What are TensorFlow Callbacks?
Callbacks are functions or blocks of code that are executed at specific stages of the training process. They allow you to interact with the model at various points such as:
- At the start and end of an epoch
- Before and after a batch is processed
- At the start and end of training
These interactions can be used to implement custom behavior such as early stopping, learning rate scheduling, saving model checkpoints, logging metrics, and more.
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