torch_logspace: Logspace

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Logspace

Description

Logspace

Usage

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)
}

torch documentation built on June 7, 2023, 6:19 p.m.