TransposedConv1D

@frozen
public struct TransposedConv1D<Scalar> : Layer where Scalar : TensorFlowFloatingPoint

A 1-D transposed convolution layer (e.g. temporal transposed convolution over images).

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

  • The 1-D convolution kernel.

    Declaration

    public var filter: Tensor<Scalar>

  • The bias vector.

    Declaration

    public var bias: Tensor<Scalar>

  • The element-wise activation function.

    Declaration

    @noDerivative
public let activation: Activation

  • The strides of the sliding window for spatial dimensions.

    Declaration

    @noDerivative
public let stride: Int

  • The padding algorithm for convolution.

    Declaration

    @noDerivative
public let padding: Padding

  • The paddingIndex property allows us to handle computation based on padding.

    Declaration

    @noDerivative
public let paddingIndex: Int

  • The element-wise activation function type.

    Declaration

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

  • Creates a TransposedConv1D 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,
  stride: Int = 1,
  padding: Padding = .valid
)

    Parameters

    filter

    The 3-D convolution kernel.
    bias

    The bias vector.
    activation

    The element-wise activation function.
    strides

    The strides of the sliding window for spatial dimensions.
    padding

    The padding algorithm for convolution.

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

    Declaration

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

    Parameters

    input

    The input to the layer.

    Return Value

    The output.

  • Creates a TransposedConv1D layer with the specified filter shape, strides, padding, and element-wise activation function. The filter tensor is initialized using Glorot uniform initialization with the specified generator. The bias vector is initialized with zeros.

    Declaration

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

    Parameters

    filterShape

    The shape of the 3-D convolution kernel.
    strides

    The strides of the sliding window for spatial dimensions.
    padding

    The padding algorithm for convolution.
    activation

    The element-wise activation function.
    generator

    The random number generator for initialization.