Github user tolgap commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-70637578
I gave a shot at trying out the ANN with the MNIST dataset. I find it
difficult to get the correct error rate from the output of the predictor.
Given an output `RDD[(Vector, Vector)]`, with both `Vector` being 10
dimensional (0..9), how do I get the error rate out of it? I've tried:
```Scala
val errRate = output.map {
T =>
val p = T._2.toArray
val l = T._1.toArray
(p(0) - l(0)) * (p(0) - l(0)) +
(p(1) - l(1)) * (p(1) - l(1)) +
(p(2) - l(2)) * (p(2) - l(2)) +
(p(3) - l(3)) * (p(3) - l(3)) +
(p(4) - l(4)) * (p(4) - l(4)) +
(p(5) - l(5)) * (p(5) - l(5)) +
(p(6) - l(6)) * (p(6) - l(6)) +
(p(7) - l(7)) * (p(7) - l(7)) +
(p(8) - l(8)) * (p(8) - l(8)) +
(p(9) - l(9)) * (p(9) - l(9))
}.reduce((u,v) => u + v)
```
But this gives me an error rate > `1`, namely `1708.46666263`. I am
expecting an error rate between 0 and 1. This same error rate calculation
(Squared error) is also used in the `ANNSuite` example.
p.s: I am still researching Machine Learning/Neural Network, so I am not
very knowledgable. I would very much appreciate the help.
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