# how to calculate the classification accuracy in neural network toolbox?

조회 수: 25 (최근 30일)
Mallikarjun Yelameli 2017년 5월 9일
댓글: Joana 2020년 7월 2일
net=patternnet(10);
[net,tr]=train(net,inputs,targets);
outputs=net(inputs);
[values,pred_ind]=max(outputs,[],1);
[~,actual_ind]=max(targets,[],1);
accuracy=sum(pred_ind==actual_ind)/size(inputs,2)*100;
Is this correct way to calculate the classification accuracy??
##### 댓글 수: 2없음 표시없음 숨기기
Yes, this is the correct way to calculate the accuracies, (but some times you need to round off the third decimal place to get the exact value.)
For, TEST accuracy :-
[~,pred_ind_tst]=max(outputs(:,[tr.testInd]),[],1);
[~,actual_ind_tst]=max(targets(:,[tr.testInd]),[],1);
Test_accuracy =sum(pred_ind_tst==actual_ind_tst)/size(targets(:,[tr.testInd]),2)*100
Double check your calculation using below:
plotconfusion(targets(:,[tr.testInd]),outputs(:,[tr.testInd]),'Test_accuracy ');
Joana 2020년 7월 2일
Hi
I tried the above code for calculating test accuracy and double checked with plotting confusion matrix, but the accuracy comes out to be 100% while confusion matrix gives 58.3%.
How i can save the actual test accuracy.?

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### 채택된 답변

Greg Heath 2017년 5월 11일
Search ot NEWSGROUP and ANSWERS with
greg patternnet
and
greg patternnet tutorial
Hope this helps.
Thank you for formally accepting my answer
Greg

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### 추가 답변 (2개)

Santhana Raj 2017년 5월 9일
There are various parameters that can and are used in different classification algorithms. Take a look at this wiki page:
Most generally used terms are precision, recall, true negative rate, accuracy. The most widely used is F-measure. The wiki page gives the formula for this. You can shoose one based on your application.
##### 댓글 수: 0이전 댓글 -2개 표시이전 댓글 -2개 숨기기

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Saira 2020년 6월 15일
Hi,
I have 5600 training images. I have extracted features using Principal Component Analysis (PCA). Then I am applying CNN on extracted features. My training accuracy is 30%. How to increase training accuracy?
Feature column vector size: 640*1
My training code:
% Convolutional neural network architecture
layers = [
imageInputLayer([1 640 1]);
reluLayer
fullyConnectedLayer(7);
softmaxLayer();
classificationLayer()];
options = trainingOptions('sgdm', 'Momentum',0.95, 'InitialLearnRate',0.0001, 'L2Regularization', 1e-4, 'MaxEpochs',5000, 'MiniBatchSize',8192, 'Verbose', true);

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