一、生成对抗网络百科

2014年，还在蒙特利尔读博士的Ian Goodfellow发表了论文《Generative Adversarial Networks》（网址： https://arxiv.org/abs/1406.2661），将生成对抗网络引入深度学习领域。2016年，GAN热潮席卷AI领域顶级会议，从ICLR到NIPS，大量高质量论文被发表和探讨。Yann LeCun曾评价GAN是“20年来机器学习领域最酷的想法”。

机器学习的模型可大体分为两类，生成模型（ Generative Model）和判别模型（Discriminative Model）。判别模型需要输入变量，通过某种模型来预测。生成模型是给定某种隐含信息，来随机产生观测数据。

GAN百科：

GAN（生成对抗网络）的系统全面介绍（醍醐灌顶）_打灰人的博客-CSDN博客

二、GAN代码

训练代码：

epoch=1000时的效果就不错啦

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt


class Generator(nn.Module):  # 生成器
    def __init__(self, latent_dim):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(latent_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, 784),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), 1, 28, 28)
        return img


class Discriminator(nn.Module):  # 判别器
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(784, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, img):
        img = img.view(img.size(0), -1)
        validity = self.model(img)
        return validity


def gen_img_plot(model, test_input):
    pred = np.squeeze(model(test_input).detach().cpu().numpy())
    fig = plt.figure(figsize=(4, 4))
    for i in range(16):
        plt.subplot(4, 4, i + 1)
        plt.imshow((pred[i] + 1) / 2)
        plt.axis('off')
    plt.show(block=False)
    plt.pause(3)  # 停留0.5s
    plt.close()


# 调用GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 超参数设置
lr = 0.0001
batch_size = 128
latent_dim = 100
epochs = 1000

# 数据集载入和数据变换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

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

# 测试数据 torch.randn()函数的作用是生成一组均值为0，方差为1(即标准正态分布)的随机数
# test_data = torch.randn(batch_size, latent_dim).to(device)
test_data = torch.FloatTensor(batch_size, latent_dim).to(device)

# 实例化生成器和判别器，并定义损失函数和优化器
generator = Generator(latent_dim).to(device)
discriminator = Discriminator().to(device)
adversarial_loss = nn.BCELoss()
optimizer_G = optim.Adam(generator.parameters(), lr=lr)
optimizer_D = optim.Adam(discriminator.parameters(), lr=lr)

# 开始训练模型
for epoch in range(epochs):
    for i, (imgs, _) in enumerate(train_loader):
        batch_size = imgs.shape[0]
        real_imgs = imgs.to(device)

        # 训练判别器
        z = torch.FloatTensor(batch_size, latent_dim).to(device)
        z.data.normal_(0, 1)
        fake_imgs = generator(z)  # 生成器生成假的图片

        real_labels = torch.full((batch_size, 1), 1.0).to(device)
        fake_labels = torch.full((batch_size, 1), 0.0).to(device)

        real_loss = adversarial_loss(discriminator(real_imgs), real_labels)
        fake_loss = adversarial_loss(discriminator(fake_imgs.detach()), fake_labels)
        d_loss = (real_loss + fake_loss) / 2

        optimizer_D.zero_grad()
        d_loss.backward()
        optimizer_D.step()

        # 训练生成器
        z.data.normal_(0, 1)
        fake_imgs = generator(z)

        g_loss = adversarial_loss(discriminator(fake_imgs), real_labels)
        optimizer_G.zero_grad()
        g_loss.backward()
        optimizer_G.step()

        torch.save(generator.state_dict(), "Generator_mnist.pth")

    print(f"Epoch [{epoch}/{epochs}] Loss_D: {d_loss.item():.4f} Loss_G: {g_loss.item():.4f}")

# gen_img_plot(Generator, test_data)
gen_img_plot(generator, test_data)

测试代码：

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
import random

device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu')


class Generator(nn.Module):  # 生成器
    def __init__(self, latent_dim):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(latent_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, 784),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), 1, 28, 28)
        return img


# test_data = torch.FloatTensor(128, 100).to(device)
test_data = torch.randn(128, 100).to(device)  # 随机噪声

model = Generator(100).to(device)
model.load_state_dict(torch.load('Generator_mnist.pth'))
model.eval()

pred = np.squeeze(model(test_data).detach().cpu().numpy())

for i in range(64):
    plt.subplot(8, 8, i + 1)
    plt.imshow((pred[i] + 1) / 2)
    plt.axis('off')
plt.savefig(fname='image.png', figsize=[5, 5])
plt.show()

三、结果

在超参数设置 epoch=1000，batch_size=128，lr=0.0001，latent_dim = 100 时，gan生成的权重测的结果如图所示

四，GAN的损失函数曲线

一开始训练时，我的gan的损失函数的曲线是类似这样的，就是知乎这文章里一样，生成器损失函数的曲线一直发散。首先，这个loss的曲线一看就是网络崩了，一般正常的情况，d_loss的值会一直下降然后收敛，而g_loss的曲线会先增大后减少，最后同样也会收敛。其次，网络拿到手以后先不要训练太多次，容易出现过拟合的情况。