model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Fine-tune. Make all layers trainable. for layer in model.layers: layer.trainable = True
# Continue training This example demonstrates how to use transfer learning with VGG16 for a binary classification task. Adapt it according to your dataset and objectives. The approach to preparing deep features for Crax rubra or any wildlife species involves thoughtful data collection, preprocessing, and model selection. Leveraging pre-trained models through transfer learning can significantly improve performance, especially when dealing with limited datasets.
validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size=(224, 224), batch_size=32, class_mode='categorical')
model = Model(inputs=base_model.input, outputs=predictions)
# 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)
x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x)