计算机视觉-RBM学习MATLAB源码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %功能：演示RBM学习算法在计算机视觉中的应用 %RBM学习过程； %环境：Win7，Matlab2018a %Modi: C.S %时间：2022-04-05 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function RBMLearning() %% load training and testing data clear; data = load('../data/mnist.mat'); train_x = double(data.train_x) / 255; clear('data'); %% train RBM model using train_x % set RBM parameters num_epochs = 10; batch_size = 100; input_dim = size(train_x, 2); hidden_sz = 100; rbm.alpha = 1; rbm.momentum = 0.1; rbm.W = zeros(hidden_sz, input_dim); rbm.vW = zeros(hidden_sz, input_dim); rbm.b = zeros(input_dim, 1); rbm.vb = zeros(input_dim, 1); rbm.c = zeros(hidden_sz, 1); rbm.vc = zeros(hidden_sz, 1); % train RBM using CD disp('Start to train RBM:'); rng(0); m = size(train_x, 1); num_batches = floor(m / batch_size); for i = 1 : num_epochs kk = randperm(m); err = 0; for j = 1 : num_batches batch = train_x(kk((j-1)*batch_size+1:j*batch_size),:); v1 = batch; h1 = sigmrnd(repmat(rbm.c', batch_size, 1) + v1 * rbm.W'); v2 = sigmrnd(repmat(rbm.b', batch_size, 1) + h1 * rbm.W ); h2 = sigm(repmat(rbm.c', batch_size, 1) + v2 * rbm.W'); c1 = h1' * v1; c2 = h2' * v2; rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2) / batch_size; rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / batch_size; rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / batch_size; rbm.W = rbm.W + rbm.vW; rbm.b = rbm.b + rbm.vb; rbm.c = rbm.c + rbm.vc; err = err + sum(sum((v1 - v2).^ 2)) / batch_size; end disp(['RBM train: epoch ' num2str(i) '/' num2str(num_epochs) '. Average reconstruction error is: ' num2str(err / num_batches)]); end %% visualize the trained model V = rbm.W'; minmax = [min(V(:)), max(V(:))]; sz = sqrt(input_dim); s1 = sz; s2 = sz; num=ceil(sqrt(hidden_sz)); data=minmax(2)*ones(num*s2+num-1,num*s1+num-1); x=0; y=0; for i=1:hidden_sz im = reshape(V(:,i),s1,s2)'; data(x*s2+1+x : x*s2+s2+x, y*s1+1+y : y*s1+s1+y)=im; x=x+1; if(x>=num) x=0; y=y+1; end end imagesc(data, [minmax(1) minmax(2)]); axis equal axis tight colormap gray end %% other helper functions function X = sigm(P) X = 1./(1+exp(-P)); end function X = sigmrnd(P) X = double(1./(1+exp(-P)) > rand(size(P))); end