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+### `tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)` {#compute_accidental_hits}
+
+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:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled_candidates output of CandidateSampler.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`indices`</b>: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
+    Values indicate rows in `true_classes`.
+*  <b>`ids`</b>: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
+    Values indicate positions in `sampled_candidates`.
+*  <b>`weights`</b>: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
+    Each value is `-FLOAT_MAX`.
+