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Lux.jl does a really good job at being clear with syntax and hackable. I couldn't recommend it more. https://lux.csail.mit.edu/ https://lux.csail.mit.edu/. Here
by ChrisRackauckas 3y ago
Lux.jl does a really good job at being clear with syntax and hackable. I couldn't recommend it more. https://lux.csail.mit.edu/ https://lux.csail.mit.edu/. Here's good materials to start with: https://lux.csail.mit.edu/dev/tutorials/beginner/1_Basics https://lux.csail.mit.edu/dev/tutorials/beginner/1_Basics
- Tarrosion 3y agoWhat's the user-facing difference between Lux and Flux?
- darsnack 3y agoHow you interact with parameters. Lux is similar to Flax (Jax) where the parameters are kept in a separate variable from the model definition, and they are passed in on the forward pass. Notably, this design choice allows Lux to accept parameters built with ComponentArrays.jl which can be especially helpful when working with libraries that expect flat vectors of parameters. Flux lies somewhere between Jax and PyTorch. Like PyTorch, the parameters are stored as part of the model. Unlike traditional PyTorch, Flux has “functional” conventions, e.g. `g = gradient(loss, model)` vs. `loss.backward()`. Similar to Flax, the model is a tree of parameters.