updated doc indexes

docs/LightningModule/RequiredTrainerInterface.md

@@ -29,21 +29,24 @@ Otherwise, to Define a Lightning Module, implement the following methods:
 ---
 **Minimal example**
 ```python
-import pytorch_lightning as ptl
+import os
 import torch
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
+
+import pytorch_lightning as ptl
 
 class CoolModel(ptl.LightningModule):
 
-    def __init(self):
+    def __init__(self):
         super(CoolModel, self).__init__()
         # not the best model...
         self.l1 = torch.nn.Linear(28 * 28, 10)
 
     def forward(self, x):
-        return torch.relu(self.l1(x))
+        return torch.relu(self.l1(x.view(x.size(0), -1)))
 
     def my_loss(self, y_hat, y):
         return F.cross_entropy(y_hat, y)
@@ -51,7 +54,7 @@ class CoolModel(ptl.LightningModule):
     def training_step(self, batch, batch_nb):
         x, y = batch
         y_hat = self.forward(x)
-        return {'tng_loss': self.my_loss(y_hat, y)}
+        return {'loss': self.my_loss(y_hat, y)}
 
     def validation_step(self, batch, batch_nb):
         x, y = batch
@@ -59,23 +62,23 @@ class CoolModel(ptl.LightningModule):
         return {'val_loss': self.my_loss(y_hat, y)}
 
     def validation_end(self, outputs):
-        avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
+        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        return avg_loss
+        return {'avg_val_loss': avg_loss}
 
     def configure_optimizers(self):
         return [torch.optim.Adam(self.parameters(), lr=0.02)]
 
     @ptl.data_loader
     def tng_dataloader(self):
-        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
+        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
     @ptl.data_loader
     def val_dataloader(self):
-        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
+        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
     @ptl.data_loader
     def test_dataloader(self):
-        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
+        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 ```
 
 ---