tf.losses.sigmoid_cross_entropy

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
)

Defined in tensorflow/python/ops/losses/losses_impl.py.

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

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.

@compatbility(eager) The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model. @end_compatibility