mindspore.ops.select
- mindspore.ops.select(cond, x, y)[source]
The conditional tensor determines whether the corresponding element in the output must be selected from x (if true) or y (if false) based on the value of each element.
It can be defined as:
\[\begin{split}out_i = \begin{cases} x_i, & \text{if } cond_i \\ y_i, & \text{otherwise} \end{cases}\end{split}\]- Parameters
cond (Tensor[bool]) – The condition tensor, decides which element is chosen. The shape is \((x_1, x_2, ..., x_N, ..., x_R)\).
x (Union[Tensor, int, float]) – The first Tensor or number to be selected. If x is a Tensor, the shape is or can be broadcadt to \((x_1, x_2, ..., x_N, ..., x_R)\). If x is an int or a float, it will be cast to the type of int32 or float32, and broadcast to the same shape as y. One of x and y must be a Tensor.
y (Union[Tensor, int, float]) – The second Tensor or number to be selected. If y is a Tensor, The shape is or can be broadcadt to \((x_1, x_2, ..., x_N, ..., x_R)\). If y is an int or a float, it will be cast to the type of int32 or float32, and broadcast to the same shape as x. One of x and y must be a Tensor.
- Returns
Tensor, has the same shape as cond.
- Raises
TypeError – If x or y is not a Tensor, int or float.
ValueError – The shapes of inputs can not be broadcast.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, ops >>> # 1) Both inputs are Tensor >>> >>> cond = Tensor([True, False]) >>> x = Tensor([2,3], mindspore.float32) >>> y = Tensor([1,2], mindspore.float32) >>> output = ops.select(cond, x, y) >>> print(output) [2. 2.] >>> # 2) y is a float >>> cond = Tensor([True, False]) >>> x = Tensor([2,3], mindspore.float32) >>> y = 2.0 >>> output = ops.select(cond, x, y) >>> print(output) [2. 2.]