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
_keras_pruning_callback_deprecated_msg = (
"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. "
"There is an alternative callback function that can be used instead: "
":class:`~optuna.integration.TFKerasPruningCallback`"
)
[docs]@deprecated("2.1.0", "4.0.0", text=_keras_pruning_callback_deprecated_msg)
class KerasPruningCallback(Callback):
"""Keras callback to prune unpromising trials.
See `the example <https://github.com/optuna/optuna-examples/blob/main/
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)