### `tf.nn.nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, partition_strategy='mod', name='nce_loss')` {#nce_loss}

Computes and returns the noise-contrastive estimation training loss.

See [Noise-contrastive estimation: A new estimation principle for
unnormalized statistical
models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
Also see our [Candidate Sampling Algorithms
Reference](../../extras/candidate_sampling.pdf)

Note: By default this uses a log-uniform (Zipfian) distribution for sampling,
so your labels must be sorted in order of decreasing frequency to achieve
good results.  For more details, see
[log_uniform_candidate_sampler](#log_uniform_candidate_sampler).

Note: In the case where `num_true` > 1, we assign to each target class
the target probability 1 / `num_true` so that the target probabilities
sum to 1 per-example.

Note: It would be useful to allow a variable number of target classes per
example.  We hope to provide this functionality in a future release.
For now, if you have a variable number of target classes, you can pad them
out to a constant number by either repeating them or by padding
with an otherwise unused class.

##### Args:


*  <b>`weights`</b>: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
      objects whose concatenation along dimension 0 has shape
      [num_classes, dim].  The (possibly-partitioned) class embeddings.
*  <b>`biases`</b>: A `Tensor` of shape `[num_classes]`.  The class biases.
*  <b>`labels`</b>: A `Tensor` of type `int64` and shape `[batch_size,
      num_true]`. The target classes.
*  <b>`inputs`</b>: A `Tensor` of shape `[batch_size, dim]`.  The forward
      activations of the input network.
*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>`num_classes`</b>: An `int`. The number of possible classes.
*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
*  <b>`sampled_values`</b>: a tuple of (`sampled_candidates`, `true_expected_count`,
      `sampled_expected_count`) returned by a `*_candidate_sampler` function.
      (if None, we default to `log_uniform_candidate_sampler`)
*  <b>`remove_accidental_hits`</b>: A `bool`.  Whether to remove "accidental hits"
      where a sampled class equals one of the target classes.  If set to
      `True`, this is a "Sampled Logistic" loss instead of NCE, and we are
      learning to generate log-odds instead of log probabilities.  See
      our [Candidate Sampling Algorithms Reference]
      (../../extras/candidate_sampling.pdf).
      Default is False.
*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
      if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
      Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  A `batch_size` 1-D tensor of per-example NCE losses.

