keras-linear-regression/intro-to-keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.datasets import boston_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X_train, Y_train), (X_test, Y_test) = boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "404/404 [==============================] - 0s - loss: 152.4799 - mean_squared_error: 152.4799 - mean_absolute_error: 9.6304      \n",
      "Epoch 2/1000\n",
      "404/404 [==============================] - 0s - loss: 75.1149 - mean_squared_error: 75.1149 - mean_absolute_error: 6.0855     \n",
      "Epoch 3/1000\n",
      "404/404 [==============================] - 0s - loss: 69.5583 - mean_squared_error: 69.5583 - mean_absolute_error: 6.0167     \n",
      "Epoch 4/1000\n",
      "404/404 [==============================] - 0s - loss: 68.0251 - mean_squared_error: 68.0251 - mean_absolute_error: 5.8919     \n",
      "Epoch 5/1000\n",
      "404/404 [==============================] - 0s - loss: 66.0708 - mean_squared_error: 66.0708 - mean_absolute_error: 5.8364     \n",
      "Epoch 6/1000\n",
      "404/404 [==============================] - 0s - loss: 62.0408 - mean_squared_error: 62.0408 - mean_absolute_error: 5.7741     \n",
      "Epoch 7/1000\n",
      "404/404 [==============================] - 0s - loss: 61.8625 - mean_squared_error: 61.8625 - mean_absolute_error: 5.7584     \n",
      "Epoch 8/1000\n",
      "404/404 [==============================] - 0s - loss: 61.3506 - mean_squared_error: 61.3506 - mean_absolute_error: 5.6645     \n",
      "Epoch 9/1000\n",
      "404/404 [==============================] - 0s - loss: 60.2124 - mean_squared_error: 60.2124 - mean_absolute_error: 5.6132     \n",
      "Epoch 10/1000\n",
      "404/404 [==============================] - 0s - loss: 58.6913 - mean_squared_error: 58.6913 - mean_absolute_error: 5.6017     \n",
      "Epoch 11/1000\n",
      "404/404 [==============================] - 0s - loss: 57.9432 - mean_squared_error: 57.9432 - mean_absolute_error: 5.5494     \n",
      "Epoch 12/1000\n",
      "404/404 [==============================] - 0s - loss: 56.8824 - mean_squared_error: 56.8824 - mean_absolute_error: 5.4260     \n",
      "Epoch 13/1000\n",
      "404/404 [==============================] - 0s - loss: 56.3089 - mean_squared_error: 56.3089 - mean_absolute_error: 5.4978     \n",
      "Epoch 14/1000\n",
      "404/404 [==============================] - 0s - loss: 54.2514 - mean_squared_error: 54.2514 - mean_absolute_error: 5.3452     \n",
      "Epoch 15/1000\n",
      "404/404 [==============================] - 0s - loss: 54.5575 - mean_squared_error: 54.5575 - mean_absolute_error: 5.3789     \n",
      "Epoch 16/1000\n",
      "404/404 [==============================] - 0s - loss: 53.3577 - mean_squared_error: 53.3577 - mean_absolute_error: 5.4448     \n",
      "Epoch 17/1000\n",
      "404/404 [==============================] - 0s - loss: 53.6863 - mean_squared_error: 53.6863 - mean_absolute_error: 5.3341     \n",
      "Epoch 18/1000\n",
      "404/404 [==============================] - 0s - loss: 51.5926 - mean_squared_error: 51.5926 - mean_absolute_error: 5.3244     \n",
      "Epoch 19/1000\n",
      "404/404 [==============================] - 0s - loss: 51.4714 - mean_squared_error: 51.4714 - mean_absolute_error: 5.3381     \n",
      "Epoch 20/1000\n",
      "404/404 [==============================] - 0s - loss: 51.3925 - mean_squared_error: 51.3925 - mean_absolute_error: 5.2532     \n",
      "Epoch 21/1000\n",
      "404/404 [==============================] - 0s - loss: 51.0795 - mean_squared_error: 51.0795 - mean_absolute_error: 5.2683     \n",
      "Epoch 22/1000\n",
      "404/404 [==============================] - 0s - loss: 50.5661 - mean_squared_error: 50.5661 - mean_absolute_error: 5.3529     \n",
      "Epoch 23/1000\n",
      "404/404 [==============================] - 0s - loss: 49.9413 - mean_squared_error: 49.9413 - mean_absolute_error: 5.2917     \n",
      "Epoch 24/1000\n",
      "404/404 [==============================] - 0s - loss: 49.3967 - mean_squared_error: 49.3967 - mean_absolute_error: 5.2620     \n",
      "Epoch 25/1000\n",
      "404/404 [==============================] - 0s - loss: 49.0987 - mean_squared_error: 49.0987 - mean_absolute_error: 5.3054     \n",
      "Epoch 26/1000\n",
      "404/404 [==============================] - 0s - loss: 48.0337 - mean_squared_error: 48.0337 - mean_absolute_error: 5.1885     \n",
      "Epoch 27/1000\n",
      "404/404 [==============================] - 0s - loss: 48.1433 - mean_squared_error: 48.1433 - mean_absolute_error: 5.2667     \n",
      "Epoch 28/1000\n",
      "404/404 [==============================] - 0s - loss: 47.8221 - mean_squared_error: 47.8221 - mean_absolute_error: 5.1985     \n",
      "Epoch 29/1000\n",
      "404/404 [==============================] - 0s - loss: 47.7517 - mean_squared_error: 47.7517 - mean_absolute_error: 5.1980     \n",
      "Epoch 30/1000\n",
      "404/404 [==============================] - 0s - loss: 47.9040 - mean_squared_error: 47.9040 - mean_absolute_error: 5.1902     \n",
      "Epoch 31/1000\n",
      "404/404 [==============================] - 0s - loss: 47.2879 - mean_squared_error: 47.2879 - mean_absolute_error: 5.1726     \n",
      "Epoch 32/1000\n",
      "404/404 [==============================] - 0s - loss: 47.2588 - mean_squared_error: 47.2588 - mean_absolute_error: 5.1973     \n",
      "Epoch 33/1000\n",
      "404/404 [==============================] - 0s - loss: 46.4283 - mean_squared_error: 46.4283 - mean_absolute_error: 5.1925     \n",
      "Epoch 34/1000\n",
      "404/404 [==============================] - 0s - loss: 44.8901 - mean_squared_error: 44.8901 - mean_absolute_error: 5.0216     \n",
      "Epoch 35/1000\n",
      "404/404 [==============================] - 0s - loss: 45.7442 - mean_squared_error: 45.7442 - mean_absolute_error: 5.0706     \n",
      "Epoch 36/1000\n",
      "404/404 [==============================] - 0s - loss: 45.3987 - mean_squared_error: 45.3987 - mean_absolute_error: 5.1026     \n",
      "Epoch 37/1000\n",
      "404/404 [==============================] - 0s - loss: 44.7941 - mean_squared_error: 44.7941 - mean_absolute_error: 5.0609     \n",
      "Epoch 38/1000\n",
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      "Epoch 39/1000\n",
      "404/404 [==============================] - 0s - loss: 44.6182 - mean_squared_error: 44.6182 - mean_absolute_error: 5.0569     \n",
      "Epoch 40/1000\n",
      "404/404 [==============================] - 0s - loss: 44.9005 - mean_squared_error: 44.9005 - mean_absolute_error: 5.0373     \n",
      "Epoch 41/1000\n",
      "404/404 [==============================] - 0s - loss: 44.5237 - mean_squared_error: 44.5237 - mean_absolute_error: 5.0678     \n",
      "Epoch 42/1000\n",
      "404/404 [==============================] - 0s - loss: 43.4067 - mean_squared_error: 43.4067 - mean_absolute_error: 4.9937     \n",
      "Epoch 43/1000\n",
      "404/404 [==============================] - 0s - loss: 44.1174 - mean_squared_error: 44.1174 - mean_absolute_error: 5.0577     \n",
      "Epoch 44/1000\n",
      "404/404 [==============================] - 0s - loss: 43.6039 - mean_squared_error: 43.6039 - mean_absolute_error: 5.0276     \n",
      "Epoch 45/1000\n",
      "404/404 [==============================] - 0s - loss: 43.3509 - mean_squared_error: 43.3509 - mean_absolute_error: 5.0220     \n",
      "Epoch 46/1000\n",
      "404/404 [==============================] - 0s - loss: 44.1222 - mean_squared_error: 44.1222 - mean_absolute_error: 5.0517     \n",
      "Epoch 47/1000\n",
      "404/404 [==============================] - 0s - loss: 43.8576 - mean_squared_error: 43.8576 - mean_absolute_error: 5.0526     \n",
      "Epoch 48/1000\n",
      "404/404 [==============================] - 0s - loss: 43.5052 - mean_squared_error: 43.5052 - mean_absolute_error: 4.9949     \n",
      "Epoch 49/1000\n",
      "404/404 [==============================] - 0s - loss: 43.7530 - mean_squared_error: 43.7530 - mean_absolute_error: 5.0504     \n",
      "Epoch 50/1000\n",
      "404/404 [==============================] - 0s - loss: 43.1814 - mean_squared_error: 43.1814 - mean_absolute_error: 4.9949     \n",
      "Epoch 51/1000\n",
      "404/404 [==============================] - 0s - loss: 43.6105 - mean_squared_error: 43.6105 - mean_absolute_error: 5.0426     \n",
      "Epoch 52/1000\n",
      "404/404 [==============================] - 0s - loss: 42.3304 - mean_squared_error: 42.3304 - mean_absolute_error: 4.9442     \n",
      "Epoch 53/1000\n",
      "404/404 [==============================] - 0s - loss: 43.0791 - mean_squared_error: 43.0791 - mean_absolute_error: 4.9500     \n",
      "Epoch 54/1000\n",
      "404/404 [==============================] - 0s - loss: 42.8263 - mean_squared_error: 42.8263 - mean_absolute_error: 5.0083     \n",
      "Epoch 55/1000\n",
      "404/404 [==============================] - 0s - loss: 41.9571 - mean_squared_error: 41.9571 - mean_absolute_error: 4.9297     \n",
      "Epoch 56/1000\n",
      "404/404 [==============================] - 0s - loss: 42.5178 - mean_squared_error: 42.5178 - mean_absolute_error: 4.9655     \n",
      "Epoch 57/1000\n",
      "404/404 [==============================] - 0s - loss: 41.8807 - mean_squared_error: 41.8807 - mean_absolute_error: 4.8936     \n",
      "Epoch 58/1000\n",
      "404/404 [==============================] - 0s - loss: 41.4168 - mean_squared_error: 41.4168 - mean_absolute_error: 4.8426     \n",
      "Epoch 59/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404/404 [==============================] - 0s - loss: 41.7527 - mean_squared_error: 41.7527 - mean_absolute_error: 4.8914     \n",
      "Epoch 60/1000\n",
      "404/404 [==============================] - 0s - loss: 41.6322 - mean_squared_error: 41.6322 - mean_absolute_error: 4.8575     \n",
      "Epoch 61/1000\n",
      "404/404 [==============================] - 0s - loss: 41.4964 - mean_squared_error: 41.4964 - mean_absolute_error: 4.9406     \n",
      "Epoch 62/1000\n",
      "404/404 [==============================] - 0s - loss: 41.6924 - mean_squared_error: 41.6924 - mean_absolute_error: 4.9263     \n",
      "Epoch 63/1000\n",
      "404/404 [==============================] - 0s - loss: 41.0838 - mean_squared_error: 41.0838 - mean_absolute_error: 4.9166     \n",
      "Epoch 64/1000\n",
      "404/404 [==============================] - 0s - loss: 41.6591 - mean_squared_error: 41.6591 - mean_absolute_error: 4.9054     \n",
      "Epoch 65/1000\n",
      "404/404 [==============================] - 0s - loss: 40.7573 - mean_squared_error: 40.7573 - mean_absolute_error: 4.8406     \n",
      "Epoch 66/1000\n",
      "404/404 [==============================] - 0s - loss: 41.3372 - mean_squared_error: 41.3372 - mean_absolute_error: 4.8742     \n",
      "Epoch 67/1000\n",
      "404/404 [==============================] - 0s - loss: 41.4603 - mean_squared_error: 41.4603 - mean_absolute_error: 4.8776     \n",
      "Epoch 68/1000\n",
      "404/404 [==============================] - 0s - loss: 41.0526 - mean_squared_error: 41.0526 - mean_absolute_error: 4.8571     \n",
      "Epoch 69/1000\n",
      "404/404 [==============================] - 0s - loss: 40.7915 - mean_squared_error: 40.7915 - mean_absolute_error: 4.8152     \n",
      "Epoch 70/1000\n",
      "404/404 [==============================] - 0s - loss: 41.2513 - mean_squared_error: 41.2513 - mean_absolute_error: 4.8427     \n",
      "Epoch 71/1000\n",
      "404/404 [==============================] - 0s - loss: 40.4575 - mean_squared_error: 40.4575 - mean_absolute_error: 4.8221     \n",
      "Epoch 72/1000\n",
      "404/404 [==============================] - 0s - loss: 40.1542 - mean_squared_error: 40.1542 - mean_absolute_error: 4.8607     \n",
      "Epoch 73/1000\n",
      "404/404 [==============================] - 0s - loss: 39.8429 - mean_squared_error: 39.8429 - mean_absolute_error: 4.8216     \n",
      "Epoch 74/1000\n",
      "404/404 [==============================] - 0s - loss: 40.4389 - mean_squared_error: 40.4389 - mean_absolute_error: 4.8168     \n",
      "Epoch 75/1000\n",
      "404/404 [==============================] - 0s - loss: 39.4356 - mean_squared_error: 39.4356 - mean_absolute_error: 4.7386     \n",
      "Epoch 76/1000\n",
      "404/404 [==============================] - 0s - loss: 40.1033 - mean_squared_error: 40.1033 - mean_absolute_error: 4.7885     \n",
      "Epoch 77/1000\n",
      "404/404 [==============================] - 0s - loss: 40.1261 - mean_squared_error: 40.1261 - mean_absolute_error: 4.8373     \n",
      "Epoch 78/1000\n",
      "404/404 [==============================] - 0s - loss: 38.6769 - mean_squared_error: 38.6769 - mean_absolute_error: 4.6951     \n",
      "Epoch 79/1000\n",
      "404/404 [==============================] - 0s - loss: 39.4568 - mean_squared_error: 39.4568 - mean_absolute_error: 4.7575     \n",
      "Epoch 80/1000\n",
      "404/404 [==============================] - 0s - loss: 39.9585 - mean_squared_error: 39.9585 - mean_absolute_error: 4.7386     \n",
      "Epoch 81/1000\n",
      "404/404 [==============================] - 0s - loss: 40.0942 - mean_squared_error: 40.0942 - mean_absolute_error: 4.8245     \n",
      "Epoch 82/1000\n",
      "404/404 [==============================] - 0s - loss: 39.4487 - mean_squared_error: 39.4487 - mean_absolute_error: 4.7240     \n",
      "Epoch 83/1000\n",
      "404/404 [==============================] - 0s - loss: 39.6566 - mean_squared_error: 39.6566 - mean_absolute_error: 4.7602     \n",
      "Epoch 84/1000\n",
      "404/404 [==============================] - 0s - loss: 39.7058 - mean_squared_error: 39.7058 - mean_absolute_error: 4.8047     \n",
      "Epoch 85/1000\n",
      "404/404 [==============================] - 0s - loss: 38.9166 - mean_squared_error: 38.9166 - mean_absolute_error: 4.6670     \n",
      "Epoch 86/1000\n",
      "404/404 [==============================] - 0s - loss: 39.0315 - mean_squared_error: 39.0315 - mean_absolute_error: 4.6965     \n",
      "Epoch 87/1000\n",
      "404/404 [==============================] - 0s - loss: 38.8982 - mean_squared_error: 38.8982 - mean_absolute_error: 4.7190     \n",
      "Epoch 88/1000\n",
      "404/404 [==============================] - 0s - loss: 39.1427 - mean_squared_error: 39.1427 - mean_absolute_error: 4.6594     \n",
      "Epoch 89/1000\n",
      "404/404 [==============================] - 0s - loss: 38.1898 - mean_squared_error: 38.1898 - mean_absolute_error: 4.6562     \n",
      "Epoch 90/1000\n",
      "404/404 [==============================] - 0s - loss: 39.0811 - mean_squared_error: 39.0811 - mean_absolute_error: 4.7295     \n",
      "Epoch 91/1000\n",
      "404/404 [==============================] - 0s - loss: 39.0850 - mean_squared_error: 39.0850 - mean_absolute_error: 4.6617     \n",
      "Epoch 92/1000\n",
      "404/404 [==============================] - 0s - loss: 38.6453 - mean_squared_error: 38.6453 - mean_absolute_error: 4.7404     \n",
      "Epoch 93/1000\n",
      "404/404 [==============================] - 0s - loss: 37.6441 - mean_squared_error: 37.6441 - mean_absolute_error: 4.5909     \n",
      "Epoch 94/1000\n",
      "404/404 [==============================] - 0s - loss: 39.1110 - mean_squared_error: 39.1110 - mean_absolute_error: 4.7227     \n",
      "Epoch 95/1000\n",
      "404/404 [==============================] - 0s - loss: 37.9580 - mean_squared_error: 37.9580 - mean_absolute_error: 4.6726     \n",
      "Epoch 96/1000\n",
      "404/404 [==============================] - 0s - loss: 38.4042 - mean_squared_error: 38.4042 - mean_absolute_error: 4.7110     \n",
      "Epoch 97/1000\n",
      "404/404 [==============================] - 0s - loss: 38.7351 - mean_squared_error: 38.7351 - mean_absolute_error: 4.6537     \n",
      "Epoch 98/1000\n",
      "404/404 [==============================] - 0s - loss: 38.9959 - mean_squared_error: 38.9959 - mean_absolute_error: 4.6876     \n",
      "Epoch 99/1000\n",
      "404/404 [==============================] - 0s - loss: 37.8479 - mean_squared_error: 37.8479 - mean_absolute_error: 4.6349     \n",
      "Epoch 100/1000\n",
      "404/404 [==============================] - 0s - loss: 38.7799 - mean_squared_error: 38.7799 - mean_absolute_error: 4.6882     \n",
      "Epoch 101/1000\n",
      "404/404 [==============================] - 0s - loss: 38.1242 - mean_squared_error: 38.1242 - mean_absolute_error: 4.6231     \n",
      "Epoch 102/1000\n",
      "404/404 [==============================] - 0s - loss: 37.7679 - mean_squared_error: 37.7679 - mean_absolute_error: 4.6437     \n",
      "Epoch 103/1000\n",
      "404/404 [==============================] - 0s - loss: 37.9556 - mean_squared_error: 37.9556 - mean_absolute_error: 4.5717     \n",
      "Epoch 104/1000\n",
      "404/404 [==============================] - 0s - loss: 37.7755 - mean_squared_error: 37.7755 - mean_absolute_error: 4.5860     \n",
      "Epoch 105/1000\n",
      "404/404 [==============================] - 0s - loss: 37.8125 - mean_squared_error: 37.8125 - mean_absolute_error: 4.5520     \n",
      "Epoch 106/1000\n",
      "404/404 [==============================] - 0s - loss: 37.9617 - mean_squared_error: 37.9617 - mean_absolute_error: 4.6369     \n",
      "Epoch 107/1000\n",
      "404/404 [==============================] - 0s - loss: 37.8995 - mean_squared_error: 37.8995 - mean_absolute_error: 4.5894     \n",
      "Epoch 108/1000\n",
      "404/404 [==============================] - 0s - loss: 37.3507 - mean_squared_error: 37.3507 - mean_absolute_error: 4.5714     \n",
      "Epoch 109/1000\n",
      "404/404 [==============================] - 0s - loss: 37.0200 - mean_squared_error: 37.0200 - mean_absolute_error: 4.5772     \n",
      "Epoch 110/1000\n",
      "404/404 [==============================] - 0s - loss: 36.6338 - mean_squared_error: 36.6338 - mean_absolute_error: 4.5437     \n",
      "Epoch 111/1000\n",
      "404/404 [==============================] - 0s - loss: 36.8532 - mean_squared_error: 36.8532 - mean_absolute_error: 4.5698     \n",
      "Epoch 112/1000\n",
      "404/404 [==============================] - 0s - loss: 37.4068 - mean_squared_error: 37.4068 - mean_absolute_error: 4.5696     \n",
      "Epoch 113/1000\n",
      "404/404 [==============================] - 0s - loss: 35.7599 - mean_squared_error: 35.7599 - mean_absolute_error: 4.4862     \n",
      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
      "404/404 [==============================] - 0s - loss: 36.6305 - mean_squared_error: 36.6305 - mean_absolute_error: 4.5205     \n",
      "Epoch 116/1000\n",
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      "Epoch 117/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404/404 [==============================] - 0s - loss: 36.4629 - mean_squared_error: 36.4629 - mean_absolute_error: 4.5426     \n",
      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
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      "Epoch 123/1000\n",
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      "Epoch 125/1000\n",
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      "Epoch 126/1000\n",
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      "Epoch 127/1000\n",
      "404/404 [==============================] - 0s - loss: 36.2065 - mean_squared_error: 36.2065 - mean_absolute_error: 4.4991     \n",
      "Epoch 128/1000\n",
      "404/404 [==============================] - 0s - loss: 34.9870 - mean_squared_error: 34.9870 - mean_absolute_error: 4.4027     \n",
      "Epoch 129/1000\n",
      "404/404 [==============================] - 0s - loss: 36.0359 - mean_squared_error: 36.0359 - mean_absolute_error: 4.5112     \n",
      "Epoch 130/1000\n",
      "404/404 [==============================] - 0s - loss: 36.1551 - mean_squared_error: 36.1551 - mean_absolute_error: 4.4952     \n",
      "Epoch 131/1000\n",
      "404/404 [==============================] - 0s - loss: 36.0286 - mean_squared_error: 36.0286 - mean_absolute_error: 4.4604     \n",
      "Epoch 132/1000\n",
      "404/404 [==============================] - 0s - loss: 34.8442 - mean_squared_error: 34.8442 - mean_absolute_error: 4.4073     \n",
      "Epoch 133/1000\n",
      "404/404 [==============================] - 0s - loss: 35.7628 - mean_squared_error: 35.7628 - mean_absolute_error: 4.5149     \n",
      "Epoch 134/1000\n",
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      "Epoch 135/1000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
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      "Epoch 286/1000\n",
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      "Epoch 288/1000\n"
     ]
    },
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      "404/404 [==============================] - 0s - loss: 29.5466 - mean_squared_error: 29.5466 - mean_absolute_error: 3.9187     \n",
      "Epoch 289/1000\n",
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      "Epoch 345/1000\n"
     ]
    },
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      "404/404 [==============================] - 0s - loss: 28.4888 - mean_squared_error: 28.4888 - mean_absolute_error: 3.8525     \n",
      "Epoch 346/1000\n",
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      "Epoch 360/1000\n",
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      "Epoch 381/1000\n",
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      "Epoch 400/1000\n",
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      "Epoch 402/1000\n"
     ]
    },
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      "404/404 [==============================] - 0s - loss: 27.7963 - mean_squared_error: 27.7963 - mean_absolute_error: 3.7080     \n",
      "Epoch 403/1000\n",
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      "Epoch 459/1000\n"
     ]
    },
    {
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     "text": [
      "404/404 [==============================] - 0s - loss: 27.2114 - mean_squared_error: 27.2114 - mean_absolute_error: 3.6667     \n",
      "Epoch 460/1000\n",
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     ]
    },
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     "text": [
      "404/404 [==============================] - 0s - loss: 27.3346 - mean_squared_error: 27.3346 - mean_absolute_error: 3.6398     \n",
      "Epoch 517/1000\n",
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      "Epoch 573/1000\n"
     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
      "404/404 [==============================] - 0s - loss: 26.6180 - mean_squared_error: 26.6180 - mean_absolute_error: 3.5879     \n",
      "Epoch 631/1000\n",
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     ]
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     ]
    },
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      "404/404 [==============================] - 0s - loss: 26.3591 - mean_squared_error: 26.3591 - mean_absolute_error: 3.5089     \n",
      "Epoch 958/1000\n",
      "404/404 [==============================] - 0s - loss: 26.1254 - mean_squared_error: 26.1254 - mean_absolute_error: 3.5402     \n",
      "Epoch 959/1000\n",
      "404/404 [==============================] - 0s - loss: 25.9927 - mean_squared_error: 25.9927 - mean_absolute_error: 3.4947     \n",
      "Epoch 960/1000\n",
      "404/404 [==============================] - 0s - loss: 24.7127 - mean_squared_error: 24.7127 - mean_absolute_error: 3.4742     \n",
      "Epoch 961/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2092 - mean_squared_error: 26.2092 - mean_absolute_error: 3.4497     \n",
      "Epoch 962/1000\n",
      "404/404 [==============================] - 0s - loss: 26.4505 - mean_squared_error: 26.4505 - mean_absolute_error: 3.5174     \n",
      "Epoch 963/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2858 - mean_squared_error: 26.2858 - mean_absolute_error: 3.5300     \n",
      "Epoch 964/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2384 - mean_squared_error: 26.2384 - mean_absolute_error: 3.4912     \n",
      "Epoch 965/1000\n",
      "404/404 [==============================] - 0s - loss: 25.6626 - mean_squared_error: 25.6626 - mean_absolute_error: 3.5415     \n",
      "Epoch 966/1000\n",
      "404/404 [==============================] - 0s - loss: 26.0285 - mean_squared_error: 26.0285 - mean_absolute_error: 3.4963     \n",
      "Epoch 967/1000\n",
      "404/404 [==============================] - 0s - loss: 25.4586 - mean_squared_error: 25.4586 - mean_absolute_error: 3.4492     \n",
      "Epoch 968/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2028 - mean_squared_error: 26.2028 - mean_absolute_error: 3.5247     \n",
      "Epoch 969/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2237 - mean_squared_error: 26.2237 - mean_absolute_error: 3.4918     \n",
      "Epoch 970/1000\n",
      "404/404 [==============================] - 0s - loss: 25.9664 - mean_squared_error: 25.9664 - mean_absolute_error: 3.4945     \n",
      "Epoch 971/1000\n",
      "404/404 [==============================] - 0s - loss: 25.5630 - mean_squared_error: 25.5630 - mean_absolute_error: 3.5265     \n",
      "Epoch 972/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404/404 [==============================] - 0s - loss: 26.1603 - mean_squared_error: 26.1603 - mean_absolute_error: 3.5225     \n",
      "Epoch 973/1000\n",
      "404/404 [==============================] - 0s - loss: 26.0218 - mean_squared_error: 26.0218 - mean_absolute_error: 3.5751     \n",
      "Epoch 974/1000\n",
      "404/404 [==============================] - 0s - loss: 25.9347 - mean_squared_error: 25.9347 - mean_absolute_error: 3.4792     \n",
      "Epoch 975/1000\n",
      "404/404 [==============================] - 0s - loss: 26.2807 - mean_squared_error: 26.2807 - mean_absolute_error: 3.4640     \n",
      "Epoch 976/1000\n",
      "404/404 [==============================] - 0s - loss: 25.8742 - mean_squared_error: 25.8742 - mean_absolute_error: 3.4560     \n",
      "Epoch 977/1000\n",
      "404/404 [==============================] - 0s - loss: 25.4864 - mean_squared_error: 25.4864 - mean_absolute_error: 3.5190     \n",
      "Epoch 978/1000\n",
      "404/404 [==============================] - 0s - loss: 25.0220 - mean_squared_error: 25.0220 - mean_absolute_error: 3.4531     \n",
      "Epoch 979/1000\n",
      "404/404 [==============================] - 0s - loss: 25.8328 - mean_squared_error: 25.8328 - mean_absolute_error: 3.5421     \n",
      "Epoch 980/1000\n",
      "404/404 [==============================] - 0s - loss: 24.9428 - mean_squared_error: 24.9428 - mean_absolute_error: 3.5588     \n",
      "Epoch 981/1000\n",
      "404/404 [==============================] - 0s - loss: 26.3633 - mean_squared_error: 26.3633 - mean_absolute_error: 3.4875     \n",
      "Epoch 982/1000\n",
      "404/404 [==============================] - 0s - loss: 25.8253 - mean_squared_error: 25.8253 - mean_absolute_error: 3.5116     \n",
      "Epoch 983/1000\n",
      "404/404 [==============================] - 0s - loss: 26.4831 - mean_squared_error: 26.4831 - mean_absolute_error: 3.5154     \n",
      "Epoch 984/1000\n",
      "404/404 [==============================] - 0s - loss: 25.5362 - mean_squared_error: 25.5362 - mean_absolute_error: 3.5259     \n",
      "Epoch 985/1000\n",
      "404/404 [==============================] - 0s - loss: 25.7780 - mean_squared_error: 25.7780 - mean_absolute_error: 3.5197     \n",
      "Epoch 986/1000\n",
      "404/404 [==============================] - 0s - loss: 26.3967 - mean_squared_error: 26.3967 - mean_absolute_error: 3.5408     \n",
      "Epoch 987/1000\n",
      "404/404 [==============================] - 0s - loss: 25.6272 - mean_squared_error: 25.6272 - mean_absolute_error: 3.5313     \n",
      "Epoch 988/1000\n",
      "404/404 [==============================] - 0s - loss: 25.1378 - mean_squared_error: 25.1378 - mean_absolute_error: 3.5096     \n",
      "Epoch 989/1000\n",
      "404/404 [==============================] - 0s - loss: 26.5154 - mean_squared_error: 26.5154 - mean_absolute_error: 3.4955     \n",
      "Epoch 990/1000\n",
      "404/404 [==============================] - 0s - loss: 25.4443 - mean_squared_error: 25.4443 - mean_absolute_error: 3.4675     \n",
      "Epoch 991/1000\n",
      "404/404 [==============================] - 0s - loss: 26.1981 - mean_squared_error: 26.1981 - mean_absolute_error: 3.5019     \n",
      "Epoch 992/1000\n",
      "404/404 [==============================] - 0s - loss: 25.7281 - mean_squared_error: 25.7281 - mean_absolute_error: 3.4968     \n",
      "Epoch 993/1000\n",
      "404/404 [==============================] - 0s - loss: 25.1960 - mean_squared_error: 25.1960 - mean_absolute_error: 3.4701     \n",
      "Epoch 994/1000\n",
      "404/404 [==============================] - 0s - loss: 25.7093 - mean_squared_error: 25.7093 - mean_absolute_error: 3.4890     \n",
      "Epoch 995/1000\n",
      "404/404 [==============================] - 0s - loss: 26.3761 - mean_squared_error: 26.3761 - mean_absolute_error: 3.4950     \n",
      "Epoch 996/1000\n",
      "404/404 [==============================] - 0s - loss: 25.0795 - mean_squared_error: 25.0795 - mean_absolute_error: 3.4556     \n",
      "Epoch 997/1000\n",
      "404/404 [==============================] - 0s - loss: 25.8258 - mean_squared_error: 25.8258 - mean_absolute_error: 3.5714     \n",
      "Epoch 998/1000\n",
      "404/404 [==============================] - 0s - loss: 25.9369 - mean_squared_error: 25.9369 - mean_absolute_error: 3.5279     \n",
      "Epoch 999/1000\n",
      "404/404 [==============================] - 0s - loss: 25.7215 - mean_squared_error: 25.7215 - mean_absolute_error: 3.5185     \n",
      "Epoch 1000/1000\n",
      "404/404 [==============================] - 0s - loss: 25.6472 - mean_squared_error: 25.6472 - mean_absolute_error: 3.5188     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f25c186fad0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nFeatures = X_train.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(1, input_shape=(nFeatures,), kernel_initializer='uniform'))\n",
    "model.add(Activation('linear'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',  loss='mse', metrics=['mse', 'mae'])\n",
    "\n",
    "model.fit(X_train, Y_train, batch_size=4, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_21 (Dense)             (None, 1)                 14        \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 14\n",
      "Trainable params: 14\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\r",
      " 32/102 [========>.....................] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[23.216887230966606, 23.216887230966606, 3.4574407783209109]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "\n",
    "model.evaluate(X_test, Y_test, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  7.2  18.8  19.   27.   22.2]\n",
      "[  7.19826889  18.25551987  21.38941193  29.28108978  23.72646713]\n"
     ]
    }
   ],
   "source": [
    "print Y_test[:5]\n",
    "print Y_pred[:5,0]"
   ]
  }
 ],
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