fasiha

import numpy as np
import numpy.fft as fft


def stft(x, Nwin, Nfft=None):
    """
    Short-time Fourier transform: convert a 1D vector to a 2D array

    The short-time Fourier transform (STFT) breaks a long vector into disjoint
    chunks (no overlap) and runs an FFT (Fast Fourier Transform) on each chunk.

rxwei

See the official Differentiable Programming Manifesto instead.

erogol

marschhuynh

# install_certifi.py
#
# sample script to install or update a set of default Root Certificates
# for the ssl module.  Uses the certificates provided by the certifi package:
#       https://pypi.python.org/pypi/certifi

import os
import os.path
import ssl
import stat

cyrilzakka

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

struct ContentView: View {

rxwei

Upstreaming Swift AutoDiff

Author: Richard Wei ([email protected]) 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

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).

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.

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>
    

marcrasi