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Minor tweak to allow min and max to be differentiable. #159

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May 31, 2019
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Minor tweak to allow min and max to be differentiable. #159

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12 changes: 10 additions & 2 deletions Sources/TensorFlow/Operators/Math.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -915,6 +915,7 @@ public extension Tensor where Scalar: Numeric & Comparable {
// NOTE: This overload is necessary, otherwise `min()` would refer to the variadic method
// `min(squeezingAxes:)` with zero indices.
@inlinable
@differentiable(where Scalar: TensorFlowFloatingPoint)
func min() -> Tensor {
let axes = Tensor<Int32>(rangeFrom: 0, to: Int32(rank), stride: 1)
return min(squeezingAxes: axes)
Expand All @@ -923,6 +924,7 @@ public extension Tensor where Scalar: Numeric & Comparable {
// NOTE: This overload is necessary, otherwise `max()` would refer to the variadic method
// `max(squeezingAxes:)` with zero indices.
@inlinable
@differentiable(where Scalar: TensorFlowFloatingPoint)
func max() -> Tensor {
let axes = Tensor<Int32>(rangeFrom: 0, to: Int32(rank), stride: 1)
return max(squeezingAxes: axes)
Expand All @@ -943,15 +945,18 @@ public extension Tensor where Scalar: Numeric & Comparable {
/// - Parameter axes: The dimensions to reduce.
/// - Precondition: Each value in `axes` must be in the range `-rank..<rank`.
@inlinable
@differentiable(wrt: self where Scalar: TensorFlowFloatingPoint)
func max(squeezingAxes axes: [Int]) -> Tensor {
let axes = axes.map(Int32.init)
// TODO(TF-433): Remove workaround for differentiating `map`.
let axes = {axes.map(Int32.init)}()
return max(squeezingAxes: Tensor<Int32>(axes))
}

/// Returns the maximum values along the specified axes. The reduced dimensions are removed.
/// - Parameter axes: The dimensions to reduce.
/// - Precondition: Each value in `axes` must be in the range `-rank..<rank`.
@inlinable
@differentiable(wrt: self where Scalar: TensorFlowFloatingPoint)
func max(squeezingAxes axes: Int...) -> Tensor {
return max(squeezingAxes: axes)
}
Expand All @@ -971,15 +976,18 @@ public extension Tensor where Scalar: Numeric & Comparable {
/// - Parameter axes: The dimensions to reduce.
/// - Precondition: Each value in `axes` must be in the range `-rank..<rank`.
@inlinable
@differentiable(wrt: self where Scalar: TensorFlowFloatingPoint)
func min(squeezingAxes axes: [Int]) -> Tensor {
let axes = axes.map(Int32.init)
// TODO(TF-433): Remove workaround for differentiating `map`.
let axes = {axes.map(Int32.init)}()
return min(squeezingAxes: Tensor<Int32>(axes))
}

/// Returns the minimum values along the specified axes. The reduced dimensions are removed.
/// - Parameter axes: The dimensions to reduce.
/// - Precondition: Each value in `axes` must be in the range `-rank..<rank`.
@inlinable
@differentiable(wrt: self where Scalar: TensorFlowFloatingPoint)
func min(squeezingAxes axes: Int...) -> Tensor {
return min(squeezingAxes: axes)
}
Expand Down