Neural network output convert to single matrix

Data file attached.

FIrst, I have two neural networks being tested. I am solving for errors for every iteration. Every iteration produces a unique answer. Once the neural network recalulates for the next iteration, it replaces the prevous solution. I am given only the final solution. I need all iteration solutions in a single column matrix to use for later calculations. How can I combine each answer into a single matrix?

Thought of indexing the results, but I am not sure how to apply it.

Second, how can I determine number of epoch used when trainbr is utilized.

clear

load 'beam_designs_lhs100.mat';     % beam_designs

% Normalize beam models and responses
[beamsin, PS] = mapminmax(beam_designs(:,1:5)');
[beamsout, TS] = mapminmax(beam_designs(:,6:7)');

% 2-layer network
for l1 = 3:5
    for l2 = 3:5
        % Divide designs into training and test datasets
        trainin = beamsin(:,1:600);
        trainout = beamsout(:,1:600);

        testin = beamsin(:,600+1:end);
        testout = beamsout(:,600+1:end);
        
        % Create function fitting neural network
        net = fitnet([l1,l2], 'trainlm');
        netbr = fitnet([l1,l2], 'trainbr');
        
        net.divideParam.trainRatio = 1;
        net.divideParam.valRatio = 0;
        net.divideParam.testRatio = 0;
        
        % Train the NN and evaluate its performance
        [net, tr] = train(net, trainin, trainout);
        [netbr, tr] = train(netbr, trainin, trainout);

        outputsD = net(testin(1:5,:));
        outputsB = netbr(testin(1:5,:));
        perf = perform(net, testout, outputsD);  % or use sum of squares

        % Computes the sum of squared errors and print results
        err_defD = sum((testout(1,:) - outputsD(1,:)).^2);
        err_defB = sum((testout(1,:) - outputsB(1,:)).^2);
        
        fprintf('answer %f\n',err_defD) % Dont need, the only way to visualize the solution to each iteration
        

Anything helps. Thank you.

Answer 1

[Ameer Hamza]

You can do indexing like this

count = 1; % define a counter
% 2-layer network
for l1 = 3:5
    for l2 = 3:5

and then change these lines

err_defD(count) = sum((testout(1,:) - outputsD(1,:)).^2);
err_defB(count) = sum((testout(1,:) - outputsB(1,:)).^2);
count = count+1;