tf.nn.nce_loss

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 as in the following example:

if mode == "train":
  loss = tf.nn.nce_loss(
      weights=weights,
      biases=biases,
      labels=labels,
      inputs=inputs,
      ...)
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: when doing embedding lookup on weights and bias, "div" partition strategy will be used. Support for other partition strategy will be added later.
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. Default is False.
name A name for the operation (optional).
Returns
A batch_size 1-D tensor of per-example NCE losses.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/nn/nce_loss