[refactor] Use functional variant of accuracy

Prevents Accuracy in `__repr__` of the models.

prototorch/models/glvq.py

@@ -33,7 +33,6 @@ class GLVQ(AbstractPrototypeModel):
             initializer=self.prototype_initializer(**kwargs))
 
         self.transfer_fn = get_activation(self.hparams.transfer_fn)
-        self.acc_metric = torchmetrics.Accuracy()
 
         self.loss = glvq_loss
 
@@ -73,11 +72,11 @@ class GLVQ(AbstractPrototypeModel):
 
     def log_acc(self, distances, targets, tag):
         preds = self.predict_from_distances(distances)
-        self.acc_metric(preds.int(), targets.int())
+        accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
         # `.int()` because FloatTensors are assumed to be class probabilities
 
         self.log(tag,
-                 self.acc_metric,
+                 accuracy,
                  on_step=False,
                  on_epoch=True,
                  prog_bar=True,