vgae pytorch实现

好的，我明白了。关于 VAE 的 PyTorch 实现，你可以参考以下步骤：

1. 定义编码器和解码器的网络结构；
2. 定义重参数化函数，用于从编码器的输出中采样隐变量；
3. 定义损失函数，包括重构误差和 KL 散度；
4. 定义优化器，使用反向传播算法更新网络参数；
5. 训练模型，迭代多个 epoch，每个 epoch 都对训练集进行一次完整的遍历。

以下是一个简单的 VAE 实现示例：

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

class Encoder(nn.Module):
    def __init__(self, input_dim, hidden_dim, latent_dim):
        super(Encoder, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc21 = nn.Linear(hidden_dim, latent_dim)
        self.fc22 = nn.Linear(hidden_dim, latent_dim)

    def forward(self, x):
        h = F.relu(self.fc1(x))
        mu = self.fc21(h)
        logvar = self.fc22(h)
        return mu, logvar

class Decoder(nn.Module):
    def __init__(self, latent_dim, hidden_dim, output_dim):
        super(Decoder, self).__init__()
        self.fc1 = nn.Linear(latent_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, z):
        h = F.relu(self.fc1(z))
        x = torch.sigmoid(self.fc2(h))
        return x

class VAE(nn.Module):
    def __init__(self, input_dim, hidden_dim, latent_dim):
        super(VAE, self).__init__()
        self.encoder = Encoder(input_dim, hidden_dim, latent_dim)
        self.decoder = Decoder(latent_dim, hidden_dim, input_dim)

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        z = mu + eps * std
        return z

    def forward(self, x):
        mu, logvar = self.encoder(x)
        z = self.reparameterize(mu, logvar)
        x_recon = self.decoder(z)
        return x_recon, mu, logvar

def loss_function(x_recon, x, mu, logvar):
    BCE = F.binary_cross_entropy(x_recon, x, reduction='sum')
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
    return BCE + KLD

input_dim = 784
hidden_dim = 256
latent_dim = 20
lr = 1e-3
batch_size = 128
num_epochs = 10

train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('data', train=True, download=True,
                               transform=torchvision.transforms.ToTensor()),
    batch_size=batch_size, shuffle=True)

model = VAE(input_dim, hidden_dim, latent_dim)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

for epoch in range(num_epochs):
    for i, (x, _) in enumerate(train_loader):
        x = x.view(-1, input_dim)
        optimizer.zero_grad()
        x_recon, mu, logvar = model(x)
        loss = loss_function(x_recon, x, mu, logvar)
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print("Epoch[{}/{}], Step [{}/{}], Loss: {:.4f}"
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))