2D Dropout

Randomly zero out entire channels (a channel is a 2D feature map, e.g., the \(j\)-th channel of the \(i\)-th sample in the batched input is a 2D tensor \(input[i, j]\)) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.

nn_module

Calls torch::nn_dropout2d() when trained.

Parameters

• p :: numeric(1)
  Probability of a channel to be zeroed. Default: 0.5.

• inplace :: logical(1)
  If set to TRUE, will do this operation in-place. Default: FALSE.

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 -> PipeOpTorchDropoutNd -> PipeOpTorchDropout2D

Methods

Public methods

• PipeOpTorchDropout2D$new()

• PipeOpTorchDropout2D$clone()

Inherited methods

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

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PipeOpTorchDropout2D$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchDropout2D$new(id = "nn_dropout2d", 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.

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PipeOpTorchDropout2D$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchDropout2D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("dropout2d")
pipeop
#> 
#> ── PipeOp <dropout2d>: 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(2)>
#>         id    class lower upper nlevels default  value
#>     <char>   <char> <num> <num>   <num>  <list> <list>
#> 1:       p ParamDbl     0     1     Inf     0.5 [NULL]
#> 2: inplace ParamLgl    NA    NA       2   FALSE [NULL]