提交 438a4ec6 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #889 from luotao1/dir

update chinese catalog
./doc/howto/contribute_to_paddle_en.md
\ No newline at end of file
./doc/howto/dev/contribute_to_paddle_en.md
......@@ -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 ${destination}/index.html
)
set_property(
......
......@@ -16,7 +16,7 @@ set(SPHINX_CACHE_DIR_EN "${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)
......
......@@ -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!
API
===
API中文手册
============
DataProvider API
----------------
......
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
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.
......
编译与安装
========================
==========
安装
++++
......@@ -24,4 +24,4 @@ PaddlePaddle提供数个预编译的二进制来进行安装,包括Docker镜
.. toctree::
:maxdepth: 1
cmake/build_from_source_cn.rst
\ No newline at end of file
cmake/build_from_source_cn.rst
GET STARTED
新手入门
============
.. toctree::
......
TBD
目前正在书写中。敬请期待。
\ No newline at end of file
TBD
###
目前正在书写中。敬请期待。
\ No newline at end of file
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
How to Configure Deep Models
============================
.. toctree::
:maxdepth: 1
rnn/rnn_en.rst
.. _algo_hrnn_demo:
#################
双层RNN的使用示例
#################
TBD
\ No newline at end of file
RNN相关模型
===========
.. toctree::
:maxdepth: 1
recurrent_group_cn.md
hierarchical_layer_cn.rst
hrnn_rnn_api_compare_cn.rst
RNN Models
==========
.. toctree::
:maxdepth: 1
rnn_config_en.rst
# How to Contribute Code
# Contribute Code
We sincerely appreciate your contributions. You can use fork and pull request
workflow to merge your code.
......
=======================
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.
......
###############################
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。
......@@ -51,4 +51,4 @@ TBD
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
\ No newline at end of file
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
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
......@@ -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
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。
......
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**.
......
How to Tune GPU Performance
===========================
.. toctree::
:maxdepth: 3
gpu_profiling_en.rst
# 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).
......
# 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。
......
# 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。
......
```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)
......
#########################
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。
......
# 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
......@@ -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
......
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。
......
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