from typing import Any
from typing import Dict
from typing import Optional
import warnings
import optuna
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 v2.
See `the example <https://github.com/optuna/optuna-examples/blob/main/
tfkeras/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``.
"""
def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None:
super().__init__()
_imports.check()
self._trial = trial
self._monitor = monitor
def on_epoch_end(self, epoch: int, logs: Optional[Dict[str, Any]] = None) -> None:
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
# 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)