code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip= True\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'dataset/training_set',\n",
    "    target_size = (64,64),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "validation_generator=test_datagen.flow_from_directory(\n",
    "    'dataset/test_set',\n",
    "    target_size=(64,64),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convolution initialization and layer addition\n",
    "\n",
    "cnn = tf.keras.models.Sequential()\n",
    "cnn.add(tf.keras.layers.Conv2D(\n",
    "    filters=32,\n",
    "    kernel_size=3,\n",
    "    activation='relu',\n",
    "    input_shape=[64,64,3]\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pooling \n",
    "cnn.add(tf.keras.layers.MaxPool2D(\n",
    "    pool_size=2,\n",
    "    strides=2\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(\n",
    "    filters=32,\n",
    "    kernel_size=3,\n",
    "    activation='relu'\n",
    "))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(\n",
    "    pool_size=2,\n",
    "    strides=2\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(\n",
    "    units=128,\n",
    "    activation='relu'\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(\n",
    "    units=1,\n",
    "    activation='sigmoid'\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(\n",
    "    optimizer='adam',\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "250/250 [==============================] - 127s 507ms/step - loss: 0.6760 - accuracy: 0.5738 - val_loss: 0.6737 - val_accuracy: 0.5735\n",
      "Epoch 2/5\n",
      "250/250 [==============================] - 79s 314ms/step - loss: 0.5898 - accuracy: 0.6811 - val_loss: 0.6135 - val_accuracy: 0.6595\n",
      "Epoch 3/5\n",
      "250/250 [==============================] - 59s 236ms/step - loss: 0.5484 - accuracy: 0.7172 - val_loss: 0.5130 - val_accuracy: 0.7485\n",
      "Epoch 4/5\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.5105 - accuracy: 0.7460 - val_loss: 0.4968 - val_accuracy: 0.7610\n",
      "Epoch 5/5\n",
      "250/250 [==============================] - 74s 294ms/step - loss: 0.4905 - accuracy: 0.7628 - val_loss: 0.5003 - val_accuracy: 0.7550\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1fce28890d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.fit(x = train_generator, validation_data= validation_generator, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 97ms/step\n",
      "Prediction: dog\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "img = Image.open(\"dataset/single_prediction/cat_or_dog_1.jpg\")\n",
    "img = img.resize((64, 64))\n",
    "img_array = np.array(img)\n",
    "img_array = np.expand_dims(img_array, axis=0)\n",
    "result = cnn.predict(img_array)\n",
    "if result[0][0] == 1:\n",
    "    prediction = 'dog'\n",
    "else:\n",
    "    prediction = 'cat'\n",
    "\n",
    "print(\"Prediction:\", prediction)\n"
   ]
  }
 ],
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