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Conv1d

Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R, R/gen-namespace.R
torch_conv1d.Rd

Conv1d

Usage

torch_conv1d(
  input,
  weight,
  bias = list(),
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  groups = 1L
)

Arguments

input

input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)

weight

filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)

bias

optional bias of shape \((\mbox{out\_channels})\). Default: NULL

stride

the stride of the convolving kernel. Can be a single number or a one-element tuple (sW,). Default: 1

padding

implicit paddings on both sides of the input. Can be a single number or a one-element tuple (padW,). Default: 0

dilation

the spacing between kernel elements. Can be a single number or a one-element tuple (dW,). Default: 1

groups

split input into groups, \(\mbox{in\_channels}\) should be divisible by the number of groups. Default: 1

conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 1D convolution over an input signal composed of several input planes.

See nn_conv1d() for details and output shape.

Examples

if (torch_is_installed()) {

filters = torch_randn(c(33, 16, 3))
inputs = torch_randn(c(20, 16, 50))
nnf_conv1d(inputs, filters)
}
#> torch_tensor
#> (1,.,.) = 
#>  Columns 1 to 8  -1.9721   0.4012  -0.1686   0.9370  -0.2506 -12.3634   0.3104   8.9371
#>    5.7967  -3.2337   8.0294  -0.2222 -11.2563   0.0434   0.5289  -9.4182
#>    1.2705  10.0972 -11.5183   3.3898   2.6910  -4.2843  -7.0170  -0.5558
#>   -0.4258   8.3421   7.6814   3.6658   6.2932   0.4534  -8.2000  -7.4022
#>   -5.0344  -8.3952  12.1052  -5.2411   9.0322 -12.3735   9.7334   1.2510
#>   -0.6253   2.2194  -1.7223   3.4245  -2.9102   8.8269  -6.5297  13.3282
#>   -0.6032  -6.2317   6.6873  14.1832   4.5295  -6.0733   3.7231   2.5772
#>    4.4295   0.6661   3.4860   2.6089  -5.8823  -0.4205  -8.9927   6.6897
#>   15.8334   2.5788  -6.7531   2.1371   0.3357  -5.1441 -19.2163  -3.3502
#>    7.2101   4.9102   1.6454   1.9889  -0.1296  -9.0576   1.4023   4.2188
#>   -0.1778   1.0934  -1.1782  10.2402   4.9157  -5.5493   0.6698   8.9676
#>    6.4662   8.2069  -7.8352   5.5900   2.3308   3.2582  -6.9394  -0.8818
#>    1.8453  11.2695  -1.2097  -7.3142   3.5464  14.8517   9.9521  -9.5284
#>    2.8641  -1.2875   4.2611  -5.7665  -4.2322  -3.1200   6.1232 -13.5325
#>    5.3822   4.0365  -2.8328   8.1731 -15.7333   1.9283   6.1614   3.0668
#>    4.1033   3.7994   3.6236  -8.2068  -5.8074 -11.6493  12.1785   6.2645
#>   19.7489  11.5494   6.2866   7.7508   6.7241 -10.6996  -7.7424  -8.2993
#>  -10.4325   5.5580   9.9403  -5.8744   6.1081   5.2264  -4.0049  -9.8615
#>  -12.4599   4.3089  13.9833   7.0562  -1.2945  -0.1112   3.8435  10.8694
#>    6.3934   7.7643 -10.3969  -5.9583   5.2962  -8.9333  -3.1270  -1.3038
#>    6.6776   7.0178   1.8489  -8.6961   5.2799   1.8824   0.1424  -7.5711
#>    3.1003  -0.1860   6.8955 -10.2304   6.1245 -13.6521  16.4608 -30.8328
#>   -1.2411  12.9709   0.3968  -5.9572  -3.0361   5.0683  -4.7782   6.8338
#>    8.9884  -2.8470   2.1631   3.9775  -1.6239   7.9917  -7.0367  -2.2697
#>    1.4686 -12.8909   0.2674   6.6647  -5.5488   1.0755  -2.7906  18.7766
#>   -3.3981  -4.2558  -1.5665   1.9402  -3.4117  -3.1792   4.8269   9.6574
#>   -2.4361  -0.3107   3.9676  -1.0001 -10.8681  -3.4518   5.6043   5.8122
#>    0.3577 -11.9476   2.5167   7.0826  -2.0832  -9.6350  -1.0751  -1.5074
#>   -3.5448   7.0308  -3.7205 -12.4894   1.6177  -2.1120   4.6454  -9.5807
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{20,33,48} ]