[ https://issues.apache.org/jira/browse/SINGA-204?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15351081#comment-15351081 ]
ASF subversion and git services commented on SINGA-204: ------------------------------------------------------- Commit 97648a60a9ff9feb55ef1a8b86e5372837e0b4f8 in incubator-singa's branch refs/heads/dev from [~flytosky] [ https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;h=97648a6 ] SINGA-204 Support the training of feed-forward neural nets Implement Alexnet model for Cifar10 https://code.google.com/p/cuda-convnet/ The accuracy of the test data is 0.82 (the same as reported by in the above link). NOTE: 1. do not convert from uint_8 data to float directly. Instead, cast the data to uint8_t and then to float. 2. it is necessary to substract the max value for softmax. Numeric error (nan) is likely to happen otherwise. > Support the training of feed-forward neural nets > ------------------------------------------------ > > Key: SINGA-204 > URL: https://issues.apache.org/jira/browse/SINGA-204 > Project: Singa > Issue Type: New Feature > Reporter: wangwei > Assignee: wangwei > > For feed-forward neural nets, their layers construct a directly acyclic > graph. For this ticket, we are going to implement add a FeedForwordNet to > training these nets, which > 1. consists of a set of directly connected layers, and > 2. provides functions for constructing the net by adding layers one by one > 3. provides access functions for layers and parameters. > 4. provides functions for forward and backward -- This message was sent by Atlassian JIRA (v6.3.4#6332)