gCn图神经网络代码

GCN图神经网络的代码如下所示：

import torch
import torch.nn as nn
import torch.nn.functional as F

class GraphConvolution(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(GraphConvolution, self).__init__()
        self.weight = nn.Parameter(torch.FloatTensor(input_dim, output_dim))
        self.bias = nn.Parameter(torch.FloatTensor(output_dim))

    def forward(self, adjacency, feature):
        support = torch.mm(feature, self.weight)
        output = torch.mm(adjacency, support) + self.bias
        return output

class GcnNet(nn.Module):
    def __init__(self, input_dim=1433):
        super(GcnNet, self).__init__()
        self.gcn1 = GraphConvolution(input_dim, 16)
        self.gcn2 = GraphConvolution(16, 7)

    def forward(self, adjacency, feature):
        h = F.relu(self.gcn1(adjacency, feature))
        logits = self.gcn2(adjacency, h)
        return logits

这段代码定义了一个基于GCN的图神经网络模型。首先，它定义了一个GraphConvolution类，用于实现图卷积运算。在该类中，通过两个参数weight和bias来定义图卷积的权重和偏置。然后，在forward函数中，通过输入的邻接矩阵（adjacency）和特征矩阵（feature）进行图卷积操作，并返回输出结果。接着，定义了一个GcnNet类，该类包含两个GraphConvolution层。在forward函数中，通过两个GraphConvolution层对输入进行图卷积计算，并使用ReLU作为激活函数。最后，返回模型的输出结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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