cifar10 knn to much accuracy ?
1 view (last 30 days)
Show older comments
i classify cifar10 for first group with knn and receive 100 percent accuracy i think it should happen
imgSetTrain=imageSet(fullfile(pathcifar,'cifar10Train'), 'recursive');
imgSetTest=imageSet(fullfile(pathcifar,'cifar10Test'), 'recursive');
classNames = { 'airplane' , 'automobile' , 'bird',...
'cat' , 'deer' , 'dog' , 'frog',...
'horse' , 'ship' , 'truck'};
ActDet =[];
knn=5;
for( k=1)%length(imgSetTest))
disp(k)
curSet=imgSetTest(k);
detectClass=zeros(curSet.Count,1);
for(ji=1:curSet.Count)
imgcur=gpuArray(read(curSet,ji));
% run on train
AllDist=[]; AllGroups=[];
for( kr=1:length(imgSetTrain))
kr
curSetTrain=imgSetTest(kr);
for(jir=1:curSetTrain.Count)
imgTrain=gpuArray(read(curSetTrain,jir));
AllGroups=[AllGroups;kr];
distImg=abs(imgcur-imgTrain);
distImg = sum(distImg(:))/numel(distImg);
AllDist=[AllDist ;distImg];
end
end
[dataSel indexSel]=sort(AllDist) ;
selGroups = AllGroups(indexSel(1:knn));
[js jsh]=hist(selGroups,[1:10]);
[kjs kjsi]=max(js);
detectClass(ji)=kjsi;
ActDet = [ActDet; k detectClass(ji)]
end
end
0 Comments
Answers (0)
See Also
Categories
Find more on Statistics and Machine Learning Toolbox in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!