singa-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "wangwei (JIRA)" <>
Subject [jira] [Resolved] (SINGA-176) Add loss and metric base classes
Date Tue, 28 Jun 2016 03:58:57 GMT


wangwei resolved SINGA-176.
    Resolution: Fixed

> Add loss and metric base classes
> --------------------------------
>                 Key: SINGA-176
>                 URL:
>             Project: Singa
>          Issue Type: New Feature
>            Reporter: wangwei
>            Assignee: Zheng Kaiping
> The loss base class 'Loss' is for learning objectives, which accept the prediction from
the neural net and the target (or ground truth) from the training dataset. It outputs a scalar
loss value for each data instance and computes the gradient of the loss value w.r.t the prediction
value, which would be back-propagated through the neural net.
> The metric base class 'Metric' is for evaluating the performance (e.g, accuracy) of the
neural net. It also accepts the prediction and the target, and computes the performance metrics,
which could be accuracy, false positive, etc. It does not compute the gradients.

This message was sent by Atlassian JIRA

View raw message