public <T> RandomShuffle<T> randomShuffle(Operand<T> value, RandomShuffle.Options... options)
RandomShuffle
operationvalue
- The tensor to be shuffled.options
- carries optional attributes valuesRandomShuffle
public <T extends Number> Multinomial<Long> multinomial(Operand<T> logits, Operand<Integer> numSamples, Multinomial.Options... options)
Multinomial
operationlogits
- 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 valuesMultinomial
public <T extends Number,U extends Number> StatelessRandomUniform<Float> statelessRandomUniform(Operand<T> shape, Operand<U> seed)
StatelessRandomUniform
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).StatelessRandomUniform
public <V extends Number,T extends Number,U extends Number> StatelessRandomNormal<V> statelessRandomNormal(Operand<T> shape, Operand<U> seed, Class<V> dtype)
StatelessRandomNormal
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).dtype
- The type of the output.StatelessRandomNormal
public UniformCandidateSampler uniformCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, UniformCandidateSampler.Options... options)
UniformCandidateSampler
operationtrueClasses
- A batch_size * num_true matrix, in which each row contains thenumTrue
- 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 sampledrangeMax
- The sampler will sample integers from the interval [0, range_max).options
- carries optional attributes valuesUniformCandidateSampler
public <U extends Number,T extends Number> RandomGamma<U> randomGamma(Operand<T> shape, Operand<U> alpha, RandomGamma.Options... options)
RandomGamma
operationshape
- 1-D integer tensor. Shape of independent samples to draw from eachalpha
- A tensor in which each scalar is a "shape" parameter describing theoptions
- carries optional attributes valuesRandomGamma
public <U extends Number,T extends Number> RandomUniformInt<U> randomUniformInt(Operand<T> shape, Operand<U> minval, Operand<U> maxval, RandomUniformInt.Options... options)
RandomUniformInt
operationshape
- 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 valuesRandomUniformInt
public <V extends Number,T extends Number,U extends Number> StatelessTruncatedNormal<V> statelessTruncatedNormal(Operand<T> shape, Operand<U> seed, Class<V> dtype)
StatelessTruncatedNormal
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).dtype
- The type of the output.StatelessTruncatedNormal
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)
StatelessMultinomial
operationlogits
- 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
- StatelessMultinomial
public <T extends Number,U extends Number> RandomPoisson<Long> randomPoisson(Operand<T> shape, Operand<U> rate, RandomPoisson.Options... options)
RandomPoisson
operationshape
- 1-D integer tensor. Shape of independent samples to draw from eachrate
- A tensor in which each scalar is a "rate" parameter describing theoptions
- carries optional attributes valuesRandomPoisson
public <T extends Number,U extends Number> StatelessMultinomial<Long> statelessMultinomial(Operand<T> logits, Operand<Integer> numSamples, Operand<U> seed)
StatelessMultinomial
operationlogits
- 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]).StatelessMultinomial
public <U extends Number,T extends Number> RandomUniform<U> randomUniform(Operand<T> shape, Class<U> dtype, RandomUniform.Options... options)
RandomUniform
operationshape
- The shape of the output tensor.dtype
- The type of the output.options
- carries optional attributes valuesRandomUniform
public <T extends Number,U extends Number> StatelessRandomNormal<Float> statelessRandomNormal(Operand<T> shape, Operand<U> seed)
StatelessRandomNormal
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).StatelessRandomNormal
public <U extends Number,T extends Number> Multinomial<U> multinomial(Operand<T> logits, Operand<Integer> numSamples, Class<U> outputDtype, Multinomial.Options... options)
Multinomial
operationlogits
- 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 valuesMultinomial
public AllCandidateSampler allCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, AllCandidateSampler.Options... options)
AllCandidateSampler
operationtrueClasses
- A batch_size * num_true matrix, in which each row contains thenumTrue
- Number of true labels per context.numSampled
- Number of candidates to produce.unique
- If unique is true, we sample with rejection, so that all sampledoptions
- carries optional attributes valuesAllCandidateSampler
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)
ParameterizedTruncatedNormal
operationshape
- 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 minvaloptions
- carries optional attributes valuesParameterizedTruncatedNormal
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)
RandomPoisson
operationshape
- 1-D integer tensor. Shape of independent samples to draw from eachrate
- A tensor in which each scalar is a "rate" parameter describing thedtype
- options
- carries optional attributes valuesRandomPoisson
public <U extends Number,T extends Number> TruncatedNormal<U> truncatedNormal(Operand<T> shape, Class<U> dtype, TruncatedNormal.Options... options)
TruncatedNormal
operationshape
- The shape of the output tensor.dtype
- The type of the output.options
- carries optional attributes valuesTruncatedNormal
public <T extends Number,U extends Number> StatelessTruncatedNormal<Float> statelessTruncatedNormal(Operand<T> shape, Operand<U> seed)
StatelessTruncatedNormal
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).StatelessTruncatedNormal
public <V extends Number,T extends Number,U extends Number> StatelessRandomUniform<V> statelessRandomUniform(Operand<T> shape, Operand<U> seed, Class<V> dtype)
StatelessRandomUniform
operationshape
- The shape of the output tensor.seed
- 2 seeds (shape [2]).dtype
- The type of the output.StatelessRandomUniform
public LogUniformCandidateSampler logUniformCandidateSampler(Operand<Long> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, LogUniformCandidateSampler.Options... options)
LogUniformCandidateSampler
operationtrueClasses
- A batch_size * num_true matrix, in which each row contains thenumTrue
- 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 sampledrangeMax
- The sampler will sample integers from the interval [0, range_max).options
- carries optional attributes valuesLogUniformCandidateSampler
public RecordInput recordInput(String filePattern, RecordInput.Options... options)
RecordInput
operationfilePattern
- Glob pattern for the data files.options
- carries optional attributes valuesRecordInput
public <U extends Number,T extends Number> RandomStandardNormal<U> randomStandardNormal(Operand<T> shape, Class<U> dtype, RandomStandardNormal.Options... options)
RandomStandardNormal
operationshape
- The shape of the output tensor.dtype
- The type of the output.options
- carries optional attributes valuesRandomStandardNormal
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