[jira] [Commented] (SYSTEMML-1500) Add missing loss layers to Caffe2DML

["Niketan Pansare (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SYSTEMML-1500?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16184560#comment-16184560 ] 

Niketan Pansare commented on SYSTEMML-1500:
-------------------------------------------

- Euclidean loss added in the commit: https://github.com/apache/systemml/commit/61dcc85e48a390c1bb63ee4c42aad9a3fade7d06

> 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
>
> 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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