3D Adaptive Max Pooling

Applies a 3D adaptive max pooling over an input signal composed of several input planes.

nn_module

Calls nn_adaptive_max_pool3d() during training.

State

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

Parameters

• output_size :: integer()
  The target output size. Can be a single number or a vector.

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.

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchAdaptiveMaxPool -> PipeOpTorchAdaptiveMaxPool3D

Methods

Public methods

• PipeOpTorchAdaptiveMaxPool3D$new()

• PipeOpTorchAdaptiveMaxPool3D$clone()

Inherited methods

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

---

PipeOpTorchAdaptiveMaxPool3D$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchAdaptiveMaxPool3D$new(
  id = "nn_adaptive_max_pool3d",
  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.

---

PipeOpTorchAdaptiveMaxPool3D$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchAdaptiveMaxPool3D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("adaptive_max_pool3d")
pipeop
#> 
#> ── PipeOp <adaptive_max_pool3d>: 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]