WGAN gp pytorch

对于Wasserstein GAN (WGAN)在PyTorch中的实现，你可以通过以下步骤来完成：

1. 导入所需的库：

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

2. 定义生成器和判别器网络：

class Generator(nn.Module):
    def __init__(self, latent_dim, img_shape):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256),
            nn.LeakyReLU(0.2),            nn.Linear(256, 512),
            nn.BatchNorm1d(512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, img_shape),
            nn.Tanh()
        )
        
    def forward(self, z):
        img = self.model(z)
        return img


class Discriminator(nn.Module):
    def __init__(self, img_shape):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(img_shape, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1)
        )
        
    def forward(self, img):
        validity = self.model(img)
        return validity

3. 定义WGAN损失函数：

def wgan_loss(real_imgs, fake_imgs, critic_real, critic_fake):
    return torch.mean(critic_fake) - torch.mean(critic_real)

4. 初始化生成器、判别器和优化器：

latent_dim = 100
img_shape = 784

generator = Generator(latent_dim, img_shape)
discriminator = Discriminator(img_shape)

optimizer_G = optim.RMSprop(generator.parameters(), lr=0.00005)
optimizer_D = optim.RMSprop(discriminator.parameters(), lr=0.00005)

5. 训练WGAN模型：

n_epochs = 200
clip_value = 0.01

for epoch in range(n_epochs):
    for i, (real_imgs, _) in enumerate(dataloader):
        batch_size = real_imgs.shape[0]
        real_imgs = real_imgs.view(batch_size, -1).to(device)
        
        # 训练判别器
        optimizer_D.zero_grad()
        
        z = torch.randn(batch_size, latent_dim).to(device)
        fake_imgs = generator(z)
        
        critic_real = discriminator(real_imgs)
        critic_fake = discriminator(fake_imgs.detach())
        
        d_loss = wgan_loss(real_imgs, fake_imgs, critic_real, critic_fake)
        d_loss.backward()
        optimizer_D.step()
        
        for p in discriminator.parameters():
            p.data.clamp_(-clip_value, clip_value)
        
        # 训练生成器
        if i % 5 == 0:
            optimizer_G.zero_grad()
            
            z = torch.randn(batch_size, latent_dim).to(device)
            fake_imgs = generator(z)
            
            critic_fake = discriminator(fake_imgs)
            
            g_loss = -torch.mean(critic_fake)
            g_loss.backward()
            optimizer_G.step()

这里只是一个简单的WGAN实现示例，你可以根据你的需求进行修改和扩展。记得在训练之前加载数据集，并将模型和数据移动到适当的设备上（如GPU）进行加速。