Adjusting or adding tolerances on tests. (#1125)

Author: BradLarson

Tests/TensorFlowTests/LayerTests.swift

@@ -1377,7 +1377,7 @@ final class LayerTests: XCTestCase {
     let input = Tensor<Float>(ones: [1, 2]) * Tensor<Float>([0.3, 0.7])
     let output = cell(input: input, state: state).state
     let expected = Tensor<Float>([[0.9921227, 0.9999934, 0.9921227, 0.9999934, 0.9921227]])
-    XCTAssertEqual(output, expected)
+    assertEqual(output, expected, accuracy: 1e-6)
   }
 
   func testDense() {
@@ -6769,7 +6769,7 @@ final class LayerTests: XCTestCase {
     let input = Tensor(shape: [5, 1], scalars: (0..<5).map(Float.init))
     let output = tanhLayer.inferring(from: input)
     let expected = Tensor<Float>([[0.0], [0.7615942], [0.9640276], [0.9950547], [0.9993292]])
-    XCTAssertEqual(output, expected)
+    assertEqual(output, expected, accuracy: 1e-6)
   }
 
   func testBatchNorm() {

Tests/TensorFlowTests/LossTests.swift

@@ -40,8 +40,8 @@ final class LossTests: XCTestCase {
       scalars: [0.1, 0.2, 0.3, 0.4, 0.4, 0.3, 0.2, 0.1])
 
     let loss = meanSquaredError(predicted: predicted, expected: expected)
-    let expectedLoss: Float = 23.324999
-    assertEqual(loss, Tensor(expectedLoss), accuracy: 1e-6)
+    let expectedLoss: Float = 23.325
+    assertEqual(loss, Tensor(expectedLoss), accuracy: 2e-6)
   }
 
   func testMeanSquaredLogarithmicError() {

Tests/TensorFlowTests/OperatorTests/LinearAlgebraTests.swift

@@ -136,7 +136,7 @@ final class LinearAlgebraTests: XCTestCase {
     matrix = Tensor<Float>(shape: [2, 2, 2, 2], scalars: (0..<16).map(Float.init))
     computedDet = det(matrix)
     expectedDet = Tensor<Float>([[-2.0, -2.0], [-2.0, -2.0]])
-    assertEqual(computedDet, expectedDet, accuracy: 1e-5)
+    assertEqual(computedDet, expectedDet, accuracy: 2e-5)
   }
 
   func testSlogdet() {
@@ -151,8 +151,8 @@ final class LinearAlgebraTests: XCTestCase {
     expectedSigns = Tensor<Float>([[-1.0, -1.0], [-1.0, -1.0]])
     expectedLogs = Tensor<Float>([[0.6931472, 0.6931462], [0.6931462, 0.6931435]])
     (computedSigns, computedLogs) = slogdet(input)
-    XCTAssertEqual(computedSigns, expectedSigns)
-    XCTAssertEqual(computedLogs, expectedLogs)
+    assertEqual(computedSigns, expectedSigns, accuracy: 1e-5)
+    assertEqual(computedLogs, expectedLogs, accuracy: 1e-5)
   }
 
   func testLogdet() {

Tests/TensorFlowTests/OperatorTests/MathTests.swift

@@ -226,7 +226,7 @@ final class MathOperatorTests: XCTestCase {
     let x = Tensor<Float>([-1.0, 2.0, 3.0])
     let y = elu(x)
     let expectedY = Tensor<Float>([-0.63212055, 2, 3])
-    XCTAssertEqual(y, expectedY)
+    assertEqual(y, expectedY, accuracy: 1e-6)
   }
 
   func testGelu() {

Tests/TensorFlowTests/TensorAutoDiffTests.swift

@@ -178,7 +178,7 @@ final class TensorAutoDiffTests: XCTestCase {
     let grad = gradient(at: Tensor<Float>([3.0, 4.0])) { x in
       logSoftmax(x).mean().scalarized()
     }
-    XCTAssertEqual(grad, Tensor([0.23105857, -0.2310586]))
+    assertEqual(grad, Tensor([0.23105857, -0.2310586]), accuracy: 1e-6)
   }
 
   func testScalars() {