生成对抗网络(GAN)在机器学习中被广泛应用于文字到图片的生成。这种网络结构包含一个生成器和一个判别器,生成器将随机噪声转换为图像,而判别器则致力于区分真实图像和生成器生成的图像。通过不断的对抗训练,生成器能够逐渐生成逼真的图像,使其难以被判别器区分。这种技术在图像生成、图像增强等领域具有广泛的应用前景。
一个简单的示例是使用GAN生成手写数字图像。以下是PyTorch中的示例代码:
import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torchvision.utils import save_image 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.Linear(100, 256) self.main = nn.Sequential( nn.ConvTranspose2d(256, 128, 5, stride=2, padding=2), nn.BatchNorm2d(128), nn.ReLU(True), nn.ConvTranspose2d(128, 64, 5, stride=2, padding=2), nn.BatchNorm2d(64), nn.ReLU(True), nn.ConvTranspose2d(64, 1, 4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): x = self.fc(x) x = x.view(-1, 256, 1, 1) x = self.main(x) return x # 定义判别器网络 class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.main = nn.Sequential( nn.Conv2d(1, 64, 4, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, 4, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, 4, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 1, 4, stride=1, padding=0), nn.Sigmoid() ) def forward(self, x): x = self.main(x) return x.view(-1, 1) # 定义训练函数 def train(generator, discriminator, dataloader, optimizer_G, optimizer_D, device): criterion = nn.BCELoss() real_label = 1 fake_label = 0 for epoch in range(200): for i, (data, _) in enumerate(dataloader): # 训练判别器 discriminator.zero_grad() real_data = data.to(device) batch_size = real_data.size(0) label = torch.full((batch_size,), real_label, device=device) output = discriminator(real_data).view(-1) errD_real = criterion(output, label) errD_real.backward() D_x = output.mean().item() noise = torch.randn(batch_size, 100, device=device) fake_data = generator(noise) label.fill_(fake_label) output = discriminator(fake_data.detach()).view(-1) errD_fake = criterion(output, label) errD_fake.backward() D_G_z1 = output.mean().item() errD = errD_real + errD_fake optimizer_D.step() # 训练生成器 generator.zero_grad() label.fill_(real_label) output = discriminator(fake_data).view(-1) errG = criterion(output, label) errG.backward() D_G_z2 = output.mean().item() optimizer_G.step() if i % 100 == 0: print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f' % (epoch+1, 200, i, len(dataloader), errD.item(), errG.item(), D_x, D_G_z1, D_G_z2)) # 保存生成的图像 fake = generator(fixed_noise) save_image(fake.detach(), 'generated_images_%03d.png' % epoch, normalize=True) # 加载MNIST数据集 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) dataset = datasets.MNIST(root='./数据集', train=True, transform=transform, download=True) dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True) # 定义设备 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 初始化生成器和判别器 generator = Generator().to(device) discriminator = Discriminator().to(device) # 定义优化器 optimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999)) optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999)) # 定义固定噪声用于保存生成的图像 fixed_noise = torch.randn(64, 100, device=device) # 开始训练 train(generator, discriminator, dataloader, optimizer_G, optimizer_D, device)
运行该代码将会训练一个GAN模型来生成手写数字图像,并保存生成的图像。
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