feat: Allow configurable hidden layer sizes for NNUE architecture

modules/bot/python/nnue.py

@@ -17,7 +17,7 @@ sys.path.insert(0, str(Path(__file__).parent / "src"))
 
 from generate import play_random_game_and_collect_positions
 from label import label_positions_with_stockfish
-from train import train_nnue, burst_train
+from train import train_nnue, burst_train, DEFAULT_HIDDEN_SIZES
 from export import export_to_nbai
 from tactical_positions_extractor import (
     download_and_extract_puzzle_db,
@@ -624,13 +624,22 @@ def train_interactive():
     epochs = int(Prompt.ask("Number of epochs", default="100"))
     batch_size = int(Prompt.ask("Batch size", default="16384"))
     subsample_ratio = float(Prompt.ask("Stochastic subsample ratio per epoch (1.0 = all data)", default="1.0"))
+    default_layers = ",".join(str(s) for s in DEFAULT_HIDDEN_SIZES)
+    hidden_layers_str = Prompt.ask(
+        "Hidden layer sizes (comma-separated, e.g. 1536,1024,512,256)",
+        default=default_layers
+    )
+    hidden_sizes = [int(x.strip()) for x in hidden_layers_str.split(",") if x.strip()]
     early_stopping = None
     if Confirm.ask("Enable early stopping?", default=False):
         early_stopping = int(Prompt.ask("Patience (epochs)", default="5"))
 
+    arch_str = " → ".join(str(s) for s in [768] + hidden_sizes + [1])
+
     # Confirm and start
     console.print("\n[bold]Configuration Summary:[/bold]")
     console.print(f"  Dataset: ds_v{dataset_version}")
+    console.print(f"  Architecture: {arch_str}")
     console.print(f"  Epochs: {epochs}")
     console.print(f"  Batch size: {batch_size}")
     console.print(f"  Subsample ratio: {subsample_ratio:.0%}")
@@ -665,7 +674,8 @@ def train_interactive():
             checkpoint=checkpoint,
             use_versioning=True,
             early_stopping_patience=early_stopping,
-            subsample_ratio=subsample_ratio
+            subsample_ratio=subsample_ratio,
+            hidden_sizes=hidden_sizes,
         )
         console.print("[green]✓ Training complete[/green]")
 
@@ -727,10 +737,18 @@ def burst_train_interactive():
     # Training hyperparameters
     batch_size = int(Prompt.ask("Batch size", default="16384"))
     subsample_ratio = float(Prompt.ask("Stochastic subsample ratio per epoch (1.0 = all data)", default="1.0"))
+    default_layers = ",".join(str(s) for s in DEFAULT_HIDDEN_SIZES)
+    hidden_layers_str = Prompt.ask(
+        "Hidden layer sizes (comma-separated, e.g. 1536,1024,512,256)",
+        default=default_layers
+    )
+    hidden_sizes = [int(x.strip()) for x in hidden_layers_str.split(",") if x.strip()]
+    arch_str = " → ".join(str(s) for s in [768] + hidden_sizes + [1])
 
     # Summary
     console.print("\n[bold]Configuration Summary:[/bold]")
     console.print(f"  Dataset:             ds_v{dataset_version}")
+    console.print(f"  Architecture:        {arch_str}")
     console.print(f"  Duration:            {duration_minutes:.0f} minutes")
     console.print(f"  Epochs per season:   {epochs_per_season}")
     console.print(f"  Patience:            {early_stopping_patience}")
@@ -757,6 +775,7 @@ def burst_train_interactive():
             initial_checkpoint=checkpoint,
             use_versioning=True,
             subsample_ratio=subsample_ratio,
+            hidden_sizes=hidden_sizes,
         )
         console.print("[green]✓ Burst training complete[/green]")