Comments (7)
schlichtanders
commented on June 11, 2024
1
I myself asked once about the advantages of Lux.jl vs Flux.jl for DiffEqFlux.jl
here the important answer SciML/DiffEqFlux.jl#815 (comment)
and meanwhile the issue got closed by moving to Lux.jl completely
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zahachtah
commented on June 11, 2024
1
simple test of Lux.transform didn't work. Just to add, Lux support would be nice.
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chengchingwen
commented on June 11, 2024
What does it need to make a layer Lux compatible?
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tobefreeman
commented on June 11, 2024
Why do SciML people favour Lux over Flux? Also, what is your evidence for
this, given that Flux is so much more popular overall?
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What does it need to make a layer Lux compatible?
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schlichtanders
commented on June 11, 2024
DiffEqFlux.jl is the central package for combining deep learning with differential equation. Take a look there - they even changed the official name to DiffEq For Lux.
Flux ist just older, hence there may be more packages referring to it. But actually for me Lux is already the go to package for deep learning in Julia
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chengchingwen
commented on June 11, 2024
I'm totally fine with supporting Lux.jl. But IIUC Lux.transform can wrap a Flux layer into a Lux compatible layer already?
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schlichtanders
commented on June 11, 2024
I am not experienced with the pitfalls of Lux.transform. sounds like it works kind of good, as DiffEqFlux is relying on it, but it probably has some drawbacks.
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