This module is deprecated. Instructions for updating: Use tf.losses instead.
Note: By default all the losses are collected into the GraphKeys.LOSSES collection.
Loss operations for use in training models, typically with signature like the following:
sum_of_squares(predictions, labels, weight, scope) : Tensor
All loss functions take a pair of tensors, predictions and ground truth labels. It is assumed that the shape of both these tensors is of the form [batch_size, d1, ... dN] where batch_size is the number of samples in the batch and d1 ... dN are the remaining dimensions.
The weight parameter can be used to adjust the relative weight samples within the batch. The result of each loss is a scalar average of all sample losses with non-zero weights.
Any parameter named logit should be the raw model outputs, not a normalized probability distribution (i.e., [0.0, 1.0]). target for losses taking logit should be a normalized probability distribution.