GNN预测空气质量代码
时间: 2023-11-02 11:07:30 浏览: 106
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。
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