# 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 ${{\text{{base}}}}^{{\text{{start}}}}$ to ${{\text{{base}}}}^{{\text{{end}}}}$ , inclusive, on a logarithmic scale with base base. That is, the values are:

$(\text{base}^{\text{start}}, \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps}})}, \ldots, \text{base}^{(\text{start} + (\text{steps} - 1) * \frac{\text{end} - \text{start}}{ \text{steps}})}, \text{base}^{\text{end}})$

Warning

Not providing a value for steps is deprecated. For backwards compatibility, not providing a value for steps 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 for steps 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: if None, 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 None, 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.

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])