mindspore.ops.pow

mindspore.ops.pow(input, exponent)[source]

Calculates the exponent power of each element in input.

\[out_{i} = input_{i} ^{ exponent_{i}}\]

Note

  • Inputs of input and exponent comply with the implicit type conversion rules to make the data types consistent.

  • The inputs must be two tensors or one tensor and one scalar.

  • When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them can be broadcast.

Parameters

  • input (Union[Tensor, number.Number, bool]) – The first input is a number.Number or a bool or a tensor whose data type is number or bool_.

  • exponent (Union[Tensor, number.Number, bool]) – The second input, when the first input is a Tensor, the second input should be a number.Number or bool value, or a Tensor whose data type is number or bool_. When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises

  • TypeError – If input and exponent is not one of the following: Tensor, number.Number or bool.

  • ValueError – If the shape of input and exponent are different.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> y = 3.0
>>> output = ops.pow(x, y)
>>> print(output)
[ 1.  8. 64.]
>>>
>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
>>> output = ops.pow(x, y)
>>> print(output)
[ 1. 16. 64.]