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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 with a shape that is the same as the broadcasted shape of the input input and other, 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