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 <https://github.com/optuna/optuna/blob/master/
examples/pruning/tfkeras_integration.py>`__
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.
self._trial.report(float(current_score), step=epoch)
# Prune trial if needed
if self._trial.should_prune():
message = "Trial was pruned at epoch {}.".format(epoch)
raise optuna.TrialPruned(message)