MinMaxNorm weight constraint.

Inherits From: Constraint

Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

Also available via the shortcut function tf.keras.constraints.min_max_norm.

min_value the minimum norm for the incoming weights.
max_value the maximum norm for the incoming weights.
rate rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step