殷娜的宽恕
function Solar_SAEtic;n = 300;m=20;train_x = [];test_x = [];for i = 1:n%filename = strcat(['D:/Program Files/MATLAB/R2012a/work/DeepLearn/Solar_SAE/64_64_3train/' num2str(i，'%03d') 'bmp']);%filename = strcat(['E:/matlab/work/c0/TrainImage' num2str(i，'%03d') 'bmp']);filename = strcat(['E:/image restoration/3-(' num2str(i) ')-jpg']);b = imread(filename);%c = rgb2gray(b);c=b;[ImageRow ImageCol] = size(c);c = reshape(c，[1，ImageRow*ImageCol]);train_x = [train_x;c];endfor i = 1:m%filename = strcat(['D:/Program Files/MATLAB/R2012a/work/DeepLearn/Solar_SAE/64_64_3test/' num2str(i，'%03d') 'bmp']);%filename = strcat(['E:/matlab/work/c0/TestImage' num2str(i+100，'%03d') '-bmp']);filename = strcat(['E:/image restoration/3-(' num2str(i+100) ')jpg']);b = imread(filename);%c = rgb2gray(b);c=b;[ImageRow ImageCol] = size(c);c = reshape(c，[1，ImageRow*ImageCol]);test_x = [test_x;c];endtrain_x = double(train_x)/255;test_x = double(test_x)/255;%train_y = double(train_y);%test_y = double(test_y);% Setup and train a stacked denoising autoencoder (SDAE)rng(0);%sae = saesetup([4096 500 200 50]);%ae{1}activation_function = 'sigm';%ae{1}learningRate = 5;%ae{1}inputZeroMaskedFraction = 0;%ae{2}activation_function = 'sigm';%ae{2}learningRate = 5%%ae{2}inputZeroMaskedFraction = 0;%ae{3}activation_function = 'sigm';%ae{3}learningRate = 5;%ae{3}inputZeroMaskedFraction = 0;%ae{4}activation_function = 'sigm';%ae{4}learningRate = 5;%ae{4}inputZeroMaskedFraction = 0;%umepochs = 10;%batchsize = 50;%sae = saetrain(sae， train_x， opts);%visualize(ae{1}W{1}(:，2:end)');% Use the SDAE to initialize a FFNNnn = nnsetup([4096 1500 500 200 50 200 500 1500 4096]);activation_function = 'sigm';learningRate = 03;utput = 'linear'; % output unit 'sigm' (=logistic)， 'softmax' and 'linear'%add pretrained weights%W{1} = ae{1}W{1};%W{2} = ae{2}W{1};%W{3} = ae{3}W{1};%W{4} = ae{3}W{2};%W{5} = ae{2}W{2};%W{6} = ae{1}W{2};%W{7} = ae{2}W{2};%W{8} = ae{1}W{2};% Train the FFNNumepochs = 30;batchsize = 150;tx = test_x(14，:);nn1 = nnff(nn，tx，tx);ty1 = reshape(a{9}，64，64);nn = nntrain(nn， train_x， train_x， opts);toc;tic;nn2 = nnff(nn，tx，tx);toc;tic;ty2 = reshape(a{9}，64，64);tx = reshape(tx，64，64);tz = tx - ty2;tz = im2bw(tz，1);%imshow(tx);%figure，imshow(ty2);%figure，imshow(tz);ty = cat(2，tx，ty2，tz);montage(ty);filename3 = strcat(['E:/image restoration/jpg']);e=imread(filename3);f= rgb2gray(e);f=imresize(f，[64，64]);%imshow(ty2);f=double (f)/255;[PSNR， MSE] = psnr(ty2，f)imwrite(ty2，'E:/image restoration/jpg'，'jpg');toc;%visualize(ty);%[er， bad] = nntest(nn， tx， tx);%assert(er < 1， 'Too big error'); 
