tf.nn.fixed_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=1.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=(), seed=None, name=None )
See the guide: Neural Network > Candidate Sampling
Samples a set of classes using the provided (fixed) base distribution.
This operation randomly samples a tensor of sampled classes
sampled_candidates) from the range of integers
The elements of
sampled_candidates are drawn without replacement
unique=True) or with replacement (if
the base distribution.
The base distribution is read from a file or passed in as an in-memory array. There is also an option to skew the distribution by applying a distortion power to the weights.
In addition, this operation returns tensors
sampled_expected_count representing the number of times each
of the target classes (
true_classes) and the sampled
sampled_candidates) is expected to occur in an average
tensor of sampled classes. These values correspond to
defined in this
unique=True, then these are post-rejection probabilities and we
compute them approximately.
[batch_size, num_true]. The target classes.
int. The number of target classes per training example.
int. The number of classes to randomly sample.
bool. Determines whether all sampled classes in a batch are unique.
int. The number of possible classes.
vocab_file: Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of
unigramsneeds to be passed to this operation.
distortion: The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result,
distortion = 1.0gives regular unigram sampling (as defined by the vocab file), and
distortion = 0.0gives a uniform distribution.
num_reserved_ids: Optionally some reserved IDs can be added in the range
[0, num_reserved_ids)by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0.
num_shards: A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with
shard) indicates the number of partitions that are being used in the overall computation.
shard: A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with
num_shards) indicates the particular partition number of the operation, when partitioning is being used.
unigrams: A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of
unigramsshould be passed to this operation.
int. An operation-specific seed. Default is 0.
name: A name for the operation (optional).
sampled_candidates: A tensor of type
[num_sampled]. The sampled classes.
true_expected_count: A tensor of type
float. Same shape as
true_classes. The expected counts under the sampling distribution of each of
sampled_expected_count: A tensor of type
float. Same shape as
sampled_candidates. The expected counts under the sampling distribution of each of