change fluid.layers.embedding to fluid.embedding to fix a bug of dim (#847)

06.understand_sentiment/README.cn.md

@@ -151,7 +151,7 @@ BATCH_SIZE = 128  #batch的大小
 ```python
 #文本卷积神经网络
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
@@ -183,7 +183,7 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
     #计算词向量
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
 
     #第一层栈

06.understand_sentiment/README.md

@@ -140,7 +140,7 @@ Note that `fluid.nets.sequence_conv_pool` contains both convolution and pooling
 ```python
 #Textconvolution neural network
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
@@ -172,7 +172,7 @@ The code of the stack bidirectional LSTM `stacked_lstm_net` is as follows:
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
     # Calculate word vectorvector
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
 
     #First stack

06.understand_sentiment/index.cn.html

@@ -193,7 +193,7 @@ BATCH_SIZE = 128  #batch的大小
 ```python
 #文本卷积神经网络
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
@@ -225,7 +225,7 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
     #计算词向量
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
 
     #第一层栈

06.understand_sentiment/index.html

@@ -182,7 +182,7 @@ Note that `fluid.nets.sequence_conv_pool` contains both convolution and pooling
 ```python
 #Textconvolution neural network
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
@@ -214,7 +214,7 @@ The code of the stack bidirectional LSTM `stacked_lstm_net` is as follows:
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
 
     # Calculate word vectorvector
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
 
     #First stack

06.understand_sentiment/train_conv.py

@@ -42,8 +42,7 @@ def parse_args():
 
 
 def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
-    emb = fluid.layers.embedding(
-        input=data, size=[input_dim, emb_dim], is_sparse=True)
+    emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
     conv_3 = fluid.nets.sequence_conv_pool(
         input=emb,
         num_filters=hid_dim,

06.understand_sentiment/train_dyn_rnn.py

@@ -42,8 +42,7 @@ def parse_args():
 
 
 def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
-    emb = fluid.layers.embedding(
-        input=data, size=[input_dim, emb_dim], is_sparse=True)
+    emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
     sentence = fluid.layers.fc(input=emb, size=lstm_size * 4, act='tanh')
 
     lstm, _ = fluid.layers.dynamic_lstm(sentence, size=lstm_size * 4)

06.understand_sentiment/train_stacked_lstm.py

@@ -46,8 +46,7 @@ def parse_args():
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
     assert stacked_num % 2 == 1
 
-    emb = fluid.layers.embedding(
-        input=data, size=[input_dim, emb_dim], is_sparse=True)
+    emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
 
     fc1 = fluid.layers.fc(input=emb, size=hid_dim)
     lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)