Adds a hinge loss to the training procedure.

tf.compat.v1.losses.hinge_loss( labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )

Args | |
---|---|

`labels` | The ground truth output tensor. Its shape should match the shape of logits. The values of the tensor are expected to be 0.0 or 1.0. Internally the {0,1} labels are converted to {-1,1} when calculating the hinge loss. |

`logits` | The logits, a float tensor. Note that logits are assumed to be unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive (resp. negative) binary prediction. |

`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 the loss will be added. |

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

Returns | |
---|---|

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

Raises | |
---|---|

`ValueError` | If the shapes of `logits` and `labels` don't match or if `labels` or `logits` 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`

.

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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/losses/hinge_loss