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EarlyStopping

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

Bases: pytorch_lightning.callbacks.base.Callback

Monitor a 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.

    Warning

    Setting mode='auto' has been deprecated in v1.1 and will be removed in v1.3.

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

Raises
  • MisconfigurationException – If mode is none of "min", "max", and "auto".

  • RuntimeError – If the metric monitor is not available.

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(callback_state)[source]

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

Parameters

callback_state (Dict[str, Any]) – the callback state returned by on_save_checkpoint.

on_save_checkpoint(trainer, pl_module, checkpoint)[source]

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

Parameters
  • trainer – the current Trainer instance.

  • pl_module – the current LightningModule instance.

  • checkpoint (Dict[str, Any]) – the checkpoint dictionary that will be saved.

Return type

Dict[str, Any]

Returns

The callback state.

on_validation_end(trainer, pl_module)[source]

Called when the validation loop ends.

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