how to calculate the classification accuracy in neural network toolbox?

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Mallikarjun Yelameli
评论： Joana 2020-7-2
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 个评论
Muhammad Shahzaib
Muhammad Shahzaib 2019-5-23
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 2020-7-2
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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采纳的回答

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

更多回答（2 个）

Santhana Raj
Santhana Raj 2017-5-9
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.
Saira
Saira 2020-6-15
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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