GAN缺陷数据增强代码

以下是使用GAN进行数据增强的代码示例：

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

# 定义生成器和判别器模型
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # 定义生成器的网络结构

    def forward(self, x):
        # 生成器的前向传播过程
        return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        # 定义判别器的网络结构

    def forward(self, x):
        # 判别器的前向传播过程
        return x

# 定义训练函数
def train_gan(generator, discriminator, dataloader, num_epochs):
    # 定义损失函数和优化器
    loss_function = nn.BCELoss()
    generator_optimizer = optim.Adam(generator.parameters(), lr=0.0002)
    discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)

    for epoch in range(num_epochs):
        for i, real_images in enumerate(dataloader):
            # 训练判别器
            discriminator.zero_grad()
            real_labels = torch.ones(real_images.size(0))
            real_output = discriminator(real_images)
            real_loss = loss_function(real_output, real_labels)

            fake_labels = torch.zeros(real_images.size(0))
            noise = Variable(torch.randn(real_images.size(0), 100))
            fake_images = generator(noise)
            fake_output = discriminator(fake_images.detach())
            fake_loss = loss_function(fake_output, fake_labels)

            discriminator_loss = real_loss + fake_loss
            discriminator_loss.backward()
            discriminator_optimizer.step()

            # 训练生成器
            generator.zero_grad()
            fake_output = discriminator(fake_images)
            generator_loss = loss_function(fake_output, real_labels)
            generator_loss.backward()
            generator_optimizer.step()

            if (i+1) % 100 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Discriminator Loss: {:.4f}, Generator Loss: {:.4f}'
                      .format(epoch+1, num_epochs, i+1, len(dataloader), discriminator_loss.item(), generator_loss.item()))

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# 初始化生成器和判别器
generator = Generator()
discriminator = Discriminator()

# 训练GAN模型
train_gan(generator, discriminator, dataloader, num_epochs=10)