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 <https://github.com/optuna/optuna/blob/master/
examples/pruning/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.
"""
[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
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)