add_noise_input.m~

function[error_percentage] = add_noise_input()
p = [0,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,1,1,0,0,0,1,0,1,1,1,0; %0
    0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0; %1
    1,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,1,1,1,1]; %2
t = [1,0,0; %0
    0,1,0; %1
    0,0,1]; %2

%variables to count the wrong outputs
input_size = size(p,1);
[W,b] = backprop(p, t, 1, 20, 0.1, 350);
a = zeros(length(W),1);

for h = 1:length(t)
    for i = 0:4:8
        for j = 1:10
            for k = 1:length(W)
                if (k == 1)
                    a(:,k) = W{k}*addNoise(p(h,:)',i)+b{k};
                else
                    a(:,k) = W{k}*addNoise(a(:,k-1),i)+b{k};
                end
            end
            if (a(:,-
        end
    end
end

outputs = cell(input_size, 1);
match_counter = 0;
tries = 0;

%for loop to go through all the layers of neurons
for trial = 1:10
    %add the pixel to the desired input and change it with the pixel
    for inp = 1:input_size
        new_noisy = addNoise(p(inp,:), pix);
        %calculate the output from using the changed input
        for j = 1:total_layers
            if (j == 1)
                a{j} = (W{1} * new_noisy.' + b{1});
            elseif (j == total_layers)
                a{j} = (W{2} * a{j-1} + b{2});
            else
                a{j} = (W{1} * a{j-1} + b{1});
            end
        end
        outputs{inp} = a{total_layers};
        if (outputs{inp} == t(inp,:).')
            match_counter = match_counter + 1;
        end
        tries = tries + 1;
    end
end
disp(tries)
disp(match_counter)
error_percentage = ((tries - match_counter) / tries) / 100;
end