integrating new CNN model

NN/models/__init__.py

@@ -11,11 +11,17 @@ This package contains the neural network models used in the trading system:
 PyTorch implementation only.
 """
 
-from NN.models.cnn_model import EnhancedCNNModel as CNNModel
-from NN.models.dqn_agent import DQNAgent
-from NN.models.cob_rl_model import MassiveRLNetwork, COBRLModelInterface
+# Import core models
+from NN.models.dqn_agent import DQNAgent, MassiveRLNetwork
+from NN.models.cob_rl_model import COBRLModelInterface
 from NN.models.advanced_transformer_trading import AdvancedTradingTransformer, TradingTransformerConfig
+from NN.models.standardized_cnn import StandardizedCNN  # Use the unified CNN model
+
+# Import model interfaces
 from NN.models.model_interfaces import ModelInterface, CNNModelInterface, RLAgentInterface, ExtremaTrainerInterface
 
-__all__ = ['CNNModel', 'DQNAgent', 'MassiveRLNetwork', 'COBRLModelInterface', 'AdvancedTradingTransformer', 'TradingTransformerConfig',
-           'ModelInterface', 'CNNModelInterface', 'RLAgentInterface', 'ExtremaTrainerInterface']
+# Export the unified StandardizedCNN as CNNModel for compatibility
+CNNModel = StandardizedCNN
+
+__all__ = ['CNNModel', 'StandardizedCNN', 'DQNAgent', 'MassiveRLNetwork', 'COBRLModelInterface', 'AdvancedTradingTransformer', 'TradingTransformerConfig',
+'ModelInterface', 'CNNModelInterface', 'RLAgentInterface', 'ExtremaTrainerInterface']

NN/models/enhanced_cnn.py

@@ -371,6 +371,10 @@ class EnhancedCNN(nn.Module):
             nn.Linear(128, 4)  # Low risk, medium risk, high risk, extreme risk
         )
     
+    def _memory_barrier(self, tensor: torch.Tensor) -> torch.Tensor:
+        """Create a memory barrier to prevent in-place operation issues"""
+        return tensor.detach().clone().requires_grad_(tensor.requires_grad)
+    
     def _check_rebuild_network(self, features):
         """Check if network needs to be rebuilt for different feature dimensions"""
         # Prevent rebuilding with zero or invalid dimensions

NN/training/integrate_checkpoint_management.py

@@ -40,7 +40,7 @@ from utils.training_integration import get_training_integration
 
 # Import training components
 from NN.models.dqn_agent import DQNAgent
-from NN.models.cnn_model import CNNModelTrainer, create_enhanced_cnn_model
+from NN.models.standardized_cnn import StandardizedCNN
 from core.extrema_trainer import ExtremaTrainer
 from core.negative_case_trainer import NegativeCaseTrainer
 from core.data_provider import DataProvider
@@ -100,18 +100,10 @@ class CheckpointIntegratedTrainingSystem:
             )
             logger.info("✅ DQN Agent initialized with checkpoint management")
             
-            # Initialize CNN Model with checkpoint management
-            logger.info("Initializing CNN Model with checkpoints...")
-            cnn_model, self.cnn_trainer = create_enhanced_cnn_model(
-                input_size=60,
-                feature_dim=50,
-                output_size=3
-            )
-            # Update trainer with checkpoint management
-            self.cnn_trainer.model_name = "integrated_cnn_model"
-            self.cnn_trainer.enable_checkpoints = True
-            self.cnn_trainer.training_integration = self.training_integration
-            logger.info("✅ CNN Model initialized with checkpoint management")
+            # Initialize StandardizedCNN Model with checkpoint management
+            logger.info("Initializing StandardizedCNN Model with checkpoints...")
+            self.cnn_model = StandardizedCNN(model_name="integrated_cnn_model")
+            logger.info("✅ StandardizedCNN Model initialized with checkpoint management")
             
             # Initialize ExtremaTrainer with checkpoint management
             logger.info("Initializing ExtremaTrainer with checkpoints...")