Source code for optuna.integration.tfkeras

import optuna
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Any  # NOQA
    from typing import Dict  # NOQA

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

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

[docs]class TFKerasPruningCallback(Callback): """tf.keras callback to prune unpromising trials. This callback is intend to be compatible for TensorFlow v1 and v2, but only tested with TensorFlow v1. See `the example < examples/pruning/>`__ if you want to add a pruning callback which observes the 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`` or ``val_acc``. """
[docs] def __init__(self, trial, monitor): # type: (optuna.trial.Trial, str) -> None super(TFKerasPruningCallback, self).__init__() _imports.check() self._trial = trial self._monitor = monitor
def on_epoch_end(self, epoch, logs=None): # type: (int, Dict[str, Any]) -> None logs = logs or {} current_score = logs.get(self._monitor) if current_score is None: return # Report current score and epoch to Optuna's trial., step=epoch) # Prune trial if needed if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)