Cannot reset network to smaller learning rate - Optimization parameter

olafansau55
olafansau55 New Altair Community Member
edited November 5 in Community Q&A
Hello Guys
I'm working on a final project using rapid miner on COVID-19 data. I did the parameter optimization process. I entered a value for the learning rate in the range of 0.00 - 0.99 with a step count of 10 and for training_cycles in the range 1-200 for a step count of 10. Initially, there was no problem with that value, but when I changed the number of steps in training_cycles to 200, an error appeared like "Cannot reset the network to a smaller learning rate". I want to ask why this happened and how to solve it. If anyone can help out I would really appreciate it.

Thank you~

Best Answer

  • SabaRG
    SabaRG New Altair Community Member
    edited May 2022 Answer ✓
    Dear @olafansau55
    Unfortunately, I don't know why the "Try" operator can not work with the deep learning operator, which I suggest you use to ignore this bug, and the ignore error option of optimization operator can not handle it! It is a bug with the deep learning (H2O) operator.
    But, for your case, I suggest starting the learning rate from 0.1 or 0.2 and checking when it has no error. Your problem is with the learning rate.
    Sincerely

    #BugReport

Answers

  • SabaRG
    SabaRG New Altair Community Member
    edited May 2022 Answer ✓
    Dear @olafansau55
    Unfortunately, I don't know why the "Try" operator can not work with the deep learning operator, which I suggest you use to ignore this bug, and the ignore error option of optimization operator can not handle it! It is a bug with the deep learning (H2O) operator.
    But, for your case, I suggest starting the learning rate from 0.1 or 0.2 and checking when it has no error. Your problem is with the learning rate.
    Sincerely

    #BugReport
  • olafansau55
    olafansau55 New Altair Community Member
    Thank you @SabaRG for your answer, i've changed the learning rate from 0.1 or 0.2 but it's give me the same error. Maybe I can try to use ignore an error .