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from keras.models import Sequential
from keras.layers import Dense
x, y = ...
x_val, y_val = ...
# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val))
# 2-class logistic regression in Keras
model = Sequential()
model.add(Dense(1, activation='sigmoid', input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val))
# logistic regression with L1 and L2 regularization
from keras.regularizers import l1l2
reg = l1l2(l1=0.01, l2=0.01)
model = Sequential()
model.add(Dense(1, activation='sigmoid', W_regularizer=reg, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val))
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