diff --git a/python/paddle/fluid/imperative/nn.py b/python/paddle/fluid/imperative/nn.py
index 7f3be204635cb1a772d7ddf746c5ecafa5aec67a..8757670ef82a8ed419edbcbfb12056d0f88200fb 100644
--- a/python/paddle/fluid/imperative/nn.py
+++ b/python/paddle/fluid/imperative/nn.py
@@ -99,7 +99,7 @@ class Conv2D(layers.PyLayer):
 
         self._bias_param = self._helper.create_parameter(
             attr=self._helper.bias_attr,
-            shape=[num_filter_channels],
+            shape=[num_filters],
             dtype=self._dtype,
             is_bias=True)
 
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
index 775b10e6dc73cd37a3aa38bc7cf626307d7f6a52..e9dd158295e0d27de4173b6398f72ce730dc8d74 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
@@ -29,8 +29,8 @@ from test_imperative_base import new_program_scope
 class SimpleImgConvPool(fluid.imperative.PyLayer):
     def __init__(self,
                  num_channels,
-                 filter_size,
                  num_filters,
+                 filter_size,
                  pool_size,
                  pool_stride,
                  pool_padding=0,
@@ -77,10 +77,10 @@ class MNIST(fluid.imperative.PyLayer):
         super(MNIST, self).__init__(param_attr=param_attr, bias_attr=bias_attr)
 
         self._simple_img_conv_pool_1 = SimpleImgConvPool(
-            1, 5, 20, 2, 2, act="relu")
+            1, 20, 5, 2, 2, act="relu")
 
         self._simple_img_conv_pool_2 = SimpleImgConvPool(
-            20, 5, 50, 2, 2, act="relu")
+            20, 50, 5, 2, 2, act="relu")
 
         pool_2_shape = 50 * 8 * 8
         SIZE = 10
@@ -106,18 +106,15 @@ class TestImperativeMnist(unittest.TestCase):
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
-            mnist = Conv2D(1, 20, 5)
+            #  mnist = Conv2D(1, 20, 5)
+            mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)
             train_reader = paddle.batch(
                 paddle.dataset.mnist.train(), batch_size=128)
 
-            dy_param_value = {}
-            for param in fluid.default_main_program().global_block(
-            ).all_parameters():
-                dy_param_value[param.name] = param._numpy()
-
+            dy_param_init_value = {}
             for batch_id, data in enumerate(train_reader()):
-                if batch_id >= 1:
+                if batch_id >= 2:
                     break
 
                 x_data = np.array(
@@ -133,9 +130,17 @@ class TestImperativeMnist(unittest.TestCase):
                 loss = fluid.layers.reduce_mean(cost)
                 dy_out = loss._numpy()
 
+                if batch_id == 0:
+                    for param in fluid.default_main_program().global_block(
+                    ).all_parameters():
+                        dy_param_init_value[param.name] = param._numpy()
+
                 loss._backward()
                 sgd.minimize(loss)
-                dy_filter_param = mnist._filter_param._numpy()
+                dy_param_value = {}
+                for param in fluid.default_main_program().global_block(
+                ).all_parameters():
+                    dy_param_value[param.name] = param._numpy()
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
@@ -143,7 +148,8 @@ class TestImperativeMnist(unittest.TestCase):
 
             exe = fluid.Executor(fluid.CPUPlace())
 
-            mnist = Conv2D(1, 20, 5)
+            #  mnist = Conv2D(1, 20, 5)
+            mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)
             train_reader = paddle.batch(
                 paddle.dataset.mnist.train(), batch_size=128)
@@ -156,7 +162,7 @@ class TestImperativeMnist(unittest.TestCase):
             sgd.minimize(loss)
 
             # initialize params and fetch them
-            static_param_value = {}
+            static_param_init_value = {}
             static_param_name_list = []
             for param in fluid.default_startup_program().global_block(
             ).all_parameters():
@@ -166,27 +172,35 @@ class TestImperativeMnist(unittest.TestCase):
                           fetch_list=static_param_name_list)
 
             for i in range(len(static_param_name_list)):
-                static_param_value[static_param_name_list[i]] = out[i]
+                static_param_init_value[static_param_name_list[i]] = out[i]
 
             for batch_id, data in enumerate(train_reader()):
-                if batch_id >= 1:
+                if batch_id >= 2:
                     break
 
                 x_data = np.array(
                     [x[0].reshape(1, 28, 28) for x in data]).astype('float32')
                 y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                     [128, 1])
-                static_out, static_filter_param = exe.run(
-                    fluid.default_main_program(),
-                    feed={"pixel": x_data,
-                          "label": y_data},
-                    fetch_list=[loss.name, mnist._filter_param.name])
 
+                fetch_list = [loss.name]
+                fetch_list.extend(static_param_name_list)
+                out = exe.run(fluid.default_main_program(),
+                              feed={"pixel": x_data,
+                                    "label": y_data},
+                              fetch_list=fetch_list)
+
+                static_param_value = {}
+                static_out = out[0]
+                for i in range(1, len(out)):
+                    static_param_value[static_param_name_list[i - 1]] = out[i]
+
+        for key, value in six.iteritems(static_param_init_value):
+            self.assertTrue(
+                np.allclose(value.all(), dy_param_init_value[key].all()))
+        self.assertTrue(np.allclose(static_out.all(), dy_out.all()))
         for key, value in six.iteritems(static_param_value):
             self.assertTrue(np.allclose(value.all(), dy_param_value[key].all()))
-        self.assertTrue(np.allclose(static_out.all(), dy_out.all()))
-        self.assertTrue(
-            np.allclose(static_filter_param.all(), dy_filter_param.all()))
 
 
 if __name__ == '__main__':