Defined in tensorflow/contrib/losses/python/losses/

See the guide: Losses (contrib) > Loss operations for use in neural networks.

Method that returns the loss tensor for hinge loss. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.hinge_loss instead. Note that the order of the logits and labels arguments has been changed, and to stay unweighted, reduction=Reduction.NONE


  • logits: The logits, a float tensor. Note that logits are assumed to be unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive (resp. negative) binary prediction.
  • labels: The ground truth output tensor. Its shape should match the shape of logits. The values of the tensor are expected to be 0.0 or 1.0. Internally the {0,1} labels are converted to {-1,1} when calculating the hinge loss.
  • scope: The scope for the operations performed in computing the loss.


An unweighted Tensor of same shape as logits and labels representing the loss values across the batch.


  • ValueError: If the shapes of logits and labels don't match.