3D Max Pooling

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

nn_module

Calls torch::nn_max_pool3d() during training.

State

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

Parameters

• kernel_size :: integer()
  The size of the window. Can be single number or a vector.

• stride :: integer()
  The stride of the window. Can be a single number or a vector. Default: kernel_size

• padding :: integer()
  Implicit zero paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0

• dilation :: integer()
  Controls the spacing between the kernel points; also known as the a trous algorithm. Default: 1

• ceil_mode :: logical(1)
  When True, will use ceil instead of floor to compute the output shape. Default: FALSE

Input and Output Channels

If return_indices is FALSE during construction, there is one input channel 'input' and one output channel 'output'. If return_indices is TRUE, there are two output channels 'output' and 'indices'. For an explanation see PipeOpTorch.

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchMaxPool -> PipeOpTorchMaxPool3D

Methods

Public methods

• PipeOpTorchMaxPool3D$new()

• PipeOpTorchMaxPool3D$clone()

Inherited methods

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

---

PipeOpTorchMaxPool3D$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchMaxPool3D$new(
  id = "nn_max_pool3d",
  return_indices = FALSE,
  param_vals = list()
)

Arguments

id

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

return_indices

(logical(1))
Whether to return the indices. If this is TRUE, there are two output channels "output" and "indices".

param_vals

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

---

PipeOpTorchMaxPool3D$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchMaxPool3D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("max_pool3d")
pipeop
#> 
#> ── PipeOp <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(5)>
#>             id    class lower upper nlevels        default  value
#>         <char>   <char> <num> <num>   <num>         <list> <list>
#> 1: kernel_size ParamUty    NA    NA     Inf <NoDefault[0]> [NULL]
#> 2:     padding ParamUty    NA    NA     Inf              0 [NULL]
#> 3:      stride ParamUty    NA    NA     Inf         [NULL] [NULL]
#> 4:    dilation ParamUty    NA    NA     Inf              1 [NULL]
#> 5:   ceil_mode ParamLgl    NA    NA       2          FALSE [NULL]