try to fix input dimentions

NN/models/cob_rl_model.py

@@ -250,6 +250,12 @@ class COBRLModelInterface(ModelInterface):
         
         logger.info(f"COB RL Model Interface initialized on {self.device}")
         
+    def to(self, device):
+        """PyTorch-style device movement method"""
+        self.device = device
+        self.model = self.model.to(device)
+        return self
+        
     def predict(self, cob_features: np.ndarray) -> Dict[str, Any]:
         """Make prediction using the model"""
         self.model.eval()

NN/models/dqn_agent.py

@@ -454,6 +454,13 @@ class DQNAgent:
             logger.error(f"Failed to move models to {self.device}: {str(e)}")
             return False
 
+    def to(self, device):
+        """PyTorch-style device movement method"""
+        self.device = device
+        self.policy_net = self.policy_net.to(device)
+        self.target_net = self.target_net.to(device)
+        return self
+
     def remember(self, state: np.ndarray, action: int, reward: float,
                 next_state: np.ndarray, done: bool, is_extrema: bool = False):
         """

NN/models/enhanced_cnn.py

@@ -498,10 +498,20 @@ class EnhancedCNN(nn.Module):
         """Enhanced action selection with ultra massive model predictions"""
         if explore and np.random.random() < 0.1:  # 10% random exploration
             return np.random.choice(self.n_actions)
-        
+ 
         self.eval()
-        state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
-        
+
+        # Accept both NumPy arrays and already-built torch tensors
+        if isinstance(state, torch.Tensor):
+            state_tensor = state.detach().to(self.device)
+            if state_tensor.dim() == 1:
+                state_tensor = state_tensor.unsqueeze(0)
+        else:
+            # Convert to tensor **directly on the target device** to avoid intermediate CPU copies
+            state_tensor = torch.as_tensor(state, dtype=torch.float32, device=self.device)
+            if state_tensor.dim() == 1:
+                state_tensor = state_tensor.unsqueeze(0)
+ 
         with torch.no_grad():
             q_values, extrema_pred, price_predictions, features, advanced_predictions = self(state_tensor)