Cat or Dog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv():\n",
    "  return keras.Sequential([\n",
    "      keras.layers.Conv2D(32,(3,3),input_shape=(64, 64, 3),activation=keras.activations.relu),\n",
    "      keras.layers.MaxPool2D(2,2),\n",
    "      keras.layers.Conv2D(32,(3,3), activation=keras.activations.relu),\n",
    "      keras.layers.MaxPool2D(2,2),\n",
    "      keras.layers.Flatten(),\n",
    "      keras.layers.Dense(128,activation=keras.activations.softmax),\n",
    "      keras.layers.Dense(1,activation=keras.activations.sigmoid)\n",
    "  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Change working directory to where dataset exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_model = conv()\n",
    "dnn_model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.1), \n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "dnn_model.summary()"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = train_datagen.flow_from_directory('dataset/training_set',\n",
    "                                                 target_size = (64, 64),\n",
    "                                                 batch_size = 16,\n",
    "                                                 class_mode = 'binary')\n",
    "\n",
    "test_set = test_datagen.flow_from_directory('dataset/test_set',\n",
    "                                            target_size = (64, 64),\n",
    "                                            batch_size = 16,\n",
    "                                            class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "main=dnn_model.fit_generator(training_set,\n",
    "                         steps_per_epoch = 8000,\n",
    "                         epochs = 25,\n",
    "                         validation_data = test_set,\n",
    "                         validation_steps = 2000)"
   ]
  },
  {
   "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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