Update README.md

README.md

@@ -32,12 +32,15 @@ The shared ResNet torso branches into two specialized heads:
  * **Value Head (v):** Outputs a single scalar value, typically between -1.0 (Black is winning) and +1.0 (White is winning). This score represents the network's prediction of the final game outcome from the current position.
 
 ## **Training:** 
-The model is trained on small dataset from high-quality PGN of games. The model is trained on 50.000 games during 50 hours on RTX4060. This model acctually is evaluate around 1500 Elo (without MCTS). Training uses the PyTorch framework with advanced optimization techniques, including a OneCycleLR learning rate scheduler for accelerated convergence and a large batch size of 1024.
+The model is trained on small dataset from high-quality PGN of games. The model is trained on 50.000 games during 50 hours on RTX4060. This model acctually is evaluate around 1250 Elo. Training uses the PyTorch framework with advanced optimization techniques, including a OneCycleLR learning rate scheduler for accelerated convergence and a large batch size of 1024.
 
 ## **Usage:**
 
 ```python
 
+import chess
+import torch
+
 # Define Input State (FEN)
 # Example: The initial position of a game
 fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
@@ -59,7 +62,6 @@ policy_probabilities = F.softmax(policy_logits, dim=1).squeeze(0)
 
 # Find the move with the highest predicted probability
 best_action_index = torch.argmax(policy_probabilities).item()
-best_move = ACTION_TO_MOVE[best_action_index] # e.g., 'e2e4'
 best_probability = policy_probabilities[best_action_index].item()
 
 # Extract the value prediction
@@ -68,7 +70,6 @@ expected_value = value_output.item()
 # Print Results
 print(f"FEN: {fen}")
 print(f"--- Model Prediction ---")
-print(f"Predicted Best Move: {best_move}")
 print(f"Move Probability: {best_probability:.4f}")
 print(f"Position Evaluation (Value): {expected_value:.4f}")
 print("\nInterpretation: Value close to +1.0 means White is winning, -1.0 means Black is winning.")