refine demo dataprovider and some tiny fix

ISSUE=4597359 

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1432 1ad973e4-5ce8-4261-8a94-b56d1f490c56

demo/semantic_role_labeling/dataprovider.py

@@ -22,13 +22,13 @@ def hook(settings, word_dict, label_dict, **kwargs):
     settings.label_dict = label_dict
     #all inputs are integral and sequential type
     settings.slots = [
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(2, seq_type=SequenceType.SEQUENCE),
-        integer_value(len(label_dict), seq_type=SequenceType.SEQUENCE)]
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(2),
+        integer_value_sequence(len(label_dict))]
 
 
 @provider(init_hook=hook)

demo/sentiment/dataprovider.py

@@ -17,7 +17,7 @@
 def hook(settings, dictionary, **kwargs):
     settings.word_dict = dictionary
     settings.input_types = [
-        integer_value(len(settings.word_dict), seq_type=SequenceType.SEQUENCE),
+        integer_value_sequence(len(settings.word_dict)),
         integer_value(2)]
     settings.logger.info('dict len : %d' % (len(settings.word_dict)))
 

demo/seqToseq/dataprovider.py

@@ -30,22 +30,15 @@ def hook(settings, src_dict, trg_dict, file_list, **kwargs):
     if settings.job_mode:
         settings.trg_dict = trg_dict
         settings.slots = [
-            integer_value(
-                len(settings.src_dict),
-                seq_type=SequenceType.SEQUENCE), integer_value(
-                    len(settings.trg_dict),
-                    seq_type=SequenceType.SEQUENCE), integer_value(
-                        len(settings.trg_dict),
-                        seq_type=SequenceType.SEQUENCE)
+            integer_value_sequence(len(settings.src_dict)), 
+            integer_value_sequence(len(settings.trg_dict)), 
+            integer_value_sequence(len(settings.trg_dict))
         ]
         settings.logger.info("trg dict len : %d" % (len(settings.trg_dict)))
     else:
         settings.slots = [
-            integer_value(
-                len(settings.src_dict),
-                seq_type=SequenceType.SEQUENCE), integer_value(
-                    len(open(file_list[0], "r").readlines()),
-                    seq_type=SequenceType.SEQUENCE)
+            integer_value_sequence(len(settings.src_dict)), 
+            integer_value_sequence(len(open(file_list[0], "r").readlines()))
         ]
 
 

doc/demo/quick_start/index_en.md

@@ -225,7 +225,7 @@ Performance summary: You can refer to the training and testing scripts later. In
 <br>
 
 ### Word Embedding Model
-In order to use the word embedding model, you need to change the data provider a little bit to make the input words as a sequence of word IDs. The revised data provider is listed below. You only need to change initializer() for the type of the first input. It is changed from sparse_binary_vector to sequence of intergers.  process() remains the same. This data provider can also be used for later sequence models.
+In order to use the word embedding model, you need to change the data provider a little bit to make the input words as a sequence of word IDs. The revised data provider `dataprovider_emb.py` is listed below. You only need to change initializer() for the type of the first input. It is changed from sparse_binary_vector to sequence of intergers.  process() remains the same. This data provider can also be used for later sequence models.
 
 ```python
 def initializer(settings, dictionary, **kwargs):
@@ -260,7 +260,7 @@ avg = pooling_layer(input=emb, pooling_type=AvgPooling())
 
 The other parts of the model are the same as logistic regression network.
 
-The performance is summarized in the following table:：
+The performance is summarized in the following table:
 
 <html>
 <center>
@@ -400,7 +400,7 @@ If you want to install the remote training platform, which enables distributed t
 You can use the trained model to perform prediction on the dataset with no labels. You can also evaluate the model on dataset with labels to obtain its test accuracy.
 <center> ![](./PipelineTest_en.png) </center>
 
-The test script (test.sh) is listed below. PaddlePaddle can evaluate a model on the data with labels specified in `test.list`.
+The test script is listed below. PaddlePaddle can evaluate a model on the data with labels specified in `test.list`.
 
 ```bash
 paddle train \
@@ -497,11 +497,12 @@ The scripts of data downloading, network configurations, and training scrips are
 ## Appendix
 ### Command Line Argument
 
-* --config：network architecture path.
-* --save_dir：model save directory.
-* --log_period：the logging period per batch.
-* --num_passes：number of training passes. One pass means the training would go over the whole training dataset once.* --config_args：Other configuration arguments.
-* --init_model_path：The path of the initial model parameter.
+* \--config：network architecture path.
+* \--save_dir：model save directory.
+* \--log_period：the logging period per batch.
+* \--num_passes：number of training passes. One pass means the training would go over the whole training dataset once.
+* \--config_args：Other configuration arguments.
+* \--init_model_path：The path of the initial model parameter.
 
 By default, the trainer will save model every pass. You can also specify `saving_period_by_batches` to set the frequency of batch saving. You can use `show_parameter_stats_period` to print the statistics of the parameters, which are very useful for tuning parameters. Other command line arguments can be found in <a href = "../../ui/index.html#command-line-argument">command line argument documentation</a>。
 

doc/demo/semantic_role_labeling/index.md

@@ -71,15 +71,14 @@ def hook(settings, word_dict, label_dict, **kwargs):
     settings.word_dict = word_dict
     settings.label_dict = label_dict
     #all inputs are integral and sequential type
-    settings.slots = [ 
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(len(word_dict), seq_type=SequenceType.SEQUENCE),
-        integer_value(2, seq_type=SequenceType.SEQUENCE),
-        integer_value(len(label_dict), seq_type=SequenceType.SEQUENCE)]```
-
+    settings.slots = [
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(2),
+        integer_value_sequence(len(label_dict))]
 ```
 The corresponding data iterator is as following:
 ```

doc/layer.md

@@ -0,0 +1,4 @@
+# Layer Documents
+
+* [Layer Source Code Document](source/gserver/layers/index.rst)
+* [Layer Python API Document](ui/api/trainer_config_helpers/layers_index.rst)

doc/source/gserver/layers/layer.rst

@@ -510,11 +510,24 @@ NCELayer
 ..  doxygenclass:: paddle::NCELayer
     :members:
 
+Validation Layers
+-----------------
+
 ValidationLayer
----------------
+```````````````
 ..  doxygenclass:: paddle::ValidationLayer
     :members:
 
+AucValidation
+`````````````
+..  doxygenclass:: paddle::AucValidation
+    :members:
+
+PnpairValidation
+````````````````
+..  doxygenclass:: paddle::PnpairValidation
+    :members:
+
 Check Layers
 ============
 

doc/ui/api/trainer_config_helpers/activations_index.rst

@@ -0,0 +1,7 @@
+Activations
+===========
+
+.. toctree::
+  :maxdepth: 3
+
+  activations.rst

doc_cn/demo/quick_start/index.md

@@ -207,17 +207,16 @@ classification_cost(input=output, label=label)
 
 ### 词向量模型(Word Vector)
 
-embeding模型需要稍微改变数据提供的脚本，即`dataprovider_emb.py`，词向量模型、
-卷积模型、时序模型均使用该脚
-- 文本输入类型定义为整数类型integer_value
-- 设置文本输入类型seq_type为SequenceType.SEQUENCE
+embedding模型需要稍微改变数据提供的脚本，即`dataprovider_emb.py`，词向量模型、
+卷积模型、时序模型均使用该脚本。其中文本输入类型定义为整数时序类型integer_value_sequence。
 
 ```
 def initializer(settings, dictionary, **kwargs):
     settings.word_dict = dictionary
     settings.input_types = [
         # Define the type of the first input as sequence of integer.
-        integer_value(len(dictionary), seq_type=SequenceType.SEQUENCE),
+        # The value of the integers range from 0 to len(dictrionary)-1
+        integer_value_sequence(len(dictionary)),
         # Define the second input for label id
         integer_value(2)]
 
@@ -479,12 +478,12 @@ else:
 ## 附录(Appendix)
 ### 命令行参数(Command Line Argument)
 
-* --config：网络配置
-* --save_dir：模型存储路径
-* --log_period：每隔多少batch打印一次日志
-* --num_passes：训练轮次，一个pass表示过一遍所有训练样本
-* --config_args：命令指定的参数会传入网络配置中。
-* --init_model_path：指定初始化模型路径，可用在测试或训练时指定初始化模型。
+* \--config：网络配置
+* \--save_dir：模型存储路径
+* \--log_period：每隔多少batch打印一次日志
+* \--num_passes：训练轮次，一个pass表示过一遍所有训练样本
+* \--config_args：命令指定的参数会传入网络配置中。
+* \--init_model_path：指定初始化模型路径，可用在测试或训练时指定初始化模型。
 
 默认一个pass保存一次模型，也可以通过saving_period_by_batches设置每隔多少batch保存一次模型。
 可以通过show_parameter_stats_period设置打印参数信息等。