mindspore.ops.logspace

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

Returns a Tensor whose value is evenly spaced on a logarithmic scale.

\begin{array}{r} \begin{aligned} s t e p = (e n d - s t a r t) / (s t e p s - 1) \\ o u t p u t = [b a s e^{s t a r t}, b a s e^{s t a r t + 1 * s t e p}, . . ., b a s e^{s t a r t + (s t e p s - 2) * s t e p}, b a s e^{e n d}] \end{aligned} \end{array}

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 $(s t e p,)$ . 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

>>> start = Tensor(1, mindspore.float32)
>>> end = Tensor(10, mindspore.float32)
>>> output = ops.logspace(start, end, steps = 10, base = 10, dtype=mstype.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]