A custom callback in Keras is a powerful tool to customize the behavior of a model during training, evaluation, or inference, and to implement early stopping, model checkpointing, and other custom metrics or visualizations.
To implement a custom callback in Keras, you need to create a class that inherits from the keras.callbacks.Callback class. This class should contain the methods that you want to customize, such as on_train_begin, on_train_end, on_epoch_begin, on_epoch_end, on_batch_begin, on_batch_end, and so on.
In each of these methods, you can define the behavior that you want to customize. For example, in the on_train_begin method, you can define the code that you want to execute when the training begins. Similarly, in the on_epoch_end method, you can define the code that you want to execute when an epoch ends.
Once you have defined the methods, you can instantiate the class and pass it to the fit() method of the model. This will enable the custom callback to be executed during training.
For example, if you want to implement early stopping, you can define a custom callback that checks the validation loss after each epoch and stops the training if the validation loss does not improve for a certain number of epochs.
In summary, to implement a custom callback in Keras, you need to create a class that inherits from the keras.callbacks.Callback class and define the methods that you want to customize. Then, you can instantiate the class and pass it to the fit() method of the model.
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