diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py
index bda8e22fd282f8ff4a820e4ecb6b3bb421d57890..ca3e44e5a0187da33654f4955197196b150da196 100644
--- a/python/paddle/v2/data_feeder.py
+++ b/python/paddle/v2/data_feeder.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from py_paddle import DataProviderConverter
-
+import collections
 import paddle.trainer.PyDataProvider2 as pydp2
 
 __all__ = ['DataFeeder']
@@ -35,15 +35,30 @@ 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
+
+        feeding = ['image', 'label']
+        data_types = enumerate_data_types_of_data_layers(topology)
+        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 +80,9 @@ 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):
@@ -75,6 +90,13 @@ class DataFeeder(DataProviderConverter):
         input_types = []
         if feeding is None:
             feeding = default_feeding_map(data_types)
+        elif isinstance(feeding, collections.Sequence):
+            feed_list = feeding
+            feeding = dict()
+            for i, name in enumerate(feed_list):
+                feeding[name] = i
+        elif not isinstance(feeding, dict):
+            raise TypeError("Feeding should be dict or sequence or None.")
 
         self.feeding = feeding
         for each in data_types:
diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py
index a207beb548f0fda6d7aacf5c55e53d937e18a924..f5797a86c2b71502a7791453ff86c6a486c9f185 100644
--- a/python/paddle/v2/trainer.py
+++ b/python/paddle/v2/trainer.py
@@ -81,7 +81,7 @@ class SGD(object):
         :type event_handler: (BaseEvent) => None
         :param feeding: Feeding is a map of neural network input name and array
                         index that reader returns.
-        :type feeding: dict
+        :type feeding: dict|list
         :return:
         """
         if event_handler is None: