How to analyse the results of training of neural network

Question

afef 2017-6-14

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/344723-how-to-analyse-the-results-of-training-of-neural-network

回答： Greg Heath 2017-6-15

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 =

2055   37.5494   34.0909
3439   42.8854   43.0830
9328   35.8696   37.2530
4032   35.3755   37.5494
6482   42.5889   34.4862
6008   40.5138   32.8063
2411   41.6008   33.9921
0435   34.7826   37.0553
1304   37.0553   38.5375
0435   34.8814   35.5731