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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,41 +1,56 @@ | ||
| using Lux, Zygote | ||
| using Lux, Zygote, Enzyme, ComponentArrays | ||
|
|
||
| const input = 2 | ||
| const hidden = 16 | ||
|
|
||
| model = Chain(Dense(input => hidden, Lux.relu), | ||
| Dense(hidden => hidden, Lux.relu), | ||
| Dense(hidden => 1), | ||
| first) | ||
|
|
||
| ps, st = Lux.setup(rng, model) | ||
|
|
||
| trial(model, x) = x[1] * (1 - x[1]) * x[2] * (1 - x[2]) * model(x, ps, st)[1] | ||
| function trial(model, x, ps, st) | ||
| u, st = Lux.apply(model, x, ps, st) | ||
| x[1] * (1 - x[1]) * x[2] * (1 - x[2]) * u | ||
| end | ||
|
|
||
| x = rand(Float32, input) | ||
| trial(model, x) | ||
| function loss_by_finitediff(model, x) | ||
| ε = cbrt(eps(Float32)) | ||
| ε₁ = [ε, 0] | ||
| ε₂ = [0, ε] | ||
| error = (trial(model, x + ε₁) + trial(model, x - ε₁) + trial(model, x + ε₂) + | ||
| trial(model, x - ε₂) - 4 * trial(model, x)) / | ||
| ε^2 + sin(π * x[1]) * sin(π * x[2]) | ||
| function loss_by_finitediff(model, x, ps, st) | ||
| T = eltype(x) | ||
| ε = cbrt(eps(T)) | ||
| ε₁ = [ε, zero(T)] | ||
| ε₂ = [zero(T), ε] | ||
| f(x) = trial(model, x, ps, st) | ||
| error = (f(x + ε₁) + f(x - ε₁) + f(x + ε₂) + f(x - ε₂) - 4 * f(x)) / ε^2 + | ||
| sin(π * x[1]) * sin(π * x[2]) | ||
| abs2(error) | ||
| end | ||
| function loss_by_taylordiff(model, x) | ||
| f(x) = trial(model, x) | ||
| function loss_by_taylordiff(model, x, ps, st) | ||
| f(x) = trial(model, x, ps, st) | ||
| error = derivative(f, x, Float32[1, 0], Val(2)) + | ||
| derivative(f, x, Float32[0, 1], Val(2)) + | ||
| sin(π * x[1]) * sin(π * x[2]) | ||
| abs2(error) | ||
| end | ||
| function loss_by_forwarddiff(model, x, ps, st) | ||
| f(x) = trial(model, x, ps, st) | ||
| error = derivative(f, x, Float32[1, 0], Val(2)) + | ||
| derivative(f, x, Float32[0, 1], Val(2)) + | ||
| sin(π * x[1]) * sin(π * x[2]) | ||
| abs2(error) | ||
| end | ||
|
|
||
| const input = 2 | ||
| const hidden = 16 | ||
| model = Chain(Dense(input => hidden, exp), | ||
| Dense(hidden => hidden, exp), | ||
| Dense(hidden => 1), | ||
| first) | ||
| x = rand(Float32, input) | ||
| dx = deepcopy(x) | ||
| ps, st = Lux.setup(rng, model) | ||
| ps = ps |> ComponentArray | ||
| dps = deepcopy(ps) | ||
| dx .= 0; | ||
| dps .= 0; | ||
|
|
||
| pinn_t = BenchmarkGroup("primal" => (@benchmarkable loss_by_taylordiff($model, $x)), | ||
| pinn_t = BenchmarkGroup( | ||
| "primal" => (@benchmarkable loss_by_taylordiff($model, $x, $ps, $st)), | ||
| "gradient" => (@benchmarkable gradient(loss_by_taylordiff, $model, | ||
| $x))) | ||
| pinn_f = BenchmarkGroup("primal" => (@benchmarkable loss_by_finitediff($model, $x)), | ||
| $x, $ps, $st))) | ||
| pinn_f = BenchmarkGroup( | ||
| "primal" => (@benchmarkable loss_by_finitediff($model, $x, $ps, $st)), | ||
| "gradient" => (@benchmarkable gradient($loss_by_finitediff, $model, | ||
| $x))) | ||
| $x, $ps, $st))) | ||
| pinn = BenchmarkGroup(["vector", "physical"], "taylordiff" => pinn_t, | ||
| "finitediff" => pinn_f) |
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