- Does it really make sense to you to post tens of lines of code without a sample of relevant data ???
Neural network training accuracy?
1 view (last 30 days)
Show older comments
i want to make a plot of train accurancy how can i do that?
clc
clear all
close all
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;
%
% cvFolds = crossvalind('Kfold', t, k);
% for i = 1:k
% testIdx = (cvFolds == i);
% trainIdx = ~testIdx ;
% trInd=find(trainIdx)
% tstInd=find(testIdx)
% net.trainFcn = 'trainbr'
% net.trainParam.epochs = 100;
% net.divideFcn = 'divideind';
% net.divideParam.trainInd=trInd
% net.divideParam.testInd=tstInd
% Choose a Performance Function
% net.performFcn = 'mse'; % Mean squared error
% end
k=10
cvFolds = crossvalind('kfold',t,k);
for i =1:k
testIdx = (cvFolds == i);
trainIdx = ~testIdx ;
trInd=find(trainIdx)
tstInd=find(testIdx)
end
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)
plot( testIdx ,'.');
1 Comment
Greg Heath
on 5 Dec 2018
Edited: Greg Heath
on 5 Dec 2018
2. I think the best measures of regression accuracy are
NMSE = mse( t - y ) / mse( t - mean( t ))
and
RSQUARE = 1 - NMSE (See Wikipedia)
3. Then, the goodness of fit in [0 1 ] is IMMEDIATELY recognized !
Greg
Answers (0)
See Also
Categories
Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!