Update network.py

network.py

@@ -19,13 +19,11 @@ class DynamicCognitiveNet(nn.Module):
             f'input_{i}': CognitiveNode(i, 1) for i in range(input_size)
         })
         self.output_nodes = nn.ModuleList([
-            CognitiveNode(input_size + i, input_size) 
-            for i in range(output_size)
+            CognitiveNode(input_size + i, 1) for i in range(output_size)
         ])
         
         # Structure learning parameters
         self.connection_strength = nn.ParameterDict()
-        self.recent_activations = {}
         self.init_connections()
         
         # Emotional context
@@ -63,7 +61,7 @@ class DynamicCognitiveNet(nn.Module):
                 
             if input_acts:
                 combined = sum(input_acts) / math.sqrt(len(input_acts))
-                out_act = out_node(combined)
+                out_act = out_node(combined.unsqueeze(0))
                 outputs.append(out_act)
                 
         return torch.cat(outputs)
@@ -76,9 +74,7 @@ class DynamicCognitiveNet(nn.Module):
                 new_strength = weight + self.learning_rate * reward
             else:
                 new_strength = weight * 0.9
-            self.connection_strength[conn_id] = nn.Parameter(
-                torch.clamp(new_strength, -1, 1)
-            )
+            self.connection_strength[conn_id].data = torch.clamp(new_strength, -1, 1)
             
         # Add new connections if performance is poor
         if reward < -0.5 and torch.rand(1).item() < 0.3:
@@ -92,9 +88,9 @@ class DynamicCognitiveNet(nn.Module):
         """Create new random connection between underutilized nodes"""
         # Find least active nodes
         node_activations = {
-            node_id: torch.mean(torch.stack(list(node.recent_activations.values())))
+            node_id: sum(node.recent_activations.values()) / len(node.recent_activations)
             for node_id, node in self.nodes.items()
-            if hasattr(node, 'recent_activations') and node.recent_activations
+            if node.recent_activations
         }
         
         if not node_activations: