MATLAB实现的生成对抗网络LSGAN-内含数据集和结果文件.zip

clear all; close all; clc; %% Least Squares Generative Adversarial Network %% Load Data load('mnistAll.mat') trainX = preprocess(mnist.train_images); trainY = mnist.train_labels; testX = preprocess(mnist.test_images); testY = mnist.test_labels; %% Settings settings.latent_dim = 100; 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; %% Initialization %% Generator paramsGen.FCW1 = dlarray(... initializeGaussian([256,settings.latent_dim],.02)); paramsGen.FCb1 = dlarray(zeros(256,1,'single')); paramsGen.BNo1 = dlarray(zeros(256,1,'single')); paramsGen.BNs1 = dlarray(ones(256,1,'single')); paramsGen.FCW2 = dlarray(initializeGaussian([512,256])); paramsGen.FCb2 = dlarray(zeros(512,1,'single')); paramsGen.BNo2 = dlarray(zeros(512,1,'single')); paramsGen.BNs2 = dlarray(ones(512,1,'single')); paramsGen.FCW3 = dlarray(initializeGaussian([1024,512])); paramsGen.FCb3 = dlarray(zeros(1024,1,'single')); paramsGen.BNo3 = dlarray(zeros(1024,1,'single')); paramsGen.BNs3 = dlarray(ones(1024,1,'single')); paramsGen.FCW4 = dlarray(initializeGaussian(... [prod(settings.image_size),1024])); paramsGen.FCb4 = dlarray(zeros(prod(settings.image_size)... ,1,'single')); stGen.BN1 = []; stGen.BN2 = []; stGen.BN3 = []; %% Discriminator paramsDis.FCW1 = dlarray(initializeGaussian([1024,... prod(settings.image_size)],.02)); paramsDis.FCb1 = dlarray(zeros(1024,1,'single')); paramsDis.BNo1 = dlarray(zeros(1024,1,'single')); paramsDis.BNs1 = dlarray(ones(1024,1,'single')); paramsDis.FCW2 = dlarray(initializeGaussian([512,1024])); paramsDis.FCb2 = dlarray(zeros(512,1,'single')); paramsDis.BNo2 = dlarray(zeros(512,1,'single')); paramsDis.BNs2 = dlarray(ones(512,1,'single')); paramsDis.FCW3 = dlarray(initializeGaussian([256,512])); paramsDis.FCb3 = dlarray(zeros(256,1,'single')); paramsDis.FCW4 = dlarray(initializeGaussian([1,256])); paramsDis.FCb4 = dlarray(zeros(1,1,'single')); stDis.BN1 = []; stDis.BN2 = []; % average Gradient and average Gradient squared holders avgG.Dis = []; avgGS.Dis = []; avgG.Gen = []; avgGS.Gen = []; %% Train numIterations = floor(size(trainX,2)/settings.batch_size); out = false; epoch = 0; global_iter = 0; while ~out tic; trainXshuffle = trainX(:,randperm(size(trainX,2))); fprintf('Epoch %d\n',epoch) for i=1:numIterations global_iter = global_iter+1; noise = gpdl(randn([settings.latent_dim,... settings.batch_size]),'CB'); idx = (i-1)*settings.batch_size+1:i*settings.batch_size; XBatch=gpdl(single(trainXshuffle(:,idx)),'CB'); [GradGen,GradDis,stGen,stDis] = ... dlfeval(@modelGradients,XBatch,noise,... paramsGen,paramsDis,stGen,stDis); % Update Discriminator network parameters [paramsDis,avgG.Dis,avgGS.Dis] = ... adamupdate(paramsDis, GradDis, ... avgG.Dis, avgGS.Dis, global_iter, ... settings.lrD, settings.beta1, settings.beta2); % Update Generator network parameters [paramsGen,avgG.Gen,avgGS.Gen] = ... adamupdate(paramsGen, GradGen, ... avgG.Gen, avgGS.Gen, global_iter, ... settings.lrG, settings.beta1, settings.beta2); if i==1 || rem(i,20)==0 progressplot(paramsGen,stGen,settings); % if i==1 || (epoch>=0 && i==1) % h = gcf; % % Capture the plot as an image % frame = getframe(h); % im = frame2im(frame); % [imind,cm] = rgb2ind(im,256); % % Write to the GIF File % if epoch == 0 % imwrite(imind,cm,'GANmnist.gif','gif', 'Loopcount',inf); % else % imwrite(imind,cm,'GANmnist.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 %% Helper Functions %% preprocess function x = preprocess(x) x = double(x)/255; x = (x-.5)/.5; x = reshape(x,28*28,[]); end %% extract data function x = gatext(x) x = gather(extractdata(x)); end %% gpu dl array wrapper function dlx = gpdl(x,labels) dlx = gpuArray(dlarray(x,labels)); end %% Weight initialization function parameter = initializeGaussian(parameterSize,sigma) if nargin < 2 sigma = 0.05; end parameter = randn(parameterSize, 'single') .* sigma; end %% Generator function [dly,st] = Generator(dlx,params,st) % fully connected %1 dly = fullyconnect(dlx,params.FCW1,params.FCb1); dly = leakyrelu(dly,0.2); if isempty(st.BN1) [dly,st.BN1.mu,st.BN1.sig] = batchnorm(dly,params.BNo1,params.BNs1); else [dly,st.BN1.mu,st.BN1.sig] = batchnorm(dly,params.BNo1,... params.BNs1,st.BN1.mu,st.BN1.sig); end %2 dly = fullyconnect(dly,params.FCW2,params.FCb2); dly = leakyrelu(dly,0.2); if isempty(st.BN2) [dly,st.BN2.mu,st.BN2.sig] = batchnorm(dly,params.BNo2,params.BNs2); else [dly,st.BN2.mu,st.BN2.sig] = batchnorm(dly,params.BNo2,... params.BNs2,st.BN2.mu,st.BN2.sig); end %3 dly = fullyconnect(dly,params.FCW3,params.FCb3); dly = leakyrelu(dly,0.2); if isempty(st.BN3) [dly,st.BN3.mu,st.BN3.sig] = batchnorm(dly,params.BNo3,params.BNs3); else [dly,st.BN3.mu,st.BN3.sig] = batchnorm(dly,params.BNo3,... params.BNs3,st.BN3.mu,st.BN3.sig); end %4 dly = fullyconnect(dly,params.FCW4,params.FCb4); % tanh dly = tanh(dly); end %% Discriminator function [dly,st] = Discriminator(dlx,params,st) % fully connected %1 dly = fullyconnect(dlx,params.FCW1,params.FCb1); dly = leakyrelu(dly,0.2); dly = dropout(dly); if isempty(st.BN1) [dly,st.BN1.mu,st.BN1.sig] = batchnorm(dly,params.BNo1,params.BNs1); else [dly,st.BN1.mu,st.BN1.sig] = batchnorm(dly,params.BNo1,... params.BNs1,st.BN1.mu,st.BN1.sig); end %2 dly = fullyconnect(dly,params.FCW2,params.FCb2); dly = leakyrelu(dly,0.2); dly = dropout(dly); if isempty(st.BN2) [dly,st.BN2.mu,st.BN2.sig] = batchnorm(dly,params.BNo2,params.BNs2); else [dly,st.BN2.mu,st.BN2.sig] = batchnorm(dly,params.BNo2,... params.BNs2,st.BN2.mu,st.BN2.sig); end %3 dly = fullyconnect(dly,params.FCW3,params.FCb3); dly = leakyrelu(dly,0.2); dly = dropout(dly); %4 dly = fullyconnect(dly,params.FCW4,params.FCb4); % sigmoid % dly = sigmoid(dly); end %% modelGradients function [GradGen,GradDis,stGen,stDis]=modelGradients(x,z,paramsGen,... paramsDis,stGen,stDis) [fake_images,stGen] = Generator(z,paramsGen,stGen); d_output_real = Discriminator(x,paramsDis,stDis); [d_output_fake,stDis] = Discriminator(fake_images,paramsDis,stDis); % Loss due to true or not d_loss = .5*mean((d_output_real-1).^2+(d_output_fake).^2); g_loss = .5*mean((d_output_fake-1).^2); % For each network, calculate the gradients with respect to the loss. GradGen = dlgradient(g_loss,paramsGen,'RetainData',true); GradDis = dlgradient(d_loss,paramsDis); end %% progressplot function progressplot(paramsGen,stGen,settings) r = 5; c = 5; noise = gpdl(randn([settings.latent_dim,r*c]),'CB'); gen_imgs = Generator(noise,paramsGen,stGen); 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 %% dropout function dly = dropout(dlx,p) if nargin < 2 p = .3; end n = p*10; mask = randi([1,10],size(dlx)); mask(mask<=n)=0; mask(mask>n)=1; dly = dlx.*mask; end