Image Classification Using SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Neural Network Using SVM as Final Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Of SVM\n",
    "\n",
    "- Part 1: https://www.youtube.com/watch?v=H9yACitf-KM\n",
    "- Part 2: https://www.youtube.com/watch?v=Js3GLb1xPhc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convolutional Neural Network\n",
    "\n",
    "# Importing the libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 1 - Data Preprocessing\n",
    "\n",
    "# Preprocessing the Training set\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8000 images belonging to 2 classes.\n",
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = train_datagen.flow_from_directory('Datasets/train',\n",
    "                                                 target_size = (64, 64),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'binary')\n",
    "\n",
    "# Preprocessing the Test set\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "test_set = test_datagen.flow_from_directory('Datasets/test',\n",
    "                                            target_size = (64, 64),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.regularizers import l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 2 - Building the CNN\n",
    "# Initialising the CNN\n",
    "cnn = tf.keras.models.Sequential()\n",
    "\n",
    "# Step 1 - Convolution\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32,padding=\"same\",kernel_size=3, activation='relu', strides=2, input_shape=[64, 64, 3]))\n",
    "\n",
    "# Step 2 - Pooling\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n",
    "\n",
    "# Adding a second convolutional layer\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32,padding='same',kernel_size=3, activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n",
    "\n",
    "# Step 3 - Flattening\n",
    "cnn.add(tf.keras.layers.Flatten())\n",
    "\n",
    "# Step 4 - Full Connection\n",
    "cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
    "\n",
    "# Step 5 - Output Layer\n",
    "#cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
    "## For Binary Classification\n",
    "cnn.add(Dense(1, kernel_regularizer=tf.keras.regularizers.l2(0.01),activation\n",
    "             ='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## for mulitclassification\n",
    "cnn.add(Dense(4, kernel_regularizer=tf.keras.regularizers.l2(0.01),activation\n",
    "             ='softmax'))\n",
    "cnn.compile(optimizer = 'adam', loss = 'squared_hinge', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               262272    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 272,545\n",
      "Trainable params: 272,545\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "250/250 [==============================] - 15s 58ms/step - loss: 0.8911 - accuracy: 0.5706 - val_loss: 0.9147 - val_accuracy: 0.6195\n",
      "Epoch 2/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.7251 - accuracy: 0.6611 - val_loss: 0.6736 - val_accuracy: 0.6765\n",
      "Epoch 3/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.6664 - accuracy: 0.6935 - val_loss: 0.6522 - val_accuracy: 0.6750\n",
      "Epoch 4/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.6154 - accuracy: 0.7179 - val_loss: 0.7462 - val_accuracy: 0.7195\n",
      "Epoch 5/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5937 - accuracy: 0.7272 - val_loss: 0.5680 - val_accuracy: 0.7655\n",
      "Epoch 6/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5608 - accuracy: 0.7408 - val_loss: 0.5924 - val_accuracy: 0.7690\n",
      "Epoch 7/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5497 - accuracy: 0.7483 - val_loss: 0.5979 - val_accuracy: 0.7590\n",
      "Epoch 8/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5414 - accuracy: 0.7484 - val_loss: 0.6206 - val_accuracy: 0.7655\n",
      "Epoch 9/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5317 - accuracy: 0.7551 - val_loss: 0.6008 - val_accuracy: 0.7795\n",
      "Epoch 10/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5172 - accuracy: 0.7629 - val_loss: 0.5014 - val_accuracy: 0.7840\n",
      "Epoch 11/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5146 - accuracy: 0.7657 - val_loss: 0.5078 - val_accuracy: 0.7790\n",
      "Epoch 12/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.5037 - accuracy: 0.7696 - val_loss: 0.5279 - val_accuracy: 0.7600\n",
      "Epoch 13/15\n",
      "250/250 [==============================] - 14s 55ms/step - loss: 0.4934 - accuracy: 0.7753 - val_loss: 0.5020 - val_accuracy: 0.7885\n",
      "Epoch 14/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.4789 - accuracy: 0.7860 - val_loss: 0.5044 - val_accuracy: 0.7850\n",
      "Epoch 15/15\n",
      "250/250 [==============================] - 14s 56ms/step - loss: 0.4760 - accuracy: 0.7828 - val_loss: 0.4897 - val_accuracy: 0.7865\n"
     ]
    }
   ],
   "source": [
    "# Part 3 - Training the CNN\n",
    "\n",
    "# Compiling the CNN\n",
    "cnn.compile(optimizer = 'adam', loss = 'hinge', metrics = ['accuracy'])\n",
    "\n",
    "# Training the CNN on the Training set and evaluating it on the Test set\n",
    "r=cnn.fit(x = training_set, validation_data = test_set, epochs = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "cnn.save('model_rcat_dog.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    " \n",
    "# load model\n",
    "model = load_model('model_rcat_dog.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 29, 29, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               802944    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 813,217\n",
      "Trainable params: 813,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 4 - Making a single prediction\n",
    "\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "test_image = image.load_img('Datasets/test/dogs/dog.4015.jpg', target_size = (64,64))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image=test_image/255\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = cnn.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.13195]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 4 - Making a single prediction\n",
    "\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "test_image = image.load_img('Datasets/test/cats/cat.4017.jpg', target_size = (64,64))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image=test_image/255\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = cnn.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4654408]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image classified is dog\n"
     ]
    }
   ],
   "source": [
    "if result[0]<0:\n",
    "    print(\"The image classified is cat\")\n",
    "else:\n",
    "    print(\"The image classified is dog\")"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}