Update node.py

node.py

@@ -4,13 +4,13 @@ from collections import deque
 from .memory import CognitiveMemory
 
 class CognitiveNode(nn.Module):
-    """Node dengan penanganan dimensi terintegrasi"""
+    """Unit neuron dengan operasi tensor yang aman"""
     def __init__(self, node_id: int, input_size: int):
         super().__init__()
         self.id = node_id
         self.input_size = input_size
         
-        # Parameter dengan dimensi yang sesuai
+        # Parameter dengan dimensi sesuai input
         self.weights = nn.Parameter(torch.randn(input_size) * 0.1)
         self.bias = nn.Parameter(torch.zeros(1))
         self.memory = CognitiveMemory(context_size=input_size)
@@ -18,25 +18,26 @@ class CognitiveNode(nn.Module):
         # Sistem neuromodulator
         self.dopamine = nn.Parameter(torch.tensor(0.5))
         self.serotonin = nn.Parameter(torch.tensor(0.5))
-        
-        # Aktivasi terakhir
         self.recent_activations = deque(maxlen=100)
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        # Validasi dimensi input
+        inputs = inputs.view(-1)
+        
         # Integrasi memori
         mem_context = self.memory.retrieve(inputs)
         combined = inputs * 0.7 + mem_context * 0.3
         
-        # Aktivasi dengan kontrol dimensi
+        # Operasi linear yang aman
         activation = torch.tanh(torch.dot(combined, self.weights) + self.bias)
         modulated = activation * (1 + torch.sigmoid(self.dopamine) 
                                 - torch.sigmoid(self.serotonin))
         
-        # Update memori
+        # Update memori dengan scalar value
         self.memory.add_memory(inputs, modulated.item())
         self.recent_activations.append(modulated.item())
         
-        return modulated
+        return modulated.squeeze()
 
     def update_plasticity(self, reward: float):
         """Update neurotransmitter dengan clamping"""