diff --git a/nets.py b/nets.py
index 652ea65..8703af2 100644
--- a/nets.py
+++ b/nets.py
@@ -41,7 +41,7 @@ def cnn_net(data,
     """
     # embedding layer
     emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
-    
+
     # convolution layer
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
@@ -49,7 +49,7 @@ def cnn_net(data,
         filter_size=win_size,
         act="tanh",
         pool_type="max")
-    
+
     # full connect layer
     fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
     # softmax layer
@@ -77,17 +77,17 @@ def lstm_net(data,
         input=data,
         size=[dict_dim, emb_dim],
         param_attr=fluid.ParamAttr(learning_rate=emb_lr))
-    
+
     # Lstm layer
     fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
 
     lstm_h, c = fluid.layers.dynamic_lstm(
         input=fc0, size=hid_dim * 4, is_reverse=False)
-    
+
     # max pooling layer
     lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
     lstm_max_tanh = fluid.layers.tanh(lstm_max)
-    
+
     # full connect layer
     fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
     # softmax layer
@@ -116,7 +116,7 @@ def bilstm_net(data,
         input=data,
         size=[dict_dim, emb_dim],
         param_attr=fluid.ParamAttr(learning_rate=emb_lr))
-    
+
     # bi-lstm layer
     fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
 
@@ -127,17 +127,17 @@ def bilstm_net(data,
 
     rlstm_h, c = fluid.layers.dynamic_lstm(
         input=rfc0, size=hid_dim * 4, is_reverse=True)
-    
+
     # extract last layer
     lstm_last = fluid.layers.sequence_last_step(input=lstm_h)
     rlstm_last = fluid.layers.sequence_last_step(input=rlstm_h)
 
     lstm_last_tanh = fluid.layers.tanh(lstm_last)
     rlstm_last_tanh = fluid.layers.tanh(rlstm_last)
-    
+
     # concat layer
     lstm_concat = fluid.layers.concat(input=[lstm_last, rlstm_last], axis=1)
-    
+
     # full connect layer
     fc1 = fluid.layers.fc(input=lstm_concat, size=hid_dim2, act='tanh')
     # softmax layer
@@ -180,4 +180,3 @@ def gru_net(data,
     acc = fluid.layers.accuracy(input=prediction, label=label)
 
     return avg_cost, acc, prediction
-
diff --git a/sentiment_classify.py b/sentiment_classify.py
index 524a6fe..8bd2e82 100644
--- a/sentiment_classify.py
+++ b/sentiment_classify.py
@@ -113,7 +113,7 @@ def train_net(train_reader,
     elif network == "gru_net":
         network = gru_net
     else:
-        print ("unknown network type")
+        print("unknown network type")
         return
     # word seq data
     data = fluid.layers.data(
@@ -147,8 +147,8 @@ def train_net(train_reader,
         for data in train_reader():
             # train a batch
             avg_cost_np, avg_acc_np = train_exe.run(
-                                              feed=feeder.feed(data),
-                                              fetch_list=[cost.name, acc.name])
+                feed=feeder.feed(data),
+                fetch_list=[cost.name, acc.name])
             data_size = len(data)
             total_acc += data_size * np.sum(avg_acc_np)
             total_cost += data_size * np.sum(avg_cost_np)
@@ -167,7 +167,7 @@ def eval_net(test_reader, use_gpu, model_path=None):
     Evaluation function
     """
     if model_path is None:
-        print (str(model_path) + "can not be found")
+        print(str(model_path) + "can not be found")
         return
     # set place, executor
     place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
@@ -178,7 +178,7 @@ def eval_net(test_reader, use_gpu, model_path=None):
         # load the saved model
         [inference_program, feed_target_names,
             fetch_targets] = fluid.io.load_inference_model(model_path, exe)
-        
+
         # compute 2class and 3class accuracy
         class2_acc, class3_acc = 0.0, 0.0
         total_count, neu_count = 0, 0
@@ -246,7 +246,7 @@ def main(args):
         word_dict, train_reader = utils.prepare_data(
             args.train_data_path, args.word_dict_path, args.batch_size,
             args.mode)
-        
+
         train_net(
             train_reader,
             word_dict,
@@ -257,10 +257,10 @@ def main(args):
             args.lr,
             args.batch_size,
             args.num_passes)
-    
+
     # eval mode
     elif args.mode == "eval":
-        # prepare_data to get word_dict, test_reader    
+        # prepare_data to get word_dict, test_reader
         word_dict, test_reader = utils.prepare_data(
             args.test_data_path, args.word_dict_path, args.batch_size,
             args.mode)
@@ -268,7 +268,7 @@ def main(args):
             test_reader,
             args.use_gpu,
             args.model_path)
-    
+
     # infer mode
     elif args.mode == "infer":
         # prepare_data to get word_dict, test_reader
@@ -283,4 +283,4 @@ def main(args):
 
 if __name__ == "__main__":
     args = parse_args()
-    main(args) 
+    main(args)