How to set a newtwork with two objectives?












1












$begingroup$


Suppose I have a x_train, y1_train and y2_train.



I want to construct a network (such as simple MLP) to fit y1_train and to be low correlated with y2_train (or to fit -y2_train) simultaneously.



How could I achieve this goal? Is the custom loss function a good solution?



I use keras as my tool.






machine-learning keras







share|improve this question









New contributor




LiuHao is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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    1












    $begingroup$


    Suppose I have a x_train, y1_train and y2_train.



    I want to construct a network (such as simple MLP) to fit y1_train and to be low correlated with y2_train (or to fit -y2_train) simultaneously.



    How could I achieve this goal? Is the custom loss function a good solution?



    I use keras as my tool.






    machine-learning keras







    share|improve this question









    New contributor




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







    $endgroup$















      1












      1








      1





      $begingroup$


      Suppose I have a x_train, y1_train and y2_train.



      I want to construct a network (such as simple MLP) to fit y1_train and to be low correlated with y2_train (or to fit -y2_train) simultaneously.



      How could I achieve this goal? Is the custom loss function a good solution?



      I use keras as my tool.






      machine-learning keras







      share|improve this question









      New contributor




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







      $endgroup$




      Suppose I have a x_train, y1_train and y2_train.



      I want to construct a network (such as simple MLP) to fit y1_train and to be low correlated with y2_train (or to fit -y2_train) simultaneously.



      How could I achieve this goal? Is the custom loss function a good solution?



      I use keras as my tool.






      machine-learning keras




      machine-learning keras






      share|improve this question









      New contributor




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











      share|improve this question









      New contributor




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









      share|improve this question




      share|improve this question








      edited 10 mins ago









      Martin Thoma

      6,5301556133




      6,5301556133






      New contributor




      LiuHao is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 2 hours ago









      LiuHaoLiuHao

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      New contributor





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






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          $begingroup$

          So this problem is less of a Deep learning problem and more of a logical problem. Your y1_train and y2_train can be modelled along with a pointer label that points what output to be considered in the output. Lets say we create the concatenated output as follows:



          [[0/1],[y1_train], [y2_train]]


          Where 0 could represent weather the label to be selected is y1 or y2 and so on.



          But if you are planning to create a little more complex output and train different outputs on different loss functions, here is an article you should refer to Multiple output tutorial/examples and Custom Loss Function for Unequal Weighted Multiple-Output Node Regression






          share|improve this answer









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            $begingroup$

            So this problem is less of a Deep learning problem and more of a logical problem. Your y1_train and y2_train can be modelled along with a pointer label that points what output to be considered in the output. Lets say we create the concatenated output as follows:



            [[0/1],[y1_train], [y2_train]]


            Where 0 could represent weather the label to be selected is y1 or y2 and so on.



            But if you are planning to create a little more complex output and train different outputs on different loss functions, here is an article you should refer to Multiple output tutorial/examples and Custom Loss Function for Unequal Weighted Multiple-Output Node Regression






            share|improve this answer









            $endgroup$


















              1












              $begingroup$

              So this problem is less of a Deep learning problem and more of a logical problem. Your y1_train and y2_train can be modelled along with a pointer label that points what output to be considered in the output. Lets say we create the concatenated output as follows:



              [[0/1],[y1_train], [y2_train]]


              Where 0 could represent weather the label to be selected is y1 or y2 and so on.



              But if you are planning to create a little more complex output and train different outputs on different loss functions, here is an article you should refer to Multiple output tutorial/examples and Custom Loss Function for Unequal Weighted Multiple-Output Node Regression






              share|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                So this problem is less of a Deep learning problem and more of a logical problem. Your y1_train and y2_train can be modelled along with a pointer label that points what output to be considered in the output. Lets say we create the concatenated output as follows:



                [[0/1],[y1_train], [y2_train]]


                Where 0 could represent weather the label to be selected is y1 or y2 and so on.



                But if you are planning to create a little more complex output and train different outputs on different loss functions, here is an article you should refer to Multiple output tutorial/examples and Custom Loss Function for Unequal Weighted Multiple-Output Node Regression






                share|improve this answer









                $endgroup$



                So this problem is less of a Deep learning problem and more of a logical problem. Your y1_train and y2_train can be modelled along with a pointer label that points what output to be considered in the output. Lets say we create the concatenated output as follows:



                [[0/1],[y1_train], [y2_train]]


                Where 0 could represent weather the label to be selected is y1 or y2 and so on.



                But if you are planning to create a little more complex output and train different outputs on different loss functions, here is an article you should refer to Multiple output tutorial/examples and Custom Loss Function for Unequal Weighted Multiple-Output Node Regression







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 15 mins ago









                thanatozthanatoz

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