remove dummy data, improve training , follow architecture

NN/models/enhanced_cnn_with_orderbook.py

@@ -161,7 +161,7 @@ class OrderBookEncoder(nn.Module):
         attended_features, attention_weights = self.cross_attention(combined_seq)
         
         # Flatten attended features
-        attended_flat = attended_features.view(attended_features.size(0), -1)  # [batch, 512]
+        attended_flat = attended_features.reshape(attended_features.size(0), -1)  # [batch, 512]
         
         # Combine with microstructure features
         combined_features = torch.cat([attended_flat, micro_encoded], dim=1)  # [batch, 640]
@@ -210,8 +210,7 @@ class VolumeProfileEncoder(nn.Module):
         if isinstance(volume_profile_data, list):
             if not volume_profile_data:
                 # Return zero features if no data
-                batch_size = 1
-                return torch.zeros(batch_size, self.aggregator[-1].out_features)
+                return torch.zeros(1, 256, device=torch.device('cpu'))  # Hardcoded output dim as per hidden_dim in class init
             
             # Convert to tensor
             features = []
@@ -239,7 +238,7 @@ class VolumeProfileEncoder(nn.Module):
         
         # Encode each level
         level_features = self.level_encoder(volume_tensor.view(-1, feature_dim))
-        level_features = level_features.view(batch_size, num_levels, -1)
+        level_features = level_features.reshape(batch_size, num_levels, -1)
         
         # Apply attention across levels
         attended_levels, _ = self.level_attention(level_features)
@@ -423,14 +422,14 @@ class EnhancedCNNWithOrderBook(nn.Module):
         Returns:
             Dictionary with Q-values, confidence, regime, and auxiliary predictions
         """
-        batch_size = market_data.size(0)
-        
-        # Process market data
+        # Process market data - ensure batch dimension first
         if len(market_data.shape) == 2:
             market_data = market_data.unsqueeze(0)
         
-        # Reshape for convolutional processing
-        market_reshaped = market_data.view(batch_size, -1, market_data.size(-1))
+        batch_size = market_data.size(0)  # Get correct batch size after shape adjustment
+        
+        # Reshape for convolutional processing with safe dimensions
+        market_reshaped = market_data.reshape(batch_size, -1, market_data.size(-1))
         market_features = self.market_encoder(market_reshaped.transpose(1, 2))
         
         # Process order book data
@@ -440,7 +439,7 @@ class EnhancedCNNWithOrderBook(nn.Module):
         if volume_profile_data is not None:
             volume_features = self.volume_encoder(volume_profile_data)
         else:
-            volume_features = torch.zeros(batch_size, 256, device=self.device)
+            volume_features = torch.zeros(batch_size, 256, device=market_data.device)
         
         # Fuse all features
         combined_features = torch.cat([