custreg.py

import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation


def fro_norm(w):
    """Frobenius norm."""
    return K.sqrt(K.sum(K.square(K.abs(w))))


def ort_reg(w):
    """Orthogonal regularization."""
    m = K.dot(K.transpose(w), w) - K.eye(w.shape)
    return fro_norm(m)


X = np.random.randn(100, 100)
y = np.random.randint(2, size=(100, 1))

model = Sequential()

# apply regularization here. applies regularization to the
# output (activation) of the layer
model.add(Dense(32, input_shape=(100,),
                activity_regularizer=ort_reg))
model.add(Dense(1))
model.add(Activation('softmax'))

model.compile(loss="binary_crossentropy",
              optimizer='sgd',
              metrics=['accuracy'])

model.fit(X, y, epochs=1, batch_size=32)