diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py
index 63a8917c55a83dfdf59dece51b5025831501a7c1..56976baa3d523ec878a8470cfc280a4093f62ddf 100644
--- a/python/paddle/v2/data_feeder.py
+++ b/python/paddle/v2/data_feeder.py
@@ -35,15 +35,31 @@ class DataFeeder(DataProviderConverter):
     DataFeeder converts this mini-batch data entries into Arguments in order
     to feed it to C++ interface.
     
-    The example usage:
+    The simple usage shows below
+
+    ..  code-block:: python
+
+        data_types = [('image', paddle.data_type.dense_vector(784)),
+                      ('label', paddle.data_type.integer_value(10))]
+        feeding = ['image', 'label']
+        feeder = DataFeeder(data_types=data_types, feeding=feeding)
+
+        minibatch_data = [([1.0, 2.0, 3.0, ...], 5)]
+
+        arg = feeder(minibatch_data)
+
+
+    If mini-batch data and data layers are not one to one mapping, we
+    could pass a dictionary to feeding parameter to represent the mapping
+    relationship.
 
 
     ..  code-block:: python
 
         data_types = [('image', paddle.data_type.dense_vector(784)),
                       ('label', paddle.data_type.integer_value(10))]
-        reader_dict = {'image':0, 'label':1}
-        feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict)
+        feeding = {'image':0, 'label':1}
+        feeder = DataFeeder(data_types=data_types, feeding=feeding)
         minibatch_data = [
                            ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ),  # first sample
                            ( [1.0,2.0,3.0,4.0], 5, [6,7,8] )   # second sample
@@ -65,9 +81,9 @@ class DataFeeder(DataProviderConverter):
                        a tuple of (data_name, data_type).
 
     :type data_types: list
-    :param reader_dict: A dictionary to specify the position of each data
-                        in the input data.
-    :type feeding: dict
+    :param feeding: A dictionary or a sequence to specify the position of each
+                    data in the input data.
+    :type feeding: dict|collections.Sequence|None
     """
 
     def __init__(self, data_types, feeding=None):