def compute_accidental_hits(true_classes,
sampled_candidates,
num_true,
seed=None,
name=None):
"""Compute the position ids in `sampled_candidates` matching `true_classes`.
In Candidate Sampling, this operation facilitates virtually removing
sampled classes which happen to match target classes. This is done
in Sampled Softmax and Sampled Logistic.
See our [Candidate Sampling Algorithms
Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).
We presuppose that the `sampled_candidates` are unique.
We call it an 'accidental hit' when one of the target classes
matches one of the sampled classes. This operation reports
accidental hits as triples `(index, id, weight)`, where `index`
represents the row number in `true_classes`, `id` represents the
position in `sampled_candidates`, and weight is `-FLOAT_MAX`.
The result of this op should be passed through a `sparse_to_dense`
operation, then added to the logits of the sampled classes. This
removes the contradictory effect of accidentally sampling the true
target classes as noise classes for the same example.
Args:
true_classes: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
The sampled_candidates output of CandidateSampler.
num_true: An `int`. The number of target classes per training example.
seed: An `int`. An operation-specific seed. Default is 0.
name: A name for the operation (optional).
Returns:
indices: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
Values indicate rows in `true_classes`.
ids: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
Values indicate positions in `sampled_candidates`.
weights: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
Each value is `-FLOAT_MAX`.
"""
seed1, seed2 = random_seed.get_seed(seed)
return gen_candidate_sampling_ops.compute_accidental_hits(
true_classes,
sampled_candidates,
num_true,
seed=seed1,
seed2=seed2,
name=name)
def compute_accidental_hits(true_classes, sampled_candidates, num_true,
seed=None, name=None):
"""Compute the position ids in `sampled_candidates` matching `true_classes`.
In Candidate Sampling, this operation facilitates virtually removing
sampled classes which happen to match target classes. This is done
in Sampled Softmax and Sampled Logistic.
See our [Candidate Sampling Algorithms
Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).
We presuppose that the `sampled_candidates` are unique.
We call it an 'accidental hit' when one of the target classes
matches one of the sampled classes. This operation reports
accidental hits as triples `(index, id, weight)`, where `index`
represents the row number in `true_classes`, `id` represents the
position in `sampled_candidates`, and weight is `-FLOAT_MAX`.
The result of this op should be passed through a `sparse_to_dense`
operation, then added to the logits of the sampled classes. This
removes the contradictory effect of accidentally sampling the true
target classes as noise classes for the same example.
Args:
true_classes: A `Tensor` of type `int64` and shape `[batch_size,
num_true]`. The target classes.
sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
The sampled_candidates output of CandidateSampler.
num_true: An `int`. The number of target classes per training example.
seed: An `int`. An operation-specific seed. Default is 0.
name: A name for the operation (optional).
Returns:
indices: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
Values indicate rows in `true_classes`.
ids: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
Values indicate positions in `sampled_candidates`.
weights: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
Each value is `-FLOAT_MAX`.
"""
seed1, seed2 = random_seed.get_seed(seed)
return gen_candidate_sampling_ops.compute_accidental_hits(
true_classes, sampled_candidates, num_true, seed=seed1, seed2=seed2,
name=name)