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
时间: 2024-05-12 12:15:28 浏览: 19
下面是使用 PyG 训练图卷积网络 GCN 进行骨骼识别的代码参考:
```python
import torch
import torch.nn.functional as F
from torch_geometric.datasets import HumanProteinAtlas
from torch_geometric.data import DataLoader
from torch_geometric.nn import GCNConv
# 加载数据集
dataset = HumanProteinAtlas(root='/tmp/HumanProteinAtlas')
# 创建数据加载器
loader = DataLoader(dataset, batch_size=64, shuffle=True)
# 定义模型
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
# 第一层卷积
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
# 第二层卷积
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
# 初始化模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
# 训练模型
model.train()
for epoch in range(200):
for data in loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out, data.y)
loss.backward()
optimizer.step()
# 测试模型
model.eval()
correct = 0
for data in loader:
data = data.to(device)
out = model(data)
pred = out.argmax(dim=1)
correct += int((pred == data.y).sum())
print('Accuracy: {:.2f}%'.format(correct / len(dataset) * 100))
```
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