diff --git a/python/paddle/fluid/tests/unittests/test_communicator_async.py b/python/paddle/fluid/tests/unittests/test_communicator_async.py
index d032d6d75b5b3a48ea1e752190952f4c52e23b07..a1f781bacee5d803c54b6e8ba59a96a9e589ebc7 100644
--- a/python/paddle/fluid/tests/unittests/test_communicator_async.py
+++ b/python/paddle/fluid/tests/unittests/test_communicator_async.py
@@ -30,11 +30,11 @@ from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distribu
 
 class TestCommunicator(unittest.TestCase):
     def net(self):
-        x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-        y_predict = fluid.layers.fc(input=x, size=1, act=None)
+        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+        #        y_predict = fluid.layers.fc(input=x, size=1, act=None)
         y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
-        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        cost = fluid.layers.square_error_cost(input=x, label=y)
         avg_cost = fluid.layers.mean(cost)
         return avg_cost
 
diff --git a/python/paddle/fluid/tests/unittests/test_communicator_sync.py b/python/paddle/fluid/tests/unittests/test_communicator_sync.py
index c0044d9d620796057cce0e3a51b2dec2878a0e17..fb67409a1e80bc304608e2581689fb1d53e6ac78 100644
--- a/python/paddle/fluid/tests/unittests/test_communicator_sync.py
+++ b/python/paddle/fluid/tests/unittests/test_communicator_sync.py
@@ -27,11 +27,11 @@ import paddle.distributed.fleet as fleet
 
 class TestCommunicator(unittest.TestCase):
     def net(self):
-        x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-        y_predict = fluid.layers.fc(input=x, size=1, act=None)
+        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+        #        y_predict = fluid.layers.fc(input=x, size=1, act=None)
         y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
-        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        cost = fluid.layers.square_error_cost(input=x, label=y)
         avg_cost = fluid.layers.mean(cost)
         return avg_cost