org.tensorflow.op.core

Class AllCandidateSampler

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.AllCandidateSampler

All Implemented Interfaces:

Op

@Operator
public final class AllCandidateSampler
extends PrimitiveOp

Generates labels for candidate sampling with a learned unigram distribution.

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.

Nested Class Summary

Nested Classes

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

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
static AllCandidateSampler	create(Scope scope, Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, AllCandidateSampler.Options... options) Factory method to create a class to wrap a new AllCandidateSampler operation to the graph.
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 AllCandidateSampler.Options	seed(Long seed)
static AllCandidateSampler.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.

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 AllCandidateSampler create(Scope scope,
                                         Operand<Long> trueClasses,
                                         Long numTrue,
                                         Long numSampled,
                                         Boolean unique,
                                         AllCandidateSampler.Options... options)

Factory method to create a class to wrap a new AllCandidateSampler 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 produce.

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.

options - carries optional attributes values

Returns:

a new instance of AllCandidateSampler

seed

public static AllCandidateSampler.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 AllCandidateSampler.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.