.

script/train.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 import torch.optim as optim
 from sklearn.preprocessing import StandardScaler
 
-MAX_MARGIN_LIMIT = 3
+MAX_MARGIN_LIMIT = 10
 
 class DifficultyNet(nn.Module):
     def __init__(self, input_dim):
@@ -83,7 +83,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
             
             # 현재 학습 데이터에 대한 오차 분석
             with torch.no_grad():
-                diff = torch.abs((preds_train * 11 + 1) - (y_train * 11 + 1))
+                diff = torch.abs((preds_train * 12 + 0.5) - (y_train * 12 + 0.5))
                 train_mae = torch.mean(diff).item()
                 train_max = torch.max(diff).item()
             
@@ -114,7 +114,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
                 val_df = df.sample(n=min(int(val_count), len(df)))
                 X_val = torch.FloatTensor(val_df[X_cols].values).to(device)
                 y_val_raw = val_df['상수'].values
-                preds = model(X_val) * 11 + 1
+                preds = model(X_val) * 12 + 0.5
                 y_val_tensor = torch.FloatTensor(y_val_raw).unsqueeze(1).to(device)
                 diff_tensor = torch.abs(preds - y_val_tensor)
                 mae = torch.mean(diff_tensor).item()
@@ -135,7 +135,7 @@ def train(script_dir, working_dir, data_dir, train_count, val_count, margin, val
                 if mae <= margin and max_error <= (margin * MAX_MARGIN_LIMIT):
                     print(f"   [Iter {i}] ✅ PASS (MAE: {mae:.4f}, MAX: {max_error:.4f})")
                 else:
-                    print(f"   [Iter {i}] ❌ FAIL (MAE: {mae:.4f} > {margin}, MAX: {max_error:.4f} > {margin * MAX_MARGIN_LIMIT})")
+                    print(f"   [Iter {i}] ❌ FAIL (MAE: {mae:.4f} > {margin} || MAX: {max_error:.4f} > {margin * MAX_MARGIN_LIMIT})")
                     all_passed = False
                     break