Callback classCallbacks 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.
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()
)A function that returns the custom Callback instances,
similar to the builtin callback functions.
String, the name of the custom class. (Conventionally, CamelCase).
\(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.
\(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).
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
\(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.
Additional methods or public members of the custom class.
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.
What the custom class will subclass. By default, the base keras class.
The R environment that all class methods will have as a grandparent.
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 = )
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).
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.
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()