{ }
Generates labels for candidate sampling with a learned unigram distribution.
tf.raw_ops.AllCandidateSampler(
true_classes, num_true, num_sampled, unique, seed=0, seed2=0, name=None
)
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.