differentiable adjacency_matrix and degree #123
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related to JuliaDiff/ChainRules.jl#586 |
CarloLucibello
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The state of #113 on this branch is that we get a sparse vector instead of nothing: julia> g = rand_graph(3, 2, graph_type=:coo);
julia> x = rand(3, g.num_nodes);
julia> e = rand(g.num_edges);
julia> function forward_fused(g, x, e)
out = propagate(e_mul_xj, g, +; xj=x, e=e)
return sum(abs2, out)
end
forward_fused (generic function with 1 method)
julia> gradient(e -> forward_fused(g, x, e), e)[1]
2-element SparseVector{Float64, Int64} with 2 stored entries:
[1] = 2.22276
[2] = 0.489046While this is a clear improvement it is still not ideal, we want a dense gradient returned. |
Codecov Report
@@ Coverage Diff @@
## master #123 +/- ##
==========================================
+ Coverage 84.12% 85.34% +1.21%
==========================================
Files 15 15
Lines 1266 1262 -4
==========================================
+ Hits 1065 1077 +12
+ Misses 201 185 -16
Continue to review full report at Codecov.
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CarloLucibello
changed the title
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Opened an issue in Zygote FluxML/Zygote.jl#1174 |
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Fix #113 for COO type