clean up docs (#1614)

* fixed hparams section

* docs clean up

docs/source/hyperparameters.rst

@@ -27,9 +27,9 @@ Argparser Best Practices
 ^^^^^^^^^^^^^^^^^^^^^^^^
 It is best practice to layer your arguments in three sections.
 
-    1. Trainer args (gpus, num_nodes, etc...)
-    2. Model specific arguments (layer_dim, num_layers, learning_rate, etc...)
-    3. Program arguments (data_path, cluster_email, etc...)
+1.  Trainer args (gpus, num_nodes, etc...)
+2.  Model specific arguments (layer_dim, num_layers, learning_rate, etc...)
+3.  Program arguments (data_path, cluster_email, etc...)
 
 We can do this as follows. First, in your LightningModule, define the arguments
 specific to that module. Remember that data splits or data paths may also be specific to
@@ -84,15 +84,11 @@ Finally, make sure to start the training like so:
 
     # YES
     model = LitModel(hparams)
-
-    # NO
-    # model = LitModel(learning_rate=hparams.learning_rate, ...)
-
-    # YES
     trainer = Trainer.from_argparse_args(hparams, early_stopping_callback=...)
 
     # NO
-    trainer = Trainer(gpus=hparams.gpus, ...)
+    # model = LitModel(learning_rate=hparams.learning_rate, ...)
+    #trainer = Trainer(gpus=hparams.gpus, ...)
 
 
 LightiningModule hparams
@@ -144,8 +140,8 @@ Now pass in the params when you init your model
 The line `self.hparams = hparams` is very special. This line assigns your hparams to the LightningModule.
 This does two things:
 
-    1. It adds them automatically to tensorboard logs under the hparams tab.
-    2. Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
+1.  It adds them automatically to tensorboard logs under the hparams tab.
+2.  Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
 
 Trainer args
 ^^^^^^^^^^^^