optuna.integration.FastAIV1PruningCallback

class optuna.integration.FastAIV1PruningCallback(learn, trial, monitor)[source]

FastAI callback to prune unpromising trials for fastai.

Note

This callback is for fastai<2.0.

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 (Learner) – fastai.basic_train.Learner.
trial (Trial) – A Trial corresponding to the current evaluation of the objective function.
monitor (str) – An evaluation metric for pruning, e.g. valid_loss and Accuracy. Please refer to fastai.callbacks.TrackerCallback reference for further details.

Warning

Deprecated in v2.4.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v4.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v2.4.0.

Methods

on_epoch_end(epoch, **kwargs)