doc: update example

Author: xuyxu

README.rst

@@ -49,30 +49,49 @@ Example
 
 .. code:: python
 
-    from torchensemble import VotingClassifier             # Voting is a classic ensemble strategy
+    from torchensemble import VotingClassifier  # voting is a classic ensemble strategy
 
     # Load data
     train_loader = DataLoader(...)
     test_loader = DataLoader(...)
 
-    # Define the ensemble
-    model = VotingClassifier(estimator=base_estimator,     # your deep learning model
-                             n_estimators=10)              # the number of base estimators
-
-    # Set the optimizer
-    model.set_optimizer("Adam",                            # parameter optimizer
-                        lr=learning_rate,                  # learning rate of the optimizer
-                        weight_decay=weight_decay)         # weight decay of the optimizer
-
-    # Set the scheduler
-    model.set_scheduler("CosineAnnealingLR", T_max=epochs) # (optional) learning rate scheduler
-
-    # Train
-    model.fit(train_loader,
-              epochs=epochs)                               # the number of training epochs
-
-    # Evaluate
-    acc = model.predict(test_loader)                       # testing accuracy
+    '''
+    [Step-1] Define the ensemble
+    '''
+    model = VotingClassifier(
+        estimator=base_estimator,               # here is your deep learning model
+        n_estimators=10,                        # number of base estimators
+    )
+
+    '''
+    [Step-2] Set the parameter optimizer
+    '''
+    model.set_optimizer(
+        "Adam",                                 # type of parameter optimizer
+        lr=learning_rate,                       # learning rate of parameter optimizer
+        weight_decay=weight_decay,              # weight decay of parameter optimizer
+    )
+
+    '''
+    [Step-3] Set the learning rate scheduler
+    '''
+    model.set_scheduler(
+        "CosineAnnealingLR",                    # type of learning rate scheduler
+        T_max=epochs,                           # additional arguments on the scheduler
+    )
+
+    '''
+    [Step-4] Train the ensemble
+    '''
+    model.fit(
+        train_loader,
+        epochs=epochs,                          # number of training epochs
+    )
+
+    '''
+    [Step-5] Evaluate the ensemble
+    '''
+    acc = model.predict(test_loader)            # testing accuracy
 
 Supported Ensemble
 ------------------