update the text generation demo.

nmt_without_attention/README.md

@@ -91,11 +91,11 @@ PaddleBook中[机器翻译](https://github.com/PaddlePaddle/book/blob/develop/08
 ```python
 #### Decoder
 encoder_last = paddle.layer.last_seq(input=encoded_vector)
-with paddle.layer.mixed(
+encoder_last_projected = paddle.layer.mixed(
         size=decoder_size,
-        act=paddle.activation.Tanh()) as encoder_last_projected:
-        encoder_last_projected += paddle.layer.full_matrix_projection(
-            input=encoder_last)
+        act=paddle.activation.Tanh(),
+        input=paddle.layer.full_matrix_projection(input=encoder_last))
+
 # gru step
 def gru_decoder_without_attention(enc_vec, current_word):
     '''
@@ -112,10 +112,12 @@ def gru_decoder_without_attention(enc_vec, current_word):
 
     context = paddle.layer.last_seq(input=enc_vec)
 
-    with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
-        decoder_inputs +=paddle.layer.full_matrix_projection(input=context)
-        decoder_inputs += paddle.layer.full_matrix_projection(
-                input=current_word)
+    decoder_inputs = paddle.layer.mixed(
+        size=decoder_size * 3,
+        input=[
+            paddle.layer.full_matrix_projection(input=context),
+            paddle.layer.full_matrix_projection(input=current_word)
+        ])
 
     gru_step = paddle.layer.gru_step(
         name='gru_decoder',
@@ -125,24 +127,24 @@ def gru_decoder_without_attention(enc_vec, current_word):
         output_mem=decoder_mem,
         size=decoder_size)
 
-    with paddle.layer.mixed(
-            size=target_dict_dim,
-            bias_attr=True,
-            act=paddle.activation.Softmax()) as out:
-        out += paddle.layer.full_matrix_projection(input=gru_step)
+    out = paddle.layer.mixed(
+        size=target_dict_dim,
+        bias_attr=True,
+        act=paddle.activation.Softmax(),
+        input=paddle.layer.full_matrix_projection(input=gru_step))
     return out
 ```
 
 在模型训练和测试阶段，解码器的行为有很大的不同：
 
 - **训练阶段**：目标翻译结果的词向量`trg_embedding`作为参数传递给单步逻辑`gru_decoder_without_attention()`，函数`recurrent_group()`循环调用单步逻辑执行，最后计算目标翻译与实际解码的差异cost并返回；
-- **测试阶段**：解码器根据最后一个生成的词预测下一个词，`GeneratedInputV2()`自动取出模型预测出的概率最高的$k$个词的词向量传递给单步逻辑，`beam_search()`函数调用单步逻辑函数`gru_decoder_without_attention()`完成柱搜索并作为结果返回。
+- **测试阶段**：解码器根据最后一个生成的词预测下一个词，`GeneratedInput()`自动取出模型预测出的概率最高的$k$个词的词向量传递给单步逻辑，`beam_search()`函数调用单步逻辑函数`gru_decoder_without_attention()`完成柱搜索并作为结果返回。
 
 训练和生成的逻辑分别实现在如下的`if-else`条件分支中：
 
 ```python
 decoder_group_name = "decoder_group"
-group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+group_input1 = paddle.layer.StaticInput(input=encoded_vector, is_seq=True)
 group_inputs = [group_input1]
 if not generating:
     trg_embedding = paddle.layer.embedding(
@@ -166,7 +168,7 @@ if not generating:
     return cost
 else:
 
-    trg_embedding = paddle.layer.GeneratedInputV2(
+    trg_embedding = paddle.layer.GeneratedInput(
         size=target_dict_dim,
         embedding_name='_target_language_embedding',
         embedding_size=word_vector_dim)

nmt_without_attention/index.html

@@ -133,11 +133,11 @@ PaddleBook中[机器翻译](https://github.com/PaddlePaddle/book/blob/develop/08
 ```python
 #### Decoder
 encoder_last = paddle.layer.last_seq(input=encoded_vector)
-with paddle.layer.mixed(
+encoder_last_projected = paddle.layer.mixed(
         size=decoder_size,
-        act=paddle.activation.Tanh()) as encoder_last_projected:
-        encoder_last_projected += paddle.layer.full_matrix_projection(
-            input=encoder_last)
+        act=paddle.activation.Tanh(),
+        input=paddle.layer.full_matrix_projection(input=encoder_last))
+
 # gru step
 def gru_decoder_without_attention(enc_vec, current_word):
     '''
@@ -154,10 +154,12 @@ def gru_decoder_without_attention(enc_vec, current_word):
 
     context = paddle.layer.last_seq(input=enc_vec)
 
-    with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
-        decoder_inputs +=paddle.layer.full_matrix_projection(input=context)
-        decoder_inputs += paddle.layer.full_matrix_projection(
-                input=current_word)
+    decoder_inputs = paddle.layer.mixed(
+        size=decoder_size * 3,
+        input=[
+            paddle.layer.full_matrix_projection(input=context),
+            paddle.layer.full_matrix_projection(input=current_word)
+        ])
 
     gru_step = paddle.layer.gru_step(
         name='gru_decoder',
@@ -167,24 +169,24 @@ def gru_decoder_without_attention(enc_vec, current_word):
         output_mem=decoder_mem,
         size=decoder_size)
 
-    with paddle.layer.mixed(
-            size=target_dict_dim,
-            bias_attr=True,
-            act=paddle.activation.Softmax()) as out:
-        out += paddle.layer.full_matrix_projection(input=gru_step)
+    out = paddle.layer.mixed(
+        size=target_dict_dim,
+        bias_attr=True,
+        act=paddle.activation.Softmax(),
+        input=paddle.layer.full_matrix_projection(input=gru_step))
     return out
 ```
 
 在模型训练和测试阶段，解码器的行为有很大的不同：
 
 - **训练阶段**：目标翻译结果的词向量`trg_embedding`作为参数传递给单步逻辑`gru_decoder_without_attention()`，函数`recurrent_group()`循环调用单步逻辑执行，最后计算目标翻译与实际解码的差异cost并返回；
-- **测试阶段**：解码器根据最后一个生成的词预测下一个词，`GeneratedInputV2()`自动取出模型预测出的概率最高的$k$个词的词向量传递给单步逻辑，`beam_search()`函数调用单步逻辑函数`gru_decoder_without_attention()`完成柱搜索并作为结果返回。
+- **测试阶段**：解码器根据最后一个生成的词预测下一个词，`GeneratedInput()`自动取出模型预测出的概率最高的$k$个词的词向量传递给单步逻辑，`beam_search()`函数调用单步逻辑函数`gru_decoder_without_attention()`完成柱搜索并作为结果返回。
 
 训练和生成的逻辑分别实现在如下的`if-else`条件分支中：
 
 ```python
 decoder_group_name = "decoder_group"
-group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+group_input1 = paddle.layer.StaticInput(input=encoded_vector, is_seq=True)
 group_inputs = [group_input1]
 if not generating:
     trg_embedding = paddle.layer.embedding(
@@ -208,7 +210,7 @@ if not generating:
     return cost
 else:
 
-    trg_embedding = paddle.layer.GeneratedInputV2(
+    trg_embedding = paddle.layer.GeneratedInput(
         size=target_dict_dim,
         embedding_name='_target_language_embedding',
         embedding_size=word_vector_dim)

nmt_without_attention/nmt_without_attention.py

@@ -16,7 +16,7 @@ def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
     '''
     Define the network structure of NMT, including encoder and decoder.
 
-    :param source_dict_dim: size of source dictionary 
+    :param source_dict_dim: size of source dictionary
     :type source_dict_dim : int
     :param target_dict_dim: size of target dictionary
     :type target_dict_dim: int
@@ -41,11 +41,11 @@ def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
         return_seq=True)
     #### Decoder
     encoder_last = paddle.layer.last_seq(input=encoded_vector)
-    with paddle.layer.mixed(
-            size=decoder_size,
-            act=paddle.activation.Tanh()) as encoder_last_projected:
-        encoder_last_projected += paddle.layer.full_matrix_projection(
-            input=encoder_last)
+    encoder_last_projected = paddle.layer.mixed(
+        size=decoder_size,
+        act=paddle.activation.Tanh(),
+        input=paddle.layer.full_matrix_projection(input=encoder_last))
+
     # gru step
     def gru_decoder_without_attention(enc_vec, current_word):
         '''
@@ -63,10 +63,12 @@ def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
 
         context = paddle.layer.last_seq(input=enc_vec)
 
-        with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
-            decoder_inputs += paddle.layer.full_matrix_projection(input=context)
-            decoder_inputs += paddle.layer.full_matrix_projection(
-                input=current_word)
+        decoder_inputs = paddle.layer.mixed(
+            size=decoder_size * 3,
+            input=[
+                paddle.layer.full_matrix_projection(input=context),
+                paddle.layer.full_matrix_projection(input=current_word)
+            ])
 
         gru_step = paddle.layer.gru_step(
             name='gru_decoder',
@@ -76,15 +78,15 @@ def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
             output_mem=decoder_mem,
             size=decoder_size)
 
-        with paddle.layer.mixed(
-                size=target_dict_dim,
-                bias_attr=True,
-                act=paddle.activation.Softmax()) as out:
-            out += paddle.layer.full_matrix_projection(input=gru_step)
+        out = paddle.layer.mixed(
+            size=target_dict_dim,
+            bias_attr=True,
+            act=paddle.activation.Softmax(),
+            input=paddle.layer.full_matrix_projection(input=gru_step))
         return out
 
     decoder_group_name = "decoder_group"
-    group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+    group_input1 = paddle.layer.StaticInput(input=encoded_vector, is_seq=True)
     group_inputs = [group_input1]
 
     if not generating:
@@ -109,7 +111,7 @@ def seq2seq_net(source_dict_dim, target_dict_dim, generating=False):
         return cost
     else:
 
-        trg_embedding = paddle.layer.GeneratedInputV2(
+        trg_embedding = paddle.layer.GeneratedInput(
             size=target_dict_dim,
             embedding_name='_target_language_embedding',
             embedding_size=word_vector_dim)
@@ -194,7 +196,7 @@ def generate(source_dict_dim, target_dict_dim, init_models_path):
     beam_gen = seq2seq_net(source_dict_dim, target_dict_dim, True)
     with gzip.open(init_models_path) as f:
         parameters = paddle.parameters.Parameters.from_tar(f)
-    # prob is the prediction probabilities, and id is the prediction word. 
+    # prob is the prediction probabilities, and id is the prediction word.
     beam_result = paddle.infer(
         output_layer=beam_gen,
         parameters=parameters,
@@ -244,10 +246,10 @@ def main():
     target_language_dict_dim = 30000
 
     if generating:
-        # shoud pass the right generated model's path here
+        # modify this path to speicify a trained model.
         init_models_path = 'models/nmt_without_att_params_batch_1800.tar.gz'
         if not os.path.exists(init_models_path):
-            print "Cannot find models for generation"
+            print "trained model cannot be found."
             exit(1)
         generate(source_language_dict_dim, target_language_dict_dim,
                  init_models_path)