Update iris example

examples/glvq_iris.py

@@ -3,13 +3,13 @@
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
-from sklearn.datasets import load_iris
+from prototorch.functions.competitions import wtac
-from sklearn.preprocessing import StandardScaler
-from torchinfo import summary
-
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import GLVQLoss
 from prototorch.modules.prototypes import Prototypes1D
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+from torchinfo import summary
 
 # Prepare and preprocess the data
 scaler = StandardScaler()
@@ -58,7 +58,11 @@ for epoch in range(70):
     # Compute loss
     dis, plabels = model(x_in)
     loss = criterion([dis, plabels], y_in)
-    print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f}")
+    with torch.no_grad():
+        pred = wtac(dis, plabels)
+        correct = pred.eq(y_in.view_as(pred)).sum().item()
+    acc = 100. * correct / len(x_train)
+    print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%")
 
     # Take a gradient descent step
     optimizer.zero_grad()