Fix some documentations.

ISSUE=4611579

git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1473 1ad973e4-5ce8-4261-8a94-b56d1f490c56

doc/CMakeLists.txt

@@ -45,5 +45,5 @@ sphinx_add_target(paddle_docs
                   ${SPHINX_HTML_DIR})
 
 add_dependencies(paddle_docs 
-  gen_proto_py
-  paddle_doxygen_docs)
+  gen_proto_py)
+  #paddle_doxygen_docs)

doc/demo/image_classification/image_classification.md

@@ -173,7 +173,7 @@ python -m paddle.utils.plotcurve -i $log > plot.png
 - The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`.
 
 
-After training finishes, the training and testing error curve will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below:
+After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below:
 
 <center>![Training and testing curves.](./plot.png)</center>
 

doc/demo/imagenet_model/resnet_model.md

@@ -1,6 +1,6 @@
 # Model Zoo - ImageNet #
 
-[ImageNet](http://www.image-net.org/) is a popular dataset for generic object classification. This tutorial provided convolutional neural network(CNN) models for ImageNet.
+[ImageNet](http://www.image-net.org/) is a popular dataset for generic object classification. This tutorial provides convolutional neural network(CNN) models for ImageNet.
 
 ## ResNet Introduction
 
@@ -48,11 +48,11 @@ We present three ResNet models, which are converted from the models provided by
 
 ## ResNet Model
 
-See ```demo/model_zoo/resnet/resnet.py```. This confgiure contains network of 50, 101 and 152 layers. You can specify layer number by adding argument like this ```--config_args=layer_num=50``` in command line arguments.
+See ```demo/model_zoo/resnet/resnet.py```. This config contains network of 50, 101 and 152 layers. You can specify layer number by adding argument like ```--config_args=layer_num=50``` in command line arguments.
 
 ### Network Visualization
 
-You can get a diagram of ResNet network by running the following command. The script generates dot file and then converts dot file to PNG file, which uses installed draw_dot tool in our server. If you can not access the server, just install graphviz to convert dot file.
+You can get a diagram of ResNet network by running the following commands. The script generates dot file and then converts dot file to PNG file, which uses installed draw_dot tool in our server. If you can not access the server, just install graphviz to convert dot file.
 
 ```
 cd demo/model_zoo/resnet
@@ -190,8 +190,7 @@ Second, specify layers to extract features in `Outputs()` of `resnet.py`. For ex
 Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")
 ```
 
-Third, specify model path and output directory in `extract_fea_c++.sh
-`, and then run following commands
+Third, specify model path and output directory in `extract_fea_c++.sh`, and then run the following commands.
 
 ```
 cd demo/model_zoo/resnet

doc/ui/data_provider/index.rst

@@ -10,7 +10,7 @@ customized, with sacrificing the efficiency only a little. This is extremly
 useful when you have to dynamically generate certain kinds of data according to,
 for example, the training performance.
 
-Besides, users also can also customize a C++ :code:`DataProvider` for a more
+Besides, users also can customize a C++ :code:`DataProvider` for a more
 complex usage, or for a higher efficiency.
 
 The following parameters are required to define in the PaddlePaddle network

doc/ui/data_provider/pydataprovider2.rst

@@ -17,10 +17,10 @@ how to write a simple PyDataProvider.
 
 MNIST is a handwriting classification data set. It contains 70,000 digital
 grayscale images. Labels of the training sample range from 0 to 9. All the
-images have been size-normalized and centered into images with a same size
+images have been size-normalized and centered into images with the same size
 of 28 x 28 pixels.
 
-A small part of the original data as an example can be found in the path below:
+A small part of the original data as an example is shown as below:
 
 .. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_train.txt
 
@@ -31,10 +31,9 @@ Just write path of the above data into train.list. It looks like this:
 
 .. literalinclude:: ../../../doc_cn/ui/data_provider/train.list
 
-The corresponding dataprovider can be found in the path below:
+The corresponding dataprovider is shown as below:
 
 .. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_provider.py
-   : linenos:
 
 The first line imports PyDataProvider2 package.
 The main function is the process function, that has two parameters.
@@ -45,8 +44,8 @@ This parameter is passed to the process function by PaddlePaddle.
 :code:`@provider` is a Python
 `Decorator <http://www.learnpython.org/en/Decorators>`_ .
 It sets some properties to DataProvider, and constructs a real PaddlePaddle
-DataProvider from a very sample user implemented python function. It does not
-matter if you are not familiar with `Decorator`_. You can keep it sample by
+DataProvider from a very simple user implemented python function. It does not
+matter if you are not familiar with `Decorator`_. You can keep it simple by
 just taking :code:`@provider` as a fixed mark above the provider function you
 implemented.
 
@@ -59,9 +58,9 @@ document of `input_types`_ for more details.
 
 The process method is the core part to construct a real DataProvider in
 PaddlePaddle. It implements how to open the text file, how to read one sample
-from the original text file, converted them into `input_types`_, and give them
+from the original text file, convert them into `input_types`_, and give them
 back to PaddlePaddle process at line 23.
-Note that data yields by the process function must follow a same order that
+Note that data yielded by the process function must follow the same order that
 `input_types`_ are defined.
 
 
@@ -111,7 +110,7 @@ The corresponding data provider can be found in the path below:
 
 .. literalinclude:: ../../../doc_cn/ui/data_provider/sentimental_provider.py
 
-This data provider for sequential model is a little bit complex than that
+This data provider for sequential model is a little more complex than that
 for MINST dataset.
 A new initialization method is introduced here.
 The method :code:`on_init` is configured to DataProvider by :code:`@provider`'s
@@ -243,7 +242,7 @@ parameters which your init_hook does not use.
 cache
 +++++
 DataProvider provides two simple cache strategy. They are
-* CacheType.NO_CACHE means do not cache any data, then data is read runtime by
+* 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
   implemented python module, and the rest passes will directly read data from