### 使用Python实现GAN生成数据 #### 创建基本框架 为了创建一个可以生成逼真图像的GAN，首先需要定义两个主要组件：生成器\(Generator\)和判别器\(Discriminator\)[^1]。 ```python import torch.nn as nn class Generator(nn.Module): def __init__(self, latent_dim, img_shape): super(Generator, self).__init__() def block(in_feat, out_feat, normalize=True): layers = [nn.Linear(in_feat, out_feat)] if normalize: layers.append(nn.BatchNorm1d(out_feat, 0.8)) layers.append(nn.LeakyReLU(0.2, inplace=True)) return layers self.model = nn.Sequential( *block(latent_dim, 128, normalize=False), *block(128, 256), *block(256, 512), *block(512, 1024), nn.Linear(1024, int(np.prod(img_shape))), nn.Tanh() ) def forward(self, z): img = self.model(z) img = img.view(img.size(0), *(img_shape)) return img ``` 此部分代码展示了如何建立一个简单的多层感知机作为生成器模型。它接受潜在空间向量`z`作为输入，并输出具有指定形状的新图像[^3]。 对于判别器而言： ```python class Discriminator(nn.Module): def __init__(self, img_shape): super(Discriminator, self).__init__() self.model = nn.Sequential( nn.Linear(int(np.prod(img_shape)), 512), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 256), nn.LeakyReLU(0.2, inplace=True), nn.Linear(256, 1), nn.Sigmoid(), ) def forward(self, img): img_flat = img.view(img.size(0), -1) validity = self.model(img_flat) return validity ``` 这段代码实现了另一个神经网络结构来评估传入样本的真实性得分；即给出一张图片是否属于真实数据集的概率估计值。 #### 定义损失函数与优化算法 接下来要设置好相应的损失计算方式以及参数更新策略以便于后续训练过程顺利开展: ```python adversarial_loss = torch.nn.BCELoss() generator = Generator(opt.latent_dim, (opt.channels, opt.img_size, opt.img_size)).to(device) discriminator = Discriminator((opt.channels, opt.img_size, opt.img_size)).to(device) optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) ``` 这里选择了二元交叉熵作为衡量标准之一，并分别指定了Adam优化方法给到生成器跟判别器使用[^4]。 #### 开始迭代训练循环 最后一步就是按照预定计划执行整个学习流程，在每一轮次里交替调整两方权重直至达到预期效果为止: ```python for epoch in range(num_epochs): for i, (imgs, _) in enumerate(dataloader): # Adversarial ground truths valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False) fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False) # Configure input real_imgs = Variable(imgs.type(Tensor)) # --- # Train Generator # ----------------- optimizer_G.zero_grad() # Sample noise as generator input z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim)))) # Generate a batch of images gen_imgs = generator(z) # Loss measures generator's ability to fool the discriminator g_loss = adversarial_loss(discriminator(gen_imgs), valid) g_loss.backward() optimizer_G.step() # --------------------- # Train Discriminator # --------------------- optimizer_D.zero_grad() # Measure discriminator's ability to classify real from generated samples real_loss = adversarial_loss(discriminator(real_imgs), valid) fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake) d_loss = (real_loss + fake_loss) / 2 d_loss.backward() optimizer_D.step() print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, num_epochs, i, len(dataloader), d_loss.item(), g_loss.item())) ``` 上述脚本描述了一个完整的训练周期，其中包含了针对生成器和判别器各自的前向传播、误差累积及梯度下降操作。