torch: torch_conv_transpose1d – R documentation

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torch

torch_conv_transpose1d

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

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release

torch_conv_transpose1d

Description

Usage

Arguments

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

Examples

torch

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