diff --git a/fluid/sequence_tagging_for_ner/train.py b/fluid/sequence_tagging_for_ner/train.py
index 6ed77cd5ca1d504a8b79b4f87349242b5051c539..0e05f6550f1b701748c9a2c34224b4a16dea7e8b 100644
--- a/fluid/sequence_tagging_for_ner/train.py
+++ b/fluid/sequence_tagging_for_ner/train.py
@@ -2,7 +2,7 @@ import os
 import math
 import numpy as np
 
-import paddle.v2 as paddle
+import paddle
 import paddle.fluid as fluid
 
 import reader
@@ -24,12 +24,19 @@ def test(exe, chunk_evaluator, inference_program, test_data, place):
     return chunk_evaluator.eval(exe)
 
 
-def main(train_data_file, test_data_file, vocab_file, target_file, emb_file,
-         model_save_dir, num_passes, use_gpu, parallel):
+def main(train_data_file,
+         test_data_file,
+         vocab_file,
+         target_file,
+         emb_file,
+         model_save_dir,
+         num_passes,
+         use_gpu,
+         parallel,
+         batch_size=200):
     if not os.path.exists(model_save_dir):
         os.mkdir(model_save_dir)
 
-    BATCH_SIZE = 200
     word_dict = load_dict(vocab_file)
     label_dict = load_dict(target_file)
 
@@ -62,12 +69,12 @@ def main(train_data_file, test_data_file, vocab_file, target_file, emb_file,
         paddle.reader.shuffle(
             reader.data_reader(train_data_file, word_dict, label_dict),
             buf_size=20000),
-        batch_size=BATCH_SIZE)
+        batch_size=batch_size)
     test_reader = paddle.batch(
         paddle.reader.shuffle(
             reader.data_reader(test_data_file, word_dict, label_dict),
             buf_size=20000),
-        batch_size=BATCH_SIZE)
+        batch_size=batch_size)
 
     place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
     feeder = fluid.DataFeeder(feed_list=[word, mark, target], place=place)
@@ -79,34 +86,33 @@ def main(train_data_file, test_data_file, vocab_file, target_file, emb_file,
     embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
     embedding_param.set(word_vector_values, place)
 
-    batch_id = 0
     for pass_id in xrange(num_passes):
         chunk_evaluator.reset(exe)
-        for data in train_reader():
+        for batch_id, data in enumerate(train_reader()):
             cost, batch_precision, batch_recall, batch_f1_score = exe.run(
                 fluid.default_main_program(),
                 feed=feeder.feed(data),
                 fetch_list=[avg_cost] + chunk_evaluator.metrics)
             if batch_id % 5 == 0:
+                print(cost)
                 print("Pass " + str(pass_id) + ", Batch " + str(
                     batch_id) + ", Cost " + str(cost[0]) + ", Precision " + str(
                         batch_precision[0]) + ", Recall " + str(batch_recall[0])
                       + ", F1_score" + str(batch_f1_score[0]))
-            batch_id = batch_id + 1
 
         pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(exe)
         print("[TrainSet] pass_id:" + str(pass_id) + " pass_precision:" + str(
             pass_precision) + " pass_recall:" + str(pass_recall) +
               " pass_f1_score:" + str(pass_f1_score))
-        pass_precision, pass_recall, pass_f1_score = test(
+        test_pass_precision, test_pass_recall, test_pass_f1_score = test(
             exe, chunk_evaluator, inference_program, test_reader, place)
         print("[TestSet] pass_id:" + str(pass_id) + " pass_precision:" + str(
-            pass_precision) + " pass_recall:" + str(pass_recall) +
-              " pass_f1_score:" + str(pass_f1_score))
+            test_pass_precision) + " pass_recall:" + str(test_pass_recall) +
+              " pass_f1_score:" + str(test_pass_f1_score))
 
         save_dirname = os.path.join(model_save_dir, "params_pass_%d" % pass_id)
         fluid.io.save_inference_model(save_dirname, ['word', 'mark', 'target'],
-                                      [crf_decode], exe)
+                                      crf_decode, exe)
 
 
 if __name__ == "__main__":
@@ -117,6 +123,7 @@ if __name__ == "__main__":
         target_file="data/target.txt",
         emb_file="data/wordVectors.txt",
         model_save_dir="models",
-        num_passes=1000,
+        num_passes=100,
+        batch_size=1,
         use_gpu=False,
         parallel=False)