Tuần tự

public struct Sequential<Layer1: Module, Layer2: Layer>: Module
    where Layer1.Output == Layer2.Input,
          Layer1.TangentVector.VectorSpaceScalar == Layer2.TangentVector.VectorSpaceScalar

extension Sequential: Layer where Layer1: Layer

Một lớp bao gồm hai hoặc nhiều lớp khác một cách tuần tự.

Ví dụ:

• Xây dựng mô hình perceptron 2 lớp đơn giản cho MNIST:

let inputSize = 28 * 28
let hiddenSize = 300
var classifier = Sequential {
     Dense<Float>(inputSize: inputSize, outputSize: hiddenSize, activation: relu)
     Dense<Float>(inputSize: hiddenSize, outputSize: 3, activation: identity)
 }

• Xây dựng bộ mã hóa tự động cho MNIST:

var autoencoder = Sequential {
    // The encoder.
    Dense<Float>(inputSize: 28 * 28, outputSize: 128, activation: relu)
    Dense<Float>(inputSize: 128, outputSize: 64, activation: relu)
    Dense<Float>(inputSize: 64, outputSize: 12, activation: relu)
    Dense<Float>(inputSize: 12, outputSize: 3, activation: relu)
    // The decoder.
    Dense<Float>(inputSize: 3, outputSize: 12, activation: relu)
    Dense<Float>(inputSize: 12, outputSize: 64, activation: relu)
    Dense<Float>(inputSize: 64, outputSize: 128, activation: relu)
    Dense<Float>(inputSize: 128, outputSize: imageHeight * imageWidth, activation: tanh)
}

• lớp 1
  Tuyên ngôn

  public var layer1: Layer1

• lớp 2
  Tuyên ngôn

  public var layer2: Layer2

• init(_:_:)
  Tuyên ngôn

  public init(_ layer1: Layer1, _ layer2: Layer2)

• callAsFunction(_:)
  Tuyên ngôn

  @differentiable(wrt: self)
  public func callAsFunction(_ input: Layer1.Input) -> Layer2.Output

• init(lớp :)
  Tuyên ngôn

  public init(@LayerBuilder layers: () -> `Self`)

Có sẵn ở `Layer1`: `Layer`

• callAsFunction(_:)
  Tuyên ngôn

  @differentiable
  public func callAsFunction(_ input: Layer1.Input) -> Layer2.Output