1D Adaptive Max Pooling

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

nn_module

Calls nn_adaptive_max_pool1d() during training.

Parameters

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

Input and Output Channels

There is one input channel "input". The module has two outputs, "output" and "indices", of which only "output" is an output channel by default. Set $outputs to also (or only) get the indices, e.g. outputs = c("output", "indices"), which are only computed when they are among $outputs. For an explanation see PipeOpTorch.

State

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

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchAdaptiveMaxPool -> PipeOpTorchAdaptiveMaxPool1D

Methods

Public methods

• PipeOpTorchAdaptiveMaxPool1D$new()

• PipeOpTorchAdaptiveMaxPool1D$clone()

Inherited methods

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

---

PipeOpTorchAdaptiveMaxPool1D$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchAdaptiveMaxPool1D$new(
  id = "nn_adaptive_max_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.

---

PipeOpTorchAdaptiveMaxPool1D$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchAdaptiveMaxPool1D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("adaptive_max_pool1d")
pipeop
#> 
#> ── PipeOp <adaptive_max_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]