Linear Layer

Applies a linear transformation to the incoming data: \(y = xA^T + b\).

nn_module

Calls torch::nn_linear() when trained where the parameter in_features is inferred as the second to last dimension of the input tensor.

Parameters

• out_features :: integer(1)
  The output features of the linear layer.

• bias :: logical(1)
  Whether to use a bias. Default is 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 -> PipeOpTorchLinear

Methods

Public methods

• PipeOpTorchLinear$new()

• PipeOpTorchLinear$clone()

Inherited methods

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

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PipeOpTorchLinear$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchLinear$new(id = "nn_linear", 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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PipeOpTorchLinear$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchLinear$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("linear", out_features = 10)
pipeop
#> 
#> ── PipeOp <linear>: not trained ────────────────────────────────────────────────
#> Values: out_features=10
#> 
#> ── 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: out_features ParamInt     1   Inf     Inf <NoDefault[0]>     10
#> 2:         bias ParamLgl    NA    NA       2           TRUE [NULL]