Conv3D

@frozen
public struct Conv3D<Scalar> : Layer where Scalar : TensorFlowFloatingPoint

A 3-D convolution layer for spatial/spatio-temporal convolution over images.

This layer creates a convolution filter that is convolved with the layer input to produce a tensor of outputs.

• filter

  The 5-D convolution filter.

  Declaration

  public var filter: Tensor<Scalar>

• bias

  The bias vector.

  Declaration

  public var bias: Tensor<Scalar>

• activation

  The element-wise activation function.

  Declaration

  @noDerivative
  public let activation: Activation

• strides

  The strides of the sliding window for spatial dimensions.

  Declaration

  @noDerivative
  public let strides: (Int, Int, Int)

• padding

  The padding algorithm for convolution.

  Declaration

  @noDerivative
  public let padding: Padding

• dilations

  The dilation factor for spatial/spatio temporal dimensions.

  Declaration

  @noDerivative
  public let dilations: (Int, Int, Int)

• Activation

  The element-wise activation function type.

  Declaration

  public typealias Activation = @differentiable (Tensor<Scalar>) -> Tensor<Scalar>

• init(filter:bias:activation:strides:padding:dilations:)

  Creates a Conv3D layer with the specified filter, bias, activation function, strides, and padding.

  Declaration

  public init(
    filter: Tensor<Scalar>,
    bias: Tensor<Scalar>? = nil,
    activation: @escaping Activation = identity,
    strides: (Int, Int, Int) = (1, 1, 1),
    padding: Padding = .valid,
    dilations: (Int, Int, Int) = (1, 1, 1)
  )

  Parameters

  filter	The 5-D convolution filter of shape [filter depth, filter height, filter width, input channel count, output channel count].
  bias	The bias vector of shape [output channel count].
  activation	The element-wise activation function.
  strides	The strides of the sliding window for spatial dimensions, i.e. (stride depth, stride height, stride width)
  padding	The padding algorithm for convolution.
  dilations	The dilation factor for spatial/spatio-temporal dimensions.

• forward(_:)

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

  The output spatial dimensions are computed as:

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

  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 count, input depth, input height, input width, input channel count].

  Return Value

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

• init(filterShape:strides:padding:dilations:activation:useBias:filterInitializer:biasInitializer:)

  Creates a Conv3D layer with the specified filter shape, strides, padding, dilations and element-wise activation function. The filter tensor is initialized using Glorot uniform initialization with the specified seed. The bias vector is initialized with zeros.

  Declaration

  public init(
    filterShape: (Int, Int, Int, Int, Int),
    strides: (Int, Int, Int) = (1, 1, 1),
    padding: Padding = .valid,
    dilations: (Int, Int, Int) = (1, 1, 1),
    activation: @escaping Activation = identity,
    useBias: Bool = true,
    filterInitializer: ParameterInitializer<Scalar> = glorotUniform(),
    biasInitializer: ParameterInitializer<Scalar> = zeros()
  )

  Parameters

  filterShape	The shape of the 5-D convolution filter, representing (filter depth, filter height, filter width, input channel count, output channel count).
  strides	The strides of the sliding window for spatial dimensions, i.e. (stride depth, stride height, stride width)
  padding	The padding algorithm for convolution.
  dilations	The dilation factor for spatial/spatio-temporal dimensions.
  activation	The element-wise activation function.
  filterInitializer	Initializer to use for the filter parameters.
  biasInitializer	Initializer to use for the bias parameters.