@Operator(group="nn") public final class LearnedUnigramCandidateSampler extends PrimitiveOp
See explanations of candidate sampling and the data formats at go/candidate-sampling.
For each batch, this op picks a single set of sampled candidate labels.
The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.
Modifier and Type | Class and Description |
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static class |
LearnedUnigramCandidateSampler.Options
Optional attributes for
LearnedUnigramCandidateSampler |
operation
Modifier and Type | Method and Description |
---|---|
static LearnedUnigramCandidateSampler |
create(Scope scope,
Operand<Long> trueClasses,
Long numTrue,
Long numSampled,
Boolean unique,
Long rangeMax,
LearnedUnigramCandidateSampler.Options... options)
Factory method to create a class wrapping a new LearnedUnigramCandidateSampler operation.
|
Output<Long> |
sampledCandidates()
A vector of length num_sampled, in which each element is
the ID of a sampled candidate.
|
Output<Float> |
sampledExpectedCount()
A vector of length num_sampled, for each sampled
candidate representing the number of times the candidate is expected
to occur in a batch of sampled candidates.
|
static LearnedUnigramCandidateSampler.Options |
seed(Long seed) |
static LearnedUnigramCandidateSampler.Options |
seed2(Long seed2) |
Output<Float> |
trueExpectedCount()
A batch_size * num_true matrix, representing
the number of times each candidate is expected to occur in a batch
of sampled candidates.
|
equals, hashCode, op, toString
public static LearnedUnigramCandidateSampler create(Scope scope, Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, LearnedUnigramCandidateSampler.Options... options)
scope
- current scopetrueClasses
- A batch_size * num_true matrix, in which each row contains the
IDs of the num_true target_classes in the corresponding original label.numTrue
- Number of true labels per context.numSampled
- Number of candidates to randomly sample.unique
- If unique is true, we sample with rejection, so that all sampled
candidates in a batch are unique. This requires some approximation to
estimate the post-rejection sampling probabilities.rangeMax
- The sampler will sample integers from the interval [0, range_max).options
- carries optional attributes valuespublic static LearnedUnigramCandidateSampler.Options seed(Long seed)
seed
- If either seed or seed2 are set to be non-zero, the random number
generator is seeded by the given seed. Otherwise, it is seeded by a
random seed.public static LearnedUnigramCandidateSampler.Options seed2(Long seed2)
seed2
- An second seed to avoid seed collision.public Output<Long> sampledCandidates()
public Output<Float> trueExpectedCount()
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