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torch_arange

Arange

Description

Arange

Usage

torch_arange(
  start,
  end,
  step = 1,
  dtype = NULL,
  layout = torch_strided(),
  device = NULL,
  requires_grad = FALSE
)

Arguments

start

(Number) the starting value for the set of points. Default: 0.
end

(Number) the ending value for the set of points
step

(Number) the gap between each pair of adjacent points. Default: 1.
dtype

(torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see ~torch.get_default_dtype. Otherwise, the dtype is inferred to be torch.int64.
layout

(torch.layout, optional) the desired layout of returned Tensor. Default: torch_strided.
device

(torch.device, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see torch_set_default_tensor_type). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad

(bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.

arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Returns a 1-D tensor of size ≤ft\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil with values from the interval [start, end) taken with common difference step beginning from start.

Note that non-integer step is subject to floating point rounding errors when comparing against end; to avoid inconsistency, we advise adding a small epsilon to end in such cases.

\mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}

Examples

if (torch_is_installed()) {

torch_arange(start = 0, end = 5)
torch_arange(1, 4)
torch_arange(1, 2.5, 0.5)
}
torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0
MIT + file LICENSE
Authors
Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]
Initial release

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