Callback {keras3}

Define a custom Callback class

Description

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, call Callback() and override the method associated with the stage of interest.

Usage

Callback(
  classname,
  on_epoch_begin = NULL,
  on_epoch_end = NULL,
  on_train_begin = NULL,
  on_train_end = NULL,
  on_train_batch_begin = NULL,
  on_train_batch_end = NULL,
  on_test_begin = NULL,
  on_test_end = NULL,
  on_test_batch_begin = NULL,
  on_test_batch_end = NULL,
  on_predict_begin = NULL,
  on_predict_end = NULL,
  on_predict_batch_begin = NULL,
  on_predict_batch_end = NULL,
  ...,
  public = list(),
  private = list(),
  inherit = NULL,
  parent_env = parent.frame()
)

Arguments

classname

String, the name of the custom class. (Conventionally, CamelCase).

on_epoch_begin

\(epoch, logs = NULL)

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args:

• epoch: Integer, index of epoch.

• logs: Named List. Currently no data is passed to this argument for this method but that may change in the future.

on_epoch_end

\(epoch, logs = NULL)

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args:

• epoch: Integer, index of epoch.

• logs: Named List, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model's metrics are returned. Example: list(loss = 0.2, accuracy = 0.7).

on_train_begin

\(logs = NULL)

Called at the beginning of training.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_train_end

\(logs = NULL)

Called at the end of training.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently the output of the last call to on_epoch_end() is passed to this argument for this method but that may change in the future.

on_train_batch_begin

\(batch, logs = NULL)

Called at the beginning of a training batch in fit() methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_train_batch_end

\(batch, logs=NULL)

Called at the end of a training batch in fit() methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Aggregated metric results up until this batch.

on_test_begin

\(logs = NULL)

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_test_end

\(logs = NULL)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently the output of the last call to on_test_batch_end() is passed to this argument for this method but that may change in the future.

on_test_batch_begin

\(batch, logs = NULL)

Called at the beginning of a batch in evaluate() methods.

Also called at the beginning of a validation batch in the fit() methods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile() in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_test_batch_end

\(batch, logs = NULL)

Called at the end of a batch in evaluate() methods.

Also called at the end of a validation batch in the fit() methods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile() in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Aggregated metric results up until this batch.

on_predict_begin

\(logs = NULL)

Called at the beginning of prediction.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_predict_end

\(logs = NULL)

Called at the end of prediction.

Subclasses should override for any actions to run.

Args:

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_predict_batch_begin

\(batch, logs = NULL)

Called at the beginning of a batch in predict() methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile() in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Currently no data is passed to this argument for this method but that may change in the future.

on_predict_batch_end

\(batch, logs = NULL)

Called at the end of a batch in predict() methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args:

• batch: Integer, index of batch within the current epoch.

• logs: Named list. Aggregated metric results up until this batch.

..., public

Additional methods or public members of the custom class.

private

Named list of R objects (typically, functions) to include in instance private environments. private methods will have all the same symbols in scope as public methods (See section "Symbols in Scope"). Each instance will have it's own private environment. Any objects in private will be invisible from the Keras framework and the Python runtime.

inherit

What the custom class will subclass. By default, the base keras class.

parent_env

The R environment that all class methods will have as a grandparent.

Value

A function that returns the custom Callback instances, similar to the builtin callback functions.

Examples

training_finished <- FALSE
callback_mark_finished <- Callback("MarkFinished",
  on_train_end = function(logs = NULL) {
    training_finished <<- TRUE
  }
)

model <- keras_model_sequential(input_shape = c(1)) |>
  layer_dense(1)
model |> compile(loss = 'mean_squared_error')
model |> fit(op_ones(c(1, 1)), op_ones(c(1, 1)),
             callbacks = callback_mark_finished())
stopifnot(isTRUE(training_finished))

All R function custom methods (public and private) will have the following symbols in scope:

• self: the Layer instance.

• super: the Layer superclass.

• private: An R environment specific to the class instance. Any objects defined here will be invisible to the Keras framework.

• ⁠__class__⁠ the current class type object. This will also be available as an alias symbol, the value supplied to Layer(classname = )

Attributes (accessible via ⁠self$⁠)

• params: Named list, Training parameters (e.g. verbosity, batch size, number of epochs, ...).

• model: Instance of Model. Reference of the model being trained.

The logs named list that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).

Symbols in scope

All R function custom methods (public and private) will have the following symbols in scope:

• self: The custom class instance.

• super: The custom class superclass.

• private: An R environment specific to the class instance. Any objects assigned here are invisible to the Keras framework.

• ⁠__class__⁠ and as.symbol(classname): the custom class type object.

See Also

• https://keras.io/api/callbacks/base_callback#callback-class

Other callbacks:
callback_backup_and_restore()
callback_csv_logger()
callback_early_stopping()
callback_lambda()
callback_learning_rate_scheduler()
callback_model_checkpoint()
callback_reduce_lr_on_plateau()
callback_remote_monitor()
callback_swap_ema_weights()
callback_tensorboard()
callback_terminate_on_nan()