Examples: using new API (#1056)

* using new API

* typo

docs/source/early_stopping.rst

@@ -4,7 +4,7 @@ Early stopping
 Default behavior
 ----------------
 By default early stopping will be enabled if `'val_loss'`
-is found in `validation_end()` return dict. Otherwise
+is found in `validation_epoch_end()` return dict. Otherwise
 training will proceed with early stopping disabled.
 
 Enable Early Stopping
@@ -16,7 +16,7 @@ There are two ways to enable early stopping.
 .. code-block:: python
 
     # A) Set early_stop_callback to True. Will look for 'val_loss'
-    # in validation_end() return dict. If it is not found an error is raised.
+    # in validation_epoch_end() return dict. If it is not found an error is raised.
     trainer = Trainer(early_stop_callback=True)
 
     # B) Or configure your own callback

docs/source/experiment_reporting.rst

@@ -87,7 +87,7 @@ Here we show the validation loss in the progress bar
 
 .. code-block:: python
 
-   def validation_end(self, outputs):
+   def validation_epoch_end(self, outputs):
       loss = some_loss()
       ...
 

docs/source/introduction_guide.rst

@@ -603,7 +603,7 @@ sample split in the `train_dataloader` method.
         loss = F.nll_loss(logits, y)
         return {'val_loss': loss}
 
-      def validation_end(self, outputs):
+      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}
@@ -657,7 +657,7 @@ Just like the validation loop, we define exactly the same steps for testing:
         loss = F.nll_loss(logits, y)
         return {'val_loss': loss}
 
-      def test_end(self, outputs):
+      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}