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)
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.nn.log_uniform_candidate_sampler
.
Note: In the case wherenum_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.
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] (https://www.tensorflow.org/extras/candidate_sampling.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).A batch_size
1-D tensor of per-example NCE losses.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/nce_loss