Aliases:
tf.nn.fixed_unigram_candidate_sampler
tf.random.fixed_unigram_candidate_sampler
tf.random.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
)
Defined in tensorflow/python/ops/candidate_sampling_ops.py
.
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 [0, range_max)
.
The elements of sampled_candidates
are drawn without replacement
(if unique=True
) or with replacement (if unique=False
) from
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 true_expected_count
and sampled_expected_count
representing the number of times each
of the target classes (true_classes
) and the sampled
classes (sampled_candidates
) is expected to occur in an average
tensor of sampled classes. These values correspond to Q(y|x)
defined in this
document.
If unique=True
, then these are post-rejection probabilities and we
compute them approximately.
Args:
true_classes
: ATensor
of typeint64
and shape[batch_size, num_true]
. The target classes.num_true
: Anint
. The number of target classes per training example.num_sampled
: Anint
. The number of classes to randomly sample.unique
: Abool
. Determines whether all sampled classes in a batch are unique.range_max
: Anint
. 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 ofvocab_file
andunigrams
needs 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 = 1.0
gives regular unigram sampling (as defined by the vocab file), anddistortion = 0.0
gives 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 withshard
) 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 withnum_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 ofvocab_file
andunigrams
should be passed to this operation.seed
: Anint
. An operation-specific seed. Default is 0.name
: A name for the operation (optional).
Returns:
sampled_candidates
: A tensor of typeint64
and shape[num_sampled]
. The sampled classes.true_expected_count
: A tensor of typefloat
. Same shape astrue_classes
. The expected counts under the sampling distribution of each oftrue_classes
.sampled_expected_count
: A tensor of typefloat
. Same shape assampled_candidates
. The expected counts under the sampling distribution of each ofsampled_candidates
.