tf.keras.callbacks.Callback

Class `Callback`

Abstract base class used to build new callbacks.

params: dict. Training parameters (eg. verbosity, batch size, number of epochs...).
model: instance of keras.models.Model. Reference of the model being trained.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch.

Currently, the .fit() method of the Sequential model class will include the following quantities in the logs that it passes to its callbacks:

on_epoch_end: logs include acc and loss, and optionally include val_loss (if validation is enabled in fit), and val_acc (if validation and accuracy monitoring are enabled).
on_batch_begin: logs include size, the number of samples in the current batch.
on_batch_end: logs include loss, and optionally acc (if accuracy monitoring is enabled).

__init__()

Initialize self. See help(type(self)) for accurate signature.

on_batch_begin(
    batch,
    logs=None
)

on_batch_end(
    batch,
    logs=None
)

on_epoch_begin(
    epoch,
    logs=None
)

on_epoch_end(
    epoch,
    logs=None
)

on_train_batch_begin(
    batch,
    logs=None
)

on_train_batch_end(
    batch,
    logs=None
)

on_train_begin(logs=None)

on_train_end(logs=None)

set_model(model)

set_params(params)