Proper order of optimizing hyperparameters in ANN












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I have built a neural network for approximating a certain function and decided on a metric how to evaluate the performance. Now, where do I start with optimizing to get the optimal result? I need to decide on number of layers, hidden neurons, learning rate, learning algorithms and all other hyperparameters. What is the usual procedure and order in which to optimize all network's parameters? I guess I am looking for some kind of general guidelines to follow to obtain the best possible performance










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    $begingroup$
    Possible duplicate of Is there a thumb-rule for designing neural-networks?
    $endgroup$
    – João Almeida
    yesterday
















1












$begingroup$


I have built a neural network for approximating a certain function and decided on a metric how to evaluate the performance. Now, where do I start with optimizing to get the optimal result? I need to decide on number of layers, hidden neurons, learning rate, learning algorithms and all other hyperparameters. What is the usual procedure and order in which to optimize all network's parameters? I guess I am looking for some kind of general guidelines to follow to obtain the best possible performance










share|improve this question







New contributor




Guest is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$








  • 3




    $begingroup$
    Possible duplicate of Is there a thumb-rule for designing neural-networks?
    $endgroup$
    – João Almeida
    yesterday














1












1








1





$begingroup$


I have built a neural network for approximating a certain function and decided on a metric how to evaluate the performance. Now, where do I start with optimizing to get the optimal result? I need to decide on number of layers, hidden neurons, learning rate, learning algorithms and all other hyperparameters. What is the usual procedure and order in which to optimize all network's parameters? I guess I am looking for some kind of general guidelines to follow to obtain the best possible performance










share|improve this question







New contributor




Guest is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$




I have built a neural network for approximating a certain function and decided on a metric how to evaluate the performance. Now, where do I start with optimizing to get the optimal result? I need to decide on number of layers, hidden neurons, learning rate, learning algorithms and all other hyperparameters. What is the usual procedure and order in which to optimize all network's parameters? I guess I am looking for some kind of general guidelines to follow to obtain the best possible performance







machine-learning neural-network






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  • 3




    $begingroup$
    Possible duplicate of Is there a thumb-rule for designing neural-networks?
    $endgroup$
    – João Almeida
    yesterday














  • 3




    $begingroup$
    Possible duplicate of Is there a thumb-rule for designing neural-networks?
    $endgroup$
    – João Almeida
    yesterday








3




3




$begingroup$
Possible duplicate of Is there a thumb-rule for designing neural-networks?
$endgroup$
– João Almeida
yesterday




$begingroup$
Possible duplicate of Is there a thumb-rule for designing neural-networks?
$endgroup$
– João Almeida
yesterday










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$begingroup$

My initial suggestion is to look in the literature for networks which have been implemented to do solve similar problems and start from there.
Then you should just try a bunch of different values for the parameters and see what works in your case.



See also this Cross Validated question: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw






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    $begingroup$

    My initial suggestion is to look in the literature for networks which have been implemented to do solve similar problems and start from there.
    Then you should just try a bunch of different values for the parameters and see what works in your case.



    See also this Cross Validated question: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw






    share|improve this answer









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      0












      $begingroup$

      My initial suggestion is to look in the literature for networks which have been implemented to do solve similar problems and start from there.
      Then you should just try a bunch of different values for the parameters and see what works in your case.



      See also this Cross Validated question: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw






      share|improve this answer









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        0





        $begingroup$

        My initial suggestion is to look in the literature for networks which have been implemented to do solve similar problems and start from there.
        Then you should just try a bunch of different values for the parameters and see what works in your case.



        See also this Cross Validated question: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw






        share|improve this answer









        $endgroup$



        My initial suggestion is to look in the literature for networks which have been implemented to do solve similar problems and start from there.
        Then you should just try a bunch of different values for the parameters and see what works in your case.



        See also this Cross Validated question: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw







        share|improve this answer












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        answered yesterday









        João AlmeidaJoão Almeida

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