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. If unique=True, candidates are drawn without replacement, else unique=False with replacement.
- 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) – 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) – 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, ).
- Raises
- Supported Platforms:
GPU
Examples
>>> sampler = ops.UniformCandidateSampler(1, 3, False, 4) >>> output1, output2, output3 = sampler(Tensor(np.array([[1], [3], [4], [6], [3]], dtype=np.int32))) >>> print(output1, output2, output3) [1, 1, 3], [[0.75], [0.75], [0.75], [0.75], [0.75]], [0.75, 0.75, 0.75]