CIFAR-10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shwetatatiya/anaconda3/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": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32 \n",
    "num_classes = 10\n",
    "epochs = 100\n",
    "data_augmentation = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# The data, shuffled and split between train and test sets:\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 48)        13872     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 32, 32, 48)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 48)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 48)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 80)        34640     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 16, 16, 80)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 80)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 80)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 128)         92288     \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d_1 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 500)               64500     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5010      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 211,206\n",
      "Trainable params: 211,206\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(48, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(80, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(GlobalMaxPooling2D())\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Dense(500))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# initiate RMSprop optimizer\n",
    "opt = keras.optimizers.Adam(lr=0.0001)\n",
    "\n",
    "# Let's train the model using RMSprop\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/shwetatatiya/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the retry module or similar alternatives.\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph2', histogram_freq=0, write_graph=True, write_images=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using real-time data augmentation.\n",
      "Epoch 1/100\n",
      "1562/1562 [==============================] - 253s 162ms/step - loss: 2.0428 - acc: 0.2263 - val_loss: 1.7488 - val_acc: 0.3685\n",
      "Epoch 2/100\n",
      "1562/1562 [==============================] - 245s 157ms/step - loss: 1.7451 - acc: 0.3457 - val_loss: 1.6306 - val_acc: 0.4018\n",
      "Epoch 3/100\n",
      "1562/1562 [==============================] - 238s 152ms/step - loss: 1.6335 - acc: 0.3986 - val_loss: 1.5413 - val_acc: 0.4374\n",
      "Epoch 4/100\n",
      "1562/1562 [==============================] - 236s 151ms/step - loss: 1.5561 - acc: 0.4282 - val_loss: 1.4778 - val_acc: 0.4630\n",
      "Epoch 5/100\n",
      "1562/1562 [==============================] - 237s 152ms/step - loss: 1.4986 - acc: 0.4535 - val_loss: 1.4335 - val_acc: 0.4782\n",
      "Epoch 6/100\n",
      "1562/1562 [==============================] - 235s 151ms/step - loss: 1.4423 - acc: 0.4753 - val_loss: 1.3325 - val_acc: 0.5253\n",
      "Epoch 7/100\n",
      "1562/1562 [==============================] - 235s 150ms/step - loss: 1.3961 - acc: 0.4943 - val_loss: 1.2703 - val_acc: 0.5431\n",
      "Epoch 8/100\n",
      "1562/1562 [==============================] - 238s 152ms/step - loss: 1.3571 - acc: 0.5111 - val_loss: 1.2088 - val_acc: 0.5718\n",
      "Epoch 9/100\n",
      "1562/1562 [==============================] - 238s 152ms/step - loss: 1.3205 - acc: 0.5236 - val_loss: 1.1631 - val_acc: 0.5871\n",
      "Epoch 10/100\n",
      "1562/1562 [==============================] - 237s 152ms/step - loss: 1.2809 - acc: 0.5428 - val_loss: 1.1490 - val_acc: 0.5907\n",
      "Epoch 11/100\n",
      "1562/1562 [==============================] - 2559s 2s/step - loss: 1.2464 - acc: 0.5518 - val_loss: 1.1670 - val_acc: 0.5812\n",
      "Epoch 12/100\n",
      "1562/1562 [==============================] - 244s 156ms/step - loss: 1.2254 - acc: 0.5596 - val_loss: 1.1128 - val_acc: 0.6034\n",
      "Epoch 13/100\n",
      "1562/1562 [==============================] - 236s 151ms/step - loss: 1.1964 - acc: 0.5728 - val_loss: 1.0512 - val_acc: 0.6306\n",
      "Epoch 14/100\n",
      "1562/1562 [==============================] - 236s 151ms/step - loss: 1.1738 - acc: 0.5777 - val_loss: 1.0407 - val_acc: 0.6308\n",
      "Epoch 15/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 1.1463 - acc: 0.5922 - val_loss: 1.0809 - val_acc: 0.6226\n",
      "Epoch 16/100\n",
      "1562/1562 [==============================] - 2004s 1s/step - loss: 1.1295 - acc: 0.5977 - val_loss: 0.9595 - val_acc: 0.6590\n",
      "Epoch 17/100\n",
      "1562/1562 [==============================] - 447s 286ms/step - loss: 1.1073 - acc: 0.6039 - val_loss: 1.0300 - val_acc: 0.6373\n",
      "Epoch 18/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 1.0910 - acc: 0.6107 - val_loss: 0.9289 - val_acc: 0.6690\n",
      "Epoch 19/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 1.0737 - acc: 0.6193 - val_loss: 1.0498 - val_acc: 0.6286\n",
      "Epoch 20/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 1.0564 - acc: 0.6234 - val_loss: 0.9302 - val_acc: 0.6677\n",
      "Epoch 21/100\n",
      "1562/1562 [==============================] - 232s 148ms/step - loss: 1.0426 - acc: 0.6310 - val_loss: 0.9135 - val_acc: 0.6695\n",
      "Epoch 22/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 1.0271 - acc: 0.6356 - val_loss: 0.9668 - val_acc: 0.6576\n",
      "Epoch 23/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 1.0161 - acc: 0.6403 - val_loss: 0.9326 - val_acc: 0.6680\n",
      "Epoch 24/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 0.9993 - acc: 0.6440 - val_loss: 0.9146 - val_acc: 0.6713\n",
      "Epoch 25/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.9878 - acc: 0.6505 - val_loss: 0.8749 - val_acc: 0.6862\n",
      "Epoch 26/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.9809 - acc: 0.6522 - val_loss: 0.8395 - val_acc: 0.6998\n",
      "Epoch 27/100\n",
      "1562/1562 [==============================] - 236s 151ms/step - loss: 0.9655 - acc: 0.6601 - val_loss: 0.8140 - val_acc: 0.7069\n",
      "Epoch 28/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 0.9567 - acc: 0.6621 - val_loss: 0.8933 - val_acc: 0.6853\n",
      "Epoch 29/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 0.9431 - acc: 0.6652 - val_loss: 0.9061 - val_acc: 0.6771\n",
      "Epoch 30/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 0.9352 - acc: 0.6680 - val_loss: 0.8533 - val_acc: 0.6981\n",
      "Epoch 31/100\n",
      "1562/1562 [==============================] - 234s 150ms/step - loss: 0.9249 - acc: 0.6712 - val_loss: 0.8257 - val_acc: 0.7061\n",
      "Epoch 32/100\n",
      "1562/1562 [==============================] - 232s 149ms/step - loss: 0.9137 - acc: 0.6774 - val_loss: 0.8131 - val_acc: 0.7080\n",
      "Epoch 33/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.9062 - acc: 0.6794 - val_loss: 0.8163 - val_acc: 0.7102\n",
      "Epoch 34/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8952 - acc: 0.6815 - val_loss: 0.7739 - val_acc: 0.7270\n",
      "Epoch 35/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8869 - acc: 0.6864 - val_loss: 0.8220 - val_acc: 0.7111\n",
      "Epoch 36/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8839 - acc: 0.6907 - val_loss: 0.7945 - val_acc: 0.7191\n",
      "Epoch 37/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8776 - acc: 0.6914 - val_loss: 0.8057 - val_acc: 0.7110\n",
      "Epoch 38/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8652 - acc: 0.6944 - val_loss: 0.7472 - val_acc: 0.7369\n",
      "Epoch 39/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8602 - acc: 0.6951 - val_loss: 0.7620 - val_acc: 0.7321\n",
      "Epoch 40/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8511 - acc: 0.7021 - val_loss: 0.7338 - val_acc: 0.7433\n",
      "Epoch 41/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8494 - acc: 0.7029 - val_loss: 0.7613 - val_acc: 0.7296\n",
      "Epoch 42/100\n",
      "1562/1562 [==============================] - 234s 150ms/step - loss: 0.8391 - acc: 0.7056 - val_loss: 0.7277 - val_acc: 0.7423\n",
      "Epoch 43/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8357 - acc: 0.7061 - val_loss: 0.7420 - val_acc: 0.7416\n",
      "Epoch 44/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.8277 - acc: 0.7071 - val_loss: 0.7643 - val_acc: 0.7332\n",
      "Epoch 45/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8195 - acc: 0.7118 - val_loss: 0.7143 - val_acc: 0.7474\n",
      "Epoch 46/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8159 - acc: 0.7144 - val_loss: 0.7609 - val_acc: 0.7350\n",
      "Epoch 47/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8125 - acc: 0.7153 - val_loss: 0.7305 - val_acc: 0.7465\n",
      "Epoch 48/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.8045 - acc: 0.7165 - val_loss: 0.7178 - val_acc: 0.7465\n",
      "Epoch 49/100\n",
      "1562/1562 [==============================] - 230s 148ms/step - loss: 0.7976 - acc: 0.7199 - val_loss: 0.7419 - val_acc: 0.7406\n",
      "Epoch 50/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.7944 - acc: 0.7215 - val_loss: 0.7313 - val_acc: 0.7459\n",
      "Epoch 51/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.7853 - acc: 0.7223 - val_loss: 0.6779 - val_acc: 0.7641\n",
      "Epoch 52/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.7821 - acc: 0.7238 - val_loss: 0.6734 - val_acc: 0.7663\n",
      "Epoch 53/100\n",
      "1562/1562 [==============================] - 232s 148ms/step - loss: 0.7776 - acc: 0.7274 - val_loss: 0.6957 - val_acc: 0.7592\n",
      "Epoch 54/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.7798 - acc: 0.7265 - val_loss: 0.7174 - val_acc: 0.7515\n",
      "Epoch 55/100\n",
      "1562/1562 [==============================] - 230s 148ms/step - loss: 0.7700 - acc: 0.7295 - val_loss: 0.7188 - val_acc: 0.7534\n",
      "Epoch 56/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.7684 - acc: 0.7293 - val_loss: 0.6345 - val_acc: 0.7779\n",
      "Epoch 57/100\n",
      "1562/1562 [==============================] - 232s 148ms/step - loss: 0.7652 - acc: 0.7316 - val_loss: 0.7035 - val_acc: 0.7587\n",
      "Epoch 58/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.7581 - acc: 0.7340 - val_loss: 0.7133 - val_acc: 0.7495\n",
      "Epoch 59/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7570 - acc: 0.7351 - val_loss: 0.6625 - val_acc: 0.7702\n",
      "Epoch 60/100\n",
      "1562/1562 [==============================] - 229s 147ms/step - loss: 0.7499 - acc: 0.7395 - val_loss: 0.6652 - val_acc: 0.7679\n",
      "Epoch 61/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7445 - acc: 0.7381 - val_loss: 0.6519 - val_acc: 0.7716\n",
      "Epoch 62/100\n",
      "1562/1562 [==============================] - 229s 147ms/step - loss: 0.7435 - acc: 0.7422 - val_loss: 0.6668 - val_acc: 0.7671\n",
      "Epoch 63/100\n",
      "1562/1562 [==============================] - 231s 148ms/step - loss: 0.7401 - acc: 0.7403 - val_loss: 0.6654 - val_acc: 0.7720\n",
      "Epoch 64/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7367 - acc: 0.7414 - val_loss: 0.6696 - val_acc: 0.7670\n",
      "Epoch 65/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7362 - acc: 0.7417 - val_loss: 0.6589 - val_acc: 0.7721\n",
      "Epoch 66/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7360 - acc: 0.7411 - val_loss: 0.6224 - val_acc: 0.7827\n",
      "Epoch 67/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7304 - acc: 0.7441 - val_loss: 0.7068 - val_acc: 0.7540\n",
      "Epoch 68/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7277 - acc: 0.7465 - val_loss: 0.6248 - val_acc: 0.7832\n",
      "Epoch 69/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7217 - acc: 0.7459 - val_loss: 0.6015 - val_acc: 0.7885\n",
      "Epoch 70/100\n",
      "1562/1562 [==============================] - 227s 146ms/step - loss: 0.7144 - acc: 0.7506 - val_loss: 0.6361 - val_acc: 0.7773\n",
      "Epoch 71/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7126 - acc: 0.7492 - val_loss: 0.6698 - val_acc: 0.7660\n",
      "Epoch 72/100\n",
      "1562/1562 [==============================] - 229s 146ms/step - loss: 0.7178 - acc: 0.7481 - val_loss: 0.5829 - val_acc: 0.7991\n",
      "Epoch 73/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7097 - acc: 0.7526 - val_loss: 0.6355 - val_acc: 0.7817\n",
      "Epoch 74/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.7043 - acc: 0.7527 - val_loss: 0.6363 - val_acc: 0.7834\n",
      "Epoch 75/100\n",
      "1562/1562 [==============================] - 233s 149ms/step - loss: 0.7071 - acc: 0.7528 - val_loss: 0.6157 - val_acc: 0.7890\n",
      "Epoch 76/100\n",
      "1562/1562 [==============================] - 229s 147ms/step - loss: 0.6969 - acc: 0.7550 - val_loss: 0.6229 - val_acc: 0.7836\n",
      "Epoch 77/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6966 - acc: 0.7560 - val_loss: 0.6157 - val_acc: 0.7872\n",
      "Epoch 78/100\n",
      "1562/1562 [==============================] - 229s 146ms/step - loss: 0.6950 - acc: 0.7580 - val_loss: 0.6250 - val_acc: 0.7851\n",
      "Epoch 79/100\n",
      "1562/1562 [==============================] - 229s 147ms/step - loss: 0.6928 - acc: 0.7578 - val_loss: 0.6439 - val_acc: 0.7806\n",
      "Epoch 80/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6946 - acc: 0.7576 - val_loss: 0.6373 - val_acc: 0.7815\n",
      "Epoch 81/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6878 - acc: 0.7603 - val_loss: 0.6454 - val_acc: 0.7779\n",
      "Epoch 82/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6922 - acc: 0.7583 - val_loss: 0.6775 - val_acc: 0.7638\n",
      "Epoch 83/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6878 - acc: 0.7614 - val_loss: 0.6278 - val_acc: 0.7832\n",
      "Epoch 84/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6805 - acc: 0.7634 - val_loss: 0.6335 - val_acc: 0.7802\n",
      "Epoch 85/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6802 - acc: 0.7610 - val_loss: 0.5945 - val_acc: 0.7959\n",
      "Epoch 86/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6781 - acc: 0.7667 - val_loss: 0.6304 - val_acc: 0.7829\n",
      "Epoch 87/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6744 - acc: 0.7644 - val_loss: 0.6616 - val_acc: 0.7731\n",
      "Epoch 88/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6807 - acc: 0.7623 - val_loss: 0.6240 - val_acc: 0.7855\n",
      "Epoch 89/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6719 - acc: 0.7642 - val_loss: 0.5892 - val_acc: 0.7959\n",
      "Epoch 90/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6757 - acc: 0.7638 - val_loss: 0.5942 - val_acc: 0.7946\n",
      "Epoch 91/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.6653 - acc: 0.7683 - val_loss: 0.6446 - val_acc: 0.7803\n",
      "Epoch 92/100\n",
      "1562/1562 [==============================] - 229s 146ms/step - loss: 0.6675 - acc: 0.7668 - val_loss: 0.5983 - val_acc: 0.7950\n",
      "Epoch 93/100\n",
      "1562/1562 [==============================] - 229s 146ms/step - loss: 0.6666 - acc: 0.7668 - val_loss: 0.5986 - val_acc: 0.7943\n",
      "Epoch 94/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.6674 - acc: 0.7678 - val_loss: 0.6052 - val_acc: 0.7922\n",
      "Epoch 95/100\n",
      "1562/1562 [==============================] - 230s 147ms/step - loss: 0.6656 - acc: 0.7676 - val_loss: 0.6026 - val_acc: 0.7919\n",
      "Epoch 96/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6589 - acc: 0.7682 - val_loss: 0.6131 - val_acc: 0.7891\n",
      "Epoch 97/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6607 - acc: 0.7709 - val_loss: 0.5663 - val_acc: 0.8093\n",
      "Epoch 98/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6569 - acc: 0.7703 - val_loss: 0.5620 - val_acc: 0.8037\n",
      "Epoch 99/100\n",
      "1562/1562 [==============================] - 227s 146ms/step - loss: 0.6564 - acc: 0.7704 - val_loss: 0.5940 - val_acc: 0.7962\n",
      "Epoch 100/100\n",
      "1562/1562 [==============================] - 228s 146ms/step - loss: 0.6492 - acc: 0.7745 - val_loss: 0.5824 - val_acc: 0.8013\n"
     ]
    }
   ],
   "source": [
    "if not data_augmentation:\n",
    "    print('Not using data augmentation.')\n",
    "    model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True, callbacks=[tbCallBack])\n",
    "else:\n",
    "    print('Using real-time data augmentation.')\n",
    "    # This will do preprocessing and realtime data augmentation:\n",
    "    '''\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images\n",
    "    '''\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images\n",
    "\n",
    "\n",
    "    # Compute quantities required for feature-wise normalization\n",
    "    # (std, mean, and principal components if ZCA whitening is applied).\n",
    "    datagen.fit(x_train)\n",
    "\n",
    "    # Fit the model on the batches generated by datagen.flow().\n",
    "    model.fit_generator(datagen.flow(x_train, y_train,\n",
    "                                     batch_size=batch_size),\n",
    "                        steps_per_epoch=x_train.shape[0] // batch_size,\n",
    "                        epochs=epochs,\n",
    "                        validation_data=(x_test, y_test), callbacks=[tbCallBack])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 14s 1ms/step\n",
      "Test Loss 0.5823626396656036\n",
      "Test Accuracy 0.8013\n"
     ]
    }
   ],
   "source": [
    "scores=model.evaluate(x_test,y_test,verbose=1)\n",
    "print(\"Test Loss\",scores[0])\n",
    "print(\"Test Accuracy\",scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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