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For anyone interested in really flexible differentiable graphs, Chainer is the most flexible convenient library I've used. It's all I use for prototyping neural
by imh 10y ago
For anyone interested in really flexible differentiable graphs, Chainer is the most flexible convenient library I've used. It's all I use for prototyping neural nets anymore, and I'm surprised not to see more adoption. It feels like working in numpy.
- curuinor 10y agoIn large part, in the CPU section, it _is_ working in numpy. I think of the neural network libraries, Chainer is made by the people who like actually coding the most. I mean, for example, lots of the TensorFlow type checking gets done in Eigen, where it's done by C++ template metaprogramming (that's how it worked when I looked at it, anyhow): Chainer type stuff just gets done by runtime inspection. Which one is faster? TF, by far. Which one would you rather have in _your_ codebase? Edit: after reading the damned thing, they add in more runtime type stuff. And after looking over TF back again, they still have this hybrid thing going on where it's some Eigen stuff and some runtime stuff. I mean....
- vvladymyrov 10y agoAlso there is http://pytorch.org http://pytorch.org - that what started as a fork from Chainer. On a high level it is build for the same purpose - to support dynamic graphs.