tf.nn.nce_loss

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'
)

Defined in tensorflow/python/ops/nn_impl.py.

See the guide: Neural Network > Candidate Sampling

Computes and returns the noise-contrastive estimation training loss.

See Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. Also see our Candidate Sampling Algorithms Reference

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)

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 classes to randomly sample per 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. 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.