100 lines
4.3 KiB
ReStructuredText
100 lines
4.3 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.callbacks.early_stopping import EarlyStopping
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.. _early_stopping:
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##############
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Early Stopping
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##############
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.. raw:: html
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<video width="50%" max-width="400px" controls
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poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/yt_thumbs/thumb_earlystop.png"
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src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/yt/Trainer+flags+19-+early+stopping_1.mp4"></video>
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***********************
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Stopping an Epoch Early
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***********************
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You can stop and skip the rest of the current epoch early by overriding :meth:`~pytorch_lightning.core.hooks.ModelHooks.on_train_batch_start` to return ``-1`` when some condition is met.
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If you do this repeatedly, for every epoch you had originally requested, then this will stop your entire training.
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**********************
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EarlyStopping Callback
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**********************
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The :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback can be used to monitor a metric and stop the training when no improvement is observed.
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To enable it:
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- Import :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback.
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- Log the metric you want to monitor using :meth:`~pytorch_lightning.core.lightning.LightningModule.log` method.
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- Init the callback, and set ``monitor`` to the logged metric of your choice.
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- Set the ``mode`` based on the metric needs to be monitored.
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- Pass the :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback to the :class:`~pytorch_lightning.trainer.trainer.Trainer` callbacks flag.
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.. code-block:: python
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from pytorch_lightning.callbacks.early_stopping import EarlyStopping
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class LitModel(LightningModule):
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def validation_step(self, batch, batch_idx):
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loss = ...
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self.log("val_loss", loss)
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model = LitModel()
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trainer = Trainer(callbacks=[EarlyStopping(monitor="val_loss", mode="min")])
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trainer.fit(model)
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You can customize the callbacks behaviour by changing its parameters.
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.. testcode::
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early_stop_callback = EarlyStopping(monitor="val_accuracy", min_delta=0.00, patience=3, verbose=False, mode="max")
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trainer = Trainer(callbacks=[early_stop_callback])
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Additional parameters that stop training at extreme points:
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- ``stopping_threshold``: Stops training immediately once the monitored quantity reaches this threshold.
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It is useful when we know that going beyond a certain optimal value does not further benefit us.
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- ``divergence_threshold``: Stops training as soon as the monitored quantity becomes worse than this threshold.
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When reaching a value this bad, we believes the model cannot recover anymore and it is better to stop early and run with different initial conditions.
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- ``check_finite``: When turned on, it stops training if the monitored metric becomes NaN or infinite.
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- ``check_on_train_epoch_end``: When turned on, it checks the metric at the end of a training epoch. Use this only when you are monitoring any metric logged within
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training-specific hooks on epoch-level.
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In case you need early stopping in a different part of training, subclass :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`
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and change where it is called:
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.. testcode::
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class MyEarlyStopping(EarlyStopping):
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def on_validation_end(self, trainer, pl_module):
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# override this to disable early stopping at the end of val loop
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pass
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def on_train_end(self, trainer, pl_module):
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# instead, do it at the end of training loop
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self._run_early_stopping_check(trainer)
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.. note::
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The :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback runs
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at the end of every validation epoch by default. However, the frequency of validation
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can be modified by setting various parameters in the :class:`~pytorch_lightning.trainer.trainer.Trainer`,
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for example :paramref:`~pytorch_lightning.trainer.trainer.Trainer.check_val_every_n_epoch`
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and :paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval`.
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It must be noted that the ``patience`` parameter counts the number of
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validation checks with no improvement, and not the number of training epochs.
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Therefore, with parameters ``check_val_every_n_epoch=10`` and ``patience=3``, the trainer
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will perform at least 40 training epochs before being stopped.
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