pytorch实现VAEGAN的代码

下面是一个简单的pytorch实现VAEGAN的代码，仅供参考：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# 定义VAEGAN的生成器
class Generator(nn.Module):
    def __init__(self, latent_size):
        super(Generator, self).__init__()
        self.latent_size = latent_size
        self.fc1 = nn.Linear(latent_size, 128)
        self.fc2 = nn.Linear(128, 256)
        self.fc3 = nn.Linear(256, 512)
        self.fc4 = nn.Linear(512, 28*28)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.relu(self.fc3(x))
        x = self.sigmoid(self.fc4(x))
        return x.view(-1, 1, 28, 28)

# 定义VAEGAN的判别器
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, stride=2, padding=1)
        self.fc = nn.Linear(64*4*4, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.relu(self.conv3(x))
        x = x.view(-1, 64*4*4)
        x = self.sigmoid(self.fc(x))
        return x

# 定义VAEGAN的损失函数
class VAEGANLoss(nn.Module):
    def __init__(self):
        super(VAEGANLoss, self).__init__()
        self.mse_loss = nn.MSELoss()

    def forward(self, x, target, output, discriminator_output):
        recon_loss = self.mse_loss(output, target)
        adv_loss = -torch.log(discriminator_output + 1e-8).mean()
        return recon_loss + adv_loss

# 定义训练函数
def train_vae_gan(generator, discriminator, train_loader, lr, latent_size, num_epochs):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    generator.to(device)
    discriminator.to(device)

    optimizer_g = optim.Adam(generator.parameters(), lr=lr)
    optimizer_d = optim.Adam(discriminator.parameters(), lr=lr)
    loss_fn = VAEGANLoss()

    for epoch in range(num_epochs):
        for batch_idx, (data, _) in enumerate(train_loader):
            data = data.to(device)
            batch_size = data.size(0)

            # 训练判别器
            z = torch.randn(batch_size, latent_size).to(device)
            fake_data = generator(z)
            real_output = discriminator(data)
            fake_output = discriminator(fake_data.detach())
            d_loss = loss_fn(data, data, fake_data, fake_output)

            optimizer_d.zero_grad()
            d_loss.backward()
            optimizer_d.step()

            # 训练生成器
            z = torch.randn(batch_size, latent_size).to(device)
            fake_data = generator(z)
            fake_output = discriminator(fake_data)
            g_loss = loss_fn(fake_data, data, fake_data, fake_output)

            optimizer_g.zero_grad()
            g_loss.backward()
            optimizer_g.step()

        print('Epoch [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}'
              .format(epoch+1, num_epochs, d_loss.item(), g_loss.item()))

# 加载MNIST数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)

# 定义超参数
lr = 0.0002
latent_size = 100
num_epochs = 50

# 创建VAEGAN模型并训练
generator = Generator(latent_size)
discriminator = Discriminator()
train_vae_gan(generator, discriminator, train_loader, lr, latent_size, num_epochs)

# 生成新的数据
z = torch.randn(10, latent_size).to(device)
fake_data = generator(z)

以上代码仅供参考，实际实现过程中还需要针对具体问题进行调整和优化。