tf.contrib.losses.sigmoid_cross_entropy(
logits,
multi_class_labels,
weights=1.0,
label_smoothing=0,
scope=None
)
Defined in tensorflow/contrib/losses/python/losses/loss_ops.py
.
Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)
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 size [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:
logits
: [batch_size, num_classes] logits outputs of the network .multi_class_labels
: [batch_size, num_classes] labels in (0, 1).weights
: Coefficients for the loss. The tensor must be a scalar, a tensor of shape [batch_size] or shape [batch_size, num_classes].label_smoothing
: If greater than 0 then smooth the labels.scope
: The scope for the operations performed in computing the loss.
Returns:
A scalar Tensor
representing the loss value.
Raises:
ValueError
: If the shape oflogits
doesn't match that ofmulti_class_labels
or if the shape ofweights
is invalid, or ifweights
is None.