MATLAB: Matlab Neural Network program error

Deep Learning Toolboxhidden layerinitializationneural networksoptimization

My goal is to optimize my NN for a fiting problem. So I want to test several number of neurons in the hidden layer and repeat the simulation with a new initialization using initwn function several times, finnally i'll choose the best architecture.
run('Data.m') % downloading data
Nmax=13; % maximum Number of neurones in the hidden layer
s2=10; % maximum Number of initialization
for i=1:Nmax
com=0;
while true
com=com+1;
        inputs = In'; % In dimension 4*576
        targets = Out'; % Out dimension 2*576
        hiddenLayerSize =i;
        net = feedforwardnet(hiddenLayerSize);
        net.layers{1}.transferFcn = 'tansig'; %
        net.layers{2}.transferFcn = 'tansig'; % I choosed tansig because I want to use 'trainbr' and 'initnw'
        net.initFcn = 'initlay';
        net.layers{1}.initFcn = 'initnw';
        net.layers{2}.initFcn = 'initnw';
        net = init(net);
        net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
        net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn='dividerand'; net.divideParam.trainRatio = 75/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 10/100;
net.trainFcn = 'trainbr'; % Bayesian Regularization backpropagation
net.performFcn = 'mse'; % Mean squared error
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', … 'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs);
% What is the importance of the folowing lines?
trainTargets = targets .* tr.trainMask{1}; valTargets = targets .* tr.valMask{1}; trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs)
save(sprintf('Network_%d',com), 'net') % I save networks
        if com>s2
            break;
        end
    end % end of while
end
Unfortunately I got an error in this program when I run it. It seems that the the probleme occur in trainTargets = targets .* tr.trainMask{1}; valTargets = targets .* tr.valMask{1};
Could someone help me with this issue?
I want to know if this strategy to find the best NN for my problem is good too. I want to find the optimal Neurons in the hidden layer and the good weight initialization to find a global minimum with a good generalization at the same time.

Best Answer

1. Always begin with all of the defaults on one of the nndatasets that has approximately the same input and output dimensions as your data. (help nndatasets).
2. Next try your data with as many defaults as possible.
3. Finally, replace defaults, one-by-one until you are satisfied.
4. The only defaults that I typically change are
   a. H, the number of hidden nodes
   b. net.divideFcn and corresponding ratios (especially with Timeseries)
   c. net.trainParam.goal
   d. net.trainParam.min_grad
5. If you search the NEWSGROUP and ANSWERS using either Ntrials or Nw, you will find many examples of doubleloop design over H and random initial weights.
6.Keep all data loading and preparation outside of the outer loop.
7. Initialize the RNG before the loops so you can duplicate the random data divisions and random weight initializations.
8. You can either save the current best net ( min MSEval with mse; min MSEtrn with msereg) or keep track of the initial RNG state for each design. Then you can search the Ntrials-by-numH tabulations to determine the best net, recreate it and investigate it till the cows come home.
9.WARNING: TRAINBR does not use mse. Use it's default msereg rather than specify msereg or the regularization option on mse.
10.You seem to have gotten into trouble with the data division. With TRAINBR there is only a training set and a test set. You seem to have wanted a training set and a validation set. It would be worth your while to take a look at the data division info in properties like those tabulated below. Unfortunately, tr.divideParam contains what you inputted and is incorrect. See
 a. tr.performParam,
 b. tr.valInd, 
 c. tr.valMask, 
 d. tr.best_vperf, 
 e. tr.vperf
11. if you practice on the engine_dataset and use rng(0), We can compare results.
Hope this helps.
Thank you for formally accepting my answer
Greg
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