fixed val_loss for early stopping

docs/source/introduction_guide.rst

@@ -654,7 +654,7 @@ sample split in the `train_dataloader` method.
       def validation_epoch_end(self, outputs):
         avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
         tensorboard_logs = {'val_loss': avg_loss}
-        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
+        return {'val_loss': avg_loss, 'log': tensorboard_logs}
 
       def val_dataloader(self):
         transform=transforms.Compose([transforms.ToTensor(),
@@ -710,7 +710,7 @@ Just like the validation loop, we define exactly the same steps for testing:
       def test_epoch_end(self, outputs):
         avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
         tensorboard_logs = {'val_loss': avg_loss}
-        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
+        return {'val_loss': avg_loss, 'log': tensorboard_logs}
 
       def test_dataloader(self):
         transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

docs/source/new-project.rst

@@ -100,7 +100,7 @@ To also add a validation loop add the following functions
         def validation_epoch_end(self, outputs):
             avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
             tensorboard_logs = {'val_loss': avg_loss}
-            return {'avg_val_loss': avg_loss, 'log': tensorboard_logs
+            return {'val_loss': avg_loss, 'log': tensorboard_logs
 
         def val_dataloader(self):
             # TODO: do a real train/val split