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neural network hyperparameter tuning

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Dimitri on 6 Nov 2018
Commented: Dimitri on 12 Jan 2019
since there is no hyperparameter tuning function for neural network I wanted to try the bayesopt function. I tried to recreate the example here: But this does not work. Is there a possibility to tune the number of hidden neurons? My code does not work...
[m,n] = size(Daten) ;
P = 0.7 ;
Training = Daten(1:round(P*m),:) ;
Testing = Daten(round(P*m)+1:end,:);
c = cvpartition(YTrain,'KFold',10);
minfn = @(z)kfoldLoss(fitnet(XTrain,YTrain,'CVPartition',c,...
results = bayesopt(minfn,hiddenLayerSize,'IsObjectiveDeterministic',true,...


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Accepted Answer

Don Mathis
Don Mathis on 17 Nov 2018
If you want a more complete workflow that also optimizes the learning rate, and tests the final model on your test set, you could try this:
% Make some data
Daten = rand(100, 3);
Daten(:,3) = Daten(:,1) + Daten(:,2) + .1*randn(100, 1); % Minimum asymptotic error is .1
[m,n] = size(Daten) ;
% Split into train and test
P = 0.7 ;
Training = Daten(1:round(P*m),:) ;
Testing = Daten(round(P*m)+1:end,:);
XTrain = Training(:,1:n-1);
YTrain = Training(:,n);
XTest = Testing(:,1:n-1);
YTest = Testing(:,n);
% Define a train/validation split to use inside the objective function
cv = cvpartition(numel(YTrain), 'Holdout', 1/3);
% Define hyperparameters to optimize
vars = [optimizableVariable('hiddenLayerSize', [1,20], 'Type', 'integer');
optimizableVariable('lr', [1e-3 1], 'Transform', 'log')];
% Optimize
minfn = @(T)kfoldLoss(XTrain', YTrain', cv, T.hiddenLayerSize,;
results = bayesopt(minfn, vars,'IsObjectiveDeterministic', false,...
'AcquisitionFunctionName', 'expected-improvement-plus');
T = bestPoint(results)
% Train final model on full training set using the best hyperparameters
net = feedforwardnet(T.hiddenLayerSize, 'traingd'); =;
net = train(net, XTrain', YTrain');
% Evaluate on test set and compute final rmse
ypred = net(XTest');
finalrmse = sqrt(mean((ypred - YTest').^2))
function rmse = kfoldLoss(x, y, cv, numHid, lr)
% Train net.
net = feedforwardnet(numHid, 'traingd'); = lr;
net = train(net, x(:,, y(:,;
% Evaluate on validation set and compute rmse
ypred = net(x(:, cv.test));
rmse = sqrt(mean((ypred - y(cv.test)).^2));


Dimitri on 22 Nov 2018
Now everything works. Do you happen to know why? The calculation of the rmse has nothing to do with training, right?
Best regards,
Don Mathis
Don Mathis on 26 Nov 2018
Right, but rmse is the objective function being optimized by bayesopt. I think training was succeeding, but the final test rmse calculation was broken.

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More Answers (2)

Sean de Wolski
Sean de Wolski on 6 Nov 2018
Edited: Sean de Wolski on 6 Nov 2018
This is nowhere near as easy as it should be. The shallow neural net infrastructure is old and uses row-major variables. This needs to be accounted for and you'll see it below with a ton of.' transposes. Second, you'll need to wrap around fitnet because it doesn't take in all of the options as name-value pairs like with the modern fit* functions in the statistics toolbox. Third, the training is non-deterministic unless you seed the rng yourself.
I don't understand the math behind using kfold cross validation with a neural net. Hence, I'll use holdout below which will reliably train and evaluate the network on an independent test sets.
Daten = rand(100, 3);
[m,n] = size(Daten) ;
P = 0.7 ;
Training = Daten(1:round(P*m),:) ;
Testing = Daten(round(P*m)+1:end,:);
XTrain=Training(:,1:n-1).'; % Note transposes
c = cvpartition(numel(YTrain),'Holdout', 0.25);
hiddenLayerSize=optimizableVariable('hiddenLayerSize',[1,20], 'Type', 'integer');
minfn = @(z)wrapFitNet(XTrain,YTrain, 'CVPartition', c, ...
results = bayesopt(minfn,hiddenLayerSize,'IsObjectiveDeterministic',false,...
Wrapper function
function cvrmse = wrapFitNet(x, y, varargin)
% Handle variable inputs
ip = inputParser;
ip.addParameter('hiddenLayerSize', 20);
ip.addParameter('CVPartition', cvpartition(numel(y),'Holdout', 0.10));
parse(ip, varargin{:});
cv = ip.Results.CVPartition;
hiddensz = ip.Results.hiddenLayerSize;
% Train net. You would adjust other hyper parameters here.
net = fitnet(hiddensz);
nets = train(net, x(:,'), y(:,'));
% Evaluate on test set and compute rmse
ypred = nets(x(:, cv.test.'));
cvrmse = sqrt(sum(ypred-y(cv.test.').^2)/numel(y(cv.test)));
Finally, if the only thing you want to optimize is hidden layer size, it may be easiest to just run a loop from 1:20 and try them all. Bayesian optimization really helps when you have many different parameters (trainfcn, etc.)

  1 Comment

Dimitri on 6 Nov 2018
i've just started with hyperparameter optimization and wanted to try it with the "simplest" machine learning method. my consideration was that i use the same structure for different learning methods. Therefore bayesopt, because it also works for svm, knn etc.. but you're right, a loop is probably the easiest one.
EDIT: I have a problem, see below.

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Dimitri on 10 Nov 2018
I'm sorry to bother you again, but I'm having trouble with your code. If the code runs through I get the following answer:
Additionally he doesn't plot any curves at bayesian optimization, which probably has to do with the error. I didn't change anything in your code. Can you help me again, please?


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Dimitri on 17 Nov 2018
I use Matlab 2018b. The code from you and Sean works with randomly generated data, but apparently not with my own. Unfortunately I don't get a solution with my data. I have noticed that this may have something to do with the learning rate. How do I modify your code so that I can use "traingd" as training function and "" as hyper parameter?
Don Mathis
Don Mathis on 17 Nov 2018
There's a mistake in the rmse formula. Try this:
cvrmse = sqrt(mean((ypred-y(cv.test)).^2));
Dimitri on 12 Jan 2019
This works. Thank you for your support!

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