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.