tf.compat.v1.nn.nce_loss Computes and returns the noise-contrastive estimation training loss. 
tf.compat.v1.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'
)
 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: if mode == "train":
  loss = tf.nn.nce_loss(
      weights=weights,
      biases=biases,
      labels=labels,
      inputs=inputs,
      ...,
      partition_strategy="div")
elif mode == "eval":
  logits = tf.matmul(inputs, tf.transpose(weights))
  logits = tf.nn.bias_add(logits, biases)
  labels_one_hot = tf.one_hot(labels, n_classes)
  loss = tf.nn.sigmoid_cross_entropy_with_logits(
      labels=labels_one_hot,
      logits=logits)
  loss = tf.reduce_sum(loss, axis=1)


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 tf.random.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
  weights   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)