mindspore.ops.RandomChoiceWithMask

class mindspore.ops.RandomChoiceWithMask(count=256, seed=0, seed2=0)

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

The input must be a tensor of rank not less than 1. If its rank is greater than or equal to 2, the first dimension specifies the number of samples. The index tensor and the mask tensor have the fixed shapes. The index tensor denotes the index of the nonzero sample, while the mask tensor denotes which elements in the index tensor are valid.

Parameters

• count (int) – Number of items expected to get and the number must be greater than 0. Default: 256.

• seed (int) – Random seed. Default: 0.

• seed2 (int) – Random seed2. 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.

• mask (Tensor) - The output shape is 1-D.

Raises

• TypeError – If count is not an int.

• TypeError – If neither seed nor seed2 is an int.

• TypeError – If input_x is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> 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,)