Minimizing and maximizing the loss












1














I would like to train an autoencoder in such a way that the reconstruction error will be low on some observations, and high on the others.



from keras.model import Sequential
from keras.layers import Dense
import keras.backend as K

def l1Loss(y_true, y_pred):
return K.mean(K.abs(y_true - y_pred))

model = Sequential()
model.add(Dense(5, input_dim=10, activation='relu'))
model.add(Dense(10, activation='sigmoid'))
model.compile(optimizer='adam', loss=l1Loss)

for i in range(1000):
model.train_on_batch(x_good, x_good) # minimize on low
model.train_on_batch(x_bad, x_bad, ???) # need to maximize this part, so that mse(x_bad, x_bad_reconstructed is high)


I saw something about replacing ??? with sample_weight=-np.ones(batch_size), but I have no idea if this is fitting for my goal.










share|improve this question





























    1














    I would like to train an autoencoder in such a way that the reconstruction error will be low on some observations, and high on the others.



    from keras.model import Sequential
    from keras.layers import Dense
    import keras.backend as K

    def l1Loss(y_true, y_pred):
    return K.mean(K.abs(y_true - y_pred))

    model = Sequential()
    model.add(Dense(5, input_dim=10, activation='relu'))
    model.add(Dense(10, activation='sigmoid'))
    model.compile(optimizer='adam', loss=l1Loss)

    for i in range(1000):
    model.train_on_batch(x_good, x_good) # minimize on low
    model.train_on_batch(x_bad, x_bad, ???) # need to maximize this part, so that mse(x_bad, x_bad_reconstructed is high)


    I saw something about replacing ??? with sample_weight=-np.ones(batch_size), but I have no idea if this is fitting for my goal.










    share|improve this question



























      1












      1








      1


      0





      I would like to train an autoencoder in such a way that the reconstruction error will be low on some observations, and high on the others.



      from keras.model import Sequential
      from keras.layers import Dense
      import keras.backend as K

      def l1Loss(y_true, y_pred):
      return K.mean(K.abs(y_true - y_pred))

      model = Sequential()
      model.add(Dense(5, input_dim=10, activation='relu'))
      model.add(Dense(10, activation='sigmoid'))
      model.compile(optimizer='adam', loss=l1Loss)

      for i in range(1000):
      model.train_on_batch(x_good, x_good) # minimize on low
      model.train_on_batch(x_bad, x_bad, ???) # need to maximize this part, so that mse(x_bad, x_bad_reconstructed is high)


      I saw something about replacing ??? with sample_weight=-np.ones(batch_size), but I have no idea if this is fitting for my goal.










      share|improve this question















      I would like to train an autoencoder in such a way that the reconstruction error will be low on some observations, and high on the others.



      from keras.model import Sequential
      from keras.layers import Dense
      import keras.backend as K

      def l1Loss(y_true, y_pred):
      return K.mean(K.abs(y_true - y_pred))

      model = Sequential()
      model.add(Dense(5, input_dim=10, activation='relu'))
      model.add(Dense(10, activation='sigmoid'))
      model.compile(optimizer='adam', loss=l1Loss)

      for i in range(1000):
      model.train_on_batch(x_good, x_good) # minimize on low
      model.train_on_batch(x_bad, x_bad, ???) # need to maximize this part, so that mse(x_bad, x_bad_reconstructed is high)


      I saw something about replacing ??? with sample_weight=-np.ones(batch_size), but I have no idea if this is fitting for my goal.







      keras autoencoder maximize loss






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      edited Nov 12 at 22:03









      Joel

      1,5746719




      1,5746719










      asked Nov 12 at 20:38









      ian

      388




      388
























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          If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.






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            1 Answer
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            active

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            active

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            1














            If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.






            share|improve this answer


























              1














              If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.






              share|improve this answer
























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                If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.






                share|improve this answer












                If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.







                share|improve this answer












                share|improve this answer



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                answered Dec 9 at 19:19









                maksym33

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