GAN 朋友torch

嗨！对于GAN（生成对抗网络）在PyTorch中的实现，你可以按照以下步骤进行操作：

步骤1：导入所需的库

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

步骤2：定义生成器和判别器模型

# 生成器模型
class Generator(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Generator, self).__init__()
        self.fc = nn.Linear(input_dim, 256)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(256, output_dim)
        self.tanh = nn.Tanh()
    
    def forward(self, x):
        x = self.fc(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.tanh(x)
        return x

# 判别器模型
class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super(Discriminator, self).__init__()
        self.fc = nn.Linear(input_dim, 256)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(256, 1)
        self.sigmoid = nn.Sigmoid()
    
    def forward(self, x):
        x = self.fc(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return x

步骤3：训练GAN模型

# 定义训练函数
def train(generator, discriminator, dataloader, num_epochs, generator_optimizer, discriminator_optimizer, criterion):
    generator.train()
    discriminator.train()

    for epoch in range(num_epochs):
        for i, real_images in enumerate(dataloader):