Add early stopping, ETA display, and richer result logging

- Early stopping with configurable patience (default 50 epochs)
- Per-epoch timing and ETA estimate in training output
- Log best_epoch and hidden size to results.json entries

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Henry Barnes

common/training.py

@@ -122,13 +122,16 @@ def run_training(model, train_loader, test_loader, config):
     )
 
     n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    patience = config.get('patience', 50)  # Early stopping patience
     print(f"Parameters: {n_params:,}")
+    print(f"Early stopping patience: {patience} epochs")
 
     best_acc = 0.0
     best_epoch = 0
     start_time = time.time()
 
     for epoch in range(epochs):
+        epoch_start = time.time()
         train_loss, train_acc = train_epoch(
             model, train_loader, optimizer, device,
             augment_fn=augment_fn,
@@ -137,6 +140,7 @@ def run_training(model, train_loader, test_loader, config):
         )
         test_loss, test_acc = evaluate(model, test_loader, device)
         scheduler.step()
+        epoch_time = time.time() - epoch_start
 
         if test_acc > best_acc:
             best_acc = test_acc
@@ -149,10 +153,20 @@ def run_training(model, train_loader, test_loader, config):
             }, save_path)
 
         lr = optimizer.param_groups[0]['lr']
+        elapsed = time.time() - start_time
+        remaining = epoch_time * (epochs - epoch - 1)
+        eta_str = f"{remaining/60:.0f}m" if remaining < 3600 else f"{remaining/3600:.1f}h"
+
         print(f"Epoch {epoch+1:3d}/{epochs} | "
               f"Train: {train_loss:.4f} / {train_acc*100:.1f}% | "
               f"Test: {test_loss:.4f} / {test_acc*100:.1f}% | "
-              f"LR={lr:.2e} | Best={best_acc*100:.1f}%")
+              f"LR={lr:.2e} | Best={best_acc*100:.1f}% | "
+              f"{epoch_time:.1f}s/ep | ETA {eta_str}")
+
+        # Early stopping
+        if epoch - best_epoch >= patience:
+            print(f"\nEarly stopping: no improvement for {patience} epochs")
+            break
 
     training_time = time.time() - start_time
 
@@ -184,12 +198,15 @@ def _save_result(results_file, benchmark, config, result):
     except (FileNotFoundError, json.JSONDecodeError):
         results = []
 
+    model_config = config.get('model_config', {})
     entry = {
         'benchmark': benchmark,
         'accuracy_float': round(result['best_acc'] * 100, 2),
         'n_params': result['n_params'],
         'epochs': config.get('epochs'),
-        'neuron_type': config.get('model_config', {}).get('neuron_type', 'lif'),
+        'best_epoch': result['best_epoch'] + 1,
+        'neuron_type': model_config.get('neuron_type', 'lif'),
+        'hidden': model_config.get('hidden', model_config.get('n_hidden')),
         'date': datetime.now().strftime('%Y-%m-%d'),
         'training_time_s': result['training_time_s'],
     }