Missed TensorFlow Dev Summit? Check out the video playlist. Watch recordings

RAdam

public class RAdam<Model: Differentiable>: Optimizer
where
  Model.TangentVector: VectorProtocol & PointwiseMultiplicative & ElementaryFunctions
    & KeyPathIterable,
  Model.TangentVector.VectorSpaceScalar == Float

RAdam optimizer.

Rectified Adam, a variant of Adam that introduces a term to rectify the adaptive learning rate variance.

Reference: [“On the Variance of the Adaptive Learning Rate and Beyond”] https://arxiv.org/pdf/1908.03265.pdf

  • Declaration

    public typealias Model = Model
  • The learning rate.

    Declaration

    public var learningRate: Float
  • A coefficient used to calculate the first and second moments of the gradients.

    Declaration

    public var beta1: Float
  • A coefficient used to calculate the first and second moments of the gradients.

    Declaration

    public var beta2: Float
  • A small scalar added to the denominator to improve numerical stability.

    Declaration

    public var epsilon: Float
  • The learning rate decay.

    Declaration

    public var decay: Float
  • The current step.

    Declaration

    public var step: Int
  • The first moments of the weights.

    Declaration

    public var firstMoments: Model.TangentVector
  • The second moments of the weights.

    Declaration

    public var secondMoments: Model.TangentVector
  • Declaration

    public init(
      for model: __shared Model,
      learningRate: Float = 1e-3,
      beta1: Float = 0.9,
      beta2: Float = 0.999,
      epsilon: Float = 1e-8,
      decay: Float = 0
    )
  • Declaration

    public func update(_ model: inout Model, along direction: Model.TangentVector)
  • Declaration

    public required init(copying other: RAdam, to device: Device)