神经网络理论与matlab r2007实现（人工智能基础之-MATLAB生成对抗网络系列）

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; end parameter = 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) end I = imtile(gatext(gen_imgs)); I = rescale(I); imagesc(I) title("Generated Images") colormap gray drawnow; 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)

所有的代码链接如下

https://mianbaoduo.com/o/bread/Y5eVkpZq

第三方面包多直接下载

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