► Keras 3 API documentation / Callbacks API / Base Callback class

Base Callback class

`Callback` class

keras.callbacks.Callback()

Base class used to build new callbacks.

Callbacks can be passed to keras methods such as fit(), evaluate(), and predict() in order to hook into the various stages of the model training, evaluation, and inference lifecycle.

To create a custom callback, subclass keras.callbacks.Callback and override the method associated with the stage of interest.

Example

>>> training_finished = False
>>> class MyCallback(Callback):
...   def on_train_end(self, logs=None):
...     global training_finished
...     training_finished = True
>>> model = Sequential([
...     layers.Dense(1, input_shape=(1,))])
>>> model.compile(loss='mean_squared_error')
>>> model.fit(np.array([[1.0]]), np.array([[1.0]]),
...           callbacks=[MyCallback()])
>>> assert training_finished == True

If you want to use Callback objects in a custom training loop:

You should pack all your callbacks into a single callbacks.CallbackList so they can all be called together.
You will need to manually call all the on_* methods at the appropriate locations in your loop. Like this:

Example

callbacks =  keras.callbacks.CallbackList([...])
callbacks.append(...)
callbacks.on_train_begin(...)
for epoch in range(EPOCHS):
    callbacks.on_epoch_begin(epoch)
    for i, data in dataset.enumerate():
    callbacks.on_train_batch_begin(i)
    batch_logs = model.train_step(data)
    callbacks.on_train_batch_end(i, batch_logs)
    epoch_logs = ...
    callbacks.on_epoch_end(epoch, epoch_logs)
final_logs=...
callbacks.on_train_end(final_logs)

Attributes

params: Dict. Training parameters (eg. verbosity, batch size, number of epochs...).
model: Instance of 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 (see method-specific docstrings).

Base Callback class

Callback class