diff --git a/fluid/PaddleRec/gru4rec/train.py b/fluid/PaddleRec/gru4rec/train.py
index 2b889a44ae676b7cf334ad0901436c8694d1e237..c593ad69d5dacd4baab0a1b06d446e5f092c5edb 100644
--- a/fluid/PaddleRec/gru4rec/train.py
+++ b/fluid/PaddleRec/gru4rec/train.py
@@ -17,21 +17,24 @@ SEED = 102
 def parse_args():
     parser = argparse.ArgumentParser("gru4rec benchmark.")
     parser.add_argument(
-        '--train_dir', type=str, default='train_data', help='train file address')
+        '--train_dir',
+        type=str,
+        default='train_data',
+        help='train file address')
     parser.add_argument(
-        '--vocab_path', type=str, default='vocab.txt', help='vocab file address')
-    parser.add_argument(
-        '--is_local', type=int, default=1, help='whether local')
-    parser.add_argument(
-        '--hid_size', type=int, default=100, help='hid size')
+        '--vocab_path',
+        type=str,
+        default='vocab.txt',
+        help='vocab file address')
+    parser.add_argument('--is_local', type=int, default=1, help='whether local')
+    parser.add_argument('--hid_size', type=int, default=100, help='hid size')
     parser.add_argument(
         '--model_dir', type=str, default='model_recall20', help='model dir')
     parser.add_argument(
         '--batch_size', type=int, default=5, help='num of batch size')
     parser.add_argument(
         '--print_batch', type=int, default=10, help='num of print batch')
-    parser.add_argument(
-        '--pass_num', type=int, default=10, help='num of epoch')
+    parser.add_argument('--pass_num', type=int, default=10, help='num of epoch')
     parser.add_argument(
         '--use_cuda', type=int, default=0, help='whether use gpu')
     parser.add_argument(
@@ -43,9 +46,11 @@ def parse_args():
     args = parser.parse_args()
     return args
 
+
 def get_cards(args):
     return args.num_devices
 
+
 def train():
     """ do training """
     args = parse_args()
@@ -61,23 +66,23 @@ def train():
         buffer_size=1000, word_freq_threshold=0, is_train=True)
 
     # Train program
-    src_wordseq, dst_wordseq, avg_cost, acc = net.network(vocab_size=vocab_size, hid_size=hid_size)
+    src_wordseq, dst_wordseq, avg_cost, acc = net.network(
+        vocab_size=vocab_size, hid_size=hid_size)
 
     # Optimization to minimize lost
     sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.base_lr)
     sgd_optimizer.minimize(avg_cost)
-    
+
     # Initialize executor
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
     if parallel:
         train_exe = fluid.ParallelExecutor(
-            use_cuda=use_cuda,
-            loss_name=avg_cost.name)
+            use_cuda=use_cuda, loss_name=avg_cost.name)
     else:
         train_exe = exe
-    
+
     pass_num = args.pass_num
     model_dir = args.model_dir
     fetch_list = [avg_cost.name]
@@ -96,10 +101,11 @@ def train():
                                                  place)
             lod_dst_wordseq = utils.to_lodtensor([dat[1] for dat in data],
                                                  place)
-            ret_avg_cost = train_exe.run(feed={  
-                                                "src_wordseq": lod_src_wordseq,
-                                                "dst_wordseq": lod_dst_wordseq},
-                                        fetch_list=fetch_list)
+            ret_avg_cost = train_exe.run(feed={
+                "src_wordseq": lod_src_wordseq,
+                "dst_wordseq": lod_dst_wordseq
+            },
+                                         fetch_list=fetch_list)
             avg_ppl = np.exp(ret_avg_cost[0])
             newest_ppl = np.mean(avg_ppl)
             if i % args.print_batch == 0: