tf.nn.log_uniform_candidate_sampler

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

Defined in tensorflow/python/ops/candidate_sampling_ops.py.

See the guide: Neural Network > Candidate Sampling

Samples a set of classes using a log-uniform (Zipfian) 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 an approximately log-uniform or Zipfian distribution:

P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)

This sampler is useful when the target classes approximately follow such a distribution - for example, if the classes represent words in a lexicon sorted in decreasing order of frequency. If your classes are not ordered by decreasing frequency, do not use this op.

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.
• 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.
• 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.