how can I do classification with Neural Network ?

2012 12 17
2 个回答
回答已采纳
更新时间：2015 5 11
6 次查看（30 天）

0 个投票

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

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 采纳的回答

Greg Heath
Greg Heath 2012-12-22

0 个投票

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);

更多回答（1 个）

Greg Heath
Greg Heath 2012-12-21
编辑：Greg Heath 2012-12-21

0 个投票

close all, clear all, clc
tic
[ x , t ] = iris_dataset;
whos
[ I N ] = size(x) % [ 4 150 ]
[ O N ] = size(t) % [ 3 150 ]
itrn = [ (1:25), (51:75), (101:125) ];
itst = [ (26:50), (76:100), (126:150) ];
xtrn = x( : , itrn); xtst = x( : , itst);
ttrn = t( : , itrn); ttst =t( : , itst);
trnclass0 = vec2ind(ttrn)
tstclass0 = vec2ind(ttst)
H = 5
Ntrials = 10
rng(0)
for i = 1:Ntrials
net = newpr(xtrn,ttrn,H); % See help newpr
net.divideFcn = ''; % Override default 0.7/0.15/0.15
[net tr Ytrn Etrn ] = train(net,xtrn,ttrn);
Nepochs(i,1) = tr.epoch(end);
trnclass = vec2ind(Ytrn);
Nerrtrn(i,1) = sum(trnclass ~= trnclass0);
Ytst = sim(net,xtst);
tstclass = vec2ind(Ytst);
Nerrtst(i,1) = sum(tstclass ~= tstclass0);
end
disp( 'Nepochs Nerrtrn Nerrtst ');
disp( [ Nepochs, Nerrtrn, Nerrtst ] );
time = toc
Thank you for formally accepting my answer.
Greg

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