Chapter07/Activity7.01/Activity7_01.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity 7.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import the Libraries\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPool2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "from tensorflow import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the seed and initialize the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 1\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "\n",
    "# Initialising the CNN\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the convolutional layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Convolution\n",
    "classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n",
    "classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
    "classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
    "\n",
    "# Pooling\n",
    "classifier.add(MaxPool2D(pool_size = (2, 2)))\n",
    "\n",
    "#  Flattening\n",
    "classifier.add(Flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the dense layers to the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Full ANN Connection\n",
    "classifier.add(Dense(units = 128, activation = 'relu'))\n",
    "classifier.add(Dense(128,activation='relu'))\n",
    "classifier.add(Dense(128,activation='relu'))\n",
    "classifier.add(Dense(128,activation='relu'))\n",
    "classifier.add(Dense(units = 1, activation = 'softmax'))\n",
    "# Compiling the CNN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the training and test data generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10764 images belonging to 2 classes.\n",
      "Found 2674 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = train_datagen.flow_from_directory('../dataset/training_set',\n",
    "                                                 target_size = (64, 64),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'binary')\n",
    "\n",
    "test_set = test_datagen.flow_from_directory('../dataset/test_set',\n",
    "                                            target_size = (64, 64),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "10000/10000 [==============================] - 3576s 358ms/step - loss: 8.1741 - accuracy: 0.4669 - val_loss: 11.4999 - val_accuracy: 0.4695\n",
      "Epoch 2/2\n",
      "10000/10000 [==============================] - 3354s 335ms/step - loss: 8.1764 - accuracy: 0.4667 - val_loss: 7.1875 - val_accuracy: 0.4691\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x13d70f048>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit_generator(training_set,\n",
    "                         steps_per_epoch = 10000,\n",
    "                         epochs = 2,\n",
    "                         validation_data = test_set,\n",
    "                         validation_steps = 2500,\n",
    "                         shuffle=False)"
   ]
  }
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