parameters: (logits, multi_class_labels, weight=1.0,
                            label_smoothing=0, scope=None)
docstring: 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.
docstring_summary: Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
docstring_tokens: Creates a cross - entropy loss using tf . nn . sigmoid_cross_entropy_with_logits .
function: 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.op_scope([logits, multi_class_labels],
                      scope, "sigmoid_cross_entropy_loss"):
      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)
function_tokens: def sigmoid_cross_entropy ( logits , multi_class_labels , weight = 1.0 , label_smoothing = 0 , scope = None ) : with ops . op_scope ( [ logits , multi_class_labels ] , scope , "sigmoid_cross_entropy_loss" ) : 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 )