mindspore.ops.LogSpace

class mindspore.ops.LogSpace(steps=10, base=10, dtype=mstype.float32)[source]

Returns a one-dimensional tensor of size steps whose values are evenly spaced from \(base^{start}\) to \(base^{end}\) , inclusive, on a logarithmic scale with base.

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

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

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

  • dtype (mindspore.dtype, optional) – The dtype of output, include mindspore.float16, mindspore.float32 or mindspore.float64(for GPU). Default: mindspore.float32.

Inputs:

  • start (Tensor) - Start value of interval, with shape of 0-D, dtype is float16, float32 or float64(for GPU).

  • end (Tensor) - End value of interval, with shape of 0-D, dtype is float16, float32 or float64(for GPU).

Outputs:

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

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If steps is not an int.

  • TypeError – If base is not an int.

  • TypeError – If dtype is not mindspore.float16, mindspore.float32 or mindspore.float64(for GPU).

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

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

Supported Platforms:

GPU CPU

Examples

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