mindspore.ops.RandomChoiceWithMask

class mindspore.ops.RandomChoiceWithMask(count=256, seed=0, seed2=0)[source]

Generates a random sample as index tensor with a mask tensor from a given tensor.

Refer to mindspore.ops.choice_with_mask() for more details.

Note

Random seed: a set of regular random numbers can be obtained through some complex mathematical algorithms, and the random seed determines the initial value of this random number. If the random seed is the same in two separate calls, the random number generated will not change.
Using the Philox algorithm to scramble seed and seed2 to obtain random seed so that the user doesn’t need to worry about which seed is more important.

Parameters

count (int, optional) – Number of items expected to get and the number must be greater than 0. Default: 256 .
seed (int, optional) – The operator-level random seed, used to generate random numbers, must be non-negative. Default: 0 .
seed2 (int, optional) – The global random seed, which combines with the operator-level random seed to determine the final generated random number, must be non-negative. Default: 0 .

Inputs:

input_x (Tensor[bool]) - The input tensor. The input tensor rank must be greater than or equal to 1 and less than or equal to 5.

Outputs:

Two tensors, the first one is the index tensor and the other one is the mask tensor.

index (Tensor) - The output shape is 2-D, its shape is \((count, rank of input_x)\).
mask (Tensor) - The output shape is 1-D, its shape is \((count)\).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> rnd_choice_mask = ops.RandomChoiceWithMask()
>>> input_x = Tensor(np.ones(shape=[240000, 4]).astype(np.bool))
>>> output_y, output_mask = rnd_choice_mask(input_x)
>>> result = output_y.shape
>>> print(result)
(256, 2)
>>> result = output_mask.shape
>>> print(result)
(256,)