NAME

AI::MXNet::Callback - A collection of predefined callback functions

module_checkpoint

Callback to save the module setup in the checkpoint files.

Parameters
----------
$mod : subclass of AI::MXNet::Module::Base
    The module to checkpoint.
$prefix : str
    The file prefix to checkpoint to
$period=1 : int
    How many epochs to wait before checkpointing. Default is 1.
$save_optimizer_states=0 : Bool
    Whether to save optimizer states for later training.

Returns
-------
$callback : sub ref
    The callback function that can be passed as iter_end_callback to fit.

log_train_metric

Callback to log the training evaluation result every period.

Parameters
----------
$period : Int
    The number of batches after which to log the training evaluation metric.
$auto_reset : Bool
    Whether to reset the metric after the logging.

Returns
-------
$callback : sub ref
    The callback function that can be passed as iter_epoch_callback to fit.

NAME

AI::MXNet::Speedometer - A callback that logs training speed 

DESCRIPTION

Calculate and log training speed periodically.

Parameters
----------
batch_size: int
    batch_size of data
frequent: int
    How many batches between calculations.
    Defaults to calculating & logging every 50 batches.
auto_reset: Bool
    Reset the metric after each log, defaults to true.

NAME

AI::MXNet::ProgressBar - A callback to show a progress bar.

DESCRIPTION

Shows a progress bar.

Parameters
----------
total: Int
    batch size, default is 1
length: Int
    the length of the progress bar, default is 80 chars

NAME

AI::MXNet::LogValidationMetricsCallback - A callback to log the eval metrics at the end of an epoch.