Is it OK to increase validation checks and decrease min gradient while training neural network?

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dream_buzz - 2021-07-07T11:17:27+00:00
Question: Is it OK to increase validation checks and decrease min gradient while training neural network?

My input vector is a 130*85 matrix and my target vector is 130*26 matrix. I am using the below parameters for training the network with 60 hidden nodes.     net.trainParam.max_fail = 50; net.trainParam.min_grad=1e-10; net.trainParam.show=10; net.trainParam.lr=0.01; net.trainParam.epochs=1000; net.trainParam.goal=0.001;` As you can see I have set the max_fail to 50 and min_grade to 1e-10. While the default values will be 6 and 1e-5 respectively. But with the default values, the training stops early with out reaching the performance goal. By setting these parameters,the training is stopped when the performance goal is reached. So. Is it OK to change these parameters?  

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    • Expert Answer

    Profile picture of Kshitij Singh Kshitij Singh answered . 2025-11-20

    The most important objective is to obtain an acceptable ratio of mean-square error to mean-target-variance for RANDOM subsets of NONTRAINING data. I find the best way to do this is to try to find the smallest value of H (No. of hidden layer nodes) that will yield
     
     
     mse(ytst-ttst)/var(ttst,1) <= 0.01
    i. e., 99% of a random test target subset variance is successfully modeled.
     
    In order to do this I use the default 70/15/15 trn/val/tst data division and the training data goals
     
     MSEgoal = max( 0, 0.01*(Ntrneq-Nw)*var(ttrn,0)/Ntrneq )
 MinGrad = MSEgoal/100
 net.trainParam.goal     = MSEgoal ;
 net.trainParam.min_grad = MinGrad ;
    For an I-H-O node topology obtained from N pairs of I-dimensional inputs and O-dimensional targets
     
     
     [ I N ] = size(input) %  [ 85 130 ]
 [ O N ] = size(target)%  [ 26 130 ]

 Ntrn   = N-2*round(0.15*N) %   90 training examples
 Ntrneq = Ntrn*O            % 2340 training equations

 % Nw = (I+1)*H+(H+1)*O       % Number of unknown weights
 % For a robust design desire Ntrneq >> Nw or
 % H << Hub = -1+ceil( (Ntrneq-O) / (I+O+1)) % 20

 Hmax = 20
 dH   = 2
 Hmin = 1
 Ntrials = 10

 Design many nets and choose the best using the validation set performance.

 for h = Hmin:dH:Hmax
  ....
     for i = 1: Ntrials
  ... 
     end
 end
    I have posted many, many examples on the NEWSGROUP and ANSWERS, Search on
     
     
     greg Ntrials


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