MATLAB - How to change "Validation Check" count - matlab

Question

How can I change "Validation Checks" value from 6 to higher or lower values using code?
I have following code:
% Create a Pattern Recognition Network
hiddenLayerSize = ns;
net = patternnet(hiddenLayerSize);
net.divideParam.trainRatio = trRa/100;
net.divideParam.valRatio = vaRa/100;
net.divideParam.testRatio = teRa/100;
% Train the Network
[net,tr] = train(net,inputs,targets);
% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
valTargets = targets .* tr.valMask{1};
testTargets = targets .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs);
valPerformance = perform(net,valTargets,outputs);
testPerformance = perform(net,testTargets,outputs);
I could not find a clue at http://www.mathworks.com/help/nnet/ug/train-and-apply-multilayer-neural-networks.html

Answer 1

TL;DL: net.trainParam.max_fail = 8;
I've used the example provided in the page you linked to get a working instance of nntraintool.
When you open nntraintool.m you see a small piece of documentation that says (among else):
% net.trainParam.showWindow = false;
This hinted that some properties are stored within net.trainParam. When querying it to see what it holds you get:
ans =
Function Parameters for 'trainlm'
Show Training Window Feedback showWindow: true
Show Command Line Feedback showCommandLine: false
Command Line Frequency show: 25
Maximum Epochs epochs: 1000
Maximum Training Time time: Inf
Performance Goal goal: 0
Minimum Gradient min_grad: 1e-07
Maximum Validation Checks max_fail: 6
Mu mu: 0.001
Mu Decrease Ratio mu_dec: 0.1
Mu Increase Ratio mu_inc: 10
Maximum mu mu_max: 10000000000
Here you can see how the Maximum Validation Checks is stored: in a field named max_fail. Now it was only a case of testing if it was a read-only field or not, which could easily be tested using net.trainParam.max_fail = 8; train(net,...); - which correctly changed the default value from 6 to 8.