Generates labels for candidate sampling with a log-uniform 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 Classes
| class | LogUniformCandidateSampler.Options | Optional attributes for LogUniformCandidateSampler
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Constants
| String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
| static LogUniformCandidateSampler |
create(Scope scope, Operand<TInt64> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, Options... options)
Factory method to create a class wrapping a new LogUniformCandidateSampler operation.
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| Output<TInt64> |
sampledCandidates()
A vector of length num_sampled, in which each element is
the ID of a sampled candidate.
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| Output<TFloat32> |
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.
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| static LogUniformCandidateSampler.Options |
seed(Long seed)
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| static LogUniformCandidateSampler.Options |
seed2(Long seed2)
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| Output<TFloat32> |
trueExpectedCount()
A batch_size * num_true matrix, representing
the number of times each candidate is expected to occur in a batch
of sampled candidates.
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public static LogUniformCandidateSampler create (Scope scope, Operand<TInt64> trueClasses, Long numTrue, Long numSampled, Boolean unique, Long rangeMax, Options... options)
Factory method to create a class wrapping a new LogUniformCandidateSampler operation.
Parameters
| scope | current scope |
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| 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 LogUniformCandidateSampler
public Output<TInt64> sampledCandidates ()
A vector of length num_sampled, in which each element is the ID of a sampled candidate.
public Output<TFloat32> 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.
public static LogUniformCandidateSampler.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. |
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public static LogUniformCandidateSampler.Options seed2 (Long seed2)
Parameters
| seed2 | An second seed to avoid seed collision. |
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