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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.ops.select

mindspore.ops.select(cond, x, y)

Returns the selected elements, either from input $x$ or input $y$, depending on the condition cond.

Given a tensor as input, this operation inserts a dimension of 1 at the dimension, it was invalid when both $x$ and $y$ are none. Keep in mind that the shape of the output tensor can vary depending on how many true values are in the input. Indexes are output in row-first order.

The conditional tensor acts as an optional compensation (mask), which determines whether the corresponding element / row 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:

$$\begin{array}{r}ou{t}_i=\{\begin{array}{ll}{x}_i,& \text{if }conditio{n}_i\\ y_i,& \text{otherwise}\end{array}\end{array}$$

If condition is a vector, then $x$ and $y$ are higher-dimensional matrices, then it chooses to copy that row (external dimensions) from $x$ and $y$. If condition has the same shape as $x$ and $y$, you can choose to copy these elements from $x$ and $y$.

Inputs:

• cond (Tensor[bool]) - The shape is $(x_1,x_2,...,x_N,...,x_R)$. The condition tensor, decides which element is chosen.

• x (Union[Tensor, int, float]) - The shape is $(x_1,x_2,...,x_N,...,x_R)$. The first input tensor. If x is int or 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 shape is $(x_1,x_2,...,x_N,...,x_R)$. The second input tensor. If y is int or 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.

Outputs:

Tensor, has the same shape as cond. The shape is $(x_1,x_2,...,x_N,...,x_R)$.

Raises

• TypeError – If x or y is not a Tensor, int or float.

• ValueError – The shapes of inputs not equal.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # 1) Both inputs are Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>>
>>> 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.]