Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)
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
label_smoothing is nonzero, smooth the labels towards 1/2:
new_multiclass_labels = multiclass_labels * (1 - label_smoothing)
+ 0.5 * label_smoothing
[batch_size, num_classes] logits outputs of the network .
[batch_size, num_classes] labels in (0, 1).
Coefficients for the loss. The tensor must be a scalar, a tensor of
shape [batch_size] or shape [batch_size, num_classes].
If greater than 0 then smooth the labels.
The scope for the operations performed in computing the loss.
Tensor representing the loss value.
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.