… and logistic regression

A follow-up on the collaborative filter, it occurs to me that it should be possible to add a logistic regression layer in Keras.  The following is a regularized multinomial logistic regression model:

from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.regularizers import l2

model = Sequential()
model.add(Dense(numClasses, input_shape=(numFeatures, ), \
    init='zero', W_regularizer=l2(0.01)))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy', optimizer='sgd')

As with the collaborative filter, you can easily modify the code to use multiple regularizers and different learning algorithms, say Adamax or adam instead of SGD.

Neat.

Logistic regression model in Keras

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