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TSRD_rram_classification

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  1. .gitattributes +43 -35
  2. checkpoints_v2m_part1/2025-04-11_15-04-17_train.log +743 -0
  3. checkpoints_v2m_part1/base_training_metrics.csv +51 -0
  4. checkpoints_v2m_part1/best_model.pth +3 -0
  5. checkpoints_v2m_part1/fine_tuned_model.pth +3 -0
  6. checkpoints_v2m_part1/fine_tuning_metrics.csv +51 -0
  7. checkpoints_v2m_part1/last_checkpoint.pth +3 -0
  8. checkpoints_v2m_part1/parse_log.py +62 -0
  9. checkpoints_v2m_part1/rram_mapped_model.pth +3 -0
  10. checkpoints_v2m_part1/training_plot.png +0 -0
  11. checkpoints_v2m_part1/visualizations/base_weights_heatmap.png +3 -0
  12. checkpoints_v2m_part1/visualizations/fine_tuned_weights_heatmap.png +3 -0
  13. checkpoints_v2m_part1/visualizations/mapping_error_distribution.png +3 -0
  14. checkpoints_v2m_part1/visualizations/weight_changes_heatmap.png +3 -0
  15. checkpoints_v2m_part2/2025-04-11_14-13-49_train.log +730 -0
  16. checkpoints_v2m_part2/base_training_metrics.csv +51 -0
  17. checkpoints_v2m_part2/best_model.pth +3 -0
  18. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_1.pth +3 -0
  19. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_10.pth +3 -0
  20. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_11.pth +3 -0
  21. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_12.pth +3 -0
  22. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_13.pth +3 -0
  23. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_14.pth +3 -0
  24. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_15.pth +3 -0
  25. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_16.pth +3 -0
  26. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_17.pth +3 -0
  27. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_18.pth +3 -0
  28. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_19.pth +3 -0
  29. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_2.pth +3 -0
  30. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_20.pth +3 -0
  31. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_21.pth +3 -0
  32. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_22.pth +3 -0
  33. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_23.pth +3 -0
  34. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_24.pth +3 -0
  35. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_25.pth +3 -0
  36. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_26.pth +3 -0
  37. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_27.pth +3 -0
  38. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_28.pth +3 -0
  39. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_29.pth +3 -0
  40. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_3.pth +3 -0
  41. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_30.pth +3 -0
  42. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_31.pth +3 -0
  43. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_32.pth +3 -0
  44. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_33.pth +3 -0
  45. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_34.pth +3 -0
  46. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_35.pth +3 -0
  47. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_36.pth +3 -0
  48. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_37.pth +3 -0
  49. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_38.pth +3 -0
  50. checkpoints_v2m_part2/fine_tune_checkpoints/fine_tuned_model_epoch_39.pth +3 -0
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checkpoints_v2m_part1/2025-04-11_15-04-17_train.log ADDED
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+ [2025-04-11 15:04:17] [INFO] 使用设备: cuda:0
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+ [2025-04-11 15:04:17] [INFO] 训练集注释文件: /data0/work/DuYiFan/projects/traffic_classify/full_classes/TsignRecgTrainAnnotation.txt
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+ [2025-04-11 15:04:17] [INFO] 测试集注释文件: /data0/work/DuYiFan/projects/traffic_classify/full_classes/TsignRecgTestAnnotation.txt
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+ [2025-04-11 15:04:17] [INFO] 训练图像目录: /data0/work/DuYiFan/projects/traffic_classify/full_classes/train
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+ [2025-04-11 15:04:17] [INFO] 测试图像目录: /data0/work/DuYiFan/projects/traffic_classify/full_classes/test
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+ [2025-04-11 15:04:17] [INFO] 创建数据集和数据加载器
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+ [2025-04-11 15:04:17] [INFO] 创建efficientnet-v2-m模型，类别数: 58
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+ [2025-04-11 15:04:19] [INFO] 设置损失函数、优化器和学习率调度器，初始学习率: 0.0001
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+ [2025-04-11 15:04:19] [INFO] 开始训练，总共 50 轮
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+ [2025-04-11 15:04:19] [INFO] 当前学习率: 0.000100
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+ [2025-04-11 15:04:19] [INFO] Epoch 1/50 开始训练
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+ [2025-04-11 15:04:38] [INFO] Epoch 1/50 开始验证
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+ [2025-04-11 15:04:40] [INFO] Epoch 1/50 - Train Loss: 3.5120, Train Acc: 0.1151, Val Loss: 3.4758, Val Acc: 0.1424
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+ [2025-04-11 15:04:41] [INFO] 已保存最佳模型，准确率: 0.1424
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+ [2025-04-11 15:04:41] [INFO] 当前学习率: 0.000100
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+ [2025-04-11 15:04:42] [INFO] Epoch 2/50 开始训练
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+ [2025-04-11 15:05:00] [INFO] Epoch 2/50 开始验证
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+ [2025-04-11 15:05:02] [INFO] Epoch 2/50 - Train Loss: 2.7881, Train Acc: 0.2928, Val Loss: 2.7346, Val Acc: 0.3210
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+ [2025-04-11 15:05:03] [INFO] 已保存最佳模型，准确率: 0.3210
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+ [2025-04-11 15:05:04] [INFO] 当前学习率: 0.000100
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+ [2025-04-11 15:05:04] [INFO] Epoch 3/50 开始训练
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+ [2025-04-11 15:05:22] [INFO] Epoch 3/50 开始验证
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+ [2025-04-11 15:05:25] [INFO] Epoch 3/50 - Train Loss: 1.9895, Train Acc: 0.5070, Val Loss: 1.8575, Val Acc: 0.4975
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+ [2025-04-11 15:05:25] [INFO] 已保存最佳模型，准确率: 0.4975
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+ [2025-04-11 15:05:26] [INFO] 当前学习率: 0.000099
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+ [2025-04-11 15:05:26] [INFO] Epoch 4/50 开始训练
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+ [2025-04-11 15:05:44] [INFO] Epoch 4/50 开始验证
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+ [2025-04-11 15:05:47] [INFO] Epoch 4/50 - Train Loss: 1.5039, Train Acc: 0.6036, Val Loss: 1.4558, Val Acc: 0.5647
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+ [2025-04-11 15:05:47] [INFO] 已保存最佳模型，准确率: 0.5647
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+ [2025-04-11 15:05:48] [INFO] 当前学习率: 0.000098
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+ [2025-04-11 15:05:48] [INFO] Epoch 5/50 开始训练
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+ [2025-04-11 15:06:07] [INFO] Epoch 5/50 开始验证
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+ [2025-04-11 15:06:09] [INFO] Epoch 5/50 - Train Loss: 1.1698, Train Acc: 0.6796, Val Loss: 1.2661, Val Acc: 0.6800
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+ [2025-04-11 15:06:10] [INFO] 已保存最佳模型，准确率: 0.6800
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+ [2025-04-11 15:06:11] [INFO] 当前学习率: 0.000098
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+ [2025-04-11 15:06:11] [INFO] Epoch 6/50 开始训练
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+ [2025-04-11 15:06:29] [INFO] Epoch 6/50 开始验证
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+ [2025-04-11 15:06:31] [INFO] Epoch 6/50 - Train Loss: 0.9343, Train Acc: 0.7590, Val Loss: 1.0889, Val Acc: 0.7272
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+ [2025-04-11 15:06:32] [INFO] 已保存最佳模型，准确率: 0.7272
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+ [2025-04-11 15:06:33] [INFO] 当前学习率: 0.000097
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+ [2025-04-11 15:06:33] [INFO] Epoch 7/50 开始训练
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+ [2025-04-11 15:06:51] [INFO] Epoch 7/50 开始验证
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+ [2025-04-11 15:06:54] [INFO] Epoch 7/50 - Train Loss: 0.8004, Train Acc: 0.8151, Val Loss: 0.9226, Val Acc: 0.7773
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+ [2025-04-11 15:06:54] [INFO] 已保存最佳模型，准确率: 0.7773
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+ [2025-04-11 15:06:55] [INFO] 当前学习率: 0.000095
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+ [2025-04-11 15:06:55] [INFO] Epoch 8/50 开始训练
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+ [2025-04-11 15:07:14] [INFO] Epoch 8/50 开始验证
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+ [2025-04-11 15:07:16] [INFO] Epoch 8/50 - Train Loss: 0.6381, Train Acc: 0.8717, Val Loss: 0.8111, Val Acc: 0.7934
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+ [2025-04-11 15:07:16] [INFO] 已保存最佳模型，准确率: 0.7934
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+ [2025-04-11 15:07:17] [INFO] 当前学习率: 0.000094
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+ [2025-04-11 15:07:17] [INFO] Epoch 9/50 开始训练
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+ [2025-04-11 15:07:36] [INFO] Epoch 9/50 开始验证
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+ [2025-04-11 15:07:39] [INFO] Epoch 9/50 - Train Loss: 0.5269, Train Acc: 0.8974, Val Loss: 0.6490, Val Acc: 0.8355
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+ [2025-04-11 15:07:39] [INFO] 已保存最佳模型，准确率: 0.8355
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+ [2025-04-11 15:07:40] [INFO] 当前学习率: 0.000092
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+ [2025-04-11 15:07:40] [INFO] Epoch 10/50 开始训练
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+ [2025-04-11 15:07:59] [INFO] Epoch 10/50 开始验证
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+ [2025-04-11 15:08:01] [INFO] Epoch 10/50 - Train Loss: 0.3630, Train Acc: 0.9374, Val Loss: 0.5186, Val Acc: 0.8786
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+ [2025-04-11 15:08:01] [INFO] 已保存最佳模型，准确率: 0.8786
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+ [2025-04-11 15:08:02] [INFO] 当前学习率: 0.000091
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+ [2025-04-11 15:08:02] [INFO] Epoch 11/50 开始训练
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+ [2025-04-11 15:08:21] [INFO] Epoch 11/50 开始验证
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+ [2025-04-11 15:08:23] [INFO] Epoch 11/50 - Train Loss: 0.2655, Train Acc: 0.9508, Val Loss: 0.4091, Val Acc: 0.9017
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+ [2025-04-11 15:08:24] [INFO] 已保存最佳模型，准确率: 0.9017
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+ [2025-04-11 15:08:25] [INFO] 当前学习率: 0.000089
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+ [2025-04-11 15:08:25] [INFO] Epoch 12/50 开始训练
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+ [2025-04-11 15:08:43] [INFO] Epoch 12/50 开始验证
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+ [2025-04-11 15:08:46] [INFO] Epoch 12/50 - Train Loss: 0.2500, Train Acc: 0.9549, Val Loss: 0.5203, Val Acc: 0.8626
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+ [2025-04-11 15:08:47] [INFO] 当前学习率: 0.000087
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+ [2025-04-11 15:08:47] [INFO] Epoch 13/50 开始训练
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+ [2025-04-11 15:09:05] [INFO] Epoch 13/50 开始验证
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+ [2025-04-11 15:09:08] [INFO] Epoch 13/50 - Train Loss: 0.2335, Train Acc: 0.9530, Val Loss: 0.4292, Val Acc: 0.8877
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+ [2025-04-11 15:09:09] [INFO] 当前学习率: 0.000084
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+ [2025-04-11 15:09:09] [INFO] Epoch 14/50 开始训练
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+ [2025-04-11 15:09:28] [INFO] Epoch 14/50 开始验证
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+ [2025-04-11 15:09:30] [INFO] Epoch 14/50 - Train Loss: 0.1710, Train Acc: 0.9657, Val Loss: 0.3083, Val Acc: 0.9228
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+ [2025-04-11 15:09:30] [INFO] 已保存最佳模型，准确率: 0.9228
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+ [2025-04-11 15:09:31] [INFO] 当前学习率: 0.000082
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+ [2025-04-11 15:09:31] [INFO] Epoch 15/50 开始训练
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+ [2025-04-11 15:09:50] [INFO] Epoch 15/50 开始验证
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+ [2025-04-11 15:09:52] [INFO] Epoch 15/50 - Train Loss: 0.1300, Train Acc: 0.9758, Val Loss: 0.3492, Val Acc: 0.9107
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+ [2025-04-11 15:09:53] [INFO] 当前学习率: 0.000080
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+ [2025-04-11 15:09:53] [INFO] Epoch 16/50 开始训练
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+ [2025-04-11 15:10:12] [INFO] Epoch 16/50 开始验证
85
+ [2025-04-11 15:10:14] [INFO] Epoch 16/50 - Train Loss: 0.1371, Train Acc: 0.9729, Val Loss: 0.4028, Val Acc: 0.9047
86
+ [2025-04-11 15:10:15] [INFO] 当前学习率: 0.000077
87
+ [2025-04-11 15:10:15] [INFO] Epoch 17/50 开始训练
88
+ [2025-04-11 15:10:34] [INFO] Epoch 17/50 开始验证
89
+ [2025-04-11 15:10:36] [INFO] Epoch 17/50 - Train Loss: 0.1029, Train Acc: 0.9796, Val Loss: 0.2834, Val Acc: 0.9268
90
+ [2025-04-11 15:10:37] [INFO] 已保存最佳模型，准确率: 0.9268
91
+ [2025-04-11 15:10:38] [INFO] 当前学习率: 0.000074
92
+ [2025-04-11 15:10:38] [INFO] Epoch 18/50 开始训练
93
+ [2025-04-11 15:10:56] [INFO] Epoch 18/50 开始验证
94
+ [2025-04-11 15:10:58] [INFO] Epoch 18/50 - Train Loss: 0.1109, Train Acc: 0.9777, Val Loss: 0.2368, Val Acc: 0.9268
95
+ [2025-04-11 15:10:59] [INFO] 当前学习率: 0.000072
96
+ [2025-04-11 15:10:59] [INFO] Epoch 19/50 开始训练
97
+ [2025-04-11 15:11:18] [INFO] Epoch 19/50 开始验证
98
+ [2025-04-11 15:11:20] [INFO] Epoch 19/50 - Train Loss: 0.1003, Train Acc: 0.9770, Val Loss: 0.2490, Val Acc: 0.9248
99
+ [2025-04-11 15:11:21] [INFO] 当前学习率: 0.000069
100
+ [2025-04-11 15:11:21] [INFO] Epoch 20/50 开始训练
101
+ [2025-04-11 15:11:40] [INFO] Epoch 20/50 开始验证
102
+ [2025-04-11 15:11:42] [INFO] Epoch 20/50 - Train Loss: 0.0969, Train Acc: 0.9784, Val Loss: 0.2582, Val Acc: 0.9298
103
+ [2025-04-11 15:11:43] [INFO] 已保存最佳模型，准确率: 0.9298
104
+ [2025-04-11 15:11:44] [INFO] 当前学习率: 0.000066
105
+ [2025-04-11 15:11:44] [INFO] Epoch 21/50 开始训练
106
+ [2025-04-11 15:12:02] [INFO] Epoch 21/50 开始验证
107
+ [2025-04-11 15:12:05] [INFO] Epoch 21/50 - Train Loss: 0.0661, Train Acc: 0.9847, Val Loss: 0.2623, Val Acc: 0.9408
108
+ [2025-04-11 15:12:05] [INFO] 已保存最佳模型，准确率: 0.9408
109
+ [2025-04-11 15:12:06] [INFO] 当前学习率: 0.000063
110
+ [2025-04-11 15:12:06] [INFO] Epoch 22/50 开始训练
111
+ [2025-04-11 15:12:25] [INFO] Epoch 22/50 开始验证
112
+ [2025-04-11 15:12:27] [INFO] Epoch 22/50 - Train Loss: 0.0559, Train Acc: 0.9849, Val Loss: 0.2204, Val Acc: 0.9488
113
+ [2025-04-11 15:12:27] [INFO] 已保存最佳模型，准确率: 0.9488
114
+ [2025-04-11 15:12:28] [INFO] 当前学习率: 0.000060
115
+ [2025-04-11 15:12:28] [INFO] Epoch 23/50 开始训练
116
+ [2025-04-11 15:12:47] [INFO] Epoch 23/50 开始验证
117
+ [2025-04-11 15:12:49] [INFO] Epoch 23/50 - Train Loss: 0.0489, Train Acc: 0.9880, Val Loss: 0.2380, Val Acc: 0.9408
118
+ [2025-04-11 15:12:50] [INFO] 当前学习率: 0.000057
119
+ [2025-04-11 15:12:50] [INFO] Epoch 24/50 开始训练
120
+ [2025-04-11 15:13:09] [INFO] Epoch 24/50 开始验证
121
+ [2025-04-11 15:13:11] [INFO] Epoch 24/50 - Train Loss: 0.0533, Train Acc: 0.9892, Val Loss: 0.3553, Val Acc: 0.8927
122
+ [2025-04-11 15:13:12] [INFO] 当前学习率: 0.000054
123
+ [2025-04-11 15:13:12] [INFO] Epoch 25/50 开始训练
124
+ [2025-04-11 15:13:31] [INFO] Epoch 25/50 开始验证
125
+ [2025-04-11 15:13:33] [INFO] Epoch 25/50 - Train Loss: 0.0507, Train Acc: 0.9880, Val Loss: 0.2503, Val Acc: 0.9218
126
+ [2025-04-11 15:13:34] [INFO] 当前学习率: 0.000050
127
+ [2025-04-11 15:13:34] [INFO] Epoch 26/50 开始训练
128
+ [2025-04-11 15:13:52] [INFO] Epoch 26/50 开始验证
129
+ [2025-04-11 15:13:55] [INFO] Epoch 26/50 - Train Loss: 0.0491, Train Acc: 0.9902, Val Loss: 0.2637, Val Acc: 0.9188
130
+ [2025-04-11 15:13:56] [INFO] 当前学习率: 0.000047
131
+ [2025-04-11 15:13:56] [INFO] Epoch 27/50 开始训练
132
+ [2025-04-11 15:14:14] [INFO] Epoch 27/50 开始验证
133
+ [2025-04-11 15:14:17] [INFO] Epoch 27/50 - Train Loss: 0.0369, Train Acc: 0.9904, Val Loss: 0.2795, Val Acc: 0.9127
134
+ [2025-04-11 15:14:18] [INFO] 当前学习率: 0.000044
135
+ [2025-04-11 15:14:18] [INFO] Epoch 28/50 开始训练
136
+ [2025-04-11 15:14:36] [INFO] Epoch 28/50 开始验证
137
+ [2025-04-11 15:14:38] [INFO] Epoch 28/50 - Train Loss: 0.0289, Train Acc: 0.9959, Val Loss: 0.3164, Val Acc: 0.9107
138
+ [2025-04-11 15:14:39] [INFO] 当前学习率: 0.000041
139
+ [2025-04-11 15:14:39] [INFO] Epoch 29/50 开始训练
140
+ [2025-04-11 15:14:57] [INFO] Epoch 29/50 开始验证
141
+ [2025-04-11 15:14:59] [INFO] Epoch 29/50 - Train Loss: 0.0331, Train Acc: 0.9935, Val Loss: 0.3443, Val Acc: 0.9087
142
+ [2025-04-11 15:15:00] [INFO] 当前学习率: 0.000038
143
+ [2025-04-11 15:15:00] [INFO] Epoch 30/50 开始训练
144
+ [2025-04-11 15:15:19] [INFO] Epoch 30/50 开始验证
145
+ [2025-04-11 15:15:21] [INFO] Epoch 30/50 - Train Loss: 0.0241, Train Acc: 0.9971, Val Loss: 0.2360, Val Acc: 0.9448
146
+ [2025-04-11 15:15:22] [INFO] 当前学习率: 0.000035
147
+ [2025-04-11 15:15:22] [INFO] Epoch 31/50 开始训练
148
+ [2025-04-11 15:15:41] [INFO] Epoch 31/50 开始验证
149
+ [2025-04-11 15:15:43] [INFO] Epoch 31/50 - Train Loss: 0.0229, Train Acc: 0.9966, Val Loss: 0.2604, Val Acc: 0.9358
150
+ [2025-04-11 15:15:44] [INFO] 当前学习率: 0.000032
151
+ [2025-04-11 15:15:44] [INFO] Epoch 32/50 开始训练
152
+ [2025-04-11 15:16:03] [INFO] Epoch 32/50 开始验证
153
+ [2025-04-11 15:16:05] [INFO] Epoch 32/50 - Train Loss: 0.0340, Train Acc: 0.9940, Val Loss: 0.2789, Val Acc: 0.9258
154
+ [2025-04-11 15:16:06] [INFO] 当前学习率: 0.000029
155
+ [2025-04-11 15:16:06] [INFO] Epoch 33/50 开始训练
156
+ [2025-04-11 15:16:25] [INFO] Epoch 33/50 开始验证
157
+ [2025-04-11 15:16:27] [INFO] Epoch 33/50 - Train Loss: 0.0209, Train Acc: 0.9978, Val Loss: 0.3414, Val Acc: 0.9198
158
+ [2025-04-11 15:16:28] [INFO] 当前学习率: 0.000027
159
+ [2025-04-11 15:16:28] [INFO] Epoch 34/50 开始训练
160
+ [2025-04-11 15:16:46] [INFO] Epoch 34/50 开始验证
161
+ [2025-04-11 15:16:49] [INFO] Epoch 34/50 - Train Loss: 0.0187, Train Acc: 0.9983, Val Loss: 0.4294, Val Acc: 0.9067
162
+ [2025-04-11 15:16:50] [INFO] 当前学习率: 0.000024
163
+ [2025-04-11 15:16:50] [INFO] Epoch 35/50 开始训练
164
+ [2025-04-11 15:17:08] [INFO] Epoch 35/50 开始验证
165
+ [2025-04-11 15:17:11] [INFO] Epoch 35/50 - Train Loss: 0.0186, Train Acc: 0.9978, Val Loss: 0.3243, Val Acc: 0.9388
166
+ [2025-04-11 15:17:12] [INFO] 当前学习率: 0.000021
167
+ [2025-04-11 15:17:12] [INFO] Epoch 36/50 开始训练
168
+ [2025-04-11 15:17:30] [INFO] Epoch 36/50 开始验证
169
+ [2025-04-11 15:17:32] [INFO] Epoch 36/50 - Train Loss: 0.0122, Train Acc: 0.9993, Val Loss: 0.3431, Val Acc: 0.9278
170
+ [2025-04-11 15:17:33] [INFO] 当前学习率: 0.000019
171
+ [2025-04-11 15:17:33] [INFO] Epoch 37/50 开始训练
172
+ [2025-04-11 15:17:52] [INFO] Epoch 37/50 开始验证
173
+ [2025-04-11 15:17:54] [INFO] Epoch 37/50 - Train Loss: 0.0124, Train Acc: 0.9993, Val Loss: 0.3521, Val Acc: 0.9248
174
+ [2025-04-11 15:17:55] [INFO] 当前学习率: 0.000017
175
+ [2025-04-11 15:17:56] [INFO] Epoch 38/50 开始训练
176
+ [2025-04-11 15:18:14] [INFO] Epoch 38/50 开始验证
177
+ [2025-04-11 15:18:16] [INFO] Epoch 38/50 - Train Loss: 0.0160, Train Acc: 0.9981, Val Loss: 0.3614, Val Acc: 0.9298
178
+ [2025-04-11 15:18:17] [INFO] 当前学习率: 0.000014
179
+ [2025-04-11 15:18:17] [INFO] Epoch 39/50 开始训练
180
+ [2025-04-11 15:18:36] [INFO] Epoch 39/50 开始验证
181
+ [2025-04-11 15:18:38] [INFO] Epoch 39/50 - Train Loss: 0.0139, Train Acc: 0.9986, Val Loss: 0.4236, Val Acc: 0.9107
182
+ [2025-04-11 15:18:39] [INFO] 当前学习率: 0.000012
183
+ [2025-04-11 15:18:39] [INFO] Epoch 40/50 开始训练
184
+ [2025-04-11 15:18:57] [INFO] Epoch 40/50 开始验证
185
+ [2025-04-11 15:19:00] [INFO] Epoch 40/50 - Train Loss: 0.0116, Train Acc: 0.9998, Val Loss: 0.4263, Val Acc: 0.9127
186
+ [2025-04-11 15:19:01] [INFO] 当前学习率: 0.000010
187
+ [2025-04-11 15:19:01] [INFO] Epoch 41/50 开始训练
188
+ [2025-04-11 15:19:19] [INFO] Epoch 41/50 开始验证
189
+ [2025-04-11 15:19:22] [INFO] Epoch 41/50 - Train Loss: 0.0141, Train Acc: 0.9990, Val Loss: 0.4753, Val Acc: 0.9027
190
+ [2025-04-11 15:19:23] [INFO] 当前学习率: 0.000009
191
+ [2025-04-11 15:19:23] [INFO] Epoch 42/50 开始训练
192
+ [2025-04-11 15:19:41] [INFO] Epoch 42/50 开始验证
193
+ [2025-04-11 15:19:44] [INFO] Epoch 42/50 - Train Loss: 0.0128, Train Acc: 0.9995, Val Loss: 0.7317, Val Acc: 0.8857
194
+ [2025-04-11 15:19:45] [INFO] 当前学习率: 0.000007
195
+ [2025-04-11 15:19:45] [INFO] Epoch 43/50 开始训练
196
+ [2025-04-11 15:20:03] [INFO] Epoch 43/50 开始验证
197
+ [2025-04-11 15:20:06] [INFO] Epoch 43/50 - Train Loss: 0.0119, Train Acc: 0.9993, Val Loss: 0.7612, Val Acc: 0.8826
198
+ [2025-04-11 15:20:07] [INFO] 当前学习率: 0.000006
199
+ [2025-04-11 15:20:07] [INFO] Epoch 44/50 开始训练
200
+ [2025-04-11 15:20:25] [INFO] Epoch 44/50 开始验证
201
+ [2025-04-11 15:20:28] [INFO] Epoch 44/50 - Train Loss: 0.0101, Train Acc: 0.9995, Val Loss: 0.4204, Val Acc: 0.9107
202
+ [2025-04-11 15:20:29] [INFO] 当前学习率: 0.000004
203
+ [2025-04-11 15:20:29] [INFO] Epoch 45/50 开始训练
204
+ [2025-04-11 15:20:47] [INFO] Epoch 45/50 开始验证
205
+ [2025-04-11 15:20:49] [INFO] Epoch 45/50 - Train Loss: 0.0126, Train Acc: 1.0000, Val Loss: 0.4008, Val Acc: 0.9137
206
+ [2025-04-11 15:20:50] [INFO] 当前学习率: 0.000003
207
+ [2025-04-11 15:20:50] [INFO] Epoch 46/50 开始训练
208
+ [2025-04-11 15:21:09] [INFO] Epoch 46/50 开始验证
209
+ [2025-04-11 15:21:11] [INFO] Epoch 46/50 - Train Loss: 0.0120, Train Acc: 0.9988, Val Loss: 0.4120, Val Acc: 0.9178
210
+ [2025-04-11 15:21:12] [INFO] 当前学习率: 0.000003
211
+ [2025-04-11 15:21:12] [INFO] Epoch 47/50 开始训练
212
+ [2025-04-11 15:21:31] [INFO] Epoch 47/50 开始验证
213
+ [2025-04-11 15:21:33] [INFO] Epoch 47/50 - Train Loss: 0.0088, Train Acc: 1.0000, Val Loss: 0.4181, Val Acc: 0.9157
214
+ [2025-04-11 15:21:34] [INFO] 当前学习率: 0.000002
215
+ [2025-04-11 15:21:34] [INFO] Epoch 48/50 开始训练
216
+ [2025-04-11 15:21:53] [INFO] Epoch 48/50 开始验证
217
+ [2025-04-11 15:21:55] [INFO] Epoch 48/50 - Train Loss: 0.0114, Train Acc: 0.9995, Val Loss: 0.4700, Val Acc: 0.9057
218
+ [2025-04-11 15:21:56] [INFO] 当前学习率: 0.000001
219
+ [2025-04-11 15:21:56] [INFO] Epoch 49/50 开始训练
220
+ [2025-04-11 15:22:15] [INFO] Epoch 49/50 开始验证
221
+ [2025-04-11 15:22:17] [INFO] Epoch 49/50 - Train Loss: 0.0107, Train Acc: 0.9993, Val Loss: 0.4309, Val Acc: 0.9137
222
+ [2025-04-11 15:22:18] [INFO] 当前学习率: 0.000001
223
+ [2025-04-11 15:22:18] [INFO] Epoch 50/50 开始训练
224
+ [2025-04-11 15:22:37] [INFO] Epoch 50/50 开始验证
225
+ [2025-04-11 15:22:39] [INFO] Epoch 50/50 - Train Loss: 0.0122, Train Acc: 0.9990, Val Loss: 0.4090, Val Acc: 0.9087
226
+ [2025-04-11 15:22:40] [INFO] 绘制训练过程图表
227
+ [2025-04-11 15:22:41] [INFO] 标准训练完成!
228
+ [2025-04-11 15:22:41] [INFO] 评估原始模型性能...
229
+ [2025-04-11 15:22:43] [INFO] 评估结果 - Loss: 0.4090, Accuracy: 0.9087
230
+ [2025-04-11 15:22:43] [INFO] 开始执行RRAM映射...
231
+ [2025-04-11 15:22:43] [INFO] 加载了 100 个RRAM电导值
232
+ [2025-04-11 15:22:43] [INFO] features.0.0.weight 的平均映射误差: 0.018905
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+ [2025-04-11 15:22:43] [INFO] features.0.1.weight 的平均映射误差: 0.031780
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+ [2025-04-11 15:22:43] [INFO] features.1.0.block.0.0.weight 的平均映射误差: 0.005872
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+ [2025-04-11 15:22:43] [INFO] features.1.0.block.0.1.weight 的平均映射误差: 0.033922
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+ [2025-04-11 15:22:43] [INFO] features.1.1.block.0.0.weight 的平均映射误差: 0.004029
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+ [2025-04-11 15:22:43] [INFO] features.1.1.block.0.1.weight 的平均映射误差: 0.032434
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+ [2025-04-11 15:22:43] [INFO] features.1.2.block.0.0.weight 的平均映射误差: 0.003632
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+ [2025-04-11 15:22:43] [INFO] features.1.2.block.0.1.weight 的平均映射误差: 0.033747
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+ [2025-04-11 15:22:43] [INFO] features.2.0.block.0.0.weight 的平均映射误差: 0.003266
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+ [2025-04-11 15:22:43] [INFO] features.2.0.block.0.1.weight 的平均映射误差: 0.032917
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+ [2025-04-11 15:22:43] [INFO] features.2.0.block.1.0.weight 的平均映射误差: 0.006467
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+ [2025-04-11 15:22:43] [INFO] features.2.0.block.1.1.weight 的平均映射误差: 0.033586
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+ [2025-04-11 15:22:43] [INFO] features.2.1.block.0.0.weight 的平均映射误差: 0.001786
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+ [2025-04-11 15:22:43] [INFO] features.2.1.block.0.1.weight 的平均映射误差: 0.033159
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+ [2025-04-11 15:22:43] [INFO] features.2.1.block.1.0.weight 的平均映射误差: 0.003039
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+ [2025-04-11 15:22:43] [INFO] features.2.1.block.1.1.weight 的平均映射误差: 0.034852
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+ [2025-04-11 15:22:43] [INFO] features.2.2.block.0.0.weight 的平均映射误差: 0.001770
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+ [2025-04-11 15:22:43] [INFO] features.2.2.block.0.1.weight 的平均映射误差: 0.033568
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+ [2025-04-11 15:22:43] [INFO] features.2.2.block.1.0.weight 的平均映射误差: 0.002761
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+ [2025-04-11 15:22:43] [INFO] features.2.2.block.1.1.weight 的平均映射误差: 0.032742
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+ [2025-04-11 15:22:43] [INFO] features.2.3.block.0.0.weight 的平均映射误差: 0.001789
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+ [2025-04-11 15:22:43] [INFO] features.2.3.block.0.1.weight 的平均映射误差: 0.034785
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+ [2025-04-11 15:22:43] [INFO] features.2.3.block.1.0.weight 的平均映射误差: 0.002686
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+ [2025-04-11 15:22:43] [INFO] features.2.3.block.1.1.weight 的平均映射误差: 0.031939
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+ [2025-04-11 15:22:43] [INFO] features.2.4.block.0.0.weight 的平均映射误差: 0.001811
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+ [2025-04-11 15:22:43] [INFO] features.2.4.block.0.1.weight 的平均映射误差: 0.037460
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+ [2025-04-11 15:22:43] [INFO] features.2.4.block.1.0.weight 的平均映射误差: 0.002625
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+ [2025-04-11 15:22:43] [INFO] features.2.4.block.1.1.weight 的平均映射误差: 0.034390
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+ [2025-04-11 15:22:43] [INFO] features.3.0.block.0.0.weight 的平均映射误差: 0.002088
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+ [2025-04-11 15:22:43] [INFO] features.3.0.block.0.1.weight 的平均映射误差: 0.032756
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+ [2025-04-11 15:22:43] [INFO] features.3.0.block.1.0.weight 的平均映射误差: 0.003913
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+ [2025-04-11 15:22:43] [INFO] features.3.0.block.1.1.weight 的平均映射误差: 0.034175
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+ [2025-04-11 15:22:43] [INFO] features.3.1.block.0.0.weight 的平均映射误差: 0.001622
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+ [2025-04-11 15:22:43] [INFO] features.3.1.block.0.1.weight 的平均映射误差: 0.036317
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+ [2025-04-11 15:22:43] [INFO] features.3.1.block.1.0.weight 的平均映射误差: 0.002014
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+ [2025-04-11 15:22:43] [INFO] features.3.1.block.1.1.weight 的平均映射误差: 0.034580
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+ [2025-04-11 15:22:43] [INFO] features.3.2.block.0.0.weight 的平均映射误差: 0.001615
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+ [2025-04-11 15:22:43] [INFO] features.3.2.block.0.1.weight 的平均映射误差: 0.045919
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+ [2025-04-11 15:22:43] [INFO] features.3.2.block.1.0.weight 的平均映射误差: 0.001930
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+ [2025-04-11 15:22:43] [INFO] features.3.2.block.1.1.weight 的平均映射误差: 0.032456
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+ [2025-04-11 15:22:43] [INFO] features.3.3.block.0.0.weight 的平均映射误差: 0.001619
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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361
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362
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363
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364
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369
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370
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371
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372
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373
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377
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379
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380
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381
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383
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384
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385
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387
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388
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389
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390
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391
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392
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393
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395
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396
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397
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398
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399
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400
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401
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402
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403
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405
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408
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410
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411
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412
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413
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420
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421
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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493
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495
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499
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500
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501
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502
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532
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533
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534
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535
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536
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539
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540
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542
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548
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+ [2025-04-11 15:22:43] [INFO] features.6.16.block.0.0.weight 的平均映射误差: 0.001568
577
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.0.1.weight 的平均映射误差: 0.041166
578
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.1.0.weight 的平均映射误差: 0.001717
579
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.1.1.weight 的平均映射误差: 0.047619
580
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.2.fc1.weight 的平均映射误差: 0.000693
581
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.2.fc2.weight 的平均映射误差: 0.001177
582
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.3.0.weight 的平均映射误差: 0.001543
583
+ [2025-04-11 15:22:43] [INFO] features.6.16.block.3.1.weight 的平均映射误差: 0.036831
584
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.0.0.weight 的平均映射误差: 0.001559
585
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.0.1.weight 的平均映射误差: 0.042565
586
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.1.0.weight 的平均映射误差: 0.001687
587
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.1.1.weight 的平均映射误差: 0.048230
588
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.2.fc1.weight 的平均映射误差: 0.000823
589
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.2.fc2.weight 的平均映射误差: 0.001306
590
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.3.0.weight 的平均映射误差: 0.001531
591
+ [2025-04-11 15:22:43] [INFO] features.6.17.block.3.1.weight 的平均映射误差: 0.037562
592
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.0.0.weight 的平均映射误差: 0.001858
593
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.0.1.weight 的平均映射误差: 0.032060
594
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.1.0.weight 的平均映射误差: 0.002120
595
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.1.1.weight 的平均映射误差: 0.032251
596
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.2.fc1.weight 的平均映射误差: 0.001538
597
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.2.fc2.weight 的平均映射误差: 0.001701
598
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.3.0.weight 的平均映射误差: 0.001624
599
+ [2025-04-11 15:22:43] [INFO] features.7.0.block.3.1.weight 的平均映射误差: 0.034763
600
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.0.0.weight 的平均映射误差: 0.001547
601
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.0.1.weight 的平均映射误差: 0.040559
602
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.1.0.weight 的平均映射误差: 0.001798
603
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.1.1.weight 的平均映射误差: 0.039211
604
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.2.fc1.weight 的平均映射误差: 0.001259
605
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.2.fc2.weight 的平均映射误差: 0.001628
606
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.3.0.weight 的平均映射误差: 0.001517
607
+ [2025-04-11 15:22:43] [INFO] features.7.1.block.3.1.weight 的平均映射误差: 0.046713
608
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.0.0.weight 的平均映射误差: 0.001524
609
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.0.1.weight 的平均映射误差: 0.046619
610
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.1.0.weight 的平均映射误差: 0.002099
611
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.1.1.weight 的平均映射误差: 0.043570
612
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.2.fc1.weight 的平均映射误差: 0.001207
613
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.2.fc2.weight 的平均映射误差: 0.001289
614
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.3.0.weight 的平均映射误差: 0.001486
615
+ [2025-04-11 15:22:43] [INFO] features.7.2.block.3.1.weight 的平均映射误差: 0.031953
616
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.0.0.weight 的平均映射误差: 0.001456
617
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.0.1.weight 的平均映射误差: 0.044966
618
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.1.0.weight 的平均映射误差: 0.002393
619
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.1.1.weight 的平均映射误差: 0.038847
620
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.2.fc1.weight 的平均映射误差: 0.001172
621
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.2.fc2.weight 的平均映射误差: 0.001262
622
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.3.0.weight 的平均映射误差: 0.001414
623
+ [2025-04-11 15:22:43] [INFO] features.7.3.block.3.1.weight 的平均映射误差: 0.032231
624
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.0.0.weight 的平均映射误差: 0.001427
625
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.0.1.weight 的平均映射误差: 0.035093
626
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.1.0.weight 的平均映射误差: 0.002034
627
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.1.1.weight 的平均映射误差: 0.032436
628
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.2.fc1.weight 的平均映射误差: 0.001459
629
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.2.fc2.weight 的平均映射误差: 0.001212
630
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.3.0.weight 的平均映射误差: 0.001388
631
+ [2025-04-11 15:22:43] [INFO] features.7.4.block.3.1.weight 的平均映射误差: 0.037126
632
+ [2025-04-11 15:22:43] [INFO] features.8.0.weight 的平均映射误差: 0.001619
633
+ [2025-04-11 15:22:43] [INFO] features.8.1.weight 的平均映射误差: 0.035800
634
+ [2025-04-11 15:22:43] [INFO] classifier.1.weight 的平均映射误差: 0.001411
635
+ [2025-04-11 15:22:45] [INFO] 评估结果 - Loss: 0.8530, Accuracy: 0.8285
636
+ [2025-04-11 15:22:46] [INFO] RRAM映射模型已保存到 checkpoints/rram_mapped_model.pth
637
+ [2025-04-11 15:22:46] [INFO] RRAM映射前后精度对比: 原始 0.9087 vs RRAM映射后 0.8285, 变化: -0.0802
638
+ [2025-04-11 15:22:46] [INFO] 开始微调全连接层 (epochs=50, lr=5e-05)...
639
+ [2025-04-11 15:22:46] [INFO] 微调过程中的模型将保存到: checkpoints/fine_tune_checkpoints
640
+ [2025-04-11 15:23:06] [INFO] Fine-tuning Epoch 1/50 - Train Acc: 0.9966, Val Acc: 0.9057
641
+ [2025-04-11 15:23:06] [INFO] 已保存第 1 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_1.pth
642
+ [2025-04-11 15:23:27] [INFO] Fine-tuning Epoch 2/50 - Train Acc: 0.9952, Val Acc: 0.9178
643
+ [2025-04-11 15:23:28] [INFO] 已保存第 2 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_2.pth
644
+ [2025-04-11 15:23:47] [INFO] Fine-tuning Epoch 3/50 - Train Acc: 0.9964, Val Acc: 0.9107
645
+ [2025-04-11 15:23:48] [INFO] 已保存第 3 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_3.pth
646
+ [2025-04-11 15:24:08] [INFO] Fine-tuning Epoch 4/50 - Train Acc: 0.9942, Val Acc: 0.9178
647
+ [2025-04-11 15:24:09] [INFO] 已保存第 4 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_4.pth
648
+ [2025-04-11 15:24:29] [INFO] Fine-tuning Epoch 5/50 - Train Acc: 0.9950, Val Acc: 0.9228
649
+ [2025-04-11 15:24:30] [INFO] 已保存第 5 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_5.pth
650
+ [2025-04-11 15:24:50] [INFO] Fine-tuning Epoch 6/50 - Train Acc: 0.9976, Val Acc: 0.9168
651
+ [2025-04-11 15:24:50] [INFO] 已保存第 6 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_6.pth
652
+ [2025-04-11 15:25:11] [INFO] Fine-tuning Epoch 7/50 - Train Acc: 0.9988, Val Acc: 0.9007
653
+ [2025-04-11 15:25:11] [INFO] 已保存第 7 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_7.pth
654
+ [2025-04-11 15:25:32] [INFO] Fine-tuning Epoch 8/50 - Train Acc: 0.9990, Val Acc: 0.9147
655
+ [2025-04-11 15:25:33] [INFO] 已保存第 8 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_8.pth
656
+ [2025-04-11 15:25:54] [INFO] Fine-tuning Epoch 9/50 - Train Acc: 0.9971, Val Acc: 0.8696
657
+ [2025-04-11 15:25:55] [INFO] 已保存第 9 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_9.pth
658
+ [2025-04-11 15:26:16] [INFO] Fine-tuning Epoch 10/50 - Train Acc: 0.9974, Val Acc: 0.8977
659
+ [2025-04-11 15:26:17] [INFO] 已保存第 10 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_10.pth
660
+ [2025-04-11 15:26:38] [INFO] Fine-tuning Epoch 11/50 - Train Acc: 0.9921, Val Acc: 0.8556
661
+ [2025-04-11 15:26:39] [INFO] 已保存第 11 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_11.pth
662
+ [2025-04-11 15:27:01] [INFO] Fine-tuning Epoch 12/50 - Train Acc: 0.9942, Val Acc: 0.8927
663
+ [2025-04-11 15:27:01] [INFO] 已保存第 12 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_12.pth
664
+ [2025-04-11 15:27:23] [INFO] Fine-tuning Epoch 13/50 - Train Acc: 0.9952, Val Acc: 0.9077
665
+ [2025-04-11 15:27:24] [INFO] 已保存第 13 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_13.pth
666
+ [2025-04-11 15:27:45] [INFO] Fine-tuning Epoch 14/50 - Train Acc: 0.9966, Val Acc: 0.8706
667
+ [2025-04-11 15:27:46] [INFO] 已保存第 14 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_14.pth
668
+ [2025-04-11 15:28:07] [INFO] Fine-tuning Epoch 15/50 - Train Acc: 0.9957, Val Acc: 0.8857
669
+ [2025-04-11 15:28:08] [INFO] 已保存第 15 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_15.pth
670
+ [2025-04-11 15:28:29] [INFO] Fine-tuning Epoch 16/50 - Train Acc: 0.9983, Val Acc: 0.9077
671
+ [2025-04-11 15:28:30] [INFO] 已保存第 16 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_16.pth
672
+ [2025-04-11 15:28:52] [INFO] Fine-tuning Epoch 17/50 - Train Acc: 0.9952, Val Acc: 0.8897
673
+ [2025-04-11 15:28:52] [INFO] 已保存第 17 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_17.pth
674
+ [2025-04-11 15:29:14] [INFO] Fine-tuning Epoch 18/50 - Train Acc: 0.9954, Val Acc: 0.8756
675
+ [2025-04-11 15:29:15] [INFO] 已保存第 18 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_18.pth
676
+ [2025-04-11 15:29:36] [INFO] Fine-tuning Epoch 19/50 - Train Acc: 0.9966, Val Acc: 0.9017
677
+ [2025-04-11 15:29:37] [INFO] 已保存第 19 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_19.pth
678
+ [2025-04-11 15:29:58] [INFO] Fine-tuning Epoch 20/50 - Train Acc: 0.9990, Val Acc: 0.8907
679
+ [2025-04-11 15:29:59] [INFO] 已保存第 20 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_20.pth
680
+ [2025-04-11 15:30:21] [INFO] Fine-tuning Epoch 21/50 - Train Acc: 0.9995, Val Acc: 0.8987
681
+ [2025-04-11 15:30:22] [INFO] 已保存第 21 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_21.pth
682
+ [2025-04-11 15:30:43] [INFO] Fine-tuning Epoch 22/50 - Train Acc: 0.9983, Val Acc: 0.8887
683
+ [2025-04-11 15:30:44] [INFO] 已保存第 22 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_22.pth
684
+ [2025-04-11 15:31:06] [INFO] Fine-tuning Epoch 23/50 - Train Acc: 0.9971, Val Acc: 0.8957
685
+ [2025-04-11 15:31:07] [INFO] 已保存第 23 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_23.pth
686
+ [2025-04-11 15:31:28] [INFO] Fine-tuning Epoch 24/50 - Train Acc: 0.9986, Val Acc: 0.8957
687
+ [2025-04-11 15:31:29] [INFO] 已保存第 24 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_24.pth
688
+ [2025-04-11 15:31:50] [INFO] Fine-tuning Epoch 25/50 - Train Acc: 0.9981, Val Acc: 0.8947
689
+ [2025-04-11 15:31:51] [INFO] 已保存第 25 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_25.pth
690
+ [2025-04-11 15:32:13] [INFO] Fine-tuning Epoch 26/50 - Train Acc: 0.9976, Val Acc: 0.9468
691
+ [2025-04-11 15:32:13] [INFO] 已保存第 26 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_26.pth
692
+ [2025-04-11 15:32:35] [INFO] Fine-tuning Epoch 27/50 - Train Acc: 0.9981, Val Acc: 0.9288
693
+ [2025-04-11 15:32:36] [INFO] 已保存第 27 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_27.pth
694
+ [2025-04-11 15:32:56] [INFO] Fine-tuning Epoch 28/50 - Train Acc: 0.9993, Val Acc: 0.9338
695
+ [2025-04-11 15:32:57] [INFO] 已保存第 28 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_28.pth
696
+ [2025-04-11 15:33:17] [INFO] Fine-tuning Epoch 29/50 - Train Acc: 0.9986, Val Acc: 0.9238
697
+ [2025-04-11 15:33:18] [INFO] 已保存第 29 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_29.pth
698
+ [2025-04-11 15:33:39] [INFO] Fine-tuning Epoch 30/50 - Train Acc: 0.9971, Val Acc: 0.9408
699
+ [2025-04-11 15:33:40] [INFO] 已保存第 30 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_30.pth
700
+ [2025-04-11 15:34:01] [INFO] Fine-tuning Epoch 31/50 - Train Acc: 0.9976, Val Acc: 0.8967
701
+ [2025-04-11 15:34:02] [INFO] 已保存第 31 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_31.pth
702
+ [2025-04-11 15:34:22] [INFO] Fine-tuning Epoch 32/50 - Train Acc: 0.9964, Val Acc: 0.9278
703
+ [2025-04-11 15:34:23] [INFO] 已保存第 32 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_32.pth
704
+ [2025-04-11 15:34:43] [INFO] Fine-tuning Epoch 33/50 - Train Acc: 0.9966, Val Acc: 0.9248
705
+ [2025-04-11 15:34:44] [INFO] 已保存第 33 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_33.pth
706
+ [2025-04-11 15:35:04] [INFO] Fine-tuning Epoch 34/50 - Train Acc: 0.9986, Val Acc: 0.8987
707
+ [2025-04-11 15:35:05] [INFO] 已保存第 34 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_34.pth
708
+ [2025-04-11 15:35:25] [INFO] Fine-tuning Epoch 35/50 - Train Acc: 0.9976, Val Acc: 0.9278
709
+ [2025-04-11 15:35:26] [INFO] 已保存第 35 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_35.pth
710
+ [2025-04-11 15:35:46] [INFO] Fine-tuning Epoch 36/50 - Train Acc: 0.9976, Val Acc: 0.9137
711
+ [2025-04-11 15:35:47] [INFO] 已保存第 36 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_36.pth
712
+ [2025-04-11 15:36:07] [INFO] Fine-tuning Epoch 37/50 - Train Acc: 0.9986, Val Acc: 0.9258
713
+ [2025-04-11 15:36:08] [INFO] 已保存第 37 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_37.pth
714
+ [2025-04-11 15:36:28] [INFO] Fine-tuning Epoch 38/50 - Train Acc: 0.9998, Val Acc: 0.9127
715
+ [2025-04-11 15:36:29] [INFO] 已保存第 38 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_38.pth
716
+ [2025-04-11 15:36:50] [INFO] Fine-tuning Epoch 39/50 - Train Acc: 0.9986, Val Acc: 0.9298
717
+ [2025-04-11 15:36:50] [INFO] 已保存第 39 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_39.pth
718
+ [2025-04-11 15:37:11] [INFO] Fine-tuning Epoch 40/50 - Train Acc: 0.9983, Val Acc: 0.9097
719
+ [2025-04-11 15:37:12] [INFO] 已保存第 40 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_40.pth
720
+ [2025-04-11 15:37:33] [INFO] Fine-tuning Epoch 41/50 - Train Acc: 0.9983, Val Acc: 0.8997
721
+ [2025-04-11 15:37:34] [INFO] 已保存第 41 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_41.pth
722
+ [2025-04-11 15:37:54] [INFO] Fine-tuning Epoch 42/50 - Train Acc: 0.9995, Val Acc: 0.9097
723
+ [2025-04-11 15:37:55] [INFO] 已保存第 42 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_42.pth
724
+ [2025-04-11 15:38:15] [INFO] Fine-tuning Epoch 43/50 - Train Acc: 0.9986, Val Acc: 0.9107
725
+ [2025-04-11 15:38:16] [INFO] 已保存第 43 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_43.pth
726
+ [2025-04-11 15:38:36] [INFO] Fine-tuning Epoch 44/50 - Train Acc: 0.9962, Val Acc: 0.9007
727
+ [2025-04-11 15:38:37] [INFO] 已保存第 44 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_44.pth
728
+ [2025-04-11 15:38:58] [INFO] Fine-tuning Epoch 45/50 - Train Acc: 0.9981, Val Acc: 0.9117
729
+ [2025-04-11 15:38:59] [INFO] 已保存第 45 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_45.pth
730
+ [2025-04-11 15:39:20] [INFO] Fine-tuning Epoch 46/50 - Train Acc: 0.9993, Val Acc: 0.9208
731
+ [2025-04-11 15:39:21] [INFO] 已保存第 46 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_46.pth
732
+ [2025-04-11 15:39:42] [INFO] Fine-tuning Epoch 47/50 - Train Acc: 0.9998, Val Acc: 0.9178
733
+ [2025-04-11 15:39:43] [INFO] 已保存第 47 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_47.pth
734
+ [2025-04-11 15:40:04] [INFO] Fine-tuning Epoch 48/50 - Train Acc: 1.0000, Val Acc: 0.9208
735
+ [2025-04-11 15:40:05] [INFO] 已保存第 48 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_48.pth
736
+ [2025-04-11 15:40:26] [INFO] Fine-tuning Epoch 49/50 - Train Acc: 0.9998, Val Acc: 0.9198
737
+ [2025-04-11 15:40:27] [INFO] 已保存第 49 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_49.pth
738
+ [2025-04-11 15:40:48] [INFO] Fine-tuning Epoch 50/50 - Train Acc: 0.9990, Val Acc: 0.9308
739
+ [2025-04-11 15:40:49] [INFO] 已保存第 50 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_50.pth
740
+ [2025-04-11 15:40:51] [INFO] 评估结果 - Loss: 0.4584, Accuracy: 0.9308
741
+ [2025-04-11 15:40:52] [INFO] 微调模型已保存到 checkpoints/fine_tuned_model.pth
742
+ [2025-04-11 15:40:52] [INFO] 微调前后精度对比: RRAM映射 0.8285 vs 微调后 0.9308, 变化: 0.1023
743
+ [2025-04-11 15:40:52] [INFO] 所有处理完成!
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checkpoints_v2m_part1/last_checkpoint.pth ADDED
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checkpoints_v2m_part1/parse_log.py ADDED
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1
+ import re
2
+ import pandas as pd
3
+
4
+ def parse_log_file(log_file_path):
5
+ # 初始化存储数据的列表
6
+ base_training_data = []
7
+ fine_tuning_data = []
8
+
9
+ with open(log_file_path, 'r', encoding='utf-8') as f:
10
+ content = f.read()
11
+
12
+ # 提取基础训练数据
13
+ base_pattern = r'Epoch (\d+)/50 - Train Loss: ([\d.]+), Train Acc: ([\d.]+), Val Loss: ([\d.]+), Val Acc: ([\d.]+)'
14
+ base_matches = re.finditer(base_pattern, content)
15
+
16
+ for match in base_matches:
17
+ epoch = int(match.group(1))
18
+ train_loss = float(match.group(2))
19
+ train_acc = float(match.group(3))
20
+ val_loss = float(match.group(4))
21
+ val_acc = float(match.group(5))
22
+
23
+ # 如果epoch小于等于50，认为是基础训练数据
24
+ if epoch <= 50:
25
+ base_training_data.append({
26
+ 'epoch': epoch,
27
+ 'train_loss': train_loss,
28
+ 'train_acc': train_acc,
29
+ 'val_loss': val_loss,
30
+ 'val_acc': val_acc
31
+ })
32
+
33
+ # 提取微调训练数据
34
+ fine_tune_pattern = r'Fine-tuning Epoch (\d+)/50 - Train Acc: ([\d.]+), Val Acc: ([\d.]+)'
35
+ fine_tune_matches = re.finditer(fine_tune_pattern, content)
36
+
37
+ for match in fine_tune_matches:
38
+ epoch = int(match.group(1))
39
+ train_acc = float(match.group(2))
40
+ val_acc = float(match.group(3))
41
+
42
+ fine_tuning_data.append({
43
+ 'epoch': epoch,
44
+ 'train_acc': train_acc,
45
+ 'val_acc': val_acc
46
+ })
47
+
48
+ # 转换为DataFrame并保存为CSV
49
+ if base_training_data:
50
+ base_df = pd.DataFrame(base_training_data)
51
+ base_df.to_csv('base_training_metrics.csv', index=False)
52
+ print(f"基础训练数据已保存到 base_training_metrics.csv，共 {len(base_training_data)} 条记录")
53
+
54
+ if fine_tuning_data:
55
+ fine_tune_df = pd.DataFrame(fine_tuning_data)
56
+ fine_tune_df.to_csv('fine_tuning_metrics.csv', index=False)
57
+ print(f"微调训练数据已保存到 fine_tuning_metrics.csv，共 {len(fine_tuning_data)} 条记录")
58
+
59
+ if __name__ == '__main__':
60
+ # 指定日志文件路径
61
+ log_file_path = '2025-04-11_15-04-17_train.log'
62
+ parse_log_file(log_file_path)
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+ size 213315346
checkpoints_v2m_part1/training_plot.png ADDED
checkpoints_v2m_part1/visualizations/base_weights_heatmap.png ADDED

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checkpoints_v2m_part1/visualizations/fine_tuned_weights_heatmap.png ADDED

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checkpoints_v2m_part1/visualizations/mapping_error_distribution.png ADDED

Git LFS Details

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checkpoints_v2m_part1/visualizations/weight_changes_heatmap.png ADDED

Git LFS Details

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1
+ [2025-04-11 14:13:49] [INFO] 使用设备: cuda:0
2
+ [2025-04-11 14:13:49] [INFO] 训练集注释文件: /data0/work/DuYiFan/projects/traffic_classify/4_directions/TsignRecgTrainAnnotation.txt
3
+ [2025-04-11 14:13:49] [INFO] 测试集注释文件: /data0/work/DuYiFan/projects/traffic_classify/4_directions/TsignRecgTestAnnotation.txt
4
+ [2025-04-11 14:13:49] [INFO] 训练图像目录: /data0/work/DuYiFan/projects/traffic_classify/4_directions/train
5
+ [2025-04-11 14:13:49] [INFO] 测试图像目录: /data0/work/DuYiFan/projects/traffic_classify/4_directions/test
6
+ [2025-04-11 14:13:49] [INFO] 创建数据集和数据加载器
7
+ [2025-04-11 14:13:49] [INFO] 创建efficientnet-v2-m模型，类别数: 4
8
+ [2025-04-11 14:13:50] [INFO] 设置损失函数、优化器和学习率调度器，初始学习率: 0.0001
9
+ [2025-04-11 14:13:50] [INFO] 开始训练，总共 50 轮
10
+ [2025-04-11 14:13:50] [INFO] 当前学习率: 0.000100
11
+ [2025-04-11 14:13:50] [INFO] Epoch 1/50 开始训练
12
+ [2025-04-11 14:13:52] [INFO] Epoch 1/50 开始验证
13
+ [2025-04-11 14:13:52] [INFO] Epoch 1/50 - Train Loss: 1.4098, Train Acc: 0.2183, Val Loss: 1.3692, Val Acc: 0.4483
14
+ [2025-04-11 14:13:52] [INFO] 已保存最佳模型，准确率: 0.4483
15
+ [2025-04-11 14:13:53] [INFO] 当前学习率: 0.000100
16
+ [2025-04-11 14:13:53] [INFO] Epoch 2/50 开始训练
17
+ [2025-04-11 14:13:54] [INFO] Epoch 2/50 开始验证
18
+ [2025-04-11 14:13:55] [INFO] Epoch 2/50 - Train Loss: 1.3061, Train Acc: 0.5352, Val Loss: 1.3508, Val Acc: 0.4483
19
+ [2025-04-11 14:13:56] [INFO] 当前学习率: 0.000100
20
+ [2025-04-11 14:13:56] [INFO] Epoch 3/50 开始训练
21
+ [2025-04-11 14:13:56] [INFO] Epoch 3/50 开始验证
22
+ [2025-04-11 14:13:57] [INFO] Epoch 3/50 - Train Loss: 1.2258, Train Acc: 0.7042, Val Loss: 1.3288, Val Acc: 0.4483
23
+ [2025-04-11 14:13:58] [INFO] 当前学习率: 0.000099
24
+ [2025-04-11 14:13:58] [INFO] Epoch 4/50 开始训练
25
+ [2025-04-11 14:13:59] [INFO] Epoch 4/50 开始验证
26
+ [2025-04-11 14:13:59] [INFO] Epoch 4/50 - Train Loss: 1.1423, Train Acc: 0.7042, Val Loss: 1.3089, Val Acc: 0.4483
27
+ [2025-04-11 14:14:00] [INFO] 当前学习率: 0.000098
28
+ [2025-04-11 14:14:00] [INFO] Epoch 5/50 开始训练
29
+ [2025-04-11 14:14:01] [INFO] Epoch 5/50 开始验证
30
+ [2025-04-11 14:14:01] [INFO] Epoch 5/50 - Train Loss: 1.0667, Train Acc: 0.7042, Val Loss: 1.2984, Val Acc: 0.4483
31
+ [2025-04-11 14:14:03] [INFO] 当前学习率: 0.000098
32
+ [2025-04-11 14:14:03] [INFO] Epoch 6/50 开始训练
33
+ [2025-04-11 14:14:03] [INFO] Epoch 6/50 开始验证
34
+ [2025-04-11 14:14:04] [INFO] Epoch 6/50 - Train Loss: 0.9744, Train Acc: 0.7042, Val Loss: 1.2977, Val Acc: 0.4483
35
+ [2025-04-11 14:14:05] [INFO] 当前学习率: 0.000097
36
+ [2025-04-11 14:14:05] [INFO] Epoch 7/50 开始训练
37
+ [2025-04-11 14:14:06] [INFO] Epoch 7/50 开始验证
38
+ [2025-04-11 14:14:06] [INFO] Epoch 7/50 - Train Loss: 0.9159, Train Acc: 0.7042, Val Loss: 1.3075, Val Acc: 0.4483
39
+ [2025-04-11 14:14:07] [INFO] 当前学习率: 0.000095
40
+ [2025-04-11 14:14:07] [INFO] Epoch 8/50 开始训练
41
+ [2025-04-11 14:14:08] [INFO] Epoch 8/50 开始验证
42
+ [2025-04-11 14:14:08] [INFO] Epoch 8/50 - Train Loss: 0.8672, Train Acc: 0.7042, Val Loss: 1.3161, Val Acc: 0.4483
43
+ [2025-04-11 14:14:09] [INFO] 当前学习率: 0.000094
44
+ [2025-04-11 14:14:09] [INFO] Epoch 9/50 开始训练
45
+ [2025-04-11 14:14:10] [INFO] Epoch 9/50 开始验证
46
+ [2025-04-11 14:14:10] [INFO] Epoch 9/50 - Train Loss: 0.8349, Train Acc: 0.7042, Val Loss: 1.3154, Val Acc: 0.4483
47
+ [2025-04-11 14:14:11] [INFO] 当前学习率: 0.000092
48
+ [2025-04-11 14:14:11] [INFO] Epoch 10/50 开始训练
49
+ [2025-04-11 14:14:12] [INFO] Epoch 10/50 开始验证
50
+ [2025-04-11 14:14:13] [INFO] Epoch 10/50 - Train Loss: 0.8062, Train Acc: 0.7042, Val Loss: 1.3091, Val Acc: 0.4483
51
+ [2025-04-11 14:14:14] [INFO] 当前学习率: 0.000091
52
+ [2025-04-11 14:14:14] [INFO] Epoch 11/50 开始训练
53
+ [2025-04-11 14:14:15] [INFO] Epoch 11/50 开始验证
54
+ [2025-04-11 14:14:15] [INFO] Epoch 11/50 - Train Loss: 0.7764, Train Acc: 0.7042, Val Loss: 1.2824, Val Acc: 0.4483
55
+ [2025-04-11 14:14:16] [INFO] 当前学习率: 0.000089
56
+ [2025-04-11 14:14:16] [INFO] Epoch 12/50 开始训练
57
+ [2025-04-11 14:14:17] [INFO] Epoch 12/50 开始验证
58
+ [2025-04-11 14:14:17] [INFO] Epoch 12/50 - Train Loss: 0.7459, Train Acc: 0.7042, Val Loss: 1.2373, Val Acc: 0.4483
59
+ [2025-04-11 14:14:18] [INFO] 当前学习率: 0.000087
60
+ [2025-04-11 14:14:18] [INFO] Epoch 13/50 开始训练
61
+ [2025-04-11 14:14:19] [INFO] Epoch 13/50 开始验证
62
+ [2025-04-11 14:14:20] [INFO] Epoch 13/50 - Train Loss: 0.7026, Train Acc: 0.7042, Val Loss: 1.2288, Val Acc: 0.4483
63
+ [2025-04-11 14:14:21] [INFO] 当前学习率: 0.000084
64
+ [2025-04-11 14:14:21] [INFO] Epoch 14/50 开始训练
65
+ [2025-04-11 14:14:21] [INFO] Epoch 14/50 开始验证
66
+ [2025-04-11 14:14:22] [INFO] Epoch 14/50 - Train Loss: 0.6678, Train Acc: 0.7042, Val Loss: 1.2415, Val Acc: 0.4483
67
+ [2025-04-11 14:14:23] [INFO] 当前学习率: 0.000082
68
+ [2025-04-11 14:14:23] [INFO] Epoch 15/50 开始训练
69
+ [2025-04-11 14:14:24] [INFO] Epoch 15/50 开始验证
70
+ [2025-04-11 14:14:24] [INFO] Epoch 15/50 - Train Loss: 0.6473, Train Acc: 0.7042, Val Loss: 1.2037, Val Acc: 0.4483
71
+ [2025-04-11 14:14:25] [INFO] 当前学习率: 0.000080
72
+ [2025-04-11 14:14:25] [INFO] Epoch 16/50 开始训练
73
+ [2025-04-11 14:14:26] [INFO] Epoch 16/50 开始验证
74
+ [2025-04-11 14:14:26] [INFO] Epoch 16/50 - Train Loss: 0.6035, Train Acc: 0.7394, Val Loss: 1.1331, Val Acc: 0.4483
75
+ [2025-04-11 14:14:27] [INFO] 当前学习率: 0.000077
76
+ [2025-04-11 14:14:27] [INFO] Epoch 17/50 开始训练
77
+ [2025-04-11 14:14:28] [INFO] Epoch 17/50 开始验证
78
+ [2025-04-11 14:14:29] [INFO] Epoch 17/50 - Train Loss: 0.5741, Train Acc: 0.7535, Val Loss: 1.1263, Val Acc: 0.4483
79
+ [2025-04-11 14:14:30] [INFO] 当前学习率: 0.000074
80
+ [2025-04-11 14:14:30] [INFO] Epoch 18/50 开始训练
81
+ [2025-04-11 14:14:31] [INFO] Epoch 18/50 开始验证
82
+ [2025-04-11 14:14:31] [INFO] Epoch 18/50 - Train Loss: 0.5583, Train Acc: 0.7887, Val Loss: 1.1548, Val Acc: 0.4483
83
+ [2025-04-11 14:14:32] [INFO] 当前学习率: 0.000072
84
+ [2025-04-11 14:14:32] [INFO] Epoch 19/50 开始训练
85
+ [2025-04-11 14:14:33] [INFO] Epoch 19/50 开始验证
86
+ [2025-04-11 14:14:33] [INFO] Epoch 19/50 - Train Loss: 0.5233, Train Acc: 0.8028, Val Loss: 1.1269, Val Acc: 0.5172
87
+ [2025-04-11 14:14:34] [INFO] 已保存最佳模型，准确率: 0.5172
88
+ [2025-04-11 14:14:35] [INFO] 当前学习率: 0.000069
89
+ [2025-04-11 14:14:35] [INFO] Epoch 20/50 开始训练
90
+ [2025-04-11 14:14:35] [INFO] Epoch 20/50 开始验证
91
+ [2025-04-11 14:14:36] [INFO] Epoch 20/50 - Train Loss: 0.5189, Train Acc: 0.7746, Val Loss: 1.1425, Val Acc: 0.3793
92
+ [2025-04-11 14:14:37] [INFO] 当前学习率: 0.000066
93
+ [2025-04-11 14:14:37] [INFO] Epoch 21/50 开始训练
94
+ [2025-04-11 14:14:38] [INFO] Epoch 21/50 开始验证
95
+ [2025-04-11 14:14:38] [INFO] Epoch 21/50 - Train Loss: 0.5140, Train Acc: 0.7958, Val Loss: 1.1988, Val Acc: 0.3448
96
+ [2025-04-11 14:14:39] [INFO] 当前学习率: 0.000063
97
+ [2025-04-11 14:14:39] [INFO] Epoch 22/50 开始训练
98
+ [2025-04-11 14:14:40] [INFO] Epoch 22/50 开始验证
99
+ [2025-04-11 14:14:40] [INFO] Epoch 22/50 - Train Loss: 0.5165, Train Acc: 0.7817, Val Loss: 1.2972, Val Acc: 0.3448
100
+ [2025-04-11 14:14:41] [INFO] 当前学习率: 0.000060
101
+ [2025-04-11 14:14:41] [INFO] Epoch 23/50 开始训练
102
+ [2025-04-11 14:14:42] [INFO] Epoch 23/50 开始验证
103
+ [2025-04-11 14:14:43] [INFO] Epoch 23/50 - Train Loss: 0.4809, Train Acc: 0.7958, Val Loss: 1.1992, Val Acc: 0.3448
104
+ [2025-04-11 14:14:44] [INFO] 当前学习率: 0.000057
105
+ [2025-04-11 14:14:44] [INFO] Epoch 24/50 开始训练
106
+ [2025-04-11 14:14:45] [INFO] Epoch 24/50 开始验证
107
+ [2025-04-11 14:14:45] [INFO] Epoch 24/50 - Train Loss: 0.4835, Train Acc: 0.7887, Val Loss: 1.1661, Val Acc: 0.3793
108
+ [2025-04-11 14:14:46] [INFO] 当前学习率: 0.000054
109
+ [2025-04-11 14:14:46] [INFO] Epoch 25/50 开始训练
110
+ [2025-04-11 14:14:47] [INFO] Epoch 25/50 开始验证
111
+ [2025-04-11 14:14:47] [INFO] Epoch 25/50 - Train Loss: 0.4557, Train Acc: 0.7958, Val Loss: 1.2521, Val Acc: 0.3448
112
+ [2025-04-11 14:14:48] [INFO] 当前学习率: 0.000050
113
+ [2025-04-11 14:14:48] [INFO] Epoch 26/50 开始训练
114
+ [2025-04-11 14:14:49] [INFO] Epoch 26/50 开始验证
115
+ [2025-04-11 14:14:50] [INFO] Epoch 26/50 - Train Loss: 0.4590, Train Acc: 0.8169, Val Loss: 1.4326, Val Acc: 0.2759
116
+ [2025-04-11 14:14:51] [INFO] 当前学习率: 0.000047
117
+ [2025-04-11 14:14:51] [INFO] Epoch 27/50 开始训练
118
+ [2025-04-11 14:14:51] [INFO] Epoch 27/50 开始验证
119
+ [2025-04-11 14:14:52] [INFO] Epoch 27/50 - Train Loss: 0.4430, Train Acc: 0.8239, Val Loss: 1.4415, Val Acc: 0.2759
120
+ [2025-04-11 14:14:53] [INFO] 当前学习率: 0.000044
121
+ [2025-04-11 14:14:53] [INFO] Epoch 28/50 开始训练
122
+ [2025-04-11 14:14:54] [INFO] Epoch 28/50 开始验证
123
+ [2025-04-11 14:14:54] [INFO] Epoch 28/50 - Train Loss: 0.4579, Train Acc: 0.8310, Val Loss: 1.3938, Val Acc: 0.3448
124
+ [2025-04-11 14:14:55] [INFO] 当前学习率: 0.000041
125
+ [2025-04-11 14:14:55] [INFO] Epoch 29/50 开始训练
126
+ [2025-04-11 14:14:56] [INFO] Epoch 29/50 开始验证
127
+ [2025-04-11 14:14:56] [INFO] Epoch 29/50 - Train Loss: 0.4221, Train Acc: 0.8662, Val Loss: 1.2294, Val Acc: 0.3448
128
+ [2025-04-11 14:14:57] [INFO] 当前学习率: 0.000038
129
+ [2025-04-11 14:14:57] [INFO] Epoch 30/50 开始训练
130
+ [2025-04-11 14:14:58] [INFO] Epoch 30/50 开始验证
131
+ [2025-04-11 14:14:59] [INFO] Epoch 30/50 - Train Loss: 0.4264, Train Acc: 0.8310, Val Loss: 1.0815, Val Acc: 0.4138
132
+ [2025-04-11 14:15:00] [INFO] 当前学习率: 0.000035
133
+ [2025-04-11 14:15:00] [INFO] Epoch 31/50 开始训练
134
+ [2025-04-11 14:15:01] [INFO] Epoch 31/50 开始验证
135
+ [2025-04-11 14:15:01] [INFO] Epoch 31/50 - Train Loss: 0.3995, Train Acc: 0.8662, Val Loss: 1.0540, Val Acc: 0.5172
136
+ [2025-04-11 14:15:02] [INFO] 当前学习率: 0.000032
137
+ [2025-04-11 14:15:02] [INFO] Epoch 32/50 开始训练
138
+ [2025-04-11 14:15:03] [INFO] Epoch 32/50 开始验证
139
+ [2025-04-11 14:15:03] [INFO] Epoch 32/50 - Train Loss: 0.3980, Train Acc: 0.8380, Val Loss: 1.0917, Val Acc: 0.5172
140
+ [2025-04-11 14:15:04] [INFO] 当前学习率: 0.000029
141
+ [2025-04-11 14:15:04] [INFO] Epoch 33/50 开始训练
142
+ [2025-04-11 14:15:05] [INFO] Epoch 33/50 开始验证
143
+ [2025-04-11 14:15:06] [INFO] Epoch 33/50 - Train Loss: 0.3591, Train Acc: 0.8803, Val Loss: 1.0186, Val Acc: 0.5172
144
+ [2025-04-11 14:15:07] [INFO] 当前学习率: 0.000027
145
+ [2025-04-11 14:15:07] [INFO] Epoch 34/50 开始训练
146
+ [2025-04-11 14:15:07] [INFO] Epoch 34/50 开始验证
147
+ [2025-04-11 14:15:08] [INFO] Epoch 34/50 - Train Loss: 0.3420, Train Acc: 0.8803, Val Loss: 1.0275, Val Acc: 0.4483
148
+ [2025-04-11 14:15:09] [INFO] 当前学习率: 0.000024
149
+ [2025-04-11 14:15:09] [INFO] Epoch 35/50 开始训练
150
+ [2025-04-11 14:15:10] [INFO] Epoch 35/50 开始验证
151
+ [2025-04-11 14:15:10] [INFO] Epoch 35/50 - Train Loss: 0.3773, Train Acc: 0.8592, Val Loss: 1.0903, Val Acc: 0.4828
152
+ [2025-04-11 14:15:11] [INFO] 当前学习率: 0.000021
153
+ [2025-04-11 14:15:11] [INFO] Epoch 36/50 开始训练
154
+ [2025-04-11 14:15:12] [INFO] Epoch 36/50 开始验证
155
+ [2025-04-11 14:15:12] [INFO] Epoch 36/50 - Train Loss: 0.3629, Train Acc: 0.8873, Val Loss: 1.1087, Val Acc: 0.5172
156
+ [2025-04-11 14:15:13] [INFO] 当前学习率: 0.000019
157
+ [2025-04-11 14:15:13] [INFO] Epoch 37/50 开始训练
158
+ [2025-04-11 14:15:14] [INFO] Epoch 37/50 开始验证
159
+ [2025-04-11 14:15:15] [INFO] Epoch 37/50 - Train Loss: 0.3062, Train Acc: 0.8944, Val Loss: 1.1035, Val Acc: 0.5172
160
+ [2025-04-11 14:15:16] [INFO] 当前学习率: 0.000017
161
+ [2025-04-11 14:15:16] [INFO] Epoch 38/50 开始训练
162
+ [2025-04-11 14:15:16] [INFO] Epoch 38/50 开始验证
163
+ [2025-04-11 14:15:17] [INFO] Epoch 38/50 - Train Loss: 0.3355, Train Acc: 0.9085, Val Loss: 1.0940, Val Acc: 0.5172
164
+ [2025-04-11 14:15:18] [INFO] 当前学习率: 0.000014
165
+ [2025-04-11 14:15:18] [INFO] Epoch 39/50 开始训练
166
+ [2025-04-11 14:15:19] [INFO] Epoch 39/50 开始验证
167
+ [2025-04-11 14:15:19] [INFO] Epoch 39/50 - Train Loss: 0.3338, Train Acc: 0.8803, Val Loss: 1.0815, Val Acc: 0.5172
168
+ [2025-04-11 14:15:20] [INFO] 当前学习率: 0.000012
169
+ [2025-04-11 14:15:20] [INFO] Epoch 40/50 开始训练
170
+ [2025-04-11 14:15:21] [INFO] Epoch 40/50 开始验证
171
+ [2025-04-11 14:15:21] [INFO] Epoch 40/50 - Train Loss: 0.3105, Train Acc: 0.8803, Val Loss: 1.0742, Val Acc: 0.5172
172
+ [2025-04-11 14:15:22] [INFO] 当前学习率: 0.000010
173
+ [2025-04-11 14:15:22] [INFO] Epoch 41/50 开始训练
174
+ [2025-04-11 14:15:23] [INFO] Epoch 41/50 开始验证
175
+ [2025-04-11 14:15:24] [INFO] Epoch 41/50 - Train Loss: 0.3438, Train Acc: 0.8873, Val Loss: 1.0633, Val Acc: 0.5862
176
+ [2025-04-11 14:15:24] [INFO] 已保存最佳模型，准确率: 0.5862
177
+ [2025-04-11 14:15:25] [INFO] 当前学习率: 0.000009
178
+ [2025-04-11 14:15:25] [INFO] Epoch 42/50 开始训练
179
+ [2025-04-11 14:15:26] [INFO] Epoch 42/50 开始验证
180
+ [2025-04-11 14:15:26] [INFO] Epoch 42/50 - Train Loss: 0.3150, Train Acc: 0.8944, Val Loss: 1.0631, Val Acc: 0.5862
181
+ [2025-04-11 14:15:27] [INFO] 当前学习率: 0.000007
182
+ [2025-04-11 14:15:28] [INFO] Epoch 43/50 开始训练
183
+ [2025-04-11 14:15:28] [INFO] Epoch 43/50 开始验证
184
+ [2025-04-11 14:15:29] [INFO] Epoch 43/50 - Train Loss: 0.3168, Train Acc: 0.8944, Val Loss: 1.0575, Val Acc: 0.5862
185
+ [2025-04-11 14:15:30] [INFO] 当前学习率: 0.000006
186
+ [2025-04-11 14:15:30] [INFO] Epoch 44/50 开始训练
187
+ [2025-04-11 14:15:31] [INFO] Epoch 44/50 开始验证
188
+ [2025-04-11 14:15:31] [INFO] Epoch 44/50 - Train Loss: 0.2939, Train Acc: 0.9085, Val Loss: 1.0698, Val Acc: 0.5862
189
+ [2025-04-11 14:15:32] [INFO] 当前学习率: 0.000004
190
+ [2025-04-11 14:15:32] [INFO] Epoch 45/50 开始训练
191
+ [2025-04-11 14:15:33] [INFO] Epoch 45/50 开始验证
192
+ [2025-04-11 14:15:33] [INFO] Epoch 45/50 - Train Loss: 0.3333, Train Acc: 0.8662, Val Loss: 1.0725, Val Acc: 0.5862
193
+ [2025-04-11 14:15:34] [INFO] 当前学习率: 0.000003
194
+ [2025-04-11 14:15:34] [INFO] Epoch 46/50 开始训练
195
+ [2025-04-11 14:15:35] [INFO] Epoch 46/50 开始验证
196
+ [2025-04-11 14:15:35] [INFO] Epoch 46/50 - Train Loss: 0.3176, Train Acc: 0.8803, Val Loss: 1.0823, Val Acc: 0.5862
197
+ [2025-04-11 14:15:36] [INFO] 当前学习率: 0.000003
198
+ [2025-04-11 14:15:36] [INFO] Epoch 47/50 开始训练
199
+ [2025-04-11 14:15:37] [INFO] Epoch 47/50 开始验证
200
+ [2025-04-11 14:15:38] [INFO] Epoch 47/50 - Train Loss: 0.2840, Train Acc: 0.9225, Val Loss: 1.0824, Val Acc: 0.5862
201
+ [2025-04-11 14:15:39] [INFO] 当前学习率: 0.000002
202
+ [2025-04-11 14:15:39] [INFO] Epoch 48/50 开始训练
203
+ [2025-04-11 14:15:40] [INFO] Epoch 48/50 开始验证
204
+ [2025-04-11 14:15:40] [INFO] Epoch 48/50 - Train Loss: 0.2919, Train Acc: 0.9014, Val Loss: 1.0881, Val Acc: 0.5862
205
+ [2025-04-11 14:15:41] [INFO] 当前学习率: 0.000001
206
+ [2025-04-11 14:15:41] [INFO] Epoch 49/50 开始训练
207
+ [2025-04-11 14:15:42] [INFO] Epoch 49/50 开始验证
208
+ [2025-04-11 14:15:42] [INFO] Epoch 49/50 - Train Loss: 0.2736, Train Acc: 0.9085, Val Loss: 1.0791, Val Acc: 0.5862
209
+ [2025-04-11 14:15:43] [INFO] 当前学习率: 0.000001
210
+ [2025-04-11 14:15:43] [INFO] Epoch 50/50 开始训练
211
+ [2025-04-11 14:15:44] [INFO] Epoch 50/50 开始验证
212
+ [2025-04-11 14:15:45] [INFO] Epoch 50/50 - Train Loss: 0.3232, Train Acc: 0.9014, Val Loss: 1.0816, Val Acc: 0.5862
213
+ [2025-04-11 14:15:46] [INFO] 绘制训练过程图表
214
+ [2025-04-11 14:15:46] [INFO] 标准训练完成!
215
+ [2025-04-11 14:15:46] [INFO] ���估原始模型性能...
216
+ [2025-04-11 14:15:47] [INFO] 评估结果 - Loss: 1.0816, Accuracy: 0.5862
217
+ [2025-04-11 14:15:47] [INFO] 开始执行RRAM映射...
218
+ [2025-04-11 14:15:47] [INFO] 加载了 100 个RRAM电导值
219
+ [2025-04-11 14:15:47] [INFO] features.0.0.weight 的平均映射误差: 0.018786
220
+ [2025-04-11 14:15:47] [INFO] features.0.1.weight 的平均映射误差: 0.033996
221
+ [2025-04-11 14:15:47] [INFO] features.1.0.block.0.0.weight 的平均映射误差: 0.005890
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+ [2025-04-11 14:15:47] [INFO] features.1.0.block.0.1.weight 的平均映射误差: 0.035606
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+ [2025-04-11 14:15:47] [INFO] features.1.1.block.0.0.weight 的平均映射误差: 0.004046
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+ [2025-04-11 14:15:47] [INFO] features.1.1.block.0.1.weight 的平均映射误差: 0.034027
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+ [2025-04-11 14:15:47] [INFO] features.1.2.block.0.0.weight 的平均映射误差: 0.003640
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+ [2025-04-11 14:15:47] [INFO] features.1.2.block.0.1.weight 的平均映射误差: 0.035464
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+ [2025-04-11 14:15:47] [INFO] features.2.0.block.0.0.weight 的平均映射误差: 0.003260
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+ [2025-04-11 14:15:47] [INFO] features.2.0.block.0.1.weight 的平均映射误差: 0.035470
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+ [2025-04-11 14:15:47] [INFO] features.2.0.block.1.0.weight 的平均映射误差: 0.006487
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+ [2025-04-11 14:15:47] [INFO] features.2.0.block.1.1.weight 的平均映射误差: 0.035480
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+ [2025-04-11 14:15:47] [INFO] features.2.1.block.0.0.weight 的平均映射误差: 0.001782
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+ [2025-04-11 14:15:47] [INFO] features.2.1.block.0.1.weight 的平均映射误差: 0.035523
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+ [2025-04-11 14:15:47] [INFO] features.2.1.block.1.0.weight 的平均映射误差: 0.003041
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+ [2025-04-11 14:15:47] [INFO] features.2.1.block.1.1.weight 的平均映射误差: 0.037262
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+ [2025-04-11 14:15:47] [INFO] features.2.2.block.0.0.weight 的平均映射误差: 0.001776
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+ [2025-04-11 14:15:47] [INFO] features.2.2.block.0.1.weight 的平均映射误差: 0.036067
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+ [2025-04-11 14:15:47] [INFO] features.2.2.block.1.0.weight 的平均映射误差: 0.002761
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+ [2025-04-11 14:15:47] [INFO] features.2.2.block.1.1.weight 的平均映射误差: 0.035264
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+ [2025-04-11 14:15:47] [INFO] features.2.3.block.0.0.weight 的平均映射误差: 0.001791
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+ [2025-04-11 14:15:47] [INFO] features.2.3.block.0.1.weight 的平均映射误差: 0.036800
241
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250
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256
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257
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258
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259
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260
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261
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262
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266
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267
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268
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269
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271
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272
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273
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274
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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358
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359
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360
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361
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362
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363
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365
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366
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367
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368
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369
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370
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372
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377
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378
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379
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380
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382
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383
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384
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385
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386
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387
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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462
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463
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464
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472
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482
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527
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528
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530
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535
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536
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540
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542
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543
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544
+ [2025-04-11 14:15:47] [INFO] features.6.13.block.2.fc2.weight 的平均映射误差: 0.001051
545
+ [2025-04-11 14:15:47] [INFO] features.6.13.block.3.0.weight 的平均映射误差: 0.001553
546
+ [2025-04-11 14:15:47] [INFO] features.6.13.block.3.1.weight 的平均映射误差: 0.041267
547
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.0.0.weight 的平均映射误差: 0.001584
548
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.0.1.weight 的平均映射误差: 0.042771
549
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.1.0.weight 的平均映射误差: 0.001726
550
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.1.1.weight 的平均映射误差: 0.040882
551
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.2.fc1.weight 的平均映射误差: 0.000707
552
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.2.fc2.weight 的平均映射误差: 0.000825
553
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.3.0.weight 的平均映射误差: 0.001558
554
+ [2025-04-11 14:15:47] [INFO] features.6.14.block.3.1.weight 的平均映射误差: 0.037846
555
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.0.0.weight 的平均映射误差: 0.001579
556
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.0.1.weight 的平均映射误差: 0.044733
557
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.1.0.weight 的平均映射误差: 0.001702
558
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.1.1.weight 的平均映射误差: 0.040810
559
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.2.fc1.weight 的平均映射误差: 0.000664
560
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.2.fc2.weight 的平均映射误差: 0.000729
561
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.3.0.weight 的平均映射误差: 0.001565
562
+ [2025-04-11 14:15:47] [INFO] features.6.15.block.3.1.weight 的平均映射误差: 0.037453
563
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.0.0.weight 的平均映射误差: 0.001561
564
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.0.1.weight 的平均映射误差: 0.041804
565
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.1.0.weight 的平均映射误差: 0.001678
566
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.1.1.weight 的平均映射误差: 0.048582
567
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.2.fc1.weight 的平均映射误差: 0.000723
568
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.2.fc2.weight 的平均映射误差: 0.000981
569
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.3.0.weight 的平均映射误差: 0.001532
570
+ [2025-04-11 14:15:47] [INFO] features.6.16.block.3.1.weight 的平均映射误差: 0.039843
571
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.0.0.weight 的平均映射误差: 0.001553
572
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.0.1.weight 的平均映射误差: 0.043473
573
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.1.0.weight 的平均映射误差: 0.001663
574
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.1.1.weight 的平均映射误差: 0.049348
575
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.2.fc1.weight 的平均映射误差: 0.000675
576
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.2.fc2.weight 的平均映射误差: 0.001071
577
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.3.0.weight 的平均映射误差: 0.001521
578
+ [2025-04-11 14:15:47] [INFO] features.6.17.block.3.1.weight 的平均映射误差: 0.040201
579
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.0.0.weight 的平均映射误差: 0.001853
580
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.0.1.weight 的平均映射误差: 0.032322
581
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.1.0.weight 的平均映射误差: 0.002048
582
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.1.1.weight 的平均映射误差: 0.033082
583
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.2.fc1.weight 的平均映射误差: 0.001504
584
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.2.fc2.weight 的平均映射误差: 0.001695
585
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.3.0.weight 的平均映射误差: 0.001625
586
+ [2025-04-11 14:15:47] [INFO] features.7.0.block.3.1.weight 的平均映射误差: 0.034974
587
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.0.0.weight 的平均映射误差: 0.001534
588
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.0.1.weight 的平均映射误差: 0.041334
589
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.1.0.weight 的平均映射误差: 0.001756
590
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.1.1.weight 的平均映射误差: 0.040078
591
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.2.fc1.weight 的平均映射误差: 0.001124
592
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.2.fc2.weight 的平均映射误差: 0.001550
593
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.3.0.weight 的平均映射误差: 0.001506
594
+ [2025-04-11 14:15:47] [INFO] features.7.1.block.3.1.weight 的平均映射误差: 0.048265
595
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.0.0.weight 的平均映射误差: 0.001509
596
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.0.1.weight 的平均映射误差: 0.047545
597
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.1.0.weight 的平均映射误差: 0.002202
598
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.1.1.weight 的平均映射误差: 0.044394
599
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.2.fc1.weight 的平均映射误差: 0.000866
600
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.2.fc2.weight 的平均映射误差: 0.001280
601
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.3.0.weight 的平均映射误差: 0.001464
602
+ [2025-04-11 14:15:47] [INFO] features.7.2.block.3.1.weight 的平均映射误差: 0.037660
603
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.0.0.weight 的平均映射误差: 0.001420
604
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.0.1.weight 的平均映射误差: 0.045454
605
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.1.0.weight 的平均映射误差: 0.002457
606
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.1.1.weight 的平均映射误差: 0.039783
607
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.2.fc1.weight 的平均映射误差: 0.000898
608
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.2.fc2.weight 的平均映射误差: 0.001218
609
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.3.0.weight 的平均映射误差: 0.001374
610
+ [2025-04-11 14:15:47] [INFO] features.7.3.block.3.1.weight 的平均映射误差: 0.038010
611
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.0.0.weight 的平均映射误差: 0.001374
612
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.0.1.weight 的平均映射误差: 0.035527
613
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.1.0.weight 的平均映射误差: 0.002129
614
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.1.1.weight 的平均映射误差: 0.034052
615
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.2.fc1.weight 的平均映射误差: 0.001206
616
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.2.fc2.weight 的平均映射误差: 0.001140
617
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.3.0.weight 的平均映射误差: 0.001325
618
+ [2025-04-11 14:15:47] [INFO] features.7.4.block.3.1.weight 的平均映射误差: 0.039857
619
+ [2025-04-11 14:15:47] [INFO] features.8.0.weight 的平均映射误差: 0.001615
620
+ [2025-04-11 14:15:47] [INFO] features.8.1.weight 的平均映射误差: 0.035214
621
+ [2025-04-11 14:15:47] [INFO] classifier.1.weight 的平均映射误差: 0.001788
622
+ [2025-04-11 14:15:47] [INFO] 评估结果 - Loss: 1.3406, Accuracy: 0.2414
623
+ [2025-04-11 14:15:47] [INFO] RRAM映射模型已保存到 checkpoints/rram_mapped_model.pth
624
+ [2025-04-11 14:15:47] [INFO] RRAM映射前后精度对比: 原始 0.5862 vs RRAM映射后 0.2414, 变化: -0.3448
625
+ [2025-04-11 14:15:47] [INFO] 开始微调全连接层 (epochs=50, lr=5e-05)...
626
+ [2025-04-11 14:15:47] [INFO] 微调过程中的模型将保存到: checkpoints/fine_tune_checkpoints
627
+ [2025-04-11 14:15:49] [INFO] Fine-tuning Epoch 1/50 - Train Acc: 0.8521, Val Acc: 0.5172
628
+ [2025-04-11 14:15:50] [INFO] 已保存第 1 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_1.pth
629
+ [2025-04-11 14:15:51] [INFO] Fine-tuning Epoch 2/50 - Train Acc: 0.9085, Val Acc: 0.5517
630
+ [2025-04-11 14:15:52] [INFO] 已保存第 2 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_2.pth
631
+ [2025-04-11 14:15:53] [INFO] Fine-tuning Epoch 3/50 - Train Acc: 0.9085, Val Acc: 0.6207
632
+ [2025-04-11 14:15:54] [INFO] 已保存第 3 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_3.pth
633
+ [2025-04-11 14:15:55] [INFO] Fine-tuning Epoch 4/50 - Train Acc: 0.8873, Val Acc: 0.7586
634
+ [2025-04-11 14:15:56] [INFO] 已保存第 4 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_4.pth
635
+ [2025-04-11 14:15:57] [INFO] Fine-tuning Epoch 5/50 - Train Acc: 0.8944, Val Acc: 0.7586
636
+ [2025-04-11 14:15:58] [INFO] 已保存第 5 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_5.pth
637
+ [2025-04-11 14:15:59] [INFO] Fine-tuning Epoch 6/50 - Train Acc: 0.9577, Val Acc: 0.8276
638
+ [2025-04-11 14:16:00] [INFO] 已保存第 6 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_6.pth
639
+ [2025-04-11 14:16:01] [INFO] Fine-tuning Epoch 7/50 - Train Acc: 0.9014, Val Acc: 0.8621
640
+ [2025-04-11 14:16:02] [INFO] 已保存第 7 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_7.pth
641
+ [2025-04-11 14:16:03] [INFO] Fine-tuning Epoch 8/50 - Train Acc: 0.9155, Val Acc: 0.7241
642
+ [2025-04-11 14:16:04] [INFO] 已保存第 8 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_8.pth
643
+ [2025-04-11 14:16:05] [INFO] Fine-tuning Epoch 9/50 - Train Acc: 0.9225, Val Acc: 0.5862
644
+ [2025-04-11 14:16:06] [INFO] 已保存第 9 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_9.pth
645
+ [2025-04-11 14:16:08] [INFO] Fine-tuning Epoch 10/50 - Train Acc: 0.9648, Val Acc: 0.5862
646
+ [2025-04-11 14:16:08] [INFO] 已保存第 10 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_10.pth
647
+ [2025-04-11 14:16:10] [INFO] Fine-tuning Epoch 11/50 - Train Acc: 0.9577, Val Acc: 0.5862
648
+ [2025-04-11 14:16:10] [INFO] 已保存第 11 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_11.pth
649
+ [2025-04-11 14:16:12] [INFO] Fine-tuning Epoch 12/50 - Train Acc: 0.9577, Val Acc: 0.6207
650
+ [2025-04-11 14:16:12] [INFO] 已保存第 12 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_12.pth
651
+ [2025-04-11 14:16:14] [INFO] Fine-tuning Epoch 13/50 - Train Acc: 0.9789, Val Acc: 0.6207
652
+ [2025-04-11 14:16:15] [INFO] 已保存第 13 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_13.pth
653
+ [2025-04-11 14:16:16] [INFO] Fine-tuning Epoch 14/50 - Train Acc: 0.9789, Val Acc: 0.7931
654
+ [2025-04-11 14:16:17] [INFO] 已保存第 14 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_14.pth
655
+ [2025-04-11 14:16:18] [INFO] Fine-tuning Epoch 15/50 - Train Acc: 0.9718, Val Acc: 0.9310
656
+ [2025-04-11 14:16:19] [INFO] 已保存第 15 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_15.pth
657
+ [2025-04-11 14:16:20] [INFO] Fine-tuning Epoch 16/50 - Train Acc: 0.9577, Val Acc: 0.8276
658
+ [2025-04-11 14:16:21] [INFO] 已保存第 16 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_16.pth
659
+ [2025-04-11 14:16:22] [INFO] Fine-tuning Epoch 17/50 - Train Acc: 0.9789, Val Acc: 0.8966
660
+ [2025-04-11 14:16:23] [INFO] 已保存第 17 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_17.pth
661
+ [2025-04-11 14:16:24] [INFO] Fine-tuning Epoch 18/50 - Train Acc: 0.9718, Val Acc: 0.8276
662
+ [2025-04-11 14:16:25] [INFO] 已保存第 18 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_18.pth
663
+ [2025-04-11 14:16:26] [INFO] Fine-tuning Epoch 19/50 - Train Acc: 0.9718, Val Acc: 0.8276
664
+ [2025-04-11 14:16:27] [INFO] 已保存第 19 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_19.pth
665
+ [2025-04-11 14:16:28] [INFO] Fine-tuning Epoch 20/50 - Train Acc: 0.9930, Val Acc: 0.7931
666
+ [2025-04-11 14:16:29] [INFO] 已保存第 20 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_20.pth
667
+ [2025-04-11 14:16:30] [INFO] Fine-tuning Epoch 21/50 - Train Acc: 0.9577, Val Acc: 0.7931
668
+ [2025-04-11 14:16:31] [INFO] 已保存第 21 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_21.pth
669
+ [2025-04-11 14:16:32] [INFO] Fine-tuning Epoch 22/50 - Train Acc: 0.9930, Val Acc: 0.7931
670
+ [2025-04-11 14:16:33] [INFO] 已保存第 22 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_22.pth
671
+ [2025-04-11 14:16:34] [INFO] Fine-tuning Epoch 23/50 - Train Acc: 0.9789, Val Acc: 0.8276
672
+ [2025-04-11 14:16:35] [INFO] 已保存第 23 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_23.pth
673
+ [2025-04-11 14:16:36] [INFO] Fine-tuning Epoch 24/50 - Train Acc: 0.9930, Val Acc: 0.8276
674
+ [2025-04-11 14:16:37] [INFO] 已保存第 24 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_24.pth
675
+ [2025-04-11 14:16:38] [INFO] Fine-tuning Epoch 25/50 - Train Acc: 0.9859, Val Acc: 0.8621
676
+ [2025-04-11 14:16:39] [INFO] 已保存第 25 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_25.pth
677
+ [2025-04-11 14:16:40] [INFO] Fine-tuning Epoch 26/50 - Train Acc: 0.9859, Val Acc: 0.7931
678
+ [2025-04-11 14:16:41] [INFO] 已保存第 26 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_26.pth
679
+ [2025-04-11 14:16:43] [INFO] Fine-tuning Epoch 27/50 - Train Acc: 0.9930, Val Acc: 0.7931
680
+ [2025-04-11 14:16:43] [INFO] 已保存第 27 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_27.pth
681
+ [2025-04-11 14:16:45] [INFO] Fine-tuning Epoch 28/50 - Train Acc: 1.0000, Val Acc: 0.7586
682
+ [2025-04-11 14:16:45] [INFO] 已保存第 28 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_28.pth
683
+ [2025-04-11 14:16:47] [INFO] Fine-tuning Epoch 29/50 - Train Acc: 1.0000, Val Acc: 0.7586
684
+ [2025-04-11 14:16:47] [INFO] 已保存第 29 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_29.pth
685
+ [2025-04-11 14:16:49] [INFO] Fine-tuning Epoch 30/50 - Train Acc: 0.9859, Val Acc: 0.7931
686
+ [2025-04-11 14:16:49] [INFO] 已保存第 30 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_30.pth
687
+ [2025-04-11 14:16:51] [INFO] Fine-tuning Epoch 31/50 - Train Acc: 0.9930, Val Acc: 0.7931
688
+ [2025-04-11 14:16:52] [INFO] 已保存第 31 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_31.pth
689
+ [2025-04-11 14:16:53] [INFO] Fine-tuning Epoch 32/50 - Train Acc: 0.9930, Val Acc: 0.8276
690
+ [2025-04-11 14:16:54] [INFO] 已保存第 32 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_32.pth
691
+ [2025-04-11 14:16:55] [INFO] Fine-tuning Epoch 33/50 - Train Acc: 1.0000, Val Acc: 0.9310
692
+ [2025-04-11 14:16:56] [INFO] 已保存第 33 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_33.pth
693
+ [2025-04-11 14:16:57] [INFO] Fine-tuning Epoch 34/50 - Train Acc: 1.0000, Val Acc: 0.9310
694
+ [2025-04-11 14:16:58] [INFO] 已保存第 34 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_34.pth
695
+ [2025-04-11 14:16:59] [INFO] Fine-tuning Epoch 35/50 - Train Acc: 0.9859, Val Acc: 0.8621
696
+ [2025-04-11 14:17:00] [INFO] 已保存第 35 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_35.pth
697
+ [2025-04-11 14:17:01] [INFO] Fine-tuning Epoch 36/50 - Train Acc: 1.0000, Val Acc: 0.8621
698
+ [2025-04-11 14:17:02] [INFO] 已保存第 36 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_36.pth
699
+ [2025-04-11 14:17:03] [INFO] Fine-tuning Epoch 37/50 - Train Acc: 0.9718, Val Acc: 0.8966
700
+ [2025-04-11 14:17:04] [INFO] 已保存第 37 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_37.pth
701
+ [2025-04-11 14:17:05] [INFO] Fine-tuning Epoch 38/50 - Train Acc: 1.0000, Val Acc: 0.8621
702
+ [2025-04-11 14:17:06] [INFO] 已保存第 38 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_38.pth
703
+ [2025-04-11 14:17:07] [INFO] Fine-tuning Epoch 39/50 - Train Acc: 1.0000, Val Acc: 0.8276
704
+ [2025-04-11 14:17:08] [INFO] 已保存第 39 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_39.pth
705
+ [2025-04-11 14:17:09] [INFO] Fine-tuning Epoch 40/50 - Train Acc: 1.0000, Val Acc: 0.8276
706
+ [2025-04-11 14:17:10] [INFO] 已保存第 40 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_40.pth
707
+ [2025-04-11 14:17:11] [INFO] Fine-tuning Epoch 41/50 - Train Acc: 0.9930, Val Acc: 0.8276
708
+ [2025-04-11 14:17:12] [INFO] 已保存第 41 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_41.pth
709
+ [2025-04-11 14:17:14] [INFO] Fine-tuning Epoch 42/50 - Train Acc: 1.0000, Val Acc: 0.8276
710
+ [2025-04-11 14:17:14] [INFO] 已保存第 42 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_42.pth
711
+ [2025-04-11 14:17:16] [INFO] Fine-tuning Epoch 43/50 - Train Acc: 1.0000, Val Acc: 0.8276
712
+ [2025-04-11 14:17:16] [INFO] 已保存第 43 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_43.pth
713
+ [2025-04-11 14:17:18] [INFO] Fine-tuning Epoch 44/50 - Train Acc: 0.9859, Val Acc: 0.8621
714
+ [2025-04-11 14:17:18] [INFO] 已保存第 44 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_44.pth
715
+ [2025-04-11 14:17:20] [INFO] Fine-tuning Epoch 45/50 - Train Acc: 0.9930, Val Acc: 0.8621
716
+ [2025-04-11 14:17:20] [INFO] 已保存第 45 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_45.pth
717
+ [2025-04-11 14:17:22] [INFO] Fine-tuning Epoch 46/50 - Train Acc: 0.9859, Val Acc: 0.8621
718
+ [2025-04-11 14:17:22] [INFO] 已保存第 46 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_46.pth
719
+ [2025-04-11 14:17:24] [INFO] Fine-tuning Epoch 47/50 - Train Acc: 0.9930, Val Acc: 0.8621
720
+ [2025-04-11 14:17:25] [INFO] 已保存第 47 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_47.pth
721
+ [2025-04-11 14:17:26] [INFO] Fine-tuning Epoch 48/50 - Train Acc: 1.0000, Val Acc: 0.8276
722
+ [2025-04-11 14:17:27] [INFO] 已保存第 48 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_48.pth
723
+ [2025-04-11 14:17:28] [INFO] Fine-tuning Epoch 49/50 - Train Acc: 0.9789, Val Acc: 0.7931
724
+ [2025-04-11 14:17:29] [INFO] 已保存第 49 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_49.pth
725
+ [2025-04-11 14:17:30] [INFO] Fine-tuning Epoch 50/50 - Train Acc: 0.9930, Val Acc: 0.8276
726
+ [2025-04-11 14:17:31] [INFO] 已保存第 50 轮微调模型到: checkpoints/fine_tune_checkpoints/fine_tuned_model_epoch_50.pth
727
+ [2025-04-11 14:17:31] [INFO] 评估结果 - Loss: 0.5838, Accuracy: 0.8276
728
+ [2025-04-11 14:17:32] [INFO] 微调模型已保存到 checkpoints/fine_tuned_model.pth
729
+ [2025-04-11 14:17:32] [INFO] 微调前后精度对比: RRAM映射 0.2414 vs 微调后 0.8276, 变化: 0.5862
730
+ [2025-04-11 14:17:32] [INFO] 所有处理完成!
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