13.2 Build AlexNet and test on CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's construct AlexNet in Keras!\n",
    "\n",
    "#### First let's load and prep our CIFAR10 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "10000 test samples\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, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.regularizers import l2\n",
    "\n",
    "# Loads the CIFAR dataset\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Display our data shape/dimensions\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",
    "# Now we one hot encode outputs\n",
    "num_classes = 10\n",
    "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": [
    "### Now let's create our layers to replicate AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 96)        34944     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 32, 32, 96)        384       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 96)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 256)       614656    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 16, 16, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPaddin (None, 10, 10, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 10, 10, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 10, 10, 512)       2048      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 10, 10, 512)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 512)         0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPaddin (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 7, 7, 1024)        4719616   \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPaddin (None, 9, 9, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 9, 9, 1024)        9438208   \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 9, 9, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 9, 9, 1024)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 4, 4, 1024)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3072)              50334720  \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 3072)              12288     \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 3072)              0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 3072)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4096)              12587008  \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 4096)              16384     \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                40970     \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 78,990,642\n",
      "Trainable params: 78,970,462\n",
      "Non-trainable params: 20,180\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "l2_reg = 0\n",
    "\n",
    "# Initialize model\n",
    "model = Sequential()\n",
    "\n",
    "# 1st Conv Layer \n",
    "model.add(Conv2D(96, (11, 11), input_shape=x_train.shape[1:],\n",
    "    padding='same', kernel_regularizer=l2(l2_reg)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 2nd Conv Layer \n",
    "model.add(Conv2D(256, (5, 5), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 3rd Conv Layer \n",
    "model.add(ZeroPadding2D((1, 1)))\n",
    "model.add(Conv2D(512, (3, 3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 4th Conv Layer \n",
    "model.add(ZeroPadding2D((1, 1)))\n",
    "model.add(Conv2D(1024, (3, 3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 5th Conv Layer \n",
    "model.add(ZeroPadding2D((1, 1)))\n",
    "model.add(Conv2D(1024, (3, 3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 1st FC Layer\n",
    "model.add(Flatten())\n",
    "model.add(Dense(3072))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# 2nd FC Layer\n",
    "model.add(Dense(4096))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# 3rd FC Layer\n",
    "model.add(Dense(num_classes))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy',\n",
    "              optimizer = keras.optimizers.Adadelta(),\n",
    "              metrics = ['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let us train AlexNet on our CIFAR10 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      " 4640/50000 [=>............................] - ETA: 1:59:10 - loss: 1.9930 - acc: 0.2756"
     ]
    }
   ],
   "source": [
    "# Training Parameters\n",
    "batch_size = 32\n",
    "epochs = 1\n",
    "\n",
    "history = model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_data=(x_test, y_test),\n",
    "          shuffle=True)\n",
    "\n",
    "model.save(\"/home/deeplearningcv/DeepLearningCV/Trained Models/CIFAR10_AlexNet_1_Epoch.h5\")\n",
    "\n",
    "# Evaluate the performance of our trained model\n",
    "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": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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