# tf.nn.uniform_candidate_sampler

tf.nn.uniform_candidate_sampler(
true_classes,
num_true,
num_sampled,
unique,
range_max,
seed=None,
name=None
)


See the guide: Neural Network > Candidate Sampling

Samples a set of classes using a uniform base distribution.

This operation randomly samples a tensor of sampled classes (sampled_candidates) from the range of integers [0, range_max).

The elements of sampled_candidates are drawn without replacement (if unique=True) or with replacement (if unique=False) from the base distribution.

The base distribution for this operation is the uniform distribution over the range of integers [0, range_max).

In addition, this operation returns tensors true_expected_count and sampled_expected_count representing the number of times each of the target classes (true_classes) and the sampled classes (sampled_candidates) is expected to occur in an average tensor of sampled classes. These values correspond to Q(y|x) defined in this document. If unique=True, then these are post-rejection probabilities and we compute them approximately.

#### Args:

• true_classes: A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
• num_true: An int. The number of target classes per training example.
• num_sampled: An int. The number of classes to randomly sample. The sampled_candidates return value will have shape [num_sampled]. If unique=True, num_sampled must be less than or equal to range_max.
• unique: A bool. Determines whether all sampled classes in a batch are unique.
• range_max: An int. The number of possible classes.
• seed: An int. An operation-specific seed. Default is 0.
• name: A name for the operation (optional).

#### Returns:

• sampled_candidates: A tensor of type int64 and shape [num_sampled]. The sampled classes, either with possible duplicates (unique=False) or all unique (unique=True). In either case, sampled_candidates is independent of the true classes.
• true_expected_count: A tensor of type float. Same shape as true_classes. The expected counts under the sampling distribution of each of true_classes.
• sampled_expected_count: A tensor of type float. Same shape as sampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates.