Merge pull request #879 from luotao1/build

add exclude_patterns for conf.py.in

Author: reyoung

cmake/FindSphinx.cmake

@@ -72,6 +72,7 @@ function( Sphinx_add_target target_name builder conf cache source destination )
     ${source}
     ${destination}
     COMMENT "Generating sphinx documentation: ${builder}"
+    COMMAND ln -s ${destination}/index_*.html ${destination}/index.html
     )
 
   set_property(
@@ -143,4 +144,4 @@ function( Sphinx_add_targets target_base_name conf source base_destination )
 
     add_dependencies( ${target_base_name}_linkcheck ${_dependencies} )
   endif()
-endfunction()
+endfunction()

doc/api/data_provider/pydataprovider2_en.rst

@@ -1,4 +1,4 @@
-..  _api_pydataprovider2_en:
+..  _api_pydataprovider2:
 
 PyDataProvider2
 ===============
@@ -104,7 +104,7 @@ And PaddlePadle will do all of the rest things\:
 
 Is this cool?
 
-..  _api_pydataprovider2_en_sequential_model:
+..  _api_pydataprovider2_sequential_model:
 
 DataProvider for the sequential model
 -------------------------------------

doc/api/predict/swig_py_paddle_en.rst

@@ -23,7 +23,7 @@ python's :code:`help()` function. Let's walk through the above python script:
 
 * At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
   PaddlePaddle with command line arguments, for more about command line arguments
-  see :ref:`cmd_detail_introduction_en` .
+  see :ref:`cmd_detail_introduction` .
 * Parse the configuration file that is used in training with :code:`parse_config()`.
   Because data to predict with always have no label, and output of prediction work
   normally is the output layer rather than the cost layer, so you should modify
@@ -36,7 +36,7 @@ python's :code:`help()` function. Let's walk through the above python script:
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
       class DataProviderConverter that can accept the same input data with
       PyDataProvider2, for more information please refer to document
-      of :ref:`api_pydataprovider2_en` .
+      of :ref:`api_pydataprovider2` .
 * Do the prediction with :code:`forwardTest()`, which takes the converted
   input data and outputs the activations of the output layer.
 

doc/conf.py.cn.in

@@ -79,7 +79,7 @@ language = 'zh_CN'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['_build']
+exclude_patterns = ['_build', '**/*_en*', '*_en*']
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

doc/conf.py.en.in

@@ -80,7 +80,7 @@ language = None
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['_build']
+exclude_patterns = ['_build', '**/*_cn*', '*_cn*']
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

doc/howto/cmd_parameter/detail_introduction_en.md

@@ -1,5 +1,5 @@
 ```eval_rst
-..  _cmd_detail_introduction_en:
+..  _cmd_detail_introduction:
 ```
 
 # Detail Description

doc/howto/cmd_parameter/index_en.md

@@ -1,5 +1,5 @@
 ```eval_rst
-..  _cmd_line_index_en:
+..  _cmd_line_index:
 ```
 # How to Set Command-line Parameters
 

doc/howto/deep_model/rnn/rnn_en.rst

@@ -30,7 +30,7 @@ Then at the :code:`process` function, each :code:`yield` function will return th
     yield src_ids, trg_ids, trg_ids_next
 
 
-For more details description of how to write a data provider, please refer to :ref:`api_pydataprovider2_en` . The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
+For more details description of how to write a data provider, please refer to :ref:`api_pydataprovider2` . The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
 
 ===============================================
 Configure Recurrent Neural Network Architecture
@@ -246,6 +246,6 @@ The code is listed below:
     outputs(beam_gen)
 
 
-Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling_en` for more details.
+Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling` for more details.
 
 The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`.

doc/tutorials/rec/ml_dataset_en.md

@@ -1,6 +1,5 @@
 ```eval_rst
-..  _demo_ml_dataset_en:
-
+..  _demo_ml_dataset:
 ```
 
 # MovieLens Dataset

doc/tutorials/rec/ml_regression_en.rst

@@ -16,7 +16,7 @@ Data Preparation
 ````````````````
 Download and extract dataset
 ''''''''''''''''''''''''''''
-We use :ref:`demo_ml_dataset_en` here. 
+We use :ref:`demo_ml_dataset` here. 
 To download and unzip the dataset, simply run the following commands.
 
 ..  code-block:: bash
@@ -264,7 +264,7 @@ In this :code:`dataprovider.py`, we should set\:
 * use_seq\: Whether this :code:`dataprovider.py` in sequence mode or not.
 * process\: Return each sample of data to :code:`paddle`.
 
-The data provider details document see :ref:`api_pydataprovider2_en`.
+The data provider details document see :ref:`api_pydataprovider2`.
 
 Train
 `````
@@ -280,7 +280,7 @@ The run.sh is shown as follow:
 It just start a paddle training process, write the log to `log.txt`,
 then print it on screen.
 
-Each command line argument in :code:`run.sh`, please refer to the :ref:`cmd_line_index_en` page. The short description of these arguments is shown as follow.
+Each command line argument in :code:`run.sh`, please refer to the :ref:`cmd_line_index` page. The short description of these arguments is shown as follow.
 
 *  config\: Tell paddle which file is neural network configuration.
 *  save_dir\: Tell paddle save model into './output'