GaussianDropout

public struct GaussianDropout<Scalar> : ParameterlessLayer where Scalar : TensorFlowFloatingPoint

GaussianDropout multiplies the input with the noise sampled from a normal distribution with mean 1.0.

Because this is a regularization layer, it is only active during training time. During inference, GaussianDropout passes through the input unmodified.

• TangentVector

  Declaration

  public typealias TangentVector = EmptyTangentVector

• probability

  Declaration

  @noDerivative
  public let probability: Scalar

• standardDeviation

  Declaration

  @noDerivative
  public let standardDeviation: Scalar

• init(probability:)

  Creates a Gaussian dropout layer.

  Precondition

  probability must be a value between 0 and 1 (inclusive).

  Declaration

  public init(probability: Scalar)

  Parameters

  probability

  The probability of a node dropping out.

• forward(_:)

  Applies multiplicative 1-centered Gaussian noise to the input during training only.

  Declaration

  @differentiable
  public func forward(_ input: Tensor<Scalar>) -> Tensor<Scalar>