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.
- 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