1794: Tidy up `Maxout` r=mcabbott a=mcabbott Maxout is from #698 . This: * adds pretty printing * changes the explicit signature to `Maxout(layer, layer, layer)`, rather than providing a tuple, to be more like other layers (with deprecation) * adds more examples to the docstring, and combines the two * changes not to use `mapreduce`. I see now this was a performance choice at the time, discussed here #647 (comment) , but with Zygote this is much slower. Before: ``` julia> using Flux julia> m3 = Maxout(() -> Dense(5, 7, tanh), 3) Maxout{Tuple{Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}}}((Dense(5, 7, tanh), Dense(5, 7, tanh), Dense(5, 7, tanh))) julia> x = rand(Float32, 5, 11); julia> `@btime` gradient(sum∘m3, $x); min 112.792 μs, mean 123.774 μs (930 allocations, 49.09 KiB. GC mean 3.71%) ``` After: ``` julia> m3 = Maxout(() -> Dense(5, 7, tanh), 3) Maxout( Dense(5, 7, tanh), # 42 parameters Dense(5, 7, tanh), # 42 parameters Dense(5, 7, tanh), # 42 parameters ) # Total: 6 arrays, 126 parameters, 888 bytes. julia> x = rand(Float32, 5, 11); julia> `@btime` gradient(sum∘m3, $x); min 34.541 μs, mean 38.448 μs (493 allocations, 32.48 KiB. GC mean 6.63%) ``` Co-authored-by: Michael Abbott <32575566+mcabbott@users.noreply.github.com>
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Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable.
] add FluxSee the documentation or the model zoo for examples.
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