dmlc--dgl
zhjwy9343
90d86fcbe3
* [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. * [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. * [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 * [Feature] Add input embedding layer * [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 * [Doc] Refactor and split chapter 4 * [Fix] Remove CompGCN example codes * [Doc] Add chapter 2 refactor and split * [Fix] code format of savenload * [Doc] Split chapter 3 * [Doc] Add introduction phrase of chapter 2 * [Doc] Add introduction phrase of chapter 2 * [Doc] Add introduction phrase of chapter 3 * Fix * Update chapter 2 * Update chapter 3 * Update chapter 4 Co-authored-by: mufeili <mufeili1996@gmail.com>
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.. _guide-nn:
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Chapter 3: Building GNN Modules
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===============================
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DGL NN module consists of building blocks for GNN models. An NN module inherits
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from `Pytorch’s NN Module <https://pytorch.org/docs/1.2.0/_modules/torch/nn/modules/module.html>`__, `MXNet Gluon’s NN Block <http://mxnet.incubator.apache.org/versions/1.6/api/python/docs/api/gluon/nn/index.html>`__ and `TensorFlow’s Keras
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Layer <https://www.tensorflow.org/api_docs/python/tf/keras/layers>`__, depending on the DNN framework backend in use. In a DGL NN
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module, the parameter registration in construction function and tensor
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operation in forward function are the same with the backend framework.
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In this way, DGL code can be seamlessly integrated into the backend
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framework code. The major difference lies in the message passing
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operations that are unique in DGL.
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DGL has integrated many commonly used
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:ref:`apinn-pytorch-conv`, :ref:`apinn-pytorch-dense-conv`, :ref:`apinn-pytorch-pooling`,
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and
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:ref:`apinn-pytorch-util`. We welcome your contribution!
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This chapter takes :class:`~dgl.nn.pytorch.conv.SAGEConv` with Pytorch backend as an example
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to introduce how to build a custom DGL NN Module.
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Roadmap
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-------
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* :ref:`guide-nn-construction`
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* :ref:`guide-nn-forward`
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* :ref:`guide-nn-heterograph`
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.. toctree::
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:maxdepth: 1
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:hidden:
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:glob:
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nn-construction
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nn-forward
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nn-heterograph
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