problem with rng during retraining of a neural network

Hello

I have a trained a neural network on a data set and tested its generalization on an independent sample. I have deliberately reduced the training equations and increased the number of hidden nodes to make the net overfit since I am going to use some methods to avoid overfitting later. The problem is with rng. I have saved the rng state but when I use the same state for traing my net again, I get different results! here is the code:

[x,t]=bodyfat_dataset;
Q = size(x,2);
Q1 = floor(Q*0.30);
Q2 = Q-Q1;
Q3=floor(Q2*0.15);
ind = randperm(Q1+Q3);
ind1 = ind(1:Q1);
ind2 = ind(Q1+(1:Q3));
x1 = x(:,ind1);
t1 = t(:,ind1);
x2 = x(:,ind2);
t2 = t(:,ind2);
[I Ntrn] = size (x1);
[O Ntrn] = size (t1);
Ntrneq   = prod(size(t1));
MSEtrn00 = mean(var(t1',1))  ;
Hub = -1 + ceil( (Ntrneq-O) / (I+O+1))
MSEgoal =  0.01*MSEtrn00;
MinGrad =   MSEgoal/10;
Hmax = 10;
dH=1
Hmin =0
Ntrials = 20
rng(0)
 j=0
 for h = Hmin:dH:Hmax
    j=j+1
    if h==0
        net = newff(x1,t1,[]);
        Nw = (I+1)*O
    else
        net = newff(x1,t1,h);
        Nw = (I+1)*h+(h+1)*O
    end
Ndof = Ntrneq-Nw;
    net.divideFcn           = 'dividetrain';
    net.trainParam.goal     = MSEgoal;
    net.trainParam.min_grad = MinGrad;
for i = 1:Ntrials
        h      = h
        ntrial = i
        net    = configure(net,x1,t1);
        [ net tr Ytrn  ] = train(net,x1,t1);
ytrn       = round(Ytrn)
        MSEtrn     = mse(t1-ytrn);
R2trn= 1-MSEtrn/MSEtrn00;
        Ytst       = net(x2)
MSEtst(i,j)=mse(net,t2,Ytst);
s(i,j) = rng;
end
end

.....................................

suppose that the best result was obtained for s(3,2) then for retraining of my net:

rng(s(3,2))
net=newff(x1,t1,2);
net.divideFcn='dividetrain';
net=train(net,x1,t1);
y2=net(x2);
perf=mse(net,t2,y2)
% the perf is diffrent than that of s(3,2) during the previous training!!!

Answer 1

[Greg Heath]

randperm is used without initializing the RNG.

Thank you for formally accepting my answer

Greg

[Mohamad]

Dear Greg Thank you very much. The rng was initialized but the problem with this code was not solved!! best

[Greg Heath]

I do not understand your reply:

Randperm was called before rng(0). Therefore, if you run the code multiple times you will get multiple answers.