Merge pull request #666 from wopeizl/windows/fix04

fix the word2ver on windows test=develop

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 = [[211]]  # 'among'
-        data2 = [[6]]  # 'a'
-        data3 = [[96]]  # 'group'
-        data4 = [[4]]  # 'of'
-        lod = [[1]]
+        data1 = [[211L]]  # 'among'
+        data2 = [[6L]]  # 'a'
+        data3 = [[96L]]  # 'group'
+        data4 = [[4L]]  # 'of'
+        lod = [[1L]]
 
         first_word = fluid.create_lod_tensor(data1, lod, place)
         second_word = fluid.create_lod_tensor(data2, lod, place)

05.recommender_system/train.py

@@ -271,26 +271,26 @@ 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([[1]], [[1]], place)
+        user_id = fluid.create_lod_tensor([[1L]], [[1]], place)
 
         assert feed_target_names[1] == "gender_id"
-        gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
+        gender_id = fluid.create_lod_tensor([[1L]], [[1]], place)
 
         assert feed_target_names[2] == "age_id"
-        age_id = fluid.create_lod_tensor([[0]], [[1]], place)
+        age_id = fluid.create_lod_tensor([[0L]], [[1]], place)
 
         assert feed_target_names[3] == "job_id"
-        job_id = fluid.create_lod_tensor([[10]], [[1]], place)
+        job_id = fluid.create_lod_tensor([[10L]], [[1]], place)
 
         assert feed_target_names[4] == "movie_id"
-        movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
+        movie_id = fluid.create_lod_tensor([[783L]], [[1]], place)
 
         assert feed_target_names[5] == "category_id"
-        category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
+        category_id = fluid.create_lod_tensor([[10L, 8L, 9L]], [[3]], place)
 
         assert feed_target_names[6] == "movie_title"
-        movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]],
-                                              [[5]], place)
+        movie_title = fluid.create_lod_tensor(
+            [[1069L, 4140L, 2923L, 710L, 988L]], [[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.

06.understand_sentiment/train_conv.py

@@ -185,7 +185,7 @@ def infer(use_cuda, params_dirname=None):
         UNK = word_dict['<unk>']
         lod = []
         for c in reviews:
-            lod.append([word_dict.get(words, UNK) for words in c])
+            lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
 
         base_shape = [[len(c) for c in lod]]
 

06.understand_sentiment/train_dyn_rnn.py

@@ -197,7 +197,7 @@ def infer(use_cuda, params_dirname=None):
         UNK = word_dict['<unk>']
         lod = []
         for c in reviews:
-            lod.append([word_dict.get(words, UNK) for words in c])
+            lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
 
         base_shape = [[len(c) for c in lod]]
 

06.understand_sentiment/train_stacked_lstm.py

@@ -195,7 +195,7 @@ def infer(use_cuda, params_dirname=None):
         UNK = word_dict['<unk>']
         lod = []
         for c in reviews:
-            lod.append([word_dict.get(words, UNK) for words in c])
+            lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
 
         base_shape = [[len(c) for c in lod]]