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















            -

            Popular posts from this blog

            How to label and detect the document text images

            Image
            1 $begingroup$ This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
            Read more

            Vallis Paradisi

            Image
            Coordinata: .mw-parser-output .geo-default,.mw-parser-output .geo-dms,.mw-parser-output .geo-dec{display:inline}.mw-parser-output .geo-nondefault,.mw-parser-output .geo-multi-punct{display:none}.mw-parser-output .longitude,.mw-parser-output .latitude{white-space:nowrap} 33°03′S 71°37′W  /  33.05°S 71.617°W  / -33.05; -71.617 Vallis Paradisi Res apud Vicidata repertae: Civitas : Chilia Numerus incolarum : 296 655 Zona horaria : UTC-4 Situs interretialis Nomen officiale : Valparaíso Gubernium Consilium : municipal council of Valparaíso Geographia Superficies : 401 600 000 Metrum quadratum Coniunctiones urbium Urbes gemellae : Ovetum , Badalona , Santa Fe , Ora longa , Sciamhaevum , Quantunia , Callao , Bat Yam , Melaka , Novorossiysk , Busan , Corduba , Odessa , Rosario , Malacca , Barcino , Soteropolis , Franciscopolis (California) , Guanajuato, Silao Tabula aut despectus Despectus in portum sinumque et urbem Vallis Paradisi Vallis Para...
            Read more

            Tabula Rosettana

            Image
            Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
            Read more