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)

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 (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. |

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

© 2020 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/versions/r2.3/api_docs/python/tf/compat/v1/nn/nce_loss