Source code for optuna.integration.tfkeras

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/blob/master/ examples/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)