diff --git a/fluid/DeepASR/data_utils/util.py b/fluid/DeepASR/data_utils/util.py
index 0a48f4696547377dbe89934355e8eaac38966fab..27f5ba8305c0e72852230658858d77fed9d233a4 100644
--- a/fluid/DeepASR/data_utils/util.py
+++ b/fluid/DeepASR/data_utils/util.py
@@ -28,7 +28,7 @@ def to_lodtensor(data, place):
 def lodtensor_to_ndarray(lod_tensor):
     """conver lodtensor to ndarray
     """
-    dims = lod_tensor.get_dims()
+    dims = lod_tensor._get_dims()
     ret = np.zeros(shape=dims).astype('float32')
     for i in xrange(np.product(dims)):
         ret.ravel()[i] = lod_tensor.get_float_element(i)
diff --git a/fluid/DeepASR/model_utils/model.py b/fluid/DeepASR/model_utils/model.py
index 3f4fdccfc93bb14496ce54ae59e49651eca2a537..8ae7e66fc781226c0316fceee65cfb805a1770ba 100644
--- a/fluid/DeepASR/model_utils/model.py
+++ b/fluid/DeepASR/model_utils/model.py
@@ -88,7 +88,7 @@ def stacked_lstmp_model(frame_dim,
         # When the execution place is specified to CUDAPlace, the program will
         # run on all $CUDA_VISIBLE_DEVICES GPUs. Otherwise the program will 
         # run on all CPU devices.
-        places = fluid.layers.get_places()
+        places = fluid.layers.device.get_places()
         pd = fluid.layers.ParallelDo(places)
         with pd.do():
             feat_ = pd.read_input(feature)
diff --git a/fluid/language_model/train_on_cloud.py b/fluid/language_model/train_on_cloud.py
index 50b7adc05d8e19ca4be7e5f585f07ec67a1cc5fb..17318400a951944c4bc6a4290b1cbfd3bc5680fa 100644
--- a/fluid/language_model/train_on_cloud.py
+++ b/fluid/language_model/train_on_cloud.py
@@ -187,7 +187,7 @@ def do_train(train_reader,
                        init_low_bound, init_high_bound)
         avg_cost = fluid.layers.mean(x=cost)
     else:
-        places = fluid.layers.get_places()
+        places = fluid.layers.device.get_places()
         pd = fluid.layers.ParallelDo(places)
         with pd.do():
             cost = network(
diff --git a/fluid/sequence_tagging_for_ner/network_conf.py b/fluid/sequence_tagging_for_ner/network_conf.py
index 5eaa704f67641bd9bb98bbac162a0adb7a72c246..17ee1951bd5955ca7a8534b50380e3f694c047f4 100644
--- a/fluid/sequence_tagging_for_ner/network_conf.py
+++ b/fluid/sequence_tagging_for_ner/network_conf.py
@@ -109,7 +109,7 @@ def ner_net(word_dict_len, label_dict_len, parallel, stack_num=2):
         name="target", shape=[1], dtype='int64', lod_level=1)
 
     if parallel:
-        places = fluid.layers.get_places()
+        places = fluid.layers.device.get_places()
         pd = fluid.layers.ParallelDo(places)
         with pd.do():
             word_ = pd.read_input(word)
diff --git a/fluid/text_classification/train.py b/fluid/text_classification/train.py
index 9078f4788319dbf76677c86eef53445fa1e85c1a..0ef923544f18d62f5a049ac79353ce4884b8d195 100644
--- a/fluid/text_classification/train.py
+++ b/fluid/text_classification/train.py
@@ -34,7 +34,7 @@ def train(train_reader,
     if not parallel:
         cost, acc, prediction = network(data, label, len(word_dict))
     else:
-        places = fluid.layers.get_places(device_count=2)
+        places = fluid.layers.device.get_places(device_count=2)
         pd = fluid.layers.ParallelDo(places)
         with pd.do():
             cost, acc, prediction = network(