TastyPancakes/Flux.jl
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fix hintify

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This commit is contained in:
Mike J Innes 2017-02-01 20:26:38 +05:30
parent 07433c13bd
commit cd86dfdf07
3 changed files with 5 additions and 4 deletions
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2
src/Flux.jl
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@ -4,7 +4,7 @@ using MacroTools, Lazy, DataFlow, Juno
using DataFlow: graphm, syntax, prewalk!, postwalk!, prewalk, postwalk, using DataFlow: graphm, syntax, prewalk!, postwalk!, prewalk, postwalk,
iscyclic, Constant, constant, isconstant, group, Split, splitnode, iscyclic, Constant, constant, isconstant, group, Split, splitnode,
detuple, value, inputs, thread!, value, inputs, Split, splitnode, inputnode, detuple, value, inputs, thread!, value, inputs, Split, splitnode, inputnode,
spliceinputs, bumpinputs, Frame, applylines spliceinputs, bumpinputs, Line, Frame, applylines
using Juno: Tree, Row using Juno: Tree, Row
# Zero Flux Given # Zero Flux Given

6
src/compiler/shape.jl
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@ -15,12 +15,12 @@ end
ihint(f, ctx::Context, h::Hint, x) = vertex(h, x) ihint(f, ctx::Context, h::Hint, x) = vertex(h, x)
ihint(f, args...) = f(args...) ihint(f, args...) = f(args...)
hintify(c::Constant) = hintify(state(c.value)) hintify(ctx, c::Constant) = hintify(ctx, state(c.value))
hintify(xs::AbstractArray) = vertex(Hint(size(xs)), constant(:_)) hintify(ctx, xs::AbstractArray) = vertex(Hint(size(xs)), constant(:_))
interpshape = mux(ilinev, ihint, iargs, ituple, hintify) interpshape = mux(ilinev, ihint, iargs, ituple, hintify)
function hintify(f, xs...) function hintify(ctx, f, xs...)
sh = infer(f, map(gethint, xs)...) sh = infer(f, map(gethint, xs)...)
sh ≠ nothing ? vertex(Hint(sh), vertex(f, xs...)) : sh ≠ nothing ? vertex(Hint(sh), vertex(f, xs...)) :
!any(x->x==nothing, xs) && graph(f) ≠ nothing ? interpret(Context(interpshape), graph(f), xs...) : !any(x->x==nothing, xs) && graph(f) ≠ nothing ? interpret(Context(interpshape), graph(f), xs...) :

1
test/basic.jl
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@ -21,4 +21,5 @@ end
let a1 = Affine(10, 20), a2 = Affine(20, 15) let a1 = Affine(10, 20), a2 = Affine(20, 15)
tlp = TLP(a1, a2) tlp = TLP(a1, a2)
@test tlp(xs) ≈ softmax(a2(σ(a1(xs)))) @test tlp(xs) ≈ softmax(a2(σ(a1(xs))))
@test Flux.infer(tlp, (1, 10)) == (1,15)
end end