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, Convolution1D, Convolution2D and Convolution3D have a unified API.

These layers expose 2 keyword arguments:

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

Available constraints

  • max_norm(max_value=2, axis=0): maximum-norm constraint
  • non_neg(): non-negativity constraint
  • unit_norm(): unit-norm constraint, enforces the matrix to have unit norm along the last axis