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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)
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.
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References:
Noise-contrastive estimation - A new estimation principle for unnormalized statistical models: Gutmann et al., 2010 (pdf)