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Merge pull request optuna#4666 from gen740/remove-tensorflow-integration
Remove `tensorflow` integration
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,82 +1,4 @@ | ||
| import optuna | ||
| from optuna._imports import try_import | ||
| from optuna_integration.tensorflow import TensorFlowPruningHook | ||
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| 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 | ||
|
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||
| if not _imports.is_successful(): | ||
| SessionRunHook = object # NOQA | ||
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| class TensorFlowPruningHook(SessionRunHook): | ||
| """TensorFlow SessionRunHook to prune unpromising trials. | ||
| See `the example <https://github.com/optuna/optuna-examples/tree/main/ | ||
| tensorflow/tensorflow_estimator_integration.py>`_ | ||
| 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. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| trial: optuna.trial.Trial, | ||
| estimator: "tf.estimator.Estimator", | ||
| metric: str, | ||
| run_every_steps: int, | ||
| ) -> None: | ||
| _imports.check() | ||
|
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||
| 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) | ||
|
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| def begin(self) -> None: | ||
| self._global_step_tensor = tf.compat.v1.train.get_global_step() | ||
|
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||
| def before_run( | ||
| self, run_context: "tf.estimator.SessionRunContext" | ||
| ) -> "tf.estimator.SessionRunArgs": | ||
| del run_context | ||
| return tf.estimator.SessionRunArgs(self._global_step_tensor) | ||
|
|
||
| def after_run( | ||
| self, | ||
| run_context: "tf.estimator.SessionRunContext", | ||
| run_values: "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") | ||
| self._trial.report(float(current_score), 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) | ||
| __all__ = ["TensorFlowPruningHook"] |
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