net_validation.m

function [scores, labels] = net_validation(folder, net, meanImage, class)
	% imdb is a matlab struct with several fields, such as:
	%	- images: contains data, labels, ids dataset mean, etc.
	%	- meta: contains meta info useful for statistics and visualization
	%	- any other you want to add


    %define classes for joint+view
    folder = 'C:/Users/Imon/Documents/MATLAB/BaoDoJoints/Deep Bone/FINAL SET';
	Class = dir([folder, '/Unknow_test_paper/*.jpeg']);
    scores = zeros(numel(Class), 13);
	% loading positive samples
	for i=1:numel(Class)
		im = imread([folder '/Unknow_test_paper/', Class(i).name]);
        filename= [folder '/Unknow_paper_results/', num2str(i),'.png'];
        %resizedIm = imresize(im, [256, 256]);
        
        resizedIm = imresize((255*mat2gray(im(:,:,1))), [227, 227]);
        rgbImage = cat(3, resizedIm, resizedIm, resizedIm);
		image =  single(rgbImage - meanImage);
        
        net.eval({'input',image}) ;
        scores(i,:) = squeeze(net.vars(23).value);
        
        
        f = figure(i);
        set(gcf,'units','normalized','position',[0 0 1 1])
        imshow(mat2gray(rgbImage));
   
        
        C = strsplit(Class(i).name,'_');
        ori_label = strcat(C{1,1}, C{1,2});
        %ori_label = Class(i).name;
        caption1 = strcat('Original label:', ori_label);
        %caption2 = strcat('Prediction:', class(find(scores(i,:) == max(scores(i,:)))), ' Prob:', num2str(max(scores(i,:))));
        maxscore =  class(find(scores(i,:) == max(scores(i,:))));
        caption2 = strcat(' Prediction:', ori_label);
        labels(i) = find(scores(i,:) == max(scores(i,:)));
        S = {caption1; caption2};
        title(S);
        saveas(f, filename, 'png');
        clear im image resizedIm rgbImage f C;
   end
    
        
        
end