diff --git a/04.word2vec/train.py b/04.word2vec/train.py
index 3c28c8e6377d785a4e8d329866c2f253cdaa98fe..f296768324917ea9f7affaeea3c3b08683914a10 100644
--- a/04.word2vec/train.py
+++ b/04.word2vec/train.py
@@ -188,11 +188,11 @@ def infer(use_cuda, params_dirname=None):
         # meaning there is only one level of detail and there is only one sequence of
         # one word on this level.
         # Note that recursive_sequence_lengths should be a list of lists.
-        data1 = [[211L]]  # 'among'
-        data2 = [[6L]]  # 'a'
-        data3 = [[96L]]  # 'group'
-        data4 = [[4L]]  # 'of'
-        lod = [[1L]]
+        data1 = [[numpy.int64(211)]]  # 'among'
+        data2 = [[numpy.int64(6)]]  # 'a'
+        data3 = [[numpy.int64(96)]]  # 'group'
+        data4 = [[numpy.int64(4)]]  # 'of'
+        lod = [[numpy.int64(1)]]
 
         first_word = fluid.create_lod_tensor(data1, lod, place)
         second_word = fluid.create_lod_tensor(data2, lod, place)
diff --git a/05.recommender_system/train.py b/05.recommender_system/train.py
index 7b1f971b0a54f380345fc613d01ae5a24f9cc9eb..569cb0eccd42d3170d329cf01bfdb06611190f50 100644
--- a/05.recommender_system/train.py
+++ b/05.recommender_system/train.py
@@ -271,26 +271,28 @@ def infer(use_cuda, params_dirname):
         # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
         # level of detail info, indicating that `data` consists of two sequences
         # of length 3 and 2, respectively.
-        user_id = fluid.create_lod_tensor([[1L]], [[1]], place)
+        user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
 
         assert feed_target_names[1] == "gender_id"
-        gender_id = fluid.create_lod_tensor([[1L]], [[1]], place)
+        gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
 
         assert feed_target_names[2] == "age_id"
-        age_id = fluid.create_lod_tensor([[0L]], [[1]], place)
+        age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
 
         assert feed_target_names[3] == "job_id"
-        job_id = fluid.create_lod_tensor([[10L]], [[1]], place)
+        job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
 
         assert feed_target_names[4] == "movie_id"
-        movie_id = fluid.create_lod_tensor([[783L]], [[1]], place)
+        movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
 
         assert feed_target_names[5] == "category_id"
-        category_id = fluid.create_lod_tensor([[10L, 8L, 9L]], [[3]], place)
+        category_id = fluid.create_lod_tensor(
+            [np.array([10, 8, 9], dtype='int64')], [[3]], place)
 
         assert feed_target_names[6] == "movie_title"
         movie_title = fluid.create_lod_tensor(
-            [[1069L, 4140L, 2923L, 710L, 988L]], [[5]], place)
+            [np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
+            place)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.