1D Adaptive Average Pooling — mlr_pipeops_nn_adaptive_avg

Applies a 1D adaptive average pooling over an input signal composed of several input planes.

nn_module

Calls nn_adaptive_avg_pool1d() during training.

Parameters

output_size :: integer(1)
The target output size. A single number.

Input and Output Channels

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

State

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

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchAdaptiveAvgPool -> PipeOpTorchAdaptiveAvgPool1D

Methods

Inherited methods

`PipeOpTorchAdaptiveAvgPool1D$new()`

Creates a new instance of this R6 class.

Usage

PipeOpTorchAdaptiveAvgPool1D$new(
  id = "nn_adaptive_avg_pool1d",
  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.

`PipeOpTorchAdaptiveAvgPool1D$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpTorchAdaptiveAvgPool1D$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("adaptive_avg_pool1d")
pipeop
#> 
#> ── PipeOp <adaptive_avg_pool1d>: not trained ───────────────────────────────────
#> Values: list()
#> 
#> ── 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(1)>
#>             id    class lower upper nlevels        default  value
#>         <char>   <char> <num> <num>   <num>         <list> <list>
#> 1: output_size ParamUty    NA    NA     Inf <NoDefault[0]> [NULL]