optuna.integration.FastAIPruningCallback¶

class optuna.integration.FastAIPruningCallback(learn: Learner, trial: optuna.trial.Trial, monitor: str)[source]¶

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 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.

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')])

Parameters

learn – fastai.basic_train.Learner.
trial – A 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 for further details.

__init__(learn: Learner, trial: optuna.trial.Trial, monitor: str) → None [source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(learn, trial, monitor)	Initialize self.
`on_epoch_end`(epoch, **kwargs)