Crax Rat May 2026

# Building the model base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Freeze base layers for layer in base_model.layers: layer.trainable = False crax rat

# Fine-tune. Make all layers trainable. for layer in model.layers: layer.trainable = True steps_per_epoch=train_generator.samples // 32

train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=32, class_mode='categorical') validation_steps=validation_generator.samples // 32

# Training history = model.fit(train_generator, steps_per_epoch=train_generator.samples // 32, validation_data=validation_generator, validation_steps=validation_generator.samples // 32, epochs=10)

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])