torch_conv_transpose1d: Conv_transpose1d

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Conv_transpose1d

Description

Conv_transpose1d

Usage

torch_conv_transpose1d(
  input,
  weight,
  bias = list(),
  stride = 1L,
  padding = 0L,
  output_padding = 0L,
  groups = 1L,
  dilation = 1L
)

Arguments

input	input tensor of shape (\mbox{minibatch} , \mbox{in\_channels} , iW)
weight	filters of shape (\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)
bias	optional bias of shape (\mbox{out\_channels}). Default: NULL
stride	the stride of the convolving kernel. Can be a single number or a tuple ⁠(sW,)⁠. Default: 1
padding	dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ⁠(padW,)⁠. Default: 0
output_padding	additional size added to one side of each dimension in the output shape. Can be a single number or a tuple (out_padW). Default: 0
groups	split input into groups, \mbox{in\_channels} should be divisible by the number of groups. Default: 1
dilation	the spacing between kernel elements. Can be a single number or a tuple ⁠(dW,)⁠. Default: 1

conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution".

See nn_conv_transpose1d() for details and output shape.

Examples

if (torch_is_installed()) {

inputs = torch_randn(c(20, 16, 50))
weights = torch_randn(c(16, 33, 5))
nnf_conv_transpose1d(inputs, weights)
}

torch documentation built on June 7, 2023, 6:19 p.m.