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mindspore.ops.add

mindspore.ops.add(input, other)

Adds other value to input Tensor.

$$ou{t}_i=inpu{t}_i+othe{r}_i$$

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.

• When the input is a Tensor, the 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, 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 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
>>> import mindspore
>>> 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