torch_logspace function

Logspace

torch_logspace(
  start,
  end,
  steps = 100,
  base = 10,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE
)

Arguments

• start: (float) the starting value for the set of points
• end: (float) the ending value for the set of points
• steps: (int) number of points to sample between start and end. Default: 100.
• base: (float) base of the logarithm function. Default: 10.0.
• dtype: (torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type).
• 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.

logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Returns a one-dimensional tensor of steps points logarithmically spaced with base base between ${\mbox{base}}^{\mbox{start}}$ and ${\mbox{base}}^{\mbox{end}}$.

The output tensor is 1-D of size steps.

Examples

if (torch_is_installed()) {

torch_logspace(start=-10, end=10, steps=5)
torch_logspace(start=0.1, end=1.0, steps=5)
torch_logspace(start=0.1, end=1.0, steps=1)
torch_logspace(start=2, end=2, steps=1, base=2)
}