How to calculate accuracy for neural network algorithms?

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How to calculate accuracy for neural network algorithms?
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Adam on 14 Mar 2019
I'm pretty sure this is a topic with literally thousands of hits if you google it! Or are you asking specifically about a Matlab coded network, in which case showing some code helps.

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Accepted Answer

Greg Heath
Greg Heath on 15 Mar 2019
I normalize the mean-square-error
MSE = mse(error) = mse(output-target)
by the minimum MSE obtained when the output is a constant.
If the output is a constant, the MSE is minimized when that constant is
the average of the target. For a 1-D target
NMSE = mse(output-target) / mse(target-mean(target))
= mse(error) / var(target,1)
This is related to the R-square statistic (AKA as R2) via
Rsquare = R2 = 1 - NMSE
Both NMSE and R2 are contained in [0,1].
I have posted zillions of examples in both the NEWSGROUP and ANSWERS.
Just search using
Thank you for formally accepting my answer
Osama Tabbakh
Osama Tabbakh on 15 Jul 2019
But what I do not understand is in the way of R-square statistic you calculate with the consideration that the behavior between the target and the output is linear. But when the behavior is nonlinear, then you get high accuracy, although the network produces a large error.

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