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Multi task learning with missing labels in Keras tutorial question



The Next CEO of Stack Overflow
2019 Community Moderator ElectionOnline machine learning tutorialHow to deal with string labels in multi-class classification with keras?Evaluating machine learning model with missing featuresMulti-task learning for Multi-label classification?Multitask learning NN only trains on a few tasksMulti task learning in KerasMulti task learning architecture for Multi-label classificationWhy is my Keras model not learning image segmentation?Training multi-label classifier with unbalanced samples in KerasApplying CNN for cross sectional data










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$begingroup$


https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/



I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:



from keras import backend as K
def masked_loss_function(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
return K.binary_crossentropy(y_true * mask, y_pred * mask)

model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])


Thanks so much!










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    0












    $begingroup$


    https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/



    I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:



    from keras import backend as K
    def masked_loss_function(y_true, y_pred):
    mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
    return K.binary_crossentropy(y_true * mask, y_pred * mask)

    model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])


    Thanks so much!










    share|improve this question









    New contributor




    scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/



      I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:



      from keras import backend as K
      def masked_loss_function(y_true, y_pred):
      mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
      return K.binary_crossentropy(y_true * mask, y_pred * mask)

      model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])


      Thanks so much!










      share|improve this question









      New contributor




      scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/



      I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:



      from keras import backend as K
      def masked_loss_function(y_true, y_pred):
      mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
      return K.binary_crossentropy(y_true * mask, y_pred * mask)

      model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])


      Thanks so much!







      classification keras beginner multitask-learning






      share|improve this question









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      scouts9 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









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      share|improve this question




      share|improve this question








      edited 1 hour ago









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      asked 1 hour ago









      scouts9scouts9

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      scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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