Add more models

README.md

@@ -48,13 +48,17 @@ To assist in the development process, you may also find it useful to install
 - Neural Gas
 
 ## Work in Progress
+
 - CBC
+- LVQMLN
+- GMLVQ
+- Limited-Rank GMLVQ
 
 ## Planned models
-- GMLVQ
+
 - Local-Matrix GMLVQ
-- Limited-Rank GMLVQ
 - GTLVQ
 - RSLVQ
 - PLVQ
-- LVQMLN
+- SILVQ
+- KNN

prototorch/models/glvq.py

@@ -37,32 +37,28 @@ class GLVQ(AbstractPrototypeModel):
 
     def training_step(self, train_batch, batch_idx):
         x, y = train_batch
-        x = x.view(x.size(0), -1)
+        x = x.view(x.size(0), -1)  # flatten
         dis = self(x)
         plabels = self.proto_layer.component_labels
         mu = glvq_loss(dis, y, prototype_labels=plabels)
         loss = mu.sum(dim=0)
-        self.log("train_loss", loss)
+
+        # Compute training accuracy
         with torch.no_grad():
             preds = wtac(dis, plabels)
-        # self.train_acc.update(preds.int(), y.int())
+        # `.int()` because FloatTensors are assumed to be class probabilities
-        self.train_acc(
+        self.train_acc(preds.int(), y.int())
-            preds.int(),
-            y.int())  # FloatTensors are assumed to be class probabilities
-        self.log(
-            "acc",
-            self.train_acc,
-            on_step=False,
-            on_epoch=True,
-            prog_bar=True,
-            logger=True,
-        )
-        return loss
 
-    # def training_epoch_end(self, outs):
+        # Logging
-    #     # Calling `self.train_acc.compute()` is
+        self.log("train_loss", loss)
-    #     # automatically done by setting `on_epoch=True` when logging in `self.training_step(...)`
+        self.log("acc",
-    #     self.log("train_acc_epoch", self.train_acc.compute())
+                 self.train_acc,
+                 on_step=False,
+                 on_epoch=True,
+                 prog_bar=True,
+                 logger=True)
+
+        return loss
 
     def predict(self, x):
         # model.eval()  # ?!
@@ -76,8 +72,9 @@ class GLVQ(AbstractPrototypeModel):
 class ImageGLVQ(GLVQ):
     """GLVQ for training on image data.
 
-    GLVQ model that constrains the prototypes to the range [0, 1] by
+    GLVQ model that constrains the prototypes to the range [0, 1] by clamping
-    clamping after updates.
+    after updates.
+
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         self.proto_layer.components.data.clamp_(0.0, 1.0)
@@ -89,6 +86,7 @@ class SiameseGLVQ(GLVQ):
     GLVQ model that applies an arbitrary transformation on the inputs and the
     prototypes before computing the distances between them. The weights in the
     transformation pipeline are only learned from the inputs.
+
     """
     def __init__(self,
                  hparams,
@@ -107,14 +105,18 @@ class SiameseGLVQ(GLVQ):
     def forward(self, x):
         self.sync_backbones()
         protos, _ = self.proto_layer()
-
         latent_x = self.backbone(x)
         latent_protos = self.backbone_dependent(protos)
-
         dis = euclidean_distance(latent_x, latent_protos)
         return dis
 
     def predict_latent(self, x):
+        """Predict `x` assuming it is already embedded in the latent space.
+
+        Only the prototypes are embedded in the latent space using the
+        backbone.
+
+        """
         # model.eval()  # ?!
         with torch.no_grad():
             protos, plabels = self.proto_layer()
@@ -122,3 +124,51 @@ class SiameseGLVQ(GLVQ):
             d = euclidean_distance(x, latent_protos)
             y_pred = wtac(d, plabels)
         return y_pred.numpy()
+
+
+class GMLVQ(GLVQ):
+    """Generalized Matrix Learning Vector Quantization."""
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, **kwargs)
+        self.omega_layer = torch.nn.Linear(self.hparams.input_dim,
+                                           self.latent_dim,
+                                           bias=False)
+
+    def forward(self, x):
+        protos, _ = self.proto_layer()
+        latent_x = self.omega_layer(x)
+        latent_protos = self.omega_layer(protos)
+        dis = euclidean_distance(latent_x, latent_protos)
+        return dis
+
+
+class LVQMLN(GLVQ):
+    """Learning Vector Quantization Multi-Layer Network.
+
+    GLVQ model that applies an arbitrary transformation on the inputs, BUT NOT
+    on the prototypes before computing the distances between them. This of
+    course, means that the prototypes no longer live the input space, but
+    rather in the embedding space.
+
+    """
+    def __init__(self,
+                 hparams,
+                 backbone_module=torch.nn.Identity,
+                 backbone_params={},
+                 **kwargs):
+        super().__init__(hparams, **kwargs)
+        self.backbone = backbone_module(**backbone_params)
+
+    def forward(self, x):
+        latent_protos, _ = self.proto_layer()
+        latent_x = self.backbone(x)
+        dis = euclidean_distance(latent_x, latent_protos)
+        return dis
+
+    def predict_latent(self, x):
+        """Predict `x` assuming it is already embedded in the latent space."""
+        with torch.no_grad():
+            latent_protos, plabels = self.proto_layer()
+            d = euclidean_distance(x, latent_protos)
+            y_pred = wtac(d, plabels)
+        return y_pred.numpy()