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https://issues.apache.org/jira/browse/SINGA-137?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15525491#comment-15525491
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wangwei commented on SINGA-137:
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Yes. We would include popular models like VGG.
1. We have provided the IO component for saving snapshots, which could be used
like this
https://github.com/apache/incubator-singa/blob/master/examples/imagenet/alexnet.cc#L289.
Adding a save/load function into net class
(https://github.com/apache/incubator-singa/blob/master/include/singa/model/feed_forward_net.h)
would be useful. You are also welcomed to help implement it.
2. There are examples of vgg/alexnet/resnet using pysinga. We have trained CPP
versions of resent and alexnet over ImageNet, and would create a webpage to
share them.
BTW, we have tested the size of vgg-16 using protobuf for serialization, which
is about 600MB https://issues.apache.org/jira/browse/SINGA-252 (would be merged
into master soon).
> To be compatible with Caffe's data format and neural net configuration
> ----------------------------------------------------------------------
>
> Key: SINGA-137
> URL: https://issues.apache.org/jira/browse/SINGA-137
> Project: Singa
> Issue Type: New Feature
> Reporter: wangwei
> Assignee: Xiangrui
> Labels: Caffe
>
> Caffe has many built-in models and a large user base.
> If we can train over Caffe's data (input data prepared using Caffe's tools)
> and neural net configuration, it would help users to switch from Caffe to
> SINGA for distributed training.
> Here are two options.
> 1. update SINGA's variable name and some data structure to be consistent with
> Caffe.
> 2. write scripts/tools to convert Caffe's configuration protocol into SINGA
> protocol.
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