diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 8acea6155c588f2e8e5ad009cd8f0a0c09afb92b..19624a704f1848131d909ee73ee0b5984e1a2e07 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -25,11 +25,18 @@ class Inference(object):
     :type parameters: paddle.v2.parameters.Parameters
     """
 
-    def __init__(self, output_layer, parameters):
+    def __init__(self, output_layer, parameters, data_types=None):
         import py_paddle.swig_paddle as api
-        topo = topology.Topology(output_layer)
-        gm = api.GradientMachine.createFromConfigProto(
-            topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+        if isinstance(output_layer, str):
+            gm = api.GradientMachine.createByConfigProtoStr(output_layer)
+            if data_types is None:
+                raise ValueError("data_types != None when using protobuf bin")
+            self.__data_types__ = data_types
+        else:
+            topo = topology.Topology(output_layer)
+            gm = api.GradientMachine.createFromConfigProto(
+                topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+            self.__data_types__ = topo.data_type()
         for param in gm.getParameters():
             val = param.getBuf(api.PARAMETER_VALUE)
             name = param.getName()
@@ -43,7 +50,6 @@ class Inference(object):
             # called here, but it's better to call this function in one place.
             param.setValueUpdated()
         self.__gradient_machine__ = gm
-        self.__data_types__ = topo.data_type()
 
     def iter_infer(self, input, feeding=None):
         from data_feeder import DataFeeder