Erosion2D

@frozen
public struct Erosion2D<Scalar> : Layer where Scalar : TensorFlowFloatingPoint

A 2-D morphological erosion layer

This layer returns the morphogical erosion of the input tensor with the provided filters

• filter

  The 4-D dilation filter.

  Declaration

  public var filter: Tensor<Scalar>

• strides

  The strides of the sliding window for spatial dimensions.

  Declaration

  @noDerivative
  public let strides: (Int, Int)

• padding

  The padding algorithm for dilation.

  Declaration

  @noDerivative
  public let padding: Padding

• rates

  The dilation factor for spatial dimensions.

  Declaration

  @noDerivative
  public let rates: (Int, Int)

• init(filter:strides:rates:padding:)

  Creates a Erosion2D layer with the specified filter, strides, dilations and padding.

  Declaration

  public init(
    filter: Tensor<Scalar>,
    strides: (Int, Int) = (1, 1),
    rates: (Int, Int) = (1, 1),
    padding: Padding = .valid
  )

  Parameters

  filter	The 4-D dilation filter of shape [filter height, filter width, input channel count, output channel count].
  strides	The strides of the sliding window for spatial dimensions, i.e. (stride height, stride width).
  rates	The dilation rates for spatial dimensions, i.e. (dilation height, dilation width).
  padding	The padding algorithm for dilation.

• forward(_:)

  Returns the output obtained from applying the layer to the given input.

  The output spatial dimensions are computed as:

  output height = (input height + 2 * padding height - (dilation height * (filter height - 1) + 1)) / stride height + 1

  output width = (input width + 2 * padding width - (dilation width * (filter width - 1) + 1)) / stride width + 1

  and padding sizes are determined by the padding scheme.

  Note

  Padding size equals zero when using .valid.

  Declaration

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

  Parameters

  input

  The input to the layer of shape [batch size, input height, input width, input channel count].

  Return Value

  The output of shape [batch count, output height, output width, output channel count].