[ https://issues.apache.org/jira/browse/SYSTEMML-1500?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Niketan Pansare reassigned SYSTEMML-1500: ----------------------------------------- Assignee: Niketan Pansare > Add missing loss layers to Caffe2DML > ------------------------------------ > > Key: SYSTEMML-1500 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1500 > Project: SystemML > Issue Type: Sub-task > Reporter: Niketan Pansare > Assignee: Niketan Pansare > > Multinomial Logistic Loss > Infogain Loss - a generalization of MultinomialLogisticLossLayer. > Softmax with Loss - computes the multinomial logistic loss of the softmax of > its inputs. It’s conceptually identical to a softmax layer followed by a > multinomial logistic loss layer, but provides a more numerically stable > gradient. > Sum-of-Squares / Euclidean - computes the sum of squares of differences of > its two inputs, 12N∑Ni=1∥x1i−x2i∥2212N∑i=1N‖xi1−xi2‖22. > Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or > squared hinge loss (L2). > Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, > often used for predicting targets interpreted as probabilities. > Accuracy / Top-k layer - scores the output as an accuracy with respect to > target – it is not actually a loss and has no backward step. > Contrastive Loss -- This message was sent by Atlassian JIRA (v6.4.14#64029)