Source code for optuna.integration.keras

import optuna
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Dict  # NOQA

with optuna._imports.try_import() as _imports:
    from keras.callbacks import Callback

if not _imports.is_successful():
    Callback = object  # NOQA

[docs]class KerasPruningCallback(Callback): """Keras callback to prune unpromising trials. See `the example < examples/pruning/>`__ if you want to add a pruning callback which observes validation accuracy. Args: trial: A :class:`~optuna.trial.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. """
[docs] def __init__(self, trial, monitor, interval=1): # type: (optuna.trial.Trial, str, int) -> None super(KerasPruningCallback, self).__init__() _imports.check() self._trial = trial self._monitor = monitor self._interval = interval
def on_epoch_end(self, epoch, logs=None): # type: (int, Dict[str, float]) -> None if (epoch + 1) % self._interval != 0: return logs = logs or {} current_score = logs.get(self._monitor) if current_score is None: return, step=epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)