notebook/02_keras_eval_performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# MLP with automatic validation set\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load pima indians dataset\n",
    "dataset = numpy.loadtxt(\"pima-indians-diabetes.csv\", delimiter=\",\")\n",
    "\n",
    "# split into input (X) and output (Y) variables\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(24, input_dim=8, init='uniform', activation='relu'))\n",
    "model.add(Dense(16, init='uniform', activation='relu'))\n",
    "model.add(Dense(8, init='uniform', activation='relu'))\n",
    "model.add(Dense(1, init='uniform', activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 514 samples, validate on 254 samples\n",
      "Epoch 1/150\n",
      "514/514 [==============================] - 0s - loss: 0.6856 - acc: 0.6401 - val_loss: 0.6650 - val_acc: 0.6732\n",
      "Epoch 2/150\n",
      "514/514 [==============================] - 0s - loss: 0.6707 - acc: 0.6401 - val_loss: 0.6552 - val_acc: 0.6732\n",
      "Epoch 3/150\n",
      "514/514 [==============================] - 0s - loss: 0.6649 - acc: 0.6401 - val_loss: 0.6516 - val_acc: 0.6732\n",
      "Epoch 4/150\n",
      "514/514 [==============================] - 0s - loss: 0.6576 - acc: 0.6401 - val_loss: 0.6471 - val_acc: 0.6732\n",
      "Epoch 5/150\n",
      "514/514 [==============================] - 0s - loss: 0.6491 - acc: 0.6401 - val_loss: 0.6383 - val_acc: 0.6732\n",
      "Epoch 6/150\n",
      "514/514 [==============================] - 0s - loss: 0.6456 - acc: 0.6401 - val_loss: 0.6321 - val_acc: 0.6732\n",
      "Epoch 7/150\n",
      "514/514 [==============================] - 0s - loss: 0.6366 - acc: 0.6401 - val_loss: 0.6264 - val_acc: 0.6732\n",
      "Epoch 8/150\n",
      "514/514 [==============================] - 0s - loss: 0.6301 - acc: 0.6479 - val_loss: 0.6133 - val_acc: 0.7362\n",
      "Epoch 9/150\n",
      "514/514 [==============================] - 0s - loss: 0.6224 - acc: 0.6751 - val_loss: 0.6094 - val_acc: 0.6732\n",
      "Epoch 10/150\n",
      "514/514 [==============================] - 0s - loss: 0.6182 - acc: 0.6809 - val_loss: 0.6074 - val_acc: 0.6417\n",
      "Epoch 11/150\n",
      "514/514 [==============================] - 0s - loss: 0.6128 - acc: 0.6693 - val_loss: 0.6002 - val_acc: 0.6929\n",
      "Epoch 12/150\n",
      "514/514 [==============================] - 0s - loss: 0.6160 - acc: 0.6809 - val_loss: 0.5886 - val_acc: 0.7165\n",
      "Epoch 13/150\n",
      "514/514 [==============================] - 0s - loss: 0.6124 - acc: 0.6809 - val_loss: 0.5969 - val_acc: 0.6850\n",
      "Epoch 14/150\n",
      "514/514 [==============================] - 0s - loss: 0.6044 - acc: 0.7004 - val_loss: 0.5852 - val_acc: 0.7047\n",
      "Epoch 15/150\n",
      "514/514 [==============================] - 0s - loss: 0.6011 - acc: 0.6693 - val_loss: 0.5872 - val_acc: 0.7047\n",
      "Epoch 16/150\n",
      "514/514 [==============================] - 0s - loss: 0.6109 - acc: 0.6732 - val_loss: 0.5751 - val_acc: 0.7244\n",
      "Epoch 17/150\n",
      "514/514 [==============================] - 0s - loss: 0.6023 - acc: 0.6790 - val_loss: 0.5736 - val_acc: 0.7008\n",
      "Epoch 18/150\n",
      "514/514 [==============================] - 0s - loss: 0.5969 - acc: 0.6946 - val_loss: 0.5724 - val_acc: 0.7008\n",
      "Epoch 19/150\n",
      "514/514 [==============================] - 0s - loss: 0.5970 - acc: 0.6887 - val_loss: 0.5704 - val_acc: 0.7165\n",
      "Epoch 20/150\n",
      "514/514 [==============================] - 0s - loss: 0.5937 - acc: 0.6868 - val_loss: 0.5703 - val_acc: 0.7087\n",
      "Epoch 21/150\n",
      "514/514 [==============================] - 0s - loss: 0.5929 - acc: 0.6965 - val_loss: 0.5696 - val_acc: 0.7047\n",
      "Epoch 22/150\n",
      "514/514 [==============================] - 0s - loss: 0.5879 - acc: 0.7043 - val_loss: 0.5672 - val_acc: 0.7087\n",
      "Epoch 23/150\n",
      "514/514 [==============================] - 0s - loss: 0.5864 - acc: 0.6946 - val_loss: 0.5684 - val_acc: 0.6969\n",
      "Epoch 24/150\n",
      "514/514 [==============================] - 0s - loss: 0.5767 - acc: 0.7101 - val_loss: 0.5672 - val_acc: 0.7047\n",
      "Epoch 25/150\n",
      "514/514 [==============================] - 0s - loss: 0.5810 - acc: 0.7179 - val_loss: 0.5622 - val_acc: 0.7008\n",
      "Epoch 26/150\n",
      "514/514 [==============================] - 0s - loss: 0.5789 - acc: 0.7101 - val_loss: 0.5742 - val_acc: 0.7165\n",
      "Epoch 27/150\n",
      "514/514 [==============================] - 0s - loss: 0.5728 - acc: 0.7198 - val_loss: 0.5711 - val_acc: 0.7087\n",
      "Epoch 28/150\n",
      "514/514 [==============================] - 0s - loss: 0.5749 - acc: 0.7140 - val_loss: 0.6655 - val_acc: 0.5315\n",
      "Epoch 29/150\n",
      "514/514 [==============================] - 0s - loss: 0.5795 - acc: 0.6946 - val_loss: 0.5671 - val_acc: 0.7087\n",
      "Epoch 30/150\n",
      "514/514 [==============================] - 0s - loss: 0.5717 - acc: 0.7160 - val_loss: 0.5692 - val_acc: 0.7047\n",
      "Epoch 31/150\n",
      "514/514 [==============================] - 0s - loss: 0.5636 - acc: 0.7179 - val_loss: 0.5711 - val_acc: 0.7047\n",
      "Epoch 32/150\n",
      "514/514 [==============================] - 0s - loss: 0.5810 - acc: 0.7101 - val_loss: 0.5805 - val_acc: 0.7087\n",
      "Epoch 33/150\n",
      "514/514 [==============================] - 0s - loss: 0.5723 - acc: 0.7179 - val_loss: 0.5680 - val_acc: 0.6969\n",
      "Epoch 34/150\n",
      "514/514 [==============================] - 0s - loss: 0.5624 - acc: 0.7296 - val_loss: 0.5552 - val_acc: 0.7008\n",
      "Epoch 35/150\n",
      "514/514 [==============================] - 0s - loss: 0.5595 - acc: 0.7296 - val_loss: 0.5609 - val_acc: 0.7047\n",
      "Epoch 36/150\n",
      "514/514 [==============================] - 0s - loss: 0.5664 - acc: 0.7179 - val_loss: 0.5570 - val_acc: 0.7008\n",
      "Epoch 37/150\n",
      "514/514 [==============================] - 0s - loss: 0.5587 - acc: 0.7412 - val_loss: 0.5664 - val_acc: 0.7087\n",
      "Epoch 38/150\n",
      "514/514 [==============================] - 0s - loss: 0.5572 - acc: 0.7335 - val_loss: 0.5477 - val_acc: 0.7008\n",
      "Epoch 39/150\n",
      "514/514 [==============================] - 0s - loss: 0.5615 - acc: 0.7218 - val_loss: 0.5523 - val_acc: 0.7047\n",
      "Epoch 40/150\n",
      "514/514 [==============================] - 0s - loss: 0.5550 - acc: 0.7529 - val_loss: 0.5562 - val_acc: 0.7283\n",
      "Epoch 41/150\n",
      "514/514 [==============================] - 0s - loss: 0.5512 - acc: 0.7374 - val_loss: 0.5431 - val_acc: 0.7047\n",
      "Epoch 42/150\n",
      "514/514 [==============================] - 0s - loss: 0.5599 - acc: 0.7354 - val_loss: 0.5504 - val_acc: 0.7205\n",
      "Epoch 43/150\n",
      "514/514 [==============================] - 0s - loss: 0.5495 - acc: 0.7374 - val_loss: 0.5453 - val_acc: 0.6929\n",
      "Epoch 44/150\n",
      "514/514 [==============================] - 0s - loss: 0.5479 - acc: 0.7315 - val_loss: 0.5462 - val_acc: 0.7244\n",
      "Epoch 45/150\n",
      "514/514 [==============================] - 0s - loss: 0.5443 - acc: 0.7354 - val_loss: 0.5680 - val_acc: 0.6929\n",
      "Epoch 46/150\n",
      "514/514 [==============================] - 0s - loss: 0.5480 - acc: 0.7412 - val_loss: 0.5465 - val_acc: 0.7165\n",
      "Epoch 47/150\n",
      "514/514 [==============================] - 0s - loss: 0.5541 - acc: 0.7218 - val_loss: 0.5403 - val_acc: 0.7165\n",
      "Epoch 48/150\n",
      "514/514 [==============================] - 0s - loss: 0.5446 - acc: 0.7237 - val_loss: 0.5372 - val_acc: 0.7205\n",
      "Epoch 49/150\n",
      "514/514 [==============================] - 0s - loss: 0.5433 - acc: 0.7432 - val_loss: 0.5358 - val_acc: 0.7126\n",
      "Epoch 50/150\n",
      "514/514 [==============================] - 0s - loss: 0.5399 - acc: 0.7412 - val_loss: 0.5452 - val_acc: 0.6850\n",
      "Epoch 51/150\n",
      "514/514 [==============================] - 0s - loss: 0.5317 - acc: 0.7510 - val_loss: 0.5327 - val_acc: 0.7244\n",
      "Epoch 52/150\n",
      "514/514 [==============================] - 0s - loss: 0.5445 - acc: 0.7432 - val_loss: 0.5464 - val_acc: 0.6969\n",
      "Epoch 53/150\n",
      "514/514 [==============================] - 0s - loss: 0.5350 - acc: 0.7510 - val_loss: 0.5253 - val_acc: 0.7323\n",
      "Epoch 54/150\n",
      "514/514 [==============================] - 0s - loss: 0.5331 - acc: 0.7432 - val_loss: 0.5272 - val_acc: 0.7362\n",
      "Epoch 55/150\n",
      "514/514 [==============================] - 0s - loss: 0.5308 - acc: 0.7490 - val_loss: 0.5272 - val_acc: 0.7441\n",
      "Epoch 56/150\n",
      "514/514 [==============================] - 0s - loss: 0.5320 - acc: 0.7374 - val_loss: 0.5417 - val_acc: 0.6969\n",
      "Epoch 57/150\n",
      "514/514 [==============================] - 0s - loss: 0.5379 - acc: 0.7490 - val_loss: 0.5327 - val_acc: 0.7441\n",
      "Epoch 58/150\n",
      "514/514 [==============================] - 0s - loss: 0.5313 - acc: 0.7296 - val_loss: 0.5563 - val_acc: 0.6969\n",
      "Epoch 59/150\n",
      "514/514 [==============================] - 0s - loss: 0.5364 - acc: 0.7374 - val_loss: 0.5274 - val_acc: 0.7362\n",
      "Epoch 60/150\n",
      "514/514 [==============================] - 0s - loss: 0.5249 - acc: 0.7490 - val_loss: 0.5272 - val_acc: 0.7402\n",
      "Epoch 61/150\n",
      "514/514 [==============================] - 0s - loss: 0.5330 - acc: 0.7432 - val_loss: 0.5357 - val_acc: 0.7480\n",
      "Epoch 62/150\n",
      "514/514 [==============================] - 0s - loss: 0.5299 - acc: 0.7393 - val_loss: 0.5379 - val_acc: 0.7244\n",
      "Epoch 63/150\n",
      "514/514 [==============================] - 0s - loss: 0.5228 - acc: 0.7432 - val_loss: 0.5356 - val_acc: 0.7323\n",
      "Epoch 64/150\n",
      "514/514 [==============================] - 0s - loss: 0.5249 - acc: 0.7412 - val_loss: 0.5262 - val_acc: 0.7480\n",
      "Epoch 65/150\n",
      "514/514 [==============================] - 0s - loss: 0.5246 - acc: 0.7354 - val_loss: 0.5490 - val_acc: 0.7165\n",
      "Epoch 66/150\n",
      "514/514 [==============================] - 0s - loss: 0.5258 - acc: 0.7374 - val_loss: 0.5135 - val_acc: 0.7480\n",
      "Epoch 67/150\n",
      "514/514 [==============================] - 0s - loss: 0.5248 - acc: 0.7490 - val_loss: 0.5392 - val_acc: 0.7362\n",
      "Epoch 68/150\n",
      "514/514 [==============================] - 0s - loss: 0.5289 - acc: 0.7432 - val_loss: 0.5377 - val_acc: 0.7205\n",
      "Epoch 69/150\n",
      "514/514 [==============================] - 0s - loss: 0.5205 - acc: 0.7393 - val_loss: 0.5159 - val_acc: 0.7756\n",
      "Epoch 70/150\n",
      "514/514 [==============================] - 0s - loss: 0.5197 - acc: 0.7451 - val_loss: 0.5244 - val_acc: 0.7283\n",
      "Epoch 71/150\n",
      "514/514 [==============================] - 0s - loss: 0.5161 - acc: 0.7607 - val_loss: 0.5237 - val_acc: 0.7598\n",
      "Epoch 72/150\n",
      "514/514 [==============================] - 0s - loss: 0.5172 - acc: 0.7432 - val_loss: 0.5082 - val_acc: 0.7756\n",
      "Epoch 73/150\n",
      "514/514 [==============================] - 0s - loss: 0.5251 - acc: 0.7393 - val_loss: 0.5090 - val_acc: 0.7677\n",
      "Epoch 74/150\n",
      "514/514 [==============================] - 0s - loss: 0.5131 - acc: 0.7451 - val_loss: 0.5176 - val_acc: 0.7598\n",
      "Epoch 75/150\n",
      "514/514 [==============================] - 0s - loss: 0.5160 - acc: 0.7607 - val_loss: 0.5151 - val_acc: 0.7756\n",
      "Epoch 76/150\n",
      "514/514 [==============================] - 0s - loss: 0.5086 - acc: 0.7490 - val_loss: 0.5087 - val_acc: 0.7677\n",
      "Epoch 77/150\n",
      "514/514 [==============================] - 0s - loss: 0.5112 - acc: 0.7549 - val_loss: 0.5125 - val_acc: 0.7598\n",
      "Epoch 78/150\n",
      "514/514 [==============================] - 0s - loss: 0.5117 - acc: 0.7549 - val_loss: 0.5048 - val_acc: 0.7835\n",
      "Epoch 79/150\n",
      "514/514 [==============================] - 0s - loss: 0.5169 - acc: 0.7490 - val_loss: 0.5169 - val_acc: 0.7717\n",
      "Epoch 80/150\n",
      "514/514 [==============================] - 0s - loss: 0.5107 - acc: 0.7568 - val_loss: 0.5060 - val_acc: 0.7874\n",
      "Epoch 81/150\n",
      "514/514 [==============================] - 0s - loss: 0.5191 - acc: 0.7568 - val_loss: 0.5159 - val_acc: 0.7559\n",
      "Epoch 82/150\n",
      "514/514 [==============================] - 0s - loss: 0.5105 - acc: 0.7393 - val_loss: 0.4992 - val_acc: 0.7874\n",
      "Epoch 83/150\n",
      "514/514 [==============================] - 0s - loss: 0.5039 - acc: 0.7374 - val_loss: 0.5164 - val_acc: 0.7559\n",
      "Epoch 84/150\n",
      "514/514 [==============================] - 0s - loss: 0.5097 - acc: 0.7393 - val_loss: 0.5006 - val_acc: 0.7913\n",
      "Epoch 85/150\n",
      "514/514 [==============================] - 0s - loss: 0.5066 - acc: 0.7471 - val_loss: 0.5077 - val_acc: 0.7638\n",
      "Epoch 86/150\n",
      "514/514 [==============================] - 0s - loss: 0.5082 - acc: 0.7451 - val_loss: 0.5082 - val_acc: 0.7717\n",
      "Epoch 87/150\n",
      "514/514 [==============================] - 0s - loss: 0.5016 - acc: 0.7471 - val_loss: 0.5070 - val_acc: 0.7835\n",
      "Epoch 88/150\n",
      "514/514 [==============================] - 0s - loss: 0.5016 - acc: 0.7588 - val_loss: 0.5036 - val_acc: 0.7677\n",
      "Epoch 89/150\n",
      "514/514 [==============================] - 0s - loss: 0.5038 - acc: 0.7490 - val_loss: 0.5076 - val_acc: 0.7598\n",
      "Epoch 90/150\n",
      "514/514 [==============================] - 0s - loss: 0.5015 - acc: 0.7568 - val_loss: 0.5106 - val_acc: 0.7638\n",
      "Epoch 91/150\n",
      "514/514 [==============================] - 0s - loss: 0.5047 - acc: 0.7529 - val_loss: 0.5109 - val_acc: 0.7677\n",
      "Epoch 92/150\n",
      "514/514 [==============================] - 0s - loss: 0.5094 - acc: 0.7432 - val_loss: 0.5065 - val_acc: 0.7638\n",
      "Epoch 93/150\n",
      "514/514 [==============================] - 0s - loss: 0.4975 - acc: 0.7626 - val_loss: 0.5147 - val_acc: 0.7835\n",
      "Epoch 94/150\n",
      "514/514 [==============================] - 0s - loss: 0.5052 - acc: 0.7490 - val_loss: 0.4985 - val_acc: 0.7953\n",
      "Epoch 95/150\n",
      "514/514 [==============================] - 0s - loss: 0.5014 - acc: 0.7704 - val_loss: 0.5099 - val_acc: 0.7520\n",
      "Epoch 96/150\n",
      "514/514 [==============================] - 0s - loss: 0.5071 - acc: 0.7549 - val_loss: 0.4988 - val_acc: 0.7913\n",
      "Epoch 97/150\n",
      "514/514 [==============================] - 0s - loss: 0.4986 - acc: 0.7607 - val_loss: 0.4988 - val_acc: 0.7835\n",
      "Epoch 98/150\n",
      "514/514 [==============================] - 0s - loss: 0.5047 - acc: 0.7451 - val_loss: 0.5093 - val_acc: 0.7756\n",
      "Epoch 99/150\n",
      "514/514 [==============================] - 0s - loss: 0.5113 - acc: 0.7529 - val_loss: 0.5048 - val_acc: 0.7874\n",
      "Epoch 100/150\n",
      "514/514 [==============================] - 0s - loss: 0.4975 - acc: 0.7568 - val_loss: 0.4894 - val_acc: 0.7953\n",
      "Epoch 101/150\n",
      "514/514 [==============================] - 0s - loss: 0.4963 - acc: 0.7646 - val_loss: 0.5042 - val_acc: 0.7638\n",
      "Epoch 102/150\n",
      "514/514 [==============================] - 0s - loss: 0.4918 - acc: 0.7607 - val_loss: 0.4883 - val_acc: 0.7913\n",
      "Epoch 103/150\n",
      "514/514 [==============================] - 0s - loss: 0.4969 - acc: 0.7743 - val_loss: 0.5245 - val_acc: 0.7520\n",
      "Epoch 104/150\n",
      "514/514 [==============================] - 0s - loss: 0.4975 - acc: 0.7568 - val_loss: 0.4873 - val_acc: 0.7874\n",
      "Epoch 105/150\n",
      "514/514 [==============================] - 0s - loss: 0.4901 - acc: 0.7704 - val_loss: 0.5006 - val_acc: 0.7835\n",
      "Epoch 106/150\n",
      "514/514 [==============================] - 0s - loss: 0.5015 - acc: 0.7471 - val_loss: 0.4894 - val_acc: 0.7874\n",
      "Epoch 107/150\n",
      "514/514 [==============================] - 0s - loss: 0.4949 - acc: 0.7607 - val_loss: 0.5097 - val_acc: 0.7598\n",
      "Epoch 108/150\n",
      "514/514 [==============================] - 0s - loss: 0.5057 - acc: 0.7646 - val_loss: 0.4904 - val_acc: 0.7795\n",
      "Epoch 109/150\n",
      "514/514 [==============================] - 0s - loss: 0.4940 - acc: 0.7626 - val_loss: 0.4959 - val_acc: 0.7677\n",
      "Epoch 110/150\n",
      "514/514 [==============================] - 0s - loss: 0.4837 - acc: 0.7607 - val_loss: 0.5270 - val_acc: 0.7441\n",
      "Epoch 111/150\n",
      "514/514 [==============================] - 0s - loss: 0.5051 - acc: 0.7471 - val_loss: 0.4887 - val_acc: 0.7874\n",
      "Epoch 112/150\n",
      "514/514 [==============================] - 0s - loss: 0.4857 - acc: 0.7626 - val_loss: 0.5034 - val_acc: 0.7638\n",
      "Epoch 113/150\n",
      "514/514 [==============================] - 0s - loss: 0.4912 - acc: 0.7607 - val_loss: 0.4886 - val_acc: 0.7835\n",
      "Epoch 114/150\n",
      "514/514 [==============================] - 0s - loss: 0.4886 - acc: 0.7646 - val_loss: 0.4979 - val_acc: 0.7756\n",
      "Epoch 115/150\n",
      "514/514 [==============================] - 0s - loss: 0.4874 - acc: 0.7685 - val_loss: 0.4829 - val_acc: 0.7953\n",
      "Epoch 116/150\n",
      "514/514 [==============================] - 0s - loss: 0.4852 - acc: 0.7665 - val_loss: 0.4819 - val_acc: 0.7835\n",
      "Epoch 117/150\n",
      "514/514 [==============================] - 0s - loss: 0.4894 - acc: 0.7588 - val_loss: 0.5052 - val_acc: 0.7717\n",
      "Epoch 118/150\n",
      "514/514 [==============================] - 0s - loss: 0.4879 - acc: 0.7607 - val_loss: 0.5001 - val_acc: 0.7756\n",
      "Epoch 119/150\n",
      "514/514 [==============================] - 0s - loss: 0.4882 - acc: 0.7685 - val_loss: 0.4893 - val_acc: 0.7795\n",
      "Epoch 120/150\n",
      "514/514 [==============================] - 0s - loss: 0.4851 - acc: 0.7626 - val_loss: 0.4930 - val_acc: 0.7756\n",
      "Epoch 121/150\n",
      "514/514 [==============================] - 0s - loss: 0.4809 - acc: 0.7665 - val_loss: 0.5030 - val_acc: 0.7795\n",
      "Epoch 122/150\n",
      "514/514 [==============================] - 0s - loss: 0.4880 - acc: 0.7607 - val_loss: 0.4895 - val_acc: 0.7677\n",
      "Epoch 123/150\n",
      "514/514 [==============================] - 0s - loss: 0.4836 - acc: 0.7685 - val_loss: 0.5034 - val_acc: 0.7835\n",
      "Epoch 124/150\n",
      "514/514 [==============================] - 0s - loss: 0.4826 - acc: 0.7685 - val_loss: 0.4920 - val_acc: 0.7795\n",
      "Epoch 125/150\n",
      "514/514 [==============================] - 0s - loss: 0.4813 - acc: 0.7665 - val_loss: 0.4882 - val_acc: 0.7795\n",
      "Epoch 126/150\n",
      "514/514 [==============================] - 0s - loss: 0.4768 - acc: 0.7607 - val_loss: 0.5019 - val_acc: 0.7638\n",
      "Epoch 127/150\n",
      "514/514 [==============================] - 0s - loss: 0.4780 - acc: 0.7568 - val_loss: 0.4958 - val_acc: 0.7677\n",
      "Epoch 128/150\n",
      "514/514 [==============================] - 0s - loss: 0.4774 - acc: 0.7763 - val_loss: 0.4838 - val_acc: 0.7795\n",
      "Epoch 129/150\n",
      "514/514 [==============================] - 0s - loss: 0.4856 - acc: 0.7549 - val_loss: 0.4730 - val_acc: 0.7795\n",
      "Epoch 130/150\n",
      "514/514 [==============================] - 0s - loss: 0.4747 - acc: 0.7802 - val_loss: 0.4837 - val_acc: 0.7913\n",
      "Epoch 131/150\n",
      "514/514 [==============================] - 0s - loss: 0.4728 - acc: 0.7685 - val_loss: 0.4902 - val_acc: 0.7795\n",
      "Epoch 132/150\n",
      "514/514 [==============================] - 0s - loss: 0.4784 - acc: 0.7607 - val_loss: 0.4817 - val_acc: 0.7874\n",
      "Epoch 133/150\n",
      "514/514 [==============================] - 0s - loss: 0.4743 - acc: 0.7763 - val_loss: 0.4892 - val_acc: 0.7835\n",
      "Epoch 134/150\n",
      "514/514 [==============================] - 0s - loss: 0.4717 - acc: 0.7879 - val_loss: 0.4978 - val_acc: 0.7677\n",
      "Epoch 135/150\n",
      "514/514 [==============================] - 0s - loss: 0.4699 - acc: 0.7607 - val_loss: 0.4820 - val_acc: 0.7756\n",
      "Epoch 136/150\n",
      "514/514 [==============================] - 0s - loss: 0.4732 - acc: 0.7685 - val_loss: 0.4838 - val_acc: 0.7953\n",
      "Epoch 137/150\n",
      "514/514 [==============================] - 0s - loss: 0.4869 - acc: 0.7529 - val_loss: 0.4992 - val_acc: 0.7598\n",
      "Epoch 138/150\n",
      "514/514 [==============================] - 0s - loss: 0.4716 - acc: 0.7782 - val_loss: 0.4761 - val_acc: 0.7756\n",
      "Epoch 139/150\n",
      "514/514 [==============================] - 0s - loss: 0.4734 - acc: 0.7821 - val_loss: 0.4833 - val_acc: 0.7835\n",
      "Epoch 140/150\n",
      "514/514 [==============================] - 0s - loss: 0.4653 - acc: 0.7860 - val_loss: 0.4744 - val_acc: 0.7913\n",
      "Epoch 141/150\n",
      "514/514 [==============================] - 0s - loss: 0.4668 - acc: 0.7782 - val_loss: 0.4687 - val_acc: 0.7953\n",
      "Epoch 142/150\n",
      "514/514 [==============================] - 0s - loss: 0.4673 - acc: 0.7763 - val_loss: 0.4742 - val_acc: 0.7677\n",
      "Epoch 143/150\n",
      "514/514 [==============================] - 0s - loss: 0.4760 - acc: 0.7763 - val_loss: 0.4711 - val_acc: 0.7953\n",
      "Epoch 144/150\n",
      "514/514 [==============================] - 0s - loss: 0.4880 - acc: 0.7724 - val_loss: 0.4974 - val_acc: 0.7835\n",
      "Epoch 145/150\n",
      "514/514 [==============================] - 0s - loss: 0.4715 - acc: 0.7607 - val_loss: 0.4677 - val_acc: 0.8110\n",
      "Epoch 146/150\n",
      "514/514 [==============================] - 0s - loss: 0.4755 - acc: 0.7646 - val_loss: 0.4880 - val_acc: 0.7717\n",
      "Epoch 147/150\n",
      "514/514 [==============================] - 0s - loss: 0.4654 - acc: 0.7802 - val_loss: 0.4755 - val_acc: 0.7992\n",
      "Epoch 148/150\n",
      "514/514 [==============================] - 0s - loss: 0.4728 - acc: 0.7802 - val_loss: 0.4892 - val_acc: 0.7953\n",
      "Epoch 149/150\n",
      "514/514 [==============================] - 0s - loss: 0.4655 - acc: 0.7821 - val_loss: 0.4726 - val_acc: 0.7913\n",
      "Epoch 150/150\n",
      "514/514 [==============================] - 0s - loss: 0.4653 - acc: 0.7918 - val_loss: 0.4770 - val_acc: 0.7795\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x114278850>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X, Y, validation_split=0.33, nb_epoch=150, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s     \n",
      "\n",
      "acc: 78.39%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.46261142566800117, 0.78385416666666663]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the model\n",
    "scores = model.evaluate(X, Y)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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