{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the CNN\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1 - Convolution\n",
    "classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2 - Pooling\n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a second convolutional layer\n",
    "classifier.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3 - Flattening\n",
    "classifier.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4 - Full connection\n",
    "classifier.add(Dense(units = 128, activation = 'relu'))\n",
    "classifier.add(Dense(units = 1, activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the CNN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partie 2 - CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n",
    "\n",
    "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.fit_generator(training_set,\n",
    "                         steps_per_epoch = 8000,\n",
    "                         epochs = 25,\n",
    "                         validation_data = test_set,\n",
    "                         validation_steps = 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partie 3 - Prédictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "training_set.class_indices\n",
    "if result[0][0] == 1:\n",
    "    prediction = 'dog'\n",
    "else:\n",
    "    prediction = 'cat'"
   ]
  }
 ],
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