Merge pull request #889 from luotao1/dir

update chinese catalog

CONTRIBUTING.md

-./doc/howto/contribute_to_paddle_en.md
\ No newline at end of file

CONTRIBUTING.md

+./doc/howto/dev/contribute_to_paddle_en.md

cmake/FindSphinx.cmake

@@ -72,7 +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
+    COMMAND ln -sf ${destination}/index_*.html ${destination}/index.html
     )
 
   set_property(

doc/CMakeLists.txt

@@ -16,7 +16,7 @@ set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
 set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
 
 configure_file(
-    "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.en.in"
+    "${CMAKE_CURRENT_SOURCE_DIR}/templates/conf.py.en.in"
     "${BINARY_BUILD_DIR_EN}/conf.py"
     @ONLY)
 
@@ -41,7 +41,7 @@ set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees")
 set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html")
 
 configure_file(
-    "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.cn.in"
+    "${CMAKE_CURRENT_SOURCE_DIR}/templates/conf.py.cn.in"
     "${BINARY_BUILD_DIR_CN}/conf.py"
     @ONLY)
 

doc/about/index_en.rst

@@ -11,4 +11,4 @@ We hope to build an active open source community both by providing feedback and
 Credits
 --------
 
-We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/blob/develop/authors>`_ of PaddlePaddle!
+We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/graphs/contributors>`_ of PaddlePaddle!

doc/api/index_cn.rst

-API
-===
+API中文手册
+============
 
 DataProvider API
 ----------------

doc/getstarted/basic_usage/index_cn.rst

-简介
-====
+经典的线性回归任务
+==================
 
 PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍将向你展示如何利用PaddlePaddle来解决一个经典的线性回归问题。
 
-1. 一个经典的任务
------------------
+任务简介
+--------
 
 我们展示如何用PaddlePaddle解决 `单变量的线性回归 <https://www.baidu.com/s?wd=单变量线性回归>`_ 问题。线性回归的输入是一批点 `(x, y)` ，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 `w` 和 `b` 。
 
 一个例子是房产估值。我们假设房产的价格（y）是其大小（x）的一个线性函数，那么我们可以通过收集市场上房子的大小和价格，用来估计线性函数的参数w 和 b。
 
-2. 准备数据
+准备数据
 -----------
 
 假设变量 `x` 和 `y` 的真实关系为： `y = 2x + 0.3 + ε`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为线性回归的输入。下面脚本符合PaddlePaddle期待的读取数据的Python程序的模式。
@@ -28,7 +28,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
             x = random.random()
             yield [x], [2*x+0.3]
 
-3. 训练模型
+训练模型
 -----------
 
 为了还原 `y = 2x + 0.3`，我们先从一条随机的直线 `y' = wx + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `y'` 和 `y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。
@@ -79,7 +79,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
 
 PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` y = 2x + 0.3 `
 
-4. 模型检验
+模型检验
 -----------
 
 训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用学习的模型对另外一组测试数据进行预测，评价预测的效果。在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。
@@ -106,10 +106,3 @@ PaddlePaddle将每个模型参数作为一个numpy数组单独存为一个文件
 从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型一致。
 
 这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入、模型训练和最后的结果验证。
-
-5. 推荐后续阅读
----------------
-
-- `安装/编译 <../build_and_install/index.html>`_ ：PaddlePaddle的安装与编译文档。
-- `快速入门 <../demo/quick_start/index.html>`_ ：使用商品评论分类任务，系统性的介绍如何一步步改进，最终得到产品级的深度模型。
-- `示例 <../demo/index.html>`_ ：各种实用案例，涵盖图像、文本、推荐等多个领域。
\ No newline at end of file

doc/getstarted/basic_usage/index_en.rst

-Basic Usage
-=============
+Simple Linear Regression
+========================
 
 PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on.
 
-1. A Classic Problem
----------------------
+Problem Background
+------------------
 
 Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression <https://en.wikipedia.org/wiki/Simple_linear_regression>`_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets.
 
-2. Prepare the Data
---------------------
+Prepare the Data
+-----------------
 
 Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types.
 
@@ -26,8 +26,8 @@ Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's se
                 x = random.random()
                 yield [x], [2*x+0.3]
 
-3. Train a NeuralNetwork
--------------------------
+Train a NeuralNetwork
+----------------------
 
 To recover this relationship between ``X`` and ``Y``, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line ``Y' = wX + b`` , then we gradually adapt ``w`` and ``b`` to minimize the difference between ``Y'`` and ``Y``. Here is what it looks like in PaddlePaddle:
 
@@ -73,8 +73,8 @@ Now that everything is ready, you can train the network with a simple command li
 This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path ``./output``. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess.
 
 
-4. Evaluate the Model
------------------------
+Evaluate the Model
+-------------------
 
 Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: ``w=2, b=0.3``, thus a better option is to check out model parameters directly.
 

doc/getstarted/build_and_install/index_cn.rst

 编译与安装
-========================
+==========
 
 安装
 ++++

doc/getstarted/index_cn.rst

-GET STARTED
+新手入门
 ============
 
 ..  toctree::

doc/howto/concepts/nn_cn.rst

-TBD
-
-目前正在书写中。敬请期待。
\ No newline at end of file

doc/howto/concepts/program_concepts_cn.rst

-TBD
-###
-
-目前正在书写中。敬请期待。
\ No newline at end of file

doc/howto/deep_model/index_cn.rst

-How to Configure Deep Models
-============================
-
-..  toctree::
-  :maxdepth: 1
-
-  rnn/recurrent_group_cn.md
-  rnn/hierarchical_layer_cn.rst
-  rnn/hrnn_rnn_api_compare_cn.rst
-  rnn/hrnn_demo_cn.rst

doc/howto/deep_model/index_en.rst

-How to Configure Deep Models
-============================
-
-..  toctree::
-  :maxdepth: 1
-
-  rnn/rnn_en.rst

doc/howto/deep_model/rnn/hrnn_demo_cn.rst

-..	_algo_hrnn_demo:
-
-#################
-双层RNN的使用示例
-#################
-
-TBD
\ No newline at end of file

doc/howto/deep_model/rnn/index_cn.rst

+RNN相关模型
+===========
+
+..  toctree::
+  :maxdepth: 1
+
+  recurrent_group_cn.md
+  hierarchical_layer_cn.rst
+  hrnn_rnn_api_compare_cn.rst

doc/howto/deep_model/rnn/index_en.rst

+RNN Models
+==========
+
+..  toctree::
+  :maxdepth: 1
+
+  rnn_config_en.rst

doc/howto/contribute_to_paddle_en.md → doc/howto/dev/contribute_to_paddle_en.md

-# How to Contribute Code
+# Contribute Code
 
 We sincerely appreciate your contributions. You can use fork and pull request
 workflow to merge your code.

doc/howto/new_layer/index_en.rst → doc/howto/dev/new_layer_en.rst

-=======================
-How to Write New Layers
-=======================
+================
+Write New Layers
+================
 
 This tutorial will guide you to write customized layers in PaddlePaddle. We will utilize fully connected layer as an example to guide you through the following steps for writing a new layer.
 

doc/howto/write_docs/index_cn.rst → doc/howto/dev/write_docs_cn.rst

-###############################
-如何贡献/修改PaddlePaddle的文档
-###############################
+##################
+如何贡献/修改文档
+##################
 
 PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
 

doc/howto/index_cn.rst

-HOW TO
-=======
+进阶指南
+========
 
-Usage
--------
+使用说明
+--------
 
 ..  toctree::
   :maxdepth: 1
 
-  concepts/use_concepts_cn.rst
-  cluster/k8s/paddle_on_k8s_cn.md
-  cluster/k8s/distributed_training_on_k8s_cn.md
+  usage/concepts/use_concepts_cn.rst
+  usage/cluster/k8s/k8s_cn.md
+  usage/cluster/k8s/k8s_distributed_cn.md
 
-Development
-------------
+开发标准
+--------
 
 ..  toctree::
   :maxdepth: 1
 
-  write_docs/index_cn.rst
-  deep_model/index_cn.rst
+  dev/write_docs_cn.rst
+  dev/contribute_to_paddle_cn.md
 
-Optimization
--------------
+模型配置
+--------
 
 ..  toctree::
   :maxdepth: 1
+
+  deep_model/rnn/index_cn.rst
+
+性能优化
+--------
+
+..  toctree::
+  :maxdepth: 1
+
+  optimization/gpu_profiling_cn.rst

doc/howto/index_en.rst

@@ -7,9 +7,8 @@ Usage
 ..  toctree::
   :maxdepth: 1
 
-  cmd_parameter/index_en.md
-  deep_model/index_en.rst
-  cluster/cluster_train_en.md
+  usage/cmd_parameter/index_en.md
+  usage/cluster/cluster_train_en.md
 
 Development
 ------------
@@ -17,8 +16,16 @@ Development
 ..  toctree::
   :maxdepth: 1
 
-  new_layer/index_en.rst
-  contribute_to_paddle_en.md
+  dev/new_layer_en.rst
+  dev/contribute_to_paddle_en.md
+
+Configuration
+-------------
+
+..  toctree::
+  :maxdepth: 1
+
+  deep_model/rnn/index_en.rst
 
 Optimization
 -------------
@@ -26,4 +33,4 @@ Optimization
 ..  toctree::
   :maxdepth: 1
 
-  optimization/index_en.rst
+  optimization/gpu_profiling_en.rst

doc/howto/optimization/gpu_profiling_cn.rst

-PaddlePaddle 性能分析与调优
-=====================================
+==================
+GPU性能分析与调优
+==================
+
+..  contents::
 
 此教程将向您分步介绍如何使用内置的定时工具、 **nvprof** 或 **nvvp** 来运行性能分析和调优。
 

doc/howto/optimization/gpu_profiling_en.rst

-Profiling on PaddlePaddle
-=========================
+====================
+Tune GPU Performance 
+====================
+
+..  contents::
 
 This tutorial will guide you step-by-step through how to conduct profiling and performance tuning using built-in timer, **nvprof** and **nvvp**.
 

doc/howto/optimization/index_en.rst

-How to Tune GPU Performance
-===========================
-
-.. toctree::
-  :maxdepth: 3
-
-  gpu_profiling_en.rst

doc/howto/cluster/cluster_train_en.md → doc/howto/usage/cluster/cluster_train_en.md

-# How to Run Distributed Training
+# Run Distributed Training
 
 In this article, we explain how to run distributed Paddle training jobs on clusters.  We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation).
 

doc/howto/cluster/k8s/paddle_on_k8s_cn.md → doc/howto/usage/cluster/k8s/k8s_cn.md

-# Paddle On Kubernetes：单机训练
+# Kubernetes 单机训练
 
 在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。
 

doc/howto/cluster/k8s/distributed_training_on_k8s_cn.md → doc/howto/usage/cluster/k8s/k8s_distributed_cn.md

-
-# PaddlePaddle on Kubernetes：分布式训练
+# Kubernetes 分布式训练
 
 前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。
 

doc/howto/cmd_parameter/index_en.md → doc/howto/usage/cmd_parameter/index_en.md

 ```eval_rst
 ..  _cmd_line_index:
 ```
-# How to Set Command-line Parameters
+# Set Command-line Parameters
 
 * [Use Case](use_case_en.md)
 * [Arguments](arguments_en.md)

doc/howto/concepts/use_concepts_cn.rst → doc/howto/usage/concepts/use_concepts_cn.rst

-#########################
-PaddlePaddle 基本使用概念
-#########################
+############
+基本使用概念
+############
 
 PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。
 

doc/tutorials/index_cn.md

-# TUTORIALS
-There are several examples and demos here.
+# 完整教程
 
-## Quick Start
+## 快速入门
 
-* [Quick Start](quick_start/index_cn.rst)
+使用商品评论分类任务，系统性的介绍如何一步步改进，最终得到产品级的深度模型。
 
-## Image
+* [阅读教程](quick_start/index_cn.rst)
+
+## 图像
 
 * TBD
 
-## NLP
+## 自然语言处理
 
-* [Sentiment Analysis](sentiment_analysis/index_cn.md)
-* [Semantic Role Labeling](semantic_role_labeling/index_cn.rst)
+* [情感分类](sentiment_analysis/index_cn.md)
+* [语义角色标注](semantic_role_labeling/index_cn.md)
 
-## Recommendation
+## 个性化推荐
 
 * TBD
 
-## Model Zoo
+## 常用模型
 
 * TBD

doc/tutorials/index_en.md

@@ -17,7 +17,6 @@ There are several examples and demos here.
 
 ## Recommendation
 
-* [MovieLens Dataset](rec/ml_dataset_en.md)
 * [MovieLens Regression](rec/ml_regression_en.rst)
 
 ## Model Zoo

doc/tutorials/quick_start/index_cn.rst

-PaddlePaddle快速入门教程
-========================
+=============
+快速入门教程
+=============
 
 我们将以 `文本分类问题 <https://en.wikipedia.org/wiki/Document_classification>`_ 为例,
 介绍PaddlePaddle的基本使用方法。