SwiftDigits

Training the model

Training the model by scikit-learn from Swift using Swift for TensorFlow which makes it possible to combine Swift with Python through @dynamicMemberLookup and @dynamicCallable.

import Python

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

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)

print("train: \(classifier.score(X_train, y_train))")
print("test:  \(classifier.score(X_test, y_test))")

Converting the model to Core ML

let coremltools = Python.import("coremltools")
let coreml_model = coremltools.converters.sklearn.convert(classifier)
coreml_model.save("Digits.mlmodel")

Using the model from Core ML

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))

How to build the app

1. git submodule update --init --recursive
2. Open SwiftDigits.xcworkspace in Xcode and build it.

License

MIT