Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).
nn_module
Calls torch::nn_softplus() when trained.
Parameters
beta::numeric(1)
The beta value for the Softplus formulation. Default: 1threshold::numeric(1)
Values above this revert to a linear function. Default: 20
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 -> PipeOpTorchSoftPlus
Methods
PipeOpTorchSoftPlus$new()
Creates a new instance of this R6 class.
Usage
PipeOpTorchSoftPlus$new(id = "nn_softplus", 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.
Examples
# Construct the PipeOp
pipeop = nn("softplus")
pipeop
#>
#> ── PipeOp <softplus>: 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: beta ParamDbl -Inf Inf Inf 1 [NULL]
#> 2: threshold ParamDbl -Inf Inf Inf 20 [NULL]