Repository: qoncept/TensorSwift Branch: master Commit: f4616d049b2d Files: 55 Total size: 12.4 MB Directory structure: gitextract_qun949lr/ ├── .gitignore ├── LICENSE ├── Package.swift ├── README.md ├── Sources/ │ ├── MNIST/ │ │ ├── AppDelegate.swift │ │ ├── Assets.xcassets/ │ │ │ └── AppIcon.appiconset/ │ │ │ └── Contents.json │ │ ├── Base.lproj/ │ │ │ ├── LaunchScreen.storyboard │ │ │ └── Main.storyboard │ │ ├── Canvas.swift │ │ ├── CanvasView.swift │ │ ├── Classifier.swift │ │ ├── Info.plist │ │ ├── Line.swift │ │ ├── Models/ │ │ │ ├── W_conv1 │ │ │ ├── W_conv2 │ │ │ ├── W_fc1 │ │ │ ├── W_fc2 │ │ │ ├── b_conv1 │ │ │ ├── b_conv2 │ │ │ ├── b_fc1 │ │ │ └── b_fc2 │ │ ├── String.swift │ │ └── ViewController.swift │ └── TensorSwift/ │ ├── Dimension.swift │ ├── Info.plist │ ├── Operators.swift │ ├── Shape.swift │ ├── Tensor.swift │ ├── TensorMath.swift │ ├── TensorNN.swift │ ├── TensorSwift.h │ └── Utils.swift ├── TensorSwift.podspec ├── TensorSwift.xcodeproj/ │ ├── Configs/ │ │ └── Project.xcconfig │ ├── TensorSwiftTests_Info.plist │ ├── TensorSwift_Info.plist │ ├── project.pbxproj │ └── project.xcworkspace/ │ └── contents.xcworkspacedata └── Tests/ ├── LinuxMain.swift ├── MNISTTests/ │ ├── Array.swift │ ├── ClassifierTests.swift │ ├── Downloader.swift │ ├── DownloaderTests.swift │ ├── Info.plist │ ├── MNISTTests-Bridging-Header.h │ └── SHA1.swift └── TensorSwiftTests/ ├── CalculationPerformanceTests.swift ├── DimensionTests.swift ├── Info.plist ├── PowerTests.swift ├── TensorMathTest.swift ├── TensorNNTests.swift ├── TensorSwiftSample.swift ├── TensorSwiftTests.swift └── TensorTests.swift ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Xcode # build/ *.pbxuser !default.pbxuser *.mode1v3 !default.mode1v3 *.mode2v3 !default.mode2v3 *.perspectivev3 !default.perspectivev3 xcuserdata *.xccheckout *.moved-aside DerivedData *.hmap *.ipa *.xcuserstate # SwiftPackageManager # .build /*.xcodeproj/xcshareddata/ /*.xcodeproj/project.xcworkspace/xcuserdata/ /*.xcodeproj/xcuserdata/ # CocoaPods # # We recommend against adding the Pods directory to your .gitignore. However # you should judge for yourself, the pros and cons are mentioned at: # http://guides.cocoapods.org/using/using-cocoapods.html#should-i-ignore-the-pods-directory-in-source-control # # Pods/ # Carthage # # Add this line if you want to avoid checking in source code from Carthage dependencies. # Carthage/Checkouts Carthage/Build ================================================ FILE: LICENSE ================================================ The MIT License (MIT) Copyright (c) 2016 Qoncept, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: Package.swift ================================================ // swift-tools-version:4.0 // The swift-tools-version declares the minimum version of Swift required to build this package. import PackageDescription let package = Package( name: "TensorSwift", products: [ // Products define the executables and libraries produced by a package, and make them visible to other packages. .library( name: "TensorSwift", targets: ["TensorSwift"]), ], dependencies: [ // Dependencies declare other packages that this package depends on. // .package(url: /* package url */, from: "1.0.0"), ], targets: [ // Targets are the basic building blocks of a package. A target can define a module or a test suite. // Targets can depend on other targets in this package, and on products in packages which this package depends on. .target( name: "TensorSwift", dependencies: []), .testTarget( name: "TensorSwiftTests", dependencies: ["TensorSwift"]), ] ) ================================================ FILE: README.md ================================================ # TensorSwift _TensorSwift_ is a lightweight library to calculate tensors, which has similar APIs to [_TensorFlow_](https://www.tensorflow.org/)'s. _TensorSwift_ is useful to simulate calculating tensors in Swift **using models trained by _TensorFlow_**. ```swift let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12]) let sum = a + b // Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18]) let mul = a * b // Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72]) let c = Tensor(shape: [3, 1], elements: [7, 8, 9]) let matmul = a.matmul(c) // Tensor(shape: [2, 1], elements: [50, 122]) let zeros = Tensor(shape: [2, 3, 4]) let ones = Tensor(shape: [2, 3, 4], element: 1) ``` ## Deep MNIST for Experts ![deep-mnist.gif](Resources/DeepMnist.gif) The following code shows how to simulate [Deep MNIST for Experts](https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html), a tutorial of _TensorFlow_, by _TensorSwift_. ```swift public struct Classifier { public let W_conv1: Tensor public let b_conv1: Tensor public let W_conv2: Tensor public let b_conv2: Tensor public let W_fc1: Tensor public let b_fc1: Tensor public let W_fc2: Tensor public let b_fc2: Tensor public func classify(_ x_image: Tensor) -> Int { let h_conv1 = (x_image.conv2d(filter: W_conv1, strides: [1, 1, 1]) + b_conv1).relu() let h_pool1 = h_conv1.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1]) let h_conv2 = (h_pool1.conv2d(filter: W_conv2, strides: [1, 1, 1]) + b_conv2).relu() let h_pool2 = h_conv2.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1]) let h_pool2_flat = h_pool2.reshaped([1, Dimension(7 * 7 * 64)]) let h_fc1 = (h_pool2_flat.matmul(W_fc1) + b_fc1).relu() let y_conv = (h_fc1.matmul(W_fc2) + b_fc2).softmax() return y_conv.elements.enumerated().max { $0.1 < $1.1 }!.0 } } ``` ## Installation ### Swift Package Manager ```swift .Package(url: "git@github.com:qoncept/TensorSwift.git", from: "0.2.0"), ``` ### CocoaPods ``` pod 'TensorSwift', '~> 0.2' ``` ### Carthage ``` github "qoncept/TensorSwift" ~> 0.2 ``` ## License [The MIT License](LICENSE) ================================================ FILE: Sources/MNIST/AppDelegate.swift ================================================ import UIKit @UIApplicationMain class AppDelegate: UIResponder, UIApplicationDelegate { var window: UIWindow? func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool { // Override point for customization after application launch. return true } func applicationWillResignActive(_ application: UIApplication) { // Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state. // Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game. } func applicationDidEnterBackground(_ application: UIApplication) { // Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later. // If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits. } func applicationWillEnterForeground(_ application: UIApplication) { // Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background. } func applicationDidBecomeActive(_ application: UIApplication) { // Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface. } func applicationWillTerminate(_ application: UIApplication) { // Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:. } } ================================================ FILE: Sources/MNIST/Assets.xcassets/AppIcon.appiconset/Contents.json ================================================ { "images" : [ { "idiom" : "iphone", "size" : "29x29", "scale" : "2x" }, { "idiom" : "iphone", "size" : "29x29", "scale" : "3x" }, { "idiom" : "iphone", "size" : "40x40", "scale" : "2x" }, { "idiom" : "iphone", "size" : "40x40", "scale" : "3x" }, { "idiom" : "iphone", "size" : "60x60", "scale" : "2x" }, { "idiom" : "iphone", "size" : "60x60", "scale" : "3x" }, { "idiom" : "ipad", "size" : "29x29", "scale" : "1x" }, { "idiom" : "ipad", "size" : "29x29", "scale" : "2x" }, { "idiom" : "ipad", "size" : "40x40", "scale" : "1x" }, { "idiom" : "ipad", "size" : "40x40", "scale" : "2x" }, { "idiom" : "ipad", "size" : "76x76", "scale" : "1x" }, { "idiom" : "ipad", "size" : "76x76", "scale" : "2x" } ], "info" : { "version" : 1, "author" : "xcode" } } ================================================ FILE: Sources/MNIST/Base.lproj/LaunchScreen.storyboard ================================================ ================================================ FILE: Sources/MNIST/Base.lproj/Main.storyboard ================================================ ================================================ FILE: Sources/MNIST/Canvas.swift ================================================ import CoreGraphics struct Canvas { var lines: [Line] = [Line()] mutating func draw(_ point: CGPoint) { lines[lines.endIndex - 1].points.append(point) } mutating func newLine() { lines.append(Line()) } } ================================================ FILE: Sources/MNIST/CanvasView.swift ================================================ import UIKit class CanvasView: UIView { private(set) var canvas: Canvas required init?(coder: NSCoder) { canvas = Canvas() super.init(coder: coder) self.addGestureRecognizer(UIPanGestureRecognizer(target: self, action: #selector(CanvasView.onPanGesture(_:)))) } var image: UIImage { UIGraphicsBeginImageContext(bounds.size) layer.render(in: UIGraphicsGetCurrentContext()!) let result = UIGraphicsGetImageFromCurrentImageContext() UIGraphicsEndImageContext() return result! } override func draw(_ rect: CGRect) { let context = UIGraphicsGetCurrentContext() for line in canvas.lines { context?.setLineWidth(20.0) context?.setStrokeColor(UIColor(red: 0.0, green: 0.0, blue: 0.0, alpha: 1.0).cgColor) context?.setLineCap(.round) context?.setLineJoin(.round) for (index, point) in line.points.enumerated() { if index == 0 { context?.move(to: CGPoint(x: point.x, y: point.y)) } else { context?.addLine(to: CGPoint(x: point.x, y: point.y)) } } } context?.strokePath() } @objc func onPanGesture(_ gestureRecognizer: UIPanGestureRecognizer) { canvas.draw(gestureRecognizer.location(in: self)) if gestureRecognizer.state == .ended { canvas.newLine() } setNeedsDisplay() } func clear() { canvas = Canvas() setNeedsDisplay() } } ================================================ FILE: Sources/MNIST/Classifier.swift ================================================ import Foundation import TensorSwift public struct Classifier { public let W_conv1: Tensor public let b_conv1: Tensor public let W_conv2: Tensor public let b_conv2: Tensor public let W_fc1: Tensor public let b_fc1: Tensor public let W_fc2: Tensor public let b_fc2: Tensor public func classify(_ x_image: Tensor) -> Int { let h_conv1 = (x_image.conv2d(filter: W_conv1, strides: [1, 1, 1]) + b_conv1).relu() let h_pool1 = h_conv1.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1]) let h_conv2 = (h_pool1.conv2d(filter: W_conv2, strides: [1, 1, 1]) + b_conv2).relu() let h_pool2 = h_conv2.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1]) let h_pool2_flat = h_pool2.reshaped([1, Dimension(7 * 7 * 64)]) let h_fc1 = (h_pool2_flat.matmul(W_fc1) + b_fc1).relu() let y_conv = (h_fc1.matmul(W_fc2) + b_fc2).softmax() return y_conv.elements.enumerated().max { $0.1 < $1.1 }!.0 } } extension Classifier { public init(path: String) { W_conv1 = Tensor(shape: [5, 5, 1, 32], elements: loadFloatArray(path, file: "W_conv1")) b_conv1 = Tensor(shape: [32], elements: loadFloatArray(path, file: "b_conv1")) W_conv2 = Tensor(shape: [5, 5, 32, 64], elements: loadFloatArray(path, file: "W_conv2")) b_conv2 = Tensor(shape: [64], elements: loadFloatArray(path, file: "b_conv2")) W_fc1 = Tensor(shape: [Dimension(7 * 7 * 64), 1024], elements: loadFloatArray(path, file: "W_fc1")) b_fc1 = Tensor(shape: [1024], elements: loadFloatArray(path, file: "b_fc1")) W_fc2 = Tensor(shape: [1024, 10], elements: loadFloatArray(path, file: "W_fc2")) b_fc2 = Tensor(shape: [10], elements: loadFloatArray(path, file: "b_fc2")) } } private func loadFloatArray(_ directory: String, file: String) -> [Float] { let data = try! Data(contentsOf: URL(fileURLWithPath: directory.stringByAppendingPathComponent(file))) return Array(UnsafeBufferPointer(start: UnsafeMutablePointer(mutating: (data as NSData).bytes.bindMemory(to: Float.self, capacity: data.count)), count: data.count / 4)) } ================================================ FILE: Sources/MNIST/Info.plist ================================================ CFBundleDevelopmentRegion en CFBundleExecutable $(EXECUTABLE_NAME) CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion 6.0 CFBundleName $(PRODUCT_NAME) CFBundlePackageType APPL CFBundleShortVersionString 1.0 CFBundleVersion 1 LSRequiresIPhoneOS UILaunchStoryboardName LaunchScreen UIMainStoryboardFile Main UIRequiredDeviceCapabilities armv7 UISupportedInterfaceOrientations UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight UISupportedInterfaceOrientations~ipad UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight NSAppTransportSecurity NSExceptionDomains yann.lecun.com NSAllowsArbitraryLoads ================================================ FILE: Sources/MNIST/Line.swift ================================================ import CoreGraphics struct Line { var points: [CGPoint] init() { self.init([]) } init(_ points: [CGPoint]) { self.points = points } } ================================================ FILE: Sources/MNIST/Models/W_fc1 ================================================ [File too large to display: 12.2 MB] ================================================ FILE: Sources/MNIST/Models/b_conv1 ================================================ Ԝ=7=֩=x=o=s=Z=^=[;=ߋ=fVj=f=m=Z=T===e=W==3== =/X=='^=؍="=`==[[=d= ================================================ FILE: Sources/MNIST/Models/b_conv2 ================================================ ==^= ={F=k=D=_ڴ=f==zI=Ŧ=ܕ=9==== =ذ=\==u='===l=Ҭ=ա=,===O=PͰ==Ķ= ===.==숵=_=9=~==Li==Ds=k6==9=s===YJ=H===%Π=.==ׇ==͔= ================================================ FILE: Sources/MNIST/Models/b_fc2 ================================================ =K=II==G==6==16=y%= ================================================ FILE: Sources/MNIST/String.swift ================================================ import Foundation extension String { public func stringByAppendingPathComponent(_ str: String) -> String { return (self as NSString).appendingPathComponent(str) } } ================================================ FILE: Sources/MNIST/ViewController.swift ================================================ import UIKit import TensorSwift class ViewController: UIViewController { @IBOutlet private var canvasView: CanvasView! private let inputSize = 28 private let classifier = Classifier(path: Bundle.main.resourcePath!) @IBAction func onPressClassifyButton(_ sender: UIButton) { let input: Tensor do { let image = canvasView.image let cgImage = image.cgImage! var pixels = [UInt8](repeating: 0, count: inputSize * inputSize) let context = CGContext(data: &pixels, width: inputSize, height: inputSize, bitsPerComponent: 8, bytesPerRow: inputSize, space: CGColorSpaceCreateDeviceGray(), bitmapInfo: 0)! context.clear(CGRect(x: 0.0, y: 0.0, width: CGFloat(inputSize), height: CGFloat(inputSize))) let rect = CGRect(x: 0.0, y: 0.0, width: CGFloat(inputSize), height: CGFloat(inputSize)) context.draw(cgImage, in: rect) input = Tensor(shape: [Dimension(inputSize), Dimension(inputSize), 1], elements: pixels.map { (pixel: UInt8) -> Float in -(Float(pixel) / 255.0 - 0.5) + 0.5 }) } let estimatedLabel = classifier.classify(input) let alertController = UIAlertController(title: "\(estimatedLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { _ in self.canvasView.clear() }) present(alertController, animated: true, completion: nil) } } ================================================ FILE: Sources/TensorSwift/Dimension.swift ================================================ public struct Dimension { public let value: Int public init(_ value: Int) { guard value >= 0 else { fatalError("`value` must be greater than or equal to 0: \(value)") } self.value = value } } extension Dimension: ExpressibleByIntegerLiteral { public init(integerLiteral value: Int) { self.init(value) } } extension Dimension: Equatable {} public func ==(lhs: Dimension, rhs: Dimension) -> Bool { return lhs.value == rhs.value } extension Dimension: CustomStringConvertible { public var description: String { return value.description } } public func +(lhs: Dimension, rhs: Dimension) -> Dimension { return Dimension(lhs.value + rhs.value) } public func -(lhs: Dimension, rhs: Dimension) -> Dimension { return Dimension(lhs.value - rhs.value) } public func *(lhs: Dimension, rhs: Dimension) -> Dimension { return Dimension(lhs.value * rhs.value) } public func /(lhs: Dimension, rhs: Dimension) -> Dimension { return Dimension(lhs.value / rhs.value) } ================================================ FILE: Sources/TensorSwift/Info.plist ================================================ CFBundleDevelopmentRegion en CFBundleExecutable $(EXECUTABLE_NAME) CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion 6.0 CFBundleName $(PRODUCT_NAME) CFBundlePackageType FMWK CFBundleShortVersionString 1.0 CFBundleSignature ???? CFBundleVersion $(CURRENT_PROJECT_VERSION) NSPrincipalClass ================================================ FILE: Sources/TensorSwift/Operators.swift ================================================ infix operator ** : PowerPrecedence precedencegroup PowerPrecedence { associativity: right higherThan: MultiplicationPrecedence } ================================================ FILE: Sources/TensorSwift/Shape.swift ================================================ public struct Shape { public let dimensions: [Dimension] public func volume() -> Int { return dimensions.reduce(1) { $0 * $1.value } } public init(_ dimensions: [Dimension]) { self.dimensions = dimensions } } extension Shape: ExpressibleByArrayLiteral { public init(arrayLiteral elements: Dimension...) { self.init(elements) } } extension Shape: Equatable {} public func ==(lhs: Shape, rhs: Shape) -> Bool { return lhs.dimensions == rhs.dimensions } extension Shape: CustomStringConvertible { public var description: String { return dimensions.description } } ================================================ FILE: Sources/TensorSwift/Tensor.swift ================================================ #if os(iOS) || os(OSX) import Accelerate #endif public struct Tensor { public typealias Element = Float public let shape: Shape public fileprivate(set) var elements: [Element] public init(shape: Shape, elements: [Element]) { let volume = shape.volume() precondition(elements.count >= volume, "`elements.count` must be greater than or equal to `shape.volume`: elements.count = \(elements.count), shape.volume = \(shape.volume())") self.shape = shape self.elements = (elements.count == volume) ? elements : Array(elements[0.. Tensor { return Tensor(shape: shape, elements: elements) } } extension Tensor { // like CollentionType internal func index(_ indices: [Int]) -> Int { assert(indices.count == shape.dimensions.count, "`indices.count` must be \(shape.dimensions.count): \(indices.count)") return zip(shape.dimensions, indices).reduce(0) { assert(0 <= $1.1 && $1.1 < $1.0.value, "Illegal index: indices = \(indices), shape = \(shape)") return $0 * $1.0.value + $1.1 } } public subscript(indices: Int...) -> Element { get { return elements[index(indices)] } set { elements[index(indices)] = newValue } } public func volume() -> Int { return shape.volume() } } extension Tensor: Sequence { public func makeIterator() -> IndexingIterator<[Element]> { return elements.makeIterator() } } extension Tensor: Equatable {} public func ==(lhs: Tensor, rhs: Tensor) -> Bool { return lhs.shape == rhs.shape && lhs.elements == rhs.elements } internal func commutativeBinaryOperation(_ lhs: Tensor, _ rhs: Tensor, operation: (Float, Float) -> Float) -> Tensor { let lSize = lhs.shape.dimensions.count let rSize = rhs.shape.dimensions.count if lSize == rSize { precondition(lhs.shape == rhs.shape, "Incompatible shapes of tensors: lhs.shape = \(lhs.shape), rhs.shape = \(rhs.shape)") return Tensor(shape: lhs.shape, elements: zipMap(lhs.elements, rhs.elements, operation: operation)) } let a: Tensor let b: Tensor if lSize < rSize { a = rhs b = lhs } else { a = lhs b = rhs } assert(hasSuffix(array: a.shape.dimensions, suffix: b.shape.dimensions), "Incompatible shapes of tensors: lhs.shape = \(lhs.shape), rhs.shape = \(rhs.shape)") return Tensor(shape: a.shape, elements: zipMapRepeat(a.elements, b.elements, operation: operation)) } internal func noncommutativeBinaryOperation(_ lhs: Tensor, _ rhs: Tensor, operation: (Float, Float) -> Float) -> Tensor { let lSize = lhs.shape.dimensions.count let rSize = rhs.shape.dimensions.count if lSize == rSize { precondition(lhs.shape == rhs.shape, "Incompatible shapes of tensors: lhs.shape = \(lhs.shape), rhs.shape = \(rhs.shape)") return Tensor(shape: lhs.shape, elements: zipMap(lhs.elements, rhs.elements, operation: operation)) } else if lSize < rSize { precondition(hasSuffix(array: rhs.shape.dimensions, suffix: lhs.shape.dimensions), "Incompatible shapes of tensors: lhs.shape = \(lhs.shape), rhs.shape = \(rhs.shape)") return Tensor(shape: rhs.shape, elements: zipMapRepeat(rhs.elements, lhs.elements, operation: { operation($1, $0) })) } else { precondition(hasSuffix(array: lhs.shape.dimensions, suffix: rhs.shape.dimensions), "Incompatible shapes of tensors: lhs.shape = \(lhs.shape), rhs.shape = \(rhs.shape)") return Tensor(shape: lhs.shape, elements: zipMapRepeat(lhs.elements, rhs.elements, operation: operation)) } } public func +(lhs: Tensor, rhs: Tensor) -> Tensor { return commutativeBinaryOperation(lhs, rhs, operation: +) } public func -(lhs: Tensor, rhs: Tensor) -> Tensor { return noncommutativeBinaryOperation(lhs, rhs, operation: -) } public func *(lhs: Tensor, rhs: Tensor) -> Tensor { return commutativeBinaryOperation(lhs, rhs, operation: *) } public func /(lhs: Tensor, rhs: Tensor) -> Tensor { return noncommutativeBinaryOperation(lhs, rhs, operation: /) } public func *(lhs: Tensor, rhs: Float) -> Tensor { return Tensor(shape: lhs.shape, elements: lhs.elements.map { $0 * rhs }) } public func *(lhs: Float, rhs: Tensor) -> Tensor { return Tensor(shape: rhs.shape, elements: rhs.elements.map { lhs * $0 }) } public func /(lhs: Tensor, rhs: Float) -> Tensor { return Tensor(shape: lhs.shape, elements: lhs.elements.map { $0 / rhs }) } public func /(lhs: Float, rhs: Tensor) -> Tensor { return Tensor(shape: rhs.shape, elements: rhs.elements.map { lhs / $0 }) } extension Tensor { // Matrix public func matmul(_ tensor: Tensor) -> Tensor { precondition(shape.dimensions.count == 2, "This tensor is not a matrix: shape = \(shape)") precondition(tensor.shape.dimensions.count == 2, "The given tensor is not a matrix: shape = \(tensor.shape)") precondition(tensor.shape.dimensions[0] == shape.dimensions[1], "Incompatible shapes of matrices: self.shape = \(shape), tensor.shape = \(tensor.shape)") #if os(iOS) || os(OSX) let result = Tensor(shape: [shape.dimensions[0], tensor.shape.dimensions[1]]) let n = Int32(tensor.shape.dimensions[1].value) let k = Int32(shape.dimensions[1].value) cblas_sgemm( CblasRowMajor, // Order CblasNoTrans, // TransA CblasNoTrans, // TransB Int32(shape.dimensions[0].value), // M n, // N k, // K 1.0, // alpha elements, // A k, // lda tensor.elements, // B n, // ldb 1.0, // beta UnsafeMutablePointer(mutating: result.elements), // C n // ldc ) return result #else let n = shape.dimensions[1].value let numRows = shape.dimensions[0] let numCols = tensor.shape.dimensions[1] let leftHead = UnsafeMutablePointer(self.elements) let rightHead = UnsafeMutablePointer(tensor.elements) let elements = [Float](count: (numCols * numRows).value, repeatedValue: 0.0) for r in 0..(elements) + r * numCols.value let left = leftHead[r * n + i] var rightPointer = rightHead + i * numCols.value for _ in 0.. Tensor { return noncommutativeBinaryOperation(lhs, rhs, operation: powf) } public func **(lhs: Tensor, rhs: Tensor.Element) -> Tensor { return Tensor(shape: lhs.shape, elements: lhs.elements.map { powf($0, rhs) }) } public func **(lhs: Tensor.Element, rhs: Tensor) -> Tensor { return Tensor(shape: rhs.shape, elements: rhs.elements.map { powf(lhs, $0) }) } extension Tensor { public func sin() -> Tensor { return Tensor(shape: shape, elements: elements.map(sinf)) } public func cos() -> Tensor { return Tensor(shape: shape, elements: elements.map(cosf)) } public func tan() -> Tensor { return Tensor(shape: shape, elements: elements.map(tanf)) } public func asin() -> Tensor { return Tensor(shape: shape, elements: elements.map(asinf)) } public func acos() -> Tensor { return Tensor(shape: shape, elements: elements.map(acosf)) } public func atan() -> Tensor { return Tensor(shape: shape, elements: elements.map(atanf)) } public func sinh() -> Tensor { return Tensor(shape: shape, elements: elements.map(sinhf)) } public func cosh() -> Tensor { return Tensor(shape: shape, elements: elements.map(coshf)) } public func tanh() -> Tensor { return Tensor(shape: shape, elements: elements.map(tanhf)) } public func exp() -> Tensor { return Tensor(shape: shape, elements: elements.map(expf)) } public func log() -> Tensor { return Tensor(shape: shape, elements: elements.map(logf)) } public func sqrt() -> Tensor { return Tensor(shape: shape, elements: elements.map(sqrtf)) } public func cbrt() -> Tensor { return Tensor(shape: shape, elements: elements.map(cbrtf)) } } extension Tensor { public func sigmoid() -> Tensor { return Tensor(shape: shape, elements: elements.map { 1.0 / (1.0 + expf(-$0)) }) } } ================================================ FILE: Sources/TensorSwift/TensorNN.swift ================================================ import Darwin #if os(iOS) || os(OSX) import Accelerate #endif extension Tensor { public func softmax() -> Tensor { let exps = exp() let sum = exps.elements.reduce(0.0, +) return exps / sum } public func relu() -> Tensor { return Tensor(shape: shape, elements: elements.map { fmax($0, 0.0) }) } } extension Tensor { public func maxPool(kernelSize: [Int], strides: [Int]) -> Tensor { // padding = Same precondition(shape.dimensions.count == 3, "`shape.dimensions.count` must be 3: \(shape.dimensions.count)") precondition(kernelSize.count == 3, "`ksize.count` must be 3: \(kernelSize.count)") precondition(kernelSize[2] == 1, "`ksize[3]` != 1 is not supported: \(kernelSize[2])") precondition(strides.count == 3, "`strides.count` must be 3: \(strides.count)") precondition(strides[2] == 1, "`strides[2]` != 1 is not supported: \(strides[2])") let inRows = shape.dimensions[0].value let inCols = shape.dimensions[1].value let numChannels = shape.dimensions[2].value let filterHeight = kernelSize[0] let filterWidth = kernelSize[1] let inMinDy = -(filterHeight - 1) / 2 let inMaxDy = inMinDy + filterHeight - 1 let inMinDx = -(filterWidth - 1) / 2 let inMaxDx = inMinDx + filterWidth - 1 let rowStride = strides[0] let colStride = strides[1] let outRows = shape.dimensions[0].value.ceilDiv(rowStride) let outCols = shape.dimensions[1].value.ceilDiv(colStride) // Initialize with -infinity for maximization. let elements = [Element](repeating: -Float.infinity, count: outCols * outRows * numChannels) for y in 0..(mutating: self.elements) + (inY * inCols + inMinX) * numChannels for _ in inMinX...inMaxX { var outPointer = UnsafeMutablePointer(mutating: elements) + outPixelIndex * numChannels for _ in 0.. Tensor { // padding = Same let inChannels = filter.shape.dimensions[2].value precondition(shape.dimensions.count == 3, "`shape.dimensions.count` must be 3: \(shape.dimensions.count)") precondition(filter.shape.dimensions.count == 4, "`filter.shape.dimensions.count` must be 4: \(filter.shape.dimensions.count)") precondition(strides.count == 3, "`strides.count` must be 3: \(strides.count)") precondition(strides[2] == 1, "`strides[2]` must be 1") precondition(shape.dimensions[2].value == inChannels, "The number of channels of this tensor and the filter are not compatible: \(shape.dimensions[2]) != \(inChannels)") let inRows = shape.dimensions[0].value let inCols = shape.dimensions[1].value let filterHeight = filter.shape.dimensions[0].value let filterWidth = filter.shape.dimensions[1].value let inMinDy = -(filterHeight - 1) / 2 let inMaxDy = inMinDy + filterHeight - 1 let inMinDx = -(filterWidth - 1) / 2 let inMaxDx = inMinDx + filterWidth - 1 let rowStride = strides[0] let colStride = strides[1] let outRows = inRows.ceilDiv(rowStride) let outCols = inCols.ceilDiv(colStride) let outChannels = filter.shape.dimensions[3].value #if os(iOS) || os(OSX) let elementsPointer = UnsafePointer(elements) // a.shape == [outRows * outCols, rowSize] let rowSize = filterHeight * filterWidth * inChannels let a = [Float](repeating: 0, count: outRows * outCols * rowSize) for y in 0..(mutating: a) + ((y * outCols + x) * filterHeight - Swift.min(inY0 + inMinDy, 0)) * filterWidth * inChannels var src = elementsPointer + (inMinY * inCols + inMinX) * inChannels for _ in inMinY...inMaxY { memcpy(dest - Swift.min(inX0 + inMinDx, 0) * inChannels, src, (inMinX...inMaxX).count * inChannels * MemoryLayout.size) dest += filterWidth * inChannels src += inCols * inChannels } } } let result = Tensor(shape: [Dimension(outRows), Dimension(outCols), Dimension(outChannels)]) let n = Int32(outChannels) let k = Int32(rowSize) // Calculate [M, N] matrix, it automatically turns into [outRows, outCols, outChannels] Tensor cblas_sgemm( CblasRowMajor, // Order CblasNoTrans, // TransA CblasNoTrans, // TransB Int32(outRows * outCols), // M n, // N k, // K 1.0, // alpha UnsafePointer(a), // A k, // lda UnsafePointer(filter.elements), // B n, // ldb 1.0, // beta UnsafeMutablePointer(mutating: result.elements), // C n // ldc ) return result #else let elements = [Float](count: outCols * outRows * outChannels, repeatedValue: 0.0) for y in 0.., _ o2: Int, _ mat: UnsafeMutablePointer, _ oo: Int, _ out: UnsafeMutablePointer) { var v = vec + o1 for i in 0.. //! Project version number for TensorSwift. FOUNDATION_EXPORT double TensorSwiftVersionNumber; //! Project version string for TensorSwift. FOUNDATION_EXPORT const unsigned char TensorSwiftVersionString[]; // In this header, you should import all the public headers of your framework using statements like #import ================================================ FILE: Sources/TensorSwift/Utils.swift ================================================ extension Int { internal func ceilDiv(_ rhs: Int) -> Int { return (self + rhs - 1) / rhs } } internal func hasSuffix(array: [Element], suffix: [Element]) -> Bool { guard array.count >= suffix.count else { return false } return zip(array[(array.count - suffix.count).. Float) -> [Float] { var result: [Float] = [] for i in a.indices { result.append(operation(a[i], b[i])) } return result } internal func zipMapRepeat(_ a: [Float], _ infiniteB: [Float], operation: (Float, Float) -> Float) -> [Float] { var result: [Float] = [] for i in a.indices { result.append(operation(a[i], infiniteB[i % infiniteB.count])) } return result } ================================================ FILE: TensorSwift.podspec ================================================ Pod::Spec.new do |s| s.name = "TensorSwift" s.version = "0.2.2" s.summary = "TensorSwift is a lightweight library to calculate tensors, which has similar APIs to TensorFlow's." s.homepage = "https://github.com/qoncept/TensorSwift" s.license = { :type => "MIT", :file => "LICENSE" } s.authors = { "Yuta Koshizawa" => "koshizawa@qoncept.co.jp", "Takehiro Araki" => "araki@qoncept.co.jp" } s.ios.deployment_target = "8.0" s.osx.deployment_target = "10.9" # s.watchos.deployment_target = "2.0" # s.tvos.deployment_target = "9.0" s.source = { :git => "https://github.com/qoncept/TensorSwift.git", :tag => "#{s.version}" } s.source_files = "Sources/*.swift" end ================================================ FILE: TensorSwift.xcodeproj/Configs/Project.xcconfig ================================================ PRODUCT_NAME = $(TARGET_NAME) SUPPORTED_PLATFORMS = macosx iphoneos iphonesimulator appletvos appletvsimulator watchos watchsimulator MACOSX_DEPLOYMENT_TARGET = 10.10 DYLIB_INSTALL_NAME_BASE = @rpath OTHER_SWIFT_FLAGS = -DXcode COMBINE_HIDPI_IMAGES = YES USE_HEADERMAP = NO ================================================ FILE: TensorSwift.xcodeproj/TensorSwiftTests_Info.plist ================================================ CFBundleDevelopmentRegion en CFBundleExecutable $(EXECUTABLE_NAME) CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion 6.0 CFBundleName $(PRODUCT_NAME) CFBundlePackageType BNDL CFBundleShortVersionString 1.0 CFBundleSignature ???? 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defaultConfigurationName = Debug; }; "_______Confs_TensorSwift" /* Build configuration list for PBXNativeTarget "TensorSwift" */ = { isa = XCConfigurationList; buildConfigurations = ( "___DebugConf_TensorSwift" /* Debug */, _ReleaseConf_TensorSwift /* Release */, ); defaultConfigurationIsVisible = 0; defaultConfigurationName = Debug; }; "_______Confs_TensorSwiftTests" /* Build configuration list for PBXNativeTarget "TensorSwiftTests" */ = { isa = XCConfigurationList; buildConfigurations = ( "___DebugConf_TensorSwiftTests" /* Debug */, _ReleaseConf_TensorSwiftTests /* Release */, ); defaultConfigurationIsVisible = 0; defaultConfigurationName = Debug; }; /* End XCConfigurationList section */ }; rootObject = __RootObject_ /* Project object */; } ================================================ FILE: TensorSwift.xcodeproj/project.xcworkspace/contents.xcworkspacedata ================================================ ================================================ FILE: Tests/LinuxMain.swift ================================================ import XCTest @testable import TensorSwiftTests XCTMain([ testCase(TensorSwiftTests.allTests), testCase(DimensionTests.allTests), testCase(PowerTests.allTests), testCase(TensorTests.allTests), testCase(TensorNNTests.allTests), testCase(CalculationPerformanceTests.allTests), testCase(TensorSwiftSample.allTests), ]) ================================================ FILE: Tests/MNISTTests/Array.swift ================================================ extension Array { func grouped(_ count: Int) -> [[Element]] { var result: [[Element]] = [] var group: [Element] = [] for element in self { group.append(element) if group.count == count { result.append(group) group = [] } } return result } } ================================================ FILE: Tests/MNISTTests/ClassifierTests.swift ================================================ import XCTest import TensorSwift @testable import MNIST class ClassifierTests: XCTestCase { func testClassify() { let classifier = Classifier(path: Bundle(for: ViewController.self).resourcePath!) let (images, labels) = downloadTestData() let count = 1000 let xArray: [[Float]] = images.withUnsafeBytes { ptr in [UInt8](UnsafeBufferPointer(start: UnsafePointer(ptr + 16), count: 28 * 28 * count)) .map { Float($0) / 255.0 } .grouped(28 * 28) } let yArray: [Int] = labels.withUnsafeBytes { ptr in [UInt8](UnsafeBufferPointer(start: UnsafePointer(ptr + 8), count: count)) .map { Int($0) } } let accuracy = Float(zip(xArray, yArray) .reduce(0) { $0 + (classifier.classify(Tensor(shape: [28, 28, 1], elements: $1.0)) == $1.1 ? 1 : 0) }) / Float(yArray.count) print("accuracy: \(accuracy)") XCTAssertGreaterThan(accuracy, 0.97) } } ================================================ FILE: Tests/MNISTTests/Downloader.swift ================================================ import Foundation func downloadTestData() -> (images: Data, labels: Data) { let baseUrl = "http://yann.lecun.com/exdb/mnist/" let testImagesUrl = URL(string: baseUrl)!.appendingPathComponent("t10k-images-idx3-ubyte.gz") let testLabelsUrl = URL(string: baseUrl)!.appendingPathComponent("t10k-labels-idx1-ubyte.gz") print("download: \(testImagesUrl)") let testImages = try! Data(contentsOf: testImagesUrl) print("download: \(testLabelsUrl)") let testLabels = try! Data(contentsOf: testLabelsUrl) return (images: ungzip(testImages)!, labels: ungzip(testLabels)!) } private func ungzip(_ source: Data) -> Data? { guard source.count > 0 else { return nil } var stream: z_stream = z_stream.init(next_in: UnsafeMutablePointer(mutating: (source as NSData).bytes.bindMemory(to: Bytef.self, capacity: source.count)), avail_in: uint(source.count), total_in: 0, next_out: nil, avail_out: 0, total_out: 0, msg: nil, state: nil, zalloc: nil, zfree: nil, opaque: nil, data_type: 0, adler: 0, reserved: 0) guard inflateInit2_(&stream, MAX_WBITS + 32, ZLIB_VERSION, Int32(MemoryLayout.size)) == Z_OK else { return nil } let data = NSMutableData() while stream.avail_out == 0 { let bufferSize = 0x10000 let buffer: UnsafeMutablePointer = UnsafeMutablePointer.allocate(capacity: bufferSize) stream.next_out = buffer stream.avail_out = uint(MemoryLayout.size(ofValue: buffer)) inflate(&stream, Z_FINISH) let length: size_t = MemoryLayout.size(ofValue: buffer) - Int(stream.avail_out) if length > 0 { data.append(buffer, length: length) } buffer.deallocate(capacity: bufferSize) } inflateEnd(&stream) return (NSData(data: data as Data) as Data) } ================================================ FILE: Tests/MNISTTests/DownloaderTests.swift ================================================ import XCTest class DownloaderTests: XCTestCase { func testDownloadTestData() { let testData = downloadTestData() XCTAssertEqual(testData.images.count, 7840016) XCTAssertEqual(testData.images.sha1, "65e11ec1fd220343092a5070b58418b5c2644e26") } } ================================================ FILE: Tests/MNISTTests/Info.plist ================================================ NSAppTransportSecurity NSExceptionDomains yann.lecun.com NSAllowsArbitraryLoads CFBundleDevelopmentRegion en CFBundleExecutable $(EXECUTABLE_NAME) CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion 6.0 CFBundleName $(PRODUCT_NAME) CFBundlePackageType BNDL CFBundleShortVersionString 1.0 CFBundleVersion 1 ================================================ FILE: Tests/MNISTTests/MNISTTests-Bridging-Header.h ================================================ #ifndef MNISTTests_Bridging_Header_h #define MNISTTests_Bridging_Header_h #import #import "zlib.h" #endif /* MNISTTests_Bridging_Header_h */ ================================================ FILE: Tests/MNISTTests/SHA1.swift ================================================ import Foundation extension Data { var sha1: String { let data = self var digest = [UInt8](repeating: 0, count: Int(CC_SHA1_DIGEST_LENGTH)) CC_SHA1((data as NSData).bytes, CC_LONG(data.count), &digest) let hexBytes = digest.map { String(format: "%02hhx", $0) } return hexBytes.joined(separator: "") } } ================================================ FILE: Tests/TensorSwiftTests/CalculationPerformanceTests.swift ================================================ import XCTest @testable import TensorSwift private func getTensor1000x1000() -> Tensor { let elements = [Float](repeating: 0.1 ,count: 1000*1000) return Tensor(shape: [1000, 1000], elements: elements) } private func getTensor1x1000() -> Tensor { let elements = [Float](repeating: 0, count: 1000) return Tensor(shape: [1, 1000], elements: elements) } class CalculationPerformanceTests : XCTestCase { func testElementAccess(){ let W = getTensor1000x1000() measure{ for _ in 0..<100000{ let _ = W[500,500] } } } func testElementAccessRaw(){ let W = getTensor1000x1000() measure{ for _ in 0..<100000{ let _ = W.elements[500*W.shape.dimensions[1].value + 500] } } } func testMultiplication(){ let W = getTensor1000x1000() let x = getTensor1x1000() measure{ let _ = x.matmul(W) } } func testMultiplicationRaw() { let W = getTensor1000x1000() let x = getTensor1x1000() measure{ let xRow = x.shape.dimensions[0].value let WRow = W.shape.dimensions[0].value let WColumn = W.shape.dimensions[1].value var elements = [Float](repeating: 0, count: 1000) for r in 0.. () throws -> Void)] { return [ ("testElementAccess", testElementAccess), ("testElementAccessRaw", testElementAccessRaw), ("testElementMultiplication", testMultiplication), ("testElementMultiplicationRaw", testMultiplicationRaw), ] } } ================================================ FILE: Tests/TensorSwiftTests/DimensionTests.swift ================================================ import XCTest @testable import TensorSwift class DimensionTests: XCTestCase { func testAdd() { do { let a = Dimension(2) let b = Dimension(3) XCTAssertEqual(a + b, 5) } } func testSub() { do { let a = Dimension(3) let b = Dimension(2) XCTAssertEqual(a - b, 1) } } func testMul() { do { let a = Dimension(2) let b = Dimension(3) XCTAssertEqual(a * b, 6) } } func testDiv() { do { let a = Dimension(6) let b = Dimension(2) XCTAssertEqual(a / b, 3) } } static var allTests : [(String, (DimensionTests) -> () throws -> Void)] { return [ ("testAdd", testAdd), ("testSub", testSub), ("testMul", testMul), ("testDiv", testDiv), ] } } ================================================ FILE: Tests/TensorSwiftTests/Info.plist ================================================ CFBundleDevelopmentRegion en CFBundleExecutable $(EXECUTABLE_NAME) CFBundleIdentifier $(PRODUCT_BUNDLE_IDENTIFIER) CFBundleInfoDictionaryVersion 6.0 CFBundleName $(PRODUCT_NAME) CFBundlePackageType BNDL CFBundleShortVersionString 1.0 CFBundleSignature ???? CFBundleVersion 1 ================================================ FILE: Tests/TensorSwiftTests/PowerTests.swift ================================================ import XCTest @testable import TensorSwift class PowerTests: XCTestCase { func testScalar() { let tensor = Tensor(shape: [2, 2], elements: [1, 2, 3, 4]) let scalar = Tensor.Element(2) XCTAssertEqual(tensor ** scalar, Tensor(shape: [2, 2], elements: [1, 4, 9, 16])) XCTAssertEqual(scalar ** tensor, Tensor(shape: [2, 2], elements: [2, 4, 8, 16])) XCTAssertEqual(scalar ** tensor ** scalar, Tensor(shape: [2, 2], elements: [2, 16, 512, 65536])) } func testMatrices() { let tensor = Tensor(shape: [2, 2], elements: [1, 2, 3, 4]) let tensor2 = Tensor(shape: [2, 2], elements: [2, 2, 2, 2]) XCTAssertEqual(tensor ** tensor2, Tensor(shape: [2, 2], elements: [1, 4, 9, 16])) XCTAssertEqual(tensor ** tensor, Tensor(shape: [2, 2], elements: [1, 4, 27, 256])) XCTAssertEqual(tensor ** tensor2 ** tensor, Tensor(shape: [2, 2], elements: [1, 16, powf(3, 8), powf(4, 16)])) } static var allTests : [(String, (PowerTests) -> () throws -> Void)] { return [ ("testScalar", testScalar), ("testMatrices", testMatrices), ] } } ================================================ FILE: Tests/TensorSwiftTests/TensorMathTest.swift ================================================ import XCTest @testable import TensorSwift class TensorMathTest: XCTestCase { func testPow() { do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [6, 5, 4, 3, 2, 1]) XCTAssertEqual(a ** b, Tensor(shape: [2, 3], elements: [1, 32, 81, 64, 25, 6])) XCTAssertEqual(b ** a, Tensor(shape: [2, 3], elements: [6, 25, 64, 81, 32, 1])) } do { let a = Tensor(shape: [2, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [2], elements: [2, 3]) XCTAssertEqual(a ** b, Tensor(shape: [2, 3, 2], elements: [1, 8, 9, 64, 25, 216, 49, 512, 81, 1000, 121, 1728])) XCTAssertEqual(b ** a, Tensor(shape: [2, 3, 2], elements: [2, 9, 8, 81, 32, 729, 128, 6561, 512, 59049, 2048, 531441])) } do { let a = Tensor(shape: [2, 1, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [3, 2], elements: [6, 5, 4, 3, 2, 1]) XCTAssertEqual(a ** b, Tensor(shape: [2, 1, 3, 2], elements: [1, 32, 81, 64, 25, 6, 117649, 32768, 6561, 1000, 121, 12])) XCTAssertEqual(b ** a, Tensor(shape: [2, 1, 3, 2], elements: [6, 25, 64, 81, 32, 1, 279936, 390625, 262144, 59049, 2048, 1])) } do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b: Float = 2.0 XCTAssertEqual(a ** b, Tensor(shape: [2, 3], elements: [1, 4, 9, 16, 25, 36])) XCTAssertEqual(b ** a, Tensor(shape: [2, 3], elements: [2, 4, 8, 16, 32, 64])) } } func testSigmoid() { do { let a = Tensor(shape: [2, 3], elements: [-10, -1, 0, 1, 10, 100]) XCTAssertEqual(a.sigmoid(), Tensor(shape: [2, 3], elements: [4.53978719e-05, 2.68941432e-01, 5.00000000e-01, 7.31058598e-01, 9.99954581e-01, 1.00000000e+00])) } } } ================================================ FILE: Tests/TensorSwiftTests/TensorNNTests.swift ================================================ import XCTest @testable import TensorSwift class TensorNNTests: XCTestCase { func testMaxPool() { do { let a = Tensor(shape: [2,3,1], elements: [0,1,2,3,4,5]) let r = a.maxPool(kernelSize: [1,3,1], strides: [1,1,1]) XCTAssertEqual(r, Tensor(shape: [2,3,1], elements: [1,2,2,4,5,5])) } do { let a = Tensor(shape: [2,2,2], elements: [0,1,2,3,4,5,6,7]) do { let r = a.maxPool(kernelSize:[1,2,1], strides: [1,1,1]) XCTAssertEqual(r, Tensor(shape: [2,2,2], elements: [2, 3, 2, 3, 6, 7, 6, 7])) } do { let r = a.maxPool(kernelSize:[1,2,1], strides: [1,2,1]) XCTAssertEqual(r, Tensor(shape: [2,1,2], elements: [2, 3, 6, 7])) } } } func testConv2d() { do { let a = Tensor(shape: [2,4,1], elements: [1,2,3,4,5,6,7,8]) do { let filter = Tensor(shape: [2,1,1,2], elements: [1,2,1,2]) let result = a.conv2d(filter: filter, strides: [1,1,1]) XCTAssertEqual(result, Tensor(shape: [2,4,2], elements: [6,12,8,16,10,20,12,24,5,10,6,12,7,14,8,16])) } do { let filter = Tensor(shape: [1,1,1,5], elements: [1,2,1,2,3]) let result = a.conv2d(filter: filter, strides: [1,1,1]) XCTAssertEqual(result, Tensor(shape: [2,4,5], elements: [1,2,1,2,3,2,4,2,4,6,3,6,3,6,9,4,8,4,8,12,5,10,5,10,15,6,12,6,12,18,7,14,7,14,21,8,16,8,16,24])) } } do { let a = Tensor(shape: [2,2,4], elements: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]) let filter = Tensor(shape: [1,1,4,2], elements: [1,2,1,2,3,2,1,1]) let result = a.conv2d(filter: filter, strides: [1,1,1]) XCTAssertEqual(result, Tensor(shape: [2,2,2], elements: [16, 16, 40, 44, 64, 72, 88, 100])) } do { let a = Tensor(shape: [4,2,2], elements: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]) let filter = Tensor(shape: [2,2,2,1], elements: [1,2,1,2,3,2,1,1]) let result = a.conv2d(filter: filter, strides: [2,2,1]) XCTAssertEqual(result, Tensor(shape: [2,1,1], elements: [58,162])) } do { let a = Tensor(shape: [4,4,1], elements: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]) let filter = Tensor(shape: [3,3,1,1], elements: [1,2,1,2,3,2,1,1,1]) let result = a.conv2d(filter: filter, strides: [3,3,1]) XCTAssertEqual(result, Tensor(shape: [2,2,1], elements: [18,33,95,113])) } do { let a = Tensor(shape: [1,3,1], elements: [1,2,3]) let filter = Tensor(shape: [1,3,1,2], elements: [1,1,2,2,3,3]) let result = a.conv2d(filter: filter, strides: [1,1,1]) XCTAssertEqual(result, Tensor(shape: [1,3,2], elements: [8, 8, 14, 14, 8, 8])) } } func testMaxPoolPerformance(){ let image = Tensor(shape: [28,28,3], element: 0.1) measure{ _ = image.maxPool(kernelSize: [2,2,1], strides: [2,2,1]) } } func testConv2dPerformance(){ let image = Tensor(shape: [28,28,1], element: 0.1) let filter = Tensor(shape: [5,5,1,16], element: 0.1) measure{ _ = image.conv2d(filter: filter, strides: [1,1,1]) } } static var allTests : [(String, (TensorNNTests) -> () throws -> Void)] { return [ ("testMaxPool", testMaxPool), ("testConv2d", testConv2d), ("testMaxPoolPerformance", testMaxPoolPerformance), ("testConv2dPerformance", testConv2dPerformance), ] } } ================================================ FILE: Tests/TensorSwiftTests/TensorSwiftSample.swift ================================================ import XCTest import TensorSwift class TensorSwiftSample: XCTestCase { func testSample() { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12]) let sum = a + b // Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18]) let mul = a * b // Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72]) let c = Tensor(shape: [3, 1], elements: [7, 8, 9]) let matmul = a.matmul(c) // Tensor(shape: [2, 1], elements: [50, 122]) let zeros = Tensor(shape: [2, 3, 4]) let ones = Tensor(shape: [2, 3, 4], element: 1) XCTAssertEqual(sum, Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18])) XCTAssertEqual(mul, Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72])) XCTAssertEqual(matmul, Tensor(shape: [2, 1], elements: [50, 122])) XCTAssertEqual(zeros, Tensor(shape: [2, 3, 4], elements: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) XCTAssertEqual(ones, Tensor(shape: [2, 3, 4], elements: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) } static var allTests : [(String, (TensorSwiftSample) -> () throws -> Void)] { return [ ("testSample", testSample), ] } } ================================================ FILE: Tests/TensorSwiftTests/TensorSwiftTests.swift ================================================ // // TensorSwiftTests.swift // TensorSwift // // Created by Araki Takehiro on 2016/09/28. // // import XCTest class TensorSwiftTests: XCTestCase { override func setUp() { super.setUp() // Put setup code here. This method is called before the invocation of each test method in the class. } override func tearDown() { // Put teardown code here. This method is called after the invocation of each test method in the class. super.tearDown() } static var allTests : [(String, (TensorSwiftTests) -> () throws -> Void)] { return [ ] } } ================================================ FILE: Tests/TensorSwiftTests/TensorTests.swift ================================================ import XCTest @testable import TensorSwift class TensorTests: XCTestCase { func testIndex() { do { let a = Tensor(shape: []) XCTAssertEqual(a.index([]), 0) } do { let a = Tensor(shape: [7]) XCTAssertEqual(a.index([3]), 3) } do { let a = Tensor(shape: [5, 7]) XCTAssertEqual(a.index([1, 2]), 9) } do { let a = Tensor(shape: [5, 7, 11]) XCTAssertEqual(a.index([3, 1, 2]), 244) } } func testAdd() { do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12]) let r = a + b XCTAssertEqual(r, Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18])) } do { let a = Tensor(shape: [2, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [2], elements: [100, 200]) XCTAssertEqual(a + b, Tensor(shape: [2, 3, 2], elements: [101, 202, 103, 204, 105, 206, 107, 208, 109, 210, 111, 212])) XCTAssertEqual(b + a, Tensor(shape: [2, 3, 2], elements: [101, 202, 103, 204, 105, 206, 107, 208, 109, 210, 111, 212])) } do { let a = Tensor(shape: [2, 1, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [3, 2], elements: [100, 200, 300, 400, 500, 600]) XCTAssertEqual(a + b, Tensor(shape: [2, 1, 3, 2], elements: [101, 202, 303, 404, 505, 606, 107, 208, 309, 410, 511, 612])) XCTAssertEqual(b + a, Tensor(shape: [2, 1, 3, 2], elements: [101, 202, 303, 404, 505, 606, 107, 208, 309, 410, 511, 612])) } } func testSub() { do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [12, 11, 10, 9, 8, 7]) let r = a - b XCTAssertEqual(r, Tensor(shape: [2, 3], elements: [-11, -9, -7, -5, -3, -1])) } do { let a = Tensor(shape: [2, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [2], elements: [100, 200]) XCTAssertEqual(a - b, Tensor(shape: [2, 3, 2], elements: [-99, -198, -97, -196, -95, -194, -93, -192, -91, -190, -89, -188])) XCTAssertEqual(b - a, Tensor(shape: [2, 3, 2], elements: [99, 198, 97, 196, 95, 194, 93, 192, 91, 190, 89, 188])) } do { let a = Tensor(shape: [2, 1, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [3, 2], elements: [100, 200, 300, 400, 500, 600]) XCTAssertEqual(a - b, Tensor(shape: [2, 1, 3, 2], elements: [-99, -198, -297, -396, -495, -594, -93, -192, -291, -390, -489, -588])) XCTAssertEqual(b - a, Tensor(shape: [2, 1, 3, 2], elements: [99, 198, 297, 396, 495, 594, 93, 192, 291, 390, 489, 588])) } } func testMul() { do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12]) XCTAssertEqual(a * b, Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72])) XCTAssertEqual(b * a, Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72])) } do { let a = Tensor(shape: [2, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [2], elements: [10, 100]) XCTAssertEqual(a * b, Tensor(shape: [2, 3, 2], elements: [10, 200, 30, 400, 50, 600, 70, 800, 90, 1000, 110, 1200])) XCTAssertEqual(b * a, Tensor(shape: [2, 3, 2], elements: [10, 200, 30, 400, 50, 600, 70, 800, 90, 1000, 110, 1200])) } do { let a = Tensor(shape: [2, 1, 3, 2], elements: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) let b = Tensor(shape: [3, 2], elements: [10, 100, 1000, -10, -100, -1000]) XCTAssertEqual(a * b, Tensor(shape: [2, 1, 3, 2], elements: [10, 200, 3000, -40, -500, -6000, 70, 800, 9000, -100, -1100, -12000])) XCTAssertEqual(b * a, Tensor(shape: [2, 1, 3, 2], elements: [10, 200, 3000, -40, -500, -6000, 70, 800, 9000, -100, -1100, -12000])) } do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b: Float = 2.0 XCTAssertEqual(a * b, Tensor(shape: [2, 3], elements: [2, 4, 6, 8, 10, 12])) XCTAssertEqual(b * a, Tensor(shape: [2, 3], elements: [2, 4, 6, 8, 10, 12])) } } func testDiv() { do { let a = Tensor(shape: [2, 3], elements: [2048, 512, 128, 32, 8, 2]) let b = Tensor(shape: [2, 3], elements: [2, 4, 8, 16, 32, 64]) XCTAssertEqual(a / b, Tensor(shape: [2, 3], elements: [1024, 128, 16, 2, 0.25, 0.03125])) XCTAssertEqual(b / a, Tensor(shape: [2, 3], elements: [0.0009765625, 0.0078125, 0.0625, 0.5, 4, 32])) } do { let a = Tensor(shape: [2, 3, 2], elements: [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]) let b = Tensor(shape: [2], elements: [8, 2]) XCTAssertEqual(a / b, Tensor(shape: [2, 3, 2], elements: [0.25, 2.0, 1.0, 8.0, 4.0, 32.0, 16.0, 128.0, 64.0, 512.0, 256.0, 2048.0])) XCTAssertEqual(b / a, Tensor(shape: [2, 3, 2], elements: [4.0, 0.5, 1.0, 0.125, 0.25, 0.03125, 0.0625, 0.0078125, 0.015625, 0.001953125, 0.00390625, 0.00048828125])) } do { let a = Tensor(shape: [3, 1, 2, 2], elements: [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]) let b = Tensor(shape: [2, 2], elements: [8, 2, -8, -2]) XCTAssertEqual(a / b, Tensor(shape: [3, 1, 2, 2], elements: [0.25, 2.0, -1.0, -8.0, 4.0, 32.0, -16.0, -128.0, 64.0, 512.0, -256.0, -2048.0])) XCTAssertEqual(b / a, Tensor(shape: [3, 1, 2, 2], elements: [4.0, 0.5, -1.0, -0.125, 0.25, 0.03125, -0.0625, -0.0078125, 0.015625, 0.001953125, -0.00390625, -0.00048828125])) } do { let a = Tensor(shape:[2, 3], elements: [ 1, 2, 4, 8, 16, 32]) let b: Float = 2.0 XCTAssertEqual(a / b, Tensor(shape: [2, 3], elements: [0.5, 1, 2, 4, 8, 16])) XCTAssertEqual(b / a, Tensor(shape: [2, 3], elements: [2, 1, 0.5, 0.25, 0.125, 0.0625])) } } func testMatmul() { do { let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6]) let b = Tensor(shape: [3, 4], elements: [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) let r = a.matmul(b) XCTAssertEqual(r, Tensor(shape: [2, 4], elements: [74, 80, 86, 92, 173, 188, 203, 218])) } do { let a = Tensor(shape: [3, 3], elements: [1, 1, 1, 2, 2, 2, 3, 3, 3]) let b = Tensor(shape: [3, 3], elements: [1, 1, 1, 2, 2, 2, 3, 3, 3]) let r = a.matmul(b) XCTAssertEqual(r, Tensor(shape: [3, 3], elements: [6, 6, 6, 12, 12, 12, 18, 18, 18])) } } func testMatmulPerformance(){ let a = Tensor(shape: [1000, 1000], element: 0.1) let b = Tensor(shape: [1000, 1000], element: 0.1) measure{ _ = a.matmul(b) } } static var allTests : [(String, (TensorTests) -> () throws -> Void)] { return [ ("testIndex", testIndex), ("testAdd", testAdd), ("testSub", testSub), ("testMul", testMul), ("testDiv", testDiv), ("testMatmul", testMatmul), ("testMatmulPerformance", testMatmulPerformance), ] } }