mindspore.mint.mul

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mindspore.mint.mul(input, other)[source]

Multiplies two tensors element-wise.

\[out_{i} = input_{i} * other_{i}\]

Note

  • When the two inputs have different shapes, they must be able to broadcast to a common shape.

  • The two inputs can not be bool type at the same time, [True, Tensor(True, bool_), Tensor(np.array([True]), bool_)] are all considered bool type.

  • The two inputs comply with the implicit type conversion rules to make the data types consistent.

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_.

  • other (Union[Tensor, number.Number, bool]) –

    The second input, which is a number.Number or a bool or 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 other is not one of the following: Tensor, number.Number, bool.

  • ValueError – If input and other are not the same shape.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
>>> output = mint.mul(x, y)
>>> print(output)
[ 4. 10. 18.]