Source code for optuna.integration.tensorflow

import optuna
from optuna._imports import try_import

if optuna.type_checking.TYPE_CHECKING:
    from typing import Optional  # NOQA

with try_import() as _imports:
    import tensorflow as tf
    from tensorflow.estimator import SessionRunHook
    from tensorflow_estimator.python.estimator.early_stopping import read_eval_metrics

if not _imports.is_successful():
    SessionRunHook = object  # NOQA

[docs]class TensorFlowPruningHook(SessionRunHook): """TensorFlow SessionRunHook to prune unpromising trials. See `the example < pruning/>`_ if you want to add a pruning hook to TensorFlow's estimator. Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. estimator: An estimator which you will use. metric: An evaluation metric for pruning, e.g., ``accuracy`` and ``loss``. run_every_steps: An interval to watch the summary file. """
[docs] def __init__(self, trial, estimator, metric, run_every_steps): # type: (optuna.trial.Trial, tf.estimator.Estimator, str, int) -> None _imports.check() self._trial = trial self._estimator = estimator self._current_summary_step = -1 self._metric = metric self._global_step_tensor = None self._timer = tf.estimator.SecondOrStepTimer(every_secs=None, every_steps=run_every_steps)
def begin(self): # type: () -> None self._global_step_tensor = tf.compat.v1.train.get_global_step() def before_run(self, run_context): # type: (tf.estimator.SessionRunContext) -> tf.estimator.SessionRunArgs del run_context return tf.estimator.SessionRunArgs(self._global_step_tensor) def after_run(self, run_context, run_values): # type: (tf.estimator.SessionRunContext, tf.estimator.SessionRunValues) -> None global_step = run_values.results # Get eval metrics every n steps. if self._timer.should_trigger_for_step(global_step): self._timer.update_last_triggered_step(global_step) eval_metrics = read_eval_metrics(self._estimator.eval_dir()) else: eval_metrics = None if eval_metrics: summary_step = next(reversed(eval_metrics)) latest_eval_metrics = eval_metrics[summary_step] # If there exists a new evaluation summary. if summary_step > self._current_summary_step: current_score = latest_eval_metrics[self._metric] if current_score is None: current_score = float("nan"), step=summary_step) self._current_summary_step = summary_step if self._trial.should_prune(): message = "Trial was pruned at iteration {}.".format(self._current_summary_step) raise optuna.TrialPruned(message)