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thepooonsrohitgr7SkafteNicki
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fix typos in validation_step and test_step docs (#5438)
* fixed docs in lightning.py * few more Co-authored-by: Rohit Gupta <[email protected]> Co-authored-by: Nicki Skafte <[email protected]> (cherry picked from commit 499d503)
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pytorch_lightning/core/lightning.py

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@@ -623,14 +623,14 @@ def validation_step(self, *args, **kwargs):
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for val_batch in val_data:
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out = validation_step(val_batch)
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val_outs.append(out)
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validation_epoch_end(val_outs)
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validation_epoch_end(val_outs)
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Args:
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batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): The index of this batch
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dataloader_idx (int): The index of the dataloader that produced this batch
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(only if multiple val datasets used)
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(only if multiple val dataloaders used)
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Return:
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Any of.
@@ -679,11 +679,11 @@ def validation_step(self, batch, batch_idx):
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# log the outputs!
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self.log_dict({'val_loss': loss, 'val_acc': val_acc})
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If you pass in multiple val datasets, validation_step will have an additional argument.
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If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument.
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.. code-block:: python
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# CASE 2: multiple validation datasets
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# CASE 2: multiple validation dataloaders
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def validation_step(self, batch, batch_idx, dataloader_idx):
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# dataloader_idx tells you which dataset this is.
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@@ -815,7 +815,7 @@ def test_step(self, *args, **kwargs):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): The index of this batch.
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dataloader_idx (int): The index of the dataloader that produced this batch
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(only if multiple test datasets used).
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(only if multiple test dataloaders used).
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Return:
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Any of.
@@ -855,17 +855,17 @@ def test_step(self, batch, batch_idx):
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# log the outputs!
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self.log_dict({'test_loss': loss, 'test_acc': test_acc})
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If you pass in multiple validation datasets, :meth:`test_step` will have an additional
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If you pass in multiple test dataloaders, :meth:`test_step` will have an additional
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argument.
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.. code-block:: python
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# CASE 2: multiple test datasets
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# CASE 2: multiple test dataloaders
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def test_step(self, batch, batch_idx, dataloader_idx):
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# dataloader_idx tells you which dataset this is.
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Note:
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If you don't need to validate you don't need to implement this method.
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If you don't need to test you don't need to implement this method.
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Note:
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When the :meth:`test_step` is called, the model has been put in eval mode and

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