Update examples/glvq_iris.py script

examples/glvq_iris.py

@@ -5,6 +5,7 @@ import torch
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
+from torchinfo import summary
 
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import GLVQLoss
@@ -27,7 +28,7 @@ class Model(torch.nn.Module):
             input_dim=2,
             prototypes_per_class=3,
             nclasses=3,
-            prototype_initializer='stratified_random',
+            prototype_initializer="stratified_random",
             data=[x_train, y_train])
 
     def forward(self, x):
@@ -40,21 +41,24 @@ class Model(torch.nn.Module):
 # Build the GLVQ model
 model = Model()
 
+# Print summary using torchinfo (might be buggy/incorrect)
+print(summary(model))
+
 # Optimize using SGD optimizer from `torch.optim`
 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
+criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
 
 x_in = torch.Tensor(x_train)
 y_in = torch.Tensor(y_train)
 
 # Training loop
-title = 'Prototype Visualization'
+title = "Prototype Visualization"
 fig = plt.figure(title)
 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}')
+    print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f}")
 
     # Take a gradient descent step
     optimizer.zero_grad()
@@ -64,23 +68,23 @@ for epoch in range(70):
     # Get the prototypes form the model
     protos = model.proto_layer.prototypes.data.numpy()
     if np.isnan(np.sum(protos)):
-        print('Stopping training because of `nan` in prototypes.')
+        print("Stopping training because of `nan` in prototypes.")
         break
 
     # Visualize the data and the prototypes
     ax = fig.gca()
     ax.cla()
     ax.set_title(title)
-    ax.set_xlabel('Data dimension 1')
-    ax.set_ylabel('Data dimension 2')
-    cmap = 'viridis'
-    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
+    ax.set_xlabel("Data dimension 1")
+    ax.set_ylabel("Data dimension 2")
+    cmap = "viridis"
+    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
     ax.scatter(protos[:, 0],
                protos[:, 1],
                c=plabels,
                cmap=cmap,
-               edgecolor='k',
-               marker='D',
+               edgecolor="k",
+               marker="D",
                s=50)
 
     # Paint decision regions

setup.py

@@ -27,6 +27,7 @@ DATASETS = [
 EXAMPLES = [
     "sklearn",
     "matplotlib",
+    "torchinfo",
 ]
 TESTS = ["pytest"]
 ALL = DOCS + DATASETS + EXAMPLES + TESTS