使用超像素标签进行gat分类的代码
时间: 2023-07-05 10:15:03 浏览: 65
这里提供一个使用PyTorch实现使用超像素标签进行GAT分类的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.datasets import MNISTSuperpixels
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GATConv
# 定义超参数
lr = 0.01
epochs = 100
# 加载MNIST超像素数据集
train_dataset = MNISTSuperpixels(root='data/', train=True)
test_dataset = MNISTSuperpixels(root='data/', train=False)
# 定义数据加载器
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义GAT模型
class GAT(nn.Module):
def __init__(self, in_channels, out_channels):
super(GAT, self).__init__()
self.conv1 = GATConv(in_channels, 64, heads=8)
self.conv2 = GATConv(64 * 8, out_channels, concat=False)
def forward(self, x, edge_index):
x = F.dropout(x, p=0.6, training=self.training)
x = F.elu(self.conv1(x, edge_index))
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
# 定义模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GAT(train_dataset.num_features, train_dataset.num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(epochs):
model.train()
for batch in train_loader:
batch = batch.to(device)
out = model(batch.x, batch.edge_index)
loss = F.nll_loss(out[batch.train_mask], batch.y[batch.train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
correct = 0
for batch in test_loader:
batch = batch.to(device)
out = model(batch.x, batch.edge_index)
pred = out.argmax(dim=1)
correct += int((pred[batch.test_mask] == batch.y[batch.test_mask]).sum())
acc = correct / len(test_dataset)
print(f'Epoch: {epoch+1:03d}, Test Acc: {acc:.4f}')
```
这个代码示例使用了PyTorch Geometric库,其中`GATConv`表示GAT层,`MNISTSuperpixels`表示MNIST超像素数据集。在训练过程中,我们使用了节点的`train_mask`进行训练,使用节点的`test_mask`进行测试。最终输出测试集的准确率。
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