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). Also see Section 3 of (Jean et al., 2014) for the math.

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` . |

`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. |

Returns | |
---|---|

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

On Using Very Large Target Vocabulary for Neural Machine Translation: Jean et al., 2014 (pdf)

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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/sampled_softmax_loss