PReLU Activation Function

Applies element-wise the function \(PReLU(x) = max(0,x) + weight * min(0,x)\) where weight is a learnable parameter.

nn_module

Calls torch::nn_prelu() when trained.

Parameters

• num_parameters :: integer(1)
  Number of a parameters to learn. Although it takes an integer as input, only two values are legitimate: 1, or the number of channels of the input. Default: 1.

• init :: numeric(1)
  The initial value of a. Default: 0.25.

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 -> PipeOpTorchPReLU

Methods

Public methods

• PipeOpTorchPReLU$new()

• PipeOpTorchPReLU$clone()

Inherited methods

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

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PipeOpTorchPReLU$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchPReLU$new(id = "nn_prelu", 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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PipeOpTorchPReLU$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchPReLU$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("prelu")
pipeop
#> 
#> ── PipeOp <prelu>: 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: num_parameters ParamInt     1   Inf     Inf       1 [NULL]
#> 2:           init ParamDbl  -Inf   Inf     Inf    0.25 [NULL]