tf.nn.compute_accidental_hits( true_classes, sampled_candidates, num_true, seed=None, name=None )
Defined in tensorflow/python/ops/candidate_sampling_ops.py
.
See the guide: Neural Network > Candidate Sampling
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.
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.
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).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
.
© 2018 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/compute_accidental_hits