Add comments for functions

python/paddle/v2/trainer.py

@@ -16,6 +16,10 @@ class BaseEvent(object):
 
 
 class CompleteTrainOneBatch(BaseEvent):
+    """
+    Event On One Batch Training Complete.
+    """
+
     def __init__(self, pass_id, batch_id, cost):
         self.pass_id = pass_id
         self.batch_id = batch_id
@@ -38,6 +42,11 @@ class ITrainer(object):
 
 class SGDTrainer(ITrainer):
     def __init__(self, update_equation):
+        """
+        Simple SGD Trainer.
+
+        :param update_equation: Maybe we should give a DSL for update equation?
+        """
         if not isinstance(update_equation, paddle.v2.optimizer.Optimizer):
             raise ValueError()
 
@@ -52,6 +61,21 @@ class SGDTrainer(ITrainer):
               event_handler=None,
               batch_size=32,
               data_types=None):
+        """
+        Training method. Will train num_passes of input data.
+
+        :param train_data_reader:
+        :param topology: Network Topology, a protobuf ModelConfig message.
+        :param parameters: The parameter pools.
+        :param num_passes: The total train passes.
+        :param test_data_reader:
+        :param event_handler: Event handler. A method will be invoked when event
+                              occurred.
+        :type event_handler: (BaseEvent) => None
+        :param batch_size: Not important, will be removed after data refactor.
+        :param data_types: Not important, will be removed after data refactor.
+        :return:
+        """
         if event_handler is None:
             event_handler = default_event_handler
 
@@ -66,6 +90,9 @@ class SGDTrainer(ITrainer):
         assert isinstance(updater, api.ParameterUpdater)
         updater.init(gm)
 
+        gm.start()
+        out_args = api.Arguments.createArguments(0)
+
         data_types_lists = []
         for each in topology.input_layer_names:
             if each not in data_types:
@@ -74,22 +101,11 @@ class SGDTrainer(ITrainer):
 
         converter = DataProviderConverter(input_types=data_types_lists)
 
-        def input_reorder(func):
-            for item in func():
-                retv = []
-                for __layer_name__ in topology.input_layer_names:
-                    retv.append(item[__layer_name__])
-                yield retv
-
-        gm.start()
-
-        out_args = api.Arguments.createArguments(0)
         for pass_id in xrange(num_passes):
             updater.startPass()
             for batch_id, data_batch in enumerate(
-                    __generator_to_batch__(
-                        input_reorder(train_data_reader),
-                        batch_size=batch_size)):
+                    __data_reader_to_batch__(train_data_reader, batch_size,
+                                             topology)):
                 pass_type = updater.startBatch(len(data_batch))
                 gm.forwardBackward(converter(data_batch), out_args, pass_type)
                 for each_param in gm.getParameters():
@@ -108,7 +124,25 @@ class SGDTrainer(ITrainer):
         gm.finish()
 
 
+def __data_reader_to_batch__(reader, batch_size, topology):
+    """
+    This function is not important, and will be removed when data refactored.
+    """
+
+    def input_reorder(func):
+        for item in func():
+            retv = []
+            for __layer_name__ in topology.input_layer_names:
+                retv.append(item[__layer_name__])
+            yield retv
+
+    return __generator_to_batch__(input_reorder(reader), batch_size=batch_size)
+
+
 def __generator_to_batch__(generator, batch_size):
+    """
+    This function is not important, and will be removed when data refactored.
+    """
     ret_val = list()
     for each_item in generator:
         ret_val.append(each_item)
@@ -139,6 +173,9 @@ def __copy_parameter_from_pool__(gm, pool):
 
 def __check_train_args__(train_data_reader, topology, parameters,
                          test_data_reader, event_handler, **kwargs):
+    """
+    Check train function's argument types
+    """
     if not callable(train_data_reader) or not isinstance(train_data_reader(),
                                                          collections.Iterator):
         raise ValueError('train_data_reader should be a function, '