Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/8648#discussion_r39191027
--- Diff: mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala ---
@@ -573,47 +582,27 @@ private[ml] class FeedForwardModel private(
case _ =>
throw new UnsupportedOperationException("Non-functional layer not
supported at the top")
}
+ // backward pass (back-propagate deltas given errors)
deltas(L) = new BDM[Double](0, 0)
deltas(L - 1) = newE
for (i <- (L - 2) to (0, -1)) {
deltas(i) = layerModels(i + 1).prevDelta(deltas(i + 1), outputs(i +
1))
}
- val grads = new Array[Array[Double]](layerModels.length)
- for (i <- 0 until layerModels.length) {
- val input = if (i==0) data else outputs(i - 1)
- grads(i) = layerModels(i).grad(deltas(i), input)
+ // forward pass (forward-propagate gradients given inputs)
+ val grads = layerModels.zipWithIndex.map { case (layer, i) =>
+ val input = if (i == 0) data else outputs(i - 1)
+ layer.grad(deltas(i), input)
}
- // update cumGradient
+ // update cumulative gradients
val cumGradientArray = cumGradient.toArray
- var offset = 0
- // TODO: extract roll
- for (i <- 0 until grads.length) {
- val gradArray = grads(i)
- var k = 0
- while (k < gradArray.length) {
- cumGradientArray(offset + k) += gradArray(k)
- k += 1
- }
- offset += gradArray.length
+ grads.flatten.zipWithIndex.foreach { case (newGrad, i) =>
+ cumGradientArray(i) += newGrad
}
newError
}
- // TODO: do we really need to copy the weights? they should be read-only
override def weights(): Vector = {
- // TODO: extract roll
- var size = 0
- for (i <- 0 until layerModels.length) {
- size += layerModels(i).size
- }
- val array = new Array[Double](size)
- var offset = 0
- for (i <- 0 until layerModels.length) {
- val layerWeights = layerModels(i).weights().toArray
- System.arraycopy(layerWeights, 0, array, offset, layerWeights.length)
- offset += layerWeights.length
- }
- Vectors.dense(array)
+ Vectors.dense(layerModels.flatMap(_.weights().toArray))
--- End diff --
I imagine System.arraycopy would be much faster and efficient here
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