FEC in Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/michal/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Conv2D, MaxPool2D, Dense\n",
    "from keras.layers import Flatten, Dropout\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>pixels</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9686</th>\n",
       "      <td>6</td>\n",
       "      <td>194 201 207 204 203 201 198 195 192 187 182 18...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24647</th>\n",
       "      <td>5</td>\n",
       "      <td>36 29 33 32 37 36 40 37 35 31 22 22 22 17 17 2...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6701</th>\n",
       "      <td>0</td>\n",
       "      <td>128 64 107 191 188 160 131 101 58 49 70 83 88 ...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25096</th>\n",
       "      <td>3</td>\n",
       "      <td>111 170 185 184 188 192 192 196 203 204 205 21...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33639</th>\n",
       "      <td>6</td>\n",
       "      <td>157 165 117 44 51 79 112 123 130 134 140 149 1...</td>\n",
       "      <td>PrivateTest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       emotion                                             pixels        Usage\n",
       "9686         6  194 201 207 204 203 201 198 195 192 187 182 18...     Training\n",
       "24647        5  36 29 33 32 37 36 40 37 35 31 22 22 22 17 17 2...     Training\n",
       "6701         0  128 64 107 191 188 160 131 101 58 49 70 83 88 ...     Training\n",
       "25096        3  111 170 185 184 188 192 192 196 203 204 205 21...     Training\n",
       "33639        6  157 165 117 44 51 79 112 123 130 134 140 149 1...  PrivateTest"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/fer2013/fer2013.csv')\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def error_rate(pred, target):\n",
    "    return np.mean(pred!=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classification_rate(p, t):\n",
    "    return np.mean(p == t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ohe(labels):\n",
    "    ohe_lab = np.zeros((labels.shape[0], len(set(labels))))\n",
    "    for i in range(len(labels)):\n",
    "        ohe_lab[i, labels[i]] = 1\n",
    "    return ohe_lab     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rearrange(X):\n",
    "    return (X.transpose(0, 2, 3, 1) / 255).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_all = df['emotion'].astype(int)\n",
    "X_all = []\n",
    "for i in df['pixels']:\n",
    "    X_all.append([int(p) for p in i.split()]) #acts like getData()\n",
    "X_all, Y_all = np.array(X_all), np.array(Y_all)\n",
    "X_all = np.reshape(X_all, (X_all.shape[0], 1, 48, 48)) #acts like getImageData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 4953, 1: 547, 2: 5121, 3: 8989, 4: 6077, 5: 4002, 6: 6198})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(Y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = rearrange(X_all)\n",
    "Y = to_categorical(Y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35887, 48, 48, 1)\n",
      "<class 'numpy.ndarray'>\n",
      "(35887, 7)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(type(X))\n",
    "print(Y.shape)\n",
    "print(type(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14940b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n00Ns+Mb0EBu+MT3Ehm9MD/l/UkESc/E5S/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1293bd518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmAqx4RtTITZ8YyrEhm9MhfwHv45zP3E/IvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12904c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDd+YFmLDN6aF2PCNaSE2fGNayP8Hmr4rOT1lNJ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1293d9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1493ed7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOEEr+CILAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129174550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAEICAYAAAB/KknhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz9VilEXe7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12906b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show one of each emtion randomly\n",
    "label_map = ['Anger', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
    "\n",
    "for i in range(7):\n",
    "    x, y = X_all[Y_all==i], Y_all[Y_all==i]\n",
    "    N = len(y)\n",
    "    j = np.random.choice(N)\n",
    "    plt.imshow(x[j].reshape(48,48), cmap='gray')\n",
    "    plt.title(label_map[y[j]])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_shape = X.shape[1:]\n",
    "num_classes = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X[:-1000]\n",
    "Y_train = Y[:-1000]\n",
    "X_test = X[-1000:]\n",
    "Y_test = Y[-1000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 48, 48, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 24, 24, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 12, 12, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               1180160   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 7)                 903       \n",
      "=================================================================\n",
      "Total params: 1,368,583\n",
      "Trainable params: 1,368,583\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', \n",
    "                 padding='same', use_bias=True, input_shape=input_shape))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', \n",
    "                 padding='same', use_bias=True))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', \n",
    "                 padding='same', use_bias=True))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "rms = keras.optimizers.RMSprop(lr=1e-5, rho=0.99)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 34887 samples, validate on 1000 samples\n",
      "Epoch 1/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.8414 - acc: 0.2496 - val_loss: 1.8290 - val_acc: 0.2200\n",
      "Epoch 2/50\n",
      "34887/34887 [==============================] - 113s 3ms/step - loss: 1.8099 - acc: 0.2514 - val_loss: 1.8225 - val_acc: 0.2200\n",
      "Epoch 3/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.8040 - acc: 0.2514 - val_loss: 1.8139 - val_acc: 0.2200\n",
      "Epoch 4/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.7982 - acc: 0.2514 - val_loss: 1.8107 - val_acc: 0.2200\n",
      "Epoch 5/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.7919 - acc: 0.2517 - val_loss: 1.8021 - val_acc: 0.2220\n",
      "Epoch 6/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.7846 - acc: 0.2529 - val_loss: 1.7965 - val_acc: 0.2200\n",
      "Epoch 7/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.7758 - acc: 0.2576 - val_loss: 1.7883 - val_acc: 0.2210\n",
      "Epoch 8/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.7661 - acc: 0.2657 - val_loss: 1.7738 - val_acc: 0.2480\n",
      "Epoch 9/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.7551 - acc: 0.2771 - val_loss: 1.7590 - val_acc: 0.2680\n",
      "Epoch 10/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.7426 - acc: 0.2919 - val_loss: 1.7455 - val_acc: 0.2700\n",
      "Epoch 11/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.7290 - acc: 0.3085 - val_loss: 1.7361 - val_acc: 0.2760\n",
      "Epoch 12/50\n",
      "34887/34887 [==============================] - 114s 3ms/step - loss: 1.7148 - acc: 0.3181 - val_loss: 1.7154 - val_acc: 0.3430\n",
      "Epoch 13/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.7005 - acc: 0.3331 - val_loss: 1.6991 - val_acc: 0.3220\n",
      "Epoch 14/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6856 - acc: 0.3430 - val_loss: 1.6869 - val_acc: 0.3300\n",
      "Epoch 15/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6719 - acc: 0.3508 - val_loss: 1.6648 - val_acc: 0.3570\n",
      "Epoch 16/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6584 - acc: 0.3572 - val_loss: 1.6605 - val_acc: 0.3350\n",
      "Epoch 17/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6463 - acc: 0.3631 - val_loss: 1.6601 - val_acc: 0.3330\n",
      "Epoch 18/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6358 - acc: 0.3701 - val_loss: 1.6290 - val_acc: 0.3560\n",
      "Epoch 19/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6257 - acc: 0.3742 - val_loss: 1.6116 - val_acc: 0.3930\n",
      "Epoch 20/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.6165 - acc: 0.3785 - val_loss: 1.6024 - val_acc: 0.3960\n",
      "Epoch 21/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6086 - acc: 0.3823 - val_loss: 1.5990 - val_acc: 0.3740\n",
      "Epoch 22/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.6007 - acc: 0.3872 - val_loss: 1.5864 - val_acc: 0.4040\n",
      "Epoch 23/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5937 - acc: 0.3893 - val_loss: 1.5888 - val_acc: 0.3820\n",
      "Epoch 24/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5871 - acc: 0.3897 - val_loss: 1.5778 - val_acc: 0.3950\n",
      "Epoch 25/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5814 - acc: 0.3948 - val_loss: 1.5801 - val_acc: 0.3900\n",
      "Epoch 26/50\n",
      "34887/34887 [==============================] - 117s 3ms/step - loss: 1.5759 - acc: 0.3969 - val_loss: 1.5767 - val_acc: 0.3830\n",
      "Epoch 27/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5709 - acc: 0.3978 - val_loss: 1.5559 - val_acc: 0.4090\n",
      "Epoch 28/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5664 - acc: 0.4024 - val_loss: 1.5902 - val_acc: 0.3710\n",
      "Epoch 29/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5622 - acc: 0.4030 - val_loss: 1.5518 - val_acc: 0.4070\n",
      "Epoch 30/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5578 - acc: 0.4044 - val_loss: 1.5394 - val_acc: 0.4230\n",
      "Epoch 31/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5539 - acc: 0.4058 - val_loss: 1.5391 - val_acc: 0.4130\n",
      "Epoch 32/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5506 - acc: 0.4092 - val_loss: 1.5378 - val_acc: 0.4270\n",
      "Epoch 33/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5473 - acc: 0.4108 - val_loss: 1.5327 - val_acc: 0.4270\n",
      "Epoch 34/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5435 - acc: 0.4118 - val_loss: 1.5306 - val_acc: 0.4080\n",
      "Epoch 35/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5408 - acc: 0.4135 - val_loss: 1.5243 - val_acc: 0.4270\n",
      "Epoch 36/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5382 - acc: 0.4146 - val_loss: 1.5411 - val_acc: 0.4020\n",
      "Epoch 37/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5350 - acc: 0.4145 - val_loss: 1.5308 - val_acc: 0.4140\n",
      "Epoch 38/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5318 - acc: 0.4150 - val_loss: 1.5297 - val_acc: 0.4040\n",
      "Epoch 39/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5288 - acc: 0.4163 - val_loss: 1.5163 - val_acc: 0.4100\n",
      "Epoch 40/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5269 - acc: 0.4179 - val_loss: 1.5320 - val_acc: 0.4030\n",
      "Epoch 41/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5242 - acc: 0.4201 - val_loss: 1.5098 - val_acc: 0.4330\n",
      "Epoch 42/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5209 - acc: 0.4218 - val_loss: 1.5113 - val_acc: 0.4350\n",
      "Epoch 43/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5194 - acc: 0.4222 - val_loss: 1.5103 - val_acc: 0.4230\n",
      "Epoch 44/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5161 - acc: 0.4226 - val_loss: 1.5297 - val_acc: 0.4130\n",
      "Epoch 45/50\n",
      "34887/34887 [==============================] - 117s 3ms/step - loss: 1.5144 - acc: 0.4258 - val_loss: 1.5182 - val_acc: 0.3970\n",
      "Epoch 46/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5119 - acc: 0.4255 - val_loss: 1.5056 - val_acc: 0.4150\n",
      "Epoch 47/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5088 - acc: 0.4259 - val_loss: 1.5251 - val_acc: 0.4080\n",
      "Epoch 48/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5069 - acc: 0.4263 - val_loss: 1.5344 - val_acc: 0.3980\n",
      "Epoch 49/50\n",
      "34887/34887 [==============================] - 115s 3ms/step - loss: 1.5048 - acc: 0.4285 - val_loss: 1.5050 - val_acc: 0.4040\n",
      "Epoch 50/50\n",
      "34887/34887 [==============================] - 116s 3ms/step - loss: 1.5023 - acc: 0.4290 - val_loss: 1.5050 - val_acc: 0.4260\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train,\n",
    "                   batch_size = 256,\n",
    "                   epochs = 50,\n",
    "                   verbose = 1,\n",
    "                   validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX0Qkyij4RUSijIJfRCTK/H+OjvkLF2B8IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14940beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177c45358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIrPutLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129179cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.ones(len(history.history['acc']))-history.history['acc'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmkD+PwF2u1jKJG+tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14939b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.ones(len(history.history['val_acc']))-history.history['val_acc'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}