用pytorch写一段WGAN代码，用于解决结构化数据的样本不均衡问题

好的，我可以回答这个问题。以下是一个简单的用 PyTorch 实现 WGAN 的代码，用于解决结构化数据的样本不均衡问题：

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

# 定义数据集
class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        return self.data[index]

# 定义生成器和判别器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(100, 128)
        self.fc2 = nn.Linear(128, 256)
        self.fc3 = nn.Linear(256, 784)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = nn.functional.tanh(self.fc3(x))
        return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 1)

    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义 WGAN 模型
class WGAN(nn.Module):
    def __init__(self):
        super(WGAN, self).__init__()
        self.generator = Generator()
        self.discriminator = Discriminator()

    def forward(self, x):
        return self.generator(x)

    def train(self, data, epochs=100, batch_size=128, lr=.0001, clip_value=.01):
        optimizer_g = optim.RMSprop(self.generator.parameters(), lr=lr)
        optimizer_d = optim.RMSprop(self.discriminator.parameters(), lr=lr)

        for epoch in range(epochs):
            for i, real_data in enumerate(DataLoader(data, batch_size=batch_size, shuffle=True)):
                # 训练判别器
                for _ in range(5):
                    self.discriminator.zero_grad()
                    real_data = real_data.view(-1, 784)
                    real_data = real_data.to(torch.float32)
                    real_data.requires_grad = True
                    real_loss = self.discriminator(real_data)
                    real_loss.backward()

                    noise = torch.randn(batch_size, 100)
                    fake_data = self.generator(noise).detach()
                    fake_loss = self.discriminator(fake_data)
                    fake_loss.backward()

                    optimizer_d.step()

                    for p in self.discriminator.parameters():
                        p.data.clamp_(-clip_value, clip_value)

                # 训练生成器
                self.generator.zero_grad()
                noise = torch.randn(batch_size, 100)
                fake_data = self.generator(noise)
                fake_loss = self.discriminator(fake_data)
                fake_loss.backward()
                optimizer_g.step()

            print(f"Epoch {epoch+1}/{epochs}, Generator Loss: {fake_loss.item()}, Discriminator Loss: {real_loss.item()}")

这段代码实现了一个简单的 WGAN 模型，用于生成 MNIST 手写数字数据集的样本。你可以根据自己的需求修改代码，例如改变数据集、调整网络结构等。