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mindspore.ops.logspace

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

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

$$\begin{array}{r}\begin{array}{rl}& step=(end-start)/(steps-1)\\ & output=[bas{e}^{start},bas{e}^{start+1\ast step},...,bas{e}^{start+(steps-2)\ast step},bas{e}^{end}]\end{array}\end{array}$$

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]