org.tensorflow.op.core

Class FixedUnigramCandidateSampler

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.FixedUnigramCandidateSampler

All Implemented Interfaces:

Op

@Operator
public final class FixedUnigramCandidateSampler
extends PrimitiveOp

Generates labels for candidate sampling with a learned unigram distribution.

A unigram sampler could use a fixed unigram distribution read from a file or passed in as an in-memory array instead of building up the distribution from data on the fly. There is also an option to skew the distribution by applying a distortion power to the weights.

The vocabulary file should be in CSV-like format, with the last field being the weight associated with the word.

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.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	FixedUnigramCandidateSampler.Options Optional attributes for FixedUnigramCandidateSampler

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
static FixedUnigramCandidateSampler	create(Scope scope, Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, FixedUnigramCandidateSampler.Options... options) Factory method to create a class to wrap a new FixedUnigramCandidateSampler operation to the graph.
static FixedUnigramCandidateSampler.Options	distortion(Float distortion)
static FixedUnigramCandidateSampler.Options	numReservedIds(Long numReservedIds)
static FixedUnigramCandidateSampler.Options	numShards(Long numShards)
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 FixedUnigramCandidateSampler.Options	seed(Long seed)
static FixedUnigramCandidateSampler.Options	seed2(Long seed2)
static FixedUnigramCandidateSampler.Options	shard(Long shard)
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.
static FixedUnigramCandidateSampler.Options	unigrams(List<Float> unigrams)
static FixedUnigramCandidateSampler.Options	vocabFile(String vocabFile)

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

create

public static FixedUnigramCandidateSampler create(Scope scope,
                                                  Operand<Long> trueClasses,
                                                  Long numTrue,
                                                  Long numSampled,
                                                  Boolean unique,
                                                  Long rangeMax,
                                                  FixedUnigramCandidateSampler.Options... options)

Factory method to create a class to wrap a new FixedUnigramCandidateSampler operation to the graph.

Parameters:

scope - current graph scope

trueClasses - 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 values

Returns:

a new instance of FixedUnigramCandidateSampler

vocabFile

public static FixedUnigramCandidateSampler.Options vocabFile(String vocabFile)

Parameters:

vocabFile - Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of vocab_file and unigrams needs to be passed to this op.

distortion

public static FixedUnigramCandidateSampler.Options distortion(Float distortion)

Parameters:

distortion - The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, distortion = 1.0 gives regular unigram sampling (as defined by the vocab file), and distortion = 0.0 gives a uniform distribution.

numReservedIds

public static FixedUnigramCandidateSampler.Options numReservedIds(Long numReservedIds)

Parameters:

numReservedIds - Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0.

numShards

public static FixedUnigramCandidateSampler.Options numShards(Long numShards)

Parameters:

numShards - A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'shard') indicates the number of partitions that are being used in the overall computation.

shard

public static FixedUnigramCandidateSampler.Options shard(Long shard)

Parameters:

shard - A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'num_shards') indicates the particular partition number of a sampler op, when partitioning is being used.

unigrams

public static FixedUnigramCandidateSampler.Options unigrams(List<Float> unigrams)

Parameters:

unigrams - A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of vocab_file and unigrams should be passed to this op.

seed

public static FixedUnigramCandidateSampler.Options seed(Long seed)

Parameters:

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.

seed2

public static FixedUnigramCandidateSampler.Options seed2(Long seed2)

Parameters:

seed2 - An second seed to avoid seed collision.

sampledCandidates

public Output<Long> sampledCandidates()

A vector of length num_sampled, in which each element is the ID of a sampled candidate.

trueExpectedCount

public 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. If unique=true, then this is a probability.

sampledExpectedCount

public 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. If unique=true, then this is a probability.