Port tests from apple/swift (#136)

Copied the apple/swift TensorFlow tests.

Author: eaplatanios

Tests/DeepLearningTests/InitializerTests.swift

@@ -0,0 +1,83 @@
+// Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import XCTest
+@testable import DeepLearning
+
+final class InitializerTests: XCTestCase {
+    func testInitializers() {
+        let scalar = Tensor<Float>(1)
+        let matrix: Tensor<Float> = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
+        let broadcastScalar = Tensor<Float>(broadcasting: 10, rank: 3)
+        let some4d = Tensor<Float>(shape: [2, 1, 2, 1], scalars: [2, 3, 4, 5])
+        XCTAssertEqual(ShapedArray(shape: [2, 1, 2, 1], scalars: [2, 3, 4, 5]), some4d.array)
+        XCTAssertEqual(ShapedArray(shape: [], scalars: [1]), scalar.array)
+        XCTAssertEqual(ShapedArray(shape: [2, 3], scalars: [1, 2, 3, 4, 5, 6]), matrix.array)
+        XCTAssertEqual(ShapedArray(shape: [1, 1, 1], scalars: [10]), broadcastScalar.array)
+    }
+
+    func testFactoryInitializers() {
+        let x = Tensor<Float>(ones: [1, 10])
+        XCTAssertEqual(ShapedArray(repeating: 1, shape: [1, 10]), x.array)
+    }
+
+    func testNumericInitializers() {
+        let x = Tensor<Float>(oneHotAtIndices: [0, 2, -1, 1], depth: 3)
+        XCTAssertEqual(ShapedArray(
+            shape: [4, 3],
+            scalars: [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0]), x.array)
+    }
+
+    func testScalarToTensorConversion() {
+        let tensor = Tensor<Float>(broadcasting: 42, rank: 4)
+        XCTAssertEqual([1, 1, 1, 1], tensor.shape)
+        XCTAssertEqual([42], tensor.scalars)
+    }
+
+    func testArrayConversion() {
+        let array3D = ShapedArray(repeating: 1.0, shape: [2, 3, 4])
+        let tensor3D = Tensor(array3D)
+        XCTAssertEqual(array3D, tensor3D.array)
+    }
+
+    func testDataTypeCast() {
+        let x = Tensor<Int32>(ones: [5, 5])
+        let ints = Tensor<Int64>(x)
+        let floats = Tensor<Float>(x)
+        let u32s = Tensor<UInt32>(floats)
+        XCTAssertEqual(ShapedArray(repeating: 1, shape: [5, 5]), ints.array)
+        XCTAssertEqual(ShapedArray(repeating: 1, shape: [5, 5]), floats.array)
+        XCTAssertEqual(ShapedArray(repeating: 1, shape: [5, 5]), u32s.array)
+    }
+
+    func testBoolToNumericCast() {
+        let bools = Tensor<Bool>(shape: [2, 2], scalars: [true, false, true, false])
+        let ints = Tensor<Int64>(bools)
+        let floats = Tensor<Float>(bools)
+        let i8s = Tensor<Int8>(bools)
+        XCTAssertEqual(ShapedArray(shape: [2, 2], scalars: [1, 0, 1, 0]), ints.array)
+        XCTAssertEqual(ShapedArray(shape: [2, 2], scalars: [1, 0, 1, 0]), floats.array)
+        XCTAssertEqual(ShapedArray(shape: [2, 2], scalars: [1, 0, 1, 0]), i8s.array)
+    }
+
+    static var allTests = [
+        ("testInitializers", testInitializers),
+        ("testFactoryInitializers", testFactoryInitializers),
+        ("testNumericInitializers", testNumericInitializers),
+        ("testScalarToTensorConversion", testScalarToTensorConversion),
+        ("testArrayConversion", testArrayConversion),
+        ("testDataTypeCast", testDataTypeCast),
+        ("testBoolToNumericCast", testBoolToNumericCast)
+    ]
+}

Tests/DeepLearningTests/LayerTests.swift

@@ -199,7 +199,7 @@ final class LayerTests: XCTestCase {
                         [[ 0.066890605,   0.049586136, 0.024610005,  0.09341654]],
                         [[ 0.065792546,   0.009325638, 0.06439907,  0.114802904]],
                         [[ 0.055909205, 0.00035158166, 0.054020774,  0.09812111]]])
-        let (𝛁rnn, 𝛁inputs) = pullback(.init(inputs.map { SimpleRNNCell<Float>.State($0) }))
+        let (𝛁rnn, _) = pullback(.init(inputs.map { SimpleRNNCell<Float>.State($0) }))
         XCTAssertEqual(𝛁rnn.cell.weight,
                        [[          0.0,           0.0,           0.0,           0.0],
                         [-0.0051169936,  0.0014167001,  0.0074189613,   0.017496519],