optuna.integration.KerasPruningCallback

class optuna.integration.KerasPruningCallback(trial: optuna.trial._trial.Trial, monitor: str, interval: int = 1)

Keras callback to prune unpromising trials.

See the example if you want to add a pruning callback which observes validation accuracy.

Parameters

• trial – A Trial corresponding to the current evaluation of the objective function.

• monitor – An evaluation metric for pruning, e.g., val_loss and val_accuracy. Please refer to keras.Callback reference for further details.

• interval – Check if trial should be pruned every n-th epoch. By default interval=1 and pruning is performed after every epoch. Increase interval to run several epochs faster before applying pruning.

__init__(trial: optuna.trial._trial.Trial, monitor: str, interval: int = 1) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(trial, monitor[, interval])	Initialize self.
on_epoch_end(epoch[, logs])