ml-finance-python

03_cifar10_image_classification.ipynb

(464530B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# CIFAR10 Image Classification"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Fast-forward to 2012, and we move on to the deeper and more modern AlexNet architecture. We will use the CIFAR10 dataset that uses 60,000 ImageNet samples, compressed to 32x32 pixel resolution (from the original 224x224), but still with three color channels. There are only 10 of the original 1,000 classes. See the notebook cifar10_image_classification for implementation details; we will skip here over some repetitive steps. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Run inside docker container for GPU acceleration"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "See [tensorflow guide](https://www.tensorflow.org/install/docker) and more detailed [instructions](https://blog.sicara.com/tensorflow-gpu-opencv-jupyter-docker-10705b6cd1d)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "`docker run -it -p 8889:8888 -v /path/to/machine-learning-for-trading/17_convolutions_neural_nets:/cnn --name tensorflow tensorflow/tensorflow:latest-gpu-py3 bash`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Inside docker container: \n",
     "`jupyter notebook --ip 0.0.0.0 --no-browser --allow-root`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Imports"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "Using TensorFlow backend.\n"
      ]
     }
    ],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import keras\n",
     "from keras.utils import np_utils\n",
     "from keras.datasets import cifar10\n",
     "from keras.models import Sequential\n",
     "from keras.preprocessing.image import ImageDataGenerator\n",
     "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\n",
     "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
     "from keras.layers.normalization import BatchNormalization\n",
     "from keras import backend as K"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed(42)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Load CIFAR-10 Data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "CIFAR10 can also be downloaded from keras, and we similarly rescale the pixel values and one-hot encode the ten class labels. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# load the pre-shuffled train and test data\n",
     "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Visualize the First 30 Training Images"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "cifar10_labels = {0: 'airplane',\n",
     "                  1: 'automobile',\n",
     "                  2: 'bird',\n",
     "                  3: 'cat',\n",
     "                  4: 'deer',\n",
     "                  5: 'dog',\n",
     "                  6: 'frog',\n",
     "                  7: 'horse',\n",
     "                  8: 'ship',\n",
     "                  9: 'truck'}"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "num_classes = len(cifar10_labels)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(32, 32, 3)"
       ]
      },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "height, width, channels = X_train.shape[1:]\n",
     "input_shape = height, width, channels\n",
     "input_shape"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x360 with 30 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=3, ncols=10, figsize=(20, 5))\n",
     "axes = axes.flatten()\n",
     "for i, ax in enumerate(axes):\n",
     "    ax.imshow(np.squeeze(X_train[i]))\n",
     "    ax.axis('off')\n",
     "    ax.set_title(cifar10_labels[y_train[i, 0]])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Rescale the Images"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "# rescale [0,255] --> [0,1]\n",
     "X_train = X_train.astype('float32')/255\n",
     "X_test = X_test.astype('float32')/255 "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### One-hot label encoding"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
     "y_test = keras.utils.to_categorical(y_test, num_classes)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train-Test split"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_train, X_valid = X_train[5000:], X_train[:5000]\n",
     "y_train, y_valid = y_train[5000:], y_train[:5000]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "(45000, 32, 32, 3)"
       ]
      },
      "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# shape of training set\n",
     "X_train.shape"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "45000 train samples\n",
       "10000 test samples\n",
       "5000 validation samples\n"
      ]
     }
    ],
    "source": [
     "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": "markdown",
    "metadata": {},
    "source": [
     "## Feedforward Neural Network"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We first train a two-layer feedforward network on 50,000 training samples for training for 20 epochs to achieve a test accuracy of 44.22%. We also experiment with a three-layer convolutional net with 500K parameters for 67.07% test accuracy."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Model Architecture "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "mlp = Sequential([\n",
     "    Flatten(input_shape=input_shape, name='input'),\n",
     "    Dense(1000, activation='relu', name='hidden_layer_1'),\n",
     "    Dropout(0.2, name='droput_1'),\n",
     "    Dense(512, activation='relu', name='hidden_layer_2'),\n",
     "    Dropout(0.2, name='dropout_2'),\n",
     "    Dense(num_classes, activation='softmax', name='output')\n",
     "])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "input (Flatten)              (None, 3072)              0         \n",
       "_________________________________________________________________\n",
       "hidden_layer_1 (Dense)       (None, 1000)              3073000   \n",
       "_________________________________________________________________\n",
       "droput_1 (Dropout)           (None, 1000)              0         \n",
       "_________________________________________________________________\n",
       "hidden_layer_2 (Dense)       (None, 512)               512512    \n",
       "_________________________________________________________________\n",
       "dropout_2 (Dropout)          (None, 512)               0         \n",
       "_________________________________________________________________\n",
       "output (Dense)               (None, 10)                5130      \n",
       "=================================================================\n",
       "Total params: 3,590,642\n",
       "Trainable params: 3,590,642\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
     }
    ],
    "source": [
     "mlp.summary()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compile the Model "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
     "mlp.compile(loss='categorical_crossentropy', \n",
     "              optimizer='adam', \n",
     "              metrics=['accuracy'])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train the Model "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "mlp_path = 'models/cifar10.mlp.weights.best.hdf5'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
     "checkpointer = ModelCheckpoint(filepath=mlp_path, \n",
     "                               verbose=1, \n",
     "                               save_best_only=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "tensorboard = TensorBoard(log_dir='./logs/mlp', \n",
     "                          histogram_freq=1, \n",
     "                          batch_size=32,\n",
     "                          write_graph=True,\n",
     "                          write_grads=False,\n",
     "                          update_freq='epoch')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Train on 45000 samples, validate on 5000 samples\n",
       "Epoch 1/20\n",
       " - 4s - loss: 1.9870 - acc: 0.2702 - val_loss: 1.8040 - val_acc: 0.3470\n",
       "\n",
       "Epoch 00001: val_loss improved from inf to 1.80397, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 2/20\n",
       " - 4s - loss: 1.8563 - acc: 0.3209 - val_loss: 1.7918 - val_acc: 0.3574\n",
       "\n",
       "Epoch 00002: val_loss improved from 1.80397 to 1.79179, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 3/20\n",
       " - 4s - loss: 1.8163 - acc: 0.3374 - val_loss: 1.7227 - val_acc: 0.3746\n",
       "\n",
       "Epoch 00003: val_loss improved from 1.79179 to 1.72267, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 4/20\n",
       " - 4s - loss: 1.7955 - acc: 0.3450 - val_loss: 1.7177 - val_acc: 0.3956\n",
       "\n",
       "Epoch 00004: val_loss improved from 1.72267 to 1.71774, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 5/20\n",
       " - 4s - loss: 1.7679 - acc: 0.3573 - val_loss: 1.6933 - val_acc: 0.3926\n",
       "\n",
       "Epoch 00005: val_loss improved from 1.71774 to 1.69330, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 6/20\n",
       " - 4s - loss: 1.7540 - acc: 0.3613 - val_loss: 1.7462 - val_acc: 0.3840\n",
       "\n",
       "Epoch 00006: val_loss did not improve from 1.69330\n",
       "Epoch 7/20\n",
       " - 4s - loss: 1.7410 - acc: 0.3658 - val_loss: 1.6759 - val_acc: 0.3918\n",
       "\n",
       "Epoch 00007: val_loss improved from 1.69330 to 1.67594, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 8/20\n",
       " - 4s - loss: 1.7207 - acc: 0.3769 - val_loss: 1.6777 - val_acc: 0.4106\n",
       "\n",
       "Epoch 00008: val_loss did not improve from 1.67594\n",
       "Epoch 9/20\n",
       " - 4s - loss: 1.7106 - acc: 0.3833 - val_loss: 1.6812 - val_acc: 0.4034\n",
       "\n",
       "Epoch 00009: val_loss did not improve from 1.67594\n",
       "Epoch 10/20\n",
       " - 4s - loss: 1.7052 - acc: 0.3863 - val_loss: 1.6236 - val_acc: 0.4274\n",
       "\n",
       "Epoch 00010: val_loss improved from 1.67594 to 1.62361, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 11/20\n",
       " - 4s - loss: 1.6923 - acc: 0.3895 - val_loss: 1.6313 - val_acc: 0.4120\n",
       "\n",
       "Epoch 00011: val_loss did not improve from 1.62361\n",
       "Epoch 12/20\n",
       " - 4s - loss: 1.6843 - acc: 0.3922 - val_loss: 1.6428 - val_acc: 0.4130\n",
       "\n",
       "Epoch 00012: val_loss did not improve from 1.62361\n",
       "Epoch 13/20\n",
       " - 4s - loss: 1.6801 - acc: 0.3975 - val_loss: 1.6125 - val_acc: 0.4276\n",
       "\n",
       "Epoch 00013: val_loss improved from 1.62361 to 1.61248, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 14/20\n",
       " - 4s - loss: 1.6706 - acc: 0.3990 - val_loss: 1.6525 - val_acc: 0.4266\n",
       "\n",
       "Epoch 00014: val_loss did not improve from 1.61248\n",
       "Epoch 15/20\n",
       " - 4s - loss: 1.6693 - acc: 0.4014 - val_loss: 1.6088 - val_acc: 0.4218\n",
       "\n",
       "Epoch 00015: val_loss improved from 1.61248 to 1.60883, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 16/20\n",
       " - 4s - loss: 1.6572 - acc: 0.4046 - val_loss: 1.5909 - val_acc: 0.4412\n",
       "\n",
       "Epoch 00016: val_loss improved from 1.60883 to 1.59088, saving model to cifar10.mlp.weights.best.hdf5\n",
       "Epoch 17/20\n",
       " - 4s - loss: 1.6548 - acc: 0.4044 - val_loss: 1.6040 - val_acc: 0.4360\n",
       "\n",
       "Epoch 00017: val_loss did not improve from 1.59088\n",
       "Epoch 18/20\n",
       " - 4s - loss: 1.6537 - acc: 0.4052 - val_loss: 1.5921 - val_acc: 0.4322\n",
       "\n",
       "Epoch 00018: val_loss did not improve from 1.59088\n",
       "Epoch 19/20\n",
       " - 4s - loss: 1.6469 - acc: 0.4062 - val_loss: 1.5967 - val_acc: 0.4416\n",
       "\n",
       "Epoch 00019: val_loss did not improve from 1.59088\n",
       "Epoch 20/20\n",
       " - 4s - loss: 1.6406 - acc: 0.4103 - val_loss: 1.5899 - val_acc: 0.4370\n",
       "\n",
       "Epoch 00020: val_loss improved from 1.59088 to 1.58988, saving model to cifar10.mlp.weights.best.hdf5\n"
      ]
     }
    ],
    "source": [
     "hist = mlp.fit(X_train,\n",
     "               y_train,\n",
     "               batch_size=32,\n",
     "               epochs=20,\n",
     "               validation_data=(x_valid, y_valid),\n",
     "               callbacks=[checkpointer, tensorboard],\n",
     "               verbose=2,\n",
     "               shuffle=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Load best model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
     "# load the weights that yielded the best validation accuracy\n",
     "mlp.load_weights(mlp_path)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Test Classification Accuracy"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Test accuracy: 44.22%\n"
      ]
     }
    ],
    "source": [
     "# evaluate and print test accuracy\n",
     "accuracy = mlp.evaluate(X_test, y_test, verbose=0)[1]\n",
     "print('Test accuracy: {:.2%}'.format(accuracy))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Convolutional Neural Network"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
     "# https://stackoverflow.com/questions/35114376/error-when-computing-summaries-in-tensorflow/35117760#35117760\n",
     "K.clear_session()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Model Architecture"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn = Sequential([\n",
     "    Conv2D(filters=16, kernel_size=2, padding='same',\n",
     "           activation='relu', input_shape=input_shape, name='CONV1'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL1'),\n",
     "    Conv2D(filters=32, kernel_size=2, padding='same', activation='relu', name='CONV2'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL2'),\n",
     "    Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', name='CONV3'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL3'),\n",
     "    Dropout(0.3, name='DROP1'),\n",
     "    Flatten(name='FLAT1'),\n",
     "    Dense(500, activation='relu', name='FC1'),\n",
     "    Dropout(0.4, name='DROP2'),\n",
     "    Dense(10, activation='softmax', name='FC2')]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "CONV1 (Conv2D)               (None, 32, 32, 16)        208       \n",
       "_________________________________________________________________\n",
       "POOL1 (MaxPooling2D)         (None, 16, 16, 16)        0         \n",
       "_________________________________________________________________\n",
       "CONV2 (Conv2D)               (None, 16, 16, 32)        2080      \n",
       "_________________________________________________________________\n",
       "POOL2 (MaxPooling2D)         (None, 8, 8, 32)          0         \n",
       "_________________________________________________________________\n",
       "CONV3 (Conv2D)               (None, 8, 8, 64)          8256      \n",
       "_________________________________________________________________\n",
       "POOL3 (MaxPooling2D)         (None, 4, 4, 64)          0         \n",
       "_________________________________________________________________\n",
       "DROP1 (Dropout)              (None, 4, 4, 64)          0         \n",
       "_________________________________________________________________\n",
       "FLAT1 (Flatten)              (None, 1024)              0         \n",
       "_________________________________________________________________\n",
       "FC1 (Dense)                  (None, 500)               512500    \n",
       "_________________________________________________________________\n",
       "DROP2 (Dropout)              (None, 500)               0         \n",
       "_________________________________________________________________\n",
       "FC2 (Dense)                  (None, 10)                5010      \n",
       "=================================================================\n",
       "Total params: 528,054\n",
       "Trainable params: 528,054\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
     }
    ],
    "source": [
     "cnn.summary()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compile the Model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn.compile(loss='categorical_crossentropy',\n",
     "            optimizer='adam',\n",
     "            metrics=['accuracy'])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train the Model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn_path = 'models/cifar10.cnn.weights.best.hdf5'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
     "checkpointer = ModelCheckpoint(filepath=cnn_path, \n",
     "                               verbose=1, \n",
     "                               save_best_only=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
     "tensorboard = TensorBoard(log_dir='./logs/cnn', \n",
     "                          histogram_freq=1, \n",
     "                          batch_size=32,\n",
     "                          write_graph=True,\n",
     "                          write_grads=False,\n",
     "                          update_freq='epoch')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Train on 45000 samples, validate on 5000 samples\n",
       "Epoch 1/20\n",
       " - 3s - loss: 1.5899 - acc: 0.4226 - val_loss: 1.4604 - val_acc: 0.4636\n",
       "\n",
       "Epoch 00001: val_loss improved from inf to 1.46040, saving model to weights/cifar10.cnn.weights.best.hdf5\n",
       "Epoch 2/20\n",
       " - 3s - loss: 1.2710 - acc: 0.5473 - val_loss: 1.2745 - val_acc: 0.5438\n",
       "\n",
       "Epoch 00002: val_loss improved from 1.46040 to 1.27447, saving model to weights/cifar10.cnn.weights.best.hdf5\n",
       "Epoch 3/20\n",
       " - 3s - loss: 1.1529 - acc: 0.5928 - val_loss: 1.0080 - val_acc: 0.6506\n",
       "\n",
       "Epoch 00003: val_loss improved from 1.27447 to 1.00804, saving model to weights/cifar10.cnn.weights.best.hdf5\n",
       "Epoch 4/20\n",
       " - 3s - loss: 1.0868 - acc: 0.6182 - val_loss: 1.0180 - val_acc: 0.6418\n",
       "\n",
       "Epoch 00004: val_loss did not improve from 1.00804\n",
       "Epoch 5/20\n",
       " - 3s - loss: 1.0414 - acc: 0.6385 - val_loss: 1.0208 - val_acc: 0.6478\n",
       "\n",
       "Epoch 00005: val_loss did not improve from 1.00804\n",
       "Epoch 6/20\n",
       " - 3s - loss: 1.0153 - acc: 0.6463 - val_loss: 0.9969 - val_acc: 0.6610\n",
       "\n",
       "Epoch 00006: val_loss improved from 1.00804 to 0.99688, saving model to weights/cifar10.cnn.weights.best.hdf5\n",
       "Epoch 7/20\n",
       " - 3s - loss: 0.9978 - acc: 0.6548 - val_loss: 0.9361 - val_acc: 0.6750\n",
       "\n",
       "Epoch 00007: val_loss improved from 0.99688 to 0.93611, saving model to weights/cifar10.cnn.weights.best.hdf5\n",
       "Epoch 8/20\n",
       " - 3s - loss: 0.9966 - acc: 0.6605 - val_loss: 0.9450 - val_acc: 0.6728\n",
       "\n",
       "Epoch 00008: val_loss did not improve from 0.93611\n",
       "Epoch 9/20\n",
       " - 3s - loss: 0.9863 - acc: 0.6662 - val_loss: 0.9678 - val_acc: 0.6760\n",
       "\n",
       "Epoch 00009: val_loss did not improve from 0.93611\n",
       "Epoch 10/20\n",
       " - 3s - loss: 0.9800 - acc: 0.6668 - val_loss: 1.1120 - val_acc: 0.6264\n",
       "\n",
       "Epoch 00010: val_loss did not improve from 0.93611\n",
       "Epoch 11/20\n",
       " - 3s - loss: 0.9862 - acc: 0.6672 - val_loss: 1.0151 - val_acc: 0.6590\n",
       "\n",
       "Epoch 00011: val_loss did not improve from 0.93611\n",
       "Epoch 12/20\n",
       " - 3s - loss: 0.9955 - acc: 0.6656 - val_loss: 1.0753 - val_acc: 0.6432\n",
       "\n",
       "Epoch 00012: val_loss did not improve from 0.93611\n",
       "Epoch 13/20\n",
       " - 3s - loss: 1.0046 - acc: 0.6668 - val_loss: 1.2666 - val_acc: 0.6052\n",
       "\n",
       "Epoch 00013: val_loss did not improve from 0.93611\n",
       "Epoch 14/20\n",
       " - 3s - loss: 1.0156 - acc: 0.6590 - val_loss: 1.3022 - val_acc: 0.5702\n",
       "\n",
       "Epoch 00014: val_loss did not improve from 0.93611\n",
       "Epoch 15/20\n",
       " - 3s - loss: 1.0317 - acc: 0.6593 - val_loss: 1.1539 - val_acc: 0.6310\n",
       "\n",
       "Epoch 00015: val_loss did not improve from 0.93611\n",
       "Epoch 16/20\n",
       " - 3s - loss: 1.0442 - acc: 0.6523 - val_loss: 1.0976 - val_acc: 0.6490\n",
       "\n",
       "Epoch 00016: val_loss did not improve from 0.93611\n",
       "Epoch 17/20\n",
       " - 3s - loss: 1.0782 - acc: 0.6472 - val_loss: 1.1564 - val_acc: 0.6316\n",
       "\n",
       "Epoch 00017: val_loss did not improve from 0.93611\n",
       "Epoch 18/20\n",
       " - 3s - loss: 1.0837 - acc: 0.6435 - val_loss: 0.9460 - val_acc: 0.6888\n",
       "\n",
       "Epoch 00018: val_loss did not improve from 0.93611\n",
       "Epoch 19/20\n",
       " - 3s - loss: 1.1012 - acc: 0.6361 - val_loss: 1.0408 - val_acc: 0.6428\n",
       "\n",
       "Epoch 00019: val_loss did not improve from 0.93611\n",
       "Epoch 20/20\n",
       " - 3s - loss: 1.1142 - acc: 0.6329 - val_loss: 1.6174 - val_acc: 0.6362\n",
       "\n",
       "Epoch 00020: val_loss did not improve from 0.93611\n"
      ]
     }
    ],
    "source": [
     "hist = cnn.fit(x_train, \n",
     "               y_train, \n",
     "               batch_size=32, \n",
     "               epochs=20,\n",
     "               validation_data=(x_valid, y_valid), \n",
     "               callbacks=[checkpointer, tensorboard], \n",
     "               verbose=2, \n",
     "               shuffle=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Load best model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn.load_weights(cnn_path)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Test set accuracy"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Accuracy: 67.07%\n"
      ]
     }
    ],
    "source": [
     "accuracy = cnn.evaluate(x_test, y_test, verbose=0)[1]\n",
     "print('Accuracy: {:.2%}'.format(accuracy))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Evaluate Predictions"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_hat = cnn.predict(x_test)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x576 with 32 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(nrows=4, ncols=8, figsize=(20, 8))\n",
     "axes = axes.flatten()\n",
     "images = np.random.choice(x_test.shape[0], size=32, replace=False)\n",
     "for i, (ax, idx) in enumerate(zip(axes, images)):\n",
     "    ax.imshow(np.squeeze(x_test[idx]))\n",
     "    ax.axis('off')\n",
     "    pred_idx, true_idx = np.argmax(y_hat[idx]), np.argmax(y_test[idx])\n",
     "    if pred_idx == true_idx:        \n",
     "        ax.set_title('{} (✓)'.format(cifar10_labels[pred_idx]), color=\"green\")\n",
     "    else:\n",
     "        ax.set_title(\"{} ({})\".format(cifar10_labels[pred_idx], cifar10_labels[true_idx]), color='red')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## CNN with Image Augmentation"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "A common trick to enhance performance is to artificially increase the size of the training set by creating synthetic data. This involves randomly shifting or horizontally flipping the image, or introducing noise into the image."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Create and configure augmented image generator"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Keras includes an ImageDataGenerator for this purpose that we can configure and fit to the training data as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "datagen = ImageDataGenerator(\n",
     "    width_shift_range=0.1,  # randomly horizontal shift\n",
     "    height_shift_range=0.1,  # randomly vertial shift\n",
     "    horizontal_flip=True)  # randomly horizontalflip"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "# fit augmented image generator on data\n",
     "datagen.fit(X_train)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Visualize subset of training data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The result shows how the augmented images have been altered in various ways as expected:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "n_images = 6\n",
     "x_train_subset = X_train[:n_images]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
        "<Figure size 1440x288 with 6 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     },
     {
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       "text/plain": [
        "<Figure size 1440x288 with 6 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "# original images\n",
     "fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(20,4))\n",
     "for i, (ax, img) in enumerate(zip(axes, x_train_subset)):\n",
     "    ax.imshow(img)\n",
     "    ax.axis('off')\n",
     "fig.suptitle('Subset of Original Training Images', fontsize=20)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9)\n",
     "fig.savefig('images/original')\n",
     "\n",
     "# augmented images\n",
     "fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(20,4))\n",
     "for x_batch in datagen.flow(x_train_subset, batch_size=n_images, shuffle=False):\n",
     "    for i, ax in enumerate(axes):\n",
     "        ax.imshow(x_batch[i])\n",
     "        ax.axis('off')\n",
     "#     fig.suptitle('Augmented Images', fontsize=20)\n",
     "    break;\n",
     "fig.suptitle('Augmented Images', fontsize=20);\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9)\n",
     "fig.savefig('images/augmented')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train Augmented Images"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
     "K.clear_session()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn_aug_path = 'models/cifar10.augmented.cnn.weights.best.hdf5'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
     "checkpointer = ModelCheckpoint(filepath=cnn_aug_path, \n",
     "                               verbose=1, \n",
     "                               save_best_only=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
     "tensorboard = TensorBoard(log_dir='./logs/cnn_aug',\n",
     "                          histogram_freq=1,\n",
     "                          batch_size=32,\n",
     "                          write_graph=True,\n",
     "                          write_grads=False,\n",
     "                          update_freq='epoch')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn = Sequential([\n",
     "    Conv2D(filters=16, kernel_size=2, padding='same',\n",
     "           activation='relu', input_shape=input_shape, name='CONV1'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL1'),\n",
     "    Conv2D(filters=32, kernel_size=2, padding='same', activation='relu', name='CONV2'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL2'),\n",
     "    Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', name='CONV3'),\n",
     "    MaxPooling2D(pool_size=2, name='POOL3'),\n",
     "    Dropout(0.3, name='DROP1'),\n",
     "    Flatten(name='FLAT1'),\n",
     "    Dense(500, activation='relu', name='FC1'),\n",
     "    Dropout(0.4, name='DROP2'),\n",
     "    Dense(10, activation='softmax', name='FC2')]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn.compile(loss='categorical_crossentropy',\n",
     "            optimizer='adam',\n",
     "            metrics=['accuracy'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 32\n",
     "epochs = 20"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
       " - 11s - loss: 1.6158 - acc: 0.4087 - val_loss: 1.3774 - val_acc: 0.5012\n",
       "\n",
       "Epoch 00001: val_loss improved from inf to 1.37738, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 2/20\n",
       " - 11s - loss: 1.3582 - acc: 0.5123 - val_loss: 1.1724 - val_acc: 0.5846\n",
       "\n",
       "Epoch 00002: val_loss improved from 1.37738 to 1.17241, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 3/20\n",
       " - 11s - loss: 1.2461 - acc: 0.5545 - val_loss: 1.0761 - val_acc: 0.6208\n",
       "\n",
       "Epoch 00003: val_loss improved from 1.17241 to 1.07606, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 4/20\n",
       " - 11s - loss: 1.1795 - acc: 0.5782 - val_loss: 1.0266 - val_acc: 0.6334\n",
       "\n",
       "Epoch 00004: val_loss improved from 1.07606 to 1.02659, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 5/20\n",
       " - 11s - loss: 1.1184 - acc: 0.6008 - val_loss: 0.9402 - val_acc: 0.6626\n",
       "\n",
       "Epoch 00005: val_loss improved from 1.02659 to 0.94016, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 6/20\n",
       " - 11s - loss: 1.0792 - acc: 0.6180 - val_loss: 0.9342 - val_acc: 0.6706\n",
       "\n",
       "Epoch 00006: val_loss improved from 0.94016 to 0.93417, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 7/20\n",
       " - 11s - loss: 1.0535 - acc: 0.6270 - val_loss: 0.9041 - val_acc: 0.6808\n",
       "\n",
       "Epoch 00007: val_loss improved from 0.93417 to 0.90409, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 8/20\n",
       " - 11s - loss: 1.0215 - acc: 0.6392 - val_loss: 0.8475 - val_acc: 0.7146\n",
       "\n",
       "Epoch 00008: val_loss improved from 0.90409 to 0.84751, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 9/20\n",
       " - 11s - loss: 0.9996 - acc: 0.6464 - val_loss: 0.8171 - val_acc: 0.7162\n",
       "\n",
       "Epoch 00009: val_loss improved from 0.84751 to 0.81715, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 10/20\n",
       " - 11s - loss: 0.9732 - acc: 0.6564 - val_loss: 0.8470 - val_acc: 0.7048\n",
       "\n",
       "Epoch 00010: val_loss did not improve from 0.81715\n",
       "Epoch 11/20\n",
       " - 11s - loss: 0.9637 - acc: 0.6639 - val_loss: 0.8323 - val_acc: 0.7102\n",
       "\n",
       "Epoch 00011: val_loss did not improve from 0.81715\n",
       "Epoch 12/20\n",
       " - 11s - loss: 0.9462 - acc: 0.6647 - val_loss: 0.8075 - val_acc: 0.7140\n",
       "\n",
       "Epoch 00012: val_loss improved from 0.81715 to 0.80745, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 13/20\n",
       " - 11s - loss: 0.9319 - acc: 0.6704 - val_loss: 0.8284 - val_acc: 0.7156\n",
       "\n",
       "Epoch 00013: val_loss did not improve from 0.80745\n",
       "Epoch 14/20\n",
       " - 11s - loss: 0.9279 - acc: 0.6745 - val_loss: 0.8644 - val_acc: 0.6886\n",
       "\n",
       "Epoch 00014: val_loss did not improve from 0.80745\n",
       "Epoch 15/20\n",
       " - 11s - loss: 0.9077 - acc: 0.6787 - val_loss: 0.7595 - val_acc: 0.7368\n",
       "\n",
       "Epoch 00015: val_loss improved from 0.80745 to 0.75952, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 16/20\n",
       " - 10s - loss: 0.9053 - acc: 0.6813 - val_loss: 0.7495 - val_acc: 0.7382\n",
       "\n",
       "Epoch 00016: val_loss improved from 0.75952 to 0.74949, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 17/20\n",
       " - 11s - loss: 0.8983 - acc: 0.6842 - val_loss: 0.7426 - val_acc: 0.7404\n",
       "\n",
       "Epoch 00017: val_loss improved from 0.74949 to 0.74256, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 18/20\n",
       " - 10s - loss: 0.8906 - acc: 0.6847 - val_loss: 0.7792 - val_acc: 0.7308\n",
       "\n",
       "Epoch 00018: val_loss did not improve from 0.74256\n",
       "Epoch 19/20\n",
       " - 11s - loss: 0.8757 - acc: 0.6931 - val_loss: 0.7281 - val_acc: 0.7566\n",
       "\n",
       "Epoch 00019: val_loss improved from 0.74256 to 0.72814, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n",
       "Epoch 20/20\n",
       " - 11s - loss: 0.8707 - acc: 0.6938 - val_loss: 0.7143 - val_acc: 0.7598\n",
       "\n",
       "Epoch 00020: val_loss improved from 0.72814 to 0.71429, saving model to weights/cifar10.augmented.cnn.weights.best.hdf5\n"
      ]
     }
    ],
    "source": [
     "hist = cnn.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
     "                         steps_per_epoch=x_train.shape[0] // batch_size,\n",
     "                         epochs=epochs,\n",
     "                         validation_data=(x_valid, y_valid), \n",
     "                         callbacks=[checkpointer, tensorboard], \n",
     "                         verbose=2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Load best model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
     "cnn.load_weights(cnn_aug_path)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Test set accuracy"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The test accuracy for the three-layer CNN improves markedly to 74.79% after training on the larger, augmented data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Accuracy: 74.79%\n"
      ]
     }
    ],
    "source": [
     "accuracy = cnn.evaluate(x_test, y_test, verbose=0)[1]\n",
     "print('Accuracy: {:.2%}'.format(accuracy))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## AlexNet"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We also need to simplify the AlexNet architecture in response to the lower dimensionality of CIFAR10 images relative to the ImageNet samples used in the competition. We use the original number of filters but make them smaller (see notebook for implementation). The summary shows the five convolutional layers followed by two fully-connected layers with frequent use of batch normalization, for a total of 21.5 million parameters:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Define Architecture"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "K.clear_session()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "alexnet = Sequential([\n",
     "\n",
     "    # 1st Convolutional Layer\n",
     "    Conv2D(96, (3,3), strides=(2,2), activation='relu', padding='same', input_shape=input_shape, name='CONV_1'),\n",
     "    MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='POOL_1'),\n",
     "    BatchNormalization(name='NORM_1'),\n",
     "    \n",
     "    # 2nd Convolutional Layer\n",
     "    Conv2D(filters=256, kernel_size=(5, 5), padding='same', activation='relu', name='CONV2'),\n",
     "    MaxPooling2D(pool_size=(3, 3), strides=(2,2), name='POOL2'),\n",
     "    BatchNormalization(name='NORM_2'),\n",
     "    \n",
     "    # 3rd Convolutional Layer\n",
     "    Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu', name='CONV3'),\n",
     "    # 4th Convolutional Layer\n",
     "    Conv2D(filters=384, kernel_size=(3, 3), padding='same', activation='relu', name='CONV4'),\n",
     "    # 5th Convolutional Layer\n",
     "    Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu', name='CONV5'),\n",
     "    MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='POOL5'),\n",
     "    BatchNormalization(name='NORM_5'),\n",
     "    \n",
     "    # Fully Connected Layers\n",
     "    Flatten(name='FLAT'),\n",
     "    Dense(4096, input_shape=(32*32*3,), activation='relu', name='FC1'),\n",
     "    Dropout(0.4, name='DROP1'),\n",
     "    Dense(4096, activation='relu', name='FC2'),\n",
     "    Dropout(0.4, name='DROP2'),\n",
     "    Dense(num_classes, activation='softmax')\n",
     "])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "CONV_1 (Conv2D)              (None, 16, 16, 96)        2688      \n",
       "_________________________________________________________________\n",
       "POOL_1 (MaxPooling2D)        (None, 8, 8, 96)          0         \n",
       "_________________________________________________________________\n",
       "NORM_1 (BatchNormalization)  (None, 8, 8, 96)          384       \n",
       "_________________________________________________________________\n",
       "CONV2 (Conv2D)               (None, 8, 8, 256)         614656    \n",
       "_________________________________________________________________\n",
       "POOL2 (MaxPooling2D)         (None, 3, 3, 256)         0         \n",
       "_________________________________________________________________\n",
       "NORM_2 (BatchNormalization)  (None, 3, 3, 256)         1024      \n",
       "_________________________________________________________________\n",
       "CONV3 (Conv2D)               (None, 3, 3, 384)         885120    \n",
       "_________________________________________________________________\n",
       "CONV4 (Conv2D)               (None, 3, 3, 384)         1327488   \n",
       "_________________________________________________________________\n",
       "CONV5 (Conv2D)               (None, 3, 3, 256)         884992    \n",
       "_________________________________________________________________\n",
       "POOL5 (MaxPooling2D)         (None, 1, 1, 256)         0         \n",
       "_________________________________________________________________\n",
       "NORM_5 (BatchNormalization)  (None, 1, 1, 256)         1024      \n",
       "_________________________________________________________________\n",
       "FLAT (Flatten)               (None, 256)               0         \n",
       "_________________________________________________________________\n",
       "FC1 (Dense)                  (None, 4096)              1052672   \n",
       "_________________________________________________________________\n",
       "DROP1 (Dropout)              (None, 4096)              0         \n",
       "_________________________________________________________________\n",
       "FC2 (Dense)                  (None, 4096)              16781312  \n",
       "_________________________________________________________________\n",
       "DROP2 (Dropout)              (None, 4096)              0         \n",
       "_________________________________________________________________\n",
       "dense_1 (Dense)              (None, 10)                40970     \n",
       "=================================================================\n",
       "Total params: 21,592,330\n",
       "Trainable params: 21,591,114\n",
       "Non-trainable params: 1,216\n",
       "_________________________________________________________________\n"
      ]
     }
    ],
    "source": [
     "alexnet.summary()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compile Model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "alexnet.compile(loss='categorical_crossentropy',\n",
     "                optimizer='adam',\n",
     "                metrics=['accuracy'])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train Model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 32\n",
     "epochs = 20"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "alexnet_path = 'models/cifar10.augmented.alexnet.weights.best.hdf5'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
     "checkpointer = ModelCheckpoint(filepath=alexnet_path, \n",
     "                               verbose=1, \n",
     "                               save_best_only=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "tensorboard = TensorBoard(log_dir='./logs/alexnet', \n",
     "                          histogram_freq=1, \n",
     "                          batch_size=batch_size,\n",
     "                          write_graph=True,\n",
     "                          write_grads=False,\n",
     "                          update_freq='epoch')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 1.7045 - acc: 0.3855 - val_loss: 1.5554 - val_acc: 0.4586\n",
       "\n",
       "Epoch 00001: val_loss improved from inf to 1.55539, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 2/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 1.4137 - acc: 0.5014 - val_loss: 1.5889 - val_acc: 0.4762\n",
       "\n",
       "Epoch 00002: val_loss did not improve from 1.55539\n",
       "Epoch 3/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 1.2087 - acc: 0.5785 - val_loss: 1.4027 - val_acc: 0.5114\n",
       "\n",
       "Epoch 00003: val_loss improved from 1.55539 to 1.40270, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 4/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 1.1051 - acc: 0.6204 - val_loss: 1.1670 - val_acc: 0.6126\n",
       "\n",
       "Epoch 00004: val_loss improved from 1.40270 to 1.16702, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 5/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 1.0394 - acc: 0.6442 - val_loss: 0.9912 - val_acc: 0.6594\n",
       "\n",
       "Epoch 00005: val_loss improved from 1.16702 to 0.99116, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 6/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.9506 - acc: 0.6781 - val_loss: 0.9463 - val_acc: 0.6726\n",
       "\n",
       "Epoch 00006: val_loss improved from 0.99116 to 0.94629, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 7/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.8909 - acc: 0.6998 - val_loss: 0.8934 - val_acc: 0.6930\n",
       "\n",
       "Epoch 00007: val_loss improved from 0.94629 to 0.89337, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 8/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.8525 - acc: 0.7154 - val_loss: 0.8005 - val_acc: 0.7336\n",
       "\n",
       "Epoch 00008: val_loss improved from 0.89337 to 0.80054, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 9/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.7979 - acc: 0.7314 - val_loss: 0.9394 - val_acc: 0.6994\n",
       "\n",
       "Epoch 00009: val_loss did not improve from 0.80054\n",
       "Epoch 10/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.7739 - acc: 0.7417 - val_loss: 0.8567 - val_acc: 0.7094\n",
       "\n",
       "Epoch 00010: val_loss did not improve from 0.80054\n",
       "Epoch 11/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.7395 - acc: 0.7523 - val_loss: 0.8152 - val_acc: 0.7286\n",
       "\n",
       "Epoch 00011: val_loss did not improve from 0.80054\n",
       "Epoch 12/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.7138 - acc: 0.7626 - val_loss: 0.7294 - val_acc: 0.7544\n",
       "\n",
       "Epoch 00012: val_loss improved from 0.80054 to 0.72942, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 13/20\n",
       "1406/1406 [==============================] - 24s 17ms/step - loss: 0.6673 - acc: 0.7772 - val_loss: 0.7657 - val_acc: 0.7456\n",
       "\n",
       "Epoch 00013: val_loss did not improve from 0.72942\n",
       "Epoch 14/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.6710 - acc: 0.7772 - val_loss: 0.8086 - val_acc: 0.7388\n",
       "\n",
       "Epoch 00014: val_loss did not improve from 0.72942\n",
       "Epoch 15/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.6272 - acc: 0.7897 - val_loss: 0.6494 - val_acc: 0.7820\n",
       "\n",
       "Epoch 00015: val_loss improved from 0.72942 to 0.64940, saving model to models/cifar10.augmented.alexnet.weights.best.hdf5\n",
       "Epoch 16/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.6164 - acc: 0.7949 - val_loss: 1.5029 - val_acc: 0.6598\n",
       "\n",
       "Epoch 00016: val_loss did not improve from 0.64940\n",
       "Epoch 17/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.5968 - acc: 0.8044 - val_loss: 0.7309 - val_acc: 0.7574\n",
       "\n",
       "Epoch 00017: val_loss did not improve from 0.64940\n",
       "Epoch 18/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.6588 - acc: 0.7818 - val_loss: 0.6926 - val_acc: 0.7852\n",
       "\n",
       "Epoch 00018: val_loss did not improve from 0.64940\n",
       "Epoch 19/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.5598 - acc: 0.8152 - val_loss: 0.8458 - val_acc: 0.7644\n",
       "\n",
       "Epoch 00019: val_loss did not improve from 0.64940\n",
       "Epoch 20/20\n",
       "1406/1406 [==============================] - 23s 16ms/step - loss: 0.5492 - acc: 0.8179 - val_loss: 0.6891 - val_acc: 0.7756\n",
       "\n",
       "Epoch 00020: val_loss did not improve from 0.64940\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "<keras.callbacks.History at 0x7fda96ce4eb8>"
       ]
      },
      "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "alexnet.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size),\n",
     "                      steps_per_epoch=X_train.shape[0] // batch_size,\n",
     "                      epochs=epochs,\n",
     "                      validation_data=(X_valid, y_valid),\n",
     "                      callbacks=[checkpointer, tensorboard],\n",
     "                      verbose=1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "alexnet.load_weights(alexnet_path)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "After training for 20 episodes, each of which takes a little under 30 seconds on a single GPU, we obtain 76.84% test accuracy."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Accuracy: 76.84%\n"
      ]
     }
    ],
    "source": [
     "accuracy = alexnet.evaluate(X_test, y_test, verbose=0)[1]\n",
     "print('Accuracy: {:.2%}'.format(accuracy))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
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