Update README.md

README.md

@@ -10,8 +10,7 @@ To use lightning, define a model that implements these 10 functions:
 | training_step  |  Called with a batch of data during training | data from your dataloaders  | tuple: scalar, dict  |
 | validation_step  |  Called with a batch of data during validation  | data from your dataloaders  | tuple: scalar, dict |
 | validation_end  |  Collate metrics from all validation steps |  outputs: array where each item is the output of a validation step | dict: for logging |
-| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved |
-|  load_model_specific |   |   |   |    
+| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved | 
 
 #### Model training   
 | Name  | Description  |  Input |  Return |
@@ -37,6 +36,7 @@ class ExampleModel(RootModule):
     def __init__(self):
         self.l1 = nn.Linear(100, 20)
     
+    # ---------------
     # TRAINING
     def training_step(self, data_batch):
         # your dataloader decides what each batch looks like
@@ -71,7 +71,8 @@ class ExampleModel(RootModule):
         
         # return a dict
         return {'total_acc': np.mean(total_accs)}
-        
+     
+     # ---------------
      # SAVING
      def get_save_dict(self):
         # lightning saves for you. Here's your chance to say what you want to save
@@ -84,6 +85,8 @@ class ExampleModel(RootModule):
         self.load_state_dict(checkpoint['state_dict'])
         pass
     
+    
+    # ---------------
     # TRAINING CONFIG
     def configure_optimizers(self):
         # give lightning the list of optimizers you want to use.
@@ -104,7 +107,8 @@ class ExampleModel(RootModule):
     @property
     def test_dataloader(self):
         return pytorch_dataloader('test')
-    
+        
+    # ---------------
     # MODIFY YOUR COMMAND LINE ARGS
     @staticmethod
     def add_model_specific_args(parent_parser):