
Last chance! 50% off unlimited learning
Sale ends in
Linspace
torch_linspace(
start,
end,
steps = 100,
dtype = NULL,
layout = torch_strided(),
device = NULL,
requires_grad = FALSE
)
(float) the starting value for the set of points
(float) the ending value for the set of points
(int) number of points to sample between start
and end
. Default: 100
.
(torch.dtype
, optional) the desired data type of returned tensor. Default: if NULL
, uses a global default (see torch_set_default_tensor_type
).
(torch.layout
, optional) the desired layout of returned Tensor. Default: torch_strided
.
(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.
(bool, optional) If autograd should record operations on the returned tensor. Default: FALSE
.
Returns a one-dimensional tensor of steps
equally spaced points between start
and end
.
The output tensor is 1-D of size steps
.
# NOT RUN {
if (torch_is_installed()) {
torch_linspace(3, 10, steps=5)
torch_linspace(-10, 10, steps=5)
torch_linspace(start=-10, end=10, steps=5)
torch_linspace(start=-10, end=10, steps=1)
}
# }
Run the code above in your browser using DataLab