Why can't we just have compute networks that can be used for anything, from computational linear algebra to deep learning?
You mean, like Tensorflow?
Have you looked at the APIs for any of these libraries? I think there is a perception that these are all for NN only because NN is the hot topic that everyone is jumping on noe; but tf for example is a general matrix manipulation library with a bunch of amazing, extra NN stuff shipped alongside it.
Re:Tensorflow sucks
by homerowilson on Sunday October 08, 2017 @07:41PM
(#15430637)
This is a good observation! Tensorflow is a practical and flexible dataflow implementation. See for instance Greta, https://github.com/gavinsimpson/greta -- a framework for Bayesian stats built on tensorflow.
Re:Tensorflow sucks
by throw65554 on Sunday October 08, 2017 @07:38PM
(#15430632)
PyTorch and Chainer take this approach.
CuPy is even designed to be drop-in compatible with Numpy; I don't think there is much support for LAPACK routines at this point though.
Doing MATLAB-esque work with tensorflow would require some special tolerance for pain, or would require one to be a masochist.
Why can't we just have compute networks that can be used for anything, from computational linear algebra to deep learning?
> Let’s be honest, when you have about half a dozen open source high-level libraries out there built on top of your already high-level library to make your library usable, you know something has gone terribly wrong
I consider that not a bug but a feature.