Common TensorFlow Callbacks
TensorFlow provides several built-in callbacks that can be very useful:
- EarlyStopping: Stops training when a monitored metric has stopped improving.
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
- ModelCheckpoint: Saves the model at specified intervals.
model_checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='model.h5', save_best_only=True)
- LearningRateScheduler: Schedules changes to the learning rate during training.
def scheduler(epoch, lr):
if epoch < 10:
return lr
else:
return lr * tf.math.exp(-0.1)
lr_scheduler = tf.keras.callbacks.LearningRateScheduler(scheduler)
- TensorBoard: Logs data for visualization in TensorBoard.
tensorboard = tf.keras.callbacks.TensorBoard(log_dir='./logs')
tf.keras.callbacks.Callback | Tensorflow Callbacks
TensorFlow Callbacks are a powerful tool for enhancing the training process of neural networks. These callbacks provide the ability to monitor and modify the behavior of the model during training, evaluation, or inference. In this article, we will explore what callbacks are, how to implement them, and some common types of callbacks provided by TensorFlow.
Table of Content
- What are TensorFlow Callbacks?
- Common TensorFlow Callbacks
- Custom Callbacks
- Effective Training with TensorFlow Callbacks
- Conclusion