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Group Normalization

Source: R/PipeOpTorchGroupNorm.R
mlr_pipeops_nn_group_norm.Rd

Applies Group Normalization for last certain number of dimensions.

nn_module

Calls torch::nn_group_norm() when trained. The parameter num_channels is inferred as the second dimension of the input shape.

Parameters

  • num_groups :: integer(1)
    The number of groups to separate the channels into. Must divide the number of channels. Setting it to 1 normalizes over all channels at once (layer normalization), setting it to the number of channels normalizes each channel on its own (instance normalization).

  • eps :: numeric(1)
    A value added to the denominator for numerical stability. Default: 1e-5.

  • affine :: logical(1)
    Whether to learn per-channel affine parameters initialized to 1 for weights and to 0 for biases. Default: TRUE.

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 -> PipeOpTorchGroupNorm

Methods

Public methods

Inherited methods

PipeOpTorchGroupNorm$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchGroupNorm$new(id = "nn_group_norm", 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.

PipeOpTorchGroupNorm$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchGroupNorm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("group_norm", num_groups = 1)
pipeop
#> 
#> ── PipeOp <group_norm>: not trained ────────────────────────────────────────────
#> Values: num_groups=1
#> 
#> ── 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(3)>
#>            id    class lower upper nlevels        default  value
#>        <char>   <char> <num> <num>   <num>         <list> <list>
#> 1: num_groups ParamInt     1   Inf     Inf <NoDefault[0]>      1
#> 2:        eps ParamDbl     0   Inf     Inf          1e-05 [NULL]
#> 3:     affine ParamLgl    NA    NA       2           TRUE [NULL]