org.tensorflow.op

Class RandomOps

java.lang.Object

org.tensorflow.op.RandomOps

public final class RandomOps
extends Object

An API for building random operations as Ops

See Also:

Ops}

Method Summary

Modifier and Type	Method and Description
AllCandidateSampler	allCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, AllCandidateSampler.Options... options) Builds an AllCandidateSampler operation
LogUniformCandidateSampler	logUniformCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, LogUniformCandidateSampler.Options... options) Builds an LogUniformCandidateSampler operation
<U extends Number,T extends Number> Multinomial<U>	multinomial(Operand<T> logits, Operand<Integer> numSamples, Class<U> outputDtype, Multinomial.Options... options) Builds an Multinomial operation
<T extends Number> Multinomial<Long>	multinomial(Operand<T> logits, Operand<Integer> numSamples, Multinomial.Options... options) Builds an Multinomial operation
<U extends Number,T extends Number> ParameterizedTruncatedNormal<U>	parameterizedTruncatedNormal(Operand<T> shape, Operand<U> means, Operand<U> stdevs, Operand<U> minvals, Operand<U> maxvals, ParameterizedTruncatedNormal.Options... options) Builds an ParameterizedTruncatedNormal operation
<U extends Number,T extends Number> RandomGamma<U>	randomGamma(Operand<T> shape, Operand<U> alpha, RandomGamma.Options... options) Builds an RandomGamma operation
<V extends Number,T extends Number,U extends Number> RandomPoisson<V>	randomPoisson(Operand<T> shape, Operand<U> rate, Class<V> dtype, RandomPoisson.Options... options) Builds an RandomPoisson operation
<T extends Number,U extends Number> RandomPoisson<Long>	randomPoisson(Operand<T> shape, Operand<U> rate, RandomPoisson.Options... options) Builds an RandomPoisson operation
<T> RandomShuffle<T>	randomShuffle(Operand<T> value, RandomShuffle.Options... options) Builds an RandomShuffle operation
<U extends Number,T extends Number> RandomStandardNormal<U>	randomStandardNormal(Operand<T> shape, Class<U> dtype, RandomStandardNormal.Options... options) Builds an RandomStandardNormal operation
<U extends Number,T extends Number> RandomUniform<U>	randomUniform(Operand<T> shape, Class<U> dtype, RandomUniform.Options... options) Builds an RandomUniform operation
<U extends Number,T extends Number> RandomUniformInt<U>	randomUniformInt(Operand<T> shape, Operand<U> minval, Operand<U> maxval, RandomUniformInt.Options... options) Builds an RandomUniformInt operation
RecordInput	recordInput(String filePattern, RecordInput.Options... options) Builds an RecordInput operation
<T extends Number,U extends Number> StatelessMultinomial<Long>	statelessMultinomial(Operand<T> logits, Operand<Integer> numSamples, Operand<U> seed) Builds an StatelessMultinomial operation
<V extends Number,T extends Number,U extends Number> StatelessMultinomial<V>	statelessMultinomial(Operand<T> logits, Operand<Integer> numSamples, Operand<U> seed, Class<V> outputDtype) Builds an StatelessMultinomial operation
<T extends Number,U extends Number> StatelessRandomNormal<Float>	statelessRandomNormal(Operand<T> shape, Operand<U> seed) Builds an StatelessRandomNormal operation
<V extends Number,T extends Number,U extends Number> StatelessRandomNormal<V>	statelessRandomNormal(Operand<T> shape, Operand<U> seed, Class<V> dtype) Builds an StatelessRandomNormal operation
<T extends Number,U extends Number> StatelessRandomUniform<Float>	statelessRandomUniform(Operand<T> shape, Operand<U> seed) Builds an StatelessRandomUniform operation
<V extends Number,T extends Number,U extends Number> StatelessRandomUniform<V>	statelessRandomUniform(Operand<T> shape, Operand<U> seed, Class<V> dtype) Builds an StatelessRandomUniform operation
<T extends Number,U extends Number> StatelessTruncatedNormal<Float>	statelessTruncatedNormal(Operand<T> shape, Operand<U> seed) Builds an StatelessTruncatedNormal operation
<V extends Number,T extends Number,U extends Number> StatelessTruncatedNormal<V>	statelessTruncatedNormal(Operand<T> shape, Operand<U> seed, Class<V> dtype) Builds an StatelessTruncatedNormal operation
<U extends Number,T extends Number> TruncatedNormal<U>	truncatedNormal(Operand<T> shape, Class<U> dtype, TruncatedNormal.Options... options) Builds an TruncatedNormal operation
UniformCandidateSampler	uniformCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, UniformCandidateSampler.Options... options) Builds an UniformCandidateSampler operation

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

randomShuffle

public <T> RandomShuffle<T> randomShuffle(Operand<T> value,
                                          RandomShuffle.Options... options)

Builds an RandomShuffle operation

Parameters:

value - The tensor to be shuffled.

options - carries optional attributes values

Returns:

a new instance of RandomShuffle

See Also:

RandomShuffle

multinomial

public <T extends Number> Multinomial<Long> multinomial(Operand<T> logits,
                                                        Operand<Integer> numSamples,
                                                        Multinomial.Options... options)

Builds an Multinomial operation

Parameters:

logits - 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`

numSamples - 0-D. Number of independent samples to draw for each row slice.

options - carries optional attributes values

Returns:

a new instance of Multinomial

See Also:

Multinomial

statelessRandomUniform

public <T extends Number,U extends Number> StatelessRandomUniform<Float> statelessRandomUniform(Operand<T> shape,
                                                                                                Operand<U> seed)

Builds an StatelessRandomUniform operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

Returns:

a new instance of StatelessRandomUniform

See Also:

StatelessRandomUniform

statelessRandomNormal

public <V extends Number,T extends Number,U extends Number> StatelessRandomNormal<V> statelessRandomNormal(Operand<T> shape,
                                                                                                           Operand<U> seed,
                                                                                                           Class<V> dtype)

Builds an StatelessRandomNormal operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

dtype - The type of the output.

Returns:

a new instance of StatelessRandomNormal

See Also:

StatelessRandomNormal

uniformCandidateSampler

public UniformCandidateSampler uniformCandidateSampler(Operand<Long> trueClasses,
                                                       Long numTrue,
                                                       Long numSampled,
                                                       Boolean unique,
                                                       Long rangeMax,
                                                       UniformCandidateSampler.Options... options)

Builds an UniformCandidateSampler operation

Parameters:

trueClasses - A batch_size * num_true matrix, in which each row contains the

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

rangeMax - The sampler will sample integers from the interval [0, range_max).

options - carries optional attributes values

Returns:

a new instance of UniformCandidateSampler

See Also:

UniformCandidateSampler

randomGamma

public <U extends Number,T extends Number> RandomGamma<U> randomGamma(Operand<T> shape,
                                                                      Operand<U> alpha,
                                                                      RandomGamma.Options... options)

Builds an RandomGamma operation

Parameters:

shape - 1-D integer tensor. Shape of independent samples to draw from each

alpha - A tensor in which each scalar is a "shape" parameter describing the

options - carries optional attributes values

Returns:

a new instance of RandomGamma

See Also:

RandomGamma

randomUniformInt

public <U extends Number,T extends Number> RandomUniformInt<U> randomUniformInt(Operand<T> shape,
                                                                                Operand<U> minval,
                                                                                Operand<U> maxval,
                                                                                RandomUniformInt.Options... options)

Builds an RandomUniformInt operation

Parameters:

shape - The shape of the output tensor.

minval - 0-D. Inclusive lower bound on the generated integers.

maxval - 0-D. Exclusive upper bound on the generated integers.

options - carries optional attributes values

Returns:

a new instance of RandomUniformInt

See Also:

RandomUniformInt

statelessTruncatedNormal

public <V extends Number,T extends Number,U extends Number> StatelessTruncatedNormal<V> statelessTruncatedNormal(Operand<T> shape,
                                                                                                                 Operand<U> seed,
                                                                                                                 Class<V> dtype)

Builds an StatelessTruncatedNormal operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

dtype - The type of the output.

Returns:

a new instance of StatelessTruncatedNormal

See Also:

StatelessTruncatedNormal

statelessMultinomial

public <V extends Number,T extends Number,U extends Number> StatelessMultinomial<V> statelessMultinomial(Operand<T> logits,
                                                                                                         Operand<Integer> numSamples,
                                                                                                         Operand<U> seed,
                                                                                                         Class<V> outputDtype)

Builds an StatelessMultinomial operation

Parameters:

logits - 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`

numSamples - 0-D. Number of independent samples to draw for each row slice.

seed - 2 seeds (shape [2]).

outputDtype -

Returns:

a new instance of StatelessMultinomial

See Also:

StatelessMultinomial

randomPoisson

public <T extends Number,U extends Number> RandomPoisson<Long> randomPoisson(Operand<T> shape,
                                                                             Operand<U> rate,
                                                                             RandomPoisson.Options... options)

Builds an RandomPoisson operation

Parameters:

shape - 1-D integer tensor. Shape of independent samples to draw from each

rate - A tensor in which each scalar is a "rate" parameter describing the

options - carries optional attributes values

Returns:

a new instance of RandomPoisson

See Also:

RandomPoisson

statelessMultinomial

public <T extends Number,U extends Number> StatelessMultinomial<Long> statelessMultinomial(Operand<T> logits,
                                                                                           Operand<Integer> numSamples,
                                                                                           Operand<U> seed)

Builds an StatelessMultinomial operation

Parameters:

logits - 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`

numSamples - 0-D. Number of independent samples to draw for each row slice.

seed - 2 seeds (shape [2]).

Returns:

a new instance of StatelessMultinomial

See Also:

StatelessMultinomial

randomUniform

public <U extends Number,T extends Number> RandomUniform<U> randomUniform(Operand<T> shape,
                                                                          Class<U> dtype,
                                                                          RandomUniform.Options... options)

Builds an RandomUniform operation

Parameters:

shape - The shape of the output tensor.

dtype - The type of the output.

options - carries optional attributes values

Returns:

a new instance of RandomUniform

See Also:

RandomUniform

statelessRandomNormal

public <T extends Number,U extends Number> StatelessRandomNormal<Float> statelessRandomNormal(Operand<T> shape,
                                                                                              Operand<U> seed)

Builds an StatelessRandomNormal operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

Returns:

a new instance of StatelessRandomNormal

See Also:

StatelessRandomNormal

multinomial

public <U extends Number,T extends Number> Multinomial<U> multinomial(Operand<T> logits,
                                                                      Operand<Integer> numSamples,
                                                                      Class<U> outputDtype,
                                                                      Multinomial.Options... options)

Builds an Multinomial operation

Parameters:

logits - 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`

numSamples - 0-D. Number of independent samples to draw for each row slice.

outputDtype -

options - carries optional attributes values

Returns:

a new instance of Multinomial

See Also:

Multinomial

allCandidateSampler

public AllCandidateSampler allCandidateSampler(Operand<Long> trueClasses,
                                               Long numTrue,
                                               Long numSampled,
                                               Boolean unique,
                                               AllCandidateSampler.Options... options)

Builds an AllCandidateSampler operation

Parameters:

trueClasses - A batch_size * num_true matrix, in which each row contains the

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

options - carries optional attributes values

Returns:

a new instance of AllCandidateSampler

See Also:

AllCandidateSampler

parameterizedTruncatedNormal

public <U extends Number,T extends Number> ParameterizedTruncatedNormal<U> parameterizedTruncatedNormal(Operand<T> shape,
                                                                                                        Operand<U> means,
                                                                                                        Operand<U> stdevs,
                                                                                                        Operand<U> minvals,
                                                                                                        Operand<U> maxvals,
                                                                                                        ParameterizedTruncatedNormal.Options... options)

Builds an ParameterizedTruncatedNormal operation

Parameters:

shape - The shape of the output tensor. Batches are indexed by the 0th dimension.

means - The mean parameter of each batch.

stdevs - The standard deviation parameter of each batch. Must be greater than 0.

minvals - The minimum cutoff. May be -infinity.

maxvals - The maximum cutoff. May be +infinity, and must be more than the minval

options - carries optional attributes values

Returns:

a new instance of ParameterizedTruncatedNormal

See Also:

ParameterizedTruncatedNormal

randomPoisson

public <V extends Number,T extends Number,U extends Number> RandomPoisson<V> randomPoisson(Operand<T> shape,
                                                                                           Operand<U> rate,
                                                                                           Class<V> dtype,
                                                                                           RandomPoisson.Options... options)

Builds an RandomPoisson operation

Parameters:

shape - 1-D integer tensor. Shape of independent samples to draw from each

rate - A tensor in which each scalar is a "rate" parameter describing the

dtype -

options - carries optional attributes values

Returns:

a new instance of RandomPoisson

See Also:

RandomPoisson

truncatedNormal

public <U extends Number,T extends Number> TruncatedNormal<U> truncatedNormal(Operand<T> shape,
                                                                              Class<U> dtype,
                                                                              TruncatedNormal.Options... options)

Builds an TruncatedNormal operation

Parameters:

shape - The shape of the output tensor.

dtype - The type of the output.

options - carries optional attributes values

Returns:

a new instance of TruncatedNormal

See Also:

TruncatedNormal

statelessTruncatedNormal

public <T extends Number,U extends Number> StatelessTruncatedNormal<Float> statelessTruncatedNormal(Operand<T> shape,
                                                                                                    Operand<U> seed)

Builds an StatelessTruncatedNormal operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

Returns:

a new instance of StatelessTruncatedNormal

See Also:

StatelessTruncatedNormal

statelessRandomUniform

public <V extends Number,T extends Number,U extends Number> StatelessRandomUniform<V> statelessRandomUniform(Operand<T> shape,
                                                                                                             Operand<U> seed,
                                                                                                             Class<V> dtype)

Builds an StatelessRandomUniform operation

Parameters:

shape - The shape of the output tensor.

seed - 2 seeds (shape [2]).

dtype - The type of the output.

Returns:

a new instance of StatelessRandomUniform

See Also:

StatelessRandomUniform

logUniformCandidateSampler

public LogUniformCandidateSampler logUniformCandidateSampler(Operand<Long> trueClasses,
                                                             Long numTrue,
                                                             Long numSampled,
                                                             Boolean unique,
                                                             Long rangeMax,
                                                             LogUniformCandidateSampler.Options... options)

Builds an LogUniformCandidateSampler operation

Parameters:

trueClasses - A batch_size * num_true matrix, in which each row contains the

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

rangeMax - The sampler will sample integers from the interval [0, range_max).

options - carries optional attributes values

Returns:

a new instance of LogUniformCandidateSampler

See Also:

LogUniformCandidateSampler

recordInput

public RecordInput recordInput(String filePattern,
                               RecordInput.Options... options)

Builds an RecordInput operation

Parameters:

filePattern - Glob pattern for the data files.

options - carries optional attributes values

Returns:

a new instance of RecordInput

See Also:

RecordInput

randomStandardNormal

public <U extends Number,T extends Number> RandomStandardNormal<U> randomStandardNormal(Operand<T> shape,
                                                                                        Class<U> dtype,
                                                                                        RandomStandardNormal.Options... options)

Builds an RandomStandardNormal operation

Parameters:

shape - The shape of the output tensor.

dtype - The type of the output.

options - carries optional attributes values

Returns:

a new instance of RandomStandardNormal

See Also:

RandomStandardNormal