Datasets
Models
Downloads: 1
Diff of
/xception.ipynb
[000000]
..
[2ed9d9]
Switch to side-by-side view
--- a +++ b/xception.ipynb @@ -0,0 +1,259 @@ +{ + "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": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}