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TensorFlow 1 version
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View source on GitHub
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Samples a set of classes using a log-uniform (Zipfian) base distribution.
tf.random.log_uniform_candidate_sampler(
true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None
)
Used in the notebooks
| Used in the tutorials |
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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.
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num_true
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An int. The number of target classes per training example.
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num_sampled
|
An int. The number of classes to randomly sample.
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unique
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A bool. Determines whether all sampled classes in a batch are
unique.
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range_max
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An int. The number of possible classes.
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seed
|
An int. An operation-specific seed. Default is 0.
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name
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A name for the operation (optional). |
Returns | |
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sampled_candidates
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A tensor of type int64 and shape [num_sampled].
The sampled classes.
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true_expected_count
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A tensor of type float. Same shape as
true_classes. The expected counts under the sampling distribution
of each of true_classes.
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sampled_expected_count
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A tensor of type float. Same shape as
sampled_candidates. The expected counts under the sampling distribution
of each of sampled_candidates.
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