使用数据集QM7bDataset实现VGAE编码
时间: 2023-12-27 16:05:22 浏览: 27
好的,我理解了你的问题。您可以按照以下步骤使用QM7b数据集实现VGAE编码:
1. 下载QM7b数据集并将其放置在适当的文件夹中。
2. 使用PyTorch加载数据集。您可以使用以下代码:
```
from torch_geometric.datasets import QM7bDataset
dataset = QM7bDataset(root='/path/to/dataset', transform=None)
```
3. 构建图形。您可以使用以下代码:
```
from torch_geometric.data import DataLoader
loader = DataLoader(dataset, batch_size=32, shuffle=True)
```
4. 定义模型。您可以使用以下代码:
```
import torch
import torch.nn as nn
from torch_geometric.nn import VGAE
class MyModel(nn.Module):
def __init__(self, in_channels, hidden_channels):
super(MyModel, self).__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv2 = GCNConv(hidden_channels, hidden_channels)
self.conv_mu = GCNConv(hidden_channels, hidden_channels)
self.conv_logstd = GCNConv(hidden_channels, hidden_channels)
self.vgae = VGAE(self.conv_mu, self.conv_logstd)
def forward(self, x, edge_index):
x = F.relu(self.conv1(x, edge_index))
x = F.relu(self.conv2(x, edge_index))
z = self.vgae.encode(x, edge_index)
return z
```
5. 训练模型。您可以使用以下代码:
```
import torch.optim as optim
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MyModel(dataset.num_features, hidden_channels=16).to(device)
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()
model.train()
for epoch in range(10):
total_loss = 0
for data in loader:
data = data.to(device)
optimizer.zero_grad()
z = model(data.x, data.edge_index)
loss = criterion(z, data.y)
loss.backward()
optimizer.step()
total_loss += loss.item() * data.num_graphs
total_loss /= len(dataset)
print(f'Epoch {epoch}, Loss {total_loss:.4f}')
```
这就是使用QM7b数据集实现VGAE编码的基本步骤。请注意,这只是一个示例,您可能需要根据模型和数据的特定要求进行更改。