Merge by Concatenation

Concatenates multiple tensors on a given dimension. No broadcasting rules are applied here, you must reshape the tensors before to have the same shape.

nn_module

Calls nn_merge_cat() when trained.

Parameters

• dim :: integer(1)
  The dimension along which to concatenate the tensors. The default is -1, i.e., the last dimension.

Input and Output Channels

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

PipeOpTorchMerges has either a vararg input channel if the constructor argument innum is not set, or input channels "input1", ..., "input<innum>". There is one output channel "output". For an explanation see PipeOpTorch.

State

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

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchMerge -> PipeOpTorchMergeCat

Methods

Public methods

• PipeOpTorchMergeCat$new()

• PipeOpTorchMergeCat$speak()

• PipeOpTorchMergeCat$clone()

Inherited methods

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

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PipeOpTorchMergeCat$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchMergeCat$new(id = "nn_merge_cat", innum = 0, param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

innum

(integer(1))
The number of inputs. Default is 0 which means there is one vararg input channel.

param_vals

(list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.

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PipeOpTorchMergeCat$speak()

What does the cat say?

Usage

PipeOpTorchMergeCat$speak()

---

PipeOpTorchMergeCat$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchMergeCat$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("merge_cat")
pipeop
#> 
#> ── PipeOp <merge_cat>: not trained ─────────────────────────────────────────────
#> Values: list()
#> 
#> ── Input channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>     ... ModelDescriptor    Task
#> 
#> ── Output channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>  output ModelDescriptor    Task
# The available parameters
pipeop$param_set
#> <ParamSet(1)>
#>        id    class lower upper nlevels default  value
#>    <char>   <char> <num> <num>   <num>  <list> <list>
#> 1:    dim ParamInt  -Inf   Inf     Inf      -1 [NULL]