mindspore.ops.UniformCandidateSampler

class mindspore.ops.UniformCandidateSampler(num_true, num_sampled, unique, range_max, seed=0, remove_accidental_hits=False)[source]

Uniform candidate sampler.

This function samples a set of classes(sampled_candidates) from [0, range_max-1] based on uniform distribution.

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

Parameters

  • num_true (int) – The number of target classes in each training example.

  • num_sampled (int) – The number of classes to randomly sample. The sampled_candidates will have a shape of num_sampled. If unique=True, num_sampled must be less than or equal to range_max.

  • unique (bool) – Whether all sampled classes in a batch are unique.

  • range_max (int) – The number of possible classes, must be non-negative.

  • seed (int, optional) – Used for random number generation, must be non-negative. If seed has a value of 0, the seed will be replaced with a randomly generated value. Default: 0 .

  • remove_accidental_hits (bool, optional) – Whether accidental hit is removed. Default: False .

Inputs:

  • true_classes (Tensor) - A Tensor. The target classes with a Tensor shape of \((batch\_size, num\_true)\).

Outputs:

  • sampled_candidates (Tensor) - The sampled_candidates is independent of the true classes. Shape: \((num\_sampled, )\).

  • true_expected_count (Tensor) - The expected counts under the sampling distribution of each of true_classes. Shape: \((batch\_size, num\_true)\).

  • sampled_expected_count (Tensor) - The expected counts under the sampling distribution of each of sampled_candidates. Shape: \((num\_sampled, )\).

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> sampler = ops.UniformCandidateSampler(1, 3, False, 4, 1)
>>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int64)))
>>> print(output1.shape)
(3,)
>>> print(output2.shape)
(5, 1)
>>> print(output3.shape)
(3,)