稀疏自编码器pytorch代码

很抱歉，根据提供的引用内容，没有直接给出稀疏自编码器的PyTorch代码。但是，以下是一个简单的稀疏自编码器的PyTorch代码示例，供您参考：

import torch
import torch.nn as nn
import torch.optim as optim

class SparseAutoencoder(nn.Module):
    def __init__(self, input_size, hidden_size, sparsity_target, sparsity_weight):
        super(SparseAutoencoder, self).__init__()
        self.encoder = nn.Linear(input_size, hidden_size)
        self.decoder = nn.Linear(hidden_size, input_size)
        self.sparsity_target = sparsity_target
        self.sparsity_weight = sparsity_weight
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        hidden = self.sigmoid(self.encoder(x))
        output = self.sigmoid(self.decoder(hidden))
        return output, hidden

    def training_step(self, x, optimizer):
        optimizer.zero_grad()
        output, hidden = self.forward(x)
        reconstruction_loss = nn.MSELoss()(output, x)
        sparsity_loss = torch.mean(torch.abs(hidden.mean(dim=0) - self.sparsity_target))
        loss = reconstruction_loss + self.sparsity_weight * sparsity_loss
        loss.backward()
        optimizer.step()
        return loss.item()

# Example usage
input_size = 784
hidden_size = 64
sparsity_target = 0.1
sparsity_weight = 0.2
model = SparseAutoencoder(input_size, hidden_size, sparsity_target, sparsity_weight)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
    for batch_idx, (data, _) in enumerate(train_loader):
        data = data.view(data.size(0), -1)
        loss = model.training_step(data, optimizer)
        # ...

在这个示例中，我们定义了一个名为SparseAutoencoder的PyTorch模型类，它包含一个编码器和一个解码器，以及一个用于计算稀疏损失的目标和权重。在训练过程中，我们使用Adam优化器来最小化重建损失和稀疏损失的加权和。请注意，这只是一个简单的示例，您可能需要根据您的具体需求进行修改和调整。