mindspore.numpy.float_power(x1, x2, dtype=None)[source]

First array elements raised to powers from second array, element-wise.

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. This differs from the power function in that integers, float16, and float64 are promoted to floats with a minimum precision of float32 so that the result is always inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers.


Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported. Integers and floats are promoted to float32 instead of float64.

  • x1 (Tensor) – the bases.

  • x2 (Tensor) – the exponenets.

  • dtype (mindspore.dtype, optional) – defaults to None. Overrides the dtype of the output Tensor.


Tensor or scalar, the bases in x1 raised to the exponents in x2. This is a scalar if both x1 and x2 are scalars.

Supported Platforms:

Ascend GPU CPU


>>> import mindspore.numpy as np
>>> x1 = np.arange(6)
>>> x2 = np.array(3)
>>> output = np.float_power(x1, x2)
>>> print(output)
[  0.   1.   8.  27.  64. 125.]