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
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglosses
dimension).label_smoothing
: If greater than0
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 oflogits
doesn't match that ofmulti_class_labels
or if the shape ofweights
is invalid, or ifweights
is None. Also ifmulti_class_labels
orlogits
is None.
Eager Compatibility
The loss_collection
argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a tf.keras.Model
.