Callbacks for Monitoring and Fine-tuning

Keras includes several built-in callbacks that can be used during training for various purposes:

• EarlyStopping: Stop training when the validation loss starts to increase.
• ModelCheckpoint: Save the model weights at every epoch or when the performance improves.
• LearningRateScheduler: Adjust the learning rate dynamically during training.
• Example:pythonCopy codefrom keras.callbacks import EarlyStopping, ModelCheckpoint early_stop = EarlyStopping(monitor='val_loss', patience=5) model_checkpoint = ModelCheckpoint('best_model.h5', save_best_only=True) model.fit(x_train, y_train, callbacks=[early_stop, model_checkpoint])