人工智能基础之-MATLAB生成对抗网络系列(持续更新)

因为之前用生成对抗网络及众多变体生成诸如心电信号，肌电信号，脑电信号，微震信号，机械振动信号，雷达信号等，但生成的信号在频谱或者时频谱上表现很差，所以暂时先不涉及到这些复杂信号，仅仅以手写数字图像为例进行说明，因为Python相关的资源太多了，我就不凑热闹了，使用的编程环境为MALAB R2021B。

首先看一下对抗自编码器AAE(Adversarial AutoEncoder)，关于AAE的大致理解，可以查看如下文章

AAE(Adversarial Autoencoders)浅解 - 嘎嘎小鱼仔的文章 - 知乎 https://zhuanlan.zhihu.com/p/382958740

AAE根据变分自编码器VAE发展而来，其发展之处就在于加入了对抗的思想。

上半部分就是一个简单典型的自编码器AE结构，包含输入层input layer，编码层encoder layer, 隐层hidden layer, 解码层decoder layer , 输出层output layer。encoder把真实分布x映射为隐层z, decoder 再将z解码还原成x。AAE的特点就在于在隐层hidden layer中引入了对抗的思想来优化隐层的z，判别器discriminator 需要在隐层判断采样后的真实数据和生成器encoder所产生的假数据。因此discriminator的目的就是使得q(z | x) 不断向p(z)靠近。

​Adversarial Autoencoders论文链接：https://arxiv.org/abs/1511.0564

下面直接上代码

首先，导入相关的mnist手写数字图

load('mnistAll.mat')

然后对训练、测试图像进行预处理

trainX = preprocess(mnist.train_images); trainY = mnist.train_labels;%训练标签testX = preprocess(mnist.test_images); testY = mnist.test_labels;%测试标签

preprocess为归一化函数，如下

function x = preprocess(x)x = double(x)/255;x = (x-.5)/.5;x = reshape(x,28*28,[]);end

然后进行参数设置，包括潜变量空间维度，batch_size大小，学习率，最大迭代次数等等

settings.latent_dim = 10;settings.batch_size = 32; settings.image_size = [28,28,1]; settings.lrD = 0.0002; settings.lrG = 0.0002; settings.beta1 = 0.5;settings.beta2 = 0.999; settings.maxepochs = 50;

下面进行编码器初始化，代码还是很容易看懂的

paramsEn.FCW1 = dlarray(initializeGaussian([512,...     prod(settings.image_size)],.02));paramsEn.FCb1 = dlarray(zeros(512,1,'single'));paramsEn.FCW2 = dlarray(initializeGaussian([512,512]));paramsEn.FCb2 = dlarray(zeros(512,1,'single'));paramsEn.FCW3 = dlarray(initializeGaussian([2*settings.latent_dim,512]));paramsEn.FCb3 = dlarray(zeros(2*settings.latent_dim,1,'single'));

解码器初始化

paramsDe.FCW1 = dlarray(initializeGaussian([512,settings.latent_dim],.02));paramsDe.FCb1 = dlarray(zeros(512,1,'single'));paramsDe.FCW2 = dlarray(initializeGaussian([512,512]));paramsDe.FCb2 = dlarray(zeros(512,1,'single'));paramsDe.FCW3 = dlarray(initializeGaussian([prod(settings.image_size),512]));paramsDe.FCb3 = dlarray(zeros(prod(settings.image_size),1,'single'));

判别器初始化

paramsDis.FCW1 = dlarray(initializeGaussian([512,settings.latent_dim],.02));paramsDis.FCb1 = dlarray(zeros(512,1,'single'));paramsDis.FCW2 = dlarray(initializeGaussian([256,512]));paramsDis.FCb2 = dlarray(zeros(256,1,'single'));paramsDis.FCW3 = dlarray(initializeGaussian([1,256]));paramsDis.FCb3 = dlarray(zeros(1,1,'single'));%平均梯度和平均梯度平方数组avgG.Dis = []; avgGS.Dis = []; avgG.En = []; avgGS.En = [];avgG.De = []; avgGS.De = [];

开始训练

dlx = gpdl(trainX(:,1),'CB');dly = Encoder(dlx,paramsEn);numIterations = floor(size(trainX,2)/settings.batch_size);out = false; epoch = 0; global_iter = 0;while ~out    tic;     shuffleid = randperm(size(trainX,2));    trainXshuffle = trainX(:,shuffleid);    fprintf('Epoch %d\n',epoch)     for i=1:numIterations        global_iter = global_iter+1;        idx = (i-1)*settings.batch_size+1:i*settings.batch_size;        XBatch=gpdl(single(trainXshuffle(:,idx)),'CB');        [GradEn,GradDe,GradDis] = ...                dlfeval(@modelGradients,XBatch,...                paramsEn,paramsDe,paramsDis,settings);        % 更新判别器网络参数        [paramsDis,avgG.Dis,avgGS.Dis] = ...            adamupdate(paramsDis, GradDis, ...            avgG.Dis, avgGS.Dis, global_iter, ...            settings.lrD, settings.beta1, settings.beta2);        % 更新编码器网络参数        [paramsEn,avgG.En,avgGS.En] = ...            adamupdate(paramsEn, GradEn, ...            avgG.En, avgGS.En, global_iter, ...            settings.lrG, settings.beta1, settings.beta2);                % 更新解码器网络参数        [paramsDe,avgG.De,avgGS.De] = ...            adamupdate(paramsDe, GradDe, ...            avgG.De, avgGS.De, global_iter, ...            settings.lrG, settings.beta1, settings.beta2);                if i==1 || rem(i,20)==0            progressplot(paramsDe,settings);            if i==1                 h = gcf;                % 捕获图像                frame = getframe(h);                 im = frame2im(frame);                 [imind,cm] = rgb2ind(im,256);                 % 写入 GIF 文件                if epoch == 0                  imwrite(imind,cm,'AAEmnist.gif','gif', 'Loopcount',inf);                 else                   imwrite(imind,cm,'AAEmnist.gif','gif','WriteMode','append');                 end             end        end            end    elapsedTime = toc;    disp("Epoch "+epoch+". Time taken for epoch = "+elapsedTime + "s")    epoch = epoch+1;    if epoch == settings.maxepochs        out = true;    end    end

下面是完整的辅助函数

模型的梯度计算函数

function [GradEn,GradDe,GradDis]=modelGradients(x,paramsEn,paramsDe,paramsDis,settings)dly = Encoder(x,paramsEn);latent_fake = dly(1:settings.latent_dim,:)+...    dly(settings.latent_dim+1:2*settings.latent_dim)*...    randn(settings.latent_dim,settings.batch_size);latent_real = gpdl(randn(settings.latent_dim,settings.batch_size),'CB');%训练判别器d_output_fake = Discriminator(latent_fake,paramsDis);d_output_real = Discriminator(latent_real,paramsDis);d_loss = -.5*mean(log(d_output_real+eps)+log(1-d_output_fake+eps));%训练编码器和解码器x_ = Decoder(latent_fake,paramsDe);g_loss = .999*mean(mean(.5*(x_-x).^2,1))-.001*mean(log(d_output_fake+eps));%对于每个网络，计算关于损失函数的梯度[GradEn,GradDe] = dlgradient(g_loss,paramsEn,paramsDe,'RetainData',true);GradDis = dlgradient(d_loss,paramsDis);end

提取数据函数

function x = gatext(x)x = gather(extractdata(x));end

GPU深度学习数组wrapper函数

function dlx = gpdl(x,labels)dlx = gpuArray(dlarray(x,labels));end

权重初始化函数

function parameter = initializeGaussian(parameterSize,sigma)if nargin < 2    sigma = 0.05;endparameter = randn(parameterSize, 'single') .* sigma;end

dropout函数

function dly = dropout(dlx,p)if nargin < 2    p = .3;end[n,d] = rat(p);mask = randi([1,d],size(dlx));mask(mask<=n)=0;mask(mask>n)=1;dly = dlx.*mask;end

编码器函数

function dly = Encoder(dlx,params)dly = fullyconnect(dlx,params.FCW1,params.FCb1);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW2,params.FCb2);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW3,params.FCb3);dly = leakyrelu(dly,.2);end

解码器函数

function dly = Decoder(dlx,params)dly = fullyconnect(dlx,params.FCW1,params.FCb1);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW2,params.FCb2);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW3,params.FCb3);dly = leakyrelu(dly,.2);dly = tanh(dly);end

判别器函数

function dly = Discriminator(dlx,params)dly = fullyconnect(dlx,params.FCW1,params.FCb1);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW2,params.FCb2);dly = leakyrelu(dly,.2);dly = fullyconnect(dly,params.FCW3,params.FCb3);dly = sigmoid(dly);end

动态进度图

function progressplot(paramsDe,settings)r = 5; c = 5;noise = gpdl(randn([settings.latent_dim,r*c]),'CB');gen_imgs = Decoder(noise,paramsDe);gen_imgs = reshape(gen_imgs,28,28,[]);fig = gcf;if ~isempty(fig.Children)    delete(fig.Children)endI = imtile(gatext(gen_imgs));I = rescale(I);imagesc(I)title("Generated Images")colormap graydrawnow;end

最后，看一下生成的GIF动态图

​以后会讲

（1）辅助分类器生成对抗网络Auxiliary Classifier Generative Adversarial Network

​（2）条件生成对抗网络Conditional Generative Adversarial Network

​（3）深层卷积生成对抗网络Deep Convolutional Generative Adversarial Network

​（4）最基础的生成对抗网络Basic Generative Adversarial Network

​（5）Info Generative Adversarial Network

​（6）最小二乘生成对抗网络Least Squares Generative Adversarial Network

​（7）著名的Pixels-to-Pixels

​（8）半监督生成对抗网络Semi-Supervised Generative Adversarial Network

​（9）著名的Wasserstein Generative Adversarial Network

​

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相应的参考文献如下

• Y. LeCun and C. Cortes, “MNIST handwritten digitdatabase,” 2010. [MNIST]
• J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, andL. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” inCVPR09, 2009. [Apple2Orange (ImageNet)]
• R. Tyleček and R. Šára, “Spatial pattern templates forrecognition of objects with regular structure,” inProc.GCPR, (Saarbrucken, Germany), 2013. [Facade]
• Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learn-ing face attributes in the wild,” inProceedings of In-ternational Conference on Computer Vision (ICCV),December 2015. [CelebA]
• Goodfellow, Ian J. et al. “Generative Adversarial Networks.” ArXiv abs/1406.2661 (2014): n. pag. (GAN)
• Radford, Alec et al. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” CoRR abs/1511.06434 (2015): n. pag. (DCGAN)
• Denton, Emily L. et al. “Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks.” ArXiv abs/1611.06430 (2017): n. pag. (CGAN)
• Odena, Augustus et al. “Conditional Image Synthesis with Auxiliary Classifier GANs.” ICML (2016). (ACGAN)
• Chen, Xi et al. “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.” NIPS (2016). (InfoGAN)
• Makhzani, Alireza et al. “Adversarial Autoencoders.” ArXiv abs/1511.05644 (2015): n. pag. (AAE)
• Isola, Phillip et al. “Image-to-Image Translation with Conditional Adversarial Networks.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016): 5967-5976. (Pix2Pix)
• J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpairedimage-to-image translation using cycle-consistent ad-versarial networks,” 2017. (CycleGAN)
• Arjovsky, Martín et al. “Wasserstein GAN.” ArXiv abs/1701.07875 (2017): n. pag. (WGAN)
• Odena, Augustus. “Semi-Supervised Learning with Generative Adversarial Networks.” ArXiv abs/1606.01583 (2016): n. pag. (SGAN)