Layer Normalization

Applies Layer Normalization for last certain number of dimensions.

nn_module

Calls torch::nn_layer_norm() when trained. The parameter normalized_shape is inferred as the dimensions of the last dims dimensions of the input shape.

Parameters

• dims :: integer(1)
  The number of dimensions over which will be normalized (starting from the last dimension).

• elementwise_affine :: logical(1)
  Whether to learn affine-linear parameters initialized to 1 for weights and to 0 for biases. The default is TRUE.

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

State

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

Input and Output Channels

One input channel called "input" and one output channel called "output". For an explanation see PipeOpTorch.

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchLayerNorm

Methods

Public methods

• PipeOpTorchLayerNorm$new()

• PipeOpTorchLayerNorm$clone()

Inherited methods

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

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PipeOpTorchLayerNorm$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchLayerNorm$new(id = "nn_layer_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.

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PipeOpTorchLayerNorm$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchLayerNorm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("layer_norm", dims = 1)
pipeop
#> 
#> ── PipeOp <layer_norm>: not trained ────────────────────────────────────────────
#> Values: dims=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:               dims ParamInt     1   Inf     Inf <NoDefault[0]>      1
#> 2: elementwise_affine ParamLgl    NA    NA       2           TRUE [NULL]
#> 3:                eps ParamDbl     0   Inf     Inf          1e-05 [NULL]