diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py
index 75c2f08132dcae291ea4d5d70edfe804a702dd18..3aa2199bcb7126325e573f3c84442b52f4a3f21c 100644
--- a/demo/mnist/api_train_v2.py
+++ b/demo/mnist/api_train_v2.py
@@ -132,7 +132,8 @@ def main():
 
     # output is a softmax layer. It returns probabilities.
     # Shape should be (100, 10)
-    probs = paddle.infer(output=predict, parameters=parameters, input=test_data)
+    probs = paddle.infer(
+        output_layer=predict, parameters=parameters, input=test_data)
     print probs.shape
 
 
diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 2ad4d9d1ab037a210b81b4ee63bce267f296bd83..53510d80c9d92b42ebbe120cc6f4166b09198ae5 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -9,8 +9,8 @@ __all__ = ['infer']
 
 
 class Inference(object):
-    def __init__(self, output, parameters):
-        topo = topology.Topology(output)
+    def __init__(self, output_layer, parameters):
+        topo = topology.Topology(output_layer)
         gm = api.GradientMachine.createFromConfigProto(
             topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
         for param in gm.getParameters():
@@ -70,13 +70,7 @@ class Inference(object):
             return retv
 
 
-def infer(output,
-          parameters,
-          input=None,
-          batch_size=None,
-          reader=None,
-          feeding=None,
-          field='value'):
+def infer(output_layer, parameters, input=None, feeding=None, field='value'):
     """
     Infer a neural network by given neural network output and parameters.  The
     user should pass either a batch of input data or reader method.
@@ -89,19 +83,13 @@ def infer(output,
                               batch_size=32)
         print result
 
-    :param output: output of the neural network that would be inferred
-    :type output: paddle.v2.config_base.Layer
+    :param output_layer: output of the neural network that would be inferred
+    :type output_layer: paddle.v2.config_base.Layer
     :param parameters: parameters of the neural network.
     :type parameters: paddle.v2.parameters.Parameters
     :param input: input data batch. Should be a python iterable object, and each
                   element is the data batch.
     :type input: collections.Iterable
-    :param batch_size: the batch size when perform inference. Default is the
-                       length of input.
-    :type batch_size: int
-    :param reader: input data reader creator in batch. If this field is set, the
-                   `input` and `batch_size` will be ignored.
-    :type reader: callable
     :param feeding: Reader dictionary. Default could generate from input
                         value.
     :param field: The prediction field. It should in [`value`, `ids`]. `value`
@@ -112,10 +100,5 @@ def infer(output,
     :rtype: numpy.ndarray
     """
 
-    inferer = Inference(output=output, parameters=parameters)
-    return inferer.infer(
-        field=field,
-        input=input,
-        batch_size=batch_size,
-        reader=reader,
-        feeding=feeding)
+    inferer = Inference(output_layer=output_layer, parameters=parameters)
+    return inferer.infer(field=field, input=input, feeding=feeding)