Update examples

README.md

@@ -28,12 +28,12 @@ The plugin should then be available for use in your Python environment as
 - Generalized Relevance Learning Vector Quantization (GRLVQ)
 - Generalized Matrix Learning Vector Quantization (GMLVQ)
 - Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
-- Learning Vector Quantization Multi-Layer Network (LVQMLN)
 - Siamese GLVQ
 - Neural Gas (NG)
 
 ## Work in Progress
 
+- Learning Vector Quantization Multi-Layer Network (LVQMLN)
 - Classification-By-Components Network (CBC)
 - Learning Vector Quantization 2.1 (LVQ2.1)
 

examples/glvq_spiral.py

@@ -30,7 +30,7 @@ if __name__ == "__main__":
                                                                    noise=1e-1))
 
     # Callbacks
-    vis = pt.models.VisGLVQ2D(train_ds, show_last_only=False, block=True)
+    vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
     snan = StopOnNaN(model.proto_layer.components)
 
     # Setup trainer

examples/gmlvq_iris.py

@@ -21,7 +21,8 @@ if __name__ == "__main__":
         distribution=(nclasses, prototypes_per_class),
         input_dim=x_train.shape[1],
         latent_dim=x_train.shape[1],
-        lr=0.01,
+        proto_lr=0.01,
+        bb_lr=0.01,
     )
 
     # Initialize the model

examples/knn_iris.py

@@ -23,7 +23,7 @@ if __name__ == "__main__":
     model = pt.models.KNN(hparams, data=train_ds)
 
     # Callbacks
-    vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
+    vis = pt.models.VisGLVQ2D(data=(x_train, y_train), resolution=200)
 
     # Setup trainer
     trainer = pl.Trainer(max_epochs=1, callbacks=[vis], gpus=0)

examples/liramlvq_tecator.py

@@ -23,7 +23,8 @@ if __name__ == "__main__":
         distribution=(nclasses, prototypes_per_class),
         input_dim=100,
         latent_dim=2,
-        lr=0.001,
+        proto_lr=0.001,
+        bb_lr=0.001,
     )
 
     # Initialize the model

examples/lvqmln_iris.py

@@ -0,0 +1,54 @@
+"""LVQMLN example using all four dimensions of the Iris dataset."""
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+
+from siamese_glvq_iris import Backbone
+
+if __name__ == "__main__":
+    # Dataset
+    train_ds = pt.datasets.Iris()
+
+    # Reproducibility
+    pl.utilities.seed.seed_everything(seed=42)
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds,
+                                               num_workers=0,
+                                               batch_size=150)
+
+    # Hyperparameters
+    hparams = dict(
+        distribution=[1, 2, 2],
+        proto_lr=0.001,
+        bb_lr=0.001,
+    )
+
+    # Initialize the backbone
+    backbone = Backbone()
+
+    # Initialize the model
+    model = pt.models.LVQMLN(
+        hparams,
+        prototype_initializer=pt.components.SSI(train_ds, transform=backbone),
+        backbone=backbone,
+    )
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = pt.models.VisSiameseGLVQ2D(
+        data=train_ds,
+        map_protos=False,
+        border=0.1,
+        resolution=500,
+        axis_off=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=0)
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/siamese_glvq_iris.py

@@ -6,7 +6,6 @@ import torch
 
 
 class Backbone(torch.nn.Module):
-    """Two fully connected layers with ReLU activation."""
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):
         super().__init__()
         self.input_size = input_size
@@ -14,11 +13,11 @@ class Backbone(torch.nn.Module):
         self.latent_size = latent_size
         self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
         self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
-        self.relu = torch.nn.ReLU()
+        self.activation = torch.nn.Sigmoid()
 
     def forward(self, x):
-        x = self.relu(self.dense1(x))
-        out = self.relu(self.dense2(x))
+        x = self.activation(self.dense1(x))
+        out = self.activation(self.dense2(x))
         return out
 
 
@@ -41,11 +40,15 @@ if __name__ == "__main__":
         bb_lr=0.01,
     )
 
+    # Initialize the backbone
+    backbone = Backbone()
+
     # Initialize the model
     model = pt.models.SiameseGLVQ(
         hparams,
         prototype_initializer=pt.components.SMI(train_ds),
-        backbone_module=Backbone,
+        backbone=backbone,
+        both_path_gradients=True,
     )
 
     # Model summary