diff --git a/python/paddle/v2/optimizer.py b/python/paddle/v2/optimizer.py
index 7bac1ea3b989a085d45e7988bcb3fc0329c1fba8..887b2567a14fe2161f97d4d0f0a79f8105b4c2cf 100644
--- a/python/paddle/v2/optimizer.py
+++ b/python/paddle/v2/optimizer.py
@@ -45,6 +45,14 @@ class Optimizer(object):
         return swig_api.ParameterUpdater.createRemoteUpdater(
             self.__opt_conf__, pass_num, use_sparse_updater)
 
+    def create_updater(self, is_local, num_passes, use_sparse_updater):
+        if is_local:
+            parameter_updater = self.create_local_updater()
+        else:
+            parameter_updater = self.create_remote_updater(num_passes,
+                                                           use_sparse_updater)
+        return parameter_updater
+
 
 class Momentum(Optimizer):
     def __init__(self, momentum=None, sparse=False, **kwargs):
diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py
index c1f964a8106d0887d401560cea7d9a184e10abf8..9caaeca2efe15ed59763ce851ad8da7fe2a6d120 100644
--- a/python/paddle/v2/trainer.py
+++ b/python/paddle/v2/trainer.py
@@ -102,13 +102,9 @@ class SGD(object):
             event_handler = default_event_handler
         __check_train_args__(**locals())
 
-        if self.__is_local__:
-            parameter_updater = self.__optimizer__.create_local_updater()
-        else:
-            parameter_updater = self.__optimizer__.create_remote_updater(
-                num_passes, self.__use_sparse_updater__)
-        self.__parameter_updater__ = parameter_updater
-        parameter_updater.init(self.__gradient_machine__)
+        self.__parameter_updater__ = self.__optimizer__.create_updater(
+            self.__is_local__, num_passes, self.__use_sparse_updater__)
+        self.__parameter_updater__.init(self.__gradient_machine__)
 
         self.__gradient_machine__.start()
         batch_evaluator = self.__gradient_machine__.makeEvaluator()
@@ -120,27 +116,28 @@ class SGD(object):
         for pass_id in xrange(num_passes):
             event_handler(v2_event.BeginPass(pass_id))
             pass_evaluator.start()
-            parameter_updater.startPass()
+            self.__parameter_updater__.startPass()
             for batch_id, data_batch in enumerate(reader()):
                 batch_evaluator.start()
                 event_handler(
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
-                pass_type = parameter_updater.startBatch(len(data_batch))
+                pass_type = self.__parameter_updater__.startBatch(
+                    len(data_batch))
                 in_args = feeder(data_batch)
                 if self.use_remote_sparse_updater():
                     self.__gradient_machine__.prefetch(in_args)
-                    parameter_updater.getParametersRemote()
+                    self.__parameter_updater__.getParametersRemote()
                 self.__gradient_machine__.forwardBackward(in_args, out_args,
                                                           pass_type)
                 self.__gradient_machine__.eval(pass_evaluator)
                 self.__gradient_machine__.eval(batch_evaluator)
                 for each_param in self.__gradient_machine__.getNonStaticParameters(
                 ):
-                    parameter_updater.update(each_param)
+                    self.__parameter_updater__.update(each_param)
                 cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
-                parameter_updater.finishBatch(cost)
+                self.__parameter_updater__.finishBatch(cost)
                 batch_evaluator.finish()
                 event_handler(
                     v2_event.EndIteration(
@@ -149,7 +146,7 @@ class SGD(object):
                         cost=cost,
                         evaluator=batch_evaluator))
 
-            parameter_updater.finishPass()
+            self.__parameter_updater__.finishPass()
             pass_evaluator.finish()
             event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
         self.__gradient_machine__.finish()