Minimizing and maximizing the loss
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
edited Nov 12 '18 at 22:03
Joel
1,5726719
asked Nov 12 '18 at 20:38
ian
388
add a comment |
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
edited Nov 12 '18 at 22:03
Joel
1,5726719
asked Nov 12 '18 at 20:38
ian
388
add a comment |
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
edited Nov 12 '18 at 22:03
Joel
1,5726719
asked Nov 12 '18 at 20:38
ian
388
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
keras autoencoder maximize loss
edited Nov 12 '18 at 22:03
Joel
1,5726719
asked Nov 12 '18 at 20:38
ian
388
edited Nov 12 '18 at 22:03
Joel
1,5726719
asked Nov 12 '18 at 20:38
ian
388
edited Nov 12 '18 at 22:03
Joel
1,5726719
edited Nov 12 '18 at 22:03
Joel
1,5726719
edited Nov 12 '18 at 22:03
Joel
1,5726719
1,5726719
asked Nov 12 '18 at 20:38
ian
388
asked Nov 12 '18 at 20:38
ian
388
asked Nov 12 '18 at 20:38
ian
388
388
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.
answered Dec 9 '18 at 19:19
maksym33
263
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.
answered Dec 9 '18 at 19:19
maksym33
263
add a comment |
If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.
answered Dec 9 '18 at 19:19
maksym33
263
add a comment |
If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.
answered Dec 9 '18 at 19:19
maksym33
263
If you set sample weight to negative numbers, then minimizing it would in fact lead to maximization of its absolute value.
answered Dec 9 '18 at 19:19
maksym33
263
answered Dec 9 '18 at 19:19
maksym33
263
answered Dec 9 '18 at 19:19
maksym33
263
answered Dec 9 '18 at 19:19
maksym33
263
263
add a comment |
add a comment |
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