torch_conv_transpose1d function

Conv_transpose1d

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)
}