updated docs

README.md

@@ -42,27 +42,34 @@ To use lightning do 2 things:
 ```python
 import pytorch_lightning as ptl
 import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
 
 class CoolModel(ptl.LightningModule):
 
     def __init(self):
+        # not the best model...
         self.l1 = torch.nn.Linear(28*28, 10)
 
     def forward(self, x):
-        return self.l1(x)
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)
 
     def training_step(self, batch, batch_nb):
         x, y = batch
         y_hat = self.forward(x)
-        return {'tng_loss': some_loss(y_hat, y)}
+        return {'tng_loss': self.my_loss(y_hat, y)}
 
     def validation_step(self, batch, batch_nb):
         x, y = batch
         y_hat = self.forward(x)
-        return {'val_loss': some_loss(y_hat, y)}
+        return {'val_loss': self.my_loss(y_hat, y)}
 
     def configure_optimizers(self):
-        return [optim.Adam(self.parameters(), lr=0.02)]
+        return [torch.optim.Adam(self.parameters(), lr=0.02)]
 
     @ptl.data_loader
     def tng_dataloader(self):
@@ -74,8 +81,7 @@ class CoolModel(ptl.LightningModule):
 
     @ptl.data_loader
     def test_dataloader(self):
-        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)   
-
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)    
 ```
 
 2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)    

docs/LightningModule/RequiredTrainerInterface.md

@@ -26,6 +26,53 @@ Otherwise, to Define a Lightning Module, implement the following methods:
 - [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)
 - [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)
 
+---
+**Minimal example**
+```python
+import pytorch_lightning as ptl
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
+
+class CoolModel(ptl.LightningModule):
+
+    def __init(self):
+        # not the best model...
+        self.l1 = torch.nn.Linear(28*28, 10)
+
+    def forward(self, x):
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)
+
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'tng_loss': self.my_loss(y_hat, y)}
+
+    def validation_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'val_loss': self.my_loss(y_hat, y)}
+
+    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)
+
+    @ptl.data_loader
+    def val_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
+
+    @ptl.data_loader
+    def test_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)   
+```
+
 ---
 
 ### training_step

pytorch_lightning/root_module/root_module.py

@@ -64,14 +64,6 @@ def configure_optimizers(self):
         """
         raise NotImplementedError
 
-    def loss(self, *args, **kwargs):
-        """
-        Expand model_out into your components
-        :param model_out:
-        :return:
-        """
-        raise NotImplementedError
-
     def summarize(self):
         model_summary = ModelSummary(self)
         print(model_summary)