tf.random.uniform_candidate_sampler

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Samples a set of classes using a uniform base distribution.

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Compat aliases for migration

tf.compat.v1.nn.uniform_candidate_sampler, tf.compat.v1.random.uniform_candidate_sampler

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

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 the uniform distribution over the range of integers [0, range_max).

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.

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. Thesampled_candidates return value will have shape [num_sampled]. Ifunique=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). As noted above, sampled_candidates may overlap with true classes.
true_expected_count	A tensor of type float. Same shape astrue_classes. The expected counts under the sampling distribution of each of true_classes.
sampled_expected_count	A tensor of type float. Same shape assampled_candidates. The expected counts under the sampling distribution of each of sampled_candidates.

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

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