org.bytedeco.javacpp

Class tensorflow.FixedUnigramCandidateSampler.Attrs

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.tensorflow.FixedUnigramCandidateSampler.Attrs

All Implemented Interfaces:

AutoCloseable

Enclosing class:

tensorflow.FixedUnigramCandidateSampler

public static class tensorflow.FixedUnigramCandidateSampler.Attrs
extends Pointer

Optional attribute setters for FixedUnigramCandidateSampler

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
Attrs() Default native constructor.
Attrs(long size) Native array allocator.
Attrs(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
float	distortion_()
tensorflow.FixedUnigramCandidateSampler.Attrs	distortion_(float distortion_)
tensorflow.FixedUnigramCandidateSampler.Attrs	Distortion(float x) The distortion is used to skew the unigram probability distribution.
long	num_reserved_ids_()
tensorflow.FixedUnigramCandidateSampler.Attrs	num_reserved_ids_(long num_reserved_ids_)
long	num_shards_()
tensorflow.FixedUnigramCandidateSampler.Attrs	num_shards_(long num_shards_)
tensorflow.FixedUnigramCandidateSampler.Attrs	NumReservedIds(long x) Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users.
tensorflow.FixedUnigramCandidateSampler.Attrs	NumShards(long x) A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism.
tensorflow.FixedUnigramCandidateSampler.Attrs	position(long position)
long	seed_()
tensorflow.FixedUnigramCandidateSampler.Attrs	seed_(long seed_)
tensorflow.FixedUnigramCandidateSampler.Attrs	Seed(long x) If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed.
long	seed2_()
tensorflow.FixedUnigramCandidateSampler.Attrs	seed2_(long seed2_)
tensorflow.FixedUnigramCandidateSampler.Attrs	Seed2(long x) An second seed to avoid seed collision.
long	shard_()
tensorflow.FixedUnigramCandidateSampler.Attrs	shard_(long shard_)
tensorflow.FixedUnigramCandidateSampler.Attrs	Shard(long x) A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism.
FloatPointer	unigrams_()
tensorflow.FixedUnigramCandidateSampler.Attrs	unigrams_(FloatPointer unigrams_)
tensorflow.FixedUnigramCandidateSampler.Attrs	Unigrams(float... x)
tensorflow.FixedUnigramCandidateSampler.Attrs	Unigrams(FloatBuffer x)
tensorflow.FixedUnigramCandidateSampler.Attrs	Unigrams(FloatPointer x) A list of unigram counts or probabilities, one per ID in sequential order.
BytePointer	vocab_file_()
tensorflow.FixedUnigramCandidateSampler.Attrs	vocab_file_(BytePointer vocab_file_)
tensorflow.FixedUnigramCandidateSampler.Attrs	VocabFile(BytePointer x) Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID.
tensorflow.FixedUnigramCandidateSampler.Attrs	VocabFile(String x)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

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

Constructor Detail

Attrs

public Attrs()

Default native constructor.

Attrs

public Attrs(long size)

Native array allocator. Access with Pointer.position(long).

Attrs

public Attrs(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

position

public tensorflow.FixedUnigramCandidateSampler.Attrs position(long position)

Overrides:

position in class Pointer

VocabFile

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs VocabFile(@tensorflow.StringPiece
                                                                       BytePointer x)

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. Defaults to ""

VocabFile

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs VocabFile(@tensorflow.StringPiece
                                                                       String x)

Distortion

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Distortion(float x)

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. Defaults to 1

NumReservedIds

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs NumReservedIds(@Cast(value="tensorflow::int64")
                                                                            long x)

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. Defaults to 0

NumShards

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs NumShards(@Cast(value="tensorflow::int64")
                                                                       long x)

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. Defaults to 1

Shard

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Shard(@Cast(value="tensorflow::int64")
                                                                   long x)

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. Defaults to 0

Unigrams

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Unigrams(@tensorflow.ArraySlice
                                                                      FloatPointer x)

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. Defaults to []

Unigrams

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Unigrams(@tensorflow.ArraySlice
                                                                      FloatBuffer x)

Unigrams

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Unigrams(@tensorflow.ArraySlice
                                                                      float... x)

Seed

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Seed(@Cast(value="tensorflow::int64")
                                                                  long x)

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. Defaults to 0

Seed2

@ByVal
public tensorflow.FixedUnigramCandidateSampler.Attrs Seed2(@Cast(value="tensorflow::int64")
                                                                   long x)

An second seed to avoid seed collision. Defaults to 0

vocab_file_

@tensorflow.StringPiece
public BytePointer vocab_file_()

vocab_file_

public tensorflow.FixedUnigramCandidateSampler.Attrs vocab_file_(BytePointer vocab_file_)

distortion_

public float distortion_()

distortion_

public tensorflow.FixedUnigramCandidateSampler.Attrs distortion_(float distortion_)

num_reserved_ids_

@Cast(value="tensorflow::int64")
public long num_reserved_ids_()

num_reserved_ids_

public tensorflow.FixedUnigramCandidateSampler.Attrs num_reserved_ids_(long num_reserved_ids_)

num_shards_

@Cast(value="tensorflow::int64")
public long num_shards_()

num_shards_

public tensorflow.FixedUnigramCandidateSampler.Attrs num_shards_(long num_shards_)

shard_

@Cast(value="tensorflow::int64")
public long shard_()

shard_

public tensorflow.FixedUnigramCandidateSampler.Attrs shard_(long shard_)

unigrams_

@tensorflow.ArraySlice
public FloatPointer unigrams_()

unigrams_

public tensorflow.FixedUnigramCandidateSampler.Attrs unigrams_(FloatPointer unigrams_)

seed_

@Cast(value="tensorflow::int64")
public long seed_()

seed_

public tensorflow.FixedUnigramCandidateSampler.Attrs seed_(long seed_)

seed2_

@Cast(value="tensorflow::int64")
public long seed2_()

seed2_

public tensorflow.FixedUnigramCandidateSampler.Attrs seed2_(long seed2_)