def sigmoid_cross_entropy(logits, multi_class_labels, weight=1.0,
label_smoothing=0, scope=None):
"""Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
`weight` acts as a coefficient for the loss. If a scalar is provided,
then the loss is simply scaled by the given value. If `weight` 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] target labels in (0, 1).
weight: 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 `predictions` doesn't match that of `targets` or
if the shape of `weight` is invalid or if `weight` is None.
"""
with ops.name_scope(scope, "sigmoid_cross_entropy_loss",
[logits, multi_class_labels]):
logits.get_shape().assert_is_compatible_with(multi_class_labels.get_shape())
multi_class_labels = math_ops.cast(multi_class_labels, logits.dtype)
if label_smoothing > 0:
multi_class_labels = (multi_class_labels * (1 - label_smoothing) +
0.5 * label_smoothing)
losses = nn.sigmoid_cross_entropy_with_logits(logits, multi_class_labels,
name="xentropy")
return compute_weighted_loss(losses, weight)
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