Comments (6)
Dense does support input arrays of arbitrary sizes by reshaping the input and the result (e.g. https://github.com/avik-pal/Lux.jl/blob/main/src/layers/basic.jl#L650)
julia> using Lux, Random
julia> d = Dense(1,2)
Dense(1 => 2) # 4 parameters
julia> ps, st = Lux.setup(Random.default_rng(), d);
julia> x = randn(1,2,3,4,5);
julia> size(d(x, ps, st)[1])
(2, 2, 3, 4, 5)
from lux.jl.
Ok I was refering to this in documantation
Input
x must be a Matrix of size in_dims × B or a Vector of length in_dims
In http://lux.csail.mit.edu/dev/api/layers/
Thank You for fast response !!
from lux.jl.
Thanks for pointing it out. Fixed docs in #124.
from lux.jl.
Happy to help @avik-pal , generally with so huge speed of development like is present in this repository it is fully understendable !
just checking Lux.Conv has
Image data should be stored in WHCN order (width, height, channels, batch)
so it is truly conv2D only - hence no possibility of 3d convolutions at the moment ?
from lux.jl.
No, it works for 3d see the inputs section. It is mentioned as an example of data ordering.
from lux.jl.
ok, fantastic ! so in this case it can be written
(width, height, channels, batch)
or (width, height,depth, channels, batch)
from lux.jl.
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