how to calculate the classification accuracy in neural network toolbox?

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??

[Muhammad Shahzaib]

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]

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.?

Answer 1

[Greg Heath]

Search ot NEWSGROUP and ANSWERS with

 greg patternnet

and

 greg patternnet tutorial

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Santhana Raj]

There are various parameters that can and are used in different classification algorithms. Take a look at this wiki page:

Wiki page on Precision and Recall

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.

Answer 3

[Saira]

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);