tf.nn.learned_unigram_candidate_sampler
tf.nn.learned_unigram_candidate_sampler
tf.nn.learned_unigram_candidate_sampler
learned_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None )
Defined in tensorflow/python/ops/candidate_sampling_ops.py
.
See the guide: Neural Network > Candidate Sampling
Samples a set of classes from a distribution learned during training.
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 constructed on the fly during training. It is a unigram distribution over the target classes seen so far during training. Every integer in [0, range_max)
begins with a weight of 1, and is incremented by 1 each time it is seen as a target class. The base distribution is not saved to checkpoints, so it is reset when the model is reloaded.
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. -
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
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/learned_unigram_candidate_sampler