notebooks/vggnet_in_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGGNet in TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we fit a model inspired by the \"very deep\" convolutional network [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) to classify MNIST digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jonkrohn/DLTFpT/blob/master/notebooks/vggnet_in_tensorflow.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import BatchNormalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_valid, y_valid) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(60000, 28, 28, 1).astype('float32')\n",
    "X_valid = X_valid.reshape(10000, 28, 28, 1).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train /= 255\n",
    "X_valid /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = 10\n",
    "y_train = to_categorical(y_train, n_classes)\n",
    "y_valid = to_categorical(y_valid, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Design neural network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, 3, activation='relu', padding='same', input_shape=(28, 28, 1)))\n",
    "model.add(Conv2D(64, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Conv2D(128, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(128, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Conv2D(256, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(256, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(256, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(2, 1)) # default stride is 2\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(Conv2D(512, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(2, 1)) # default stride is 2\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 28, 28, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 14, 14, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 14, 14, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 7, 7, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 7, 7, 256)         590080    \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 7, 7, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 256)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 3, 3, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 3, 3, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 3, 3, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 3, 3, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 2, 2, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 1, 1, 512)         2048      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              2101248   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                40970     \n",
      "=================================================================\n",
      "Total params: 33,642,954\n",
      "Trainable params: 33,640,010\n",
      "Non-trainable params: 2,944\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configure model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "60000/60000 [==============================] - 597s 10ms/sample - loss: 0.4775 - accuracy: 0.8731 - val_loss: 2.7757 - val_accuracy: 0.6111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f402c3f3fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size=128, epochs=1, verbose=1, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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