wehub-resource-sync
102 lines
3.7 KiB
ReStructuredText
102 lines
3.7 KiB
ReStructuredText
.. _guide_cn-training:
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第5章：训练图神经网络
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=====================================================
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:ref:`(English Version) <guide-training>`
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概述
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--------
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本章通过使用 :ref:`guide_cn-message-passing` 中介绍的消息传递方法和 :ref:`guide_cn-nn` 中介绍的图神经网络模块，
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讲解了如何对小规模的图数据进行节点分类、边分类、链接预测和整图分类的图神经网络的训练。
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本章假设用户的图以及所有的节点和边特征都能存进GPU。对于无法全部载入的情况，请参考用户指南的 :ref:`guide_cn-minibatch`。
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后续章节的内容均假设用户已经准备好了图和节点/边的特征数据。如果用户希望使用DGL提供的数据集或其他兼容
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``DGLDataset`` 的数据(如 :ref:`guide_cn-data-pipeline` 所述)，
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可以使用类似以下代码的方法获取单个图数据集的图数据。
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.. code:: python
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import dgl
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dataset = dgl.data.CiteseerGraphDataset()
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graph = dataset[0]
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注意: 本章代码使用PyTorch作为DGL的后端框架。
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.. _guide_cn-training-heterogeneous-graph-example:
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异构图训练的样例数据
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~~~~~~~~~~~~~~~~~~~~~~~~~
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有时用户会想在异构图上进行图神经网络的训练。本章会以下面代码所创建的一个异构图为例，来演示如何进行节点分类、边分类和链接预测的训练。
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这个 ``hetero_graph`` 异构图有以下这些边的类型：
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- ``('user', 'follow', 'user')``
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- ``('user', 'followed-by', 'user')``
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- ``('user', 'click', 'item')``
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- ``('item', 'clicked-by', 'user')``
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- ``('user', 'dislike', 'item')``
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- ``('item', 'disliked-by', 'user')``
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.. code:: python
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import numpy as np
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import torch
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n_users = 1000
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n_items = 500
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n_follows = 3000
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n_clicks = 5000
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n_dislikes = 500
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n_hetero_features = 10
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n_user_classes = 5
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n_max_clicks = 10
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follow_src = np.random.randint(0, n_users, n_follows)
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follow_dst = np.random.randint(0, n_users, n_follows)
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click_src = np.random.randint(0, n_users, n_clicks)
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click_dst = np.random.randint(0, n_items, n_clicks)
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dislike_src = np.random.randint(0, n_users, n_dislikes)
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dislike_dst = np.random.randint(0, n_items, n_dislikes)
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hetero_graph = dgl.heterograph({
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('user', 'follow', 'user'): (follow_src, follow_dst),
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('user', 'followed-by', 'user'): (follow_dst, follow_src),
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('user', 'click', 'item'): (click_src, click_dst),
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('item', 'clicked-by', 'user'): (click_dst, click_src),
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('user', 'dislike', 'item'): (dislike_src, dislike_dst),
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('item', 'disliked-by', 'user'): (dislike_dst, dislike_src)})
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hetero_graph.nodes['user'].data['feature'] = torch.randn(n_users, n_hetero_features)
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hetero_graph.nodes['item'].data['feature'] = torch.randn(n_items, n_hetero_features)
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hetero_graph.nodes['user'].data['label'] = torch.randint(0, n_user_classes, (n_users,))
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hetero_graph.edges['click'].data['label'] = torch.randint(1, n_max_clicks, (n_clicks,)).float()
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# 在user类型的节点和click类型的边上随机生成训练集的掩码
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hetero_graph.nodes['user'].data['train_mask'] = torch.zeros(n_users, dtype=torch.bool).bernoulli(0.6)
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hetero_graph.edges['click'].data['train_mask'] = torch.zeros(n_clicks, dtype=torch.bool).bernoulli(0.6)
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本章路线图
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------------
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本章共有四节，每节对应一种图学习任务。
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* :ref:`guide_cn-training-node-classification`
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* :ref:`guide_cn-training-edge-classification`
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* :ref:`guide_cn-training-link-prediction`
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* :ref:`guide_cn-training-graph-classification`
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* :ref:`guide_cn-training-graph-eweight`
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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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training-node
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training-edge
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training-link
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training-graph
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