Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.

tf.losses.sigmoid_cross_entropy( multi_class_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )

`weights`

acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `weights`

is a tensor of shape `[batch_size]`

, then the loss weights apply to each corresponding sample.

If `label_smoothing`

is nonzero, smooth the labels towards 1/2:

new_multiclass_labels = multiclass_labels * (1 - label_smoothing) + 0.5 * label_smoothing

Args | |
---|---|

`multi_class_labels` | `[batch_size, num_classes]` target integer labels in `{0, 1}` . |

`logits` | Float `[batch_size, num_classes]` logits outputs of the network. |

`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). |

`label_smoothing` | If greater than `0` then smooth the labels. |

`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 `Tensor` of the same type as `logits` . If `reduction` is `NONE` , this has the same shape as `logits` ; otherwise, it is scalar. |

Raises | |
---|---|

`ValueError` | If the shape of `logits` doesn't match that of `multi_class_labels` or if the shape of `weights` is invalid, or if `weights` is None. Also if `multi_class_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`

.

© 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/r1.15/api_docs/python/tf/losses/sigmoid_cross_entropy