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Computes and returns the noise-contrastive estimation training loss.
tf.compat.v2.nn.nce_loss(
weights, biases, labels, inputs, num_sampled, num_classes, num_true=1,
sampled_values=None, remove_accidental_hits=False, name='nce_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)
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.
|