# 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.

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))
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.