Add loss transfer function to glvq

examples/glvq_spiral.py

@@ -35,6 +35,8 @@ if __name__ == "__main__":
         prototype_initializer=cinit.SSI(torch.Tensor(x_train),
                                         torch.Tensor(y_train),
                                         noise=1e-7),
+        transfer_function="sigmoid_beta",
+        transfer_beta=10.0,
         lr=0.01,
     )
 

prototorch/models/glvq.py

@@ -2,6 +2,7 @@ import pytorch_lightning as pl
 import torch
 import torchmetrics
 from prototorch.components import LabeledComponents
+from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance,
                                             squared_euclidean_distance)
@@ -21,11 +22,14 @@ class GLVQ(AbstractPrototypeModel):
         # Default Values
         self.hparams.setdefault("distance", euclidean_distance)
         self.hparams.setdefault("optimizer", torch.optim.Adam)
+        self.hparams.setdefault("transfer_function", "identity")
+        self.hparams.setdefault("transfer_beta", 10.0)
 
         self.proto_layer = LabeledComponents(
             labels=(self.hparams.nclasses, self.hparams.prototypes_per_class),
             initializer=self.hparams.prototype_initializer)
 
+        self.transfer_function = get_activation(self.hparams.transfer_function)
         self.train_acc = torchmetrics.Accuracy()
 
     @property
@@ -43,7 +47,9 @@ class GLVQ(AbstractPrototypeModel):
         dis = self(x)
         plabels = self.proto_layer.component_labels
         mu = glvq_loss(dis, y, prototype_labels=plabels)
-        loss = mu.sum(dim=0)
+        batch_loss = self.transfer_function(mu,
+                                            beta=self.hparams.transfer_beta)
+        loss = batch_loss.sum(dim=0)
 
         # Compute training accuracy
         with torch.no_grad():