Day 69.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10:Object Recognition in Images Using Deep Convolutional Neural Network(DCNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Libraries\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D \n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import cifar-10 dataset\n",
    "from keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "  1761280/170498071 [..............................] - ETA: 30:24"
     ]
    }
   ],
   "source": [
    "# load cifar-10 dataset\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining the dataset¶\n",
    "print(\"Shape of training data: 'features'\", X_train.shape)\n",
    "print(\"Shape of training data: 'labels'\", y_train.shape)\n",
    "print(\"Shape of test data: 'features'\", X_test.shape)\n",
    "print(\"Shape of test data: 'labels'\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corresponding classes for the labels\n",
    "print('Sample training images and their labels: ' + str([z[0] for z in y_train[0:10]])) \n",
    "print('Corresponding classes for the labels: ' + str([cifar_classes[z[0]] for z in y_train[0:10]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 images of CIFAR \n",
    "import matplotlib.pyplot as plt\n",
    "for z in range(10):\n",
    "    images = X_train[z]\n",
    "    plt.imshow(images)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the dataset\n",
    "cifar_num_classes = 10\n",
    "# Transform label indices to one-hot encoded vectors\n",
    "y_train = to_categorical(y_train, cifar_num_classes)\n",
    "y_test = to_categorical(y_test, cifar_num_classes)\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization of pixel values between 0 to 1 range \n",
    "X_train /= 255.0\n",
    "X_test /= 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the CNN model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same', input_shape=(32, 32, 3)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), padding='same', input_shape=(32, 32, 3)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(80))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(cifar_num_classes))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary of model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimize the weights\n",
    "opt = SGD(lr=0.01, momentum=0.9, decay=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the model\n",
    "history = model.fit(X_train, y_train, batch_size=64, epochs=100, verbose=2, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Visualizing the Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the result (accuracy)\n",
    "def plotAccuracy(history):  \n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('model Accuracy')\n",
    "    plt.ylabel('acc')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'validation'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotAccuracy(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the result (Loss)\n",
    "def plotLosses(history):  \n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'validation'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotLosses(history)"
   ]
  }
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