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Added '@differentiable' to some overloads of 'min(_:_:)' and 'max(_:_:)'. #360

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Jul 14, 2019
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Added '@differentiable' to some overloads of 'min(_:_:)' and 'max(_:_:)'. #360

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20 changes: 12 additions & 8 deletions Sources/TensorFlow/Operators/Math.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -1228,22 +1228,23 @@ public func max<T>(_ lhs: Tensor<T>, _ rhs: Tensor<T>) -> Tensor<T> where T: Num

@inlinable
internal func _vjpMax<T: TensorFlowFloatingPoint>(
_ x: Tensor<T>, _ y: Tensor<T>
_ x: Tensor<T>,
_ y: Tensor<T>
) -> (Tensor<T>, (Tensor<T>) -> (Tensor<T>, Tensor<T>)) {
let value = max(x, y)
return (value, { v in _vjpMinMaxHelper(x, y, originalValue: value, seed: v) })
}

/// Returns the element-wise maximum of the scalar and the tensor, broadcasting the scalar.
@inlinable
// @differentiable(where T: TensorFlowFloatingPoint)
@differentiable(wrt: rhs where T: TensorFlowFloatingPoint)
public func max<T>(_ lhs: T, _ rhs: Tensor<T>) -> Tensor<T> where T: Numeric & Comparable {
max(Tensor(lhs), rhs)
}

/// Returns the element-wise maximum of the scalar and the tensor, broadcasting the scalar.
@inlinable
// @differentiable(where T: TensorFlowFloatingPoint)
@differentiable(wrt: lhs where T: TensorFlowFloatingPoint)
public func max<T>(_ lhs: Tensor<T>, _ rhs: T) -> Tensor<T> where T: Numeric & Comparable {
max(lhs, Tensor(rhs))
}
Expand All @@ -1258,22 +1259,23 @@ public func min<T>(_ lhs: Tensor<T>, _ rhs: Tensor<T>) -> Tensor<T> where T: Num

@inlinable
internal func _vjpMin<T: TensorFlowFloatingPoint>(
_ x: Tensor<T>, _ y: Tensor<T>
_ x: Tensor<T>,
_ y: Tensor<T>
) -> (Tensor<T>, (Tensor<T>) -> (Tensor<T>, Tensor<T>)) {
let value = min(x, y)
return (value, { v in _vjpMinMaxHelper(x, y, originalValue: value, seed: v) })
}

/// Returns the element-wise minimum of the scalar and the tensor, broadcasting the scalar.
@inlinable
// @differentiable(where T: TensorFlowFloatingPoint)
@differentiable(wrt: rhs where T: TensorFlowFloatingPoint)
public func min<T>(_ lhs: T, _ rhs: Tensor<T>) -> Tensor<T> where T: Numeric & Comparable {
min(Tensor(lhs), rhs)
}

/// Returns the element-wise minimum of the scalar and the tensor, broadcasting the scalar.
@inlinable
// @differentiable(where T: TensorFlowFloatingPoint)
@differentiable(wrt: lhs where T: TensorFlowFloatingPoint)
public func min<T>(_ lhs: Tensor<T>, _ rhs: T) -> Tensor<T> where T: Numeric & Comparable {
min(lhs, Tensor(rhs))
}
Expand All @@ -1297,7 +1299,8 @@ internal func _vjpMinMaxHelper<T: TensorFlowFloatingPoint>(
/// Returns the cosine similarity between `x` and `y`.
@differentiable
public func cosineSimilarity<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>, _ y: Tensor<Scalar>
_ x: Tensor<Scalar>,
_ y: Tensor<Scalar>
) -> Tensor<Scalar> {
(x * y).sum() / (sqrt(x.squared().sum()) * sqrt(y.squared().sum()))
}
Expand All @@ -1306,7 +1309,8 @@ public func cosineSimilarity<Scalar: TensorFlowFloatingPoint>(
/// `1 - cosineSimilarity(x, y)`.
@differentiable
public func cosineDistance<Scalar: TensorFlowFloatingPoint>(
_ x: Tensor<Scalar>, _ y: Tensor<Scalar>
_ x: Tensor<Scalar>,
_ y: Tensor<Scalar>
) -> Tensor<Scalar> {
1 - cosineSimilarity(x, y)
}
Expand Down