MATLAB: How can I do classification with Neural Network

associative memoryclassificationnn

i want to do data classification by using learning rule.i try to write some code but i must change my learning rate to get optimum solition.
if true
load fisheriris
traindata=[meas(1:25,:);meas(51:75,:);meas(101:125,:)];
testdata=[meas(26:50,:);meas(76:100,:);meas(126:150,:)];
traindata_class=[zeros(25,1);ones(25,1);-ones(25,1)];
testdata_class=[zeros(25,1);ones(25,1);-ones(25,1)];
traindata=traindata';
testdata=testdata';
traindata_class=traindata_class';
testdata_class=testdata_class';
net=newff(traindata,traindata_class,[5],{'tansig'},'traingdx');
net.trainParam.epochs=100;
net.trainParam.max_fail=20;
[net,tr]=train(net,traindata,traindata_class); cikis_ysa=sim(net,testdata); cikis_ysa=round(cikis_ysa);
hata=0;
for i=1:75
    if (cikis_ysa(i)-testdata_class(i)~=0)
        hata=hata+1;
    end
end
yuzdehata=100*hata/75;
dogruluk=100-yuzdehata;
end

Best Answer

Your major mistakes were
1. Overriding the newff defaults maxepochs = 1000 and max_fail = 6
2. Not overriding the data division default trn/val/tst = 0.7/0.15/0.15
3. Not searching over 10 or more random weight initializations
When I make the change
net.divideFcn = '';
the max_fail specification is moot.
Then, intializing the random number generator and looping over
Ntrials = 20
weight initialization trials:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
rng(0)
for i = 1:Ntrials
net=newff(traindata,traindata_class,[5],{'tansig'},'traingdx');
net.divideFcn = '';
[net, tr Ytrn ] = train(net,traindata,traindata_class);
Nepochs(i,1) = tr.epoch(end);
ytrn = round(Ytrn);
Nerrtrn(i,1) = sum(ytrn ~= traindata_class);
ytst = round( sim(net,testdata) );
Nerrtst(i,1) = sum(ytst ~= testdata_class);
end
disp( 'Nepochs Nerrtrn Nerrtst' )
disp( [ Nepochs , Nerrtrn , Nerrtst ] )
Nepochs Nerrtrn Nerrtst
   100     7     8
   100    18    15
   100    12    15
   100     9    13
   100    13    12
   100     7    10
   100     8    12
   100    30    29
   100    10    11
   100    48    49
   100     6    11
   100     8     9
   100    29    25
   100    15    15
   100     7    10
   100    42    43
   100     6    13
   100    37    37
   100     6    12
   100     9     8
Whereas the newff default maxepochs = 1000 yields
Nepochs Nerrtrn Nerrtst
        1000           0           7
        1000           0           7
        1000           1           7
        1000           1           8
        1000           1           7
        1000           1           7
        1000           1           8
        1000           3           7
        1000           0           7
        1000           1           7
        1000           1           7
        1000           1           7
        1000           0           7
        1000           2           7
        1000           0           7
        1000           3           6
        1000           0           7
        1000           0           7
        1000           1           7
        1000           3           6
However, you would probably do much better using the newff default training and learning functions.
net = newff(traindata,traindata_class,5);
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