torch / torch
torch.logspace¶
-
torch.
logspace
(start, end, steps, base=10.0, *, 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 from to , inclusive, on a logarithmic scale with basebase
. That is, the values 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
base (float) – base of the logarithm function. Default:
10.0
.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.logspace(start=-10, end=10, steps=5) tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10]) >>> torch.logspace(start=0.1, end=1.0, steps=5) tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000]) >>> torch.logspace(start=0.1, end=1.0, steps=1) tensor([1.2589]) >>> torch.logspace(start=2, end=2, steps=1, base=2) tensor([4.0])
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