-04 - root_mean_squared_error: 0.0128 - val_loss: 2.0650e-04 - val_root_mean_squared_error: 0.0144\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4952/5000\n", + "1/1 - 0s - loss: 1.6223e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.2549e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4953/5000\n", + "1/1 - 0s - loss: 1.5295e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.7815e-04 - val_root_mean_squared_error: 0.0133\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4954/5000\n", + "1/1 - 0s - loss: 1.4601e-04 - root_mean_squared_error: 0.0121 - val_loss: 1.1877e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4955/5000\n", + "1/1 - 0s - loss: 1.3717e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.4750e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4956/5000\n", + "1/1 - 0s - loss: 1.3116e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.2068e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4957/5000\n", + "1/1 - 0s - loss: 1.2704e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.2830e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4958/5000\n", + "1/1 - 0s - loss: 1.2542e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.3086e-04 - val_root_mean_squared_error: 0.0114\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4959/5000\n", + "1/1 - 0s - loss: 1.2569e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.2070e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4960/5000\n", + "1/1 - 0s - loss: 1.2724e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.4343e-04 - val_root_mean_squared_error: 0.0120\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4961/5000\n", + "1/1 - 0s - loss: 1.2956e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.1865e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4962/5000\n", + "1/1 - 0s - loss: 1.3189e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.5382e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4963/5000\n", + "1/1 - 0s - loss: 1.3458e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.1795e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4964/5000\n", + "1/1 - 0s - loss: 1.3616e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5971e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4965/5000\n", + "1/1 - 0s - loss: 1.3817e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.1717e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4966/5000\n", + "1/1 - 0s - loss: 1.3812e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.5984e-04 - val_root_mean_squared_error: 0.0126\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4967/5000\n", + "1/1 - 0s - loss: 1.3863e-04 - root_mean_squared_error: 0.0118 - val_loss: 1.1635e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4968/5000\n", + "1/1 - 0s - loss: 1.3686e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.5463e-04 - val_root_mean_squared_error: 0.0124\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4969/5000\n", + "1/1 - 0s - loss: 1.3574e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.1605e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4970/5000\n", + "1/1 - 0s - loss: 1.3315e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.4652e-04 - val_root_mean_squared_error: 0.0121\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4971/5000\n", + "1/1 - 0s - loss: 1.3128e-04 - root_mean_squared_error: 0.0115 - val_loss: 1.1669e-04 - val_root_mean_squared_error: 0.0108\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4972/5000\n", + "1/1 - 0s - loss: 1.2901e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.3834e-04 - val_root_mean_squared_error: 0.0118\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4973/5000\n", + "1/1 - 0s - loss: 1.2739e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.1822e-04 - val_root_mean_squared_error: 0.0109\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4974/5000\n", + "1/1 - 0s - loss: 1.2597e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.3161e-04 - val_root_mean_squared_error: 0.0115\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4975/5000\n", + "1/1 - 0s - loss: 1.2501e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.2028e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4976/5000\n", + "1/1 - 0s - loss: 1.2434e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.2658e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4977/5000\n", + "1/1 - 0s - loss: 1.2393e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2251e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4978/5000\n", + "1/1 - 0s - loss: 1.2370e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2304e-04 - val_root_mean_squared_error: 0.0111\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4979/5000\n", + "1/1 - 0s - loss: 1.2359e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2477e-04 - val_root_mean_squared_error: 0.0112\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4980/5000\n", + "1/1 - 0s - loss: 1.2356e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2068e-04 - val_root_mean_squared_error: 0.0110\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4981/5000\n", + "1/1 - 0s - loss: 1.2360e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2708e-04 - val_root_mean_squared_error: 0.0113\n", + "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", + "Epoch 4982/5000\n", + "1/1 - 0s - loss: 1.2371e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.1918e-04 - val_root_mean_squared_error: 0.0109\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 983/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0310 - val_root_mean_squared_error: 0.1760\n", + "Epoch 4983/5000\n", + "1/1 - 0s - loss: 1.2387e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.2960e-04 - val_root_mean_squared_error: 0.0114\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 984/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "Epoch 4984/5000\n", + "1/1 - 0s - loss: 1.2412e-04 - root_mean_squared_error: 0.0111 - val_loss: 1.1814e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 985/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0815 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1759\n", + "Epoch 4985/5000\n", + "1/1 - 0s - loss: 1.2445e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.3273e-04 - val_root_mean_squared_error: 0.0115\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 986/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", + "Epoch 4986/5000\n", + "1/1 - 0s - loss: 1.2493e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1725e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 987/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1758\n", + "Epoch 4987/5000\n", + "1/1 - 0s - loss: 1.2551e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.3717e-04 - val_root_mean_squared_error: 0.0117\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 988/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "Epoch 4988/5000\n", + "1/1 - 0s - loss: 1.2640e-04 - root_mean_squared_error: 0.0112 - val_loss: 1.1654e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 989/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0309 - val_root_mean_squared_error: 0.1757\n", + "Epoch 4989/5000\n", + "1/1 - 0s - loss: 1.2744e-04 - root_mean_squared_error: 0.0113 - val_loss: 1.4401e-04 - val_root_mean_squared_error: 0.0120\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 990/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0814 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "Epoch 4990/5000\n", + "1/1 - 0s - loss: 1.2912e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.1654e-04 - val_root_mean_squared_error: 0.0108\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 991/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1756\n", + "Epoch 4991/5000\n", + "1/1 - 0s - loss: 1.3103e-04 - root_mean_squared_error: 0.0114 - val_loss: 1.5507e-04 - val_root_mean_squared_error: 0.0125\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 992/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", + "Epoch 4992/5000\n", + "1/1 - 0s - loss: 1.3426e-04 - root_mean_squared_error: 0.0116 - val_loss: 1.1845e-04 - val_root_mean_squared_error: 0.0109\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 993/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1755\n", + "Epoch 4993/5000\n", + "1/1 - 0s - loss: 1.3772e-04 - root_mean_squared_error: 0.0117 - val_loss: 1.7312e-04 - val_root_mean_squared_error: 0.0132\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 994/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", + "Epoch 4994/5000\n", + "1/1 - 0s - loss: 1.4387e-04 - root_mean_squared_error: 0.0120 - val_loss: 1.2420e-04 - val_root_mean_squared_error: 0.0111\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 995/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0813 - val_loss: 0.0308 - val_root_mean_squared_error: 0.1754\n", + "Epoch 4995/5000\n", + "1/1 - 0s - loss: 1.4960e-04 - root_mean_squared_error: 0.0122 - val_loss: 2.0129e-04 - val_root_mean_squared_error: 0.0142\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 996/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", + "Epoch 4996/5000\n", + "1/1 - 0s - loss: 1.6039e-04 - root_mean_squared_error: 0.0127 - val_loss: 1.3517e-04 - val_root_mean_squared_error: 0.0116\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 997/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1753\n", + "Epoch 4997/5000\n", + "1/1 - 0s - loss: 1.6784e-04 - root_mean_squared_error: 0.0130 - val_loss: 2.3788e-04 - val_root_mean_squared_error: 0.0154\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 998/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "Epoch 4998/5000\n", + "1/1 - 0s - loss: 1.8299e-04 - root_mean_squared_error: 0.0135 - val_loss: 1.4714e-04 - val_root_mean_squared_error: 0.0121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 999/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1752\n", + "Epoch 4999/5000\n", + "1/1 - 0s - loss: 1.8689e-04 - root_mean_squared_error: 0.0137 - val_loss: 2.6351e-04 - val_root_mean_squared_error: 0.0162\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n", - "Epoch 1000/1000\n", - "1/1 - 0s - loss: 0.0066 - root_mean_squared_error: 0.0812 - val_loss: 0.0307 - val_root_mean_squared_error: 0.1751\n", + "Epoch 5000/5000\n", + "1/1 - 0s - loss: 1.9837e-04 - root_mean_squared_error: 0.0141 - val_loss: 1.4595e-04 - val_root_mean_squared_error: 0.0121\n", "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5475,7 +18117,7 @@ "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=1000, validation_data=(X_dev, y_dev), verbose=2, shuffle=False, callbacks=[model_checkpoint_callback])\n", + "history = model.fit(train_X, train_y, epochs=5000, 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", @@ -5485,7 +18127,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -5496,7 +18138,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 185, "metadata": {}, "outputs": [], "source": [ @@ -5506,7 +18148,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ @@ -5523,7 +18165,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ @@ -5547,7 +18189,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 188, "metadata": {}, "outputs": [], "source": [ @@ -5581,12 +18223,12 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 189, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -5600,7 +18242,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 53364.70144205812.\n" + "The test root mean squared error is 60721.38074846454.\n" ] } ], @@ -5611,12 +18253,12 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 190, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5630,7 +18272,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 58259.14410631176.\n" + "The test root mean squared error is 9861.972216549791.\n" ] } ], @@ -5641,12 +18283,12 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 191, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -5663,7 +18305,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 192, "metadata": {}, "outputs": [ { @@ -5671,10 +18313,10 @@ "output_type": "stream", "text": [ " Count\n", - "0 410131\n", - "1 332086\n", - "2 352166\n", - "3 333230\n", + "0 646156\n", + "1 502435\n", + "2 484473\n", + "3 370624\n", " Count\n", "0 488981\n", "1 336030\n", @@ -5692,7 +18334,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -5718,7 +18360,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -5735,14 +18377,14 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test root mean squared error is 109683.84377496077.\n" + "The test root mean squared error is 150172.5420424786.\n" ] } ],