def uniform_candidate_sampler(true_classes,
num_true,
num_sampled,
unique,
range_max,
seed=None,
name=None):
"""Samples a set of classes using a uniform 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 for this operation is the uniform distribution
over the range of integers `[0, range_max)`.
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](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.
Args:
true_classes: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
num_true: An `int`. The number of target classes per training example.
num_sampled: An `int`. The number of classes to randomly sample per batch.
unique: A `bool`. Determines whether all sampled classes in a batch are
unique.
range_max: An `int`. The number of possible classes.
seed: An `int`. An operation-specific seed. Default is 0.
name: A name for the operation (optional).
Returns:
sampled_candidates: A tensor of type `int64` and shape `[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 `true_classes`.
sampled_expected_count: A tensor of type `float`. Same shape as
`sampled_candidates`. The expected counts under the sampling distribution
of each of `sampled_candidates`.
"""
seed1, seed2 = random_seed.get_seed(seed)
return gen_candidate_sampling_ops._uniform_candidate_sampler(true_classes,
num_true,
num_sampled,
unique,
range_max,
seed=seed1,
seed2=seed2,
name=name)
def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
range_max, seed=None, name=None):
"""Samples a set of classes using a uniform 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 for this operation is the uniform distribution
over the range of integers `[0, range_max)`.
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](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.
Args:
true_classes: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
num_true: An `int`. The number of target classes per training example.
num_sampled: An `int`. The number of classes to randomly sample. The
`sampled_candidates` return value will have shape `[num_sampled]`. If
`unique=True`, `num_sampled` must be less than or equal to `range_max`.
unique: A `bool`. Determines whether all sampled classes in a batch are
unique.
range_max: An `int`. The number of possible classes.
seed: An `int`. An operation-specific seed. Default is 0.
name: A name for the operation (optional).
Returns:
sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The
sampled classes, either with possible duplicates (`unique=False`) or all
unique (`unique=True`). In either case, `sampled_candidates` is
independent of the true classes.
true_expected_count: A tensor of type `float`. Same shape as
`true_classes`. The expected counts under the sampling distribution
of each of `true_classes`.
sampled_expected_count: A tensor of type `float`. Same shape as
`sampled_candidates`. The expected counts under the sampling distribution
of each of `sampled_candidates`.
"""
seed1, seed2 = random_seed.get_seed(seed)
return gen_candidate_sampling_ops._uniform_candidate_sampler(
true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
seed2=seed2, name=name)
