iOSエンジニアのための、SwiftからPythonのライブラリを使って機械学習する方法 / Machine Learning using Python from Swift for iOS Engineer

Swift 4.2では他言語のAPIをSwiftから簡単に呼び出せるようになります。これ自体は汎用的な仕組みですが、最大の目的はSwiftからPythonの資産を活用できるようにし、Python並の機械学習を実現することのようです。日々Swiftを書いているiOSエンジニアにとってこれは大きなチャンスです。このトークではiOSエンジニアに向けて、SwiftのコードでPythonのライブラリを使って機械学習する方法を紹介します。

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1. iOS ΤϯδχΞͷͨΊͷɺ Swi$ ͔Β Python ͷϥΠϒϥϦΛ ࢖ͬͯػցֶश͢Δํ๏ Yuta Koshizawa @koher
3. ! ػցֶशೖ໳
5. ! ෼ྨʢ Classifica)on ʣ
6. ྫ: ݂ӷݕࠪʹΑΔഏ͕Μͷ਍அ
7. ྫ: ݂ӷݕࠪʹΑΔഏ͕Μͷ਍அ ݈߁ͳਓͷσʔλ ඃݕऀ ID CYFRA (ng/ml) CEA (ng/ml) 0001 3.0 4.0 0002 2.8 4.5 0003 3.1 3.7 ... ... ...
8. ྫ: ݂ӷݕࠪʹΑΔഏ͕Μͷ਍அ ഏ͕Μͷਓͷσʔλ ඃݕऀ ID CYFRA (ng/ml) CEA (ng/ml) 1001 4.0 6.0 1002 3.4 7.0 1003 4.9 4.8 ... ... ...
9. ID: 0001, CYFRA: 3.0 ng/ml, CEA: 4.0 ng/ml
10. ID: 0002, CYFRA: 2.8 ng/ml, CEA: 4.5 ng/ml
11. ID: 0003, CYFRA: 3.1 ng/ml, CEA: 3.7 ng/ml
12. ɾɾɾ
13. ID: 1001, CYFRA: 4.0 ng/ml, CEA: 6.0 ng/ml
14. ID: 1002, CYFRA: 3.4 ng/ml, CEA: 7.0 ng/ml
15. ID: 1003, CYFRA: 4.9 ng/ml, CEA: 4.8 ng/ml
16. ɾɾɾ
17. ઢͷԼ → ݈߁ɺઢͷ্ → ഏ͕Μ
18. CYFRA: 3.2 ng/ml, CEA: 3.8 ng/ml
19. CYFRA: 4.5 ng/ml, CEA: 5.9 ng/ml
20. ػցֶश ! → ྑͦ͞͏ͳ௚ઢΛܾΊΔ
21. y = a x + b
22. y = a x + b
23. y = a x + b
24. y = -1.78 x + 11.1
25. ௚ઢͰ෼ྨ
26. ௚ઢͰ෼ྨ
27. ͜Μͳσʔλͩͱʁ
28. ௚ઢͰ෼ྨ͢Δͷ͸ແཧ͋Γ
29. ͜ΜͳۂઢͰ෼ྨ͍ͨ͠
30. ௚ઢͷࣜͰ͸ແཧ
31. ΑΓෳࡶͳ͕ࣜඞཁ
32. ෳࡶͳํ͕දݱྗ͕ߴͯ͘ྑ͍ʁ
33. ͞ΒʹෳࡶͳࣜʹΑΔۂઢ
34. CYFRA: 3.2 ng/ml, CEA: 4.7 ng/ml
35. CYFRA: 3.2 ng/ml, CEA: 4.7 ng/ml
36. ! ෳࡶͳϞσϧͳΒྑ͍ͱ͍͏Θ͚Ͱ͸ͳ͍
37. ! ൚Խʢ Generaliza*on ʣ
38. ͲͷΑ͏ʹ൚ԽೳྗΛධՁ͢Ε͹Α͍ʁ
39. ! ! ܇࿅σʔλʢ Training data ʣ ! ςετσʔλʢ Test data ʣ ʹ෼͚ͯ൚ԽೳྗΛධՁ͢Δɻ
40. શσʔλ
41. ܇࿅σʔλ
42. ܇࿅σʔλΛ࢖ֶͬͯश
43. ܇࿅σʔλ: ਫ਼౓ 100%
44. ςετσʔλΛ࢖ͬͯධՁ
45. ςετσʔλ: ਫ਼౓ 90.0%
46. ܇࿅σʔλΛ࢖ֶͬͯश
47. ܇࿅σʔλ: ਫ਼౓ 97.1%
48. ςετσʔλΛ࢖ͬͯධՁ
49. ςετσʔλ: ਫ਼౓ 95.8%
50. ܇࿅σʔλΛ࢖ֶͬͯश
51. ܇࿅σʔλ: ਫ਼౓ 89.6%
52. ςετσʔλΛ࢖ͬͯධՁ
53. ςετσʔλ: ਫ਼౓ 85.8%
56. ܇࿅σʔλΛ 10 ഒʹ૿΍ͯ͠ΈΔ
57. Overfi&ng ͷͱ͖ʢ܇࿅σʔλʣ
58. Overfi&ng ͷͱ͖ʢ܇࿅σʔλʣ
59. Overfi&ng ͷͱ͖ʢςετσʔλ: 90.0%ʣ
60. Overfi&ng ͷͱ͖ʢςετσʔλ: 95.0%ʣ
61. Underfi'ng ͷͱ͖ʢ܇࿅σʔλʣ
62. Underfi'ng ͷͱ͖ʢ܇࿅σʔλʣ
63. Underfi'ng ͷͱ͖ʢςετσʔλ: 85.8%ʣ
64. Underfi'ng ͷͱ͖ʢςετσʔλ: 85.8%ʣ
65. ! ! ద߹ෆ଍ʢ Underfi(ng ʣ ! ա৒ద߹ʢ Overfi'ng ʣ Λ۠ผͯ͠ରԠํ๏Λߟ͑Δ
66. ಛ௃ྔ͕ 3 ݸ͋Δ৔߹ ඃݕऀ ID CYFRA (ng/ml) CEA (ng/ml) SCC (ng/ml) 0001 3.0 4.0 1.1 0002 2.8 4.5 1.4 0003 3.1 3.7 0.9 ... ... ... ...
67. ಛ௃ྔ͕ 3 ݸ͋Δ৔߹
68. ಛ௃ྔ͕ 3 ݸ͋Δ৔߹
69. ! 4 ࣍ݩɾߴ࣍ݩۭؒΛ Πϝʔδ͢Δํ๏
70. ࣍ݩ
71. ෼ྨͷͨΊʹ͸ۭؒΛ෼ׂ͢Δ
72. ࣍ݩͱஅ໘
73. ࣍ݩͱஅ໘
74. ࣍ݩͱஅ໘
75. ࣍ݩͱஅ໘
76. ! ௒ۂ໘ʢ Hypersurface ʣ
77. ۂ͕ͬͯͳ͍ͱ͖͸ ! ௒ฏ໘ʢ Hyperplane ʣ
78. ! ๏ଇੑΛݟ͚ͭग़͢ N ࣍ݩۭؒ͸ N - 1 ࣍ݩۭؒͰ෼ׂͰ͖Δ
79. ۭؒʢ 3 ࣍ݩʣ + ࣌ؒʢ 1 ࣍ݩʣ = 4 ࣍ݩʁ
80. 4 ࣍ݩۭ࣌ؒͷஅ໘
81. ݱࡏ͸ 4 ࣍ݩۭ࣌ؒΛ աڈͱະདྷʹ෼͚Δ௒ฏ໘
82. ! 4 ࣍ݩ͸ۭ࣌ؒͰߟ͑Δͱ૝૾͠΍͍͢
83. 2 ࣍ݩۭؒΛۂઢͰ෼͚Δ͜ͱΛߟ͑Δ
84. 10 × 10 ͷ֨ࢠঢ়ͷσʔλ
85. 10 × 10 ͷ֨ࢠঢ়ͷσʔλ
86. ֨ࢠঢ়ͷσʔλͱ࣍ݩ • 2 ࣍ݩͩͱ ݅ • 3 ࣍ݩͩͱ ݅ • 4 ࣍ݩͩͱ ݅ • ... • 80 ࣍ݩͩͱ ݅
87. ֨ࢠঢ়ͷσʔλͱ࣍ݩ • 2 ࣍ݩͩͱ ݅ • 3 ࣍ݩͩͱ ݅ • 4 ࣍ݩͩͱ ݅ • ... • 80 ࣍ݩͩͱ ݅ ← ؍ଌՄೳͳӉ஦ʹ͋Δݪࢠͷ਺
88. ! ߴ࣍ݩۭؒ͸ലେ ୯७ʹσʔλ݅਺Λ૿΍͢ํ๏Ͱ௅Ήͷ͸ແ๳
89. खॻ͖਺ࣈͷ෼ྨ 1 1 h$p:/ /scikit-learn.org/stable/datasets/index.html#op;cal-recogni;on-of-handwri$en-digits-data-set
90. 8 × 8 = 64 ըૉ
91. ೖྗ͸ 64 ࣍ݩͷσʔλ
92. ߴ࣍ݩۭؒͰͷ෼ྨͷΠϝʔδ
93. ߴ࣍ݩۭؒͰͷ෼ྨͷΠϝʔδ
94. ߴ࣍ݩۭؒͰͷ෼ྨͷΠϝʔδ
95. ߴ࣍ݩۭؒͰͷ෼ྨͷΠϝʔδ
96. ߴ࣍ݩۭؒͰͷ෼ྨͷΠϝʔδ
97. ! ߴ࣍ݩͰ͸ઢܗϞσϧ΋༗༻
98. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
99. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
100. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
101. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
102. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
103. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
104. खॻ͖਺ࣈͷ෼ྨʢ݁Ռʣ train: 0.9925760950259837 test: 0.9666666666666667
105. ! Swi% ͔Βಈతܕ෇ݴޠʹ ࿈ܞ͢Δͷʹ͔ܽͤͳ͍৽ػೳ
106. SE-0195: @dynamicMemberLookup 2 2 h$ps:/ /github.com/apple/swi6-evolu9on/blob/master/proposals/0195-dynamic-member-lookup.md
107. SE-0216: @dynamicCallable 3 3 h$ps:/ /github.com/apple/swi6-evolu9on/blob/master/proposals/0216-dynamic-callable.md
108. ੩తܕ෇ݴޠͱͷ࿈ܞ import UIKit class ViewController: UIViewController { override func viewDidLoad() { super.viewDidLoad() } }
109. ੩తܕ෇ݴޠͱͷ࿈ܞ import UIKit class ViewController: UIViewController { override func viewDidLoad() { super.viewDidLoab() // ίϯύΠϧΤϥʔ // ^ } }
110. ಈతܕ෇ݴޠͱͷ࿈ܞ import NumPy let a = array([[1, 2], [3, 4]]) print(a.shape)
111. ಈతܕ෇ݴޠͱͷ࿈ܞ import NumPy let a = array([[1, 2], [3, 4]]) print(a.shabe) // ͜Ε͕ΤϥʔʹͳΔ͔࣮ߦ࣌·ͰΘ͔Βͳ͍ // ^
112. ಈతܕ෇ݴޠͱͷ࿈ܞ import NumPy let a = array([[1, 2], [3, 4]]) print(a["shape"]) // ͜Μͳײ͕͡ Swift ʹೃછΈͦ͏
113. ಈతܕ෇ݴޠͱͷ࿈ܞ import NumPy let a = array([[1, 2], [3, 4]]) print(a.shape) // Ͱ΋͜͏ॻ͚ͳ͍ͱਏ͍
114. SE-0195: @dynamicMemberLookup _ = foo.bar foo.bar = x // @dynamicMemberLookup Ͱ↑͕↓ͷγϯλοΫεγϡΨʔʹͳΔ _ = foo[dynamicMember: "bar"] foo[dynamicMember: "bar"] = x
115. SE-0195: @dynamicMemberLookup @dynamicMemberLookup struct Foo { subscript(dynamicMember member: String) -> Foo { ... } }
116. SE-0216: @dynamicCallable foo(42, "XYZ") foo(a: 42, b: "XYZ") // @dynamicCallable Ͱ↑͕↓ͷγϯλοΫεγϡΨʔʹͳΔ foo.dynamicallyCall(withArguments: [42, "XYZ"]) foo.dynamicallyCall(withKeywordArguments: ["a": 42, "b": "XYZ"])
117. SE-0216: @dynamicCallable @dynamicCallable struct Foo { ... @discardableResult func dynamicallyCall(withArguments: [FooConvertible]) -> Foo { ... } @discardableResult func dynamicallyCall(withKeywordArguments: [String: FooConvertible]) -> Foo { ... } }
118. Swi$ for TensorFlow 4 4 h$ps:/ /medium.com/tensorflow/introducing-swi8-for-tensorflow-b75722c58df0
119. ! Swi% ͔Β Python ͷ ϥΠϒϥϦΛ࢖͏ํ๏
120. Python import numpy as np a = np.array([[1, 2, 3], [4, 5, 6]]) print(a.shape)
121. Swi$ import Python let np = Python.import("numpy") let a = np.array([[1, 2, 3], [4, 5, 6]]) print(a.shape)
123. Swi$ import Python let np = Python.import("numpy") let a = np.array([[1, 2, 3], [4, 5, 6]]) print(a.shape)
124. Swi$ import Python let np: PythonObject = Python.import("numpy") let a: PythonObject = np.array([[1, 2, 3], [4, 5, 6]]) print(a.shape)
125. PythonObject 5 @dynamicCallable @dynamicMemberLookup public struct PythonObject { ... } 5 h$ps:/ /github.com/apple/swi6/blob/tensorflow/stdlib/public/Python/Python.swi6
126. PythonObject 5 public extension PythonObject { subscript(dynamicMember memberName: String) -> PythonObject { get { guard let member = checking[dynamicMember: memberName] else { fatalError("Could not access PythonObject member '\(memberName)'") } return member } nonmutating set { let selfObject = ownedPyObject defer { Py_DecRef(selfObject) } let valueObject = newValue.ownedPyObject defer { Py_DecRef(valueObject) } if PyObject_SetAttrString(selfObject, memberName, valueObject) == -1 { try! throwPythonErrorIfPresent() fatalError(...) } } } ... } 5 h$ps:/ /github.com/apple/swi6/blob/tensorflow/stdlib/public/Python/Python.swi6
127. PythonObject 5 public extension PythonObject { subscript(dynamicMember memberName: String) -> PythonObject { get { guard let member = checking[dynamicMember: memberName] else { fatalError("Could not access PythonObject member '\(memberName)'") } return member } nonmutating set { let selfObject = ownedPyObject defer { Py_DecRef(selfObject) } let valueObject = newValue.ownedPyObject defer { Py_DecRef(valueObject) } if PyObject_SetAttrString(selfObject, memberName, valueObject) == -1 { try! throwPythonErrorIfPresent() fatalError(...) } } } ... } 5 h$ps:/ /github.com/apple/swi6/blob/tensorflow/stdlib/public/Python/Python.swi6
128. ! Swi% ͔Β Python ͷ ϥΠϒϥϦΛ࢖ͬͯػցֶश
129. खॻ͖਺ࣈͷ෼ྨʢ Python ʣ # Python # scikit-learn ͷؔ਺΍ΫϥεΛ import from sklearn.datasets import load_digits from sklearn.svm import LinearSVC from sklearn.model_selection import train_test_split # ֶश classifier = LinearSVC() dataset = load_digits() X_train, X_test, y_train, y_test = \ train_test_split(dataset["data"], dataset["target"]) classifier.fit(X_train, y_train) # ධՁ print(f"train: {classifier.score(X_train, y_train)}") print(f"test: {classifier.score(X_test, y_test)}")
130. खॻ͖਺ࣈͷ෼ྨʢ Swi% ʣ import Python // scikit-learn ͷؔ਺΍ΫϥεΛ import let load_digits = Python.import("sklearn.datasets").load_digits let LinearSVC = Python.import("sklearn.svm").LinearSVC let train_test_split = Python.import("sklearn.model_selection").train_test_split // ֶश let classifier = LinearSVC() let dataset = load_digits() let (X_train, X_test, y_train, y_test) = train_test_split(dataset["data"], dataset["target"]).tuple4 classifier.fit(X_train, y_train) // ධՁ print("train: \(classifier.score(X_train, y_train))") print("test: \(classifier.score(X_test, y_test))")
131. ࣮ߦͷ༷ࢠ
132. ! ϞσϧΛ Core ML ༻ʹ ม׵ͯ͠ iOS ΞϓϦͰ࢖͏
133. coremltools 6 6 h$ps:/ /github.com/apple/coremltools
134. Core ML ༻ͷϞσϧΛ࡞੒ // coremltools ͷ import let coremltools = Python.import("coremltools") // Ϟσϧͷม׵ͱอଘ let coreml_model = coremltools.converters.sklearn .convert(classifier) coreml_model.save("Digits.mlmodel")
135. coremltools Λ import // coremltools ͷ import let coremltools = Python.import("coremltools") // Ϟσϧͷม׵ͱอଘ let coreml_model = coremltools.converters.sklearn .convert(classifier) coreml_model.save("Digits.mlmodel")
136. ϞσϧΛม׵ // coremltools ͷ import let coremltools = Python.import("coremltools") // Ϟσϧͷม׵ͱอଘ let coreml_model = coremltools.converters.sklearn .convert(classifier) coreml_model.save("Digits.mlmodel")
137. ϞσϧΛอଘ // coremltools ͷ import let coremltools = Python.import("coremltools") // Ϟσϧͷม׵ͱอଘ let coreml_model = coremltools.converters.sklearn .convert(classifier) coreml_model.save("Digits.mlmodel")
140. iOS ΞϓϦ͔Βม׵ͨ͠ϞσϧΛར༻ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
141. ෼ྨثͷੜ੒ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
143. Digits.mlmodel ͔Β෼ྨثͷΫϥε͕ࣗಈੜ੒͞ΕΔ class Digits { var model: MLModel convenience init() { ... } func prediction(input: DigitsInput) throws -> DigitsOutput { let outFeatures = try model.prediction(from: input) let result = DigitsOutput( classLabel: outFeatures.featureValue(for: "classLabel")!.int64Value, classProbability: outFeatures.featureValue( for: "classProbability" )!.dictionaryValue as! [Int64 : Double] ) return result } ... }
144. Ϙλϯ͕ԡ͞Εͨͱ͖ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
145. ෼ྨثʹ౉͢ೖྗ஋Λੜ੒ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
146. ෼ྨ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
147. ݁ՌΛ UIAlertController Ͱදࣔ class ViewController: UIViewController { @IBOutlet var canvasView: CanvasView! private let classifier = Digits() @IBAction func pressClassifyButton(_ button: UIButton) { let image = Image<UInt8>(uiImage: canvasView.image).resizedTo(width: 8, height: 8) let input = try! MLMultiArray(shape: [8, 8], dataType: .double) var pointer = input.dataPointer.bindMemory(to: Double.self, capacity: 8 * 8) for pixel in image { pointer.pointee = Double(255 - pixel) / 16.0 pointer += 1 } let result = try! classifier.prediction(input: DigitsInput(input: input)) let alertController = UIAlertController(title: "\(result.classLabel)", message: nil, preferredStyle: .alert) alertController.addAction(UIAlertAction(title: "Dismiss", style: .default) { [weak self] _ in self?.canvasView.clear() }) present(alertController, animated: true, completion: nil) } }
148. Swi$Digits 7 7 h$ps:/ /github.com/koher/swi6-digits
149. ࣮ࡍͷͱ͜Ζ - ϓϩμΫγϣϯ؀ڥͰ࢖͏ͷ͸·ͩා͍ - ֶश͸ਪ࿦ΑΓ΋ະख़ͳٕज़Λ࢖͍΍͍͢ - Python Λ஌Βͳͯ͘ྑ͍Θ͚Ͱ͸ͳ͍ - Swift Ͱ΋ॻ͚ΔॲཧΛ Swift Ͱॻ͚Δͷ͸͏Ε͍͠ - Swift ͷະདྷΛࠓ͔ΒମݧͰ͖Δ
150. ! ࣗ෼ͰֶͿͱ͖ʹ໾ཱͭࢿྉ
151. Machine Learning | Coursera 8 8 h$ps:/ /www.coursera.org/learn/machine-learning
152. PythonͰ͸͡ΊΔػցֶश 9 9 h$ps:/ /www.oreilly.co.jp/books/9784873117980/
153. ѻΘͳ͔ͬͨτϐοΫ - ! ݕূσʔλʢ Validation data ʣ - ! લॲཧʢ Preprocessing ʣ - ! ਖ਼ଇԽʢ Regularization ʣ - ! ަࠩݕূʢ Cross-validation ʣ - ! ِཅੑʢ False Positive ʣɺِӄੑʢ False Negative ʣ - ! ద߹཰ʢ Precision ʣɺ࠶ݱ཰ʢ Recall ʣɺ ɹ F ஋ʢ F-measure ʣ - ...
155. Ϟσϧͷબ୒ʹςετσʔλΛར༻ͯ͠ ਖ਼͘͠ධՁͰ͖͍ͯΔͷ͔ʁ
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157. SE-0195: @dynamicMemberLookup 2 2 h$ps:/ /github.com/apple/swi6-evolu9on/blob/master/proposals/0195-dynamic-member-lookup.md
158. SE-0216: @dynamicCallable 3 3 h$ps:/ /github.com/apple/swi6-evolu9on/blob/master/proposals/0216-dynamic-callable.md
159. Swi$ for TensorFlow 4 4 h$ps:/ /medium.com/tensorflow/introducing-swi8-for-tensorflow-b75722c58df0
160. Swi$ for TensorFlow ͷΠϯετʔϧ 10 10 h%ps:/ /github.com/tensorflow/swi8/blob/master/Installa<on.md
161. Xcode 10 beta Λ࢖͏৔߹ sudo xcode-select -s /Applications/Xcode-beta.app/
162. Core ML ༻ʹϞσϧΛม׵ 11 11 h$ps:/ /developer.apple.com/documenta6on/coreml/conver6ngtrainedmodelstocore_ml
163. coremltools 6 6 h$ps:/ /github.com/apple/coremltools
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