mindspore.mint.add

mindspore.mint.add(input, other, *, alpha=1) → Tensor

Adds scaled other value to self.

$$ou{t}_i=sel{f}_i+alpha\times othe{r}_i$$

Note

• When self and other have different shapes, they must be able to broadcast to a common shape.

• self, other and alpha comply with the implicit type conversion rules to make the data types consistent.

Parameters

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

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

  other is a number.Number or a bool or a tensor whose data type is number or bool_.

Keyword Arguments

alpha (number.Number, optional) – A scaling factor applied to other, default 1.

Returns

Tensor with a shape that is the same as the broadcasted shape of the self and other, and the data type is the one with higher precision or higher digits among self, other and alpha.

Raises

• TypeError – If the type of other or alpha is not one of the following: Tensor, number.Number, bool.

• TypeError – If alpha is of type float but self and other are not of type float.

• TypeError – If alpha is of type bool but self and other are not of type bool.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, mint
>>> x = Tensor(1, mindspore.int32)
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> alpha = 0.5
>>> output = mint.add(x, y, alpha=alpha)  # x.add(y, alpha=alpha)
>>> print(output)
[3. 3.5 4.]
>>> # the data type of x is int32, the data type of y is float32,
>>> # alpha is a float, and the output is the data format of higher precision float32.
>>> print(output.dtype)
Float32