Loss is decreasing but val_loss not! [duplicate]












0












$begingroup$



This question already has an answer here:




  • Validation loss is not decreasing

    2 answers




If loss is decreasing but val_loss not, what is the problem and how can I fix it?



I get such vague result:
enter image description here






lstm loss-function







share|improve this question











$endgroup$



marked as duplicate by Antonio Jurić, Siong Thye Goh, Sean Owen♦ yesterday


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.


















  • $begingroup$
    Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
    $endgroup$
    – Sean Owen♦
    yesterday
















0












$begingroup$



This question already has an answer here:




  • Validation loss is not decreasing

    2 answers




If loss is decreasing but val_loss not, what is the problem and how can I fix it?



I get such vague result:
enter image description here






lstm loss-function







share|improve this question











$endgroup$



marked as duplicate by Antonio Jurić, Siong Thye Goh, Sean Owen♦ yesterday


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.


















  • $begingroup$
    Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
    $endgroup$
    – Sean Owen♦
    yesterday














0












0








0





$begingroup$



This question already has an answer here:




  • Validation loss is not decreasing

    2 answers




If loss is decreasing but val_loss not, what is the problem and how can I fix it?



I get such vague result:
enter image description here






lstm loss-function







share|improve this question











$endgroup$





This question already has an answer here:




  • Validation loss is not decreasing

    2 answers




If loss is decreasing but val_loss not, what is the problem and how can I fix it?



I get such vague result:
enter image description here





This question already has an answer here:




  • Validation loss is not decreasing

    2 answers







lstm loss-function




lstm loss-function






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 2 days ago







user145959

















asked 2 days ago









user145959user145959

1168




1168




marked as duplicate by Antonio Jurić, Siong Thye Goh, Sean Owen♦ yesterday


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.









marked as duplicate by Antonio Jurić, Siong Thye Goh, Sean Owen♦ yesterday


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.














  • $begingroup$
    Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
    $endgroup$
    – Sean Owen♦
    yesterday


















  • $begingroup$
    Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
    $endgroup$
    – Sean Owen♦
    yesterday
















$begingroup$
Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
$endgroup$
– Sean Owen♦
yesterday




$begingroup$
Are you sure this isn't backwards? It would be odd for validation loss to be consistently lower than train. Not impossible, but atypical.
$endgroup$
– Sean Owen♦
yesterday










1 Answer
1






active

oldest

votes


















3












$begingroup$

This indicates that model is not generalizing (it is over-fitting). Few options are :




  1. Get more training data

  2. Reduce complexity of model (Number of LSTM layers, complexity of dense layers)


Andrew NG has a good video on this topic :



https://www.youtube.com/watch?v=OSd30QGMl88



A tutorial specific to LSTM :



https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/






share|improve this answer









$endgroup$




















    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3












    $begingroup$

    This indicates that model is not generalizing (it is over-fitting). Few options are :




    1. Get more training data

    2. Reduce complexity of model (Number of LSTM layers, complexity of dense layers)


    Andrew NG has a good video on this topic :



    https://www.youtube.com/watch?v=OSd30QGMl88



    A tutorial specific to LSTM :



    https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/






    share|improve this answer









    $endgroup$


















      3












      $begingroup$

      This indicates that model is not generalizing (it is over-fitting). Few options are :




      1. Get more training data

      2. Reduce complexity of model (Number of LSTM layers, complexity of dense layers)


      Andrew NG has a good video on this topic :



      https://www.youtube.com/watch?v=OSd30QGMl88



      A tutorial specific to LSTM :



      https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/






      share|improve this answer









      $endgroup$
















        3












        3








        3





        $begingroup$

        This indicates that model is not generalizing (it is over-fitting). Few options are :




        1. Get more training data

        2. Reduce complexity of model (Number of LSTM layers, complexity of dense layers)


        Andrew NG has a good video on this topic :



        https://www.youtube.com/watch?v=OSd30QGMl88



        A tutorial specific to LSTM :



        https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/






        share|improve this answer









        $endgroup$



        This indicates that model is not generalizing (it is over-fitting). Few options are :




        1. Get more training data

        2. Reduce complexity of model (Number of LSTM layers, complexity of dense layers)


        Andrew NG has a good video on this topic :



        https://www.youtube.com/watch?v=OSd30QGMl88



        A tutorial specific to LSTM :



        https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 2 days ago









        Shamit VermaShamit Verma

        78426




        78426















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