Swift code for Medium article on Neuralyzer

IrisWithNeuralyzer.swift

import Neuralyzer

struct IrisData: Codable {
  let row: [Double]
}

var parsedData: [IrisData] = []
if let file = Bundle.main.path(forResource: "iris", ofType: "json") {
  do {
    let jsonDecoder = JSONDecoder()
    let data = try Data(contentsOf: URL(fileURLWithPath: file), options: .alwaysMapped)
    let result = try? jsonDecoder.decode([IrisData].self, from: data)
    if let result = result {
      parsedData = result
    } else {
      print("Could not get json from file, make sure that file contains valid json.")
    }
  } catch let error {
    print(error.localizedDescription)
  }
} else {
  print("Fail")
}

let array = parsedData.map { $0.row }
let data = Matrix<Double>(unpack: array)
data.columns
// The first 4 columns are features, the last three are one-hot classes
let X = Matrix(copy: data[0..<150, 0..<4])
let Y = Matrix(copy: data[0..<150, 4..<7])

let loss = CategoricalCrossentropy()
let optim = RMSProp(loss: loss, learningRate: 0.1)
let net = Sequential(inputSize: 4, optimizer: optim)
net.add(layer: Dense(units: 16, activation: Sigmoid()))
net.add(layer: Dense(units: 8, activation: Sigmoid()))
net.add(layer: Dense(units: 3, activation: Softmax()))
// Let's go!
let epochs = 20
let batchSize = 10
for epoch in 0..<epochs {
  let t0 = Date()
  var last = 0
  for now in stride(from: 9, to: X.rows, by: batchSize) {
    let x = X[last...now, 0..<4]
    let y = Y[last...now, 0..<3]
    net.train(input: x, target: y)
    last = now
  }
  let estimate = net.predict(input: X) // Grab estimates for all of X
  let l = loss.fx(input: estimate, target: Y) // Compute loss at current epoch
  let accuracy = evaluate(estimate: estimate, target: Y)
  let time = Date().timeIntervalSince(t0)  // How long did that take?
  // Let's track our progress!
  print("Epoch \(epoch+1)/\(epochs)")
  print("\(time)s - loss: \(l) - accuracy \(accuracy)")
}

// The last example is of type Iris-Virginica
let input = Matrix(copy: X[149..<150, 0..<4])
let names: [String] = ["Setosa", "Versicolour", "Virginica"]
print(net.predict(input: input))
print("The last example is of type Iris-\(names[2])!")

var json = net.spec  // Grab the spec for the network by itself
// Add our 'training features': optimizer and loss function
json["training"] = ["optimizer": optim.spec, "loss": loss.spec]
print(json)