optuna.integration.SkorchPruningCallback¶

class optuna.integration.SkorchPruningCallback(trial: optuna.trial._trial.Trial, monitor: str)[source]¶

Skorch callback to prune unpromising trials.

New in version 2.1.0.

Parameters

trial – A Trial corresponding to the current evaluation of the objective function.
monitor – An evaluation metric for pruning, e.g. val_loss or val_acc. The metrics are obtained from the returned dictionaries, i.e., net.histroy. The names thus depend on how this dictionary is formatted.

__init__(trial: optuna.trial._trial.Trial, monitor: str) → None [source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(trial, monitor)	Initialize self.
`on_epoch_end`(net, **kwargs)