Skip to content

Instantly share code, notes, and snippets.

@lidopypy

lidopypy/Predict_house_price.ipynb

Created April 18, 2019 03:21
Show Gist options
  • Save lidopypy/e4beabe1201d652ab9329c6b464223ec to your computer and use it in GitHub Desktop.
Save lidopypy/e4beabe1201d652ab9329c6b464223ec to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Predict_house_price.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.datasets import boston_housing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"(train_data,train_target),(test_data,test_target)=boston_housing.load_data()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(404, 13)\n",
"(102, 13)\n"
]
}
],
"source": [
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"print(train_data.shape)\n",
"print(test_data.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
" 17.9, 23.1, 19.9, 15.7, 8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
" 32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
" 23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
" 12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7, 9.6, 31.5, 24.8, 19.1,\n",
" 22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
" 15.6, 10.5, 6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5, 8.3,\n",
" 14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
" 14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
" 28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
" 19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
" 18.2, 8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
" 31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6, 5. , 14.4, 19.8, 13.8,\n",
" 19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
" 22.6, 19.6, 8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
" 27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
" 8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3, 8.8, 19.2,\n",
" 19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
" 23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
" 21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
" 17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
" 16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
" 24. , 18.5, 21.7, 19.5, 33.2, 23.2, 5. , 19.1, 12.7, 22.3, 10.2,\n",
" 13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
" 22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
" 23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. , 8.3, 23.9, 8.4, 13.8,\n",
" 7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
" 8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
" 19.7, 31.6, 24.8, 19.4, 22.8, 7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
" 19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
" 23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
" 19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
" 23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
" 33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
" 28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
" 24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8, 7. ,\n",
" 11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_target"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"mean=train_data.mean(axis=0)\n",
"train_data-=mean\n",
"\n",
"std=train_data.std(axis=0)\n",
"train_data/=std\n",
"\n",
"test_data-=mean\n",
"test_data/=std"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from keras import models\n",
"from keras import layers\n",
"def Build_model():\n",
" model = models.Sequential()\n",
" model.add(layers.Dense(64, activation='relu',input_shape=(train_data.shape[1],)))\n",
" model.add(layers.Dense(64, activation='relu'))\n",
" model.add(layers.Dense(1))\n",
" model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 303 samples, validate on 101 samples\n",
"Epoch 1/100\n",
"303/303 [==============================] - 2s 5ms/step - loss: 554.4896 - mean_absolute_error: 21.7380 - val_loss: 498.0806 - val_mean_absolute_error: 19.9687\n",
"Epoch 2/100\n",
"303/303 [==============================] - 0s 152us/step - loss: 488.3619 - mean_absolute_error: 20.1000 - val_loss: 430.9249 - val_mean_absolute_error: 18.2020\n",
"Epoch 3/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 420.3613 - mean_absolute_error: 18.3535 - val_loss: 358.5522 - val_mean_absolute_error: 16.2857\n",
"Epoch 4/100\n",
"303/303 [==============================] - 0s 124us/step - loss: 346.7087 - mean_absolute_error: 16.3811 - val_loss: 279.1034 - val_mean_absolute_error: 14.0423\n",
"Epoch 5/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 267.6957 - mean_absolute_error: 14.0941 - val_loss: 198.9775 - val_mean_absolute_error: 11.5112\n",
"Epoch 6/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 194.2321 - mean_absolute_error: 11.5698 - val_loss: 134.9469 - val_mean_absolute_error: 9.1258\n",
"Epoch 7/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 135.3857 - mean_absolute_error: 9.3334 - val_loss: 87.7119 - val_mean_absolute_error: 7.1486\n",
"Epoch 8/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 93.7141 - mean_absolute_error: 7.5414 - val_loss: 61.6006 - val_mean_absolute_error: 5.8813\n",
"Epoch 9/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 69.0519 - mean_absolute_error: 6.3993 - val_loss: 48.1773 - val_mean_absolute_error: 5.1082\n",
"Epoch 10/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 54.5777 - mean_absolute_error: 5.6037 - val_loss: 40.6706 - val_mean_absolute_error: 4.5145\n",
"Epoch 11/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 44.4373 - mean_absolute_error: 5.0314 - val_loss: 35.0743 - val_mean_absolute_error: 4.0872\n",
"Epoch 12/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 37.2586 - mean_absolute_error: 4.6194 - val_loss: 30.4780 - val_mean_absolute_error: 3.7516\n",
"Epoch 13/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 32.5369 - mean_absolute_error: 4.2199 - val_loss: 27.0423 - val_mean_absolute_error: 3.4482\n",
"Epoch 14/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 28.5600 - mean_absolute_error: 3.9747 - val_loss: 24.5376 - val_mean_absolute_error: 3.2743\n",
"Epoch 15/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 25.8237 - mean_absolute_error: 3.6614 - val_loss: 22.1604 - val_mean_absolute_error: 3.1298\n",
"Epoch 16/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 23.2770 - mean_absolute_error: 3.4857 - val_loss: 20.2223 - val_mean_absolute_error: 2.9855\n",
"Epoch 17/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 21.5174 - mean_absolute_error: 3.3725 - val_loss: 19.2943 - val_mean_absolute_error: 3.0311\n",
"Epoch 18/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 20.1946 - mean_absolute_error: 3.2967 - val_loss: 18.6034 - val_mean_absolute_error: 2.9343\n",
"Epoch 19/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 19.0858 - mean_absolute_error: 3.1483 - val_loss: 16.6791 - val_mean_absolute_error: 2.7799\n",
"Epoch 20/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 17.8155 - mean_absolute_error: 3.0187 - val_loss: 16.4399 - val_mean_absolute_error: 2.8700\n",
"Epoch 21/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 17.1216 - mean_absolute_error: 2.9915 - val_loss: 14.9246 - val_mean_absolute_error: 2.6546\n",
"Epoch 22/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 16.4290 - mean_absolute_error: 2.8987 - val_loss: 14.0311 - val_mean_absolute_error: 2.5757\n",
"Epoch 23/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 15.5577 - mean_absolute_error: 2.8074 - val_loss: 13.2399 - val_mean_absolute_error: 2.5039\n",
"Epoch 24/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 15.0770 - mean_absolute_error: 2.7682 - val_loss: 12.8226 - val_mean_absolute_error: 2.5032\n",
"Epoch 25/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 14.6325 - mean_absolute_error: 2.6787 - val_loss: 12.6537 - val_mean_absolute_error: 2.5467\n",
"Epoch 26/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 14.2251 - mean_absolute_error: 2.6488 - val_loss: 11.4780 - val_mean_absolute_error: 2.3743\n",
"Epoch 27/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 13.7024 - mean_absolute_error: 2.5812 - val_loss: 11.1288 - val_mean_absolute_error: 2.3758\n",
"Epoch 28/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 13.1888 - mean_absolute_error: 2.5116 - val_loss: 11.1474 - val_mean_absolute_error: 2.4054\n",
"Epoch 29/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 13.2211 - mean_absolute_error: 2.5802 - val_loss: 11.2085 - val_mean_absolute_error: 2.4657\n",
"Epoch 30/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 12.7332 - mean_absolute_error: 2.5231 - val_loss: 10.1189 - val_mean_absolute_error: 2.2876\n",
"Epoch 31/100\n",
"303/303 [==============================] - 0s 155us/step - loss: 12.4453 - mean_absolute_error: 2.5176 - val_loss: 10.4527 - val_mean_absolute_error: 2.2904\n",
"Epoch 32/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 12.3571 - mean_absolute_error: 2.4449 - val_loss: 10.7805 - val_mean_absolute_error: 2.3914\n",
"Epoch 33/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 11.9670 - mean_absolute_error: 2.4470 - val_loss: 9.4208 - val_mean_absolute_error: 2.1999\n",
"Epoch 34/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 12.1377 - mean_absolute_error: 2.4356 - val_loss: 9.7611 - val_mean_absolute_error: 2.2477\n",
"Epoch 35/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 11.6229 - mean_absolute_error: 2.3644 - val_loss: 9.1834 - val_mean_absolute_error: 2.2156\n",
"Epoch 36/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 11.5271 - mean_absolute_error: 2.4143 - val_loss: 9.1043 - val_mean_absolute_error: 2.1679\n",
"Epoch 37/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 11.1759 - mean_absolute_error: 2.3322 - val_loss: 10.7688 - val_mean_absolute_error: 2.3998\n",
"Epoch 38/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 11.2710 - mean_absolute_error: 2.3327 - val_loss: 9.3336 - val_mean_absolute_error: 2.2100\n",
"Epoch 39/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 10.8310 - mean_absolute_error: 2.2925 - val_loss: 8.8252 - val_mean_absolute_error: 2.1565\n",
"Epoch 40/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 10.8815 - mean_absolute_error: 2.3154 - val_loss: 8.5477 - val_mean_absolute_error: 2.0709\n",
"Epoch 41/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.6845 - mean_absolute_error: 2.2826 - val_loss: 9.2149 - val_mean_absolute_error: 2.2467\n",
"Epoch 42/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.6811 - mean_absolute_error: 2.3109 - val_loss: 8.1328 - val_mean_absolute_error: 2.0712\n",
"Epoch 43/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.4072 - mean_absolute_error: 2.2686 - val_loss: 9.8475 - val_mean_absolute_error: 2.2436\n",
"Epoch 44/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.4993 - mean_absolute_error: 2.2309 - val_loss: 8.3071 - val_mean_absolute_error: 2.0961\n",
"Epoch 45/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.3157 - mean_absolute_error: 2.2292 - val_loss: 7.9625 - val_mean_absolute_error: 2.0604\n",
"Epoch 46/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 10.0605 - mean_absolute_error: 2.2337 - val_loss: 8.1678 - val_mean_absolute_error: 2.0479\n",
"Epoch 47/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.1427 - mean_absolute_error: 2.2132 - val_loss: 9.1460 - val_mean_absolute_error: 2.2319\n",
"Epoch 48/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 10.0679 - mean_absolute_error: 2.2270 - val_loss: 7.9977 - val_mean_absolute_error: 2.0237\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 49/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.7438 - mean_absolute_error: 2.1796 - val_loss: 7.8743 - val_mean_absolute_error: 1.9942\n",
"Epoch 50/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.6335 - mean_absolute_error: 2.1823 - val_loss: 7.8477 - val_mean_absolute_error: 2.0485\n",
"Epoch 51/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 9.6480 - mean_absolute_error: 2.1900 - val_loss: 8.1353 - val_mean_absolute_error: 2.0718\n",
"Epoch 52/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 9.5996 - mean_absolute_error: 2.1769 - val_loss: 7.7602 - val_mean_absolute_error: 1.9566\n",
"Epoch 53/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 9.4092 - mean_absolute_error: 2.1272 - val_loss: 8.5879 - val_mean_absolute_error: 2.1957\n",
"Epoch 54/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.4416 - mean_absolute_error: 2.1776 - val_loss: 8.1831 - val_mean_absolute_error: 2.0572\n",
"Epoch 55/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.4224 - mean_absolute_error: 2.1388 - val_loss: 8.4147 - val_mean_absolute_error: 2.1559\n",
"Epoch 56/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.2528 - mean_absolute_error: 2.1810 - val_loss: 7.6319 - val_mean_absolute_error: 1.9660\n",
"Epoch 57/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 9.1860 - mean_absolute_error: 2.1158 - val_loss: 7.5831 - val_mean_absolute_error: 2.0025\n",
"Epoch 58/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 9.0672 - mean_absolute_error: 2.1089 - val_loss: 7.4002 - val_mean_absolute_error: 1.9255\n",
"Epoch 59/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 9.0170 - mean_absolute_error: 2.0839 - val_loss: 8.0102 - val_mean_absolute_error: 2.0120\n",
"Epoch 60/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 8.8590 - mean_absolute_error: 2.0827 - val_loss: 7.5543 - val_mean_absolute_error: 1.9180\n",
"Epoch 61/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 9.1156 - mean_absolute_error: 2.1455 - val_loss: 7.5037 - val_mean_absolute_error: 1.9817\n",
"Epoch 62/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.9182 - mean_absolute_error: 2.1039 - val_loss: 7.4493 - val_mean_absolute_error: 1.9992\n",
"Epoch 63/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 8.7095 - mean_absolute_error: 2.0813 - val_loss: 8.0619 - val_mean_absolute_error: 2.0472\n",
"Epoch 64/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.7877 - mean_absolute_error: 2.0810 - val_loss: 7.4195 - val_mean_absolute_error: 1.9908\n",
"Epoch 65/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.5254 - mean_absolute_error: 2.0457 - val_loss: 7.6732 - val_mean_absolute_error: 2.0650\n",
"Epoch 66/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.5443 - mean_absolute_error: 2.0910 - val_loss: 8.0561 - val_mean_absolute_error: 2.0810\n",
"Epoch 67/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 8.6011 - mean_absolute_error: 2.0562 - val_loss: 7.9920 - val_mean_absolute_error: 2.0214\n",
"Epoch 68/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.7141 - mean_absolute_error: 2.0791 - val_loss: 7.5519 - val_mean_absolute_error: 1.9505\n",
"Epoch 69/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.3825 - mean_absolute_error: 2.0435 - val_loss: 7.4288 - val_mean_absolute_error: 2.0102\n",
"Epoch 70/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.2462 - mean_absolute_error: 2.0321 - val_loss: 7.4796 - val_mean_absolute_error: 1.9099\n",
"Epoch 71/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.4100 - mean_absolute_error: 2.0177 - val_loss: 7.4864 - val_mean_absolute_error: 2.0223\n",
"Epoch 72/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.0687 - mean_absolute_error: 2.0054 - val_loss: 7.4411 - val_mean_absolute_error: 1.9270\n",
"Epoch 73/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.2437 - mean_absolute_error: 2.0192 - val_loss: 7.3954 - val_mean_absolute_error: 1.9908\n",
"Epoch 74/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.3323 - mean_absolute_error: 2.0469 - val_loss: 7.2606 - val_mean_absolute_error: 1.8886\n",
"Epoch 75/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.2570 - mean_absolute_error: 2.0038 - val_loss: 7.2834 - val_mean_absolute_error: 1.9836\n",
"Epoch 76/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.0712 - mean_absolute_error: 1.9996 - val_loss: 7.4701 - val_mean_absolute_error: 1.9362\n",
"Epoch 77/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.0942 - mean_absolute_error: 1.9819 - val_loss: 7.5207 - val_mean_absolute_error: 2.0629\n",
"Epoch 78/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 7.8365 - mean_absolute_error: 1.9528 - val_loss: 7.3586 - val_mean_absolute_error: 2.0097\n",
"Epoch 79/100\n",
"303/303 [==============================] - 0s 158us/step - loss: 7.7716 - mean_absolute_error: 1.9723 - val_loss: 7.2588 - val_mean_absolute_error: 1.9356\n",
"Epoch 80/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.5553 - mean_absolute_error: 1.9183 - val_loss: 8.3724 - val_mean_absolute_error: 2.1621\n",
"Epoch 81/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.9108 - mean_absolute_error: 1.9702 - val_loss: 7.9647 - val_mean_absolute_error: 2.1538\n",
"Epoch 82/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.9099 - mean_absolute_error: 1.9798 - val_loss: 7.9548 - val_mean_absolute_error: 2.1559\n",
"Epoch 83/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.8583 - mean_absolute_error: 1.9674 - val_loss: 7.3514 - val_mean_absolute_error: 1.9998\n",
"Epoch 84/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 7.6614 - mean_absolute_error: 1.9237 - val_loss: 7.2733 - val_mean_absolute_error: 1.9899\n",
"Epoch 85/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.6803 - mean_absolute_error: 1.9398 - val_loss: 7.1883 - val_mean_absolute_error: 1.9404\n",
"Epoch 86/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 7.5517 - mean_absolute_error: 1.9261 - val_loss: 7.4785 - val_mean_absolute_error: 1.8999\n",
"Epoch 87/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 7.5372 - mean_absolute_error: 1.9008 - val_loss: 7.3430 - val_mean_absolute_error: 2.0120\n",
"Epoch 88/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.5337 - mean_absolute_error: 1.9344 - val_loss: 7.2368 - val_mean_absolute_error: 1.9287\n",
"Epoch 89/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.2837 - mean_absolute_error: 1.8936 - val_loss: 7.2604 - val_mean_absolute_error: 1.8886\n",
"Epoch 90/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.3996 - mean_absolute_error: 1.8945 - val_loss: 7.2983 - val_mean_absolute_error: 1.9249\n",
"Epoch 91/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.1742 - mean_absolute_error: 1.9242 - val_loss: 7.9993 - val_mean_absolute_error: 2.1785\n",
"Epoch 92/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.2413 - mean_absolute_error: 1.9180 - val_loss: 7.1818 - val_mean_absolute_error: 1.9319\n",
"Epoch 93/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.1450 - mean_absolute_error: 1.8831 - val_loss: 7.3230 - val_mean_absolute_error: 2.0072\n",
"Epoch 94/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.0533 - mean_absolute_error: 1.8811 - val_loss: 7.8642 - val_mean_absolute_error: 2.0862\n",
"Epoch 95/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2026 - mean_absolute_error: 1.8654 - val_loss: 7.3647 - val_mean_absolute_error: 2.0228\n",
"Epoch 96/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 7.0827 - mean_absolute_error: 1.8626 - val_loss: 7.2431 - val_mean_absolute_error: 1.9845\n",
"Epoch 97/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.9152 - mean_absolute_error: 1.8507 - val_loss: 7.8764 - val_mean_absolute_error: 2.1550\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 98/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.1705 - mean_absolute_error: 1.9061 - val_loss: 7.6931 - val_mean_absolute_error: 2.0317\n",
"Epoch 99/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.8928 - mean_absolute_error: 1.8550 - val_loss: 8.5570 - val_mean_absolute_error: 2.1721\n",
"Epoch 100/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.0464 - mean_absolute_error: 1.8720 - val_loss: 7.2395 - val_mean_absolute_error: 1.9596\n",
"Train on 303 samples, validate on 101 samples\n",
"Epoch 1/100\n",
"303/303 [==============================] - 0s 908us/step - loss: 545.0965 - mean_absolute_error: 21.2431 - val_loss: 463.3152 - val_mean_absolute_error: 19.5930\n",
"Epoch 2/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 464.5970 - mean_absolute_error: 19.2101 - val_loss: 391.6526 - val_mean_absolute_error: 17.6920\n",
"Epoch 3/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 388.3923 - mean_absolute_error: 17.1773 - val_loss: 317.5015 - val_mean_absolute_error: 15.6264\n",
"Epoch 4/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 310.4136 - mean_absolute_error: 14.9752 - val_loss: 245.8114 - val_mean_absolute_error: 13.5047\n",
"Epoch 5/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 235.7623 - mean_absolute_error: 12.7717 - val_loss: 176.7511 - val_mean_absolute_error: 11.1654\n",
"Epoch 6/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 166.5705 - mean_absolute_error: 10.4524 - val_loss: 116.5685 - val_mean_absolute_error: 8.8054\n",
"Epoch 7/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 110.0588 - mean_absolute_error: 8.2125 - val_loss: 72.2263 - val_mean_absolute_error: 6.7230\n",
"Epoch 8/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 73.1298 - mean_absolute_error: 6.4506 - val_loss: 49.6051 - val_mean_absolute_error: 5.3801\n",
"Epoch 9/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 53.7726 - mean_absolute_error: 5.4226 - val_loss: 37.2683 - val_mean_absolute_error: 4.6424\n",
"Epoch 10/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 42.9329 - mean_absolute_error: 4.7227 - val_loss: 30.4910 - val_mean_absolute_error: 4.2443\n",
"Epoch 11/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 35.6661 - mean_absolute_error: 4.2639 - val_loss: 26.4989 - val_mean_absolute_error: 4.0156\n",
"Epoch 12/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 30.5906 - mean_absolute_error: 3.9303 - val_loss: 22.6642 - val_mean_absolute_error: 3.6777\n",
"Epoch 13/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 26.9285 - mean_absolute_error: 3.6677 - val_loss: 22.2955 - val_mean_absolute_error: 3.5960\n",
"Epoch 14/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 24.1097 - mean_absolute_error: 3.4692 - val_loss: 20.1639 - val_mean_absolute_error: 3.4047\n",
"Epoch 15/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 21.9322 - mean_absolute_error: 3.3074 - val_loss: 20.4685 - val_mean_absolute_error: 3.4495\n",
"Epoch 16/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 20.2030 - mean_absolute_error: 3.1481 - val_loss: 18.0141 - val_mean_absolute_error: 3.1790\n",
"Epoch 17/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 18.9872 - mean_absolute_error: 3.0324 - val_loss: 17.1306 - val_mean_absolute_error: 3.1047\n",
"Epoch 18/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 17.4468 - mean_absolute_error: 2.8748 - val_loss: 17.7914 - val_mean_absolute_error: 3.2039\n",
"Epoch 19/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 16.8917 - mean_absolute_error: 2.7956 - val_loss: 15.8516 - val_mean_absolute_error: 2.9801\n",
"Epoch 20/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 15.7720 - mean_absolute_error: 2.7060 - val_loss: 15.5951 - val_mean_absolute_error: 2.9824\n",
"Epoch 21/100\n",
"303/303 [==============================] - 0s 168us/step - loss: 15.0522 - mean_absolute_error: 2.6440 - val_loss: 15.5687 - val_mean_absolute_error: 3.0113\n",
"Epoch 22/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 14.3054 - mean_absolute_error: 2.5955 - val_loss: 14.3047 - val_mean_absolute_error: 2.8522\n",
"Epoch 23/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 13.9676 - mean_absolute_error: 2.5259 - val_loss: 14.2409 - val_mean_absolute_error: 2.8384\n",
"Epoch 24/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 13.2995 - mean_absolute_error: 2.4874 - val_loss: 15.0725 - val_mean_absolute_error: 3.0136\n",
"Epoch 25/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 12.8003 - mean_absolute_error: 2.4774 - val_loss: 13.6528 - val_mean_absolute_error: 2.7795\n",
"Epoch 26/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 12.5823 - mean_absolute_error: 2.4478 - val_loss: 13.3634 - val_mean_absolute_error: 2.7904\n",
"Epoch 27/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 12.3454 - mean_absolute_error: 2.4228 - val_loss: 14.2952 - val_mean_absolute_error: 2.9281\n",
"Epoch 28/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 12.1082 - mean_absolute_error: 2.3849 - val_loss: 13.3558 - val_mean_absolute_error: 2.7645\n",
"Epoch 29/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 11.5087 - mean_absolute_error: 2.3244 - val_loss: 13.1823 - val_mean_absolute_error: 2.7776\n",
"Epoch 30/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 11.5449 - mean_absolute_error: 2.3012 - val_loss: 12.8863 - val_mean_absolute_error: 2.7349\n",
"Epoch 31/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 11.1459 - mean_absolute_error: 2.2820 - val_loss: 14.1299 - val_mean_absolute_error: 2.9000\n",
"Epoch 32/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.8896 - mean_absolute_error: 2.2840 - val_loss: 12.8651 - val_mean_absolute_error: 2.7471\n",
"Epoch 33/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 10.5183 - mean_absolute_error: 2.2267 - val_loss: 13.3381 - val_mean_absolute_error: 2.8008\n",
"Epoch 34/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 10.6703 - mean_absolute_error: 2.2218 - val_loss: 12.3023 - val_mean_absolute_error: 2.6806\n",
"Epoch 35/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.3090 - mean_absolute_error: 2.2315 - val_loss: 12.4368 - val_mean_absolute_error: 2.6936\n",
"Epoch 36/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.5025 - mean_absolute_error: 2.1972 - val_loss: 12.2094 - val_mean_absolute_error: 2.6609\n",
"Epoch 37/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.3695 - mean_absolute_error: 2.1846 - val_loss: 12.4981 - val_mean_absolute_error: 2.7060\n",
"Epoch 38/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.0185 - mean_absolute_error: 2.1826 - val_loss: 13.0613 - val_mean_absolute_error: 2.7777\n",
"Epoch 39/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 10.1629 - mean_absolute_error: 2.2224 - val_loss: 12.3656 - val_mean_absolute_error: 2.6874\n",
"Epoch 40/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 10.0271 - mean_absolute_error: 2.1568 - val_loss: 12.6068 - val_mean_absolute_error: 2.7311\n",
"Epoch 41/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 9.5618 - mean_absolute_error: 2.1441 - val_loss: 12.3280 - val_mean_absolute_error: 2.6944\n",
"Epoch 42/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 9.6904 - mean_absolute_error: 2.1491 - val_loss: 12.0631 - val_mean_absolute_error: 2.6613\n",
"Epoch 43/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.6160 - mean_absolute_error: 2.1688 - val_loss: 12.0075 - val_mean_absolute_error: 2.6575\n",
"Epoch 44/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 9.5159 - mean_absolute_error: 2.1498 - val_loss: 11.9224 - val_mean_absolute_error: 2.6489\n",
"Epoch 45/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 9.4572 - mean_absolute_error: 2.0837 - val_loss: 13.0356 - val_mean_absolute_error: 2.8049\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 46/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.4904 - mean_absolute_error: 2.1147 - val_loss: 12.2849 - val_mean_absolute_error: 2.7033\n",
"Epoch 47/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 9.1368 - mean_absolute_error: 2.0913 - val_loss: 12.4591 - val_mean_absolute_error: 2.7387\n",
"Epoch 48/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 9.1798 - mean_absolute_error: 2.0874 - val_loss: 13.8034 - val_mean_absolute_error: 2.9147\n",
"Epoch 49/100\n",
"303/303 [==============================] - 0s 155us/step - loss: 9.2269 - mean_absolute_error: 2.1595 - val_loss: 11.2341 - val_mean_absolute_error: 2.5799\n",
"Epoch 50/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 9.1111 - mean_absolute_error: 2.0752 - val_loss: 11.6385 - val_mean_absolute_error: 2.6259\n",
"Epoch 51/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 8.9493 - mean_absolute_error: 2.0660 - val_loss: 11.6722 - val_mean_absolute_error: 2.6376\n",
"Epoch 52/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.9194 - mean_absolute_error: 2.0548 - val_loss: 11.7550 - val_mean_absolute_error: 2.6478\n",
"Epoch 53/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.7748 - mean_absolute_error: 2.0685 - val_loss: 10.9447 - val_mean_absolute_error: 2.5197\n",
"Epoch 54/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.8912 - mean_absolute_error: 2.0497 - val_loss: 12.9538 - val_mean_absolute_error: 2.7927\n",
"Epoch 55/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.5942 - mean_absolute_error: 2.0615 - val_loss: 10.9030 - val_mean_absolute_error: 2.5243\n",
"Epoch 56/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.6365 - mean_absolute_error: 2.0459 - val_loss: 11.3765 - val_mean_absolute_error: 2.5961\n",
"Epoch 57/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.3684 - mean_absolute_error: 1.9931 - val_loss: 11.9041 - val_mean_absolute_error: 2.6525\n",
"Epoch 58/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.6746 - mean_absolute_error: 2.0600 - val_loss: 11.2435 - val_mean_absolute_error: 2.5846\n",
"Epoch 59/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.4787 - mean_absolute_error: 2.0215 - val_loss: 10.9163 - val_mean_absolute_error: 2.5355\n",
"Epoch 60/100\n",
"303/303 [==============================] - 0s 109us/step - loss: 8.3196 - mean_absolute_error: 2.0163 - val_loss: 10.8264 - val_mean_absolute_error: 2.5296\n",
"Epoch 61/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.5190 - mean_absolute_error: 2.0397 - val_loss: 11.9249 - val_mean_absolute_error: 2.6834\n",
"Epoch 62/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.2419 - mean_absolute_error: 1.9718 - val_loss: 11.5043 - val_mean_absolute_error: 2.6386\n",
"Epoch 63/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.5459 - mean_absolute_error: 2.0458 - val_loss: 10.7147 - val_mean_absolute_error: 2.5235\n",
"Epoch 64/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.2164 - mean_absolute_error: 1.9939 - val_loss: 11.3479 - val_mean_absolute_error: 2.6159\n",
"Epoch 65/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 8.0878 - mean_absolute_error: 1.9909 - val_loss: 10.8049 - val_mean_absolute_error: 2.5191\n",
"Epoch 66/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 8.2091 - mean_absolute_error: 1.9797 - val_loss: 11.8423 - val_mean_absolute_error: 2.6710\n",
"Epoch 67/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.2187 - mean_absolute_error: 2.0081 - val_loss: 11.9183 - val_mean_absolute_error: 2.6893\n",
"Epoch 68/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.0507 - mean_absolute_error: 1.9842 - val_loss: 11.4521 - val_mean_absolute_error: 2.6389\n",
"Epoch 69/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.9764 - mean_absolute_error: 1.9696 - val_loss: 10.4958 - val_mean_absolute_error: 2.4880\n",
"Epoch 70/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.9274 - mean_absolute_error: 1.9608 - val_loss: 10.8498 - val_mean_absolute_error: 2.5385\n",
"Epoch 71/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.8610 - mean_absolute_error: 1.9392 - val_loss: 11.5898 - val_mean_absolute_error: 2.6459\n",
"Epoch 72/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.9164 - mean_absolute_error: 1.9675 - val_loss: 11.4262 - val_mean_absolute_error: 2.6228\n",
"Epoch 73/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.8822 - mean_absolute_error: 1.9320 - val_loss: 11.5543 - val_mean_absolute_error: 2.6382\n",
"Epoch 74/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.8108 - mean_absolute_error: 1.9760 - val_loss: 11.3652 - val_mean_absolute_error: 2.6219\n",
"Epoch 75/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.7619 - mean_absolute_error: 1.9471 - val_loss: 10.9089 - val_mean_absolute_error: 2.5601\n",
"Epoch 76/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 7.5913 - mean_absolute_error: 1.9246 - val_loss: 11.0908 - val_mean_absolute_error: 2.5846\n",
"Epoch 77/100\n",
"303/303 [==============================] - 0s 158us/step - loss: 7.6673 - mean_absolute_error: 1.9255 - val_loss: 10.6935 - val_mean_absolute_error: 2.5378\n",
"Epoch 78/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 7.6511 - mean_absolute_error: 1.9050 - val_loss: 11.8004 - val_mean_absolute_error: 2.6582\n",
"Epoch 79/100\n",
"303/303 [==============================] - 0s 124us/step - loss: 7.6467 - mean_absolute_error: 1.9462 - val_loss: 11.2229 - val_mean_absolute_error: 2.5890\n",
"Epoch 80/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.5624 - mean_absolute_error: 1.9201 - val_loss: 11.7263 - val_mean_absolute_error: 2.6642\n",
"Epoch 81/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.5513 - mean_absolute_error: 1.9417 - val_loss: 11.0603 - val_mean_absolute_error: 2.5728\n",
"Epoch 82/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 7.4080 - mean_absolute_error: 1.9023 - val_loss: 10.4410 - val_mean_absolute_error: 2.5058\n",
"Epoch 83/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.5183 - mean_absolute_error: 1.9058 - val_loss: 10.4685 - val_mean_absolute_error: 2.5030\n",
"Epoch 84/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.4962 - mean_absolute_error: 1.8704 - val_loss: 10.9579 - val_mean_absolute_error: 2.5727\n",
"Epoch 85/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 7.3294 - mean_absolute_error: 1.8913 - val_loss: 11.2440 - val_mean_absolute_error: 2.6030\n",
"Epoch 86/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.5649 - mean_absolute_error: 1.9130 - val_loss: 11.1150 - val_mean_absolute_error: 2.5742\n",
"Epoch 87/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.3052 - mean_absolute_error: 1.8921 - val_loss: 10.8477 - val_mean_absolute_error: 2.5477\n",
"Epoch 88/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.3590 - mean_absolute_error: 1.8895 - val_loss: 10.4253 - val_mean_absolute_error: 2.5028\n",
"Epoch 89/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 7.0602 - mean_absolute_error: 1.8396 - val_loss: 11.0222 - val_mean_absolute_error: 2.5686\n",
"Epoch 90/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.1832 - mean_absolute_error: 1.8975 - val_loss: 10.2296 - val_mean_absolute_error: 2.4680\n",
"Epoch 91/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.0171 - mean_absolute_error: 1.8552 - val_loss: 11.6921 - val_mean_absolute_error: 2.6544\n",
"Epoch 92/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2620 - mean_absolute_error: 1.8897 - val_loss: 11.0633 - val_mean_absolute_error: 2.5899\n",
"Epoch 93/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.9363 - mean_absolute_error: 1.8732 - val_loss: 10.2891 - val_mean_absolute_error: 2.4780\n",
"Epoch 94/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"303/303 [==============================] - 0s 109us/step - loss: 7.0837 - mean_absolute_error: 1.8501 - val_loss: 11.0923 - val_mean_absolute_error: 2.5731\n",
"Epoch 95/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2639 - mean_absolute_error: 1.9099 - val_loss: 9.8370 - val_mean_absolute_error: 2.4108\n",
"Epoch 96/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.9835 - mean_absolute_error: 1.8280 - val_loss: 9.9727 - val_mean_absolute_error: 2.4276\n",
"Epoch 97/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.9560 - mean_absolute_error: 1.8459 - val_loss: 9.8859 - val_mean_absolute_error: 2.4213\n",
"Epoch 98/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.9193 - mean_absolute_error: 1.8355 - val_loss: 10.2240 - val_mean_absolute_error: 2.4610\n",
"Epoch 99/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.8842 - mean_absolute_error: 1.8321 - val_loss: 10.6366 - val_mean_absolute_error: 2.5155\n",
"Epoch 100/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.8741 - mean_absolute_error: 1.8477 - val_loss: 10.4887 - val_mean_absolute_error: 2.4936\n",
"Train on 303 samples, validate on 101 samples\n",
"Epoch 1/100\n",
"303/303 [==============================] - 0s 1ms/step - loss: 558.7822 - mean_absolute_error: 21.7214 - val_loss: 438.7118 - val_mean_absolute_error: 19.4609\n",
"Epoch 2/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 477.8023 - mean_absolute_error: 19.9300 - val_loss: 372.8368 - val_mean_absolute_error: 17.7668\n",
"Epoch 3/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 400.5904 - mean_absolute_error: 18.0251 - val_loss: 305.7383 - val_mean_absolute_error: 15.9047\n",
"Epoch 4/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 319.4199 - mean_absolute_error: 15.8674 - val_loss: 238.1236 - val_mean_absolute_error: 13.7800\n",
"Epoch 5/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 239.6367 - mean_absolute_error: 13.3597 - val_loss: 172.7307 - val_mean_absolute_error: 11.3423\n",
"Epoch 6/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 167.9100 - mean_absolute_error: 10.6765 - val_loss: 120.0956 - val_mean_absolute_error: 9.0861\n",
"Epoch 7/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 114.1621 - mean_absolute_error: 8.3227 - val_loss: 82.8159 - val_mean_absolute_error: 7.2358\n",
"Epoch 8/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 79.6856 - mean_absolute_error: 6.6080 - val_loss: 59.8605 - val_mean_absolute_error: 5.9285\n",
"Epoch 9/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 59.0943 - mean_absolute_error: 5.5606 - val_loss: 45.4446 - val_mean_absolute_error: 4.9249\n",
"Epoch 10/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 45.5341 - mean_absolute_error: 4.8301 - val_loss: 37.7236 - val_mean_absolute_error: 4.3333\n",
"Epoch 11/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 37.1194 - mean_absolute_error: 4.3775 - val_loss: 32.5555 - val_mean_absolute_error: 3.9873\n",
"Epoch 12/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 30.6605 - mean_absolute_error: 4.0108 - val_loss: 30.2627 - val_mean_absolute_error: 3.8036\n",
"Epoch 13/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 27.7230 - mean_absolute_error: 3.7861 - val_loss: 28.4323 - val_mean_absolute_error: 3.6883\n",
"Epoch 14/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 24.9904 - mean_absolute_error: 3.5740 - val_loss: 27.2151 - val_mean_absolute_error: 3.6482\n",
"Epoch 15/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 22.7355 - mean_absolute_error: 3.4411 - val_loss: 26.7076 - val_mean_absolute_error: 3.6028\n",
"Epoch 16/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 21.4305 - mean_absolute_error: 3.3673 - val_loss: 24.7585 - val_mean_absolute_error: 3.4378\n",
"Epoch 17/100\n",
"303/303 [==============================] - 0s 152us/step - loss: 20.0734 - mean_absolute_error: 3.2272 - val_loss: 24.3228 - val_mean_absolute_error: 3.4208\n",
"Epoch 18/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 18.8037 - mean_absolute_error: 3.1364 - val_loss: 22.8495 - val_mean_absolute_error: 3.2883\n",
"Epoch 19/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 17.7744 - mean_absolute_error: 3.0994 - val_loss: 21.9099 - val_mean_absolute_error: 3.1719\n",
"Epoch 20/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 16.7754 - mean_absolute_error: 2.9948 - val_loss: 21.3743 - val_mean_absolute_error: 3.1283\n",
"Epoch 21/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 15.8346 - mean_absolute_error: 2.9165 - val_loss: 21.1111 - val_mean_absolute_error: 3.0557\n",
"Epoch 22/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 15.0767 - mean_absolute_error: 2.8197 - val_loss: 19.9865 - val_mean_absolute_error: 2.9935\n",
"Epoch 23/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 14.2518 - mean_absolute_error: 2.7880 - val_loss: 19.6347 - val_mean_absolute_error: 2.9435\n",
"Epoch 24/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 13.7750 - mean_absolute_error: 2.7426 - val_loss: 19.2447 - val_mean_absolute_error: 2.8279\n",
"Epoch 25/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 13.3249 - mean_absolute_error: 2.6776 - val_loss: 18.8195 - val_mean_absolute_error: 2.8049\n",
"Epoch 26/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 12.4978 - mean_absolute_error: 2.5949 - val_loss: 19.1020 - val_mean_absolute_error: 2.8495\n",
"Epoch 27/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 12.6007 - mean_absolute_error: 2.6036 - val_loss: 18.1600 - val_mean_absolute_error: 2.7864\n",
"Epoch 28/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 11.7793 - mean_absolute_error: 2.5399 - val_loss: 17.9418 - val_mean_absolute_error: 2.7592\n",
"Epoch 29/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 11.4054 - mean_absolute_error: 2.4564 - val_loss: 17.6703 - val_mean_absolute_error: 2.8237\n",
"Epoch 30/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 11.2139 - mean_absolute_error: 2.4438 - val_loss: 17.7804 - val_mean_absolute_error: 2.7905\n",
"Epoch 31/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.8450 - mean_absolute_error: 2.4105 - val_loss: 17.0893 - val_mean_absolute_error: 2.7801\n",
"Epoch 32/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 10.5073 - mean_absolute_error: 2.3873 - val_loss: 16.9766 - val_mean_absolute_error: 2.7445\n",
"Epoch 33/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 10.3046 - mean_absolute_error: 2.3483 - val_loss: 16.9466 - val_mean_absolute_error: 2.7613\n",
"Epoch 34/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.2518 - mean_absolute_error: 2.3245 - val_loss: 16.7643 - val_mean_absolute_error: 2.7557\n",
"Epoch 35/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 9.9391 - mean_absolute_error: 2.3075 - val_loss: 16.5721 - val_mean_absolute_error: 2.7117\n",
"Epoch 36/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 9.7098 - mean_absolute_error: 2.2720 - val_loss: 16.7549 - val_mean_absolute_error: 2.7490\n",
"Epoch 37/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 9.5387 - mean_absolute_error: 2.2566 - val_loss: 16.3186 - val_mean_absolute_error: 2.6733\n",
"Epoch 38/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 9.3342 - mean_absolute_error: 2.2462 - val_loss: 16.1302 - val_mean_absolute_error: 2.6689\n",
"Epoch 39/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 9.3790 - mean_absolute_error: 2.2600 - val_loss: 15.9001 - val_mean_absolute_error: 2.6427\n",
"Epoch 40/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 9.0469 - mean_absolute_error: 2.1796 - val_loss: 15.9344 - val_mean_absolute_error: 2.6089\n",
"Epoch 41/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 8.9577 - mean_absolute_error: 2.1874 - val_loss: 15.9934 - val_mean_absolute_error: 2.7355\n",
"Epoch 42/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"303/303 [==============================] - 0s 132us/step - loss: 8.7472 - mean_absolute_error: 2.1841 - val_loss: 15.8334 - val_mean_absolute_error: 2.6270\n",
"Epoch 43/100\n",
"303/303 [==============================] - 0s 188us/step - loss: 8.5788 - mean_absolute_error: 2.1333 - val_loss: 15.6742 - val_mean_absolute_error: 2.6091\n",
"Epoch 44/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 8.4858 - mean_absolute_error: 2.1373 - val_loss: 15.5825 - val_mean_absolute_error: 2.6657\n",
"Epoch 45/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.5276 - mean_absolute_error: 2.1446 - val_loss: 15.4505 - val_mean_absolute_error: 2.5994\n",
"Epoch 46/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.3156 - mean_absolute_error: 2.1144 - val_loss: 15.4320 - val_mean_absolute_error: 2.5782\n",
"Epoch 47/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 8.3568 - mean_absolute_error: 2.1353 - val_loss: 15.6985 - val_mean_absolute_error: 2.5771\n",
"Epoch 48/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 8.0323 - mean_absolute_error: 2.0693 - val_loss: 15.2905 - val_mean_absolute_error: 2.6237\n",
"Epoch 49/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 8.0080 - mean_absolute_error: 2.0719 - val_loss: 15.5804 - val_mean_absolute_error: 2.5977\n",
"Epoch 50/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 7.9583 - mean_absolute_error: 2.0770 - val_loss: 15.4696 - val_mean_absolute_error: 2.5843\n",
"Epoch 51/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 7.8121 - mean_absolute_error: 2.0338 - val_loss: 14.9736 - val_mean_absolute_error: 2.5624\n",
"Epoch 52/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.7310 - mean_absolute_error: 2.0499 - val_loss: 15.2876 - val_mean_absolute_error: 2.6235\n",
"Epoch 53/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.7688 - mean_absolute_error: 2.0305 - val_loss: 14.8915 - val_mean_absolute_error: 2.5677\n",
"Epoch 54/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.6622 - mean_absolute_error: 2.0423 - val_loss: 14.8616 - val_mean_absolute_error: 2.5418\n",
"Epoch 55/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.4835 - mean_absolute_error: 1.9985 - val_loss: 14.9358 - val_mean_absolute_error: 2.5826\n",
"Epoch 56/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.6197 - mean_absolute_error: 2.0101 - val_loss: 14.9481 - val_mean_absolute_error: 2.5801\n",
"Epoch 57/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.5022 - mean_absolute_error: 2.0175 - val_loss: 15.0369 - val_mean_absolute_error: 2.5734\n",
"Epoch 58/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.2856 - mean_absolute_error: 1.9668 - val_loss: 14.6046 - val_mean_absolute_error: 2.5692\n",
"Epoch 59/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2362 - mean_absolute_error: 1.9680 - val_loss: 14.7274 - val_mean_absolute_error: 2.5688\n",
"Epoch 60/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.1968 - mean_absolute_error: 1.9849 - val_loss: 15.0153 - val_mean_absolute_error: 2.5399\n",
"Epoch 61/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.1600 - mean_absolute_error: 1.9564 - val_loss: 14.8112 - val_mean_absolute_error: 2.5316\n",
"Epoch 62/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.0685 - mean_absolute_error: 1.9437 - val_loss: 14.8260 - val_mean_absolute_error: 2.6292\n",
"Epoch 63/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 6.9636 - mean_absolute_error: 1.9781 - val_loss: 15.1571 - val_mean_absolute_error: 2.5282\n",
"Epoch 64/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2027 - mean_absolute_error: 1.9625 - val_loss: 14.3113 - val_mean_absolute_error: 2.4875\n",
"Epoch 65/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.9437 - mean_absolute_error: 1.9291 - val_loss: 14.6254 - val_mean_absolute_error: 2.5237\n",
"Epoch 66/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.9385 - mean_absolute_error: 1.9349 - val_loss: 14.6411 - val_mean_absolute_error: 2.5269\n",
"Epoch 67/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.8229 - mean_absolute_error: 1.8962 - val_loss: 14.7457 - val_mean_absolute_error: 2.5547\n",
"Epoch 68/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 6.7147 - mean_absolute_error: 1.9098 - val_loss: 14.6464 - val_mean_absolute_error: 2.5585\n",
"Epoch 69/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.7747 - mean_absolute_error: 1.8861 - val_loss: 14.6714 - val_mean_absolute_error: 2.6135\n",
"Epoch 70/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 6.5581 - mean_absolute_error: 1.9114 - val_loss: 14.7181 - val_mean_absolute_error: 2.5877\n",
"Epoch 71/100\n",
"303/303 [==============================] - 0s 155us/step - loss: 6.6729 - mean_absolute_error: 1.8788 - val_loss: 14.4996 - val_mean_absolute_error: 2.5262\n",
"Epoch 72/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.6263 - mean_absolute_error: 1.8916 - val_loss: 14.8775 - val_mean_absolute_error: 2.6111\n",
"Epoch 73/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.6032 - mean_absolute_error: 1.9229 - val_loss: 14.4357 - val_mean_absolute_error: 2.5452\n",
"Epoch 74/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.4709 - mean_absolute_error: 1.8810 - val_loss: 14.5471 - val_mean_absolute_error: 2.5517\n",
"Epoch 75/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.4563 - mean_absolute_error: 1.8609 - val_loss: 14.5410 - val_mean_absolute_error: 2.5216\n",
"Epoch 76/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.3320 - mean_absolute_error: 1.8471 - val_loss: 14.5174 - val_mean_absolute_error: 2.5554\n",
"Epoch 77/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.2926 - mean_absolute_error: 1.8662 - val_loss: 14.6568 - val_mean_absolute_error: 2.5315\n",
"Epoch 78/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.4248 - mean_absolute_error: 1.8704 - val_loss: 14.4583 - val_mean_absolute_error: 2.5287\n",
"Epoch 79/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 6.2456 - mean_absolute_error: 1.7960 - val_loss: 14.2678 - val_mean_absolute_error: 2.5027\n",
"Epoch 80/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.0662 - mean_absolute_error: 1.8312 - val_loss: 14.5861 - val_mean_absolute_error: 2.5347\n",
"Epoch 81/100\n",
"303/303 [==============================] - 0s 109us/step - loss: 6.1447 - mean_absolute_error: 1.7791 - val_loss: 14.1564 - val_mean_absolute_error: 2.4825\n",
"Epoch 82/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 6.3323 - mean_absolute_error: 1.8727 - val_loss: 14.2551 - val_mean_absolute_error: 2.4939\n",
"Epoch 83/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 5.9789 - mean_absolute_error: 1.7847 - val_loss: 14.3161 - val_mean_absolute_error: 2.5127\n",
"Epoch 84/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 5.8775 - mean_absolute_error: 1.7688 - val_loss: 14.2899 - val_mean_absolute_error: 2.4752\n",
"Epoch 85/100\n",
"303/303 [==============================] - 0s 145us/step - loss: 5.9145 - mean_absolute_error: 1.8058 - val_loss: 14.2883 - val_mean_absolute_error: 2.4867\n",
"Epoch 86/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 5.8402 - mean_absolute_error: 1.7845 - val_loss: 14.1758 - val_mean_absolute_error: 2.4901\n",
"Epoch 87/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.0150 - mean_absolute_error: 1.8037 - val_loss: 14.5026 - val_mean_absolute_error: 2.5007\n",
"Epoch 88/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 5.8617 - mean_absolute_error: 1.7755 - val_loss: 15.2779 - val_mean_absolute_error: 2.6591\n",
"Epoch 89/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 5.9410 - mean_absolute_error: 1.7948 - val_loss: 14.4489 - val_mean_absolute_error: 2.5250\n",
"Epoch 90/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"303/303 [==============================] - 0s 125us/step - loss: 5.8207 - mean_absolute_error: 1.7669 - val_loss: 14.2272 - val_mean_absolute_error: 2.5029\n",
"Epoch 91/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 5.7958 - mean_absolute_error: 1.7699 - val_loss: 14.3769 - val_mean_absolute_error: 2.5352\n",
"Epoch 92/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 5.7717 - mean_absolute_error: 1.7564 - val_loss: 15.0044 - val_mean_absolute_error: 2.5807\n",
"Epoch 93/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 5.7176 - mean_absolute_error: 1.7425 - val_loss: 14.3700 - val_mean_absolute_error: 2.5464\n",
"Epoch 94/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 5.7002 - mean_absolute_error: 1.7385 - val_loss: 14.1317 - val_mean_absolute_error: 2.5055\n",
"Epoch 95/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 5.6039 - mean_absolute_error: 1.7420 - val_loss: 14.0687 - val_mean_absolute_error: 2.4756\n",
"Epoch 96/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 5.8083 - mean_absolute_error: 1.7718 - val_loss: 14.1795 - val_mean_absolute_error: 2.5083\n",
"Epoch 97/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 5.5589 - mean_absolute_error: 1.7302 - val_loss: 14.3534 - val_mean_absolute_error: 2.5477\n",
"Epoch 98/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 5.4623 - mean_absolute_error: 1.7074 - val_loss: 14.7557 - val_mean_absolute_error: 2.6577\n",
"Epoch 99/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 5.7929 - mean_absolute_error: 1.7743 - val_loss: 14.3087 - val_mean_absolute_error: 2.5286\n",
"Epoch 100/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 5.3624 - mean_absolute_error: 1.7065 - val_loss: 14.4608 - val_mean_absolute_error: 2.4993\n",
"Train on 303 samples, validate on 101 samples\n",
"Epoch 1/100\n",
"303/303 [==============================] - 0s 1ms/step - loss: 547.4053 - mean_absolute_error: 21.6343 - val_loss: 648.9108 - val_mean_absolute_error: 23.3824\n",
"Epoch 2/100\n",
"303/303 [==============================] - 0s 177us/step - loss: 490.4607 - mean_absolute_error: 20.2213 - val_loss: 588.4164 - val_mean_absolute_error: 22.0288\n",
"Epoch 3/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 435.2417 - mean_absolute_error: 18.7553 - val_loss: 524.2048 - val_mean_absolute_error: 20.5541\n",
"Epoch 4/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 375.3505 - mean_absolute_error: 17.1383 - val_loss: 449.1108 - val_mean_absolute_error: 18.7426\n",
"Epoch 5/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 309.9195 - mean_absolute_error: 15.2859 - val_loss: 371.6136 - val_mean_absolute_error: 16.7240\n",
"Epoch 6/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 244.7682 - mean_absolute_error: 13.3275 - val_loss: 292.0395 - val_mean_absolute_error: 14.4254\n",
"Epoch 7/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 182.5162 - mean_absolute_error: 11.2655 - val_loss: 220.7174 - val_mean_absolute_error: 12.0042\n",
"Epoch 8/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 130.5178 - mean_absolute_error: 9.2882 - val_loss: 165.7696 - val_mean_absolute_error: 9.9470\n",
"Epoch 9/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 93.4372 - mean_absolute_error: 7.7010 - val_loss: 124.8000 - val_mean_absolute_error: 8.4478\n",
"Epoch 10/100\n",
"303/303 [==============================] - 0s 152us/step - loss: 69.1631 - mean_absolute_error: 6.4725 - val_loss: 99.6661 - val_mean_absolute_error: 7.4628\n",
"Epoch 11/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 54.0010 - mean_absolute_error: 5.5826 - val_loss: 81.5161 - val_mean_absolute_error: 6.6612\n",
"Epoch 12/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 43.3850 - mean_absolute_error: 4.8827 - val_loss: 69.4735 - val_mean_absolute_error: 5.9770\n",
"Epoch 13/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 36.0793 - mean_absolute_error: 4.3596 - val_loss: 58.6497 - val_mean_absolute_error: 5.3983\n",
"Epoch 14/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 30.5740 - mean_absolute_error: 3.9187 - val_loss: 49.8309 - val_mean_absolute_error: 4.8905\n",
"Epoch 15/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 26.6290 - mean_absolute_error: 3.6174 - val_loss: 44.2241 - val_mean_absolute_error: 4.5159\n",
"Epoch 16/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 23.8022 - mean_absolute_error: 3.3855 - val_loss: 40.1345 - val_mean_absolute_error: 4.2634\n",
"Epoch 17/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 21.5752 - mean_absolute_error: 3.1481 - val_loss: 37.3562 - val_mean_absolute_error: 4.0026\n",
"Epoch 18/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 20.4199 - mean_absolute_error: 3.1032 - val_loss: 34.9327 - val_mean_absolute_error: 3.8869\n",
"Epoch 19/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 19.2421 - mean_absolute_error: 2.9639 - val_loss: 31.5640 - val_mean_absolute_error: 3.7126\n",
"Epoch 20/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 18.1872 - mean_absolute_error: 2.8977 - val_loss: 30.2906 - val_mean_absolute_error: 3.6667\n",
"Epoch 21/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 17.3357 - mean_absolute_error: 2.8407 - val_loss: 29.6705 - val_mean_absolute_error: 3.5360\n",
"Epoch 22/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 16.6807 - mean_absolute_error: 2.7744 - val_loss: 27.5299 - val_mean_absolute_error: 3.4735\n",
"Epoch 23/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 16.0478 - mean_absolute_error: 2.7581 - val_loss: 27.6856 - val_mean_absolute_error: 3.4383\n",
"Epoch 24/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 15.4764 - mean_absolute_error: 2.6697 - val_loss: 26.8113 - val_mean_absolute_error: 3.3945\n",
"Epoch 25/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 15.0681 - mean_absolute_error: 2.5964 - val_loss: 24.8507 - val_mean_absolute_error: 3.3210\n",
"Epoch 26/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 14.5994 - mean_absolute_error: 2.6204 - val_loss: 24.6774 - val_mean_absolute_error: 3.2767\n",
"Epoch 27/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 13.8847 - mean_absolute_error: 2.5231 - val_loss: 23.8503 - val_mean_absolute_error: 3.2583\n",
"Epoch 28/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 13.4333 - mean_absolute_error: 2.4915 - val_loss: 22.9164 - val_mean_absolute_error: 3.1279\n",
"Epoch 29/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 13.2653 - mean_absolute_error: 2.4366 - val_loss: 22.2699 - val_mean_absolute_error: 3.0988\n",
"Epoch 30/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 12.8216 - mean_absolute_error: 2.4466 - val_loss: 21.9221 - val_mean_absolute_error: 3.0808\n",
"Epoch 31/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 12.6731 - mean_absolute_error: 2.4200 - val_loss: 20.9449 - val_mean_absolute_error: 3.0163\n",
"Epoch 32/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 12.1294 - mean_absolute_error: 2.3822 - val_loss: 21.2279 - val_mean_absolute_error: 3.1067\n",
"Epoch 33/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 11.9462 - mean_absolute_error: 2.3175 - val_loss: 19.4330 - val_mean_absolute_error: 2.8861\n",
"Epoch 34/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 11.5853 - mean_absolute_error: 2.3456 - val_loss: 19.5182 - val_mean_absolute_error: 2.8834\n",
"Epoch 35/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 11.1702 - mean_absolute_error: 2.2433 - val_loss: 18.3066 - val_mean_absolute_error: 2.8761\n",
"Epoch 36/100\n",
"303/303 [==============================] - 0s 142us/step - loss: 11.0102 - mean_absolute_error: 2.3084 - val_loss: 19.0849 - val_mean_absolute_error: 2.9430\n",
"Epoch 37/100\n",
"303/303 [==============================] - 0s 149us/step - loss: 10.9464 - mean_absolute_error: 2.2483 - val_loss: 18.2281 - val_mean_absolute_error: 2.9032\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 38/100\n",
"303/303 [==============================] - 0s 168us/step - loss: 10.5083 - mean_absolute_error: 2.2123 - val_loss: 17.7351 - val_mean_absolute_error: 2.8490\n",
"Epoch 39/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 10.3443 - mean_absolute_error: 2.2182 - val_loss: 17.7196 - val_mean_absolute_error: 2.8212\n",
"Epoch 40/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 10.2738 - mean_absolute_error: 2.2159 - val_loss: 17.8171 - val_mean_absolute_error: 2.8499\n",
"Epoch 41/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 10.0408 - mean_absolute_error: 2.1745 - val_loss: 17.2285 - val_mean_absolute_error: 2.8812\n",
"Epoch 42/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 10.2105 - mean_absolute_error: 2.1658 - val_loss: 16.3817 - val_mean_absolute_error: 2.7142\n",
"Epoch 43/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 9.8000 - mean_absolute_error: 2.1458 - val_loss: 16.0743 - val_mean_absolute_error: 2.7012\n",
"Epoch 44/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 9.5561 - mean_absolute_error: 2.1253 - val_loss: 15.6136 - val_mean_absolute_error: 2.7650\n",
"Epoch 45/100\n",
"303/303 [==============================] - 0s 191us/step - loss: 9.5178 - mean_absolute_error: 2.1406 - val_loss: 15.6271 - val_mean_absolute_error: 2.7251\n",
"Epoch 46/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 9.4331 - mean_absolute_error: 2.1614 - val_loss: 15.0484 - val_mean_absolute_error: 2.6460\n",
"Epoch 47/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 9.1075 - mean_absolute_error: 2.0870 - val_loss: 15.1996 - val_mean_absolute_error: 2.7204\n",
"Epoch 48/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 9.1810 - mean_absolute_error: 2.0721 - val_loss: 15.1695 - val_mean_absolute_error: 2.6893\n",
"Epoch 49/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 9.0236 - mean_absolute_error: 2.0794 - val_loss: 14.8975 - val_mean_absolute_error: 2.7656\n",
"Epoch 50/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 8.9141 - mean_absolute_error: 2.1228 - val_loss: 14.8672 - val_mean_absolute_error: 2.6789\n",
"Epoch 51/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.5697 - mean_absolute_error: 2.0212 - val_loss: 15.1998 - val_mean_absolute_error: 2.8496\n",
"Epoch 52/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 8.9588 - mean_absolute_error: 2.1094 - val_loss: 14.3350 - val_mean_absolute_error: 2.6617\n",
"Epoch 53/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 8.5889 - mean_absolute_error: 2.0280 - val_loss: 13.5530 - val_mean_absolute_error: 2.5617\n",
"Epoch 54/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.5281 - mean_absolute_error: 2.0549 - val_loss: 13.9386 - val_mean_absolute_error: 2.6050\n",
"Epoch 55/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 8.3580 - mean_absolute_error: 2.0152 - val_loss: 13.6423 - val_mean_absolute_error: 2.6105\n",
"Epoch 56/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 8.6081 - mean_absolute_error: 2.0286 - val_loss: 13.6295 - val_mean_absolute_error: 2.6315\n",
"Epoch 57/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.2047 - mean_absolute_error: 1.9875 - val_loss: 13.5044 - val_mean_absolute_error: 2.6228\n",
"Epoch 58/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.1885 - mean_absolute_error: 1.9404 - val_loss: 13.1969 - val_mean_absolute_error: 2.5931\n",
"Epoch 59/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 8.0785 - mean_absolute_error: 2.0127 - val_loss: 13.6440 - val_mean_absolute_error: 2.6687\n",
"Epoch 60/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 8.0248 - mean_absolute_error: 1.9807 - val_loss: 13.3982 - val_mean_absolute_error: 2.6823\n",
"Epoch 61/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 8.0762 - mean_absolute_error: 1.9707 - val_loss: 12.9478 - val_mean_absolute_error: 2.5478\n",
"Epoch 62/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.8356 - mean_absolute_error: 1.9327 - val_loss: 13.4838 - val_mean_absolute_error: 2.6004\n",
"Epoch 63/100\n",
"303/303 [==============================] - 0s 139us/step - loss: 7.7859 - mean_absolute_error: 1.9294 - val_loss: 13.5028 - val_mean_absolute_error: 2.6361\n",
"Epoch 64/100\n",
"303/303 [==============================] - 0s 155us/step - loss: 7.9971 - mean_absolute_error: 1.9784 - val_loss: 13.0586 - val_mean_absolute_error: 2.5927\n",
"Epoch 65/100\n",
"303/303 [==============================] - 0s 135us/step - loss: 7.5847 - mean_absolute_error: 1.9185 - val_loss: 13.5520 - val_mean_absolute_error: 2.6480\n",
"Epoch 66/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 7.5896 - mean_absolute_error: 1.8988 - val_loss: 12.9580 - val_mean_absolute_error: 2.6120\n",
"Epoch 67/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.6788 - mean_absolute_error: 1.9252 - val_loss: 12.7534 - val_mean_absolute_error: 2.5366\n",
"Epoch 68/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.3891 - mean_absolute_error: 1.8683 - val_loss: 12.9837 - val_mean_absolute_error: 2.5526\n",
"Epoch 69/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 7.3178 - mean_absolute_error: 1.8893 - val_loss: 12.7426 - val_mean_absolute_error: 2.5561\n",
"Epoch 70/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.4085 - mean_absolute_error: 1.8746 - val_loss: 12.8602 - val_mean_absolute_error: 2.6097\n",
"Epoch 71/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2463 - mean_absolute_error: 1.8736 - val_loss: 12.7716 - val_mean_absolute_error: 2.6135\n",
"Epoch 72/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.0264 - mean_absolute_error: 1.8849 - val_loss: 12.8083 - val_mean_absolute_error: 2.6034\n",
"Epoch 73/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 7.2824 - mean_absolute_error: 1.8727 - val_loss: 12.3813 - val_mean_absolute_error: 2.5299\n",
"Epoch 74/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.1904 - mean_absolute_error: 1.8182 - val_loss: 11.9598 - val_mean_absolute_error: 2.4629\n",
"Epoch 75/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 7.1414 - mean_absolute_error: 1.8469 - val_loss: 12.5954 - val_mean_absolute_error: 2.5900\n",
"Epoch 76/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 6.9953 - mean_absolute_error: 1.8517 - val_loss: 12.6396 - val_mean_absolute_error: 2.5621\n",
"Epoch 77/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.9763 - mean_absolute_error: 1.8362 - val_loss: 12.3805 - val_mean_absolute_error: 2.4973\n",
"Epoch 78/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 7.0598 - mean_absolute_error: 1.8429 - val_loss: 12.1291 - val_mean_absolute_error: 2.5256\n",
"Epoch 79/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 6.8011 - mean_absolute_error: 1.8341 - val_loss: 12.7179 - val_mean_absolute_error: 2.6638\n",
"Epoch 80/100\n",
"303/303 [==============================] - 0s 114us/step - loss: 6.7281 - mean_absolute_error: 1.7795 - val_loss: 12.5807 - val_mean_absolute_error: 2.6312\n",
"Epoch 81/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.9238 - mean_absolute_error: 1.8258 - val_loss: 12.7149 - val_mean_absolute_error: 2.5885\n",
"Epoch 82/100\n",
"303/303 [==============================] - 0s 112us/step - loss: 6.7504 - mean_absolute_error: 1.8030 - val_loss: 12.2234 - val_mean_absolute_error: 2.5458\n",
"Epoch 83/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.5627 - mean_absolute_error: 1.7541 - val_loss: 11.7741 - val_mean_absolute_error: 2.4560\n",
"Epoch 84/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 6.6024 - mean_absolute_error: 1.7765 - val_loss: 12.4671 - val_mean_absolute_error: 2.5354\n",
"Epoch 85/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.7669 - mean_absolute_error: 1.8055 - val_loss: 11.8656 - val_mean_absolute_error: 2.5235\n",
"Epoch 86/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"303/303 [==============================] - 0s 125us/step - loss: 6.6198 - mean_absolute_error: 1.7868 - val_loss: 11.9117 - val_mean_absolute_error: 2.4924\n",
"Epoch 87/100\n",
"303/303 [==============================] - 0s 116us/step - loss: 6.6270 - mean_absolute_error: 1.7793 - val_loss: 12.4838 - val_mean_absolute_error: 2.5695\n",
"Epoch 88/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 6.3983 - mean_absolute_error: 1.7289 - val_loss: 11.7588 - val_mean_absolute_error: 2.4311\n",
"Epoch 89/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.4426 - mean_absolute_error: 1.8145 - val_loss: 11.8745 - val_mean_absolute_error: 2.4684\n",
"Epoch 90/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.3189 - mean_absolute_error: 1.7532 - val_loss: 12.2728 - val_mean_absolute_error: 2.5588\n",
"Epoch 91/100\n",
"303/303 [==============================] - 0s 155us/step - loss: 6.5179 - mean_absolute_error: 1.7548 - val_loss: 12.4064 - val_mean_absolute_error: 2.5566\n",
"Epoch 92/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 6.3138 - mean_absolute_error: 1.7316 - val_loss: 12.7792 - val_mean_absolute_error: 2.5903\n",
"Epoch 93/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.2375 - mean_absolute_error: 1.7412 - val_loss: 11.6283 - val_mean_absolute_error: 2.4393\n",
"Epoch 94/100\n",
"303/303 [==============================] - 0s 122us/step - loss: 6.5487 - mean_absolute_error: 1.7422 - val_loss: 11.9066 - val_mean_absolute_error: 2.4751\n",
"Epoch 95/100\n",
"303/303 [==============================] - 0s 132us/step - loss: 6.0688 - mean_absolute_error: 1.6940 - val_loss: 12.5155 - val_mean_absolute_error: 2.6527\n",
"Epoch 96/100\n",
"303/303 [==============================] - 0s 119us/step - loss: 6.0422 - mean_absolute_error: 1.6769 - val_loss: 11.9144 - val_mean_absolute_error: 2.4586\n",
"Epoch 97/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 6.1295 - mean_absolute_error: 1.7275 - val_loss: 12.5434 - val_mean_absolute_error: 2.6353\n",
"Epoch 98/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 5.9931 - mean_absolute_error: 1.7350 - val_loss: 11.9898 - val_mean_absolute_error: 2.5030\n",
"Epoch 99/100\n",
"303/303 [==============================] - 0s 125us/step - loss: 5.8361 - mean_absolute_error: 1.6793 - val_loss: 12.2699 - val_mean_absolute_error: 2.5122\n",
"Epoch 100/100\n",
"303/303 [==============================] - 0s 129us/step - loss: 6.0849 - mean_absolute_error: 1.7371 - val_loss: 11.7168 - val_mean_absolute_error: 2.4489\n"
]
}
],
"source": [
"from sklearn.model_selection import KFold\n",
"kf = KFold(n_splits=4)\n",
"for train,test in kf.split(train_data): \n",
" X_train, X_test, y_train, y_test = train_data[train], train_data[test], train_target[train], train_target[test]\n",
" epochs=100\n",
" model = Build_model()\n",
" model.fit(X_train,y_train,epochs=epochs,batch_size=32,validation_data=(X_test,y_test))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (pypy)",
"language": "python",
"name": "pypy"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment