From d272f29c881d1f422aaf4ffa2b3fec6222b67d59 Mon Sep 17 00:00:00 2001
From: William Falcon <falc@fb.com>
Date: Thu, 25 Jul 2019 11:52:54 -0400
Subject: [PATCH] updated docs

---
 README.md                                     | 18 ++++---
 .../RequiredTrainerInterface.md               | 47 +++++++++++++++++++
 pytorch_lightning/root_module/root_module.py  |  8 ----
 3 files changed, 59 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 3f62526eb6..8dc89cd4f7 100644
--- a/README.md
+++ b/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/)    
diff --git a/docs/LightningModule/RequiredTrainerInterface.md b/docs/LightningModule/RequiredTrainerInterface.md
index 7625be2494..5faa67a701 100644
--- a/docs/LightningModule/RequiredTrainerInterface.md
+++ b/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
diff --git a/pytorch_lightning/root_module/root_module.py b/pytorch_lightning/root_module/root_module.py
index d6e0ec9666..584c5aa4b4 100644
--- a/pytorch_lightning/root_module/root_module.py
+++ b/pytorch_lightning/root_module/root_module.py
@@ -64,14 +64,6 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
         """
         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)