帮我用Pytorch写一个两条件的GAN网络

好的，我可以回答这个问题。以下是一个基于 Pytorch 的两条件 GAN 网络的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, 256)
        self.fc3 = nn.Linear(256, output_dim)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

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

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_dim, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 1)
        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.sigmoid(self.fc3(x))
        return x

# 定义训练函数
def train_gan(generator, discriminator, data, num_epochs, batch_size, lr):
    criterion = nn.BCELoss()
    optimizer_g = optim.Adam(generator.parameters(), lr=lr)
    optimizer_d = optim.Adam(discriminator.parameters(), lr=lr)

    for epoch in range(num_epochs):
        for i in range(, len(data), batch_size):
            # 训练判别器
            discriminator.zero_grad()
            real_data = data[i:i+batch_size]
            real_labels = torch.ones(len(real_data), 1)
            fake_data = generator(torch.randn(len(real_data), 2))
            fake_labels = torch.zeros(len(real_data), 1)
            real_pred = discriminator(real_data)
            fake_pred = discriminator(fake_data.detach())
            d_loss = criterion(real_pred, real_labels) + criterion(fake_pred, fake_labels)
            d_loss.backward()
            optimizer_d.step()

            # 训练生成器
            generator.zero_grad()
            fake_data = generator(torch.randn(len(real_data), 2))
            fake_labels = torch.ones(len(real_data), 1)
            fake_pred = discriminator(fake_data)
            g_loss = criterion(fake_pred, fake_labels)
            g_loss.backward()
            optimizer_g.step()

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

# 测试代码
data = np.random.normal(size=(100, 2))
generator = Generator(2, 2)
discriminator = Discriminator(2)
train_gan(generator, discriminator, data, num_epochs=100, batch_size=32, lr=.001)

这个 GAN 网络有两个输入条件，输出也是两个维度。你可以根据自己的需求修改输入和输出的维度。