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code-philia
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mtpnet_image_models

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effientnet 02 geoglenet 0

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  1. Image/DenseNet/code/backdoor_train.log +253 -0
  2. Image/DenseNet/code/train.log +250 -0
  3. Image/DenseNet/code/train.py +10 -6
  4. Image/DenseNet/model/0/epoch1/embeddings.npy +3 -0
  5. Image/DenseNet/model/0/epoch1/subject_model.pth +3 -0
  6. Image/DenseNet/model/0/epoch10/embeddings.npy +3 -0
  7. Image/DenseNet/model/0/epoch10/subject_model.pth +3 -0
  8. Image/DenseNet/model/0/epoch11/embeddings.npy +3 -0
  9. Image/DenseNet/model/0/epoch11/subject_model.pth +3 -0
  10. Image/DenseNet/model/0/epoch12/embeddings.npy +3 -0
  11. Image/DenseNet/model/0/epoch12/subject_model.pth +3 -0
  12. Image/DenseNet/model/0/epoch13/embeddings.npy +3 -0
  13. Image/DenseNet/model/0/epoch13/subject_model.pth +3 -0
  14. Image/DenseNet/model/0/epoch14/embeddings.npy +3 -0
  15. Image/DenseNet/model/0/epoch14/subject_model.pth +3 -0
  16. Image/DenseNet/model/0/epoch15/embeddings.npy +3 -0
  17. Image/DenseNet/model/0/epoch15/subject_model.pth +3 -0
  18. Image/DenseNet/model/0/epoch16/embeddings.npy +3 -0
  19. Image/DenseNet/model/0/epoch16/subject_model.pth +3 -0
  20. Image/DenseNet/model/0/epoch17/embeddings.npy +3 -0
  21. Image/DenseNet/model/0/epoch17/subject_model.pth +3 -0
  22. Image/DenseNet/model/0/epoch18/embeddings.npy +3 -0
  23. Image/DenseNet/model/0/epoch18/subject_model.pth +3 -0
  24. Image/DenseNet/model/0/epoch19/embeddings.npy +3 -0
  25. Image/DenseNet/model/0/epoch19/subject_model.pth +3 -0
  26. Image/DenseNet/model/0/epoch2/embeddings.npy +3 -0
  27. Image/DenseNet/model/0/epoch2/subject_model.pth +3 -0
  28. Image/DenseNet/model/0/epoch20/embeddings.npy +3 -0
  29. Image/DenseNet/model/0/epoch20/subject_model.pth +3 -0
  30. Image/DenseNet/model/0/epoch21/embeddings.npy +3 -0
  31. Image/DenseNet/model/0/epoch21/subject_model.pth +3 -0
  32. Image/DenseNet/model/0/epoch22/embeddings.npy +3 -0
  33. Image/DenseNet/model/0/epoch22/subject_model.pth +3 -0
  34. Image/DenseNet/model/0/epoch23/embeddings.npy +3 -0
  35. Image/DenseNet/model/0/epoch23/subject_model.pth +3 -0
  36. Image/DenseNet/model/0/epoch24/embeddings.npy +3 -0
  37. Image/DenseNet/model/0/epoch24/subject_model.pth +3 -0
  38. Image/DenseNet/model/0/epoch3/embeddings.npy +3 -0
  39. Image/DenseNet/model/0/epoch3/subject_model.pth +3 -0
  40. Image/DenseNet/model/0/epoch4/embeddings.npy +3 -0
  41. Image/DenseNet/model/0/epoch4/subject_model.pth +3 -0
  42. Image/DenseNet/model/0/epoch5/embeddings.npy +3 -0
  43. Image/DenseNet/model/0/epoch5/subject_model.pth +3 -0
  44. Image/DenseNet/model/0/epoch6/embeddings.npy +3 -0
  45. Image/DenseNet/model/0/epoch6/subject_model.pth +3 -0
  46. Image/DenseNet/model/0/epoch7/embeddings.npy +3 -0
  47. Image/DenseNet/model/0/epoch7/subject_model.pth +3 -0
  48. Image/DenseNet/model/0/epoch8/embeddings.npy +3 -0
  49. Image/DenseNet/model/0/epoch8/subject_model.pth +3 -0
  50. Image/DenseNet/model/0/epoch9/embeddings.npy +3 -0
Image/DenseNet/code/backdoor_train.log ADDED
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1
+ 2025-03-14 16:01:30,357 - train - INFO - 开始训练 densenet
2
+ 2025-03-14 16:01:30,357 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3
3
+ 2025-03-14 16:01:35,120 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 10.94%
4
+ 2025-03-14 16:01:41,623 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.009 | Acc: 25.39%
5
+ 2025-03-14 16:01:48,359 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.872 | Acc: 29.96%
6
+ 2025-03-14 16:01:54,814 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.780 | Acc: 33.57%
7
+ 2025-03-14 16:02:02,833 - train - INFO - Epoch: 1 | Test Loss: 1.342 | Test Acc: 52.66%
8
+ 2025-03-14 16:02:03,082 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.477 | Acc: 41.41%
9
+ 2025-03-14 16:02:09,563 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.335 | Acc: 51.83%
10
+ 2025-03-14 16:02:16,183 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.242 | Acc: 55.57%
11
+ 2025-03-14 16:02:22,685 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.173 | Acc: 58.28%
12
+ 2025-03-14 16:02:30,658 - train - INFO - Epoch: 2 | Test Loss: 1.093 | Test Acc: 61.78%
13
+ 2025-03-14 16:02:41,822 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.999 | Acc: 63.28%
14
+ 2025-03-14 16:02:48,275 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.908 | Acc: 68.81%
15
+ 2025-03-14 16:02:55,542 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.893 | Acc: 69.34%
16
+ 2025-03-14 16:03:02,152 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.874 | Acc: 69.98%
17
+ 2025-03-14 16:03:09,933 - train - INFO - Epoch: 3 | Test Loss: 0.895 | Test Acc: 69.23%
18
+ 2025-03-14 16:03:10,171 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.741 | Acc: 75.00%
19
+ 2025-03-14 16:03:16,709 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.748 | Acc: 74.24%
20
+ 2025-03-14 16:03:23,165 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.743 | Acc: 74.83%
21
+ 2025-03-14 16:03:29,519 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.735 | Acc: 75.22%
22
+ 2025-03-14 16:03:37,232 - train - INFO - Epoch: 4 | Test Loss: 0.914 | Test Acc: 69.83%
23
+ 2025-03-14 16:03:48,484 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.680 | Acc: 75.78%
24
+ 2025-03-14 16:03:55,252 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.674 | Acc: 77.53%
25
+ 2025-03-14 16:04:01,807 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.667 | Acc: 77.67%
26
+ 2025-03-14 16:04:08,493 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.661 | Acc: 77.80%
27
+ 2025-03-14 16:04:16,124 - train - INFO - Epoch: 5 | Test Loss: 0.713 | Test Acc: 76.28%
28
+ 2025-03-14 16:04:16,366 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.490 | Acc: 87.50%
29
+ 2025-03-14 16:04:22,967 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.614 | Acc: 79.94%
30
+ 2025-03-14 16:04:29,555 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.626 | Acc: 79.32%
31
+ 2025-03-14 16:04:35,837 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.619 | Acc: 79.52%
32
+ 2025-03-14 16:04:43,506 - train - INFO - Epoch: 6 | Test Loss: 0.767 | Test Acc: 74.45%
33
+ 2025-03-14 16:04:55,346 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.728 | Acc: 75.00%
34
+ 2025-03-14 16:05:02,115 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.594 | Acc: 80.22%
35
+ 2025-03-14 16:05:08,580 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.593 | Acc: 80.24%
36
+ 2025-03-14 16:05:15,893 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.597 | Acc: 80.01%
37
+ 2025-03-14 16:05:24,379 - train - INFO - Epoch: 7 | Test Loss: 0.659 | Test Acc: 76.67%
38
+ 2025-03-14 16:05:24,687 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.511 | Acc: 82.81%
39
+ 2025-03-14 16:05:31,960 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.554 | Acc: 81.19%
40
+ 2025-03-14 16:05:38,472 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.564 | Acc: 81.15%
41
+ 2025-03-14 16:05:45,042 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.568 | Acc: 81.13%
42
+ 2025-03-14 16:05:53,242 - train - INFO - Epoch: 8 | Test Loss: 0.714 | Test Acc: 76.71%
43
+ 2025-03-14 16:06:05,059 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.489 | Acc: 85.16%
44
+ 2025-03-14 16:06:11,780 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.565 | Acc: 81.15%
45
+ 2025-03-14 16:06:18,537 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.564 | Acc: 81.30%
46
+ 2025-03-14 16:06:25,070 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.566 | Acc: 81.23%
47
+ 2025-03-14 16:06:32,812 - train - INFO - Epoch: 9 | Test Loss: 1.349 | Test Acc: 60.93%
48
+ 2025-03-14 16:06:33,056 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.427 | Acc: 85.94%
49
+ 2025-03-14 16:06:40,104 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.541 | Acc: 82.09%
50
+ 2025-03-14 16:06:47,429 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.546 | Acc: 81.77%
51
+ 2025-03-14 16:06:55,098 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.547 | Acc: 81.80%
52
+ 2025-03-14 16:07:03,407 - train - INFO - Epoch: 10 | Test Loss: 0.619 | Test Acc: 80.03%
53
+ 2025-03-14 16:07:15,781 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.509 | Acc: 85.94%
54
+ 2025-03-14 16:07:22,836 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.546 | Acc: 82.31%
55
+ 2025-03-14 16:07:29,380 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.547 | Acc: 82.02%
56
+ 2025-03-14 16:07:36,091 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.545 | Acc: 82.09%
57
+ 2025-03-14 16:07:44,313 - train - INFO - Epoch: 11 | Test Loss: 0.782 | Test Acc: 74.83%
58
+ 2025-03-14 16:07:44,642 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.424 | Acc: 88.28%
59
+ 2025-03-14 16:07:52,452 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.532 | Acc: 82.69%
60
+ 2025-03-14 16:07:59,454 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.535 | Acc: 82.39%
61
+ 2025-03-14 16:08:05,751 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.539 | Acc: 82.22%
62
+ 2025-03-14 16:08:13,415 - train - INFO - Epoch: 12 | Test Loss: 0.667 | Test Acc: 77.20%
63
+ 2025-03-14 16:08:24,850 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.513 | Acc: 81.25%
64
+ 2025-03-14 16:08:31,529 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.525 | Acc: 82.91%
65
+ 2025-03-14 16:08:37,988 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.524 | Acc: 82.91%
66
+ 2025-03-14 16:08:44,426 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.531 | Acc: 82.63%
67
+ 2025-03-14 16:08:52,236 - train - INFO - Epoch: 13 | Test Loss: 0.671 | Test Acc: 77.66%
68
+ 2025-03-14 16:08:52,474 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.400 | Acc: 87.50%
69
+ 2025-03-14 16:08:58,885 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.513 | Acc: 83.14%
70
+ 2025-03-14 16:09:05,594 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.526 | Acc: 82.57%
71
+ 2025-03-14 16:09:13,190 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.525 | Acc: 82.58%
72
+ 2025-03-14 16:09:21,359 - train - INFO - Epoch: 14 | Test Loss: 0.644 | Test Acc: 78.23%
73
+ 2025-03-14 16:09:32,590 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.637 | Acc: 78.91%
74
+ 2025-03-14 16:09:39,160 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.521 | Acc: 82.97%
75
+ 2025-03-14 16:09:45,611 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.523 | Acc: 82.81%
76
+ 2025-03-14 16:09:52,133 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.522 | Acc: 82.83%
77
+ 2025-03-14 16:09:59,547 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 78.16%
78
+ 2025-03-14 16:09:59,772 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.607 | Acc: 76.56%
79
+ 2025-03-14 16:10:06,285 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.508 | Acc: 83.32%
80
+ 2025-03-14 16:10:12,668 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.504 | Acc: 83.33%
81
+ 2025-03-14 16:10:19,419 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.514 | Acc: 83.00%
82
+ 2025-03-14 16:10:27,539 - train - INFO - Epoch: 16 | Test Loss: 0.622 | Test Acc: 79.45%
83
+ 2025-03-14 16:10:38,671 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.503 | Acc: 86.72%
84
+ 2025-03-14 16:10:45,253 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.503 | Acc: 83.30%
85
+ 2025-03-14 16:10:51,781 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.507 | Acc: 83.28%
86
+ 2025-03-14 16:10:58,057 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.504 | Acc: 83.31%
87
+ 2025-03-14 16:11:05,776 - train - INFO - Epoch: 17 | Test Loss: 0.701 | Test Acc: 77.26%
88
+ 2025-03-14 16:11:06,018 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.620 | Acc: 78.91%
89
+ 2025-03-14 16:11:13,323 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.498 | Acc: 83.79%
90
+ 2025-03-14 16:11:20,126 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.494 | Acc: 83.98%
91
+ 2025-03-14 16:11:26,804 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.499 | Acc: 83.79%
92
+ 2025-03-14 16:11:34,825 - train - INFO - Epoch: 18 | Test Loss: 0.751 | Test Acc: 76.20%
93
+ 2025-03-14 16:11:46,371 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.408 | Acc: 86.72%
94
+ 2025-03-14 16:11:53,283 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.485 | Acc: 84.00%
95
+ 2025-03-14 16:12:00,455 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.493 | Acc: 83.82%
96
+ 2025-03-14 16:12:06,896 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.499 | Acc: 83.56%
97
+ 2025-03-14 16:12:15,134 - train - INFO - Epoch: 19 | Test Loss: 0.663 | Test Acc: 76.95%
98
+ 2025-03-14 16:12:15,418 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.390 | Acc: 85.94%
99
+ 2025-03-14 16:12:21,907 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.500 | Acc: 83.41%
100
+ 2025-03-14 16:12:28,443 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.498 | Acc: 83.41%
101
+ 2025-03-14 16:12:35,175 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.494 | Acc: 83.63%
102
+ 2025-03-14 16:12:45,840 - train - INFO - Epoch: 20 | Test Loss: 0.561 | Test Acc: 81.65%
103
+ 2025-03-14 16:12:57,454 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.349 | Acc: 89.84%
104
+ 2025-03-14 16:13:04,132 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.477 | Acc: 83.99%
105
+ 2025-03-14 16:13:10,608 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.477 | Acc: 84.11%
106
+ 2025-03-14 16:13:17,405 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.481 | Acc: 84.17%
107
+ 2025-03-14 16:13:25,297 - train - INFO - Epoch: 21 | Test Loss: 0.593 | Test Acc: 80.85%
108
+ 2025-03-14 16:13:25,591 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.453 | Acc: 82.81%
109
+ 2025-03-14 16:13:32,327 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.466 | Acc: 84.52%
110
+ 2025-03-14 16:13:39,185 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.471 | Acc: 84.26%
111
+ 2025-03-14 16:13:45,668 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.478 | Acc: 84.05%
112
+ 2025-03-14 16:13:53,441 - train - INFO - Epoch: 22 | Test Loss: 0.665 | Test Acc: 78.56%
113
+ 2025-03-14 16:14:05,354 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.474 | Acc: 84.38%
114
+ 2025-03-14 16:14:11,690 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.474 | Acc: 84.71%
115
+ 2025-03-14 16:14:18,410 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.482 | Acc: 84.29%
116
+ 2025-03-14 16:14:24,963 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.478 | Acc: 84.44%
117
+ 2025-03-14 16:14:32,640 - train - INFO - Epoch: 23 | Test Loss: 1.058 | Test Acc: 68.37%
118
+ 2025-03-14 16:14:32,934 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.378 | Acc: 89.06%
119
+ 2025-03-14 16:14:39,551 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.477 | Acc: 84.41%
120
+ 2025-03-14 16:14:46,168 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.481 | Acc: 84.37%
121
+ 2025-03-14 16:14:52,732 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.479 | Acc: 84.30%
122
+ 2025-03-14 16:15:00,623 - train - INFO - Epoch: 24 | Test Loss: 0.636 | Test Acc: 80.17%
123
+ 2025-03-14 16:15:11,674 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.529 | Acc: 82.03%
124
+ 2025-03-14 16:15:18,136 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.454 | Acc: 85.12%
125
+ 2025-03-14 16:15:24,586 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.464 | Acc: 84.64%
126
+ 2025-03-14 16:15:31,108 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.466 | Acc: 84.54%
127
+ 2025-03-14 16:15:39,073 - train - INFO - Epoch: 25 | Test Loss: 0.572 | Test Acc: 81.28%
128
+ 2025-03-14 16:15:39,328 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.496 | Acc: 82.03%
129
+ 2025-03-14 16:15:45,847 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.468 | Acc: 84.57%
130
+ 2025-03-14 16:15:52,738 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.469 | Acc: 84.65%
131
+ 2025-03-14 16:15:59,415 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.466 | Acc: 84.73%
132
+ 2025-03-14 16:16:06,894 - train - INFO - Epoch: 26 | Test Loss: 0.816 | Test Acc: 74.91%
133
+ 2025-03-14 16:16:18,304 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.400 | Acc: 85.94%
134
+ 2025-03-14 16:16:24,917 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.469 | Acc: 84.55%
135
+ 2025-03-14 16:16:31,318 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.464 | Acc: 84.77%
136
+ 2025-03-14 16:16:37,744 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.462 | Acc: 84.86%
137
+ 2025-03-14 16:16:45,639 - train - INFO - Epoch: 27 | Test Loss: 0.561 | Test Acc: 80.96%
138
+ 2025-03-14 16:16:45,909 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.450 | Acc: 82.81%
139
+ 2025-03-14 16:16:52,939 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.464 | Acc: 84.66%
140
+ 2025-03-14 16:16:59,702 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.466 | Acc: 84.61%
141
+ 2025-03-14 16:17:06,087 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.467 | Acc: 84.57%
142
+ 2025-03-14 16:17:13,719 - train - INFO - Epoch: 28 | Test Loss: 0.692 | Test Acc: 78.32%
143
+ 2025-03-14 16:17:25,054 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.428 | Acc: 83.59%
144
+ 2025-03-14 16:17:31,720 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.457 | Acc: 84.75%
145
+ 2025-03-14 16:17:38,193 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.463 | Acc: 84.71%
146
+ 2025-03-14 16:17:44,756 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.465 | Acc: 84.69%
147
+ 2025-03-14 16:17:52,619 - train - INFO - Epoch: 29 | Test Loss: 0.517 | Test Acc: 82.95%
148
+ 2025-03-14 16:17:52,871 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.412 | Acc: 86.72%
149
+ 2025-03-14 16:17:59,452 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.442 | Acc: 85.97%
150
+ 2025-03-14 16:18:06,228 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.452 | Acc: 85.31%
151
+ 2025-03-14 16:18:12,888 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.457 | Acc: 85.07%
152
+ 2025-03-14 16:18:21,005 - train - INFO - Epoch: 30 | Test Loss: 0.656 | Test Acc: 78.35%
153
+ 2025-03-14 16:18:33,022 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.579 | Acc: 82.81%
154
+ 2025-03-14 16:18:39,739 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.443 | Acc: 85.60%
155
+ 2025-03-14 16:18:46,476 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.452 | Acc: 85.29%
156
+ 2025-03-14 16:18:52,888 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.458 | Acc: 85.02%
157
+ 2025-03-14 16:19:00,889 - train - INFO - Epoch: 31 | Test Loss: 0.530 | Test Acc: 82.67%
158
+ 2025-03-14 16:19:01,161 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.321 | Acc: 89.06%
159
+ 2025-03-14 16:19:07,656 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.446 | Acc: 85.07%
160
+ 2025-03-14 16:19:14,096 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.451 | Acc: 84.76%
161
+ 2025-03-14 16:19:20,849 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.454 | Acc: 84.86%
162
+ 2025-03-14 16:19:28,416 - train - INFO - Epoch: 32 | Test Loss: 0.834 | Test Acc: 73.96%
163
+ 2025-03-14 16:19:40,125 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.497 | Acc: 82.03%
164
+ 2025-03-14 16:19:47,217 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.447 | Acc: 85.14%
165
+ 2025-03-14 16:19:53,869 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.460 | Acc: 84.78%
166
+ 2025-03-14 16:20:00,742 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.451 | Acc: 85.11%
167
+ 2025-03-14 16:20:09,000 - train - INFO - Epoch: 33 | Test Loss: 0.449 | Test Acc: 84.73%
168
+ 2025-03-14 16:20:09,277 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.413 | Acc: 82.03%
169
+ 2025-03-14 16:20:16,108 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.448 | Acc: 85.24%
170
+ 2025-03-14 16:20:22,620 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.445 | Acc: 85.32%
171
+ 2025-03-14 16:20:29,138 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.443 | Acc: 85.38%
172
+ 2025-03-14 16:20:37,243 - train - INFO - Epoch: 34 | Test Loss: 0.531 | Test Acc: 82.30%
173
+ 2025-03-14 16:20:49,210 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.354 | Acc: 87.50%
174
+ 2025-03-14 16:20:55,814 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.432 | Acc: 85.93%
175
+ 2025-03-14 16:21:02,450 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.439 | Acc: 85.55%
176
+ 2025-03-14 16:21:08,928 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.442 | Acc: 85.40%
177
+ 2025-03-14 16:21:17,192 - train - INFO - Epoch: 35 | Test Loss: 0.597 | Test Acc: 80.39%
178
+ 2025-03-14 16:21:17,446 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.304 | Acc: 89.84%
179
+ 2025-03-14 16:21:24,132 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.440 | Acc: 85.47%
180
+ 2025-03-14 16:21:30,524 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.436 | Acc: 85.62%
181
+ 2025-03-14 16:21:36,979 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.441 | Acc: 85.50%
182
+ 2025-03-14 16:21:44,631 - train - INFO - Epoch: 36 | Test Loss: 0.642 | Test Acc: 78.95%
183
+ 2025-03-14 16:21:56,350 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.366 | Acc: 86.72%
184
+ 2025-03-14 16:22:02,994 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.435 | Acc: 85.64%
185
+ 2025-03-14 16:22:09,586 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.440 | Acc: 85.30%
186
+ 2025-03-14 16:22:16,323 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.444 | Acc: 85.23%
187
+ 2025-03-14 16:22:24,686 - train - INFO - Epoch: 37 | Test Loss: 0.630 | Test Acc: 79.88%
188
+ 2025-03-14 16:22:24,928 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.366 | Acc: 89.06%
189
+ 2025-03-14 16:22:31,317 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.420 | Acc: 85.93%
190
+ 2025-03-14 16:22:37,751 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.421 | Acc: 86.11%
191
+ 2025-03-14 16:22:44,033 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.427 | Acc: 85.95%
192
+ 2025-03-14 16:22:52,202 - train - INFO - Epoch: 38 | Test Loss: 0.530 | Test Acc: 82.57%
193
+ 2025-03-14 16:23:03,115 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.402 | Acc: 85.94%
194
+ 2025-03-14 16:23:09,617 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.437 | Acc: 85.83%
195
+ 2025-03-14 16:23:16,188 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.434 | Acc: 85.81%
196
+ 2025-03-14 16:23:22,787 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.440 | Acc: 85.63%
197
+ 2025-03-14 16:23:30,722 - train - INFO - Epoch: 39 | Test Loss: 0.605 | Test Acc: 80.62%
198
+ 2025-03-14 16:23:30,947 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.363 | Acc: 89.84%
199
+ 2025-03-14 16:23:37,305 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.437 | Acc: 85.76%
200
+ 2025-03-14 16:23:43,539 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.436 | Acc: 85.80%
201
+ 2025-03-14 16:23:49,649 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.438 | Acc: 85.76%
202
+ 2025-03-14 16:23:57,791 - train - INFO - Epoch: 40 | Test Loss: 0.522 | Test Acc: 82.90%
203
+ 2025-03-14 16:24:10,073 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.347 | Acc: 87.50%
204
+ 2025-03-14 16:24:16,616 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.431 | Acc: 85.68%
205
+ 2025-03-14 16:24:23,037 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.438 | Acc: 85.39%
206
+ 2025-03-14 16:24:29,562 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.435 | Acc: 85.55%
207
+ 2025-03-14 16:24:37,304 - train - INFO - Epoch: 41 | Test Loss: 0.576 | Test Acc: 80.45%
208
+ 2025-03-14 16:24:37,526 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.319 | Acc: 91.41%
209
+ 2025-03-14 16:24:44,058 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.426 | Acc: 86.07%
210
+ 2025-03-14 16:24:50,478 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.428 | Acc: 85.86%
211
+ 2025-03-14 16:24:56,812 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.436 | Acc: 85.59%
212
+ 2025-03-14 16:25:04,435 - train - INFO - Epoch: 42 | Test Loss: 0.617 | Test Acc: 80.75%
213
+ 2025-03-14 16:25:16,446 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.413 | Acc: 83.59%
214
+ 2025-03-14 16:25:23,070 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.414 | Acc: 86.35%
215
+ 2025-03-14 16:25:29,554 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.418 | Acc: 86.25%
216
+ 2025-03-14 16:25:35,883 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.425 | Acc: 85.97%
217
+ 2025-03-14 16:25:43,587 - train - INFO - Epoch: 43 | Test Loss: 0.571 | Test Acc: 80.94%
218
+ 2025-03-14 16:25:43,825 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.422 | Acc: 85.16%
219
+ 2025-03-14 16:25:50,820 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.430 | Acc: 85.84%
220
+ 2025-03-14 16:25:57,866 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.435 | Acc: 85.65%
221
+ 2025-03-14 16:26:04,343 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.435 | Acc: 85.64%
222
+ 2025-03-14 16:26:11,962 - train - INFO - Epoch: 44 | Test Loss: 0.544 | Test Acc: 81.97%
223
+ 2025-03-14 16:26:23,304 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.537 | Acc: 79.69%
224
+ 2025-03-14 16:26:29,717 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.416 | Acc: 86.31%
225
+ 2025-03-14 16:26:36,049 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.426 | Acc: 86.04%
226
+ 2025-03-14 16:26:42,335 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.433 | Acc: 85.66%
227
+ 2025-03-14 16:26:50,038 - train - INFO - Epoch: 45 | Test Loss: 0.509 | Test Acc: 82.87%
228
+ 2025-03-14 16:26:50,309 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.292 | Acc: 90.62%
229
+ 2025-03-14 16:26:56,772 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.407 | Acc: 86.56%
230
+ 2025-03-14 16:27:03,183 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.417 | Acc: 86.22%
231
+ 2025-03-14 16:27:09,786 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.421 | Acc: 86.09%
232
+ 2025-03-14 16:27:17,656 - train - INFO - Epoch: 46 | Test Loss: 0.557 | Test Acc: 81.43%
233
+ 2025-03-14 16:27:28,839 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.359 | Acc: 89.06%
234
+ 2025-03-14 16:27:35,181 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.414 | Acc: 86.39%
235
+ 2025-03-14 16:27:41,630 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.427 | Acc: 85.87%
236
+ 2025-03-14 16:27:47,853 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.430 | Acc: 85.79%
237
+ 2025-03-14 16:27:56,187 - train - INFO - Epoch: 47 | Test Loss: 0.579 | Test Acc: 81.69%
238
+ 2025-03-14 16:27:56,420 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.468 | Acc: 85.94%
239
+ 2025-03-14 16:28:03,669 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.428 | Acc: 85.92%
240
+ 2025-03-14 16:28:10,055 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.430 | Acc: 85.82%
241
+ 2025-03-14 16:28:16,357 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.429 | Acc: 85.72%
242
+ 2025-03-14 16:28:23,968 - train - INFO - Epoch: 48 | Test Loss: 0.595 | Test Acc: 80.28%
243
+ 2025-03-14 16:28:35,744 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.294 | Acc: 89.06%
244
+ 2025-03-14 16:28:42,339 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.401 | Acc: 86.66%
245
+ 2025-03-14 16:28:48,791 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.411 | Acc: 86.20%
246
+ 2025-03-14 16:28:55,258 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.418 | Acc: 86.02%
247
+ 2025-03-14 16:29:02,924 - train - INFO - Epoch: 49 | Test Loss: 0.536 | Test Acc: 81.65%
248
+ 2025-03-14 16:29:03,178 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.444 | Acc: 85.94%
249
+ 2025-03-14 16:29:09,522 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.410 | Acc: 87.00%
250
+ 2025-03-14 16:29:15,921 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.409 | Acc: 86.71%
251
+ 2025-03-14 16:29:22,219 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.415 | Acc: 86.36%
252
+ 2025-03-14 16:29:29,514 - train - INFO - Epoch: 50 | Test Loss: 0.619 | Test Acc: 78.84%
253
+ 2025-03-14 16:29:40,209 - train - INFO - 训练完成!
Image/DenseNet/code/train.log ADDED
@@ -0,0 +1,250 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-03-09 21:11:49,168 - train - INFO - 开始训练 densenet
2
+ 2025-03-09 21:11:49,168 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2
3
+ 2025-03-09 21:11:49,894 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.311 | Acc: 8.59%
4
+ 2025-03-09 21:11:55,683 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 1.940 | Acc: 27.50%
5
+ 2025-03-09 21:12:01,584 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.770 | Acc: 34.31%
6
+ 2025-03-09 21:12:07,368 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.653 | Acc: 38.88%
7
+ 2025-03-09 21:12:14,425 - train - INFO - Epoch: 1 | Test Loss: 1.328 | Test Acc: 53.16%
8
+ 2025-03-09 21:12:14,716 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.206 | Acc: 55.47%
9
+ 2025-03-09 21:12:21,053 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.124 | Acc: 59.37%
10
+ 2025-03-09 21:12:27,403 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.072 | Acc: 61.63%
11
+ 2025-03-09 21:12:33,389 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.033 | Acc: 63.06%
12
+ 2025-03-09 21:12:40,181 - train - INFO - Epoch: 2 | Test Loss: 1.210 | Test Acc: 61.82%
13
+ 2025-03-09 21:12:49,405 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.909 | Acc: 71.09%
14
+ 2025-03-09 21:12:56,088 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.830 | Acc: 70.80%
15
+ 2025-03-09 21:13:03,091 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.805 | Acc: 71.64%
16
+ 2025-03-09 21:13:09,972 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.787 | Acc: 72.33%
17
+ 2025-03-09 21:13:17,000 - train - INFO - Epoch: 3 | Test Loss: 0.768 | Test Acc: 73.42%
18
+ 2025-03-09 21:13:17,213 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.791 | Acc: 73.44%
19
+ 2025-03-09 21:13:22,943 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.690 | Acc: 75.70%
20
+ 2025-03-09 21:13:28,945 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.669 | Acc: 76.64%
21
+ 2025-03-09 21:13:35,287 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.662 | Acc: 76.95%
22
+ 2025-03-09 21:13:43,309 - train - INFO - Epoch: 4 | Test Loss: 0.685 | Test Acc: 76.06%
23
+ 2025-03-09 21:13:54,007 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.594 | Acc: 79.69%
24
+ 2025-03-09 21:14:00,201 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.594 | Acc: 79.16%
25
+ 2025-03-09 21:14:06,592 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.595 | Acc: 79.42%
26
+ 2025-03-09 21:14:13,084 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.594 | Acc: 79.52%
27
+ 2025-03-09 21:14:20,411 - train - INFO - Epoch: 5 | Test Loss: 0.712 | Test Acc: 75.64%
28
+ 2025-03-09 21:14:20,685 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.455 | Acc: 85.94%
29
+ 2025-03-09 21:14:27,227 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.543 | Acc: 81.23%
30
+ 2025-03-09 21:14:33,263 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.546 | Acc: 81.30%
31
+ 2025-03-09 21:14:39,203 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.547 | Acc: 81.20%
32
+ 2025-03-09 21:14:46,340 - train - INFO - Epoch: 6 | Test Loss: 0.847 | Test Acc: 73.10%
33
+ 2025-03-09 21:14:56,240 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.486 | Acc: 80.47%
34
+ 2025-03-09 21:15:03,070 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.527 | Acc: 81.69%
35
+ 2025-03-09 21:15:09,779 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.526 | Acc: 81.86%
36
+ 2025-03-09 21:15:15,807 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.526 | Acc: 81.90%
37
+ 2025-03-09 21:15:22,494 - train - INFO - Epoch: 7 | Test Loss: 0.673 | Test Acc: 77.47%
38
+ 2025-03-09 21:15:22,735 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.419 | Acc: 85.16%
39
+ 2025-03-09 21:15:28,714 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.505 | Acc: 82.74%
40
+ 2025-03-09 21:15:35,489 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.516 | Acc: 82.33%
41
+ 2025-03-09 21:15:41,666 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.510 | Acc: 82.53%
42
+ 2025-03-09 21:15:49,206 - train - INFO - Epoch: 8 | Test Loss: 0.662 | Test Acc: 77.04%
43
+ 2025-03-09 21:15:59,574 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.538 | Acc: 80.47%
44
+ 2025-03-09 21:16:06,194 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.488 | Acc: 83.35%
45
+ 2025-03-09 21:16:12,155 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.490 | Acc: 83.31%
46
+ 2025-03-09 21:16:18,332 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.495 | Acc: 83.14%
47
+ 2025-03-09 21:16:25,285 - train - INFO - Epoch: 9 | Test Loss: 0.670 | Test Acc: 77.47%
48
+ 2025-03-09 21:16:25,526 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.566 | Acc: 77.34%
49
+ 2025-03-09 21:16:31,632 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.485 | Acc: 83.08%
50
+ 2025-03-09 21:16:37,363 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.490 | Acc: 82.98%
51
+ 2025-03-09 21:16:43,737 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.492 | Acc: 83.05%
52
+ 2025-03-09 21:16:50,797 - train - INFO - Epoch: 10 | Test Loss: 0.664 | Test Acc: 77.87%
53
+ 2025-03-09 21:17:00,747 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.431 | Acc: 85.94%
54
+ 2025-03-09 21:17:06,577 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.477 | Acc: 83.89%
55
+ 2025-03-09 21:17:12,684 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.480 | Acc: 83.71%
56
+ 2025-03-09 21:17:18,559 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.480 | Acc: 83.62%
57
+ 2025-03-09 21:17:25,751 - train - INFO - Epoch: 11 | Test Loss: 0.653 | Test Acc: 77.73%
58
+ 2025-03-09 21:17:25,991 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.406 | Acc: 88.28%
59
+ 2025-03-09 21:17:32,073 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.470 | Acc: 83.87%
60
+ 2025-03-09 21:17:38,732 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.473 | Acc: 83.91%
61
+ 2025-03-09 21:17:45,448 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.474 | Acc: 83.92%
62
+ 2025-03-09 21:17:52,973 - train - INFO - Epoch: 12 | Test Loss: 0.609 | Test Acc: 79.12%
63
+ 2025-03-09 21:18:02,605 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.588 | Acc: 82.03%
64
+ 2025-03-09 21:18:09,036 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.446 | Acc: 84.57%
65
+ 2025-03-09 21:18:15,491 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.460 | Acc: 84.19%
66
+ 2025-03-09 21:18:21,601 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.456 | Acc: 84.31%
67
+ 2025-03-09 21:18:28,561 - train - INFO - Epoch: 13 | Test Loss: 0.613 | Test Acc: 79.41%
68
+ 2025-03-09 21:18:28,768 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.302 | Acc: 89.06%
69
+ 2025-03-09 21:18:35,282 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.463 | Acc: 84.21%
70
+ 2025-03-09 21:18:41,504 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.461 | Acc: 84.27%
71
+ 2025-03-09 21:18:48,232 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.458 | Acc: 84.32%
72
+ 2025-03-09 21:18:55,232 - train - INFO - Epoch: 14 | Test Loss: 0.518 | Test Acc: 81.97%
73
+ 2025-03-09 21:19:04,117 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.396 | Acc: 85.94%
74
+ 2025-03-09 21:19:10,215 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.436 | Acc: 84.96%
75
+ 2025-03-09 21:19:16,620 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.441 | Acc: 84.67%
76
+ 2025-03-09 21:19:23,308 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.446 | Acc: 84.72%
77
+ 2025-03-09 21:19:31,353 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 79.11%
78
+ 2025-03-09 21:19:31,609 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.452 | Acc: 85.16%
79
+ 2025-03-09 21:19:37,681 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.438 | Acc: 84.92%
80
+ 2025-03-09 21:19:43,368 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.446 | Acc: 84.64%
81
+ 2025-03-09 21:19:49,273 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.444 | Acc: 84.83%
82
+ 2025-03-09 21:19:57,522 - train - INFO - Epoch: 16 | Test Loss: 0.552 | Test Acc: 81.27%
83
+ 2025-03-09 21:20:09,354 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.323 | Acc: 90.62%
84
+ 2025-03-09 21:20:15,473 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.410 | Acc: 86.25%
85
+ 2025-03-09 21:20:21,552 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.439 | Acc: 85.15%
86
+ 2025-03-09 21:20:27,549 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.440 | Acc: 85.08%
87
+ 2025-03-09 21:20:35,037 - train - INFO - Epoch: 17 | Test Loss: 0.573 | Test Acc: 81.27%
88
+ 2025-03-09 21:20:35,286 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.416 | Acc: 85.94%
89
+ 2025-03-09 21:20:41,226 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.404 | Acc: 86.05%
90
+ 2025-03-09 21:20:47,415 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.418 | Acc: 85.49%
91
+ 2025-03-09 21:20:53,316 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.424 | Acc: 85.37%
92
+ 2025-03-09 21:21:00,935 - train - INFO - Epoch: 18 | Test Loss: 0.579 | Test Acc: 80.69%
93
+ 2025-03-09 21:21:11,435 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.374 | Acc: 87.50%
94
+ 2025-03-09 21:21:17,597 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.432 | Acc: 85.06%
95
+ 2025-03-09 21:21:23,798 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.430 | Acc: 85.17%
96
+ 2025-03-09 21:21:29,790 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.433 | Acc: 84.92%
97
+ 2025-03-09 21:21:38,374 - train - INFO - Epoch: 19 | Test Loss: 0.568 | Test Acc: 81.09%
98
+ 2025-03-09 21:21:39,187 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.397 | Acc: 86.72%
99
+ 2025-03-09 21:21:45,999 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.416 | Acc: 85.76%
100
+ 2025-03-09 21:21:52,755 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.423 | Acc: 85.38%
101
+ 2025-03-09 21:21:59,191 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.420 | Acc: 85.43%
102
+ 2025-03-09 21:22:06,489 - train - INFO - Epoch: 20 | Test Loss: 0.549 | Test Acc: 81.92%
103
+ 2025-03-09 21:22:16,199 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.380 | Acc: 89.06%
104
+ 2025-03-09 21:22:22,500 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.403 | Acc: 86.45%
105
+ 2025-03-09 21:22:29,352 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.408 | Acc: 86.16%
106
+ 2025-03-09 21:22:35,751 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.413 | Acc: 85.82%
107
+ 2025-03-09 21:22:42,795 - train - INFO - Epoch: 21 | Test Loss: 0.527 | Test Acc: 82.40%
108
+ 2025-03-09 21:22:43,029 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.499 | Acc: 84.38%
109
+ 2025-03-09 21:22:48,802 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.410 | Acc: 86.12%
110
+ 2025-03-09 21:22:54,812 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.418 | Acc: 85.64%
111
+ 2025-03-09 21:23:00,729 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.418 | Acc: 85.68%
112
+ 2025-03-09 21:23:07,906 - train - INFO - Epoch: 22 | Test Loss: 0.902 | Test Acc: 73.40%
113
+ 2025-03-09 21:23:17,770 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.453 | Acc: 85.16%
114
+ 2025-03-09 21:23:23,789 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.410 | Acc: 86.04%
115
+ 2025-03-09 21:23:29,984 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.413 | Acc: 85.85%
116
+ 2025-03-09 21:23:36,210 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.407 | Acc: 86.07%
117
+ 2025-03-09 21:23:43,584 - train - INFO - Epoch: 23 | Test Loss: 0.629 | Test Acc: 79.57%
118
+ 2025-03-09 21:23:43,818 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.474 | Acc: 83.59%
119
+ 2025-03-09 21:23:49,856 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.393 | Acc: 86.41%
120
+ 2025-03-09 21:23:55,816 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.399 | Acc: 86.19%
121
+ 2025-03-09 21:24:01,461 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.399 | Acc: 86.22%
122
+ 2025-03-09 21:24:08,313 - train - INFO - Epoch: 24 | Test Loss: 0.539 | Test Acc: 81.81%
123
+ 2025-03-09 21:24:17,795 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.485 | Acc: 83.59%
124
+ 2025-03-09 21:24:23,553 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.396 | Acc: 86.18%
125
+ 2025-03-09 21:24:29,445 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.402 | Acc: 86.05%
126
+ 2025-03-09 21:24:35,449 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.406 | Acc: 85.98%
127
+ 2025-03-09 21:24:42,460 - train - INFO - Epoch: 25 | Test Loss: 0.539 | Test Acc: 82.08%
128
+ 2025-03-09 21:24:42,688 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.492 | Acc: 78.91%
129
+ 2025-03-09 21:24:48,661 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.391 | Acc: 86.51%
130
+ 2025-03-09 21:24:54,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.395 | Acc: 86.36%
131
+ 2025-03-09 21:25:00,772 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.398 | Acc: 86.21%
132
+ 2025-03-09 21:25:07,465 - train - INFO - Epoch: 26 | Test Loss: 0.561 | Test Acc: 80.89%
133
+ 2025-03-09 21:25:17,361 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.332 | Acc: 85.94%
134
+ 2025-03-09 21:25:23,617 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.387 | Acc: 86.50%
135
+ 2025-03-09 21:25:29,754 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.391 | Acc: 86.45%
136
+ 2025-03-09 21:25:35,426 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.394 | Acc: 86.51%
137
+ 2025-03-09 21:25:42,482 - train - INFO - Epoch: 27 | Test Loss: 0.636 | Test Acc: 80.20%
138
+ 2025-03-09 21:25:42,739 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.327 | Acc: 89.84%
139
+ 2025-03-09 21:25:48,984 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.376 | Acc: 86.90%
140
+ 2025-03-09 21:25:55,729 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.386 | Acc: 86.62%
141
+ 2025-03-09 21:26:02,430 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.392 | Acc: 86.50%
142
+ 2025-03-09 21:26:09,534 - train - INFO - Epoch: 28 | Test Loss: 0.604 | Test Acc: 80.52%
143
+ 2025-03-09 21:26:19,870 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.393 | Acc: 89.06%
144
+ 2025-03-09 21:26:26,037 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.384 | Acc: 86.73%
145
+ 2025-03-09 21:26:32,204 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.388 | Acc: 86.72%
146
+ 2025-03-09 21:26:38,258 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.385 | Acc: 86.78%
147
+ 2025-03-09 21:26:44,989 - train - INFO - Epoch: 29 | Test Loss: 0.581 | Test Acc: 80.79%
148
+ 2025-03-09 21:26:45,232 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.339 | Acc: 88.28%
149
+ 2025-03-09 21:26:51,032 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.371 | Acc: 87.30%
150
+ 2025-03-09 21:26:56,876 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.376 | Acc: 87.18%
151
+ 2025-03-09 21:27:02,463 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.380 | Acc: 87.05%
152
+ 2025-03-09 21:27:09,305 - train - INFO - Epoch: 30 | Test Loss: 0.512 | Test Acc: 82.26%
153
+ 2025-03-09 21:27:19,501 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.309 | Acc: 87.50%
154
+ 2025-03-09 21:27:25,645 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.367 | Acc: 87.24%
155
+ 2025-03-09 21:27:32,514 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.379 | Acc: 87.03%
156
+ 2025-03-09 21:27:39,392 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.386 | Acc: 86.70%
157
+ 2025-03-09 21:27:46,716 - train - INFO - Epoch: 31 | Test Loss: 0.715 | Test Acc: 77.60%
158
+ 2025-03-09 21:27:46,951 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.417 | Acc: 87.50%
159
+ 2025-03-09 21:27:53,142 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.370 | Acc: 87.15%
160
+ 2025-03-09 21:27:59,233 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.380 | Acc: 86.96%
161
+ 2025-03-09 21:28:05,461 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.383 | Acc: 86.79%
162
+ 2025-03-09 21:28:12,214 - train - INFO - Epoch: 32 | Test Loss: 0.581 | Test Acc: 80.21%
163
+ 2025-03-09 21:28:21,517 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.527 | Acc: 82.03%
164
+ 2025-03-09 21:28:27,470 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.367 | Acc: 87.20%
165
+ 2025-03-09 21:28:33,329 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.382 | Acc: 86.78%
166
+ 2025-03-09 21:28:39,101 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.384 | Acc: 86.76%
167
+ 2025-03-09 21:28:46,139 - train - INFO - Epoch: 33 | Test Loss: 0.525 | Test Acc: 82.67%
168
+ 2025-03-09 21:28:46,389 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.350 | Acc: 89.06%
169
+ 2025-03-09 21:28:52,622 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.350 | Acc: 87.94%
170
+ 2025-03-09 21:28:58,609 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.361 | Acc: 87.54%
171
+ 2025-03-09 21:29:04,578 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.371 | Acc: 87.25%
172
+ 2025-03-09 21:29:11,580 - train - INFO - Epoch: 34 | Test Loss: 0.572 | Test Acc: 81.09%
173
+ 2025-03-09 21:29:21,637 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.500 | Acc: 83.59%
174
+ 2025-03-09 21:29:28,349 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.363 | Acc: 87.59%
175
+ 2025-03-09 21:29:34,488 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.376 | Acc: 87.18%
176
+ 2025-03-09 21:29:40,398 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.380 | Acc: 87.04%
177
+ 2025-03-09 21:29:48,658 - train - INFO - Epoch: 35 | Test Loss: 0.455 | Test Acc: 84.92%
178
+ 2025-03-09 21:29:48,921 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.357 | Acc: 89.06%
179
+ 2025-03-09 21:29:55,061 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.364 | Acc: 87.31%
180
+ 2025-03-09 21:30:00,890 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.371 | Acc: 87.18%
181
+ 2025-03-09 21:30:06,897 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.375 | Acc: 87.03%
182
+ 2025-03-09 21:30:14,235 - train - INFO - Epoch: 36 | Test Loss: 0.541 | Test Acc: 82.74%
183
+ 2025-03-09 21:30:24,234 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.431 | Acc: 84.38%
184
+ 2025-03-09 21:30:29,986 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.356 | Acc: 87.55%
185
+ 2025-03-09 21:30:36,139 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.370 | Acc: 87.21%
186
+ 2025-03-09 21:30:42,107 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.371 | Acc: 87.20%
187
+ 2025-03-09 21:30:49,263 - train - INFO - Epoch: 37 | Test Loss: 0.514 | Test Acc: 82.69%
188
+ 2025-03-09 21:30:49,531 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.229 | Acc: 90.62%
189
+ 2025-03-09 21:30:55,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.348 | Acc: 87.84%
190
+ 2025-03-09 21:31:01,689 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.358 | Acc: 87.62%
191
+ 2025-03-09 21:31:08,168 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.368 | Acc: 87.41%
192
+ 2025-03-09 21:31:15,751 - train - INFO - Epoch: 38 | Test Loss: 0.497 | Test Acc: 83.43%
193
+ 2025-03-09 21:31:25,606 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.285 | Acc: 92.97%
194
+ 2025-03-09 21:31:31,509 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.361 | Acc: 87.68%
195
+ 2025-03-09 21:31:37,804 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.362 | Acc: 87.66%
196
+ 2025-03-09 21:31:44,177 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.362 | Acc: 87.68%
197
+ 2025-03-09 21:31:50,975 - train - INFO - Epoch: 39 | Test Loss: 0.527 | Test Acc: 82.27%
198
+ 2025-03-09 21:31:51,210 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.305 | Acc: 89.06%
199
+ 2025-03-09 21:31:56,699 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.352 | Acc: 87.79%
200
+ 2025-03-09 21:32:02,349 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.361 | Acc: 87.46%
201
+ 2025-03-09 21:32:08,110 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.369 | Acc: 87.15%
202
+ 2025-03-09 21:32:15,326 - train - INFO - Epoch: 40 | Test Loss: 0.653 | Test Acc: 78.55%
203
+ 2025-03-09 21:32:25,211 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.522 | Acc: 82.03%
204
+ 2025-03-09 21:32:30,421 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.367 | Acc: 87.38%
205
+ 2025-03-09 21:32:36,104 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.363 | Acc: 87.40%
206
+ 2025-03-09 21:32:42,045 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.363 | Acc: 87.31%
207
+ 2025-03-09 21:32:49,160 - train - INFO - Epoch: 41 | Test Loss: 0.510 | Test Acc: 82.85%
208
+ 2025-03-09 21:32:49,411 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.325 | Acc: 87.50%
209
+ 2025-03-09 21:32:55,294 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.354 | Acc: 87.75%
210
+ 2025-03-09 21:33:00,458 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.364 | Acc: 87.59%
211
+ 2025-03-09 21:33:06,072 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.363 | Acc: 87.61%
212
+ 2025-03-09 21:33:12,986 - train - INFO - Epoch: 42 | Test Loss: 0.555 | Test Acc: 81.95%
213
+ 2025-03-09 21:33:23,293 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.384 | Acc: 84.38%
214
+ 2025-03-09 21:33:28,557 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.356 | Acc: 87.80%
215
+ 2025-03-09 21:33:34,297 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.364 | Acc: 87.48%
216
+ 2025-03-09 21:33:40,033 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.363 | Acc: 87.58%
217
+ 2025-03-09 21:33:47,053 - train - INFO - Epoch: 43 | Test Loss: 0.467 | Test Acc: 84.13%
218
+ 2025-03-09 21:33:47,325 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.285 | Acc: 89.84%
219
+ 2025-03-09 21:33:53,575 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.343 | Acc: 88.06%
220
+ 2025-03-09 21:33:59,731 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.350 | Acc: 87.93%
221
+ 2025-03-09 21:34:05,556 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.355 | Acc: 87.73%
222
+ 2025-03-09 21:34:12,656 - train - INFO - Epoch: 44 | Test Loss: 0.767 | Test Acc: 74.93%
223
+ 2025-03-09 21:34:23,508 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.393 | Acc: 83.59%
224
+ 2025-03-09 21:34:29,421 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.347 | Acc: 87.89%
225
+ 2025-03-09 21:34:35,133 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.342 | Acc: 88.16%
226
+ 2025-03-09 21:34:40,818 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.350 | Acc: 87.92%
227
+ 2025-03-09 21:34:47,264 - train - INFO - Epoch: 45 | Test Loss: 0.480 | Test Acc: 83.56%
228
+ 2025-03-09 21:34:47,512 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.302 | Acc: 89.06%
229
+ 2025-03-09 21:34:53,220 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.342 | Acc: 88.10%
230
+ 2025-03-09 21:34:59,094 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.347 | Acc: 88.02%
231
+ 2025-03-09 21:35:05,396 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.348 | Acc: 88.04%
232
+ 2025-03-09 21:35:11,923 - train - INFO - Epoch: 46 | Test Loss: 0.448 | Test Acc: 84.89%
233
+ 2025-03-09 21:35:22,070 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.401 | Acc: 86.72%
234
+ 2025-03-09 21:35:28,150 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.347 | Acc: 87.89%
235
+ 2025-03-09 21:35:34,314 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.354 | Acc: 87.75%
236
+ 2025-03-09 21:35:40,069 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.357 | Acc: 87.63%
237
+ 2025-03-09 21:35:46,827 - train - INFO - Epoch: 47 | Test Loss: 0.508 | Test Acc: 82.58%
238
+ 2025-03-09 21:35:47,066 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.263 | Acc: 91.41%
239
+ 2025-03-09 21:35:53,455 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.330 | Acc: 88.49%
240
+ 2025-03-09 21:35:59,739 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.336 | Acc: 88.37%
241
+ 2025-03-09 21:36:05,768 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.340 | Acc: 88.33%
242
+ 2025-03-09 21:36:12,946 - train - INFO - Epoch: 48 | Test Loss: 0.432 | Test Acc: 84.88%
243
+ 2025-03-09 21:36:22,692 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.347 | Acc: 89.06%
244
+ 2025-03-09 21:36:28,875 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.335 | Acc: 88.68%
245
+ 2025-03-09 21:36:34,927 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.336 | Acc: 88.46%
246
+ 2025-03-09 21:36:40,251 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.342 | Acc: 88.42%
247
+ 2025-03-09 21:36:47,402 - train - INFO - Epoch: 49 | Test Loss: 0.419 | Test Acc: 85.76%
248
+ 2025-03-09 21:36:47,660 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.199 | Acc: 93.75%
249
+ 2025-03-09 21:36:53,975 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.328 | Acc: 88.47%
250
+ 2025-03-09 21:36:59,800 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.342 | Acc: 88.16%
Image/DenseNet/code/train.py CHANGED
@@ -21,12 +21,14 @@ def main():
21
  model=model,
22
  trainloader=trainloader,
23
  testloader=testloader,
24
- epochs=args.epochs,
25
- lr=args.lr,
26
  device=f'cuda:{args.gpu}',
27
  save_dir='../model',
28
  model_name='densenet',
29
- save_type='0'
 
 
30
  )
31
  elif args.train_type == '1':
32
  train_model_data_augmentation(
@@ -45,14 +47,16 @@ def main():
45
  model,
46
  poison_ratio=args.poison_ratio,
47
  target_label=args.target_label,
48
- epochs=args.epochs,
49
- lr=args.lr,
50
  device=f'cuda:{args.gpu}',
51
  save_dir='../model',
52
  model_name='densenet',
53
  batch_size=args.batch_size,
54
  num_workers=args.num_workers,
55
- local_dataset_path=args.dataset_path
 
 
56
  )
57
 
58
  if __name__ == '__main__':
 
21
  model=model,
22
  trainloader=trainloader,
23
  testloader=testloader,
24
+ epochs=50,
25
+ lr=0.1,
26
  device=f'cuda:{args.gpu}',
27
  save_dir='../model',
28
  model_name='densenet',
29
+ save_type='0',
30
+ layer_name='avg_pool',
31
+ interval = 2
32
  )
33
  elif args.train_type == '1':
34
  train_model_data_augmentation(
 
47
  model,
48
  poison_ratio=args.poison_ratio,
49
  target_label=args.target_label,
50
+ epochs=50,
51
+ lr=0.1,
52
  device=f'cuda:{args.gpu}',
53
  save_dir='../model',
54
  model_name='densenet',
55
  batch_size=args.batch_size,
56
  num_workers=args.num_workers,
57
+ local_dataset_path=args.dataset_path,
58
+ layer_name='avg_pool' ,
59
+ interval = 2
60
  )
61
 
62
  if __name__ == '__main__':
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