提交 38d73571 编写于 作者: L Luo Tao 提交者: Yu Yang

fix dead link and some warning, tiny modify on some document

Change-Id: I6ba3ec1daeb3d4aee2556959e87bae7c4c1ad668
上级 2fe39f8c
......@@ -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 :doc:`Python Data Provider <../py_data_provider_wrapper>`. 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 `PyDataProvider2 <../../ui/data_provider/index.html>`_. The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
===============================================
Configure Recurrent Neural Network Architecture
......@@ -106,7 +106,7 @@ We will use the sequence to sequence model with attention as an example to demon
In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.
The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to :doc:`Layers <../trainer_config_helpers/layers>` for more details.
The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to `Layers <../../ui/api/trainer_config_helpers/layers_index.html>`_ for more details.
We also project the encoder vector to :code:`decoder_size` dimensional space, get the first instance of the backward recurrent network, and project it to :code:`decoder_size` dimensional space:
......@@ -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 :doc:`Semantic Role Labeling Demo <../../../demo/semantic_role_labeling>` 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 `Semantic Role Labeling Demo <../../demo/semantic_role_labeling/index.html>`_ for more details.
The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`.
......@@ -5,6 +5,7 @@ Install PaddlePaddle
----------------------
.. toctree::
:maxdepth: 1
:glob:
install_*
......@@ -15,6 +16,7 @@ Build from Source
If you want to hack and contribute PaddlePaddle source code, following guides can help you\:
.. toctree::
:maxdepth: 1
:glob:
build_from_source.md
......@@ -29,6 +31,7 @@ state and your experience of installation may not be smooth.
If you want to pack docker image, the following guide can help you\:
.. toctree::
:maxdepth: 1
:glob:
docker_install.md
......
......@@ -152,7 +152,6 @@ Please refer to the following section reference for details.
Reference
---------
.. _@provider::
@provider
+++++++++
......@@ -170,31 +169,28 @@ PaddlePaddle from a user defined function. Its parameters are:
usefull in sequential model, that defines batch size is counted upon sequence
or token. By default, each sample or sequence counts to 1 when calculating
batch size.
* cache is a data cache strategy, see `cache`_
* cache is a data cache strategy, see `cache`_.
* Init_hook function is invoked once the data provider is initialized,
see `init_hook`_
see `init_hook`_.
.. _input_types::
input_types
+++++++++++
PaddlePaddle has four data types, and three sequence types.
The four data types are:
* dense_vector represents dense float vector.
* sparse_binary_vector sparse binary vector, most of the value is 0, and
* :code:`dense_vector`: dense float vector.
* :code:`sparse_binary_vector`: sparse binary vector, most of the value is 0, and
the non zero elements are fixed to 1.
* sparse_float_vector sparse float vector, most of the value is 0, and some
non zero elements that can be any float value. They are given by the user.
* integer represents an integer scalar, that is especially used for label or
word index.
* :code:`sparse_float_vector`: sparse float vector, most of the value is 0, and some
non zero elements can be any float value. They are given by the user.
* :code:`integer`: an integer scalar, that is especially used for label or word index.
The three sequence types are:
The three sequence types are
* SequenceType.NO_SEQUENCE means the sample is not a sequence
* SequenceType.SEQUENCE means the sample is a sequence
* SequenceType.SUB_SEQUENCE means it is a nested sequence, that each timestep of
* :code:`SequenceType.NO_SEQUENCE` means the sample is not a sequence.
* :code:`SequenceType.SEQUENCE` means the sample is a sequence.
* :code:`SequenceType.SUB_SEQUENCE` means it is a nested sequence, that each timestep of
the input sequence is also a sequence.
Different input type has a defferenct input format. Their formats are shown
......@@ -214,36 +210,39 @@ in the above table.
where f represents a float value, i represents an integer value.
.. _init_hook::
.. _settings::
init_hook
+++++++++
init_hook is a function that is invoked once the data provoder is initialized.
Its parameters lists as follows:
* The first parameter is a settings object, which is the same to :code:'settings'
* The first parameter is a settings object, which is the same to :code:`settings`
in :code:`process` method. The object contains several attributes, including:
* settings.input_types the input types. Reference `input_types`_
* settings.logger a logging object
* :code:`settings.input_types`: the input types. Reference `input_types`_.
* :code:`settings.logger`: a logging object.
* The rest parameters are the key word arguments. It is made up of PaddpePaddle
pre-defined parameters and user defined parameters.
* PaddlePaddle defines parameters including:
* is_train is a bool parameter that indicates the DataProvider is used in
training or testing
* file_list is the list of all files.
* PaddlePaddle-defined parameters including:
* :code:`is_train` is a bool parameter that indicates the DataProvider is used in
training or testing.
* :code:`file_list` is the list of all files.
* User-defined parameters args can be set in training configuration.
Note, PaddlePaddle reserves the right to add pre-defined parameter, so please
use :code:`**kwargs` in init_hook to ensure compatibility by accepting the
parameters which your init_hook does not use.
.. _cache ::
cache
+++++
DataProvider provides two simple cache strategy. They are
* CacheType.NO_CACHE means do not cache any data, then data is read at runtime by
DataProvider provides two simple cache strategy. They are:
* :code:`CacheType.NO_CACHE` means do not cache any data, then data is read at runtime by
the user implemented python module every pass.
* CacheType.CACHE_PASS_IN_MEM means the first pass reads data by the user
* :code:`CacheType.CACHE_PASS_IN_MEM` means the first pass reads data by the user
implemented python module, and the rest passes will directly read data from
memory.
编译与安装
========================
PaddlePaddle提供数个预编译的二进制来进行安装,包括Docker镜像,ubuntu的deb安装包等。我们推荐使用Docker镜像来部署环境,同时欢迎贡献更多的安装包。
Note: The intallation packages are still in pre-release state and your experience of installation may not be smooth.
注意:目前PaddlePaddle的安装包还处在pre-release的状态,使用起来或许会不是很顺畅。
.. toctree::
:maxdepth: 1
install/index.rst
install/docker_install.rst
install/ubuntu_install.rst
cmake/index.rst
安装PaddlePaddle
==========
PaddlePaddle提供数个预编译的二进制来进行安装。他们包括Docker镜像,ubuntu的deb安装包等
。欢迎贡献更多的安装包。我们更推荐使用Docker镜像来部署PaddlePaddle环境。
Note: The intallation packages are still in pre-release
state and your experience of installation may not be smooth.
注意!目前PaddlePaddle的安装包还处在pre-release的状态,
使用起来或许会不是很顺畅。
.. toctree::
docker_install.rst
ubuntu_install.rst
PaddlePaddle的数据提供(DataProvider)介绍
==================================
========================================
数据提供(DataProvider,后用DataProvider代替)是PaddlePaddle负责提供数据的模块。其作用是将训练数据
传入内存或者显存,让神经网络可以进行训练。简单的使用,用户可以使用Python的
:code:`PyDataProvider` 来自定义传数据的过程。如果有更复杂的使用,或者需要更高的效率,
用户也可以在C++端自定义一个 :code:`DataProvider` 。
数据提供(DataProvider)是PaddlePaddle负责提供数据的模块。其作用是将训练数据传入内存或者显存,让神经网络可以进行训练。简单的使用,用户可以使用Python的 :code:`PyDataProvider` 来自定义传数据的过程。如果有更复杂的使用,或者需要更高的效率,用户也可以在C++端自定义一个 :code:`DataProvider` 。
PaddlePaddle需要用户在网络配置(trainer_config.py)中定义使用什么DataProvider,和DataProvider
的一些参数,训练文件列表(train.list)和测试文件列表(test.list)。
PaddlePaddle需要用户在网络配置(trainer_config.py)中定义使用哪种DataProvider及其参数,训练文件列表(train.list)和测试文件列表(test.list)。
其中,train.list和test.list均为本地的两个文件(推荐直接放置到训练目录,以相对路径引用)。如果
test.list不设置,或者设置为None的话,那么在训练过程中,不会执行测试操作。否则,则会根据命令行
参数指定的测试方式,在训练过程中进行测试,从而防止过拟合。
其中,train.list和test.list均为本地的两个文件(推荐直接放置到训练目录,以相对路径引用)。如果test.list不设置,或者设置为None,那么在训练过程中,不会执行测试操作。否则,会根据命令行参数指定的测试方式,在训练过程中进行测试,从而防止过拟合。
一般情况下,train.list和test.list为纯文本文件,其每一行对应这每一个数据文件。数据文件存放在
本地磁盘中,将文件的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)的方式写在train.list和
test.list中。当然,train.list和test.list也可以放置hdfs文件路径,或者数据库连接地址等等。
用户在DataProvider中需要实现如何访问其中每一个文件。
一般情况下,train.list和test.list为纯文本文件,一行对应一个数据文件,数据文件存放在本地磁盘中。将文件的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)写在train.list和test.list中。当然,train.list和test.list也可以放置hdfs文件路径,或者数据库连接地址等等。
DataProvider的具体用法和如何实现一个新的DataProvider,请参考下述文章:
用户在DataProvider中需要实现如何访问其中每一个文件。DataProvider的具体用法和如何实现一个新的DataProvider,请参考下述文章:
.. toctree::
......
......@@ -116,8 +116,6 @@ DataProvider创建的时候执行。这个初始化函数具有如下参数:
参考(Reference)
---------------
.. _@provider::
@provider
+++++++++
......@@ -134,9 +132,6 @@ DataProvider创建的时候执行。这个初始化函数具有如下参数:
* cache 是数据缓存的策略,参考 `cache`_
* init_hook 是初始化时调用的函数,参考 `init_hook`_
.. _input_types::
input_types
+++++++++++
......@@ -169,16 +164,11 @@ PaddlePaddle的数据包括四种主要类型,和三种序列模式。其中
其中,f代表一个浮点数,i代表一个整数。
.. _init_hook::
.. _settings::
init_hook
+++++++++
init_hook可以传入一个函数。这个函数在初始化的时候会被调用。这个函数的参数是:
* 第一个参数是 settings 对象。这个对象和process的第一个参数一致。具有的属性有
* settings.input_types 设置输入类型。参考 `input_types`_
* settings.logger 一个logging对象
......@@ -192,8 +182,6 @@ init_hook可以传入一个函数。这个函数在初始化的时候会被调
注意,PaddlePaddle保留添加参数的权力,所以init_hook尽量使用 :code:`**kwargs` , 来接受不使用的
函数来保证兼容性。
.. _cache::
cache
+++++
......
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