Source code for optuna.integration.keras

from typing import Dict
from typing import Optional
import warnings

import optuna
from optuna._deprecated import deprecated


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

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


[docs]@deprecated( "2.1.0", text="Recent Keras release (2.4.0) simply redirects all APIs " "in the standalone keras package to point to tf.keras. " "There is now only one Keras: tf.keras. " "There may be some breaking changes for some workflows by upgrading to keras 2.4.0. " "Test before upgrading. " "REF:https://github.com/keras-team/keras/releases/tag/2.4.0", ) class KerasPruningCallback(Callback): """Keras callback to prune unpromising trials. See `the example <https://github.com/optuna/optuna/blob/master/ examples/keras/keras_integration.py>`__ 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 <https://keras.io/callbacks/#callback>`_ 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. """ def __init__(self, trial: optuna.trial.Trial, monitor: str, interval: int = 1) -> None: super().__init__() _imports.check() self._trial = trial self._monitor = monitor self._interval = interval def on_epoch_end(self, epoch: int, logs: Optional[Dict[str, float]] = None) -> None: if (epoch + 1) % self._interval != 0: return logs = logs or {} current_score = logs.get(self._monitor) if current_score is None: message = ( "The metric '{}' is not in the evaluation logs for pruning. " "Please make sure you set the correct metric name.".format(self._monitor) ) warnings.warn(message) return self._trial.report(float(current_score), step=epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)