Nrthugu

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)

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)

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)

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)

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