oneflow.logspace¶
-
oneflow.
logspace
(start, end, steps, base=10.0, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) → Tensor¶ This function is equivalent to PyTorch’s logspace function. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.logspace.html.
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 basebase
. That is, the values are:\[(\text{base}^{\text{start}}, \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, \ldots, \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, \text{base}^{\text{end}})\]- 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, optional) – base of the logarithm function. Default:
10.0
.
- Keyword Arguments
dtype (oneflow.dtype, optional) – the data type to perform the computation in. Default: if None, uses the global default dtype (see oneflow.get_default_dtype()) when both
start
andend
are real, and corresponding complex dtype when either is complex.device (oneflow.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type
placement (oneflow.placement, optional) – the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument device.
sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional) – the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument device.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.
Example:
>>> import oneflow as flow >>> flow.logspace(start=-10, end=10, steps=2) tensor([1.0000e-10, 1.0000e+10], dtype=oneflow.float32) >>> flow.logspace(start=0.1, end=1.0, steps=5) tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000], dtype=oneflow.float32) >>> flow.logspace(start=0.1, end=1.0, steps=1) tensor([1.2589], dtype=oneflow.float32) >>> flow.logspace(start=2, end=2, steps=1, base=2) tensor([4.], dtype=oneflow.float32)