fix: if layers == 0, layers were not initialized

model2vec/train/classifier.py

@@ -66,20 +66,29 @@ def classes(self) -> np.ndarray:
 
     def construct_head(self) -> nn.Sequential:
         """Constructs a simple classifier head."""
+        modules: list[nn.Module] = []
         if self.n_layers == 0:
-            return nn.Sequential(nn.Linear(self.embed_dim, self.out_dim))
-        modules = [
-            nn.Linear(self.embed_dim, self.hidden_dim),
-            nn.ReLU(),
-        ]
-        for _ in range(self.n_layers - 1):
-            modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()])
-        modules.extend([nn.Linear(self.hidden_dim, self.out_dim)])
-
-        for module in modules:
-            if isinstance(module, nn.Linear):
-                nn.init.kaiming_uniform_(module.weight)
+            modules.append(nn.Linear(self.embed_dim, self.out_dim))
+        else:
+            # If we have a hidden layer, we should first project to hidden_dim
+            modules = [
+                nn.Linear(self.embed_dim, self.hidden_dim),
+                nn.ReLU(),
+            ]
+            for _ in range(self.n_layers - 1):
+                modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()])
+            # We always have a layer mapping from hidden to out.
+            modules.append(nn.Linear(self.hidden_dim, self.out_dim))
+
+        linear_modules = [module for module in modules if isinstance(module, nn.Linear)]
+        if linear_modules:
+            *initial, last = linear_modules
+            for module in initial:
+                nn.init.kaiming_uniform_(module.weight, nonlinearity="relu")
                 nn.init.zeros_(module.bias)
+            # Final layer does not kaiming
+            nn.init.xavier_uniform_(last.weight)
+            nn.init.zeros_(last.bias)
 
         return nn.Sequential(*modules)