Nothing
resolve_tensors <- function(tensors) {
# convert unnamed list into tuple
if (is.list(tensors) && is.null(names(tensors)))
tensors <- tuple(tensors)
# covert data frame to dict
if (is.data.frame(tensors)) {
tensors <- as.list(tensors)
}
tensors
}
#' Creates a dataset with a single element, comprising the given tensors.
#'
#' @param tensors A nested structure of tensors.
#'
#' @return A dataset.
#'
#' @family tensor datasets
#'
#' @export
tensors_dataset <- function(tensors) {
validate_tf_version()
as_tf_dataset(
tf$data$Dataset$from_tensors(tensors = resolve_tensors(tensors))
)
}
#' Creates a dataset whose elements are slices of the given tensors.
#'
#' @param tensors A nested structure of tensors, each having the same size in
#' the first dimension.
#'
#' @return A dataset.
#'
#' @family tensor datasets
#'
#' @export
tensor_slices_dataset <-function(tensors) {
validate_tf_version()
with(tf$device("cpu"), {
# https://github.com/tensorflow/tensorflow/issues/71744
as_tf_dataset(
tf$data$Dataset$from_tensor_slices(tensors = resolve_tensors(tensors))
)
})
}
#' Splits each rank-N `tf$SparseTensor` in this dataset row-wise.
#'
#' @param sparse_tensor A `tf$SparseTensor`.
#'
#' @return A dataset of rank-(N-1) sparse tensors.
#'
#' @family tensor datasets
#'
#' @export
sparse_tensor_slices_dataset <- function(sparse_tensor) {
validate_tf_version()
as_tf_dataset(
tf$data$Dataset$from_sparse_tensor_slices(sparse_tensor = sparse_tensor)
)
}
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