Image Recognition-Deep Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Recognition of cats and dogs using DeepLearning - CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Importing the Libraries_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_verify tensorflow version_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Image Train set_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_set = train_datagen.flow_from_directory(\n",
    "        'dataset/training_set',\n",
    "        target_size=(64, 64),\n",
    "        batch_size=32,\n",
    "        class_mode='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Image Test set_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory(\n",
    "        'dataset/test_set',\n",
    "        target_size=(64, 64),\n",
    "        batch_size=32,\n",
    "        class_mode='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Initialize the CNN_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the convolution layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters = 32,kernel_size = 3,activation = \"relu\",input_shape = [64,64,3]) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying MaxPooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the second convolutional layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters = 32,kernel_size = 3,activation = \"relu\"))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flattening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Adding a fully connected layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units = 128,activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units = 1,activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Training the CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compile the CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(optimizer = \"adam\",loss = \"binary_crossentropy\",metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 250 steps, validate for 63 steps\n",
      "Epoch 1/25\n",
      "250/250 [==============================] - 80s 321ms/step - loss: 0.6783 - accuracy: 0.5689 - val_loss: 0.6257 - val_accuracy: 0.6615\n",
      "Epoch 2/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.6135 - accuracy: 0.6660 - val_loss: 0.5718 - val_accuracy: 0.7095\n",
      "Epoch 3/25\n",
      "250/250 [==============================] - 79s 316ms/step - loss: 0.5589 - accuracy: 0.7105 - val_loss: 0.6091 - val_accuracy: 0.6785\n",
      "Epoch 4/25\n",
      "250/250 [==============================] - 80s 321ms/step - loss: 0.5333 - accuracy: 0.7275 - val_loss: 0.5083 - val_accuracy: 0.7580\n",
      "Epoch 5/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.5021 - accuracy: 0.7523 - val_loss: 0.4882 - val_accuracy: 0.7715\n",
      "Epoch 6/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.4806 - accuracy: 0.7634 - val_loss: 0.4892 - val_accuracy: 0.7585\n",
      "Epoch 7/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.4692 - accuracy: 0.7782 - val_loss: 0.4849 - val_accuracy: 0.7730\n",
      "Epoch 8/25\n",
      "250/250 [==============================] - 79s 318ms/step - loss: 0.4491 - accuracy: 0.7886 - val_loss: 0.4732 - val_accuracy: 0.7720\n",
      "Epoch 9/25\n",
      "250/250 [==============================] - 80s 319ms/step - loss: 0.4294 - accuracy: 0.7939 - val_loss: 0.4746 - val_accuracy: 0.7875\n",
      "Epoch 10/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.4239 - accuracy: 0.8055 - val_loss: 0.4546 - val_accuracy: 0.7920\n",
      "Epoch 11/25\n",
      "250/250 [==============================] - 82s 327ms/step - loss: 0.4027 - accuracy: 0.8167 - val_loss: 0.4485 - val_accuracy: 0.7945\n",
      "Epoch 12/25\n",
      "250/250 [==============================] - 80s 320ms/step - loss: 0.3832 - accuracy: 0.8279 - val_loss: 0.4443 - val_accuracy: 0.8010\n",
      "Epoch 13/25\n",
      "250/250 [==============================] - 79s 318ms/step - loss: 0.3741 - accuracy: 0.8319 - val_loss: 0.4556 - val_accuracy: 0.8030\n",
      "Epoch 14/25\n",
      "250/250 [==============================] - 79s 317ms/step - loss: 0.3553 - accuracy: 0.8336 - val_loss: 0.4695 - val_accuracy: 0.7820\n",
      "Epoch 15/25\n",
      "250/250 [==============================] - 85s 339ms/step - loss: 0.3492 - accuracy: 0.8439 - val_loss: 0.4950 - val_accuracy: 0.7825\n",
      "Epoch 16/25\n",
      "250/250 [==============================] - 85s 341ms/step - loss: 0.3294 - accuracy: 0.8533 - val_loss: 0.4785 - val_accuracy: 0.8075\n",
      "Epoch 17/25\n",
      "250/250 [==============================] - 83s 333ms/step - loss: 0.3232 - accuracy: 0.8605 - val_loss: 0.4712 - val_accuracy: 0.8005\n",
      "Epoch 18/25\n",
      "250/250 [==============================] - 85s 340ms/step - loss: 0.2904 - accuracy: 0.8754 - val_loss: 0.4824 - val_accuracy: 0.8155\n",
      "Epoch 19/25\n",
      "250/250 [==============================] - 84s 337ms/step - loss: 0.2909 - accuracy: 0.8769 - val_loss: 0.4837 - val_accuracy: 0.8090\n",
      "Epoch 20/25\n",
      "250/250 [==============================] - 81s 324ms/step - loss: 0.2745 - accuracy: 0.8850 - val_loss: 0.5025 - val_accuracy: 0.8065\n",
      "Epoch 21/25\n",
      "250/250 [==============================] - 48s 192ms/step - loss: 0.2689 - accuracy: 0.8845 - val_loss: 0.4742 - val_accuracy: 0.8145\n",
      "Epoch 22/25\n",
      "250/250 [==============================] - 37s 150ms/step - loss: 0.2495 - accuracy: 0.8988 - val_loss: 0.5087 - val_accuracy: 0.8015\n",
      "Epoch 23/25\n",
      "250/250 [==============================] - 41s 163ms/step - loss: 0.2397 - accuracy: 0.8979 - val_loss: 0.5415 - val_accuracy: 0.8025\n",
      "Epoch 24/25\n",
      "250/250 [==============================] - 46s 184ms/step - loss: 0.2248 - accuracy: 0.9106 - val_loss: 0.5867 - val_accuracy: 0.7835\n",
      "Epoch 25/25\n",
      "250/250 [==============================] - 45s 179ms/step - loss: 0.2070 - accuracy: 0.9178 - val_loss: 0.5274 - val_accuracy: 0.7995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e0a6dbea48>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.fit(x=train_set,validation_data = test_set, epochs = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Making the prediction of cats and dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "\n",
    "#Predicting the dog image\n",
    "test_image = image.load_img('dataset/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 = cnn.predict(test_image)\n",
    "train_set.class_indices\n",
    "if result[0][0] == 1:\n",
    "    prediction = 'dog'\n",
    "else:\n",
    "    prediction = 'cat'\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n"
     ]
    }
   ],
   "source": [
    "#Predicting the cat image\n",
    "test_image = image.load_img('dataset/prediction/cat_or_dog_2.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 = cnn.predict(test_image)\n",
    "train_set.class_indices\n",
    "if result[0][0] == 1:\n",
    "    prediction = 'dog'\n",
    "else:\n",
    "    prediction = 'cat'\n",
    "print(prediction)"
   ]
  }
 ],
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