Merge branch 'master' of https://github.com/PaddlePaddle/Paddle into link_fix

.travis.yml

@@ -50,7 +50,7 @@ before_install:
     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
-  - pip install wheel protobuf sphinx breathe recommonmark virtualenv numpy
+  - pip install wheel protobuf 'sphinx==1.4.9' breathe recommonmark virtualenv numpy
 script:
   - paddle/scripts/travis/main.sh
 notifications:

doc/ui/data_provider/pydataprovider2.rst

@@ -174,12 +174,12 @@ input_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:`sparse_binary_vector`: sparse binary vector, most of the value is 0, and
   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.
 * :code:`integer`: an integer scalar, that is especially used for label or word index.
 
@@ -200,7 +200,7 @@ in the above table.
 +----------------------+---------------------+-----------------------------------+------------------------------------------------+
 | 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, ...], ...]                      |
 +----------------------+---------------------+-----------------------------------+------------------------------------------------+
@@ -227,7 +227,7 @@ Its parameters lists as follows:
     * :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

doc/ui/index.md

@@ -7,7 +7,7 @@
 
 ## API Reference
 
-* [Model Config Interface](api/trainer_config_helpers/index.md)
+* [Model Config Interface](api/trainer_config_helpers/index.rst)
 
 ## Command Line Argument
 

doc_cn/ui/data_provider/pydataprovider2.rst

@@ -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
 
-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。
 
 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
 
 * 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_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。
 
 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
 +----------------------+---------------------+-----------------------------------+------------------------------------------------+
 | 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, ...], ...]                      |
 +----------------------+---------------------+-----------------------------------+------------------------------------------------+
@@ -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.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