Add Cosine Similarity

Sources/TensorFlow/Loss.swift

@@ -65,6 +65,19 @@ public func hingeLoss<Scalar: TensorFlowFloatingPoint>(
     return max(Tensor(1) - expected * predicted, Tensor(0)).mean()
 }
 
+/// Returns the cosine similarity 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, expected))
+public func cosineSimilarity<Scalar: TensorFlowFloatingPoint>(
+    predicted: Tensor<Scalar>, expected: Tensor<Scalar>
+) -> Tensor<Scalar> {
+    return -(expected * predicted).sum() /
+        (sqrt(expected.squared().sum()) * sqrt(predicted.squared().sum()))
+}
+
 /// Returns the squared hinge loss between predictions and expectations.
 ///
 /// - Parameters:

Tests/TensorFlowTests/LossTests.swift

@@ -78,6 +78,14 @@ final class LossTests: XCTestCase {
         let expectedLoss: Float = 0.225
         assertElementsEqual(expected: Tensor(expectedLoss), actual: loss)
     }
+
+    func testCosineSimilarityLoss() {
+        let predicted = Tensor<Float>([1, 2, 3, 4, 5, 6, 7, 8])
+        let expected = Tensor<Float>([0.5, 1, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0])
+        let loss = cosineSimilarity(predicted: predicted, expected: expected)
+        let expectedLoss: Float = -1.0
+        assertElementsEqual(expected: Tensor(expectedLoss), actual: loss)
+    }
 
     func testSquaredHingeLoss() {
         let predicted = Tensor<Float>([1, 2, 3, 4, 5, 6, 7, 8])
@@ -209,6 +217,7 @@ final class LossTests: XCTestCase {
         ("testHingeLoss", testHingeLoss),
         ("testKullbackLeiblerDivergence", testKullbackLeiblerDivergence),
         ("testCategoricalHingeLoss", testCategoricalHingeLoss),
+        ("testCosineSimilarityLoss", testCosineSimilarityLoss),
         ("testSquaredHingeLoss", testSquaredHingeLoss),
         ("testPoissonLoss", testPoissonLoss),
         ("testSoftmaxCrossEntropyWithProbabilitiesLoss",