Global Convolutional Network(GCN)网络模型

PyG（Torch Geometric）是一个用于图神经网络（Graph Neural Networks, GNNs）的Python库，它是基于PyTorch构建的。如果你想使用PyG搭建GCN（Graph Convolutional Network）网络，首先需要安装`torch_geometric`库。以下是一个简单的步骤概述： 1. **安装**: ```bash pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html pip install torch-scatter -f https://github.com/rusty1s/pytorch_scatter/blobs/main/dist/ pip install torch-sparse -f https://github.com/rusty1s/pytorch_sparse/blobs/main/dist/ pip install torch-cluster -f https://github.com/rusty1s/pytorch_cluster/blobs/main/dist/ pip install torch-geometric ``` 2. **导入所需模块**: ```python import torch from torch_geometric.nn import GCNConv, global_mean_pool ``` 3. **构建数据结构**: 使用`torch_geometric.data.Data`类来创建图的数据结构，包括节点特征和边的信息。 4. **定义模型**: ```python class GCN(torch.nn.Module): def __init__(self, in_channels, hidden_channels, out_channels): super(GCN, self).__init__() self.conv1 = GCNConv(in_channels, hidden_channels) self.conv2 = GCNConv(hidden_channels, out_channels) def forward(self, data): x, edge_index = data.x, data.edge_index x = F.relu(self.conv1(x, edge_index)) x = self.conv2(x, edge_index) return global_mean_pool(x, data.batch) ``` 5. **训练和预测**: 定义损失函数、优化器，并通过`DataLoader`加载数据进行训练。 ```python model = GCN(...).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.01) for epoch in range(100): # 假设这是训练循环 optimizer.zero_grad() output = model(data) loss = F.nll_loss(output[data.train_mask], data.y[data.train_mask]) loss.backward() optimizer.step() # 预测阶段 with torch.no_grad(): pred = model(data) ```