Cross Validation in Neural Network ?

조회 수: 2 (최근 30일)
Narges Sedre
Narges Sedre 2018년 12월 4일
답변: Greg Heath 2018년 12월 5일
i want to do a cross validation 10% of the data should be for training and the rest is for test .
i dont know how to perform that.
filename='FIFA7.xlsx';
A =xlsread(filename);
[m,n]=size(A);
T= A(:,1);
data= A(:,(2:end));
rows80=int32(floor(0.8 * m));
trainingset=A(1:rows80,:);
testset=A(rows80+1:end,:);
t=trainingset(1:rows80,1);
t_test=A(rows80+1:end,1);
% k=10
% cvFols=crossvalind('kfold',l,k);
% for i =1:k
% testIdx=(cvFolds==i);
% trainIdx=~test;
%
%
net= newff(trainingset',t');
y=sim(net,trainingset');
%net.trainParam.epoch=20;
net= train(net,trainingset',t');
y=sim(net,trainingset');
y_test=sim(net,testset');
p=0;
y1=hardlim(y');
y2= hardlims(y_test);
for(i=1:size(t,1))
if(t(i,:)==y1(i,:))
p=p+1;
end
end
trainerror =100*p/size(trainingset,1);
e=0;
y2=hardlim(y_test');
for(j=1:size(t_test,1))
if(t_test(j,:)==y2(j,:))
e=e+1;
end
end
testerror=100*e/size(t_test,1)

답변 (1개)

Greg Heath
Greg Heath 2018년 12월 5일
You may be confused. The MATLAB DEFAULT is RANDOM DATA DIVISION with
80% training
10% validation (tends to prevent overtraining)
10% testing
Depending om the size of the data, you can obtain 10 to 20 separate designs and choose the best.
Instead of storing all of the weights, I just store the current best net as I loop through the designs.
I am sure that I have posted zillions of examples in both the NEWSGROUP comp.soft.sys.matlab and ANSWERS.
Hope this helps.
Thank you for formally accepting my answer
Greg

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