this is a test pull-request, test=release/1.8 (#2142)

* this is a test pull-request, test=release/1.8

* fix doc, test=release/1.8

* fix doc, test=release/1.8

* fix doc, test=release/1.8

doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl.md

 
-# VisualDL 工具简介
 
+# VisualDL工具简介
 
 <p align="center">
   <img src="http://visualdl.bj.bcebos.com/images/vdl-logo.png" width="70%"/>
 </p>
 
-
-
 VisualDL是深度学习模型可视化分析工具，以丰富的图表呈现训练参数变化趋势、模型结构、数据样本、高维数据分布等。可帮助用户更清晰直观地理解深度学习模型训练过程及模型结构，进而实现高效的模型优化。
 
-VisualDL提供丰富的可视化功能，支持实时训练参数分析、图结构、数据样本可视化及高维数据降维呈现等诸多功能。具体功能使用方式，请参见 **VisualDL 使用指南**。项目正处于高速迭代中，敬请期待新组件的加入。
+VisualDL提供丰富的可视化功能，支持实时训练参数分析、图结构、数据样本可视化及高维数据降维呈现等诸多功能。具体功能使用方式，请参见 **VisualDL使用指南**。项目正处于高速迭代中，敬请期待新组件的加入。
 
 VisualDL原生支持python的使用， 通过在模型的Python配置中添加几行代码，便可为训练过程提供丰富的可视化支持。
 
 
-
-
-
 ## 核心亮点
 
 ### 简单易用
@@ -40,13 +35,21 @@ API设计简洁易懂，使用简单。模型结构一键实现可视化。
 
 ## 安装方式
 
-使用pip安装 VisualDL 运行范例：
+### 使用pip安装
 
 ```shell
 pip install --upgrade visualdl==2.0.0a2
 ```
 
+### 使用代码安装
+
+```
+git clone https://github.com/PaddlePaddle/VisualDL.git
+cd VisualDL
 
+python setup.py bdist_wheel
+pip install --upgrade dist/visualdl-*.whl
+```
 
 ## 使用方式
 
@@ -57,15 +60,13 @@ VisualDL将训练过程中的数据、参数等信息储存至日志文件中后
 VisualDL的后端提供了Python SDK，可通过LogWriter定制一个日志记录器，接口如下：
 
 ```python
-class LogWriter(
-                logdir=None,
+class LogWriter(logdir=None,
                 comment='',
                 max_queue=10,
                 flush_secs=120,
                 filename_suffix='',
                 write_to_disk=True,
-                **kwargs
-                )
+                **kwargs)
 ```
 
 #### 接口参数
@@ -206,4 +207,4 @@ VisualDL 是由 [PaddlePaddle](http://www.paddlepaddle.org/) 和 [ECharts](http:
 
 ## 更多细节
 
-想了解更多关于VisualDL可视化功能的使用详情介绍，请查看**Visual DL 使用指南**。
+想了解更多关于VisualDL可视化功能的使用详情介绍，请查看**VisualDL使用指南**。

doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl_usage.md

@@ -10,13 +10,13 @@ VisualDL 是一个面向深度学习任务设计的可视化工具。VisualDL
 
 |                           组件名称                           |  展示图表  | 作用                                                         |
 | :----------------------------------------------------------: | :--------: | :----------------------------------------------------------- |
-|      <a href="#1">[ Scalar](#Scalar -- 折线图组件)</a>       |   折线图   | 动态展示损失函数值、准确率等标量数据                         |
-|      <a href="#3">[Image](#Image -- 图片可视化组件)</a>      | 图片可视化 | 显示图片，可显示输入图片和处理后的结果，便于查看中间过程的变化 |
-| <a href="#6">[High Dimensional](#High Dimensional -- 数据降维组件)</a> |  数据降维  | 将高维数据映射到 2D/3D 空间来可视化嵌入，便于观察不同数据的相关性 |
+|       <a href="#1">[ Scalar](#Scalar--折线图组件)</a>        |   折线图   | 动态展示损失函数值、准确率等标量数据                         |
+|       <a href="#3">[Image](#Image--图片可视化组件)</a>       | 图片可视化 | 显示图片，可显示输入图片和处理后的结果，便于查看中间过程的变化 |
+| <a href="#6">[High Dimensional](#High-Dimensional--数据降维组件)</a> |  数据降维  | 将高维数据映射到 2D/3D 空间来可视化嵌入，便于观察不同数据的相关性 |
 
 
 
-## Scalar -- 折线图组件
+## Scalar--折线图组件
 
 ### 介绍
 
@@ -38,7 +38,7 @@ add_scalar(tag, value, step, walltime=None)
 |walltime|int|记录数据的时间戳，默认为当前时间戳|
 
 ### Demo
-下面展示了使用 Scalar 组件记录数据的示例，代码见[Scalar组件](../../demo/components/scalar_test.py)
+下面展示了使用 Scalar 组件记录数据的示例，代码见[Scalar组件](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/scalar_test.py)
 ```python
 from visualdl import LogWriter
 
@@ -114,7 +114,7 @@ visualdl --logdir ./log --port 8080
 </p>
 
 
-## Image -- 图片可视化组件
+## Image--图片可视化组件
 
 ### 介绍
 
@@ -136,7 +136,7 @@ add_image(tag, img, step, walltime=None)
 |walltime|int|记录数据的时间戳，默认为当前时间戳|
 
 ### Demo
-下面展示了使用 Image 组件记录数据的示例，代码文件请见[Image组件](../../demo/components/image_test.py)
+下面展示了使用 Image 组件记录数据的示例，代码文件请见[Image组件](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/image_test.py)
 ```python
 import numpy as np
 from PIL import Image
@@ -191,7 +191,7 @@ visualdl --logdir ./log --port 8080
 </p>
 
 
-## High Dimensional -- 数据降维组件
+## High Dimensional--数据降维组件
 
 ### 介绍
 
@@ -216,7 +216,7 @@ add_embeddings(tag, labels, hot_vectors, walltime=None)
 |walltime|int|记录数据的时间戳，默认为当前时间戳|
 
 ### Demo
-下面展示了使用 High Dimensional 组件记录数据的示例，代码见[High Dimensional组件](../../demo/components/high_dimensional_test.py)
+下面展示了使用 High Dimensional 组件记录数据的示例，代码见[High Dimensional组件](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/high_dimensional_test.py)
 ```python
 from visualdl import LogWriter
 

doc/fluid/advanced_guide/inference_deployment/inference/paddle_gpu_benchmark_en.md

@@ -8,7 +8,7 @@
 ## Test Targets
 **PaddlePaddle, Pytorch, Tensorflow**  
 
-- In test, PaddlePaddle adopts subgraph optimization to integrate TensorRT [model](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models) .
+- In test, PaddlePaddle adopts subgraph optimization to integrate TensorRT [model](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models) .
 - Native implementation is used in Pytorch. Model [address 1](https://github.com/pytorch/vision/tree/master/torchvision/models) , [address 2](https://github.com/marvis/pytorch-mobilenet) .
 - Test for TensorFlow contains test for native TF and TF—TRT. **Test for TF—TRT hasn't reached expectation wihch will be complemented later**. Model [address](https://github.com/tensorflow/models) .
 

doc/fluid/advanced_guide/performance_improving/singlenode_training_improving/training_best_practice.rst

@@ -172,7 +172,7 @@ Paddle里面使用 paddle.fluid.io. :ref:`cn_api_fluid_io_DataLoader` 接口来
 
 为降低训练的整体时间，建议用户使用异步数据读取的方式，并开启 :code:`use_double_buffer=True` 。用户可根据模型的实际情况设置数据队列的大小。
 如果数据准备的时间大于模型执行的时间，或者出现了数据队列为空的情况，就需要考虑对数据读取Reader进行加速。
-常用的方法是 **使用Python多进程准备数据** ，一个简单的使用多进程准备数据的示例，可以参考 `YOLOv3 <https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/yolov3/reader.py>`_ 。
+常用的方法是 **使用Python多进程准备数据** ，一个简单的使用多进程准备数据的示例，可以参考 `YOLOv3 <https://github.com/PaddlePaddle/models/blob/50cf1d814c1d267a4597885363597f5f8f4a50ad/dygraph/yolov3/README.md>`_ 。
 
 Python端的数据预处理，都是使用CPU完成。如果Paddle提供了相应功能的API，可将这部分预处理功能写到模型配置中，如此Paddle就可以使用GPU来完成该预处理功能，这样也可以减轻CPU预处理数据的负担，提升总体训练速度。
 

doc/fluid/beginners_guide/basic_concept/programming_guide/programming_guide.md

@@ -210,7 +210,7 @@ print(res) #[array([10])]
 
 ```
 
-限于篇幅，上面仅仅用一个最简单的例子来说明如何在声明式编程模式中实现循环操作。循环操作在很多应用中都有着重要作用，比如NLP中常用的Transformer模型，在解码（生成）阶段的Beam Search算法中，需要使用循环操作来进行候选的选取与生成，可以参考[Transformer](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT/transformer)模型的实现来进一步学习while_loop在复杂场景下的用法。
+限于篇幅，上面仅仅用一个最简单的例子来说明如何在声明式编程模式中实现循环操作。循环操作在很多应用中都有着重要作用，比如NLP中常用的Transformer模型，在解码（生成）阶段的Beam Search算法中，需要使用循环操作来进行候选的选取与生成，可以参考[Transformer](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/machine_translation/transformer)模型的实现来进一步学习while_loop在复杂场景下的用法。
 
 除while_loop之外，飞桨还提供fluid.layers.cond API来实现条件分支的操作，以及fluid.layers.switch_case和fluid.layers.case API来实现分支控制功能，具体用法请参考文档：[cond](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/layers_cn/cond_cn.html)，[switch_case](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/layers_cn/switch_case_cn.html)和[case](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/layers_cn/case_cn.html#case)
 

doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_cn.rst

@@ -85,4 +85,4 @@ PaddlePaddle提供了 :code:`fluid.data()` 算子来描述输入数据的格式
 下一步做什么？
 ##############
 
-使用PaddlePaddle实现模型时需要关注 **数据层**、**前向计算逻辑**、**损失函数** 和 **优化方法**。不同的任务需要的数据格式不同，涉及的计算逻辑不同，损失函数不同，优化方法也不同。PaddlePaddle提供了丰富的模型示例，可以以这些示例为参考来构建自己的模型结构。用户可以访问 `模型库 <https://github.com/PaddlePaddle/models/tree/develop/fluid>`_ 查看官方提供的示例。
+使用PaddlePaddle实现模型时需要关注 **数据层**、**前向计算逻辑**、**损失函数** 和 **优化方法**。不同的任务需要的数据格式不同，涉及的计算逻辑不同，损失函数不同，优化方法也不同。PaddlePaddle提供了丰富的模型示例，可以以这些示例为参考来构建自己的模型结构。用户可以访问 `模型库 <https://github.com/PaddlePaddle/models/tree/develop>`_ 查看官方提供的示例。

doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_en.rst

@@ -88,4 +88,4 @@ What to do next?
 #################
 
 Attention needs to be paid for **Data Layer**, **Forward Computing Logic**, **Loss function** and **Optimization Function** while you use PaddlePaddle to implement models.
-The data format, computing logic, loss function and optimization function are all different in different tasks. A rich number of examples of model are provided in PaddlePaddle. You can build your own model structure by referring to these examples. You can visit `Model Repository <https://github.com/PaddlePaddle/models/tree/develop/fluid>`_ to refer to examples in official documentation.
+The data format, computing logic, loss function and optimization function are all different in different tasks. A rich number of examples of model are provided in PaddlePaddle. You can build your own model structure by referring to these examples. You can visit `Model Repository <https://github.com/PaddlePaddle/models/tree/develop>`_ to refer to examples in official documentation.

doc/fluid/beginners_guide/coding_practice/single_node.rst

@@ -123,7 +123,7 @@
 
 1. :ref:`cn_api_fluid_CompiledProgram` 会将传入的 :code:`fluid.Program` 转为计算图，即Graph，因为 :code:`compiled_prog` 与传入的 :code:`train_program` 是完全不同的对象，目前还不能够对 :code:`compiled_prog` 进行保存。
 2. 多卡训练也可以使用 :ref:`cn_api_fluid_ParallelExecutor` ，但是现在推荐使用 :ref:`cn_api_fluid_CompiledProgram` .
-3. 如果 :code:`exe` 是用CUDAPlace来初始化的，模型会在GPU中运行。在显卡训练模式中，所有的显卡都将被占用。用户可以配置 `CUDA_VISIBLE_DEVICES <http://www.acceleware.com/blog/cudavisibledevices-masking-gpus>`_ 以更改被占用的显卡。
+3. 如果 :code:`exe` 是用CUDAPlace来初始化的，模型会在GPU中运行。在显卡训练模式中，所有的显卡都将被占用。用户可以配置 `CUDA_VISIBLE_DEVICES以更改被占用的显卡。
 4. 如果 :code:`exe` 是用CPUPlace来初始化的，模型会在CPU中运行。在这种情况下，多线程用于运行模型，同时线程的数目和逻辑核的数目相等。用户可以配置 ``CPU_NUM`` 以更改使用中的线程数目。
 
 进阶使用

doc/fluid/beginners_guide/coding_practice/single_node_en.rst

@@ -114,7 +114,7 @@ Notes:
 
 1. :ref:`api_fluid_CompiledProgram` will convert the input Program into a computational graph, and :code:`compiled_prog` is a completely different object from the incoming :code:`train_program`. At present, :code:`compiled_prog` can not be saved.
 2. Multi-card training can also be used: ref:`api_fluid_ParallelExecutor` , but now it is recommended to use: :ref:`api_fluid_CompiledProgram`.
-3. If :code:`exe` is initialized with CUDAPlace, the model will be run in GPU. In the mode of graphics card training, all graphics card will be occupied. Users can configure `CUDA_VISIBLE_DEVICES <http://www.acceleware.com/blog/cudavisibledevices-masking-gpus>`_ to change graphics cards that are being used. 
+3. If :code:`exe` is initialized with CUDAPlace, the model will be run in GPU. In the mode of graphics card training, all graphics card will be occupied. Users can configure `CUDA_VISIBLE_DEVICES `_ to change graphics cards that are being used. 
 4. If :code:`exe` is initialized with CPUPlace, the model will be run in CPU. In this situation, the multi-threads are used to run the model, and the number of threads is equal to the number of logic cores. Users can configure `CPU_NUM`  to change the number of threads that are being used. 
 
 Advanced Usage

doc/fluid/faq/train_cn.md

@@ -54,7 +54,7 @@ PaddlePaddle可以像tf一样根据输出，只执行模型的一部分么？
 对于LodTensor数据，分步处理每一个时间步数据的需求，大部分情况可以使用`DynamicRNN`，[参考示例](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_machine_translation.py#L69) ，其中`rnn.step_input`即是每一个时间步的数据，`rnn.memory`是需要更新的rnn的hidden state，另外还有个`rnn.static_input`是rnn外部的数据在DynamicRNN内的表示（如encoder的output），可以利用这三种数据完成所需操作。
 
 rank_table记录了每个sequence的长度，DynamicRNN中调用了lod_tensor_to_array在产生array时按照rank_table做了特殊处理（sequence从长到短排序后从前到后进行slice），每个时间步数据的batch size可能会缩小（短的sequence结束时），这是Fluid DynamicRNN的一些特殊之处。
-对于非LoDTensor数据，可以使用StaticRNN，用法与上面类似，参考[语言模型示例]( https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/unarchived/language_model/lstm/lm_model.py#L261)。
+对于非LoDTensor数据，可以使用StaticRNN，用法与上面类似，参考[语言模型示例]( https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/shared_modules/models/language_model/lm_model.py#L261)。
 
 ##### Q: NumPy读取出fc层W矩阵？
 
@@ -80,7 +80,7 @@ weight名字在构建网络的时候可以通过param_attr指定，然后用`flu
 
 + 问题描述
 
-根据models里面的[ctr例子](https://github.com/PaddlePaddle/models/blob/develop/PaddleRec/ctr/infer.py)改写了一个脚本，主要在train的同时增加test过程，方便选择模型轮次，整体训练测试过程跑通，不过无法获取accuracy？
+根据models里面的[ctr例子](https://github.com/PaddlePaddle/models/blob/develop/PaddleRec/ctr/dnn/infer.py)改写了一个脚本，主要在train的同时增加test过程，方便选择模型轮次，整体训练测试过程跑通，不过无法获取accuracy？
 
 + 问题解答
 
@@ -152,7 +152,7 @@ ln -s libnccl.so.2 libnccl.so
 
 CTR模型保存模型时报错
 
-+ 代码文件：[network_conf.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleRec/ctr/network_conf.py)只修改了最后一行：
++ 代码文件：[network_conf.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleRec/ctr/dnn/network_conf.py)只修改了最后一行：
 
 ```
 accuracy = fluid.layers.accuracy(input=predict, label=words[-1])
@@ -188,7 +188,7 @@ return accuracy, avg_cost, auc_var, batch_auc_var, py_reader
 
 可以通过param_t = fluid.global_scope().find_var(param_name).get_tensor()查看，
 param_name可以自己指定，如果不知道的话，可以通过 param_list = fluid.framework.default_main_program().block(0).all_parameters() 看一下。
- 
+
 ##### Q: 共享向量权重
 
 + 问题描述

doc/fluid/install/install_CentOS.md

@@ -56,7 +56,7 @@
         python3 -c "import platform;print(platform.architecture()[0]);print(platform.machine())"
 
 * 默认提供的安装包需要计算机支持MKL
-* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/beginners_guide/install/install_script.md)。
+* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install).
 
 ## 选择CPU/GPU
 

doc/fluid/install/install_CentOS_en.md

@@ -57,7 +57,7 @@
         python3 -c "import platform;print(platform.architecture()[0]);print(platform.machine())"
 
 * The installation package provided by default requires computer support for MKL
-* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), for instructions please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/beginners_guide/install/install_script.md)。
+* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), for instructions please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install).
 
 ## Choose CPU/GPU
 
@@ -65,19 +65,19 @@
 
 * If your computer has NVIDIA® GPU, please make sure that the following conditions are met and install the GPU version of PaddlePaddle
 
-	* **CUDA toolkit 10.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
-	* **CUDA toolkit 9.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
-	* **Hardware devices with GPU computing power over 1.0**
+    * **CUDA toolkit 10.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
+    * **CUDA toolkit 9.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
+    * **Hardware devices with GPU computing power over 1.0**
 
-		You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
+        You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
 
 * 如果您需要使用多卡环境请确保您已经正确安装nccl2，或者按照以下指令安装nccl2（这里提供的是CentOS 7，CUDA9，cuDNN7下nccl2的安装指令），更多版本的安装信息请参考NVIDIA[官方网站](https://developer.nvidia.com/nccl):
 
 
-		wget http://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm
-		rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm
-		yum update -y
-		yum install -y libnccl-2.3.7-2+cuda9.0 libnccl-devel-2.3.7-2+cuda9.0 libnccl-static-2.3.7-2+cuda9.0
+        wget http://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm
+        rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm
+        yum update -y
+        yum install -y libnccl-2.3.7-2+cuda9.0 libnccl-devel-2.3.7-2+cuda9.0 libnccl-static-2.3.7-2+cuda9.0
 
 ## Installation method
 
@@ -103,7 +103,7 @@ Here is pip installation
 You can[Verify installation succeeded or not](#check)，if you have any questions, you can refer to [FAQ](./FAQ.html)
 
 
-Note: 
+Note:
 
 * If it is python2.7, it is recommended to use the `python` command; if it is python3.x, it is recommended to use the 'python3' command
 

doc/fluid/install/install_Ubuntu.md

@@ -57,7 +57,7 @@
         python3 -c "import platform;print(platform.architecture()[0]);print(platform.machine())"
 
 * 默认提供的安装包需要计算机支持MKL
-* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/beginners_guide/install/install_script.md)。
+* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install).
 
 ## 选择CPU/GPU
 

doc/fluid/install/install_Ubuntu_en.md

@@ -57,26 +57,26 @@
         python3 -c "import platform;print(platform.architecture()[0]);print(platform.machine())"
 
 * The installation package provided by default requires computer support for MKL
-* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/beginners_guide/install/install_script.md).
+* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install).
 
 ## Choose CPU/GPU
 
 * If your computer doesn't have NVIDIA® GPU, please install CPU version of PaddlePaddle
 
 * If your computer has NVIDIA® GPU, and meet the following conditions, we command you to install PaddlePaddle
-	* **CUDA toolkit 10.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
-	* **CUDA toolkit 9.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
-	* **Hardware devices with GPU computing power over 1.0**
+    * **CUDA toolkit 10.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
+    * **CUDA toolkit 9.0 with cuDNN v7.3+(for multi card support, NCCL2.3.7 or higher)**
+    * **Hardware devices with GPU computing power over 1.0**
 
 
-	You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
+    You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
 
 * If you need to use multi card environment, please make sure that you have installed nccl2 correctly, or install nccl2 according to the following instructions (here is the installation instructions of nccl2 under ubuntu 16.04, CUDA9 and cuDNN7). For more version of installation information, please refer to NVIDIA[official website](https://developer.nvidia.com/nccl):
 
 
-	wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
-	dpkg -i nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
-	sudo apt-get install -y libnccl2=2.3.7-1+cuda9.0 libnccl-dev=2.3.7-1+cuda9.0
+    wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
+    dpkg -i nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
+    sudo apt-get install -y libnccl2=2.3.7-1+cuda9.0 libnccl-dev=2.3.7-1+cuda9.0
 
 
 

doc/fluid/user_guides/cv_case/image_classification/README.cn.md

@@ -609,4 +609,4 @@ with fluid.scope_guard(inference_scope):
 [22] http://cs231n.github.io/classification/
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/cv_case/image_classification/README.md

@@ -620,4 +620,4 @@ The traditional image classification method consists of multiple stages. The fra
 
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/cv_case/image_classification/index.cn.html

@@ -377,7 +377,7 @@ def inference_program():
 def train_program():
     predict = inference_program()
 
-    label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+    label = fluid.data(name='label', shape=[None,1], dtype='int64')
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=predict, label=label)
@@ -651,7 +651,7 @@ with fluid.scope_guard(inference_scope):
 [22] http://cs231n.github.io/classification/
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/cv_case/image_classification/index.html

@@ -662,7 +662,7 @@ The traditional image classification method consists of multiple stages. The fra
 
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/label_semantic_roles/README.cn.md

@@ -557,7 +557,7 @@ print("Inference Shape: ", np_data.shape)
 
 <a name="参考文献"></a>
 ## 参考文献
-1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
+1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](https://www.researchgate.net/publication/221012740_Chinese_Semantic_Role_Labeling_with_Shallow_Parsing)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv:1406.1078, 2014.
 4. Bahdanau D, Cho K, Bengio Y. [Neural machine translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473)[J]. arXiv preprint arXiv:1409.0473, 2014.
@@ -569,4 +569,4 @@ print("Inference Shape: ", np_data.shape)
 10. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/nlp_case/label_semantic_roles/README.md

@@ -541,7 +541,7 @@ Labeling semantic roles is an important intermediate step in many natural langua
 
 <a name="References"></a>
 ## References
-1. Sun W, Sui Z, Wang M, et al. [Chinese label semantic roles with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings Of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
+1. Sun W, Sui Z, Wang M, et al. [Chinese label semantic roles with shallow parsing](https://www.researchgate.net/publication/221012740_Chinese_Semantic_Role_Labeling_with_Shallow_Parsing)[C]//Proceedings Of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv: 1406.1078, 2014.
 4. Bahdanau D, Cho K, Bengio Y. [Neural machine translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473)[J]. arXiv preprint arXiv:1409.0473, 2014.
@@ -553,4 +553,4 @@ Labeling semantic roles is an important intermediate step in many natural langua
 10. Zhou J, Xu W. [End-to-end learning of label semantic roles using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C] //Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/nlp_case/label_semantic_roles/index.cn.html

@@ -599,7 +599,7 @@ print("Inference Shape: ", np_data.shape)
 
 <a name="参考文献"></a>
 ## 参考文献
-1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
+1. Sun W, Sui Z, Wang M, et al. [Chinese semantic role labeling with shallow parsing](https://www.researchgate.net/publication/221012740_Chinese_Semantic_Role_Labeling_with_Shallow_Parsing)[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv:1406.1078, 2014.
 4. Bahdanau D, Cho K, Bengio Y. [Neural machine translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473)[J]. arXiv preprint arXiv:1409.0473, 2014.
@@ -611,7 +611,7 @@ print("Inference Shape: ", np_data.shape)
 10. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/label_semantic_roles/index.html

@@ -430,8 +430,8 @@ The data introduction section mentions the payment of the CoNLL 2005 training se
 crf_decode = fluid.layers.crf_decoding(
     input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
-train_data = paddle.batch(
-    paddle.reader.shuffle(
+train_data = fluid.io.batch(
+    fluid.io.shuffle(
         paddle.dataset.conll05.test(), buf_size=8192),
     batch_size=BATCH_SIZE)
 
@@ -583,7 +583,7 @@ Labeling semantic roles is an important intermediate step in many natural langua
 
 <a name="References"></a>
 ## References
-1. Sun W, Sui Z, Wang M, et al. [Chinese label semantic roles with shallow parsing](http://www.aclweb.org/anthology/D09-1#page=1513)[C]//Proceedings Of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
+1. Sun W, Sui Z, Wang M, et al. [Chinese label semantic roles with shallow parsing](https://www.researchgate.net/publication/221012740_Chinese_Semantic_Role_Labeling_with_Shallow_Parsing)[C]//Proceedings Of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3. Association for Computational Linguistics, 2009: 1475-1483.
 2. Pascanu R, Gulcehre C, Cho K, et al. [How to construct deep recurrent neural networks](https://arxiv.org/abs/1312.6026)[J]. arXiv preprint arXiv:1312.6026, 2013.
 3. Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation](https://arxiv.org/abs/1406.1078)[J]. arXiv preprint arXiv: 1406.1078, 2014.
 4. Bahdanau D, Cho K, Bengio Y. [Neural machine translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473)[J]. arXiv preprint arXiv:1409.0473, 2014.
@@ -595,7 +595,7 @@ Labeling semantic roles is an important intermediate step in many natural langua
 10. Zhou J, Xu W. [End-to-end learning of label semantic roles using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C] //Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/machine_translation/README.cn.md

@@ -630,4 +630,4 @@ Ein Mann mit einem orangen Schutzhelm starrt etwas an .
 5. Papineni K, Roukos S, Ward T, et al. [BLEU: a method for automatic evaluation of machine translation](http://dl.acm.org/citation.cfm?id=1073135)[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002: 311-318.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/nlp_case/machine_translation/README.md

@@ -498,4 +498,4 @@ End-to-End neural network translation is an recently acclaimed machine translati
 
 <br/>
 
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/nlp_case/machine_translation/index.cn.html

@@ -672,7 +672,7 @@ Ein Mann mit einem orangen Schutzhelm starrt etwas an .
 5. Papineni K, Roukos S, Ward T, et al. [BLEU: a method for automatic evaluation of machine translation](http://dl.acm.org/citation.cfm?id=1073135)[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002: 311-318.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/machine_translation/index.html

@@ -540,7 +540,7 @@ End-to-End neural network translation is an recently acclaimed machine translati
 
 <br/>
 
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/understand_sentiment/README.cn.md

@@ -448,4 +448,4 @@ with fluid.scope_guard(inference_scope):
 9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/nlp_case/understand_sentiment/README.md

@@ -439,4 +439,4 @@ In this chapter, we take sentiment analysis as an example to introduce end-to-en
 9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="https://www.paddlepaddle.org.cn/" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/nlp_case/understand_sentiment/index.cn.html

@@ -490,7 +490,7 @@ with fluid.scope_guard(inference_scope):
 9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/nlp_case/understand_sentiment/index.html

@@ -481,7 +481,7 @@ In this chapter, we take sentiment analysis as an example to introduce end-to-en
 9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="https://www.paddlepaddle.org.cn/" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/rec_case/recommender_system/README.cn.md

@@ -598,4 +598,4 @@ with fluid.scope_guard(inference_scope):
 
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/rec_case/recommender_system/index.cn.html

@@ -640,7 +640,7 @@ with fluid.scope_guard(inference_scope):
 
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/simple_case/fit_a_line/README.md

@@ -389,4 +389,4 @@ In this chapter, we analyzed dataset of Boston House Price to introduce the basi
 4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/simple_case/fit_a_line/index.html

@@ -431,7 +431,7 @@ In this chapter, we analyzed dataset of Boston House Price to introduce the basi
 4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/simple_case/recognize_digits/README.cn.md

@@ -617,8 +617,8 @@ with fluid.scope_guard(inference_scope):
 6. Cireşan, Dan Claudiu, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber. ["Deep, big, simple neural nets for handwritten digit recognition."](http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00052) Neural computation 22, no. 12 (2010): 3207-3220.
 7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
 8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
-9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
+9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://classes.engr.oregonstate.edu/eecs/fall2017/cs534/extra/rosenblatt-1958.pdf) Psychological review 65, no. 6 (1958): 386.
 10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。

doc/fluid/user_guides/simple_case/recognize_digits/index.cn.html

@@ -659,11 +659,11 @@ with fluid.scope_guard(inference_scope):
 6. Cireşan, Dan Claudiu, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber. ["Deep, big, simple neural nets for handwritten digit recognition."](http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00052) Neural computation 22, no. 12 (2010): 3207-3220.
 7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
 8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
-9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
+9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://classes.engr.oregonstate.edu/eecs/fall2017/cs534/extra/rosenblatt-1958.pdf) Psychological review 65, no. 6 (1958): 386.
 10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
 
 </div>
 <!-- You can change the lines below now. -->

doc/fluid/user_guides/simple_case/word2vec/README.md

@@ -488,4 +488,4 @@ In this chapter, we introduced word vectors, the relationship between language m
 5. https://en.wikipedia.org/wiki/Singular_value_decomposition
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

doc/fluid/user_guides/simple_case/word2vec/index.html

@@ -331,15 +331,15 @@ def optimizer_func():
 
 - Now we can start training. This version is much simpler than before. We have ready-made training and test sets: `paddle.dataset.imikolov.train()` and `paddle.dataset.imikolov.test()`. Both will return a reader. In PaddlePaddle, the reader is a Python function that reads the next piece of data when called each time . It is a Python generator.
 
-`paddle.batch` will read in a reader and output a batched reader. We can also output the training of each step and batch during the training process.
+`fluid.io.batch` will read in a reader and output a batched reader. We can also output the training of each step and batch during the training process.
 
 ```python
 def train(if_use_cuda, params_dirname, is_sparse=True):
     place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace()
 
-    train_reader = paddle.batch(
+    train_reader = fluid.io.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-    test_reader = paddle.batch(
+    test_reader = fluid.io.batch(
         paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)
 
     first_word = fluid.data(name='firstw', shape=[None, 1], dtype='int64')
@@ -530,7 +530,7 @@ In this chapter, we introduced word vectors, the relationship between language m
 5. https://en.wikipedia.org/wiki/Singular_value_decomposition
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://paddlepaddleimage.cdn.bcebos.com/bookimage/camo.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="https://www.paddlepaddle.org.cn/" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
 
 </div>
 <!-- You can change the lines below now. -->