optuna.integration.fastai 源代码

import optuna
from optuna._imports import try_import
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Any  # NOQA

with try_import() as _imports:
    from fastai.basic_train import Learner  # NOQA
    from fastai.callbacks import TrackerCallback

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


[文档]class FastAIPruningCallback(TrackerCallback): """FastAI callback to prune unpromising trials for fastai. .. note:: This callback is for fastai<2.0, not the coming version developed in fastai/fastai_dev. See `the example <https://github.com/optuna/optuna/blob/master/ examples/fastai_simple.py>`__ if you want to add a pruning callback which monitors validation loss of a ``Learner``. Example: Register a pruning callback to ``learn.fit`` and ``learn.fit_one_cycle``. .. code:: learn.fit(n_epochs, callbacks=[FastAIPruningCallback(learn, trial, 'valid_loss')]) learn.fit_one_cycle( n_epochs, cyc_len, max_lr, callbacks=[FastAIPruningCallback(learn, trial, 'valid_loss')]) Args: learn: `fastai.basic_train.Learner <https://docs.fast.ai/basic_train.html#Learner>`_. trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. monitor: An evaluation metric for pruning, e.g. ``valid_loss`` and ``Accuracy``. Please refer to `fastai.Callback reference <https://docs.fast.ai/callback.html#Callback>`_ for further details. """ def __init__(self, learn, trial, monitor): # type: (Learner, optuna.trial.Trial, str) -> None super(FastAIPruningCallback, self).__init__(learn, monitor) _imports.check() self._trial = trial def on_epoch_end(self, epoch, **kwargs): # type: (int, Any) -> None value = self.get_monitor_value() if value is None: return # This conversion is necessary to avoid problems reported in issues. # - https://github.com/optuna/optuna/issue/642 # - https://github.com/optuna/optuna/issue/655. self._trial.report(float(value), step=epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)