I think the idea is it sets up Neural Networks that are then run in Torch, with some nice diagram generating tools. I don't know if that's something people will actually use, but it looks like a pretty concise way to generate a pretty complicated Neural Network, which could be a worthwhile idea considering how complex the more advanced ones are.
It's not exactly a wrapper, since you presumably still use the generated NN in LuaJIT. AI isn't my field, but it seems like a useful tool - actually setting up a complex neural network seems to be a lot of grunt work, comparatively speaking. There's probably a place for a tool that nicely abstracts over that part of the process.
This is a great service actually. Visualizing network architectures is good for teaching and discussing things. I use json in deeplearning4j and it's crazy hard to keep track of all the possible combinations of nets so it gets messy quick.
This is really cool work. There is a lackluster theano feature which allows you to print a flowchart figure for the "computation graph" corresponding to the symbolic representation of your model.
@ajtulloch's library provides what I imagined the feature would be at first glance - a comprehensible, elegant graphical representation of your NN model. And on top of that, all in Haskell, with Haskell DSL for running torch - so cool.
Hi folks, author here - thanks for the interest in the project. It's literally ~1,000 lines of Haskell that I wrote a couple of weekends ago (and don't use at all in production), but LMK if you find it useful in any way (or have feature requests). Thanks!
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[ 3.0 ms ] story [ 23.7 ms ] thread@ajtulloch's library provides what I imagined the feature would be at first glance - a comprehensible, elegant graphical representation of your NN model. And on top of that, all in Haskell, with Haskell DSL for running torch - so cool.