提交 ab86711a 编写于 作者: Q qingqing01 提交者: GitHub

Merge pull request #885 from reyoung/hotfix/fix_pydp_docs

Hotfix/fix pydp docs
...@@ -50,7 +50,7 @@ before_install: ...@@ -50,7 +50,7 @@ before_install:
fi fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
- pip install wheel protobuf sphinx breathe recommonmark virtualenv numpy - pip install wheel protobuf 'sphinx==1.4.9' breathe recommonmark virtualenv numpy
script: script:
- paddle/scripts/travis/main.sh - paddle/scripts/travis/main.sh
notifications: notifications:
......
...@@ -174,12 +174,12 @@ input_types ...@@ -174,12 +174,12 @@ input_types
+++++++++++ +++++++++++
PaddlePaddle has four data types, and three sequence types. PaddlePaddle has four data types, and three sequence types.
The four data types are: The four data types are:
* :code:`dense_vector`: dense float vector. * :code:`dense_vector`: dense float vector.
* :code:`sparse_binary_vector`: sparse binary vector, most of the value is 0, and * :code:`sparse_binary_vector`: sparse binary vector, most of the value is 0, and
the non zero elements are fixed to 1. the non zero elements are fixed to 1.
* :code:`sparse_float_vector`: sparse float vector, most of the value is 0, and some * :code:`sparse_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. 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. * :code:`integer`: an integer scalar, that is especially used for label or word index.
...@@ -200,7 +200,7 @@ in the above table. ...@@ -200,7 +200,7 @@ in the above table.
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] | | sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] | | sparse_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] | | integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
...@@ -227,7 +227,7 @@ Its parameters lists as follows: ...@@ -227,7 +227,7 @@ Its parameters lists as follows:
* :code:`is_train` is a bool parameter that indicates the DataProvider is used in * :code:`is_train` is a bool parameter that indicates the DataProvider is used in
training or testing. training or testing.
* :code:`file_list` is the list of all files. * :code:`file_list` is the list of all files.
* User-defined parameters args can be set in training configuration. * User-defined parameters args can be set in training configuration.
Note, PaddlePaddle reserves the right to add pre-defined parameter, so please Note, PaddlePaddle reserves the right to add pre-defined parameter, so please
......
...@@ -7,7 +7,7 @@ ...@@ -7,7 +7,7 @@
## API Reference ## API Reference
* [Model Config Interface](api/trainer_config_helpers/index.md) * [Model Config Interface](api/trainer_config_helpers/index.rst)
## Command Line Argument ## Command Line Argument
......
...@@ -53,7 +53,7 @@ process函数调用多次 :code:`yield` 即可。 :code:`yield` 是Python的一 ...@@ -53,7 +53,7 @@ process函数调用多次 :code:`yield` 即可。 :code:`yield` 是Python的一
.. literalinclude:: mnist_config.py .. literalinclude:: mnist_config.py
这里说明了训练数据是 'train.list',而没有测试数据。引用的DataProvider是 'mnist_provider' 这里说明了训练数据是 'train.list',而没有测试数据。引用的DataProvider是 'mnist_provider'
这个模块中的 'process' 函数。 这个模块中的 'process' 函数。
同时,根据模型配置文件中 :code:`data_layer` 的名字,用户也可以显式指定返回的数据对应关系。例如: 同时,根据模型配置文件中 :code:`data_layer` 的名字,用户也可以显式指定返回的数据对应关系。例如:
...@@ -152,7 +152,7 @@ PaddlePaddle的数据包括四种主要类型,和三种序列模式。其中 ...@@ -152,7 +152,7 @@ PaddlePaddle的数据包括四种主要类型,和三种序列模式。其中
* dense_vector 表示稠密的浮点数向量。 * dense_vector 表示稠密的浮点数向量。
* sparse_binary_vector 表示稀疏的零一向量,即大部分值为0,有值的位置只能取1 * sparse_binary_vector 表示稀疏的零一向量,即大部分值为0,有值的位置只能取1
* sparse_float_vector 表示稀疏的向量,即大部分值为0,有值的部分可以是任何浮点数 * sparse_vector 表示稀疏的向量,即大部分值为0,有值的部分可以是任何浮点数
* integer 表示整数标签。 * integer 表示整数标签。
而三种序列模式为 而三种序列模式为
...@@ -170,7 +170,7 @@ PaddlePaddle的数据包括四种主要类型,和三种序列模式。其中 ...@@ -170,7 +170,7 @@ PaddlePaddle的数据包括四种主要类型,和三种序列模式。其中
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] | | sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] | | sparse_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] | | integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] |
+----------------------+---------------------+-----------------------------------+------------------------------------------------+ +----------------------+---------------------+-----------------------------------+------------------------------------------------+
...@@ -202,7 +202,7 @@ DataProvider提供了两种简单的Cache策略。他们是 ...@@ -202,7 +202,7 @@ DataProvider提供了两种简单的Cache策略。他们是
* CacheType.NO_CACHE 不缓存任何数据,每次都会从python端读取数据 * CacheType.NO_CACHE 不缓存任何数据,每次都会从python端读取数据
* CacheType.CACHE_PASS_IN_MEM 第一个pass会从python端读取数据,剩下的pass会直接从内存里 * CacheType.CACHE_PASS_IN_MEM 第一个pass会从python端读取数据,剩下的pass会直接从内存里
读取数据。 读取数据。
注意事项 注意事项
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