Transpose 1D Convolution

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

nn_module

Calls nn_conv_transpose1d. The parameter in_channels is inferred as the second dimension of the input tensor.

Parameters

• out_channels :: integer(1)
  Number of output channels produce by the convolution.

• kernel_size :: integer()
  Size of the convolving kernel.

• stride :: integer()
  Stride of the convolution. Default: 1.

• padding :: integer()
dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of the input. Default: 0.

• output_padding :: integer()
  Additional size added to one side of the output shape. Default: 0.

• groups :: integer()
  Number of blocked connections from input channels to output channels. Default: 1

• bias :: logical(1)
  If True, adds a learnable bias to the output. Default: TRUE.

• dilation :: integer()
  Spacing between kernel elements. Default: 1.

• padding_mode :: character(1)
  The padding mode. One of "zeros", "reflect", "replicate", or "circular". Default is "zeros".

State

The state is the value calculated by the public method $shapes_out().

Input and Output Channels

One input channel called "input" and one output channel called "output". For an explanation see PipeOpTorch.

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchConvTranspose -> PipeOpTorchConvTranspose1D

Methods

Public methods

• PipeOpTorchConvTranspose1D$new()

• PipeOpTorchConvTranspose1D$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()
• PipeOpTorch$shapes_out()

---

PipeOpTorchConvTranspose1D$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchConvTranspose1D$new(id = "nn_conv_transpose1d", param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

param_vals

(list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.

---

PipeOpTorchConvTranspose1D$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchConvTranspose1D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("conv_transpose1d", kernel_size = 3, out_channels = 2)
pipeop
#> 
#> ── PipeOp <conv_transpose1d>: not trained ──────────────────────────────────────
#> Values: out_channels=2, kernel_size=3
#> 
#> ── Input channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>   input ModelDescriptor    Task
#> 
#> ── Output channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>  output ModelDescriptor    Task
# The available parameters
pipeop$param_set
#> <ParamSet(9)>
#>                id    class lower upper nlevels        default  value
#>            <char>   <char> <num> <num>   <num>         <list> <list>
#> 1:   out_channels ParamInt     1   Inf     Inf <NoDefault[0]>      2
#> 2:    kernel_size ParamUty    NA    NA     Inf <NoDefault[0]>      3
#> 3:         stride ParamUty    NA    NA     Inf              1 [NULL]
#> 4:        padding ParamUty    NA    NA     Inf              0 [NULL]
#> 5: output_padding ParamUty    NA    NA     Inf              0 [NULL]
#> 6:       dilation ParamUty    NA    NA     Inf              1 [NULL]
#> 7:         groups ParamInt     1   Inf     Inf              1 [NULL]
#> 8:           bias ParamLgl    NA    NA       2           TRUE [NULL]
#> 9:   padding_mode ParamFct    NA    NA       4          zeros [NULL]