Adjust safety margin in dataset shape analysis to account for data augmentation effects

model_training_nnn_tpu/dataset_tf.py

@@ -934,15 +934,15 @@ def analyze_dataset_shapes(dataset_tf: BrainToTextDatasetTF, sample_size: int =
         'n_features': dataset_tf.feature_dim
     }
 
-    # 5. 添加适当的安全边际 - 基于分析范围调整
+    # 5. 添加适当的安全边际 - 基于分析范围和数据增强调整
     if sample_size == -1:
-        # 全数据分析：只需要很小的边际应对可能的舍入误差
-        safety_margin = 1.02  # 2% buffer for rounding errors
-        margin_reason = "minimal buffer for full dataset analysis"
+        # 全数据分析：需要为数据增强预留空间（特别是Gaussian平滑）
+        safety_margin = 1.15  # 15% buffer for data augmentation effects
+        margin_reason = "buffer for full dataset analysis + data augmentation"
     else:
-        # 采样分析：需要更大的边际应对未采样到的极值
-        safety_margin = 1.3   # 30% buffer for sampling uncertainty
-        margin_reason = f"larger buffer due to sampling only {sample_size} trials"
+        # 采样分析：需要更大的边际应对未采样到的极值 + 数据增强
+        safety_margin = 1.35   # 35% buffer for sampling uncertainty + data augmentation
+        margin_reason = f"larger buffer due to sampling only {sample_size} trials + data augmentation"
 
     final_max_shapes = {
         'max_time_steps': int(original_max_shapes['max_time_steps'] * safety_margin),