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.CallbackListso 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).