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Show HN: Layered – Neural Networks in Python 3
- lqdc13 11y agoInteresting, but really slow to train. Partially because of the class __call__ instead of function calls among other things.
- danijar 11y agoThanks for taking a look. Do you really think __call__ affects performance that much? I'll look into OpenCL to improve performance.
- lqdc13 11y agoThe lib looks good otherwise. Might be a useful educational tool if anything. In my tests it's 2x slower, but it might not be the main reason. I didn't profile it at all. Another thing is that it seems like it doesn't use Atlas to scale to all the cores even though my Python is linked against it.
- last_mohican123 11y agoIt got me excited. But slow to train. :-(
- albertzeyer 11y agoSuch projects are nice for educational use but not so much further when you don't have any GPU implementation. It's pointed out in the Readme that this was for education but it still wasn't clear whether it's supposed to have a practical use outside of that (some educational projects develop into something useful). If it has (or maybe even if not), it should have a small comparison to other frameworks (maybe Python frameworks only), e.g. Theano, Blocks, Keras, Brainstorm, Neon, .... I think Brainstorm (https://github.com/IDSIA/brainstorm https://github.com/IDSIA/brainstorm) or Neon (https://github.com/NervanaSystems/neon https://github.com/NervanaSystems/neon) are somewhat close to your framework, because they are not based on Theano and thus do not do automatic symbolic gradient calculation but have explicit backprop code. Also, you really should point out what GPU implementation you have (if any), if it supports multi-GPU, and/or maybe other distributed setups.