A common use case is to use this method for training, and calculate the full sigmoid loss for evaluation or inference. In this case, you must set partition_strategy="div" for the two losses to be consistent, as in the following example:
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.
biases
A Tensor of shape [num_classes]. The class biases.
labels
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
inputs
A Tensor of shape [batch_size, dim]. The forward activations of the input network.
num_sampled
An int. The number of negative classes to randomly sample per batch. This single sample of negative classes is evaluated for each element in the batch.
num_classes
An int. The number of possible classes.
num_true
An int. 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 to log_uniform_candidate_sampler)
remove_accidental_hits
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 (pdf). Default is False.
partition_strategy
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.
name
A name for the operation (optional).
Returns
A batch_size 1-D tensor of per-example NCE losses.
References
Noise-contrastive estimation - A new estimation principle for unnormalized statistical models: Gutmann et al., 2010 (pdf)
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