Looking at this
<https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala#L541>
code
for (i <- (L - 2) to (0, -1)) {
layerModels(i + 1).computePrevDelta(deltas(i + 1), outputs(i + 1),
deltas(i))
}
I want to understand why are we passing outputs(i+1) instead of outputs(i)
in the code snippet above. As far as I understand this is only needed for
sigmoid activation layer which has a derivative as f'(x) = f(x) * (1-f(x)) =
outputs(i) * (1-outputs(i))
Which means in order to find prevDelta we should be using outputs(i).
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