Remarkably good idea, thanks for sharing. I like the clean readable code but efficient if needed idea. It would be amazing if the recent developments could be added as you hint at for people to learn.
many thanks On Saturday, February 28, 2015 at 4:19:18 PM UTC+1, Deniz Yuret wrote: > > KUnet.jl <https://github.com/denizyuret/KUnet.jl> (beginning deep > learning with 500 lines of Julia > <http://www.denizyuret.com/2015/02/beginning-deep-learning-with-500-lines.html> > ) is out with its alpha release. Only the basic functionality is in > place (i.e. backprop with relu, softmax, sgd, momentum, nesterov, adagrad, > dropout, l1-l2 regularization etc.) but the GPU functionality is in, its > speed is competitive with Caffe <http://caffe.berkeleyvision.org/>, and I > think convolutional and recurrent nets can be added without too much > effort. I wrote a blog post > <http://www.denizyuret.com/2015/02/beginning-deep-learning-with-500-lines.html> > about > the code structure and there is some basic documentation > <https://github.com/denizyuret/KUnet.jl/blob/master/README.md>. You can > send me suggestions for improvement (both in coding style and new > functionality) using comments > <http://www.blogger.com/comment.g?blogID=8540876&postID=328231440874481473> > to > the blog post > <http://www.denizyuret.com/2015/02/beginning-deep-learning-with-500-lines.html>, > > or using issues <https://github.com/denizyuret/KUnet.jl/issues> or pull > requests <https://help.github.com/articles/fork-a-repo/> on GitHub > <https://github.com/denizyuret/KUnet.jl>. > > I tried to make the code (cpu/gpu) generic and high level. Getting the > same code working on the GPU and the CPU in Julia proved to be a bit > challenging and showed that both a more standard treatment of CPU and GPU > arrays, and a standard syntax for in-place operations would be welcome > additions to the language. I'd like to thank Tim Holy (CUDArt), Nick > Henderson (CUBLAS), and Simon Byrne (InplaceOps) for their generous help. > > best, > deniz >
