堆叠自编码器pytorch

堆栈自编码器（Stacked Autoencoder，简称SAE）是一种深度学习模型，它由多个简单的自编码器堆叠而成，每个自编码器都会压缩输入数据，然后上一层解码器再试图从低维表示重构原始数据。PyTorch 中实现堆叠自编码器，你可以这样做：

1. 首先，安装必要的库，如 `torch` 和 `torchvision`:

   pip install torch torchvision

2. 导入所需的模块并创建基本的神经网络层（例如全连接层、激活函数等）：

   import torch
   import torch.nn as nn

   class EncoderBlock(nn.Module):
       def __init__(self, in_channels, out_channels):
           super(EncoderBlock, self).__init__()
           self.encoder = nn.Sequential(
               nn.Linear(in_channels, out_channels),
               nn.ReLU(),
               nn.BatchNorm1d(out_channels)
           )

       def forward(self, x):
           return self.encoder(x)

   class DecoderBlock(nn.Module):
       def __init__(self, in_channels, out_channels):
           super(DecoderBlock, self).__init__()
           self.decoder = nn.Sequential(
               nn.Linear(in_channels, out_channels),
               nn.ReLU(),
               nn.BatchNorm1d(out_channels),
               nn.Linear(out_channels, in_channels)
           )

       def forward(self, x):
           return self.decoder(x)

3. 创建堆叠自编码器模型，可以设置几个层次：

   class StackedAutoencoder(nn.Module):
       def __init__(self, input_dim, latent_dim, num_layers=2):
           super(StackedAutoencoder, self).__init__()
           self.encoders = nn.ModuleList()
           self.decoders = nn.ModuleList()

           for _ in range(num_layers - 1):
               self.encoders.append(EncoderBlock(input_dim, latent_dim))
               input_dim = latent_dim

           self.encoders.append(EncoderBlock(input_dim, latent_dim // 2)) # Final encoder
           self.latent_space = nn.Linear(latent_dim // 2, latent_dim) # Output of the deepest layer

           for i in reversed(range(num_layers - 1)):
               decoder_in_channels = latent_dim if i == num_layers - 2 else latent_dim // 2
               self.decoders.append(DecoderBlock(decoder_in_channels, latent_dim))

           self.reconstructor = DecoderBlock(latent_dim, input_dim) # Last decoder

       def forward(self, x):
           hiddens = []
           for encoder in self.encoders:
               x = encoder(x)
               hiddens.append(x)
           encoded = self.latent_space(hiddens[-1])

           reconstructed = encoded
           for decoder in self.decoders:
               reconstructed = decoder(reconstructed)
           return reconstructed, hiddens

4. 初始化模型，训练数据以及损失函数（如均方误差）：

   model = StackedAutoencoder(input_dim, latent_dim)
   optimizer = torch.optim.Adam(model.parameters())
   criterion = nn.MSELoss()

   # ... (加载数据和训练循环)