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Rough Optimisers sketch #17

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Rough Optimisers sketch #17

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d07855f
attempt
ericphanson May 13, 2022
3c54b5f
wip
ericphanson May 13, 2022
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4 changes: 4 additions & 0 deletions examples/Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,11 @@
[deps]
Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
Legolas = "741b9549-f6ed-4911-9fbf-4a1c0c97f0cd"
LegolasFlux = "eb5f792d-d1b1-4535-bae3-d5649ec7daa4"
MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
42 changes: 37 additions & 5 deletions examples/digits.jl
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Expand Up @@ -8,6 +8,8 @@ using StableRNGs
using Flux: onehotbatch, onecold, crossentropy, throttle
using Base.Iterators: repeated, partition
using Legolas, LegolasFlux
using Zygote
using Optimisers: Optimisers

# This should store all the information needed
# to construct the model.
Expand Down Expand Up @@ -110,17 +112,47 @@ function accuracy(m, x, y)
end

function train_model!(m; N = N_train)
loss = (x, y) -> crossentropy(m(x), y)
opt = ADAM()
state = Optimisers.setup(Optimisers.ADAM(), m) # just once
evalcb = throttle(() -> @show(accuracy(m, tX, tY)), 5)
Flux.@epochs 1 Flux.train!(loss, params(m), Iterators.take(train, N), opt; cb=evalcb)
return accuracy(m, tX, tY)
for d in Iterators.take(train, N)
m̄, _ = gradient(m, d[1]) do m, x
crossentropy(m(x), d[2])
end
state, m = Optimisers.update(state, m, m̄);
evalcb()
end
return accuracy(m, tX, tY), state
end

m = DigitsModel()

# increase N to actually train more than a tiny amount
acc = train_model!(m; N=10)
acc, state = train_model!(m; N=10)

##
#

using Arrow, Test

macro serialize_as_record(T)
name = :(Symbol("JuliaLang.", @__MODULE__, ".", string(parentmodule($T), '.', nameof($T))))
return quote
Arrow.ArrowTypes.arrowname(::Type{$T}) = $name
Arrow.ArrowTypes.ArrowType(::Type{$T}) = fieldtypes($T)
Arrow.ArrowTypes.toarrow(obj::$T) = ntuple(i -> getfield(obj, i), fieldcount($T))
Arrow.ArrowTypes.JuliaType(::Val{$name}, ::Any) = $T
Arrow.ArrowTypes.fromarrow(::Type{$T}, args...) = $T(args...)
end
end

@serialize_as_record Optimisers.ADAM
@serialize_as_record Optimisers.Leaf

Arrow.tobuffer( [(; obj=state)]; maxdepth=50)
state2 = Arrow.Table(Arrow.tobuffer( [(; obj=state)]; maxdepth=50)).obj[1]

#
##

# Let's serialize out the weights into a `DigitsRow`.
# We could save this here with `write_model_row`.
Expand Down