I found a code in Matlab Answers for regression:
x = 0:0.1:10; % input dimension 1x101
y = sin(x); % output dimension 1x101
neurons = 10;net = feedforwardnet(neurons);net = train(net,x,y);outputs = sim(net,x);w1 = net.IW{1,1};w2 = net.LW{2,1};b1 = net.b{1};b2 = net.b{2};xx = x;yy = zeros(size(y));for ii = 1:length(net.inputs{1}.processFcns) xx = feval(net.inputs{1}.processFcns{ii},... 'apply',xx,net.inputs{1}.processSettings{ii});endfor jj = 1:size(xx,2) yy(jj) = w2*tansig(w1*xx(jj) + b1) + b2;endfor kk = 1:length(net.outputs{2}.processFcns) yy = feval(net.outputs{2}.processFcns{kk},... 'reverse',yy,net.outputs{2}.processSettings{kk});end
How can I change the following equation for a classification problem, for example [x,y] = simpleclass_dataset?
yy(jj) = w2*tansig(w1*xx(jj) + b1) + b2;
Because the input and output dimensions are different from the regression example.
For simpleclass_dataset the input dimension is 2×1,000 and the output dimension is 4×1,000.
Thank you!
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