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zhjwy9343 b0a9d16f25 [Doc] Chinese User Guide chapter 1 - 4 (#2351)
* [Feature] Add full graph training with dgl built-in dataset.

* [Feature] Add full graph training with dgl built-in dataset.

* [Feature] Add full graph training with dgl built-in dataset.

* [Bug] fix model to cuda.

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* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Fix] Add random

* [Bug] Fix batch norm error

* [Doc] Test with CN in Sphinx

* [Doc] Test with CN in Sphinx

* [Doc] Remove the test CN docs.

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

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* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Doc] fill readme with new performance results

* [Doc] Add Chinese User Guide, graph and 1.5

* [Doc] Add Chinese User Guide, graph and 1.5

* Update README.md

* [Fix] Temporary remove compgcn

* [Doc] Add CN user guide chapter2

* [Test] Tunning format

* [Test] Tunning format

* [Test] Tunning format

* [Test] Tunning format

* [Test] Tunning format

* [Test] Section headers

* [Fix] Fix format errors

* [Fix] Fix format errors

* [Fix] Fix format errors

* [Doc] Add CN-EN EN-CN links

* [Doc] Add CN-EN EN-CN links

* [Doc] Copyedit chapter2

* [Doc] Copyedit chapter2

* [Doc] Remove EN in 2.1

* [Doc] Remove EN in chapter 2

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* [Doc] Add chapter 3 preface and 3.1

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* [Doc] Remove EN parts

* [Doc] Copyediting 3.1

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* [Doc] Proofreading 3.1 and 3.2

* [Doc] Proofreading 3.2 and 3.3

* [Doc] Add chapter 4 CN raw text.

* [Clean] Remove codes in other branches

* [Doc] Start to copyedit chapter 4 preface

* [Doc] copyedit CN section 4.1

* [Doc] Remove EN in User Guide Chapter 4

* [Doc] Copyedit chapter 4.1

* [Doc] copyedit cn chapter 4.2, 4.3, 4.4, and 4.5.

* [Doc] Fix errors in EN user guide graph feature and heterograph

* [Doc] 2nd round copyediting with Murph's comments

* [Doc] 3rd round copyediting with Murph's comments

* [Doc] 3rd round copyediting with Murph's comments

* [Doc] 3rd round copyediting with Murph's comments

* [Sync] syncronize with the dgl master

* [Doc] edited after Minjie's comments, 1st round

* update cub

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
2020-11-19 13:40:33 +08:00

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.. _guide_cn-nn:
第3章:构建图神经网络(GNN)模块
===================================
:ref:`(English Version) <guide-nn>`
DGL NN模块是用户构建GNN模型的基本模块。根据DGL所使用的后端深度神经网络框架,
DGL NN模块的父类取决于后端所使用的深度神经网络框架。对于PyTorch后端,
它应该继承 `PyTorch的NN模块 <https://pytorch.org/docs/1.2.0/_modules/torch/nn/modules/module.html>`__;对于MXNet后端,它应该继承
`MXNet Gluon的NN块 <http://mxnet.incubator.apache.org/versions/1.6/api/python/docs/api/gluon/nn/index.html>`__
对于TensorFlow后端,它应该继承 `Tensorflow的Keras层 <https://www.tensorflow.org/api_docs/python/tf/keras/layers>`__
在DGL NN模块中,构造函数中的参数注册和前向传播函数中使用的张量操作与后端框架一样。这种方式使得DGL的代码可以无缝嵌入到后端框架的代码中。
DGL和这些深度神经网络框架的主要差异是其独有的消息传递操作。
DGL已经集成了很多常用的 :ref:`apinn-pytorch-conv`:ref:`apinn-pytorch-dense-conv`
:ref:`apinn-pytorch-pooling`:ref:`apinn-pytorch-util`。欢迎给DGL贡献更多的模块!
本章将使用PyTorch作为后端,用 :class:`~dgl.nn.pytorch.conv.SAGEConv` 作为例子来介绍如何构建用户自己的DGL NN模块。
本章路线图
------------
* :ref:`guide_cn-nn-construction`
* :ref:`guide_cn-nn-forward`
* :ref:`guide_cn-nn-heterograph`
.. toctree::
:maxdepth: 1
:hidden:
:glob:
nn-construction
nn-forward
nn-heterograph