写一段Python代码实现编码器是由两个方向的长短时记忆网络LSTM构成的。编码器的任务是将输入的数据转换成一个潜在空间中的向量表示。为了实现这个目的编码器使用两个线性模块来估计向量的均值和协方差从而近似表示一个高斯分布。这个高斯分布可以用来生成新的数据也可以用来计算潜在空间中两个向量之间的距离。

import torch.nn as nn import torch.nn.functional as F

class Encoder(nn.Module): def init(self, input_size, hidden_size, num_layers): super(Encoder, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers

    self.lstm_forward = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
    self.lstm_backward = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
    self.linear_mean = nn.Linear(hidden_size*2, hidden_size)
    self.linear_cov = nn.Linear(hidden_size*2, hidden_size)
    
def forward(self, x):
    h0_forward = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
    c0_forward = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
    h0_backward = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
    c0_backward = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
    
    output_forward, (hn_forward, cn_forward) = self.lstm_forward(x, (h0_forward, c0_forward))
    output_backward, (hn_backward, cn_backward) = self.lstm_backward(torch.flip(x, [1]), (h0_backward, c0_backward))
    output_backward = torch.flip(output_backward, [1])
    
    output = torch.cat((output_forward, output_backward), dim=2)
    mean = self.linear_mean(output)
    cov = F.softplus(self.linear_cov(output))
    
    return mean, co