卷积变分自编码器pytorch代码

卷积变分自编码器（Convolutional Variational Autoencoder，简称CVAE）是一种基于卷积神经网络的自编码器模型，用于生成具有潜在空间表示的图像。下面是一个使用PyTorch实现的简单示例代码：

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

class Encoder(nn.Module):
    def __init__(self, latent_dim):
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1)
        self.fc_mu = nn.Linear(32*8*8, latent_dim)
        self.fc_logvar = nn.Linear(32*8*8, latent_dim)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = x.view(x.size(0), -1)
        mu = self.fc_mu(x)
        logvar = self.fc_logvar(x)
        return mu, logvar

class Decoder(nn.Module):
    def __init__(self, latent_dim):
        super(Decoder, self).__init__()
        self.fc = nn.Linear(latent_dim, 32*8*8)
        self.deconv1 = nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1)
        self.deconv2 = nn.ConvTranspose2d(16, 3, kernel_size=4, stride=2, padding=1)

    def forward(self, z):
        x = self.fc(z)
        x = x.view(x.size(0), 32, 8, 8)
        x = F.relu(self.deconv1(x))
        x = torch.sigmoid(self.deconv2(x))
        return x

class CVAE(nn.Module):
    def __init__(self, latent_dim):
        super(CVAE, self).__init__()
        self.encoder = Encoder(latent_dim)
        self.decoder = Decoder(latent_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)
        recon_x = self.decoder(z)
        return recon_x, mu, logvar

# 训练过程
def train(model, train_loader, optimizer, epoch):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        optimizer.zero_grad()
        recon_batch, mu, logvar = model(data)
        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.item()
        optimizer.step()
    print('Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))

# 损失函数
def loss_function(recon_x, x, mu, logvar):
    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 3*32*32), reduction='sum')
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
    return BCE + KLD

# 示例使用CIFAR-10数据集进行训练
latent_dim = 20
model = CVAE(latent_dim)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
train(model, train_loader, optimizer, epoch=10)

这是一个简单的卷积变分自编码器的PyTorch代码示例，其中包括了编码器、解码器和整个CVAE模型的定义，以及训练过程和损失函数的定义。你可以根据自己的需求进行修改和扩展。希望对你有帮助！