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I created training data for a neural network and below are two errors I got

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The only code that uses "FindRoot" command is the following

(*numerically searching for Gram point in its approximate region of the 5000th zero*)

grampoint[n_] := t /. FindRoot[RiemannSiegelTheta[t] - (10^12 + n - 1) Pi, {t,zeros[[5000]]}]

Also, when using the neural network to predict outputs I got the errors below

enter image description here

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Overall, the trained neural network did not predict as accurately as I hoped. Is this got to do anything with these errors? Do this errors mean the data created was not accurate or what do they mean?

EDIT:

The code that precedes the first errors is below

simplifiedzeros = Import["/Users/user1/Documents/SummerProject/OdlyzkoZerosOnly.txt", "CSV"] // Flatten;

(*creating list of the 10^12 to 10^12+10^4 zeros (imaginary part)*)
zeros = 267653395647 + simplifiedzeros;

(*numerically searching for Gram point in its approximate region of 5000th zero*)
grampoint[n_] :=  t /. FindRoot[RiemannSiegelTheta[t] - (10^12 +n - 1) Pi, {t, zeros[[5000]]}];

(*distance between zero and gram point*)
distance[n_] := zeros[[n]] - grampoint[n - 1];

(*approximation to RiemannSiegelTheta function*)
sterlingtheta[t_] := (t/2) (Log[t/(2 Pi)]) - t/2 - Pi/8 + 1/(48 t)

(*features for neural net*)
feature1[n_] := {RiemannSiegelZ[grampoint[n - 1]],RiemannSiegelZ[grampoint[n]]};
feature2[n_] := Table[Cos[sterlingtheta[grampoint[n - 1]] - grampoint[n - 1] Log[j]]/
Sqrt[j], {j, 1, 10}];
feature3[n_] := Table[Cos[sterlingtheta[grampoint[n]] - grampoint[n] Log[j]]/
Sqrt[j], {j, 1, 10}];
feature4[n_] := Table[Sin[sterlingtheta[grampoint[n - 1]] - grampoint[n - 1] Log[j]]/
Sqrt[j], {j, 2, 10}];
feature5[n_] := Table[Sin[sterlingtheta[grampoint[n]] - grampoint[n] Log[j]]/
Sqrt[j], {j, 2, 10}];

(*the features for predicting distance zero[[n]]-gram[n-1]*)
features[n_] := Join[feature1[n], feature2[n], feature3[n], feature4[n], feature5[n]];

(*the 10,000 pieces of data of features and outputs*)
netdata = Table[features[n] -> distance[n], {n, 1, 10000}];
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  • $\begingroup$ FindRoot is giving the best answer possible at machine precision for grampoint[65]. You can check consecutive floating-point values for t this way: Block[{n = 65}, RiemannSiegelTheta[grampoint[n] {1 - $MachineEpsilon, 1, 1 + $MachineEpsilon}] - (10^12 + n - 1) Pi]. If that solution is unsatisfactory, then you should set WorkingPrecision to a number, perhaps, WorkingPrecision -> 16 or higher. (Can't get a better machine-precision result this way, tho.) If machine precision is satisfactory, try setting AccuracyGoal -> -Log10[(t*deriv /. t -> zeros[[5000]]) $MachineEpsilon] $\endgroup$
    – Michael E2
    Commented Aug 20, 2021 at 16:02
  • $\begingroup$ ...where deriv = D[RiemannSiegelTheta[t], t] $\endgroup$
    – Michael E2
    Commented Aug 20, 2021 at 16:04
  • $\begingroup$ @MichaelE2 Thank you, I will give that a try. Maybe I should also manually set PrecisionGoal and AccuracyGoal to higher numbers like 16 too. $\endgroup$
    – Gabi23
    Commented Aug 20, 2021 at 16:14
  • $\begingroup$ What causes the messages is AccuracyGoal being higher than the working precision can handle. The setting I suggested lowers it to the best, achievable accuracy possible at machine precision (and you might need to lower it, not raise it, a little more, since it's an approximation). PrecisionGoal should not be set higher than the working precision and probably should be a little less, since it's impossible to avoid roundoff error. You might look up arbitrary and machine precision numbers in Numbers if you don't know about them. $\endgroup$
    – Michael E2
    Commented Aug 20, 2021 at 16:27
  • $\begingroup$ Let us continue this discussion in chat. $\endgroup$
    – Gabi23
    Commented Aug 20, 2021 at 17:24

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