Prototypes can use keras constraints.

protoflow/applications/glvq.py

@@ -12,6 +12,7 @@ class _GLVQ(Network):
     layer used by GLVQ-like models including GMLVQ and LVQMLN.
 
     """
+
     def compile(self,
                 loss=None,
                 squashing="sigmoid_beta",
@@ -59,13 +60,15 @@ class GLVQ(_GLVQ):
         prototype_initializer (str) : Method to use to set the initial
             prototype locations. (default: "mean")
     """
+
     def __init__(self,
                  nclasses,
                  input_dim,
                  prototypes_per_class=1,
                  prototype_initializer="zeros",
                  trainable_prototypes=True,
                  prototypes_dtype="float32",
+                 prototype_constraint=None,
                  distance_fn=squared_euclidean_distance,
                  **kwargs):
         super().__init__(**kwargs)
@@ -75,6 +78,7 @@ def __init__(self,
             prototypes_per_class=prototypes_per_class,
             prototype_initializer=prototype_initializer,
             trainable_prototypes=trainable_prototypes,
+            prototype_constraint=prototype_constraint,
             dtype=prototypes_dtype,
         )
         self.distance_fn = distance_fn

protoflow/layers/prototypes.py

@@ -6,14 +6,18 @@
 
 from protoflow.modules import initializers
 
+from tensorflow.python.keras import constraints
+
 
 class _Prototypes(tf.keras.layers.Layer):
     """Base class for Prototype layers in ProtoFlow."""
+
     def __init__(self,
                  nclasses=None,
                  prototypes_per_class=1,
                  prototype_distribution=None,
-                 prototype_initializer='zeros',
+                 prototype_initializer="zeros",
+                 prototype_constraint=None,
                  trainable_prototypes=True,
                  **kwargs):
         if 'input_shape' not in kwargs and 'input_dim' in kwargs:
@@ -28,6 +32,7 @@ def __init__(self,
             assert self.nclasses == len(prototype_distribution)
             self.prototype_distribution = prototype_distribution
         self.prototype_initializer = initializers.get(prototype_initializer)
+        self.prototype_constraint = constraints.get(prototype_constraint)
         self.trainable_prototypes = trainable_prototypes
 
         # Make a label list and flatten the list of lists using itertools
@@ -55,13 +60,15 @@ def get_config(self):
 
 class Prototypes1D(_Prototypes):
     """Point Prototypes."""
+
     def build(self, input_shape):
         num_of_prototypes = sum(self.prototype_distribution)
         self.prototypes = self.add_weight(
             name='prototypes',
             shape=(num_of_prototypes, input_shape[-1]),
             dtype=self.dtype,
             initializer=self.prototype_initializer,
+            constraint=self.prototype_constraint,
             trainable=self.trainable_prototypes)
         super().build(input_shape)
 
@@ -77,6 +84,7 @@ class AppendPrototypes1D(Prototypes1D):
 
     `shape_transformation`: Callable that is to be applied to get a matrix.
     """
+
     def __init__(self, shape_transformation=None, **kwargs):
         self.shape_transformation = shape_transformation or tf.identity
         super().__init__(**kwargs)