Image_classification_smallpets_directory.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the data from the previously created directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_directory = \"C:\\\\Users\\Ramy\\.keras\\datasets\\cats_dogs_small\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = os.path.join(base_directory, 'train')\n",
    "validation_directory = os.path.join(base_directory, 'validation')\n",
    "test_directory = os.path.join(base_directory, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cats_dir = os.path.join(train_directory, 'cats')\n",
    "train_dogs_dir = os.path.join(train_directory, 'dogs')\n",
    "validation_cats_dir = os.path.join(validation_directory, 'cats')\n",
    "validation_dogs_dir = os.path.join(validation_directory, 'dogs')\n",
    "test_cats_dir = os.path.join(test_directory, 'cats')\n",
    "test_dogs_dir = os.path.join(test_directory, 'dogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_cats_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data reprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rescale pixels for standardization\n",
    "train_data_generator = ImageDataGenerator(rescale=1./255)\n",
    "test_data_generator = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.preprocessing.image.ImageDataGenerator at 0x2ecae398f70>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_data_generator.flow_from_directory(train_directory, \n",
    "                                                                target_size=(150, 150), \n",
    "                                                                batch_size=20, \n",
    "                                                                class_mode='binary') # because we haveo nly two classes! binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (150, 150, 3)\n",
      "color format:  rgb\n"
     ]
    }
   ],
   "source": [
    "print('Shape: ' , train_generator.image_shape)\n",
    "print('color format: ', train_generator.color_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_generator = test_data_generator.flow_from_directory(validation_directory, \n",
    "                                                                target_size=(150, 150), \n",
    "                                                                batch_size=20, \n",
    "                                                                class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (150, 150, 3)\n",
      "color format:  rgb\n"
     ]
    }
   ],
   "source": [
    "print('Shape: ' , validation_generator.image_shape)\n",
    "print('color format: ', validation_generator.color_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### creating the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Layer 1\n",
    "model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (150,150,3)))\n",
    "model.add(layers.MaxPooling2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layer 2\n",
    "model.add(layers.Conv2D(64, (3,3), activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layer 3\n",
    "model.add(layers.Conv2D(128, (3,3), activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#layer 4\n",
    "model.add(layers.Conv2D(128, (3,3), activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#flatten  and dense layers\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 512)               66048     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,864,001\n",
      "Trainable params: 3,864,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = \"binary_crossentropy\", optimizer = optimizers.RMSprop(lr = 1e-4), metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras can't handle any empty or destroyed image. so we will use PILLOW library for that reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "100/100 [==============================] - 67s 661ms/step - loss: 0.6939 - accuracy: 0.5245 - val_loss: 0.6825 - val_accuracy: 0.5870\n",
      "Epoch 2/5\n",
      "100/100 [==============================] - 57s 572ms/step - loss: 0.6895 - accuracy: 0.5424 - val_loss: 0.6611 - val_accuracy: 0.6060\n",
      "Epoch 3/5\n",
      "100/100 [==============================] - 57s 573ms/step - loss: 0.6677 - accuracy: 0.6106 - val_loss: 0.6525 - val_accuracy: 0.5990\n",
      "Epoch 4/5\n",
      "100/100 [==============================] - 57s 564ms/step - loss: 0.6677 - accuracy: 0.5945 - val_loss: 0.6574 - val_accuracy: 0.6070\n",
      "Epoch 5/5\n",
      "100/100 [==============================] - 53s 526ms/step - loss: 0.6446 - accuracy: 0.6459 - val_loss: 0.6109 - val_accuracy: 0.6520\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator, \n",
    "                             steps_per_epoch=100, \n",
    "                             epochs= 5, \n",
    "                             validation_data=validation_generator,\n",
    "                             validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: cat_dog_small_\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('cat_dog_small_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ecb181f820>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, acc, 'bo', label= 'Training Accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label = 'Validation accuracy')\n",
    "plt.title('Acc Iteration')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ecb4041940>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, loss, 'bo', label= 'Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label = 'Validation loss')\n",
    "plt.title('Validation Iteration')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}