|  | 
| 21 | 21 | 
 | 
| 22 | 22 | function callback(state, l) #callback function to observe training | 
| 23 | 23 |     display(l) | 
| 24 |  | -    return false | 
|  | 24 | +    return l < 1e-2 | 
| 25 | 25 | end | 
| 26 | 26 | 
 | 
| 27 | 27 | u0 = Float32[200.0] | 
| @@ -58,11 +58,11 @@ optfun = OptimizationFunction(loss_adjoint, | 
| 58 | 58 |     Optimization.AutoZygote()) | 
| 59 | 59 | optprob = OptimizationProblem(optfun, pp, train_loader) | 
| 60 | 60 | 
 | 
| 61 |  | -res1 = Optimization.solve(optprob, | 
| 62 |  | -    Optimization.Sophia(; η = 0.5, | 
| 63 |  | -        λ = 0.0), callback = callback, | 
| 64 |  | -    maxiters = 1000) | 
| 65 |  | -@test 10res1.objective < l1 | 
|  | 61 | +# res1 = Optimization.solve(optprob, | 
|  | 62 | +#     Optimization.Sophia(; η = 0.5, | 
|  | 63 | +#         λ = 0.0), callback = callback, | 
|  | 64 | +#     maxiters = 1000) | 
|  | 65 | +# @test 10res1.objective < l1 | 
| 66 | 66 | 
 | 
| 67 | 67 | optfun = OptimizationFunction(loss_adjoint, | 
| 68 | 68 |     Optimization.AutoForwardDiff()) | 
| @@ -100,7 +100,7 @@ function callback(st, l, pred; doplot = false) | 
| 100 | 100 |         scatter!(pl, t, pred[1, :], label = "prediction") | 
| 101 | 101 |         display(plot(pl)) | 
| 102 | 102 |     end | 
| 103 |  | -    return false | 
|  | 103 | +    return l < 1e-3 | 
| 104 | 104 | end | 
| 105 | 105 | 
 | 
| 106 | 106 | optfun = OptimizationFunction(loss_adjoint, | 
|  | 
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