Arrays have incompatible size for this operation

%% load training data (use training_data.mat attached file)
% initialize number of nodes, weights, biases, errors and gradients.
%epochs and mini-batch size
clc
data = [out.dutycycle out.voltage out.current]
sample=1000;
labels = data(1:sample,1);
y = labels'; %output vector

images = data(1:sample,2:3);
images(:,1) = images(:,1)/400;
images(:,2) = images(:,2)/100;
images = images'; %Input vectors

hn1 = 80; %Number of neurons in the first hidden layer
hn2 = 60; %Number of neurons in the second hidden layer
%Initializing weights and biases
w12 = randn(hn1,1000).*sqrt(2/1000);
w23 = randn(hn2,hn1)*sqrt(2/hn1);
w34 = randn(1,hn2)*sqrt(2/hn2);
b12 = randn(hn1,1);
b23 = randn(hn2,1);
b34 = randn(1,1);
%learning rate
eta = 0.0058;
%Initializing errors and gradients
error4 = zeros(1,1);
error3 = zeros(hn2,1);
error2 = zeros(hn1,1);
errortot4 = zeros(1,1);
errortot3 = zeros(hn2,1);
errortot2 = zeros(hn1,1);
grad4 = zeros(1,1);
grad3 = zeros(hn2,1);
grad2 = zeros(hn1,1);
epochs = 50;
m = 10; %Minibatch size
%% Training phase 
for k = 1:epochs %Outer epoch loop
    
    batches = 1;
    
    for j = 1:sample/m
        error4 = zeros(1,1);
        error3 = zeros(hn2,1);
        error2 = zeros(hn1,1);
        errortot4 = zeros(1,1);
        errortot3 = zeros(hn2,1);
        errortot2 = zeros(hn1,1);
        grad4 = zeros(1,1);
        grad3 = zeros(hn2,1);
        grad2 = zeros(hn1,1);
        for i = batches:batches+m-1 %Loop over each minibatch
            
            %Feed forward
            a1 = images(:,i);
            z2 = w12.*a1 + b12;
            a2 = elu(z2);
            z3 = w23*a2 + b23;
            a3 = elu(z3);
            z4 = w34*a3 + b34;
            a4 = elu(z4); %Output vector
            
            %backpropagation
            error4 = (a4-y(:,i)).*elup(z4);
            error3 = (w34'*error4).*elup(z3);
            error2 = (w23'*error3).*elup(z2);
            
            errortot4 = errortot4 + error4;
            errortot3 = errortot3 + error3;
            errortot2 = errortot2 + error2;
            grad4 = grad4 + error4*a3';
            grad3 = grad3 + error3*a2';
            grad2 = grad2 + error2*a1';
        end
        
        %Gradient descent
        w34 = w34 - eta/m*grad4;
        w23 = w23 - eta/m*grad3;
        w12 = w12 - eta/m*grad2;
        b34 = b34 - eta/m*errortot4;
        b23 = b23 - eta/m*errortot3;
        b12 = b12 - eta/m*errortot2;
        
        batches = batches + m;
        
    end
    fprintf('Epochs:');
    disp(k) %Track number of epochs
    [images,y] = shuffle(images,y); %Shuffles order of the images for next epoch
end
disp('Training done!')
%note : create folder for this experiment and save the parameters.
% don't forget to keep the folder in matlab path!
save('wfour.mat','w34');
save('wthree.mat','w23');
save('wtwo.mat','w12');
save('bfour.mat','b34');
save('bthree.mat','b23');
save('btwo.mat','b12');
%% Testing phase 
% load testing data with labels ... (use testing_data.mat attached file)
testsample = 100;
labels = test(1:testsample,1);
y = labels';
% y = zeros(1,testsample);
% for i = 1:testsample
%     y(labels(i)+1,i) = 1;
% end

images = test(1:testsample,2:3); 
images(:,1) = images(:,1)/400;
images(:,2) = images(:,2)/100;%copy
images = images';
we34 = matfile('wfour.mat');
w4 = we34.w34;
we23 = matfile('wthree.mat');
w3 = we23.w23;
we12 = matfile('wtwo.mat');
w2 = we12.w12;
bi34 = matfile('bfour.mat');
b4 = bi34.b34;
bi23 = matfile('bthree.mat');
b3 = bi23.b23;
bi12 = matfile('btwo.mat');
b2 = bi12.b12;
success = 0;
n = testsample;
for i = 1:n
    out2 = elu(w2*images(:,i)+b2);
    out3 = elu(w3*out2+b3);
    out = elu(w4*out3+b4);
    big = 0;
    num = 0;
    for k = 1:10
        if out(k) > big
            num = k-1;
            big = out(k);
        end
    end
    if labels(i) == num
        success = success + 1;
    end
    
end
fprintf('Accuracy: ');
fprintf('%f',success/n*100);
disp(' %');
%% ELU activation function
function fr = elu(x)
f = zeros(length(x),1);
for i = 1:length(x)
    if x(i)>=0
        f(i) = x(i);
    else
        f(i) = 0.2*(exp(x(i))-1);
    end
end
fr = f;
end
%% derivative of the ELU activation function
function fr = elup(x)
f = zeros(length(x),1);
for i = 1:length(x)
    if x(i)>=0
        f(i) = 1;
    else
        f(i) = 0.2*exp(x(i));
    end
end
fr = f;
end
%% SHuffle function 
function [B,v] = shuffle(A,y)
cols = size(A,2);
P = randperm(cols);
B = A(:,P);
v = y(:,P);
end

[Walter Roberson]

We do not have your data or your files, we do not know the size of any of the data, and you did not indicate which line the problem occurs on.

[Venu]

Hi @Nazirul Mubin bin Afandi, Can you try changing this line z2 = w12.*a1 + b12; to z2 = w12*a1 + b12; (i.e without doing element wise multiplication, try normal matrix multiplication). If this doesn't resolve your issue, please provide your input data training_data.mat file.