torch_arange function

Arange

torch_arange(
  start,
  end,
  step = 1L,
  dtype = NULL,
  layout = NULL,
  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 $\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil$

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.

Examples

if (torch_is_installed()) {

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