Another useful regularization technique is called Max-Norm Regularization.
Implementation
layer = keras.layers.Dense(100, activation="selu", kernel_initializer="lecun_normal",
kernel_constraint=keras.constraints.max_norm(1.))
By setting a hyper-parameter r you can set a max value for weights to prevent over-fitting.
References:
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
