GNN预测空气质量代码

抱歉，由于缺乏问题的上下文和相关数据，我无法为您提供完整的GNN预测空气质量代码。但是，以下是一个简单的GNN模型，可以帮助您开始：

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

class GNN(nn.Module):

    def __init__(self, num_nodes, num_features, num_classes, hidden_dim=64):
        super(GNN, self).__init__()

        self.num_nodes = num_nodes
        self.num_features = num_features
        self.num_classes = num_classes
        self.hidden_dim = hidden_dim

        self.conv1 = nn.Conv1d(num_features, hidden_dim, kernel_size=1, bias=False)
        self.conv2 = nn.Conv1d(hidden_dim, num_classes, kernel_size=1, bias=False)

    def forward(self, adj, features):

        x = self.conv1(features)
        x = F.relu(torch.bmm(adj, x))
        x = self.conv2(x)
        x = F.relu(torch.bmm(adj, x))

        return x

# example usage
num_nodes = 100
num_features = 10
num_classes = 1

adj = torch.randn(num_nodes, num_nodes)
features = torch.randn(num_nodes, num_features)

model = GNN(num_nodes, num_features, num_classes)
out = model(adj, features)

print(out.shape) # should be (num_nodes, num_classes)

这个模型使用了一个简单的两层卷积神经网络，其中第一层卷积核大小为1，第二层卷积核大小为1。模型的输入包括邻接矩阵和节点特征矩阵，输出为每个节点对应的预测类别。请注意，这只是一个简单的示例，您需要根据您的具体问题和数据进行修改和调整。