Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
Compat aliases for migration
See Migration guide for more details.
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.
label_smoothing is nonzero, smooth the labels towards 1/2:
new_multiclass_labels = multiclass_labels * (1 - label_smoothing) + 0.5 * label_smoothing
| || |
| || Float |
| || Optional |
| || If greater than |
| ||The scope for the operations performed in computing the loss.|
| ||collection to which the loss will be added.|
| ||Type of reduction to apply to loss.|
| Weighted loss |
| || If the shape of |
loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a
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Code samples licensed under the Apache 2.0 License.