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https://issues.apache.org/jira/browse/SYSTEMML-1500?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Niketan Pansare reassigned SYSTEMML-1500:
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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
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