Adding logcosh loss

Author: Shashi456

Sources/TensorFlow/Loss.swift

@@ -86,6 +86,41 @@ public func categoricalHingeLoss<Scalar: TensorFlowFloatingPoint>(
     return max(Tensor(0), negative - positive + Tensor(1))
 }
 
+/// Helper function for Logcosh
+@differentiable(wrt: x)
+internal func logcosh<Scalar: TensorFlowFloatingPoint>(
+    x: Tensor<Scalar>
+) -> Tensor<Scalar> {
+    let y = Tensor<Scalar>([2])
+    return  x + softplus(Tensor(-2) * x) - log(y)
+}
+
+/// Returns the Logcosh loss 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> {
+    return (logcosh(x: predicted - expected)).mean()
+}
+
+
+/// Returns the Poisson loss 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 poissonLoss<Scalar: TensorFlowFloatingPoint>(
+    predicted: Tensor<Scalar>, expected: Tensor<Scalar>
+) -> Tensor<Scalar> {
+    return (predicted - expected * log(predicted)).mean()
+}
+
+
 /// Returns the Poisson loss between predictions and expectations.
 ///
 /// - Parameters:

Tests/TensorFlowTests/LossTests.swift

@@ -96,6 +96,14 @@ 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])
@@ -202,6 +210,7 @@ final class LossTests: XCTestCase {
         ("testCategoricalHingeLoss", testCategoricalHingeLoss),
         ("testSquaredHingeLoss", testSquaredHingeLoss),
         ("testPoissonLoss",testPoissonLoss),
+        ("testLogcoshLoss", testLogcoshLoss),
         ("testSoftmaxCrossEntropyWithProbabilitiesLoss",
          testSoftmaxCrossEntropyWithProbabilitiesLoss),
         ("testSoftmaxCrossEntropyWithProbabilitiesGrad",