用Pytorch写一个SDNE代码
时间: 2023-01-31 21:10:12 浏览: 68
SDNE(Structure Deep Network Embedding)是一种用于结构化数据的深度学习模型,可以将结构化数据映射到低维空间中的表示。
下面是一个使用 PyTorch 实现 SDNE 的示例代码:
```python
import torch
import torch.nn as nn
class SDNE(nn.Module):
def __init__(self, num_nodes, num_features, hidden_size, alpha=1e-6, beta=5, nu1=1e-5, nu2=1e-4):
super(SDNE, self).__init__()
self.num_nodes = num_nodes
self.num_features = num_features
self.hidden_size = hidden_size
self.alpha = alpha
self.beta = beta
self.nu1 = nu1
self.nu2 = nu2
self.encoder = nn.Sequential(
nn.Linear(num_features, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, int(hidden_size / 2)),
nn.ReLU(),
nn.Linear(int(hidden_size / 2), int(hidden_size / 4)),
nn.ReLU(),
nn.Linear(int(hidden_size / 4), int(hidden_size / 8)),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Linear(int(hidden_size / 8), int(hidden_size / 4)),
nn.ReLU(),
nn.Linear(int(hidden_size / 4), int(hidden_size / 2)),
nn.ReLU(),
nn.Linear(int(hidden_size / 2), hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, num_features)
)
def forward(self, inputs):
encoded = self.encoder(inputs)
decoded = self.decoder(encoded)
reconstruction_loss = nn.MSELoss()(decoded, inputs)
regularization_loss = self.nu1 * torch.mean(encoded ** 2) + self.nu2 * torch.mean(encoded.pow(4))
loss = reconstruction_loss + self.beta * regularization_loss
return encoded, decoded, loss
```
这个类的构造函数接受七个参数:
- `num_nodes`:图中的节点数量
- `num_features`:每个节点的特征数量
- `hidden_size