Cifar10-cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "\n",
    "# load the pre-shuffled train and test data\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the first 24 training images in the data set\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(x_train[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re-scale the Images by Dividing Every Pixel in Every Image by 255\n",
    "\n",
    "# rescale [0,255] --> [0,1]\n",
    "x_train = x_train.astype('float32')/255\n",
    "x_test = x_test.astype('float32')/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (45000, 32, 32, 3)\n",
      "45000 train samples\n",
      "10000 test samples\n",
      "5000 validation samples\n"
     ]
    }
   ],
   "source": [
    "#Break Dataset into Training, Testing, and Validation Sets\n",
    "from keras.utils import np_utils\n",
    "\n",
    "# one-hot encode the labels\n",
    "num_classes = len(np.unique(y_train))\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# break training set into training and validation sets\n",
    "(x_train, x_valid) = x_train[5000:], x_train[:5000]\n",
    "(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
    "\n",
    "# print shape of training set\n",
    "print('x_train shape:', x_train.shape)\n",
    "\n",
    "# print number of training, validation, and test images\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "print(x_valid.shape[0], 'validation samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 16)        208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 32)        2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 64)          8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 500)               512500    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5010      \n",
      "=================================================================\n",
      "Total params: 528,054\n",
      "Trainable params: 528,054\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Define the Model Architecture\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', \n",
    "                        input_shape=(32, 32, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/100\n",
      " - 28s - loss: 1.5620 - acc: 0.4311 - val_loss: 1.6411 - val_acc: 0.4526\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.64112, saving model to model.weights.best.hdf5\n",
      "Epoch 2/100\n",
      " - 21s - loss: 1.2471 - acc: 0.5567 - val_loss: 1.1797 - val_acc: 0.5672\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.64112 to 1.17966, saving model to model.weights.best.hdf5\n",
      "Epoch 3/100\n",
      " - 21s - loss: 1.1320 - acc: 0.5985 - val_loss: 1.1541 - val_acc: 0.5912\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.17966 to 1.15405, saving model to model.weights.best.hdf5\n",
      "Epoch 4/100\n",
      " - 20s - loss: 1.0641 - acc: 0.6280 - val_loss: 1.1464 - val_acc: 0.6122\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.15405 to 1.14642, saving model to model.weights.best.hdf5\n",
      "Epoch 5/100\n",
      " - 20s - loss: 1.0309 - acc: 0.6381 - val_loss: 0.9612 - val_acc: 0.6624\n",
      "\n",
      "Epoch 00005: val_loss improved from 1.14642 to 0.96122, saving model to model.weights.best.hdf5\n",
      "Epoch 6/100\n",
      " - 21s - loss: 1.0169 - acc: 0.6471 - val_loss: 1.0161 - val_acc: 0.6486\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.96122\n",
      "Epoch 7/100\n",
      " - 20s - loss: 0.9990 - acc: 0.6571 - val_loss: 0.9247 - val_acc: 0.6880\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.96122 to 0.92469, saving model to model.weights.best.hdf5\n",
      "Epoch 8/100\n",
      " - 20s - loss: 0.9952 - acc: 0.6593 - val_loss: 0.9421 - val_acc: 0.6900\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.92469\n",
      "Epoch 9/100\n",
      " - 21s - loss: 0.9984 - acc: 0.6588 - val_loss: 1.1130 - val_acc: 0.6136\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.92469\n",
      "Epoch 10/100\n",
      " - 19s - loss: 1.0161 - acc: 0.6573 - val_loss: 0.9409 - val_acc: 0.6886\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.92469\n",
      "Epoch 11/100\n",
      " - 20s - loss: 1.0188 - acc: 0.6556 - val_loss: 1.0179 - val_acc: 0.6732\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.92469\n",
      "Epoch 12/100\n",
      " - 21s - loss: 1.0430 - acc: 0.6518 - val_loss: 1.1710 - val_acc: 0.6484\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.92469\n",
      "Epoch 13/100\n",
      " - 21s - loss: 1.0619 - acc: 0.6477 - val_loss: 1.2213 - val_acc: 0.6492\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.92469\n",
      "Epoch 14/100\n",
      " - 21s - loss: 1.0769 - acc: 0.6426 - val_loss: 1.2005 - val_acc: 0.6312\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.92469\n",
      "Epoch 15/100\n",
      " - 17s - loss: 1.0975 - acc: 0.6346 - val_loss: 1.0283 - val_acc: 0.6752\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.92469\n",
      "Epoch 16/100\n",
      " - 21s - loss: 1.1099 - acc: 0.6320 - val_loss: 1.1271 - val_acc: 0.6218\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.92469\n",
      "Epoch 17/100\n",
      " - 22s - loss: 1.1402 - acc: 0.6228 - val_loss: 1.1049 - val_acc: 0.6216\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.92469\n",
      "Epoch 18/100\n",
      " - 499s - loss: 1.1599 - acc: 0.6197 - val_loss: 1.0126 - val_acc: 0.6588\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.92469\n",
      "Epoch 19/100\n",
      " - 21s - loss: 1.1741 - acc: 0.6113 - val_loss: 1.2276 - val_acc: 0.6208\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.92469\n",
      "Epoch 20/100\n",
      " - 21s - loss: 1.2022 - acc: 0.6073 - val_loss: 1.1325 - val_acc: 0.6326\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.92469\n",
      "Epoch 21/100\n",
      " - 21s - loss: 1.2166 - acc: 0.5985 - val_loss: 1.2262 - val_acc: 0.5990\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.92469\n",
      "Epoch 22/100\n",
      " - 21s - loss: 1.2354 - acc: 0.5956 - val_loss: 1.1423 - val_acc: 0.6406\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.92469\n",
      "Epoch 23/100\n",
      " - 21s - loss: 1.2570 - acc: 0.5874 - val_loss: 1.4573 - val_acc: 0.6170\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.92469\n",
      "Epoch 24/100\n",
      " - 21s - loss: 1.2631 - acc: 0.5826 - val_loss: 1.2547 - val_acc: 0.5470\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.92469\n",
      "Epoch 25/100\n",
      " - 21s - loss: 1.2841 - acc: 0.5769 - val_loss: 1.3294 - val_acc: 0.5370\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.92469\n",
      "Epoch 26/100\n",
      " - 21s - loss: 1.2999 - acc: 0.5735 - val_loss: 1.3028 - val_acc: 0.5630\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.92469\n",
      "Epoch 27/100\n",
      " - 20s - loss: 1.3113 - acc: 0.5648 - val_loss: 1.1638 - val_acc: 0.6278\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.92469\n",
      "Epoch 28/100\n",
      " - 21s - loss: 1.3341 - acc: 0.5588 - val_loss: 1.1002 - val_acc: 0.6328\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.92469\n",
      "Epoch 29/100\n",
      " - 20s - loss: 1.3521 - acc: 0.5555 - val_loss: 1.2012 - val_acc: 0.5964\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.92469\n",
      "Epoch 30/100\n",
      " - 21s - loss: 1.3626 - acc: 0.5499 - val_loss: 1.3770 - val_acc: 0.5718\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.92469\n",
      "Epoch 31/100\n",
      " - 21s - loss: 1.3733 - acc: 0.5438 - val_loss: 1.2265 - val_acc: 0.6096\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.92469\n",
      "Epoch 32/100\n",
      " - 21s - loss: 1.3828 - acc: 0.5453 - val_loss: 1.3447 - val_acc: 0.5590\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.92469\n",
      "Epoch 33/100\n",
      " - 20s - loss: 1.3980 - acc: 0.5378 - val_loss: 1.2911 - val_acc: 0.5660\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.92469\n",
      "Epoch 34/100\n",
      " - 21s - loss: 1.4012 - acc: 0.5370 - val_loss: 1.5632 - val_acc: 0.4818\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.92469\n",
      "Epoch 35/100\n",
      " - 21s - loss: 1.4091 - acc: 0.5335 - val_loss: 1.6157 - val_acc: 0.4834\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.92469\n",
      "Epoch 36/100\n",
      " - 21s - loss: 1.4265 - acc: 0.5264 - val_loss: 1.3688 - val_acc: 0.5482\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.92469\n",
      "Epoch 37/100\n",
      " - 21s - loss: 1.4547 - acc: 0.5182 - val_loss: 1.3308 - val_acc: 0.5334\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.92469\n",
      "Epoch 38/100\n",
      " - 22s - loss: 1.4594 - acc: 0.5120 - val_loss: 1.4156 - val_acc: 0.5372\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.92469\n",
      "Epoch 39/100\n",
      " - 23s - loss: 1.4697 - acc: 0.5113 - val_loss: 1.1711 - val_acc: 0.6016\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.92469\n",
      "Epoch 40/100\n",
      " - 24s - loss: 1.4733 - acc: 0.5083 - val_loss: 1.3632 - val_acc: 0.5514\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.92469\n",
      "Epoch 41/100\n",
      " - 22s - loss: 1.4981 - acc: 0.5015 - val_loss: 1.4424 - val_acc: 0.5126\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.92469\n",
      "Epoch 42/100\n",
      " - 24s - loss: 1.5197 - acc: 0.4934 - val_loss: 1.3514 - val_acc: 0.5422\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.92469\n",
      "Epoch 43/100\n",
      " - 22s - loss: 1.5223 - acc: 0.4909 - val_loss: 1.3452 - val_acc: 0.5534\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.92469\n",
      "Epoch 44/100\n",
      " - 19s - loss: 1.5252 - acc: 0.4882 - val_loss: 1.5049 - val_acc: 0.5200\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.92469\n",
      "Epoch 45/100\n",
      " - 20s - loss: 1.5413 - acc: 0.4882 - val_loss: 1.4795 - val_acc: 0.4780\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.92469\n",
      "Epoch 46/100\n",
      " - 21s - loss: 1.5397 - acc: 0.4826 - val_loss: 1.6521 - val_acc: 0.4878\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.92469\n",
      "Epoch 47/100\n",
      " - 19s - loss: 1.5423 - acc: 0.4788 - val_loss: 1.2672 - val_acc: 0.5646\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.92469\n",
      "Epoch 48/100\n",
      " - 19s - loss: 1.5502 - acc: 0.4794 - val_loss: 1.8724 - val_acc: 0.5214\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.92469\n",
      "Epoch 49/100\n",
      " - 20s - loss: 1.5622 - acc: 0.4744 - val_loss: 1.4117 - val_acc: 0.4950\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.92469\n",
      "Epoch 50/100\n",
      " - 20s - loss: 1.5558 - acc: 0.4719 - val_loss: 1.3244 - val_acc: 0.5220\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.92469\n",
      "Epoch 51/100\n",
      " - 19s - loss: 1.5612 - acc: 0.4757 - val_loss: 1.3182 - val_acc: 0.5566\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.92469\n",
      "Epoch 52/100\n",
      " - 20s - loss: 1.5596 - acc: 0.4794 - val_loss: 1.6829 - val_acc: 0.4510\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.92469\n",
      "Epoch 53/100\n",
      " - 19s - loss: 1.5806 - acc: 0.4650 - val_loss: 1.3816 - val_acc: 0.5476\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.92469\n",
      "Epoch 54/100\n",
      " - 17s - loss: 1.5828 - acc: 0.4626 - val_loss: 1.5577 - val_acc: 0.4446\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.92469\n",
      "Epoch 55/100\n",
      " - 20s - loss: 1.5656 - acc: 0.4685 - val_loss: 1.6357 - val_acc: 0.4126\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.92469\n",
      "Epoch 56/100\n",
      " - 20s - loss: 1.5778 - acc: 0.4649 - val_loss: 1.7805 - val_acc: 0.4960\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.92469\n",
      "Epoch 57/100\n",
      " - 19s - loss: 1.5775 - acc: 0.4692 - val_loss: 1.2736 - val_acc: 0.5688\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.92469\n",
      "Epoch 58/100\n",
      " - 19s - loss: 1.5838 - acc: 0.4670 - val_loss: 1.6891 - val_acc: 0.3960\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00058: val_loss did not improve from 0.92469\n",
      "Epoch 59/100\n",
      " - 20s - loss: 1.5830 - acc: 0.4616 - val_loss: 2.0241 - val_acc: 0.4282\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.92469\n",
      "Epoch 60/100\n",
      " - 20s - loss: 1.5816 - acc: 0.4655 - val_loss: 1.7095 - val_acc: 0.4938\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.92469\n",
      "Epoch 61/100\n",
      " - 19s - loss: 1.5840 - acc: 0.4598 - val_loss: 1.3279 - val_acc: 0.5630\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.92469\n",
      "Epoch 62/100\n",
      " - 19s - loss: 1.5921 - acc: 0.4605 - val_loss: 1.4806 - val_acc: 0.4956\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.92469\n",
      "Epoch 63/100\n",
      " - 19s - loss: 1.5916 - acc: 0.4570 - val_loss: 1.4969 - val_acc: 0.4792\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.92469\n",
      "Epoch 64/100\n",
      " - 19s - loss: 1.5998 - acc: 0.4617 - val_loss: 2.1257 - val_acc: 0.4496\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.92469\n",
      "Epoch 65/100\n",
      " - 19s - loss: 1.5990 - acc: 0.4618 - val_loss: 1.3964 - val_acc: 0.5088\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.92469\n",
      "Epoch 66/100\n",
      " - 19s - loss: 1.5900 - acc: 0.4631 - val_loss: 1.6689 - val_acc: 0.5098\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.92469\n",
      "Epoch 67/100\n",
      " - 19s - loss: 1.5980 - acc: 0.4599 - val_loss: 1.4312 - val_acc: 0.4854\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.92469\n",
      "Epoch 68/100\n",
      " - 20s - loss: 1.5833 - acc: 0.4627 - val_loss: 1.4660 - val_acc: 0.4844\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.92469\n",
      "Epoch 69/100\n",
      " - 20s - loss: 1.5925 - acc: 0.4599 - val_loss: 1.7231 - val_acc: 0.3546\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.92469\n",
      "Epoch 70/100\n",
      " - 21s - loss: 1.6084 - acc: 0.4549 - val_loss: 1.3979 - val_acc: 0.5464\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.92469\n",
      "Epoch 71/100\n",
      " - 19s - loss: 1.6155 - acc: 0.4537 - val_loss: 1.7147 - val_acc: 0.3618\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.92469\n",
      "Epoch 72/100\n",
      " - 19s - loss: 1.6070 - acc: 0.4535 - val_loss: 1.4284 - val_acc: 0.5132\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.92469\n",
      "Epoch 73/100\n",
      " - 18s - loss: 1.6210 - acc: 0.4510 - val_loss: 1.8233 - val_acc: 0.4524\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.92469\n",
      "Epoch 74/100\n",
      " - 18s - loss: 1.6226 - acc: 0.4495 - val_loss: 1.3842 - val_acc: 0.4906\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.92469\n",
      "Epoch 75/100\n",
      " - 19s - loss: 1.6286 - acc: 0.4449 - val_loss: 1.5213 - val_acc: 0.4512\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.92469\n",
      "Epoch 76/100\n",
      " - 20s - loss: 1.6282 - acc: 0.4470 - val_loss: 1.5053 - val_acc: 0.4758\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.92469\n",
      "Epoch 77/100\n",
      " - 19s - loss: 1.6355 - acc: 0.4446 - val_loss: 1.5595 - val_acc: 0.4758\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.92469\n",
      "Epoch 78/100\n",
      " - 19s - loss: 1.6233 - acc: 0.4454 - val_loss: 1.4582 - val_acc: 0.5152\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.92469\n",
      "Epoch 79/100\n",
      " - 20s - loss: 1.6433 - acc: 0.4423 - val_loss: 1.7852 - val_acc: 0.4684\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.92469\n",
      "Epoch 80/100\n",
      " - 19s - loss: 1.6359 - acc: 0.4414 - val_loss: 1.3636 - val_acc: 0.5170\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.92469\n",
      "Epoch 81/100\n",
      " - 19s - loss: 1.6597 - acc: 0.4345 - val_loss: 1.6109 - val_acc: 0.4114\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.92469\n",
      "Epoch 82/100\n",
      " - 19s - loss: 1.6603 - acc: 0.4290 - val_loss: 2.3681 - val_acc: 0.3518\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.92469\n",
      "Epoch 83/100\n",
      " - 19s - loss: 1.6652 - acc: 0.4291 - val_loss: 2.1623 - val_acc: 0.3546\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.92469\n",
      "Epoch 84/100\n",
      " - 18s - loss: 1.6751 - acc: 0.4299 - val_loss: 1.8498 - val_acc: 0.4262\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.92469\n",
      "Epoch 85/100\n",
      " - 20s - loss: 1.6821 - acc: 0.4259 - val_loss: 1.4367 - val_acc: 0.5098\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.92469\n",
      "Epoch 86/100\n",
      " - 21s - loss: 1.7047 - acc: 0.4164 - val_loss: 1.9862 - val_acc: 0.4898\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.92469\n",
      "Epoch 87/100\n",
      " - 22s - loss: 1.6951 - acc: 0.4148 - val_loss: 1.4783 - val_acc: 0.4620\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.92469\n",
      "Epoch 88/100\n",
      " - 19s - loss: 1.7126 - acc: 0.4124 - val_loss: 2.3860 - val_acc: 0.4046\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.92469\n",
      "Epoch 89/100\n",
      " - 20s - loss: 1.7264 - acc: 0.4065 - val_loss: 1.6238 - val_acc: 0.4336\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.92469\n",
      "Epoch 90/100\n",
      " - 19s - loss: 1.7238 - acc: 0.4077 - val_loss: 1.3254 - val_acc: 0.5328\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.92469\n",
      "Epoch 91/100\n",
      " - 20s - loss: 1.7285 - acc: 0.4069 - val_loss: 1.5322 - val_acc: 0.4738\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.92469\n",
      "Epoch 92/100\n",
      " - 21s - loss: 1.7153 - acc: 0.4129 - val_loss: 1.3997 - val_acc: 0.4968\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.92469\n",
      "Epoch 93/100\n",
      " - 19s - loss: 1.7362 - acc: 0.4033 - val_loss: 1.6300 - val_acc: 0.4176\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.92469\n",
      "Epoch 94/100\n",
      " - 20s - loss: 1.7610 - acc: 0.3982 - val_loss: 1.3947 - val_acc: 0.5370\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.92469\n",
      "Epoch 95/100\n",
      " - 19s - loss: 1.7623 - acc: 0.3924 - val_loss: 1.5791 - val_acc: 0.4302\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.92469\n",
      "Epoch 96/100\n",
      " - 17s - loss: 1.7828 - acc: 0.3901 - val_loss: 1.4778 - val_acc: 0.5018\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.92469\n",
      "Epoch 97/100\n",
      " - 17s - loss: 1.7883 - acc: 0.3776 - val_loss: 1.3977 - val_acc: 0.5266\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.92469\n",
      "Epoch 98/100\n",
      " - 19s - loss: 1.7753 - acc: 0.3835 - val_loss: 1.5998 - val_acc: 0.3818\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.92469\n",
      "Epoch 99/100\n",
      " - 20s - loss: 1.7987 - acc: 0.3794 - val_loss: 1.8269 - val_acc: 0.3850\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.92469\n",
      "Epoch 100/100\n",
      " - 20s - loss: 1.7871 - acc: 0.3786 - val_loss: 1.5998 - val_acc: 0.4736\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.92469\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "hist = model.fit(x_train, y_train, batch_size=32, epochs=100,\n",
    "          validation_data=(x_valid, y_valid), callbacks=[checkpointer], \n",
    "          verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "model.load_weights('model.weights.best.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy: 0.674\n"
     ]
    }
   ],
   "source": [
    "# evaluate and print test accuracy\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('\\n', 'Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflowGPU]",
   "language": "python",
   "name": "conda-env-tensorflowGPU-py"
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