MicrexIT

"The answer to life is \(y)."

MicrexIT

//define types inline 
let a: Int = 1
let b: Double = -2.0
var c: Float = 3.0
var d: Bool = true
var e: String = "Hello!"
// print w type
print(type(of: a)) // Prints Int
print(type(of: b)) // Prints Doube
print(type(of: c)) // Prints Float

MicrexIT

let x = 42.0 // immutable variable
// x = 21.5 this will throw an error
var y = 0   // can mutate
y = 42

MicrexIT

let pred = model(X)
// print(pred)
var predicted = [Bool]()
let npred = pred.makeNumpyArray()
for i in 0..<pred.shape[0] {
  if npred[i][0] < 0.5 {
      predicted.append(false)
  } else {
      predicted.append(true)
  }

MicrexIT

let epochCount = 30 // nbr of training steps/epochs
// TODO
// func accuracy(predictions: Tensor<Int32>, truths: Tensor<Int32>) -> Float {
//     return Tensor<Float>(predictions .== truths).mean().scalarized()
// }
// let X: Tensor<Float> = [[0, 0], [0, 1], [1, 0], [1, 1]]
// let y: Tensor<Float> = [0, 1, 1, 0]
for _ in 1...epochCount {
  let 𝛁model = gradient(at: model) { (model: NeuralNet) -> Tensor<Float> in
      let ŷ = model(X).withDerivative { print("∂L/∂ŷ =", $0) }

MicrexIT

let optimizer = SGD(for: model, learningRate: 0.05)

MicrexIT

// import TensorFlow
let hiddenSize: Int = 10
// Neural Network has a struct containing layers
struct NeuralNet: Layer { // Extends the Layer type provided by S4TF
    var layer1 = Dense<Float>(inputSize: 2, outputSize: hiddenSize, activation: relu)
    var layer2 = Dense<Float>(inputSize: hiddenSize, outputSize: 1)  // hidden layer
  
    @differentiable // tells swift compiler these function can be derived
    func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
        return input.sequenced(through: layer1, layer2)

MicrexIT

import TensorFlow
var X: Tensor<Float> = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
var y: Tensor<Float> = [0, 1, 1, 0]

MicrexIT

/**
 No imports, just plain Swift 😎
 **/
@differentiable
func power2(_ x:Double) -> Double{
  return x * x
}
gradient(at:0.5) {x in power2(x)}
// Prints 1.0
// NB: grad(x*x) = 2*x 

MicrexIT

// This cell is here to display plots inside a Jupyter Notebook.
// Do not copy it into another environment.
%include "EnableIPythonDisplay.swift"
IPythonDisplay.shell.enable_matplotlib("inline")