Usage of constraints

Functions from the constraints module allow setting constraints (eg. non-negativity) on network parameters during optimization.

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

These layers expose 2 keyword arguments:

  • W_constraint for the main weights matrix
  • b_constraint for the bias.
from keras.constraints import maxnorm
model.add(Dense(64, W_constraint = maxnorm(2)))

Available constraints

  • maxnorm(m=2): maximum-norm constraint
  • nonneg(): non-negativity constraint
  • unitnorm(): unit-norm constraint, enforces the matrix to have unit norm along the last axis