diff --git a/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl.md b/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl.md
index 77d1756eafe1a55e8b470543398a02e05fc0b109..ce709d977c50deabbd2f2d85b70f723386288a8f 100644
--- a/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl.md
+++ b/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl.md
@@ -1,23 +1,18 @@
-# VisualDL 工具简介
+# VisualDL工具简介
-
-
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**。
diff --git a/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl_usage.md b/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl_usage.md
index f191aa8cef12caf67d2e42666683fb2155aae437..179fe8143d75d50f37b55fef711d8cc5da61ce42 100644
--- a/doc/fluid/advanced_guide/evaluation_debugging/debug/visualdl_usage.md
+++ b/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 |
| :----------------------------------------------------------: | :--------: | :----------------------------------------------------------- |
-| [ Scalar](#Scalar -- 折线图组件) | 折线图 | 动态展示损失函数值、准确率等标量数据 |
-| [Image](#Image -- 图片可视化组件) | 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 |
-| [High Dimensional](#High Dimensional -- 数据降维组件) | 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](#Scalar--折线图组件) | 折线图 | 动态展示损失函数值、准确率等标量数据 |
+| [Image](#Image--图片可视化组件) | 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 |
+| [High Dimensional](#High-Dimensional--数据降维组件) | 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
-## 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
-## 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
diff --git a/doc/fluid/advanced_guide/inference_deployment/inference/paddle_gpu_benchmark_en.md b/doc/fluid/advanced_guide/inference_deployment/inference/paddle_gpu_benchmark_en.md
index 538251c033dc5fd964e0ad9326c113bedad69dc1..014335a2bf04c8e9c5e392518fc3afad67261fcd 100644
--- a/doc/fluid/advanced_guide/inference_deployment/inference/paddle_gpu_benchmark_en.md
+++ b/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) .
diff --git a/doc/fluid/advanced_guide/performance_improving/singlenode_training_improving/training_best_practice.rst b/doc/fluid/advanced_guide/performance_improving/singlenode_training_improving/training_best_practice.rst
index 95e71abd70e3605f94f3faa0aa1367db499b022b..c4fcd241904958e702c84cca3bfbe54b192544cb 100644
--- a/doc/fluid/advanced_guide/performance_improving/singlenode_training_improving/training_best_practice.rst
+++ b/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 `_ 。
+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 `_ 。
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。
diff --git a/doc/fluid/beginners_guide/basic_concept/programming_guide/programming_guide.md b/doc/fluid/beginners_guide/basic_concept/programming_guide/programming_guide.md
index cc010e7e00a2c7015777dec6870d1340b482248b..1260d4d6590a6a3584b1cb73aca843e9912ba90f 100644
--- a/doc/fluid/beginners_guide/basic_concept/programming_guide/programming_guide.md
+++ b/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)
diff --git a/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_cn.rst b/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_cn.rst
index cee9ba796020cf9d6767ced07dacaee157e4022b..dfed6f72a8bd85e686cb93c15d60a376ec2ffa16 100644
--- a/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_cn.rst
+++ b/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 `_ 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 `_ 查看官方提供的示例。
diff --git a/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_en.rst b/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_en.rst
index 991ff76f87ad82a54c0b6ff8b78803feb6afcf0b..a932c7089dbad2d36096697e3c6aa90e7dd54cb0 100644
--- a/doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_en.rst
+++ b/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 `_ 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 `_ to refer to examples in official documentation.
diff --git a/doc/fluid/beginners_guide/coding_practice/single_node.rst b/doc/fluid/beginners_guide/coding_practice/single_node.rst
index d63379fc6fdc949441d8961004472429b6ca5d0b..b93de0eeeb1c8b8a7e19aadeaf39caf783655385 100644
--- a/doc/fluid/beginners_guide/coding_practice/single_node.rst
+++ b/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 `_ 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
diff --git a/doc/fluid/beginners_guide/coding_practice/single_node_en.rst b/doc/fluid/beginners_guide/coding_practice/single_node_en.rst
index 3e69c281c55e1b5367a9795a83d4f8825e3d607e..63d7bb4f6cd3ce7f786cc72f71c99e8f016608c5 100644
--- a/doc/fluid/beginners_guide/coding_practice/single_node_en.rst
+++ b/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 `_ 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
diff --git a/doc/fluid/faq/train_cn.md b/doc/fluid/faq/train_cn.md
index dd6dbb45e9a5ccb2d9fd0544da60287e169ec6d6..f7964402aa0860833812a19b94aa20a2a1fb5b09 100644
--- a/doc/fluid/faq/train_cn.md
+++ b/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
diff --git a/doc/fluid/install/install_CentOS.md b/doc/fluid/install/install_CentOS.md
index 82afa784da00f4c433ab661358aafdadb55a19df..cbdca442556f1f18422f42d55fb4825141e341ae 100644
--- a/doc/fluid/install/install_CentOS.md
+++ b/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
diff --git a/doc/fluid/install/install_CentOS_en.md b/doc/fluid/install/install_CentOS_en.md
index 078e8b003c3bf10240a69309758cef8f9437423b..98025e3ffc93fced9662e7f5c98c20d4e8593402 100644
--- a/doc/fluid/install/install_CentOS_en.md
+++ b/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
diff --git a/doc/fluid/install/install_Ubuntu.md b/doc/fluid/install/install_Ubuntu.md
index 4f74eeed638931bcca1b1a958a630abdf7ee333f..c94e51f356e8ca7e471f25b747527fcd42f9c1e1 100644
--- a/doc/fluid/install/install_Ubuntu.md
+++ b/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
diff --git a/doc/fluid/install/install_Ubuntu_en.md b/doc/fluid/install/install_Ubuntu_en.md
index 2983ad15a931914081eab00528bba5346c2756cd..99390c458f7d35f47623672b68e1fdaac30e9567 100644
--- a/doc/fluid/install/install_Ubuntu_en.md
+++ b/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
diff --git a/doc/fluid/user_guides/cv_case/image_classification/README.cn.md b/doc/fluid/user_guides/cv_case/image_classification/README.cn.md
index cdc22a93b9ce9659744ef0a7be8a461606cb7ec2..433b9e62ba28c4f6fdf3a11a2ea7e470c57843fb 100644
--- a/doc/fluid/user_guides/cv_case/image_classification/README.cn.md
+++ b/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/
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/cv_case/image_classification/README.md b/doc/fluid/user_guides/cv_case/image_classification/README.md
index 6471b3398c19f89ead67342559d6fd6f7618d693..78c29b94a684b1e89f3e8a4e6bc3b745234d189a 100644
--- a/doc/fluid/user_guides/cv_case/image_classification/README.md
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/cv_case/image_classification/index.cn.html b/doc/fluid/user_guides/cv_case/image_classification/index.cn.html
index 6c53ac5453c9d9cc13f63c8ccfd8a33c7b688d93..c1283675fdf09965d982a70c37c89743176d9a30 100644
--- a/doc/fluid/user_guides/cv_case/image_classification/index.cn.html
+++ b/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/
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/cv_case/image_classification/index.html b/doc/fluid/user_guides/cv_case/image_classification/index.html
index 66c1a657e2ee88463dbd1e4706109aaa7d67d554..9dad6da305f0965778a2ff47398e0c8b8c20a77d 100644
--- a/doc/fluid/user_guides/cv_case/image_classification/index.html
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.cn.md b/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.cn.md
index 9fe04d2ff80d9e934097935864408c63bd657006..00be108b19fee33bbc26cdc365df2626c1638840 100644
--- a/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.cn.md
+++ b/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.cn.md
@@ -557,7 +557,7 @@ print("Inference Shape: ", np_data.shape)
## 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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.md b/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.md
index 00c5f627fb7eaeea22b37a2fb5744c617f72ab41..e53ea80e8d17f7e56820112b222350952d5753c8 100644
--- a/doc/fluid/user_guides/nlp_case/label_semantic_roles/README.md
+++ b/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
## 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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.cn.html b/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.cn.html
index c9deb3ad2ed3ceeaaec56579bd6dda5db5e95dc0..95860ba76bbd16c222ffa723cd2628d0cff2b36d 100644
--- a/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.cn.html
+++ b/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.cn.html
@@ -599,7 +599,7 @@ print("Inference Shape: ", np_data.shape)
## 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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.html b/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.html
index c7d962c7a4680bb930ef3ff98b419ffb3fdaf0c6..02a2168e542012d69ee2049f53b6cab8c179fbab 100644
--- a/doc/fluid/user_guides/nlp_case/label_semantic_roles/index.html
+++ b/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
## 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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/machine_translation/README.cn.md b/doc/fluid/user_guides/nlp_case/machine_translation/README.cn.md
index b255601f1742828862e78207315f8544b1c8f2d6..cec32c13ceb62088d121f7bcd9649aa4f1faed66 100644
--- a/doc/fluid/user_guides/nlp_case/machine_translation/README.cn.md
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/machine_translation/README.md b/doc/fluid/user_guides/nlp_case/machine_translation/README.md
index 3d3796f4c6cf5136092aca69f8d0045403f4c236..9c906790cb83a9cd59f3f9aac6c5662fe95e84fc 100644
--- a/doc/fluid/user_guides/nlp_case/machine_translation/README.md
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/machine_translation/index.cn.html b/doc/fluid/user_guides/nlp_case/machine_translation/index.cn.html
index 38a1cb1abf02d1da3640e5dc677586df10614e12..a44c53efcb069491a8c17ade1ca07fe79d8ded55 100644
--- a/doc/fluid/user_guides/nlp_case/machine_translation/index.cn.html
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/machine_translation/index.html b/doc/fluid/user_guides/nlp_case/machine_translation/index.html
index e1bd7d27c1cec1f3e47765b72869583e3941bc65..8ed8b68743d3ff2f4b740661c601b4ea77880d5f 100644
--- a/doc/fluid/user_guides/nlp_case/machine_translation/index.html
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/understand_sentiment/README.cn.md b/doc/fluid/user_guides/nlp_case/understand_sentiment/README.cn.md
index ce9cd776d6d4b726065f511abf720757764e8fe2..0ddf0365651b4fb6ae14ceab02afb18263971f7d 100644
--- a/doc/fluid/user_guides/nlp_case/understand_sentiment/README.cn.md
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/understand_sentiment/README.md b/doc/fluid/user_guides/nlp_case/understand_sentiment/README.md
index 5e18077cec00eae50663f4523841dfbb6eeb9aca..fa80ac7d8a5500fe4781b0e79e30ae4a2745a6ae 100644
--- a/doc/fluid/user_guides/nlp_case/understand_sentiment/README.md
+++ b/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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/nlp_case/understand_sentiment/index.cn.html b/doc/fluid/user_guides/nlp_case/understand_sentiment/index.cn.html
index 4af2290740ecebcdedff52a383408aa50e13fdf0..29ff80587b6ef8cbc8504ef30282fc286557938f 100644
--- a/doc/fluid/user_guides/nlp_case/understand_sentiment/index.cn.html
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/nlp_case/understand_sentiment/index.html b/doc/fluid/user_guides/nlp_case/understand_sentiment/index.html
index 45604f13c72ae2a40f6b5f971ea27ee2f65f1048..679cdd76545f63cdb5bad135434e6e7a35014681 100644
--- a/doc/fluid/user_guides/nlp_case/understand_sentiment/index.html
+++ b/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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/rec_case/recommender_system/README.cn.md b/doc/fluid/user_guides/rec_case/recommender_system/README.cn.md
index f5d07326821670df103718a879963ccf6b13874a..51219c76cda0d29458d784a9194bf0aabe88e1d9 100644
--- a/doc/fluid/user_guides/rec_case/recommender_system/README.cn.md
+++ b/doc/fluid/user_guides/rec_case/recommender_system/README.cn.md
@@ -598,4 +598,4 @@ with fluid.scope_guard(inference_scope):
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/rec_case/recommender_system/index.cn.html b/doc/fluid/user_guides/rec_case/recommender_system/index.cn.html
index b75bae0a93451775b78f03b2bec64b74e48aee8a..e35011a91afb2a7a6d4ee4b9cd23b8d39400f448 100644
--- a/doc/fluid/user_guides/rec_case/recommender_system/index.cn.html
+++ b/doc/fluid/user_guides/rec_case/recommender_system/index.cn.html
@@ -640,7 +640,7 @@ with fluid.scope_guard(inference_scope):
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/simple_case/fit_a_line/README.md b/doc/fluid/user_guides/simple_case/fit_a_line/README.md
index 2bc2b24c4d8fa3a4bff0ff29d8fb02f4437e8e95..cbe1d091cf735b5c2f41c133a6a1f3152792f227 100644
--- a/doc/fluid/user_guides/simple_case/fit_a_line/README.md
+++ b/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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/simple_case/fit_a_line/index.html b/doc/fluid/user_guides/simple_case/fit_a_line/index.html
index 3496bd7c54d28fa5c96a6e197bec906548698dee..923b357da39ebe535e84c7b5039a66bba543e7d9 100644
--- a/doc/fluid/user_guides/simple_case/fit_a_line/index.html
+++ b/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.
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/simple_case/recognize_digits/README.cn.md b/doc/fluid/user_guides/simple_case/recognize_digits/README.cn.md
index 9521e46c327d908591423a253b8cc7656539aaa9..b7b363b71372e4ed1a23fac5aad5296f209ea5c2 100644
--- a/doc/fluid/user_guides/simple_case/recognize_digits/README.cn.md
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/simple_case/recognize_digits/index.cn.html b/doc/fluid/user_guides/simple_case/recognize_digits/index.cn.html
index ce4cce576f2ae96ccf0a16177172fcb6d9ec76e7..c60fbbfbe20daa6d73f3359aeba8494e350bd409 100644
--- a/doc/fluid/user_guides/simple_case/recognize_digits/index.cn.html
+++ b/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.
-
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/doc/fluid/user_guides/simple_case/word2vec/README.md b/doc/fluid/user_guides/simple_case/word2vec/README.md
index 6393aec48987aaa61f94216858c5da5f67a7fc68..d885ca354a082779018239f67ded49a353a40f7f 100644
--- a/doc/fluid/user_guides/simple_case/word2vec/README.md
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
diff --git a/doc/fluid/user_guides/simple_case/word2vec/index.html b/doc/fluid/user_guides/simple_case/word2vec/index.html
index fca7fdc812a142b4766f25e052e9c4dcecdd5b01..b8eaef7f46b76994da5da5410035747e089c13b6 100644
--- a/doc/fluid/user_guides/simple_case/word2vec/index.html
+++ b/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
-
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.