# How to analyse the results of training of neural network

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afef on 14 Jun 2017
Answered: Greg Heath on 15 Jun 2017
Hi, i tried to create neural network for classification using nprtool and i tried to modify the code but i couldn't analyse the results and what should i do with this results .So Can anyone please tell me because i have no idea ? This is my code :
x = patientInputs;
t = patientTargets;
N=1012
I=9
O=2
[ I N ] = size(x)
[ O N ] = size(t)
Ntrn = N-2*round(0.15*N) % 708
Ntrneq = Ntrn*O %1416
%For a robust design desire Ntrneq >> Nw or
H=10
Hub = -1+ceil( (Ntrneq-O) / (I+O+1)) % Hub =117
Nw = (I+1)*H+(H+1)*O % Number of unknown weights = 122
%H << Hub = -1+ceil( (Ntrneq-O) / (I+O+1))
Ntrials = 10
rng(0)
j=0
for h =round([Hub/10, Hub/2, Hub])
j = j+1
h = h %12
Nw = (I+1)*h+(h+1)*O % 146
Ndof = Ntrneq-Nw %1270
net = patternnet(h);
net.divideFcn = 'dividerand'; % 'dividetrain'
for i = 1:Ntrials
net = configure(net,x,t);
[ net tr outputs regerrors ] = train(net,x,t);
assignedclasses = vec2ind(outputs);
trueclasses = vec2ind(t);
classerr = assignedclasses~=trueclasses;
Nerr(i,j) = sum(classerr);
% FrErr = Fraction of Errors (Nerr/N)
[FrErr(i,j),CM,IND,ROC] = confusion(t,outputs);
FN(i,j) = mean(ROC(:,1)); % Fraction of False Negatives
TN(i,j) = mean(ROC(:,2)) ; % Fraction of True Negatives
TP(i,j) = mean(ROC(:,3)); % Fraction of True Positives
end
end
PctErr=100*Nerr/N
And this are the resultas that i got :
Ntrn =
708
Ntrneq =
1416
H =
10
Hub =
117
Nw =
122
Ntrials =
10
j =
0
j =
1
h =
12
Nw =
146
Ndof =
1270
j =
2
h =
59
Nw =
710
Ndof =
706
j =
3
h =
117
Nw =
1406
Ndof =
10
PctErr =
41.2055 37.5494 34.0909
46.3439 42.8854 43.0830
38.9328 35.8696 37.2530
41.4032 35.3755 37.5494
37.6482 42.5889 34.4862
41.6008 40.5138 32.8063
38.2411 41.6008 33.9921
38.0435 34.7826 37.0553
39.1304 37.0553 38.5375
38.0435 34.8814 35.5731

Greg Heath on 15 Jun 2017
It is considered ill-mannered to post the same problem in both
See my answer in the NEWSGROUP
Greg

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