260 lines (259 with data), 11.4 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b8fc951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 980 images belonging to 2 classes.\n",
      "Found 140 images belonging to 2 classes.\n",
      "Found 280 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mohit\\AppData\\Local\\Temp\\ipykernel_33056\\3483214989.py:72: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  xception_history = xception_model.fit_generator(train_generator, steps_per_epoch=train_generator.n // train_generator.batch_size, epochs=5, validation_data=val_generator, validation_steps=val_generator.n // val_generator.batch_size, callbacks=[checkpoint])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "30/30 [==============================] - 186s 6s/step - loss: 0.1842 - accuracy: 0.9462 - val_loss: 0.1311 - val_accuracy: 0.9219\n",
      "Epoch 2/5\n",
      "30/30 [==============================] - 193s 6s/step - loss: 0.0225 - accuracy: 0.9926 - val_loss: 0.1422 - val_accuracy: 0.9297\n",
      "Epoch 3/5\n",
      "30/30 [==============================] - 190s 6s/step - loss: 0.0069 - accuracy: 0.9989 - val_loss: 0.0426 - val_accuracy: 0.9922\n",
      "Epoch 4/5\n",
      "30/30 [==============================] - 192s 6s/step - loss: 0.0070 - accuracy: 0.9979 - val_loss: 0.0151 - val_accuracy: 0.9922\n",
      "Epoch 5/5\n",
      "30/30 [==============================] - 192s 6s/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 0.9922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mohit\\AppData\\Local\\Temp\\ipykernel_33056\\3483214989.py:74: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  xception_scores = xception_model.evaluate_generator(test_generator, steps=test_samples // BATCH_SIZE)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xception Test Loss: 0.012623521499335766\n",
      "Xception Test Accuracy: 0.9921875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mohit\\AppData\\Local\\Temp\\ipykernel_33056\\3483214989.py:85: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  xception_predictions = xception_model.predict_generator(test_generator, steps=num_prediction_steps, verbose=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 11s 1s/step\n",
      "Xception Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       140\n",
      "           1       1.00      0.99      0.99       140\n",
      "\n",
      "    accuracy                           0.99       280\n",
      "   macro avg       0.99      0.99      0.99       280\n",
      "weighted avg       0.99      0.99      0.99       280\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing necessary libraries\n",
    "import os\n",
    "import random\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications import VGG16, VGG19, ResNet50, InceptionV3, Xception\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# Setting random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "random.seed(42)\n",
    "\n",
    "# Define constants\n",
    "NUM_CLASSES = 2\n",
    "IMG_SIZE = (224, 224)\n",
    "BATCH_SIZE = 32\n",
    "TRAIN_DIR = 'train'\n",
    "VAL_DIR = 'val'\n",
    "TEST_DIR = 'test'\n",
    "TRAIN_SPLIT = 0.7\n",
    "VAL_SPLIT = 0.1\n",
    "TEST_SPLIT = 0.2\n",
    "test_samples = 280\n",
    "\n",
    "# Define the base directory where the image data is located\n",
    "BASE_DIR = r\"C:\\Users\\mohit\\Downloads\\DIP Assignment 2\\Data\"\n",
    "\n",
    "# Set the paths for training, validation, and test data\n",
    "TRAIN_DIR = os.path.join(BASE_DIR, 'train')\n",
    "VAL_DIR = os.path.join(BASE_DIR, 'val')\n",
    "TEST_DIR = os.path.join(BASE_DIR, 'test')\n",
    "\n",
    "# Load and preprocess the data\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(TRAIN_DIR,target_size=IMG_SIZE,batch_size=BATCH_SIZE,class_mode='categorical')\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(VAL_DIR,target_size=IMG_SIZE,batch_size=BATCH_SIZE,class_mode='categorical')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(TEST_DIR,target_size=IMG_SIZE,batch_size=BATCH_SIZE,class_mode='categorical',shuffle=False)\n",
    "\n",
    "# Define function for creating transfer learning models\n",
    "def create_transfer_model(base_model, num_classes):\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(1024, activation='relu')(x)\n",
    "    predictions = Dense(num_classes, activation='softmax')(x)\n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "    return model\n",
    "\n",
    "# Define Xception model\n",
    "xception_base = Xception(include_top=False, weights='imagenet', input_shape=(IMG_SIZE[0], IMG_SIZE[1], 3))\n",
    "xception_model = create_transfer_model(xception_base, NUM_CLASSES)\n",
    "\n",
    "# Compile the models\n",
    "optimizer = Adam(learning_rate=0.0001)\n",
    "xception_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Define checkpoint callback to save the best model during training\n",
    "checkpoint = ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True)\n",
    "\n",
    "xception_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "xception_history = xception_model.fit_generator(train_generator, steps_per_epoch=train_generator.n // train_generator.batch_size, epochs=5, validation_data=val_generator, validation_steps=val_generator.n // val_generator.batch_size, callbacks=[checkpoint])\n",
    "\n",
    "xception_scores = xception_model.evaluate_generator(test_generator, steps=test_samples // BATCH_SIZE)\n",
    "print(\"Xception Test Loss:\", xception_scores[0])\n",
    "print(\"Xception Test Accuracy:\", xception_scores[1])\n",
    "\n",
    "# Get the number of test samples\n",
    "num_test_samples = test_generator.n\n",
    "\n",
    "# Calculate the number of steps for prediction\n",
    "num_prediction_steps = num_test_samples // test_generator.batch_size + 1\n",
    "\n",
    "# Generate predictions for all test samples\n",
    "xception_predictions = xception_model.predict_generator(test_generator, steps=num_prediction_steps, verbose=1)\n",
    "\n",
    "# Convert predictions to class labels\n",
    "xception_predicted_labels = np.argmax(xception_predictions, axis=1)\n",
    "\n",
    "# Get true class labels\n",
    "true_labels = test_generator.classes\n",
    "\n",
    "# Calculate classification report\n",
    "xception_report = classification_report(true_labels, xception_predicted_labels)\n",
    "\n",
    "print(\"Xception Classification Report:\")\n",
    "print(xception_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "17422ece",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xception_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10180\\1533048863.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxception_history\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxception_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m//\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Plot the training loss curve\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'xception_model' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xception_history = xception_model.fit(train_generator, steps_per_epoch=train_generator.n // train_generator.batch_size, epochs=3)\n",
    "\n",
    "# Plot the training loss curve\n",
    "plt.plot(xception_history.history['loss'])\n",
    "plt.title('xception Training Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6641997c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(xception_history.history['accuracy'])\n",
    "plt.title('xception Training Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e503a76d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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