图卷积网络进行骨骼识别代码_PyG训练图卷积网络GCN的代码参考

以下是使用PyG训练图卷积网络GCN进行骨骼识别的代码参考：

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import HumanProteinAtlasExpression
from torch_geometric.data import DataLoader

# Load the dataset
dataset = HumanProteinAtlasExpression(root='data/HumanProteinAtlasExpression/')
train_dataset = dataset[0:2500]
test_dataset = dataset[2500:]

# Define the model
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        x = F.relu(self.conv1(x, edge_index))
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)

        return F.log_softmax(x, dim=1)

# Train the model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

for epoch in range(1, 201):
    model.train()
    total_loss = 0
    for batch in train_loader:
        batch = batch.to(device)
        optimizer.zero_grad()
        out = model(batch)
        loss = F.nll_loss(out, batch.y)
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * batch.num_graphs

    model.eval()
    test_loss = 0
    for batch in test_loader:
        batch = batch.to(device)
        out = model(batch)
        test_loss += F.nll_loss(out, batch.y).item() * batch.num_graphs

    print(f'Epoch: {epoch:03d}, Train Loss: {total_loss / len(train_dataset):.4f}, Test Loss: {test_loss / len(test_dataset):.4f}')

在这个例子中，我们使用了 `HumanProteinAtlasExpression` 数据集，该数据集包含了人类蛋白质组中的表达数据和相关的骨骼结构信息。我们定义了一个包含两个图卷积层的GCN模型，使用Adam优化器进行训练，并在每个epoch中计算训练集和测试集上的损失。