tensorflow::ops::LearnedUnigramCandidateSampler
#include <candidate_sampling_ops.h>
Generates labels for candidate sampling with a learned unigram distribution.
Summary
See explanations of candidate sampling and the data formats at go/candidatesampling.
For each batch, this op picks a single set of sampled candidate labels.
The advantages of sampling candidates perbatch 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.
Arguments:
 scope: A Scope object
 true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
 num_true: Number of true labels per context.
 num_sampled: Number of candidates to randomly sample per batch.
 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 postrejection sampling probabilities.
 range_max: The sampler will sample integers from the interval [0, range_max).
Optional attributes (see Attrs
):
 seed: If either seed or seed2 are set to be nonzero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
 seed2: An second seed to avoid seed collision.
Returns:
Output
sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate.Output
true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability.Output
sampled_expected_count: 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. If unique=true, then this is a probability.
Constructors and Destructors 


LearnedUnigramCandidateSampler(const ::tensorflow::Scope & scope, ::tensorflow::Input true_classes, int64 num_true, int64 num_sampled, bool unique, int64 range_max)


LearnedUnigramCandidateSampler(const ::tensorflow::Scope & scope, ::tensorflow::Input true_classes, int64 num_true, int64 num_sampled, bool unique, int64 range_max, const LearnedUnigramCandidateSampler::Attrs & attrs)

Public attributes 


sampled_candidates


sampled_expected_count


true_expected_count

Public static functions 


Seed(int64 x)


Seed2(int64 x)

Structs 


tensorflow:: 
Optional attribute setters for LearnedUnigramCandidateSampler. 
Public attributes
sampled_candidates
::tensorflow::Output sampled_candidates
sampled_expected_count
::tensorflow::Output sampled_expected_count
true_expected_count
::tensorflow::Output true_expected_count
Public functions
LearnedUnigramCandidateSampler
LearnedUnigramCandidateSampler( const ::tensorflow::Scope & scope, ::tensorflow::Input true_classes, int64 num_true, int64 num_sampled, bool unique, int64 range_max )
LearnedUnigramCandidateSampler
LearnedUnigramCandidateSampler( const ::tensorflow::Scope & scope, ::tensorflow::Input true_classes, int64 num_true, int64 num_sampled, bool unique, int64 range_max, const LearnedUnigramCandidateSampler::Attrs & attrs )