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How to set a newtwork with two objectives?



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsKeras/Theano custom loss calculation - working with tensorsHow to create a custom loss function from sklearn metrics in Keras?Keras custom loss function as True Negatives by (True Negatives plus False Positives)How to join 2 neural networks in tensorflow?Custom loss function which is included gradient in KerasValidation loss keeps fluctuating about training lossProcess mining with MLcustom loss in keras, problem with batch sizeHow to run tensorflow model twice before computing the lossIs it possible to call from Keras unsupported backend function directly from tensorflow?










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











$endgroup$
















    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











    $endgroup$














      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











      $endgroup$




      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















      share|improve this question













      share|improve this question




      share|improve this question








      edited Apr 4 at 5:04









      Martin Thoma

      6,7781657135




      6,7781657135










      asked Apr 4 at 3:11









      LiuHaoLiuHao

      61




      61




















          1 Answer
          1






          active

          oldest

          votes


















          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









          $endgroup$













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            1 Answer
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            active

            oldest

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            active

            oldest

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            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












              $begingroup$

              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 Apr 4 at 4:59









                thanatozthanatoz

                689421




                689421



























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