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Batchnorm changes: fix axis handling and drop workaround for AD crasher #1

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Feb 12, 2019
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Batchnorm changes: fix axis handling and drop workaround for AD crasher #1

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57 changes: 25 additions & 32 deletions Sources/DeepLearning/Layer.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -155,8 +155,9 @@ public struct BatchNorm<Scalar>: Layer

@differentiable(wrt: (self, input))
private func applyTraining(to input: Tensor<Scalar>) -> Tensor<Scalar> {
let mean = input.mean(alongAxes: axis)
let variance = input.variance(alongAxes: axis)
let positiveAxis = (input.rank + axis) % input.rank
let mean = input.mean(alongAxes: [0, positiveAxis])
let variance = input.variance(alongAxes: [0, positiveAxis])
runningMean.value += (mean - runningMean.value) * (1 - momentum)
runningVariance.value += (
variance - runningVariance.value) * (1 - momentum)
Expand All @@ -170,41 +171,33 @@ public struct BatchNorm<Scalar>: Layer
return (input - runningMean.value) * inv + offset
}

// TODO fix crasher in the below to enable behavior that differs between
// training and inference
//
// @differentiable(wrt: (self, input), vjp: _vjpApplied(to:))
// public func applied(to input: Tensor<Scalar>) -> Tensor<Scalar> {
// if learningPhaseIndicator.training {
// return applyTraining(to: input)
// } else {
// return applyInference(to: input)
// }
// }
//
// public func _vjpApplied(to input: Tensor<Scalar>) ->
// (Tensor<Scalar>, (Tensor<Scalar>) ->
// (BatchNorm<Scalar>.CotangentVector, Tensor<Scalar>)) {
// if learningPhaseIndicator.training {
// return self.valueWithPullback(at: input) {
// $0.applyTraining(to: $1)
// }
// } else {
// return self.valueWithPullback(at: input) {
// $0.applyInference(to: $1)
// }
// }
// }
//
// Work around for now by always using training mode
@differentiable(wrt: (self, input))
@differentiable(wrt: (self, input), vjp: _vjpApplied(to:))
public func applied(to input: Tensor<Scalar>) -> Tensor<Scalar> {
return applyTraining(to: input)
if learningPhaseIndicator.training {
return applyTraining(to: input)
} else {
return applyInference(to: input)
}
}

@usableFromInline
func _vjpApplied(to input: Tensor<Scalar>) ->
(Tensor<Scalar>, (Tensor<Scalar>) ->
(BatchNorm<Scalar>.CotangentVector, Tensor<Scalar>)) {
if learningPhaseIndicator.training {
return self.valueWithPullback(at: input) {
$0.applyTraining(to: $1)
}
} else {
return self.valueWithPullback(at: input) {
$0.applyInference(to: $1)
}
}
}

public init(featureCount: Int,
learningPhaseIndicator: LearningPhaseIndicator,
axis: Int = 0,
axis: Int = -1,
momentum: Tensor<Scalar> = Tensor(0.99),
epsilon: Tensor<Scalar> = Tensor(0.001)) {
self.axis = Int32(axis)
Expand Down