diff --git a/demo/seqToseq/api_train_v2.py b/demo/seqToseq/api_train_v2.py
index 29fa4b48b0745d62ee524cd9b687d5d5b940b885..2809054e7d3a367f441188fe7f91037cfa5f1579 100644
--- a/demo/seqToseq/api_train_v2.py
+++ b/demo/seqToseq/api_train_v2.py
@@ -3,7 +3,7 @@ import sys
 import paddle.v2 as paddle
 
 
-def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
+def seqToseq_net(source_dict_dim, target_dict_dim, is_generating=False):
     ### Network Architecture
     word_vector_dim = 512  # dimension of word vector
     decoder_size = 512  # dimension of hidden unit in GRU Decoder network
@@ -120,13 +120,7 @@ def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
             eos_id=1,
             beam_size=beam_size,
             max_length=max_length)
-        #
-        # seqtext_printer_evaluator(
-        #     input=beam_gen,
-        #     id_input=data_layer(
-        #         name="sent_id", size=1),
-        #     dict_file=trg_dict_path,
-        #     result_file=gen_trans_file)
+
         return beam_gen
 
 
@@ -138,7 +132,7 @@ def main():
     source_dict_dim = target_dict_dim = dict_size
 
     # define network topology
-    cost = seqToseq_net(source_dict_dim, target_dict_dim, False)
+    cost = seqToseq_net(source_dict_dim, target_dict_dim)
     parameters = paddle.parameters.create(cost)
 
     # define optimize method and trainer
diff --git a/python/paddle/v2/layer.py b/python/paddle/v2/layer.py
index affb375ebfdae5e507d34d322cdbc066f18fce01..384de9b9d57f88e84ab6067846174bb037502dc0 100644
--- a/python/paddle/v2/layer.py
+++ b/python/paddle/v2/layer.py
@@ -526,8 +526,8 @@ def beam_search(step,
     assert num_results_per_sample <= beam_size
     # logger.warning("num_results_per_sample should be less than beam_size")
 
-    if isinstance(input, StaticInputV2) or isinstance(
-            input, BaseGeneratedInputV2):
+    if isinstance(input, StaticInputV2) or isinstance(input,
+                                                      BaseGeneratedInputV2):
         input = [input]
 
     generated_input_index = -1
@@ -574,8 +574,7 @@ def beam_search(step,
     #     reverse=False,
     #     name=name,
     #     is_generating=True)
-    tmp = recurrent_group(
-        step=__real_step__, input=real_input, name=name)
+    tmp = recurrent_group(step=__real_step__, input=real_input, name=name)
 
     return tmp