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.
Args
true_classes
A Tensor of type int64.
A batch_size * num_true matrix, in which each row contains the
IDs of the num_true target_classes in the corresponding original label.
num_true
An int that is >= 1. Number of true labels per context.
num_sampled
An int that is >= 1. Number of candidates to produce.
unique
A bool.
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.
seed
An optional int. Defaults to 0.
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
An optional int. Defaults to 0.
An second seed to avoid seed collision.
name
A name for the operation (optional).
Returns
A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]