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<h1><a href="https://nn.labml.ai/graphs/gatv2/index.html">Graph 注意力网络 v2 (GATv2)</a></h1>
<p>这是 Gatv2 运营商的 <a href="https://pytorch.org">PyTorch</a> 实现，来自论文 G <a href="https://papers.labml.ai/paper/2105.14491">raph 注意力网络有多专心？</a>。</p>
<p>GATV2 处理图形数据。图由连接节点的节点和边组成。例如，在 Cora 数据集中，节点是研究论文，边缘是连接论文的引文。</p>
G@@ <p>ATv2 运算符修复了标准 GAT 的静态注意力问题：由于标准 GAT 中的线性层紧随其后应用，因此参与节点的排名在查询节点上是无条件的。相比之下，在 GATv2 中，每个节点都可以参与任何其他节点。</p>
<p>以下是<a href="https://nn.labml.ai/graphs/gatv2/experiment.html">在 Cora 数据集上训练双层 GATv2 的训练代码</a>。</p>
<p><a href="https://app.labml.ai/run/34b1e2f6ed6f11ebb860997901a2d1e3"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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