Add logarithm-cosine loss (#227)

Author: Shashi456

Sources/TensorFlow/Loss.swift

@@ -128,6 +128,26 @@ public func categoricalHingeLoss<Scalar: TensorFlowFloatingPoint>(
     return max(Tensor(0), negative - positive + Tensor(1))
 }
 
+// Helper function for `losCoshLoss(predicted:expected:)`.
+@differentiable
+fileprivate func logCosh<Scalar: TensorFlowFloatingPoint>(
+    _ x: Tensor<Scalar>
+) -> Tensor<Scalar> {
+    x + softplus(Tensor(-2) * x) - log(Tensor(2))
+}
+
+/// Returns the logarithm of the hyperbolic cosine of the error between predictions and expectations.
+///
+/// - Parameters:
+///   - predicted: Predicted outputs from a neural network.
+///   - expected: Expected values, i.e. targets, that correspond to the correct output.
+@differentiable(wrt: predicted)
+public func logCoshLoss<Scalar: TensorFlowFloatingPoint>(
+    predicted: Tensor<Scalar>, expected: Tensor<Scalar>
+) -> Tensor<Scalar> {
+    (logCosh(predicted - expected)).mean()
+}
+
 /// Returns the Poisson loss between predictions and expectations.
 ///
 /// - Parameters:

Tests/TensorFlowTests/LossTests.swift

@@ -121,6 +121,14 @@ final class LossTests: XCTestCase {
         assertElementsEqual(expected: Tensor(expectedLoss), actual: loss)
     }
 
+    func testLogCoshLoss() {
+        let predicted = Tensor<Float>([0.2, 0.3, 0.4])
+        let expected = Tensor<Float>([1.0, 4.0, 3.0])
+        let loss = logCoshLoss(predicted: predicted, expected: expected)
+        let expectedLoss: Float = 1.7368573
+        assertElementsEqual(expected: Tensor(expectedLoss), actual: loss)
+    }
+
     func testPoissonLoss() {
         let predicted = Tensor<Float>([0.1, 0.2, 0.3])
         let expected = Tensor<Float>([1, 2, 3])
@@ -238,7 +246,8 @@ final class LossTests: XCTestCase {
         ("testKullbackLeiblerDivergence", testKullbackLeiblerDivergence),
         ("testCategoricalHingeLoss", testCategoricalHingeLoss),
         ("testSquaredHingeLoss", testSquaredHingeLoss),
-        ("testPoissonLoss", testPoissonLoss),
+        ("testPoissonLoss",testPoissonLoss),
+        ("testLogCoshLoss", testLogCoshLoss),
         ("testSoftmaxCrossEntropyWithProbabilitiesLoss",
          testSoftmaxCrossEntropyWithProbabilitiesLoss),
         ("testSoftmaxCrossEntropyWithProbabilitiesGrad",