tf.nn.nce_loss(weights, biases, inputs, labels, 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: 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.
weights
: ATensor
of shape[num_classes, dim]
, or a list ofTensor
objects whose concatenation along dimension 0 has shape [num_classes, dim]. The (possibly-partitioned) class embeddings.biases
: ATensor
of shape[num_classes]
. The class biases.inputs
: ATensor
of shape[batch_size, dim]
. The forward activations of the input network.labels
: ATensor
of typeint64
and shape[batch_size, num_true]
. The target classes.num_sampled
: Anint
. The number of classes to randomly sample per batch.num_classes
: Anint
. The number of possible classes.num_true
: Anint
. The number of target classes per training example.sampled_values
: a tuple of (sampled_candidates
,true_expected_count
,sampled_expected_count
) returned by a*_candidate_sampler
function. (if None, we default tolog_uniform_candidate_sampler
)remove_accidental_hits
: Abool
. Whether to remove "accidental hits" where a sampled class equals one of the target classes. If set toTrue
, 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.partition_strategy
: A string specifying the partitioning strategy, relevant iflen(weights) > 1
. Currently"div"
and"mod"
are supported. Default is"mod"
. Seetf.nn.embedding_lookup
for more details.name
: A name for the operation (optional).
A batch_size
1-D tensor of per-example NCE losses.