Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)
tf . contrib . losses . sigmoid_cross_entropy (
logits , multi_class_labels , weights = 1.0 , label_smoothing = 0 , scope = None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.sigmoid_cross_entropy instead. Note that the order of the predictions and labels arguments has been changed.
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 of logits
doesn't match that of
multi_class_labels
or if the shape of weights
is invalid, or if
weights
is None.