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how to calculate the classification accuracy in neural network toolbox?

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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??
  2 comentarios
Muhammad Shahzaib
Muhammad Shahzaib el 23 de Mayo de 2019
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
Joana el 2 de Jul. de 2020
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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Respuesta aceptada

Greg Heath
Greg Heath el 11 de Mayo de 2017
Search ot NEWSGROUP and ANSWERS with
greg patternnet
and
greg patternnet tutorial
Hope this helps.
Thank you for formally accepting my answer
Greg

Más respuestas (2)

Santhana Raj
Santhana Raj el 9 de Mayo de 2017
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.

Saira
Saira el 15 de Jun. de 2020
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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