生成对抗网络代码

生成对抗网络（GAN）是一种深度学习模型，由生成器和判别器两个部分组成，它们通过对抗的方式协同工作，达到生成高质量的数据样本的目的。以下是一个简单的GAN代码示例：

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

# 定义生成器
class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        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
class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        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

# 定义超参数
input_size = 100
hidden_size = 128
output_size = 1
batch_size = 64
num_epochs = 50
lr = 0.0002

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 初始化生成器和判别器
G = Generator(input_size, hidden_size, output_size)
D = Discriminator(input_size, hidden_size, output_size)

# 定义损失函数和优化器
criterion = nn.BCELoss()
G_optimizer = optim.Adam(G.parameters(), lr=lr)
D_optimizer = optim.Adam(D.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    for i, (real_images, _) in enumerate(train_loader):
        # 训练判别器
        D.zero_grad()
        real_labels = torch.ones(batch_size, 1)
        fake_labels = torch.zeros(batch_size, 1)
        real_outputs = D(real_images.view(batch_size, -1))
        real_loss = criterion(real_outputs, real_labels)
        z = torch.randn(batch_size, input_size)
        fake_images = G(z)
        fake_outputs = D(fake_images)
        fake_loss = criterion(fake_outputs, fake_labels)
        D_loss = real_loss + fake_loss
        D_loss.backward()
        D_optimizer.step()

        # 训练生成器
        G.zero_grad()
        z = torch.randn(batch_size, input_size)
        fake_images = G(z)
        fake_outputs = D(fake_images)
        G_loss = criterion(fake_outputs, real_labels)
        G_loss.backward()
        G_optimizer.step()

        # 打印损失值
        if i % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], D Loss: {:.4f}, G Loss: {:.4f}'
                  .format(epoch, num_epochs, i, len(train_loader), D_loss.item(), G_loss.item()))

在这个例子中，我们使用了PyTorch框架来实现GAN模型。首先定义了生成器和判别器的神经网络结构，然后定义了超参数和数据预处理方式。接着，我们加载MNIST数据集并迭代训练模型，其中每个迭代步骤都包括训练判别器和生成器。最后，我们在每个epoch中打印损失值以跟踪模型的训练过程。