LUS_CNN_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "resistant-macro",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "from tensorflow.keras.models import Sequential, save_model, load_model\n",
    "from tensorflow.keras.layers import Convolution2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Dropout\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "embedded-blond",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_WIDTH=254\n",
    "IMAGE_HEIGHT=254\n",
    "IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\n",
    "IMAGE_CHANNELS=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "physical-marks",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 84, 84, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 42, 42, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 42, 42, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1568)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               200832    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 211,105\n",
      "Trainable params: 211,105\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Define CNN model called classifier\n",
    "classifier = Sequential()\n",
    "\n",
    "#CNN first layer\n",
    "classifier.add(Convolution2D(32, 3, 3, input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS), activation = 'relu'))#Apply a 3x3 convolution filter with 32 filters output \n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2))) #Apply maxpooling with a 2x2 filter\n",
    "classifier.add(Dropout(0.2)) #Apply dropout with a probability of 20%\n",
    "\n",
    "#CNN second layer\n",
    "classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))\n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
    "classifier.add(Dropout(0.2))\n",
    "\n",
    "classifier.add(Flatten())\n",
    "\n",
    "#Fully connected layer\n",
    "classifier.add(Dense(128, activation = 'relu'))\n",
    "classifier.add(Dropout(0.5))\n",
    "classifier.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "direct-requirement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1934 images belonging to 2 classes.\n",
      "Found 478 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#Apply data augmentation techniques on the training dataset\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale = 1./255,\n",
    "        shear_range = 8,\n",
    "        zoom_range = 0.05,\n",
    "        width_shift_range = 0.1,\n",
    "        height_shift_range = 0.1,\n",
    "        fill_mode = 'constant',\n",
    "        cval =200\n",
    "        )\n",
    "\n",
    "\n",
    "test_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "\n",
    "#Create training dataset from local directory and apply the previously defined data augmentation train_datagen\n",
    "training_set = train_datagen.flow_from_directory(\n",
    "        'dataset/train',\n",
    "        target_size= IMAGE_SIZE,\n",
    "        batch_size=32,\n",
    "        class_mode='binary',\n",
    "        shuffle = True)\n",
    "\n",
    "#Create testing dataset from local directory and apply the previously defined data augmentation test_datagen\n",
    "test_set = test_datagen.flow_from_directory(\n",
    "        'dataset/test',\n",
    "        target_size= IMAGE_SIZE,\n",
    "        batch_size=32,\n",
    "        class_mode='binary',\n",
    "        shuffle = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fiscal-bristol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 61 steps, validate for 15 steps\n",
      "Epoch 1/5\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier model \n",
    "epochs=5 \n",
    "history = classifier.fit(\n",
    "                training_set,\n",
    "                epochs=epochs,\n",
    "                validation_data=test_set,\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "quick-conditioning",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-48685cc23f50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Training loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"validation loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display graph of training/validation loss and accuracy \n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\n",
    "ax1.plot(history.history['loss'], color='b', label=\"Training loss\")\n",
    "ax1.plot(history.history['val_loss'], color='r', label=\"validation loss\")\n",
    "ax1.set_xticks(np.arange(1, epochs, 1))\n",
    "ax1.set_yticks(np.arange(0, 1, 0.1))\n",
    "\n",
    "ax2.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
    "ax2.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
    "ax2.set_xticks(np.arange(1, epochs, 1))\n",
    "\n",
    "legend = plt.legend(loc='best', shadow=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "equal-grain",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the trained model in your local repository\n",
    "classifier.save(\"model.h5\")"
   ]
  }
 ],
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