get_activation: Get Pytorch Activation Function

In mlr3learners/mlr3learners.pycox: Adds Learners from Python Package pycox for mlr3

Description

Helper function to return a class or constructed object for pytorch activation function from torch.nn.modules.activation.

Usage

get_activation(
  activation = "relu",
  construct = TRUE,
  alpha = 1,
  dim = NULL,
  lambd = 0.5,
  min_val = -1,
  max_val = 1,
  negative_slope = 0.01,
  num_parameters = 1L,
  init = 0.25,
  lower = 1/8,
  upper = 1/3,
  beta = 1,
  threshold = 20,
  value = 20
)

Arguments

activation	(character(1)) Activation function method, see details for list of implemented methods.
construct	(logical(1)) If TRUE (default) returns constructed object, otherwise a class.
alpha	(numeric(1)) Passed to celu and elu.
dim	(integer(1)) Passed to glu, logsoftmax, softmax, and softmin.
lambd	(numeric(1)) Passed to hardshrink and softshrink.
min_val, max_val	(numeric(1)) Passed to hardtanh.
negative_slope	(numeric(1)) Passed to leakyrelu.
num_parameters	(integer(1)) Passed to prelu.
init	(numeric(1)) Passed to prelu.
lower, upper	(numeric(1)) Passed to rrelu.
beta	(numeric(1)) Passed to softplus.
threshold	(numeric(1)) Passed to softplus and threshold.
value	(numeric(1)) Passed to threshold.

Details

Implemented methods (with help pages) are

• "celu"
reticulate::py_help(torch$nn$modules$activation$CELU)

• "elu"
reticulate::py_help(torch$nn$modules$activation$ELU)

• "gelu"
reticulate::py_help(torch$nn$modules$activation$GELU)

• "glu"
reticulate::py_help(torch$nn$modules$activation$GLU)

• "hardshrink"
reticulate::py_help(torch$nn$modules$activation$Hardshrink)

• "hardsigmoid"
reticulate::py_help(torch$nn$modules$activation$Hardsigmoid)

• "hardswish"
reticulate::py_help(torch$nn$modules$activation$Hardswish)

• "hardtanh"
reticulate::py_help(torch$nn$modules$activation$Hardtanh)

• "relu6"
reticulate::py_help(torch$nn$modules$activation$ReLU6)

• "leakyrelu"
reticulate::py_help(torch$nn$modules$activation$LeakyReLU)

• "logsigmoid"
reticulate::py_help(torch$nn$modules$activation$LogSigmoid)

• "logsoftmax"
reticulate::py_help(torch$nn$modules$activation$LogSoftmax)

• "prelu"
reticulate::py_help(torch$nn$modules$activation$PReLU)

• "rrelu"
reticulate::py_help(torch$nn$modules$activation$RReLU)

• "relu"
reticulate::py_help(torch$nn$modules$activation$ReLU)

• "selu"
reticulate::py_help(torch$nn$modules$activation$SELU)

• "sigmoid"
reticulate::py_help(torch$nn$modules$activation$Sigmoid)

• "softmax"
reticulate::py_help(torch$nn$modules$activation$Softmax)

• "softmax2d"
reticulate::py_help(torch$nn$modules$activation$Softmax2d)

• "softmin"
reticulate::py_help(torch$nn$modules$activation$Softmin)

• "softplus"
reticulate::py_help(torch$nn$modules$activation$Softplus)

• "softshrink"
reticulate::py_help(torch$nn$modules$activation$Softshrink)

• "softsign"
reticulate::py_help(torch$nn$modules$activation$Softsign)

• "tanh"
reticulate::py_help(torch$nn$modules$activation$Tanh)

• "tanhshrink"
reticulate::py_help(torch$nn$modules$activation$Tanhshrink)

• "threshold"
reticulate::py_help(torch$nn$modules$activation$Threshold)

mlr3learners/mlr3learners.pycox documentation built on Sept. 24, 2020, 10:40 a.m.