torch / torch
torch.linspace¶
-
torch.
linspace
(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶ Creates a one-dimensional tensor of size
steps
whose values are evenly spaced fromstart
toend
, inclusive. That is, the value are:Warning
Not providing a value for
steps
is deprecated. For backwards compatibility, not providing a value forsteps
will create a tensor with 100 elements. Note that this behavior is not reflected in the documented function signature and should not be relied on. In a future PyTorch release, failing to provide a value forsteps
will throw a runtime error.- Parameters
start (float) – the starting value for the set of points
end (float) – the ending value for the set of points
steps (int) – size of the constructed tensor
out (Tensor, optional) – the output tensor.
dtype (
torch.dtype
, optional) – the desired data type of returned tensor. Default: ifNone
, uses a global default (seetorch.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: ifNone
, uses the current device for the default tensor type (seetorch.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
.
Example:
>>> torch.linspace(3, 10, steps=5) tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) >>> torch.linspace(-10, 10, steps=5) tensor([-10., -5., 0., 5., 10.]) >>> torch.linspace(start=-10, end=10, steps=5) tensor([-10., -5., 0., 5., 10.]) >>> torch.linspace(start=-10, end=10, steps=1) tensor([-10.])
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