mindspore.ops.sub
- mindspore.ops.sub(input, other)[source]
Subtracts the second input tensor from the first input tensor element-wise.
\[out_{i} = input_{i} - other_{i}\]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 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 are not number.Number or bool or Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input = Tensor(np.array([1, 2, 3]), mindspore.int32) >>> other = Tensor(np.array([4, 5, 6]), mindspore.int32) >>> output = ops.sub(input, other) >>> print(output) [-3 -3 -3]