Accuracy between from the equation and the validation accuracy

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TAN HOR YAN
TAN HOR YAN 2021-10-13
回答： Sahil Jain 2021-10-18
What is the different between the accuracy by using the Accuracy = sum ( diag (C)) / sum (C (:)) ×100 and the validation accuracy which run by the following coding:
[XTrain,YTrain] = digitTrain4DArrayData;
idx = randperm(size(XTrain,4),403);
XValidation = XTrain(:,:,:,idx);
XTrain(:,:,:,idx) = [];
YValidation = YTrain(idx);
YTrain(idx) = [];
layers = [
imageInputLayer([28 28 1])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(10)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'MaxEpochs',8, ...
'ValidationData',{XValidation,YValidation}, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
NET = trainNetwork(XTrain,YTrain,layers,options);

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回答（1 个）

Sahil Jain
Sahil Jain 2021-10-18
Hi. You have not mentioned what "C" is. Assuming that "C" is the confusion matrix, accuracy values evaluated using both the approaches are equivalent and give the same result. Upon replacing the last line of your code with the following, you can observe that "accuracy1" and "accuracy2" both have the same value.
[NET, info] = trainNetwork(XTrain,YTrain,layers,options);
YPred = classify(NET, XValidation);
C = confusionmat(YValidation, YPred);
accuracy1 = sum (diag (C))/sum (C(:))*100;
accuracy2 = info.FinalValidationAccuracy;

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