tf.random.log_uniform_candidate_sampler  |  TensorFlow v2.16.1 (original) (raw)

tf.random.log_uniform_candidate_sampler

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Samples a set of classes using a log-uniform (Zipfian) base distribution.

View aliases

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.nn.log_uniform_candidate_sampler, tf.compat.v1.random.log_uniform_candidate_sampler

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

Used in the notebooks

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

See the Candidate Sampling Algorithms Referencefor a quick course on Candidate Sampling.

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_countand 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 the Candidate Sampling Algorithms Reference. If unique=True, then these are post-rejection probabilities and we compute them approximately.

Note that this function (and also other *_candidate_samplerfunctions) only gives you the ingredients to implement the various Candidate Sampling algorithms listed in the big table in theCandidate Sampling Algorithms Reference. You still need to implement the algorithms yourself.

For example, according to that table, the phrase "negative samples" may mean different things in different algorithms. For instance, in NCE, "negative samples" means S_i (which is just the sampled classes) which may overlap with true classes, while in Sampled Logistic, "negative samples" means S_i - T_i which excludes the true classes. The return value sampled_candidates corresponds toS_i, not to any specific definition of "negative samples" in any specific algorithm. It's your responsibility to pick an algorithm and calculate the "negative samples" defined by that algorithm (e.g. S_i - T_i).

As another example, the true_classes argument is for calculating the true_expected_count output (as a by-product of this function's main calculation), which may be needed by some algorithms (according to that table). It's not for excluding true classes in the return value sampled_candidates. Again that step is algorithm-specific and should be carried out by you.