diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py
index f333c0af964bf5ecbc8f9b908c4e512a982eec33..5ecc96c6856508b1822652400b149b25cff75ad6 100644
--- a/python/paddle/v2/layer.py
+++ b/python/paddle/v2/layer.py
@@ -256,38 +256,3 @@ sum_cost = __convert_to_v2__(
     'sum_cost', name_prefix='sum_cost', parent_names=['input'])
 huber_cost = __convert_to_v2__(
     'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label'])
-
-if __name__ == '__main__':
-    pixel = data(name='pixel', type=data_type.dense_vector(784))
-    label = data(name='label', type=data_type.integer_value(10))
-    weight = data(name='weight', type=data_type.dense_vector(10))
-    score = data(name='score', type=data_type.dense_vector(1))
-
-    hidden = fc(input=pixel,
-                size=100,
-                act=activation.Sigmoid(),
-                param_attr=attr.Param(name='hidden'))
-    inference = fc(input=hidden, size=10, act=activation.Softmax())
-    maxid = max_id(input=inference)
-    cost1 = classification_cost(input=inference, label=label)
-    cost2 = classification_cost(input=inference, label=label, weight=weight)
-    cost3 = cross_entropy_cost(input=inference, label=label)
-    cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label)
-    cost5 = regression_cost(input=inference, label=label)
-    cost6 = regression_cost(input=inference, label=label, weight=weight)
-    cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label)
-    cost8 = rank_cost(left=score, right=score, label=score)
-    cost9 = lambda_cost(input=inference, score=score)
-    cost10 = sum_cost(input=inference)
-    cost11 = huber_cost(input=score, label=label)
-
-    # print parse_network(cost1)
-    # print parse_network(cost2)
-    # print parse_network(cost1, cost2)
-    # print parse_network(cost2)
-    # print parse_network(inference, maxid)
-    print parse_network(cost1, cost2)
-    print parse_network(cost3, cost4)
-    print parse_network(cost5, cost6)
-    print parse_network(cost7, cost8, cost9, cost10, cost11)
-    print parse_network(inference, maxid)
diff --git a/python/paddle/v2/tests/layer_test.py b/python/paddle/v2/tests/layer_test.py
index 6c4b458914701eba5d08670244d4e4a6204ed7f3..2958cbd9ebd0c38dd399b8b107ddffbcf2ed4c1e 100644
--- a/python/paddle/v2/tests/layer_test.py
+++ b/python/paddle/v2/tests/layer_test.py
@@ -16,9 +16,9 @@ import unittest
 
 import paddle.trainer_config_helpers as conf_helps
 import paddle.v2.activation as activation
+import paddle.v2.attr as attr
 import paddle.v2.data_type as data_type
 import paddle.v2.layer as layer
-import paddle.v2.attr as attr
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as parse_network
 
@@ -95,8 +95,9 @@ class RNNTest(unittest.TestCase):
             data1 = layer.data(
                 name="word", type=data_type.integer_value(dict_dim))
             embd = layer.embedding(input=data1, size=word_dim)
-            aaa = layer.recurrent_group(name="rnn", step=new_step, input=embd)
-            return str(layer.parse_network(aaa))
+            rnn_layer = layer.recurrent_group(
+                name="rnn", step=new_step, input=embd)
+            return str(layer.parse_network(rnn_layer))
 
         diff = difflib.unified_diff(test_old_rnn().splitlines(1),
                                     test_new_rnn().splitlines(1))