EarlyStopping

class pytorch_lightning.callbacks.EarlyStopping(monitor='early_stop_on', min_delta=0.0, patience=3, verbose=False, mode='auto', strict=True)

Bases: pytorch_lightning.callbacks.base.Callback

Monitor a validation metric and stop training when it stops improving.

Parameters

• monitor (str) – quantity to be monitored. Default: 'early_stop_on'.

• min_delta (float) – minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement. Default: 0.0.

• patience (int) – number of validation epochs with no improvement after which training will be stopped. Default: 3.

• verbose (bool) – verbosity mode. Default: False.

• mode (str) – one of {auto, min, max}. In min mode, training will stop when the quantity monitored has stopped decreasing; in max mode it will stop when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity. Default: 'auto'.

• strict (bool) – whether to crash the training if monitor is not found in the validation metrics. Default: True.

Example:

>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import EarlyStopping
>>> early_stopping = EarlyStopping('val_loss')
>>> trainer = Trainer(callbacks=[early_stopping])

on_load_checkpoint(checkpointed_state)

Called when loading a model checkpoint, use to reload state.

on_save_checkpoint(trainer, pl_module)

Called when saving a model checkpoint, use to persist state.

on_train_end(trainer, pl_module)

Called when the train ends.

on_train_epoch_end(trainer, pl_module)

Called when the train epoch ends.

on_validation_end(trainer, pl_module)

Called when the validation loop ends.

on_validation_epoch_end(trainer, pl_module)

Called when the val epoch ends.