mindspore.ops.logspace

mindspore.ops.logspace(start, end, steps, base=10, *, dtype=mstype.float32)[source]

Returns a 1-D Tensor with size steps whose value is from \(base^{start}\) to \(base^{end}\), and use base as the base number.

\[\begin{split}\begin{aligned} &step = (end - start)/(steps - 1)\\ &output = [base^{start}, base^{start + 1 * step}, ... , base^{start + (steps-2) * step}, base^{end}] \end{aligned}\end{split}\]

Note

  • Input base must be integer.

Parameters

  • start (Union[float, Tensor]) – Start value of interval.

  • end (Union[float, Tensor]) – End value of interval.

  • steps (int) – The steps must be a non-negative integer.

  • base (int, optional) – The base must be a non-negative integer. Default: 10 .

  • dtype (mindspore.dtype, optional) – The dtype of output. Default: mstype.float32 .

Returns

Tensor has the shape as \((step, )\). Its datatype is set by the attr ‘dtype’.

Raises

  • TypeError – If start is not a float or a Tensor.

  • TypeError – If end is not a float or a Tensor.

  • TypeError – If steps is not an int.

  • TypeError – If base is not an int.

  • ValueError – If steps is not a non-negative integer.

  • ValueError – If base is not a non-negative integer.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> start = Tensor(1, mindspore.float32)
>>> end = Tensor(10, mindspore.float32)
>>> output = ops.logspace(start, end, steps = 10, base = 10, dtype=mindspore.float32)
>>> print(output)
[1.e+01 1.e+02 1.e+03 1.e+04 1.e+05 1.e+06 1.e+07 1.e+08 1.e+09 1.e+10]