tf.contrib.losses.sigmoid_cross_entropy

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
)

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.

tf.contrib.losses.sigmoid_cross_entropy

Args

Returns

Raises