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

Keras callback to prune unpromising trials.

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

  • 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[source]

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


__init__(trial, monitor[, interval])

Initialize self.

on_epoch_end(epoch[, logs])