# tf.compat.v1.nn.sampled_softmax_loss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_impl.py#L2320-L2422) |

Computes and returns the sampled softmax training loss.

    tf.compat.v1.nn.sampled_softmax_loss(
        weights,
        biases,
        labels,
        inputs,
        num_sampled,
        num_classes,
        num_true=1,
        sampled_values=None,
        remove_accidental_hits=True,
        partition_strategy='mod',
        name='sampled_softmax_loss',
        seed=None
    )

This is a faster way to train a softmax classifier over a huge number of
classes.

This operation is for training only. It is generally an underestimate of
the full softmax loss.

A common use case is to use this method for training, and calculate the full
softmax 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.sampled_softmax_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.softmax_cross_entropy_with_logits(
          labels=labels_one_hot,
          logits=logits)

See our Candidate Sampling Algorithms Reference
([pdf](https://www.tensorflow.org/extras/candidate_sampling.pdf)).
Also see Section 3 of (Jean et al., 2014) for the math.

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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-sharded) 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. Note that this format differs from the `labels` argument of [`nn.softmax_cross_entropy_with_logits`](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits). |
| `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. Default is True. |
| `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). |
| `seed` | random seed for candidate sampling. Default to None, which doesn't set the op-level random seed for candidate sampling. |

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

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| ## References ||
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| On Using Very Large Target Vocabulary for Neural Machine Translation: [Jean et al., 2014](https://aclanthology.coli.uni-saarland.de/papers/P15-1001/p15-1001) ([pdf](http://aclweb.org/anthology/P15-1001)) ||

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