生成对抗网络——CGAN（代码+理解）

CGAN（Conditional Generative Adversarial Network）模型是一种深度学习模型，属于生成对抗网络（GAN）的一种变体。它的基本思想是通过训练生成器和判别器两个网络，使生成器能够生成与给定条件相匹配的合成数据，而判别器则负责区分真实数据和生成数据。相比于GAN，它引入了条件信息（y），使得生成器可以生成与给定条件相匹配的合成数据，从而提高了生成数据的可控性和针对性。

二、CGAN训练流程

1. 初始化

首先，初始化生成器和判别器的网络参数（本例未初始化）。

2. 数据准备

准备真实数据集和对应的条件信息。条件信息可以是类别标签、文本描述等。

# labels 即真事条件信息 for i, (imgs, labels) in enumerate(dataloader): # gen_labels 即假条件信息 gen_labels = torch.randint(0, opt.n_classes, (batch_size,))

3. 输出模型计算结果

（1）对于生成器：从随机噪声分布中采样噪声向量，并与条件信息一起输入到生成器中，生成合成数据。

gen_imgs = generator(z, gen_labels)

（2）对于判别器：将真实数据及其条件信息和生成数据及其条件信息分别输入到判别器中，得到真实数据和生成数据的判别结果。

validity_fake = discriminator(gen_imgs.detach(), gen_labels) validity_real = discriminator(imgs, labels)

4. 计算损失

（1）生成器损失：鼓励判别器对生成样本及相应条件c的判断为“真实”，即最大化log(D(G(z|c), c))。

g_loss = adversarial_loss(validity, valid)

（2）判别器损失：激励判别器正确区分真实样本(X, c)与生成样本(G(z|c), c)。

d_loss = (d_real_loss + d_fake_loss) / 2

5. 反向传播和优化

使用梯度下降算法或其他优化算法更新生成器和判别器的网络参数，以最小化各自的损失函数。

6. 迭代训练

重复步骤 3至 5，直到达到预设的训练轮数或满足其他停止条件。

三、CGAN实现

1. 模型结构

（1）生成器（Generator）

（2）判别器（Discriminator）

2. 代码

import torch import torch.nn as nn import torchvision.transforms as transforms from torchvision.utils import save_image from torch.utils.data import DataLoader from torchvision import datasets import matplotlib.pyplot as plt import argparse import numpy as np parser = argparse.ArgumentParser() parser.add_argument("--n_epochs", type=int, default=50, help="number of epochs of training") parser.add_argument("--batch_size", type=int, default=64, help="size of the batches") parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate") parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient") parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient") parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space") parser.add_argument("--n_classes", type=int, default=10, help="number of classes for dataset") parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension") parser.add_argument("--channels", type=int, default=1, help="number of image channels") parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling") opt = parser.parse_args() print(opt) dataloader = torch.utils.data.DataLoader( datasets.MNIST( "./others/", train=False, download=False, transform=transforms.Compose( [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] ), ), batch_size=opt.batch_size, shuffle=True, ) img_shape = (opt.channels, opt.img_size, opt.img_size) class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes) 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(opt.latent_dim + opt.n_classes, 128, normalize=False), *block(128, 256), *block(256, 512), *block(512, 1024), nn.Linear(1024, int(np.prod(img_shape))), # np.prod 计算所有元素的乘积 nn.Tanh() ) def forward(self, noise, labels): # 噪声样本与标签的拼接，-1 表示最后一个维度 gen_input = torch.cat((self.label_emb(labels.long()), noise), -1) img = self.model(gen_input) img = img.view(img.size(0), *img_shape) return img class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.label_embedding = nn.Embedding(opt.n_classes, opt.n_classes) self.model = nn.Sequential( nn.Linear(opt.n_classes + int(np.prod(img_shape)), 512), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 512), nn.Dropout(0.4), # 将输入单元的一部分（本例中为40%）设置为0，有助于 防止过拟合 nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 512), nn.Dropout(0.4), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 1), ) def forward(self, img, labels): d_in = torch.cat((img.view(img.size(0), -1), self.label_embedding(labels.long())), -1) validity = self.model(d_in) return validity # 实例化模型 generator = Generator() discriminator = Discriminator() # 优化器 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)) # 均方误差 adversarial_loss = torch.nn.MSELoss() def sample_image(n_row, batches_done): """Saves a grid of generated digits ranging from 0 to n_classes""" # Sample noise z = torch.randn(n_row 2, opt.latent_dim) # Get labels ranging from 0 to n_classes for n rows labels = torch.Tensor(np.array([num for _ in range(n_row) for num in range(n_row)])) gen_imgs = generator(z, labels) save_image(gen_imgs.data, "./others/images/CGAN/%d.png" % batches_done, nrow=n_row, normalize=True) def gen_img_plot(model, text_input, labels): prediction = np.squeeze(model(text_input, labels).detach().cpu().numpy()[:16]) plt.figure(figsize=(4, 4)) for i in range(16): plt.subplot(4, 4, i + 1) plt.imshow((prediction[i] + 1) / 2) plt.axis('off') plt.show() # ---------- # Training # ---------- D_loss_ = [] # 记录训练过程中判别器的损失 G_loss_ = [] # 记录训练过程中生成器的损失 for epoch in range(opt.n_epochs): # 初始化损失值 D_epoch_loss = 0 G_epoch_loss = 0 count = len(dataloader) # 返回批次数 for i, (imgs, labels) in enumerate(dataloader): batch_size = imgs.shape[0] valid = torch.ones(batch_size, 1) fake = torch.zeros(batch_size, 1) # 生成随机噪声 和 标签 z = torch.randn(batch_size, opt.latent_dim) gen_labels = torch.randint(0, opt.n_classes, (batch_size,)) # --------------------- # Train Discriminator # --------------------- optimizer_D.zero_grad() gen_imgs = generator(z, gen_labels) validity_fake = discriminator(gen_imgs.detach(), gen_labels) d_fake_loss = adversarial_loss(validity_fake, fake) validity_real = discriminator(imgs, labels) d_real_loss = adversarial_loss(validity_real, valid) d_loss = (d_real_loss + d_fake_loss) / 2 d_loss.backward() optimizer_D.step() # ----------------- # Train Generator # ----------------- optimizer_G.zero_grad() validity = discriminator(gen_imgs, gen_labels) g_loss = adversarial_loss(validity, valid) g_loss.backward() optimizer_G.step() print( "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()) ) # batches_done = epoch * len(dataloader) + i # if batches_done % opt.sample_interval == 0: # sample_image(n_row=10, batches_done=batches_done) with torch.no_grad(): D_epoch_loss += d_loss G_epoch_loss += g_loss # 求平均损失 with torch.no_grad(): D_epoch_loss /= count G_epoch_loss /= count D_loss_.append(D_epoch_loss.item()) G_loss_.append(G_epoch_loss.item()) text_input = torch.randn(opt.batch_size, opt.latent_dim) text_labels = torch.randint(0, opt.n_classes, (opt.batch_size,)) gen_img_plot(generator, text_input, text_labels) x = [epoch + 1 for epoch in range(opt.n_epochs)] plt.figure() plt.plot(x, G_loss_, 'r') plt.plot(x, D_loss_, 'b') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['G_loss', 'D_loss']) plt.show()

3. 训练结果

四、学习中产生的疑问，及文心一言回答

1. torch.cat((self.label_emb(labels.long()), noise), -1) 函数理解

2. Discriminator 模型疑问

后续更新 GAN 的其他模型结构。

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