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Loss.R
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Loss.R
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#' @title MSELossFlat
#'
#' @description Flattens input and output, same as nn$MSELoss
#'
#' @param ... parameters to pass
#'
#' @return Loss object
#' @export
MSELossFlat = function(...) {
args = list(...)
do.call(vision()$all$MSELossFlat, args)
}
#' @title L1LossFlat
#'
#' @description Flattens input and output, same as nn$L1LossFlat
#' @param ... parameters to pass
#'
#' @return Loss object
#'
#' @export
L1LossFlat = function(...) {
args = list(...)
do.call(vision()$all$L1LossFlat, args)
}
#' @title BCELossFlat
#'
#' @description Flattens input and output, same as nn$BCELoss
#' @param ... parameters to pass
#'
#' @return Loss object
#'
#' @export
BCELossFlat = function(...) {
args = list(...)
do.call(vision()$all$BCELossFlat, args)
}
#' @title AvgLoss
#'
#' @description Flattens input and output, same as nn$AvgLoss
#' @param ... parameters to pass
#'
#' @return Loss object
#'
#' @export
AvgLoss = function(...) {
args = list(...)
do.call(vision()$all$AvgLoss, args)
}
#' @title BaseLoss
#'
#' @description Flattens input and output, same as nn$BaseLoss
#' @param ... parameters to pass
#' @return Loss object
#' @export
BaseLoss = function(...) {
args = list(...)
do.call(vision()$all$BaseLoss, args)
}
#' @title HammingLoss
#'
#' @description Hamming loss for single-label classification problems
#'
#'
#' @return Loss object
#'
#' @param axis axis
#' @param sample_weight sample weight
#'
#' @export
HammingLoss <- function(axis = -1, sample_weight = NULL) {
args = list(
axis = as.integer(axis),
sample_weight = sample_weight
)
if(is.null(args$sample_weight))
args$sample_weight <- NULL
do.call(vision()$all$HammingLoss, args)
}
#' @title AdaptiveLoss
#'
#' @description Expand the `target` to match the `output` size before applying `crit`.
#'
#'
#' @return Loss object
#' @param crit critic
#'
#' @export
AdaptiveLoss <- function(crit) {
vision()$gan$AdaptiveLoss(
crit = crit
)
}
#' @title HammingLossMulti
#'
#' @description Hamming loss for multi-label classification problems
#'
#' @return Loss object
#' @param thresh threshold
#' @param sigmoid sigmoid
#' @param labels labels
#' @param sample_weight sample_weight
#'
#' @export
HammingLossMulti <- function(thresh = 0.5, sigmoid = TRUE, labels = NULL, sample_weight = NULL) {
args = list(
thresh = thresh,
sigmoid = sigmoid,
labels = labels,
sample_weight = sample_weight
)
if(is.null(args$labels))
args$labels <- NULL
if(is.null(args$sample_weight))
args$sample_weight <- NULL
do.call(fastai2$vision$all$HammingLossMulti, args)
}
#' @title AvgSmoothLoss
#'
#' @description Smooth average of the losses (exponentially weighted with `beta`)
#'
#'
#' @return Loss object
#' @param beta beta, defaults to 0.98
#'
#' @export
AvgSmoothLoss <- function(beta = 0.98) {
vision()$all$AvgSmoothLoss(
beta = beta
)
}
#' @title BCEWithLogitsLossFlat
#' @param ... parameters to pass
#'
#' @return Loss object
#' @export
BCEWithLogitsLossFlat = function(...) {
args = list(...)
do.call(vision()$all$BCEWithLogitsLossFlat, args)
}
#' @title LabelSmoothingCrossEntropy
#'
#' @description Same as `nn$Module`, but no need for subclasses to call `super()$__init__`
#'
#' @return Loss object
#'
#' @param eps epsilon
#' @param reduction reduction, defaults to mean
#'
#' @export
LabelSmoothingCrossEntropy <- function(eps = 0.1, reduction = "mean") {
vision()$all$LabelSmoothingCrossEntropy(
eps = eps,
reduction = reduction
)
}
#' @title LabelSmoothingCrossEntropyFlat
#'
#' @description Same as `nn$Module`, but no need for subclasses to call `super().__init__`
#'
#'
#' @param ... parameters to pass
#'
#' @return Loss object
#'
#' @export
LabelSmoothingCrossEntropyFlat <- function(...) {
vision()$all$LabelSmoothingCrossEntropyFlat(
...
)
}
#' @title CrossEntropyLossFlat
#'
#' @description Same as `nn$Module`, but no need for subclasses to call `super().__init__`
#'
#'
#' @param ... parameters to pass
#'
#' @return Loss object
#'
#' @export
CrossEntropyLossFlat <- function(...) {
vision()$all$CrossEntropyLossFlat(
...
)
}