rorodriguez116

import Photos

class PhotoCaptureProcessor: NSObject {
    
    lazy var context = CIContext()

    private(set) var requestedPhotoSettings: AVCapturePhotoSettings
    
    private let willCapturePhotoAnimation: () -> Void
    

JacopoMangiavacchi

import Foundation
import MLCompute
import PlaygroundSupport

let page = PlaygroundPage.current
page.needsIndefiniteExecution = true

let tensor1 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)
let tensor2 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)
let tensor3 = MLCTensor(descriptor: MLCTensorDescriptor(shape: [6, 1], dataType: .float32)!)

cweinberger

/* NOTE: check out 3-MySQLDataConvertibleEnum_Better_Version.swift. */

import Fluent
import FluentMySQL
import Vapor

protocol MySQLDataConvertibleEnum: MySQLDataConvertible, ReflectionDecodable, CaseIterable { }

extension MySQLDataConvertibleEnum where Self: RawRepresentable, Self.RawValue == String {
    func convertToMySQLData() -> MySQLData {

SumanSudhir

func downloadMovieLensDatasetIfNotPresent() -> String{
    let localURL = URL(fileURLWithPath: FileManager.default.currentDirectoryPath)
    let dataFolder = DatasetUtilities.downloadResource(
        filename: "ml-25m",
        fileExtension: "zip",
        remoteRoot: URL(string: "http://files.grouplens.org/datasets/movielens/")!,
        localStorageDirectory: localURL.appendingPathComponent("data/", isDirectory: true)
        )

    return try! String(contentsOf: dataFolder.appendingPathComponent("ratings.csv"), encoding: .utf8)

marcrasi

kongzii

import Foundation
import Python
import TensorFlow

public struct MyModel : Layer {
    public var conv1d: Conv1D<Float>
    public var dense1: Dense<Float>
    public var dropout: Dropout<Float>
    public var denseOut: Dense<Float>
    

hollance

import CoreML
import Combine

extension Publisher where Self.Output: MLFeatureProvider {
  /**
   Operator that lets you run a Core ML model as part of a Combine chain.

   It accepts an MLFeatureProvider object as input, and, if all goes well,
   returns another MLFeatureProvider with the model outputs.

dan-zheng

Retroactive derivative registration: register derivatives for functions in other modules.

Previously:

• If module A defines func foo, then its derivatives must be in the same module.

With retroactive derivative registration:

• If module A defines func foo;
• And if module B imports module A and defines @differentiating(foo) func derivativeFoo:
• Then module C can import modules A and B and differentiate func foo (e.g. via differentiation APIs).

rxwei

Upstreaming Swift AutoDiff

Author: Richard Wei (rxwei@google.com) on behalf of the Swift for TensorFlow team

Last updated: October 2, 2019

Overview

The differentiable programming feature (AutoDiff) has been incubated in the 'tensorflow' branch of apple/swift since December 2017 and released as part of the Swift for TensorFlow toolchains. The Differentiable Programming Mega-Proposal, which serves as a manifesto, received general positive feedback from the community, but there is a long way between receiving conceptual approval and obtaining Swift Evolution approval of such a large feature. We would like to merge the pieces into the 'master' branch under a gate to further development and bake the feature on master, just like Apple develops its major features

cyrilzakka

//
//  ContentView.swift
//  Scribe
//
//  Created by Cyril Zakka on 7/21/19.
//  Copyright © 2019 Cyril Zakka. All rights reserved.
//
import SwiftUI

struct ContentView: View {