/ 
AI-MXNet-1.6
River stage one • 2 direct dependents • 2 total dependents
/ AI::MXNet::Callback

NAME

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

DESCRIPTION

A collection of predefined callback functions, mainly to be used in AI::MXNet::Module::Base::fit.

SYNOPSIS

my $model = mx->mod->Module(
    symbol  => $net,
    context => $contexts
);
$model->fit(
    $data_iter,
    eval_metric         => mx->metric->Perplexity,
    kvstore             => $kv_store,
    optimizer           => $optimizer,
    optimizer_params    => {
        learning_rate => $lr,
        momentum      => $mom,
        wd            => $wd,
        clip_gradient => 5,
        rescale_grad  => 1/$batch_size,
        lr_scheduler  => AI::MXNet::FactorScheduler->new(step => 1000, factor => 0.99)
    },
    initializer         => mx->init->Xavier(factor_type => "in", magnitude => 2.34),
    num_epoch           => $num_epoch,
    batch_end_callback  => mx->callback->Speedometer($batch_size, $disp_batches),
    ($chkp_epoch ? (epoch_end_callback  => [mx->callback->module_checkpoint($model, $chkp_prefix, $chkp_epoch), \&sample]) : ())
);

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.