ImageDataGenerator

ML Project/ImageDataGenerator

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+# Import necessary libraries
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+
+# Set up data directories
+train_dir = 'train'
+test_dir = 'test'
+
+# Data Preprocessing
+train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(train_dir, target_size=(64, 64), batch_size=32, class_mode='binary')
+test_generator = test_datagen.flow_from_directory(test_dir, target_size=(64, 64), batch_size=32, class_mode='binary')
+
+# Build a Convolutional Neural Network (CNN) model
+model = Sequential()
+model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(units=128, activation='relu'))
+model.add(Dense(units=1, activation='sigmoid')
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(train_generator, steps_per_epoch=len(train_generator), epochs=10, validation_data=test_generator, validation_steps=len(test_generator))
+
+# Evaluate the model
+test_loss, test_accuracy = model.evaluate(test_generator, steps=len(test_generator))
+print("Test accuracy: {:.2f}%".format(test_accuracy * 100))
+
+# Save the model
+model.save('cat_dog_classifier.h5')