# tf.compat.v1.nn.nce_loss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/nn_impl.py#L2116-L2225) |

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]
> **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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/random/log_uniform_candidate_sampler).

> [!NOTE]
> **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]
> **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.

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| ## 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](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). |

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| ## Returns ||
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| A `batch_size` 1-D tensor of per-example NCE losses. ||

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| ## References ||
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| Noise-contrastive estimation - A new estimation principle for unnormalized statistical models: [Gutmann et al., 2010](http://proceedings.mlr.press/v9/gutmann10a) ([pdf](http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf)) ||

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