Adds a cosine-distance loss to the training procedure. (deprecated arguments)

tf.compat.v1.losses.cosine_distance( labels, predictions, axis=None, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS, dim=None )

Note that the function assumes that `predictions`

and `labels`

are already unit-normalized.

Args | |
---|---|

`labels` | `Tensor` whose shape matches 'predictions' |

`predictions` | An arbitrary matrix. |

`axis` | The dimension along which the cosine distance is computed. |

`weights` | Optional `Tensor` whose rank is either 0, or the same rank as `labels` , and must be broadcastable to `labels` (i.e., all dimensions must be either `1` , or the same as the corresponding `losses` dimension). |

`scope` | The scope for the operations performed in computing the loss. |

`loss_collection` | collection to which this loss will be added. |

`reduction` | Type of reduction to apply to loss. |

`dim` | The old (deprecated) name for `axis` . |

Returns | |
---|---|

Weighted loss float `Tensor` . If `reduction` is `NONE` , this has the same shape as `labels` ; otherwise, it is scalar. |

Raises | |
---|---|

`ValueError` | If `predictions` shape doesn't match `labels` shape, or `axis` , `labels` , `predictions` or `weights` is `None` . |

The `loss_collection`

argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a `tf.keras.Model`

.

© 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/losses/cosine_distance