Usage of regularizers

Regularizers allow to apply penalties on layer parameters or layer activity during optimization. These penalties are incorporated in the loss function that the network optimizes.

The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers Dense, TimeDistributedDense, MaxoutDense, Convolution1D, Convolution2D and Convolution3D have a unified API.

These layers expose 3 keyword arguments:

  • W_regularizer: instance of keras.regularizers.WeightRegularizer
  • b_regularizer: instance of keras.regularizers.WeightRegularizer
  • activity_regularizer: instance of keras.regularizers.ActivityRegularizer

Example

from keras.regularizers import l2, activity_l2
model.add(Dense(64, input_dim=64, W_regularizer=l2(0.01), activity_regularizer=activity_l2(0.01)))

Available penalties

keras.regularizers.WeightRegularizer(l1=0., l2=0.)

keras.regularizers.ActivityRegularizer(l1=0., l2=0.)

Shortcuts

These are shortcut functions available in keras.regularizers.

  • l1(l=0.01): L1 weight regularization penalty, also known as LASSO
  • l2(l=0.01): L2 weight regularization penalty, also known as weight decay, or Ridge
  • l1l2(l1=0.01, l2=0.01): L1-L2 weight regularization penalty, also known as ElasticNet
  • activity_l1(l=0.01): L1 activity regularization
  • activity_l2(l=0.01): L2 activity regularization
  • activity_l1l2(l1=0.01, l2=0.01): L1+L2 activity regularization