GNN回归预测python代码
时间: 2024-12-14 07:16:53 浏览: 98
GNN (Graph Neural Network) 回归预测通常涉及到将图数据输入到深度学习模型中,用于预测节点属性或整个图的连续值。在Python中,你可以使用如PyTorch Geometric或DGL(Delta Graph Library)这样的库来进行GNN的实现。这里是一个简单的例子,使用PyTorch Geometric:
```python
import torch
import torch_geometric.nn as pyg_nn
from torch_geometric.data import Data
# 假设我们有一个NodeData类,包含边和节点特征
class NodeData(Data):
def __init__(self, x, edge_index):
super(NodeData, self).__init__()
self.x = x
self.edge_index = edge_index
# 定义一个基础的GNN模型
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super(GNN, self).__init__()
self.conv1 = pyg_nn.GCNConv(in_channels, hidden_channels)
self.conv2 = pyg_nn.GCNConv(hidden_channels, out_channels)
def forward(self, data):
x = self.conv1(data.x, data.edge_index)
x = torch.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, data.edge_index)
return x
# 初始化模型、损失函数和优化器
model = GNN(in_channels=10, hidden_channels=64, out_channels=1) # 根据实际需求调整参数
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
# 假设data是一个NodeData实例
data = NodeData(x=torch.randn(100, 10), edge_index=torch.tensor([[0, 1, 2], [1, 2, 3]])) # 需要有相应的边和节点特征
for epoch in range(10): # 训练循环
model.train() # 设置模型为训练模式
optimizer.zero_grad()
pred = model(data) # 进行前向传播
loss = criterion(pred, target) # 假设target是你需要预测的目标值
loss.backward() # 反向传播
optimizer.step()
# 预测阶段
model.eval()
with torch.no_grad():
prediction = model(data).squeeze() # 没有训练梯度
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