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remove default argument closures #573

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Dec 5, 2019
Merged

remove default argument closures #573

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5731b90
remove default argument closures
Dec 4, 2019
7dcc639
work around linker error
Dec 4, 2019
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14 changes: 7 additions & 7 deletions Sources/TensorFlow/BackwardsCompatibility.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ public func hingeLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>
) -> Tensor<Scalar> {
hingeLoss(predicted: predicted, expected: expected, reduction: _mean)
hingeLoss(predicted: predicted, expected: expected, reduction: { $0.mean() })
}

/// Returns the squared hinge loss between predictions and expectations.
Expand All @@ -65,7 +65,7 @@ public func squaredHingeLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>
) -> Tensor<Scalar> {
squaredHingeLoss(predicted: predicted, expected: expected, reduction: _mean)
squaredHingeLoss(predicted: predicted, expected: expected, reduction: { $0.mean() })
}

/// Returns the categorical hinge loss between predictions and expectations.
Expand All @@ -78,7 +78,7 @@ public func categoricalHingeLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>
) -> Tensor<Scalar> {
categoricalHingeLoss(predicted: predicted, expected: expected, reduction: _mean)
categoricalHingeLoss(predicted: predicted, expected: expected, reduction: { $0.mean() })
}

/// Returns the logarithm of the hyperbolic cosine of the error between predictions and
Expand All @@ -92,7 +92,7 @@ public func logCoshLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>
) -> Tensor<Scalar> {
logCoshLoss(predicted: predicted, expected: expected, reduction: _mean)
logCoshLoss(predicted: predicted, expected: expected, reduction: { $0.mean() })
}

/// Returns the Poisson loss between predictions and expectations.
Expand All @@ -105,7 +105,7 @@ public func poissonLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>
) -> Tensor<Scalar> {
poissonLoss(predicted: predicted, expected: expected, reduction: _mean)
poissonLoss(predicted: predicted, expected: expected, reduction: { $0.mean() })
}

/// Returns the Kullback-Leibler divergence (KL divergence) between between expectations and
Expand All @@ -132,7 +132,7 @@ public func softmaxCrossEntropy<Scalar: TensorFlowFloatingPoint>(
logits: Tensor<Scalar>,
probabilities: Tensor<Scalar>
) -> Tensor<Scalar> {
softmaxCrossEntropy(logits: logits, probabilities: probabilities, reduction: _mean)
softmaxCrossEntropy(logits: logits, probabilities: probabilities, reduction: { $0.mean() })
}

/// Returns the sigmoid cross entropy (binary cross entropy) between logits and labels.
Expand All @@ -144,5 +144,5 @@ public func sigmoidCrossEntropy<Scalar: TensorFlowFloatingPoint>(
logits: Tensor<Scalar>,
labels: Tensor<Scalar>
) -> Tensor<Scalar> {
sigmoidCrossEntropy(logits: logits, labels:labels, reduction: _mean)
sigmoidCrossEntropy(logits: logits, labels:labels, reduction: { $0.mean() })
}
37 changes: 24 additions & 13 deletions Sources/TensorFlow/Loss.swift
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Expand Up @@ -22,7 +22,7 @@
public func l1Loss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>,
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = { $0.sum() }
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = _sum
) -> Tensor<Scalar> {
reduction(abs(expected - predicted))
}
Expand All @@ -37,7 +37,7 @@ public func l1Loss<Scalar: TensorFlowFloatingPoint>(
public func l2Loss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>,
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = { $0.sum() }
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = _sum
) -> Tensor<Scalar> {
reduction((expected - predicted).squared())
}
Expand Down Expand Up @@ -189,20 +189,11 @@ public func poissonLoss<Scalar: TensorFlowFloatingPoint>(
public func kullbackLeiblerDivergence<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>,
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = { $0.sum() }
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = _sum
) -> Tensor<Scalar> {
reduction(expected * log(expected / predicted))
}

/// Workaround for cross-module default parameter @differentiable functions.
/// Tensor<Scalar>.mean() is the preferred way to do this.
@differentiable
public func _mean<Scalar: TensorFlowFloatingPoint>(
_ value: Tensor<Scalar>
) -> Tensor<Scalar> {
return value.mean()
}

/// Returns the softmax cross entropy (categorical cross entropy) between logits and labels.
///
/// - Parameters:
Expand Down Expand Up @@ -310,11 +301,31 @@ public func huberLoss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>,
expected: Tensor<Scalar>,
delta: Scalar,
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = { $0.sum() }
reduction: @differentiable (Tensor<Scalar>) -> Tensor<Scalar> = _sum
) -> Tensor<Scalar> {
let error = expected - predicted
let absError = abs(error)
let quadratic = min(absError, delta)
let linear = absError - quadratic
return reduction((0.5 * quadratic * quadratic) + (delta * linear))
}

/// Workaround for TF-1030 so that we can use sum as a default argument for reductions.
/// `Tensor<Scalar>.sum()` is the preferred way to do this.
// TODO(TF-1030): Remove this and replace with `{ $0.sum() }`.
@differentiable
public func _sum<Scalar: TensorFlowFloatingPoint>(
_ value: Tensor<Scalar>
) -> Tensor<Scalar> {
return value.sum()
}

/// Workaround for TF-1030 so that we can use mean as a default argument for reductions.
/// `Tensor<Scalar>.mean()` is the preferred way to do this.
// TODO(TF-1030): Remove this and replace with `{ $0.mean() }`.
@differentiable
public func _mean<Scalar: TensorFlowFloatingPoint>(
_ value: Tensor<Scalar>
) -> Tensor<Scalar> {
return value.mean()
}
8 changes: 6 additions & 2 deletions Tests/TensorFlowTests/LossTests.swift
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Expand Up @@ -223,7 +223,9 @@ final class LossTests: XCTestCase {
do {
// Test adapted from:
// https://github.com/tensorflow/tensorflow/blob/148f07323f97ef54998f28cd95c195064ce2c426/tensorflow/python/keras/losses_test.py#L1554
let loss = huberLoss(predicted: predictions, expected: predictions, delta: 1)
// TODO(TF-1025): Remove `reduction`, use default instead.
let loss = huberLoss(
predicted: predictions, expected: predictions, delta: 1, reduction: { $0.sum() })
assertEqual(loss, Tensor(0), accuracy: 1e-6)
}

Expand All @@ -239,7 +241,9 @@ final class LossTests: XCTestCase {
// print(loss(labels, predictions))
// # tf.Tensor(0.62500006, shape=(), dtype=float32)
// ```
let loss = huberLoss(predicted: predictions, expected: labels, delta: Float(1))
// TODO(TF-1025): Remove `reduction`, use default instead.
let loss = huberLoss(
predicted: predictions, expected: labels, delta: Float(1), reduction: { $0.sum() })
assertEqual(loss, Tensor(0.62500006), accuracy: 1e-6)
}
}
Expand Down