mindspore.ops.add

mindspore.ops.add(input, other)[source]

Adds other value to input Tensor.

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

Note

One of the two inputs must be a Tensor, 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.
When input is Tensor, it’s dimension should be greater than or equal to 1.

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, 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 of the input input , other 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.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> # case 1: x and y are both Tensor.
>>> x = Tensor(np.array([1, 2, 3]).astype(np.float32))
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> output = ops.add(x, y)
>>> print(output)
[5. 7. 9.]
>>> # case 2: x is a scalar and y is a Tensor
>>> x = Tensor(1, mindspore.int32)
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> output = ops.add(x, y)
>>> print(output)
[5. 6. 7.]
>>> # the data type of x is int32, the data type of y is float32,
>>> # and the output is the data format of higher precision float32.
>>> print(output.dtype)
Float32