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R/PipeOpTorchActivation.R
#' @title ELU Activation Function
#'
#' @inherit torch::nnf_elu description
#' @section nn_module: Calls [`torch::nn_elu()`] when trained.
#' @section Parameters:
#' * `alpha` :: `numeric(1)`\cr
#' The alpha value for the ELU formulation. Default: 1.0
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#'
#' @templateVar id nn_elu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#' @export
PipeOpTorchELU = R6Class("PipeOpTorchELU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_elu", param_vals = list()) {
param_set = ps(
alpha = p_dbl(default = 1, tags = "train"),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_elu,
tags = "activation"
)
}
)
)
register_po("nn_elu", PipeOpTorchELU)
#' @title Hard Shrink Activation Function
#' @inherit torch::nnf_hardshrink description
#' @section nn_module: Calls [`torch::nn_hardshrink()`] when trained.
#' @section Parameters:
#' * `lambd` :: `numeric(1)`\cr
#' The lambda value for the Hardshrink formulation formulation. Default 0.5.
#' @templateVar id nn_hardshrink
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#' @export
PipeOpTorchHardShrink = R6Class("PipeOpTorchHardShrink",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_hardshrink", param_vals = list()) {
param_set = ps(
lambd = p_dbl(default = 0.5, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_hardshrink,
tags = "activation"
)
}
)
)
register_po("nn_hardshrink", PipeOpTorchHardShrink)
#' @title Hard Sigmoid Activation Function
#'
#' @inherit torch::nnf_hardsigmoid description
#' @section nn_module: Calls [`torch::nn_hardsigmoid()`] when trained.
#' @section Parameters:
#' No parameters.
#' @templateVar id nn_hardsigmoid
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchHardSigmoid = R6Class("PipeOpTorchHardSigmoid",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_hardsigmoid", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_hardsigmoid,
tags = "activation"
)
}
)
)
register_po("nn_hardsigmoid", PipeOpTorchHardSigmoid)
#' @title Hard Tanh Activation Function
#'
#' @inherit torch::nnf_hardtanh description
#' @section nn_module: Calls [`torch::nn_hardtanh()`] when trained.
#' @section Parameters:
#' * `min_val` :: `numeric(1)`\cr
#' Minimum value of the linear region range. Default: -1.
#' * `max_val` :: `numeric(1)`\cr
#' Maximum value of the linear region range. Default: 1.
#' * `inplace` :: `logical(1)`\cr
#' Can optionally do the operation in-place. Default: `FALSE`.
#' @templateVar id nn_hardtanh
#'
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchHardTanh = R6Class("PipeOpTorchHardTanh",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_hardtanh", param_vals = list()) {
param_set = ps(
min_val = p_dbl(default = -1, tags = "train"),
max_val = p_dbl(default = 1, tags = "train"),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_hardtanh,
tags = "activation"
)
}
)
)
register_po("nn_hardtanh", PipeOpTorchHardTanh)
#' @title Leaky ReLU Activation Function
#'
#' @inherit torch::nnf_leaky_relu description
#' @section nn_module: Calls [`torch::nn_leaky_relu()`] when trained.
#' @section Parameters:
#' * `negative_slope` :: `numeric(1)`\cr
#' Controls the angle of the negative slope. Default: 1e-2.
#' * `inplace` :: `logical(1)`\cr
#' Can optionally do the operation in-place. Default: ‘FALSE’.
#' @templateVar id nn_leaky_relu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchLeakyReLU = R6Class("PipeOpTorchLeakyReLU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_leaky_relu", param_vals = list()) {
param_set = ps(
negative_slope = p_dbl(default = 0.01, tags = "train"),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_leaky_relu,
tags = "activation"
)
}
)
)
register_po("nn_leaky_relu", PipeOpTorchLeakyReLU)
#' @title Log Sigmoid Activation Function
#'
#' @inherit torch::nnf_logsigmoid description
#' @section nn_module: Calls [`torch::nn_log_sigmoid()`] when trained.
#' @section Parameters:
#' No parameters.
#'
#' @templateVar id nn_log_sigmoid
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchLogSigmoid = R6Class("PipeOpTorchLogSigmoid",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_log_sigmoid", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_log_sigmoid,
tags = "activation"
)
}
)
)
register_po("nn_log_sigmoid", PipeOpTorchLogSigmoid)
#' @title PReLU Activation Function
#'
#' @inherit torch::nnf_prelu description
#' @section nn_module: Calls [`torch::nn_prelu()`] when trained.
#' @section Parameters:
#' * `num_parameters` :: `integer(1)`:
#' Number of a to learn. Although it takes an int as input, there is only two values are legitimate: 1, or the
#' number of channels at input. Default: 1.
#' * `init` :: `numeric(1)`\cr T
#' The initial value of a. Default: 0.25.
#' @templateVar id nn_prelu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchPReLU = R6Class("PipeOpTorchPReLU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_prelu", param_vals = list()) {
param_set = ps(
num_parameters = p_int(default = 1, lower = 1, tags = "train"),
init = p_dbl(default = 0.25, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_prelu,
tags = "activation"
)
}
)
)
register_po("nn_prelu", PipeOpTorchPReLU)
#' @title ReLU Activation Function
#'
#' @inherit torch::nnf_relu description
#' @section nn_module: Calls [`torch::nn_relu()`] when trained.
#' @section Parameters:
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#' @templateVar id nn_relu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchReLU = R6Class("PipeOpTorchReLU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_relu", param_vals = list()) {
param_set = ps(
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_relu,
tags = "activation"
)
}
)
)
register_po("nn_relu", PipeOpTorchReLU)
#' @title ReLU6 Activation Function
#'
#' @inherit torch::nnf_relu6 description
#' @section nn_module: Calls [`torch::nn_relu6()`] when trained.
#' @section Parameters:
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#' @templateVar id nn_relu6
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchReLU6 = R6Class("PipeOpTorchReLU6",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_relu6", param_vals = list()) {
param_set = ps(
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_relu6,
tags = "activation"
)
}
)
)
register_po("nn_relu6", PipeOpTorchReLU6)
#' @title RReLU Activation Function
#' @inherit torch::nnf_rrelu description
#' @section nn_module: Calls [`torch::nn_rrelu()`] when trained.
#' @section Parameters:
#' * `lower`:: `numeric(1)`\cr
#' Lower bound of the uniform distribution. Default: 1/8.
#' * `upper`:: `numeric(1)`\cr
#' Upper bound of the uniform distribution. Default: 1/3.
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#'
#' @templateVar id nn_rrelu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchRReLU = R6Class("PipeOpTorchRReLU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_rrelu", param_vals = list()) {
param_set = ps(
lower = p_dbl(default = 1 / 8, tags = "train"),
upper = p_dbl(default = 1 / 3, tags = "train"),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_rrelu,
tags = "activation"
)
}
)
)
register_po("nn_rrelu", PipeOpTorchRReLU)
#' @title SELU Activation Function
#'
#' @inherit torch::nnf_selu description
#' @section nn_module: Calls [`torch::nn_selu()`] when trained.
#' @section Parameters:
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#' @templateVar id nn_selu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchSELU = R6Class("PipeOpTorchSELU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_selu", param_vals = list()) {
param_set = ps(
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_selu,
tags = "activation"
)
}
)
)
register_po("nn_selu", PipeOpTorchSELU)
#' @title CELU Activation Function
#'
#' @inherit torch::nnf_celu description
#' @section nn_module: Calls [`torch::nn_celu()`] when trained.
#' @section Parameters:
#' * `alpha` :: `numeric(1)`\cr
#' The alpha value for the ELU formulation. Default: 1.0
#' * `inplace` :: `logical(1)`\cr
#' Whether to do the operation in-place. Default: `FALSE`.
#' @templateVar id nn_celu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchCELU = R6Class("PipeOpTorchCELU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_celu", param_vals = list()) {
param_set = ps(
alpha = p_dbl(default = 1.0, tags = "train"),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_celu,
tags = "activation"
)
}
)
)
register_po("nn_celu", PipeOpTorchCELU)
#' @title GELU Activation Function
#'
#' @inherit torch::nnf_gelu description
#' @section nn_module: Calls [`torch::nn_gelu()`] when trained.
#' @section Parameters:
#' * `approximate` :: `character(1)`\cr
#' Whether to use an approximation algorithm. Default is `"none"`.
#' @templateVar id nn_gelu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchGELU = R6Class("PipeOpTorchGELU",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_gelu", param_vals = list()) {
param_set = ps(
approximate = p_fct(default = "none", levels = c("none", "tanh"), tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_gelu,
tags = "activation"
)
}
)
)
register_po("nn_gelu", PipeOpTorchGELU)
#' @title Sigmoid Activation Function
#'
#' @inherit torch::nnf_sigmoid description
#' @section nn_module: Calls [`torch::nn_sigmoid()`] when trained.
#' @section Parameters:
#' No parameters.
#'
#' @templateVar id nn_sigmoid
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchSigmoid = R6Class("PipeOpTorchSigmoid",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_sigmoid", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_sigmoid,
tags = "activation"
)
}
)
)
register_po("nn_sigmoid", PipeOpTorchSigmoid)
#' @title SoftPlus Activation Function
#'
#' @inherit torch::nnf_softplus description
#' @section nn_module: Calls [`torch::nn_softplus()`] when trained.
#' @section Parameters:
#' * `beta` :: `numeric(1)`\cr
#' The beta value for the Softplus formulation. Default: 1
#' * `threshold` :: `numeric(1)`\cr
#' Values above this revert to a linear function. Default: 20
#' @templateVar id nn_softplus
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchSoftPlus = R6Class("PipeOpTorchSoftPlus",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_softplus", param_vals = list()) {
param_set = ps(
beta = p_dbl(default = 1, tags = "train"),
threshold = p_dbl(default = 20, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_softplus,
tags = "activation"
)
}
)
)
register_po("nn_softplus", PipeOpTorchSoftPlus)
#' @title Soft Shrink Activation Function
#'
#' @inherit torch::nnf_softshrink description
#' @section nn_module: Calls [`torch::nn_softshrink()`] when trained.
#' @section Parameters:
#' * `lamd` :: `numeric(1)`\cr
#' The lambda (must be no less than zero) value for the Softshrink formulation. Default: 0.5
#' @templateVar id nn_softshrink
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchSoftShrink = R6Class("PipeOpTorchSoftShrink",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_softshrink", param_vals = list()) {
param_set = ps(
lambd = p_dbl(default = 0.5, upper = 1, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_softshrink,
tags = "activation"
)
}
)
)
register_po("nn_softshrink", PipeOpTorchSoftShrink)
#' @title SoftSign Activation Function
#'
#' @inherit torch::nnf_softsign description
#' @section nn_module: Calls [`torch::nn_softsign()`] when trained.
#' @section Parameters:
#' No parameters.
#' @templateVar id nn_softsign
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#' @export
PipeOpTorchSoftSign = R6Class("PipeOpTorchSoftSign",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_softsign", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_softsign,
tags = "activation"
)
}
)
)
register_po("nn_softsign", PipeOpTorchSoftSign)
#' @title Tanh Activation Function
#'
#' @inherit torch::nn_tanh description
#' @section nn_module: Calls [`torch::nn_tanh()`] when trained.
#' @section Parameters:
#' No parameters.
#' @templateVar id nn_tanh
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchTanh = R6Class("PipeOpTorchTanh",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_tanh", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_tanh,
tags = "activation"
)
}
)
)
register_po("nn_tanh", PipeOpTorchTanh)
#' @title Tanh Shrink Activation Function
#'
#' @inherit torch::nnf_tanhshrink description
#' @section nn_module: Calls [`torch::nn_tanhshrink()`] when trained.
#' @section Parameters:
#' No parameters.
#' @templateVar id nn_tanhshrink
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchTanhShrink = R6Class("PipeOpTorchTanhShrink",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_tanhshrink", param_vals = list()) {
param_set = ps()
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_tanhshrink,
tags = "activation"
)
}
)
)
register_po("nn_tanhshrink", PipeOpTorchTanhShrink)
#' @title Treshold Activation Function
#'
#' @inherit torch::nnf_threshold description
#' @section nn_module: Calls [`torch::nn_threshold()`] when trained.
#' @section Parameters:
#' * `threshold` :: `numeric(1)`\cr
#' The value to threshold at.
#' * `value` :: `numeric(1)`\cr
#' The value to replace with.
#' * `inplace` :: `logical(1)`\cr
#' Can optionally do the operation in-place. Default: ‘FALSE’.
#' @templateVar id nn_threshold
#' @template pipeop_torch_channels_default
#' @templateVar param_vals threshold = 1, value = 2
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchThreshold = R6Class("PipeOpTorchThreshold",
inherit = PipeOpTorch,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_threshold", param_vals = list()) {
param_set = ps(
threshold = p_dbl(tags = c("train", "required")),
value = p_dbl(tags = c("train", "required")),
inplace = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_threshold,
tags = "activation"
)
}
)
)
register_po("nn_threshold", PipeOpTorchThreshold)
#' @title GLU Activation Function
#'
#' @inherit torch::nnf_glu description
#' @section nn_module: Calls [`torch::nn_glu()`] when trained.
#' @section Parameters:
#' * `dim` :: `integer(1)`\cr
#' Dimension on which to split the input. Default: -1
#' @templateVar id nn_glu
#' @template pipeop_torch_channels_default
#' @template pipeop_torch
#' @template pipeop_torch_example
#'
#'
#' @export
PipeOpTorchGLU = R6Class("PipeOpTorchGLU",
inherit = PipeOpTorch,
public = list(
#' @description Creates a new instance of this [R6][R6::R6Class] class.
#' @template params_pipelines
initialize = function(id = "nn_glu", param_vals = list()) {
param_set = ps(
dim = p_int(default = -1L, lower = 1L, tags = "train", special_vals = list(-1L))
)
super$initialize(
id = id,
param_set = param_set,
param_vals = param_vals,
module_generator = nn_glu,
tags = "activation"
)
}
),
private = list(
.shapes_out = function(shapes_in, param_vals, task) {
shape = shapes_in[[1L]]
true_dim = param_vals$dim %??% -1
if (true_dim < 0) {
true_dim = 1 + length(shape) + true_dim
}
d_new = shape[true_dim] / 2
if (test_integerish(d_new)) {
shape[true_dim] = d_new
} else {
stopf("Dimension %i of input tensor must be divisible by 2.", true_dim)
}
list(shape)
}
)
)
register_po("nn_glu", PipeOpTorchGLU)
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