“人工智能生成对抗网络”的代码举例

以下是一个简单的GAN代码示例，该代码使用PyTorch框架实现：

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 import Variable

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(100, 128),
            nn.ReLU(),
            nn.Linear(128, 784),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.fc(x)
        return x

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(784, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.fc(x)
        return x

# 训练过程
def train():
    batch_size = 100
    learning_rate = 0.0002
    num_epochs = 200

    # 加载MNIST数据集
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0.5,), std=(0.5,))])
    train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

    # 定义生成器和判别器
    G = Generator()
    D = Discriminator()

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

    # 开始训练
    for epoch in range(num_epochs):
        for i, (images, _) in enumerate(train_loader):
            # 训练判别器
            real_images = Variable(images.view(images.size(0), -1))
            real_labels = Variable(torch.ones(images.size(0), 1))
            fake_images = Variable(torch.randn(images.size(0), 100))
            fake_labels = Variable(torch.zeros(images.size(0), 1))

            D_optimizer.zero_grad()

            real_outputs = D(real_images)
            real_loss = criterion(real_outputs, real_labels)

            fake_images = G(fake_images)
            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
            fake_images = Variable(torch.randn(images.size(0), 100))
            fake_labels = Variable(torch.ones(images.size(0), 1))

            G_optimizer.zero_grad()

            generated_images = G(fake_images)
            outputs = D(generated_images)

            G_loss = criterion(outputs, fake_labels)
            G_loss.backward()
            G_optimizer.step()

        # 输出损失
        print('Epoch [{}/{}], D_loss: {:.4f}, G_loss: {:.4f}'.format(epoch+1, num_epochs, D_loss.data[0], G_loss.data[0]))

if __name__ == '__main__':
    train()

这个GAN程序演示了如何使用神经网络生成手写数字图像。在训练过程中，生成器的目标是生成看起来像真实图像的图像，而判别器的目标是将真实图像与生成的图像区分开来。这个GAN程序可以在MNIST数据集上进行训练，生成的图像可以与真实的手写数字图像相比较。