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out2.log
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out2.log
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relative_path classID
0 files_train/signal_data_type_1/files_0/signal_... 0
1 files_train/signal_data_type_1/files_0/signal_... 0
2 files_train/signal_data_type_1/files_0/signal_... 0
3 files_train/signal_data_type_1/files_0/signal_... 0
4 files_train/signal_data_type_1/files_0/signal_... 0
cuda:0
start training
[1, 1] loss: 0.114
[1, 21] loss: 2.305
[1, 41] loss: 2.300
[1, 61] loss: 2.305
[1, 81] loss: 2.300
[1, 101] loss: 2.299
[1, 121] loss: 2.292
[1, 141] loss: 2.285
[1, 161] loss: 2.273
Epoch: 0, Loss: 2.29348, Accuracy: 0.13
Accuracy: 0.16, Total items: 690
[[67. 0. 0. 0. 0. 0. 0. 0. 22. 0.]
[26. 0. 0. 0. 0. 0. 0. 0. 47. 0.]
[18. 0. 0. 0. 0. 0. 0. 0. 49. 0.]
[36. 0. 0. 0. 0. 0. 0. 0. 31. 0.]
[28. 0. 0. 0. 0. 0. 0. 0. 34. 0.]
[34. 0. 0. 0. 0. 0. 0. 0. 30. 0.]
[51. 0. 0. 0. 0. 0. 0. 0. 17. 0.]
[61. 0. 0. 0. 0. 0. 0. 0. 13. 0.]
[15. 0. 0. 0. 0. 0. 0. 0. 44. 0.]
[56. 0. 0. 0. 0. 0. 0. 0. 11. 0.]]
[2, 1] loss: 0.112
[2, 21] loss: 2.221
[2, 41] loss: 2.245
[2, 61] loss: 2.204
[2, 81] loss: 2.215
[2, 101] loss: 2.209
[2, 121] loss: 2.196
[2, 141] loss: 2.174
[2, 161] loss: 2.135
Epoch: 1, Loss: 2.20308, Accuracy: 0.17
Accuracy: 0.18, Total items: 690
[[19. 0. 25. 4. 0. 0. 0. 35. 6. 0.]
[ 9. 0. 18. 3. 0. 0. 0. 10. 33. 0.]
[ 4. 0. 27. 2. 0. 0. 0. 5. 29. 0.]
[10. 0. 17. 2. 0. 0. 0. 19. 19. 0.]
[ 6. 0. 21. 2. 0. 0. 0. 14. 19. 0.]
[ 5. 0. 22. 3. 0. 0. 0. 14. 20. 0.]
[10. 0. 6. 3. 0. 0. 0. 35. 14. 0.]
[13. 0. 10. 2. 0. 0. 1. 40. 8. 0.]
[ 7. 0. 13. 0. 0. 0. 0. 3. 36. 0.]
[21. 0. 12. 2. 0. 0. 0. 27. 5. 0.]]
[3, 1] loss: 0.105
[3, 21] loss: 2.193
[3, 41] loss: 2.164
[3, 61] loss: 2.171
[3, 81] loss: 2.155
[3, 101] loss: 2.157
[3, 121] loss: 2.179
[3, 141] loss: 2.188
[3, 161] loss: 2.150
Epoch: 2, Loss: 2.16125, Accuracy: 0.17
Accuracy: 0.19, Total items: 690
[[15. 0. 16. 7. 0. 0. 0. 49. 2. 0.]
[10. 0. 34. 4. 0. 0. 0. 14. 11. 0.]
[ 6. 0. 32. 6. 0. 0. 0. 8. 15. 0.]
[ 9. 0. 21. 2. 0. 0. 0. 26. 9. 0.]
[ 5. 0. 23. 6. 0. 0. 0. 20. 8. 0.]
[ 6. 0. 20. 12. 0. 0. 0. 21. 5. 0.]
[ 3. 0. 7. 5. 0. 0. 0. 44. 9. 0.]
[ 8. 0. 3. 3. 0. 0. 0. 52. 8. 0.]
[ 4. 0. 14. 4. 0. 0. 0. 9. 28. 0.]
[11. 0. 7. 2. 0. 0. 0. 44. 3. 0.]]
[4, 1] loss: 0.113
[4, 21] loss: 2.196
[4, 41] loss: 2.088
[4, 61] loss: 2.160
[4, 81] loss: 2.149
[4, 101] loss: 2.163
[4, 121] loss: 2.123
[4, 141] loss: 2.139
[4, 161] loss: 2.065
Epoch: 3, Loss: 2.13404, Accuracy: 0.20
Accuracy: 0.18, Total items: 690
[[ 3. 0. 16. 0. 0. 4. 63. 0. 3. 0.]
[ 2. 0. 27. 1. 0. 1. 22. 0. 20. 0.]
[ 0. 0. 32. 0. 0. 1. 17. 0. 17. 0.]
[ 0. 0. 20. 0. 0. 3. 34. 0. 10. 0.]
[ 1. 0. 25. 0. 0. 1. 25. 0. 10. 0.]
[ 0. 0. 17. 1. 0. 6. 31. 0. 9. 0.]
[ 0. 0. 6. 0. 0. 3. 49. 0. 10. 0.]
[ 0. 0. 4. 0. 0. 2. 62. 0. 6. 0.]
[ 0. 0. 10. 0. 1. 4. 13. 0. 31. 0.]
[ 3. 0. 7. 0. 0. 2. 52. 0. 3. 0.]]
[5, 1] loss: 0.116
[5, 21] loss: 2.015
[5, 41] loss: 2.113
[5, 61] loss: 2.065
[5, 81] loss: 2.052
[5, 101] loss: 2.139
[5, 121] loss: 2.081
[5, 141] loss: 1.913
[5, 161] loss: 2.030
Epoch: 4, Loss: 2.05108, Accuracy: 0.23
Accuracy: 0.26, Total items: 690
[[50. 5. 4. 0. 15. 0. 2. 5. 3. 5.]
[ 8. 4. 25. 0. 9. 0. 2. 2. 17. 6.]
[ 5. 5. 27. 0. 11. 0. 1. 2. 14. 2.]
[24. 2. 18. 0. 6. 0. 3. 1. 10. 3.]
[13. 5. 21. 0. 11. 0. 1. 2. 7. 2.]
[14. 6. 13. 0. 17. 0. 0. 2. 7. 5.]
[ 5. 1. 7. 0. 2. 0. 35. 3. 15. 0.]
[47. 0. 4. 0. 9. 0. 5. 3. 5. 1.]
[ 3. 1. 10. 0. 1. 0. 3. 0. 41. 0.]
[43. 1. 2. 0. 9. 0. 0. 0. 3. 9.]]
[6, 1] loss: 0.119
[6, 21] loss: 1.891
[6, 41] loss: 1.961
[6, 61] loss: 1.919
[6, 81] loss: 1.917
[6, 101] loss: 1.864
[6, 121] loss: 1.964
[6, 141] loss: 1.985
[6, 161] loss: 1.778
Epoch: 5, Loss: 1.91362, Accuracy: 0.28
Accuracy: 0.28, Total items: 690
[[16. 3. 0. 13. 0. 13. 11. 5. 2. 26.]
[ 4. 13. 0. 9. 0. 11. 2. 1. 26. 7.]
[ 2. 8. 0. 14. 1. 19. 6. 0. 15. 2.]
[11. 5. 0. 13. 0. 19. 4. 0. 8. 7.]
[ 3. 5. 0. 16. 1. 22. 3. 0. 6. 6.]
[ 2. 7. 0. 10. 0. 23. 6. 2. 8. 6.]
[ 0. 1. 0. 2. 0. 2. 33. 0. 30. 0.]
[19. 1. 0. 2. 0. 7. 22. 5. 7. 11.]
[ 0. 4. 0. 2. 2. 4. 2. 0. 44. 1.]
[ 8. 1. 0. 6. 0. 4. 0. 0. 1. 47.]]
[7, 1] loss: 0.101
[7, 21] loss: 1.851
[7, 41] loss: 1.858
[7, 61] loss: 1.836
[7, 81] loss: 1.803
[7, 101] loss: 1.776
[7, 121] loss: 1.798
[7, 141] loss: 1.789
[7, 161] loss: 1.728
Epoch: 6, Loss: 1.79298, Accuracy: 0.34
Accuracy: 0.33, Total items: 690
[[34. 5. 4. 1. 0. 1. 10. 29. 2. 3.]
[ 9. 17. 17. 1. 0. 1. 8. 3. 13. 4.]
[11. 13. 19. 1. 0. 0. 12. 3. 8. 0.]
[14. 4. 16. 1. 0. 3. 12. 10. 6. 1.]
[ 5. 9. 19. 1. 1. 4. 6. 12. 4. 1.]
[10. 5. 16. 1. 1. 4. 10. 10. 5. 2.]
[ 0. 2. 2. 0. 0. 0. 56. 1. 7. 0.]
[11. 2. 1. 0. 0. 1. 28. 28. 1. 2.]
[ 1. 4. 3. 0. 0. 0. 15. 1. 35. 0.]
[22. 0. 0. 1. 0. 0. 3. 8. 2. 31.]]
[8, 1] loss: 0.080
[8, 21] loss: 1.724
[8, 41] loss: 1.706
[8, 61] loss: 1.660
[8, 81] loss: 1.631
[8, 101] loss: 1.777
[8, 121] loss: 1.612
[8, 141] loss: 1.621
[8, 161] loss: 1.647
Epoch: 7, Loss: 1.67981, Accuracy: 0.37
Accuracy: 0.38, Total items: 690
[[36. 6. 0. 17. 7. 2. 2. 11. 1. 7.]
[ 8. 22. 7. 12. 8. 0. 2. 0. 13. 1.]
[ 3. 17. 5. 15. 13. 1. 5. 2. 5. 1.]
[ 8. 7. 2. 16. 20. 1. 3. 4. 4. 2.]
[ 2. 7. 2. 10. 28. 2. 2. 5. 4. 0.]
[ 3. 7. 0. 20. 13. 4. 2. 7. 8. 0.]
[ 1. 3. 2. 0. 2. 0. 43. 7. 9. 1.]
[24. 2. 0. 3. 5. 0. 10. 26. 1. 3.]
[ 1. 6. 0. 1. 4. 0. 2. 0. 45. 0.]
[13. 0. 0. 8. 0. 0. 1. 3. 3. 39.]]
[9, 1] loss: 0.083
[9, 21] loss: 1.579
[9, 41] loss: 1.516
[9, 61] loss: 1.595
[9, 81] loss: 1.587
[9, 101] loss: 1.553
[9, 121] loss: 1.393
[9, 141] loss: 1.474
[9, 161] loss: 1.608
Epoch: 8, Loss: 1.54000, Accuracy: 0.43
Accuracy: 0.36, Total items: 690
[[31. 22. 0. 10. 1. 3. 5. 2. 2. 13.]
[ 7. 48. 0. 5. 0. 2. 2. 0. 7. 2.]
[ 7. 43. 0. 4. 1. 1. 6. 0. 5. 0.]
[13. 29. 1. 6. 0. 6. 5. 0. 5. 2.]
[ 6. 28. 0. 10. 4. 6. 2. 1. 4. 1.]
[ 7. 24. 1. 12. 0. 5. 4. 1. 7. 3.]
[ 1. 8. 0. 0. 0. 0. 53. 2. 3. 1.]
[34. 3. 0. 2. 0. 1. 11. 16. 2. 5.]
[ 0. 12. 0. 0. 0. 0. 4. 0. 43. 0.]
[11. 2. 0. 6. 0. 0. 1. 0. 3. 44.]]
[10, 1] loss: 0.070
[10, 21] loss: 1.398
[10, 41] loss: 1.351
[10, 61] loss: 1.494
[10, 81] loss: 1.565
[10, 101] loss: 1.438
[10, 121] loss: 1.438
[10, 141] loss: 1.441
[10, 161] loss: 1.338
Epoch: 9, Loss: 1.42855, Accuracy: 0.47
Accuracy: 0.44, Total items: 690
[[38. 4. 1. 7. 5. 0. 2. 12. 0. 20.]
[ 8. 31. 8. 7. 0. 1. 3. 2. 4. 9.]
[18. 12. 11. 8. 5. 2. 4. 2. 3. 2.]
[ 9. 7. 1. 14. 10. 4. 5. 4. 1. 12.]
[ 5. 5. 3. 13. 18. 3. 2. 7. 1. 5.]
[ 7. 7. 2. 15. 5. 6. 4. 7. 4. 7.]
[ 1. 3. 3. 0. 0. 0. 50. 8. 1. 2.]
[ 6. 1. 1. 1. 1. 0. 11. 43. 0. 10.]
[ 1. 9. 1. 1. 1. 1. 6. 0. 38. 1.]
[ 2. 0. 0. 0. 1. 0. 2. 2. 2. 58.]]
[11, 1] loss: 0.071
[11, 21] loss: 1.320
[11, 41] loss: 1.422
[11, 61] loss: 1.312
[11, 81] loss: 1.178
[11, 101] loss: 1.342
[11, 121] loss: 1.296
[11, 141] loss: 1.291
[11, 161] loss: 1.262
Epoch: 10, Loss: 1.29615, Accuracy: 0.53
Accuracy: 0.44, Total items: 690
[[30. 8. 1. 10. 5. 6. 3. 5. 1. 20.]
[ 5. 24. 5. 6. 0. 4. 2. 1. 20. 6.]
[ 6. 6. 12. 15. 6. 6. 7. 0. 9. 0.]
[ 4. 4. 1. 17. 11. 7. 5. 0. 5. 13.]
[ 3. 7. 2. 12. 21. 6. 2. 2. 5. 2.]
[ 4. 8. 1. 10. 10. 15. 3. 3. 7. 3.]
[ 1. 1. 1. 0. 2. 1. 50. 3. 7. 2.]
[12. 1. 0. 2. 6. 0. 11. 30. 1. 11.]
[ 1. 2. 0. 1. 0. 1. 4. 0. 49. 1.]
[ 3. 0. 0. 0. 0. 2. 1. 0. 3. 58.]]
[12, 1] loss: 0.050
[12, 21] loss: 1.172
[12, 41] loss: 1.217
[12, 61] loss: 1.068
[12, 81] loss: 1.190
[12, 101] loss: 1.171
[12, 121] loss: 1.123
[12, 141] loss: 1.194
[12, 161] loss: 1.083
Epoch: 11, Loss: 1.15006, Accuracy: 0.58
Accuracy: 0.47, Total items: 690
[[40. 8. 4. 5. 7. 1. 2. 5. 0. 17.]
[ 9. 43. 2. 1. 3. 3. 2. 1. 4. 5.]
[11. 11. 21. 4. 10. 2. 6. 0. 2. 0.]
[10. 8. 8. 8. 19. 1. 4. 0. 1. 8.]
[ 5. 8. 6. 5. 26. 3. 3. 2. 2. 2.]
[ 4. 9. 3. 5. 16. 11. 3. 3. 4. 6.]
[ 1. 2. 1. 0. 1. 1. 50. 7. 4. 1.]
[15. 1. 1. 0. 5. 0. 11. 30. 0. 11.]
[ 1. 9. 1. 0. 1. 1. 4. 0. 40. 2.]
[ 6. 1. 0. 2. 0. 0. 1. 0. 1. 56.]]
[13, 1] loss: 0.062
[13, 21] loss: 1.078
[13, 41] loss: 0.872
[13, 61] loss: 0.972
[13, 81] loss: 1.114
[13, 101] loss: 1.049
[13, 121] loss: 0.993
[13, 141] loss: 1.010
[13, 161] loss: 0.995
Epoch: 12, Loss: 1.00755, Accuracy: 0.63
Accuracy: 0.49, Total items: 690
[[33. 7. 6. 6. 5. 9. 3. 10. 1. 9.]
[ 7. 34. 2. 3. 1. 4. 2. 1. 15. 4.]
[ 9. 6. 21. 6. 9. 4. 4. 1. 7. 0.]
[ 8. 6. 7. 13. 14. 5. 6. 3. 2. 3.]
[ 5. 5. 5. 8. 27. 6. 3. 1. 2. 0.]
[ 5. 6. 4. 4. 10. 19. 3. 3. 7. 3.]
[ 1. 2. 0. 0. 1. 1. 51. 6. 5. 1.]
[ 9. 0. 1. 2. 4. 0. 10. 42. 0. 6.]
[ 0. 6. 0. 0. 1. 1. 3. 0. 48. 0.]
[ 6. 0. 0. 0. 0. 2. 1. 3. 2. 53.]]
[14, 1] loss: 0.058
[14, 21] loss: 0.846
[14, 41] loss: 0.923
[14, 61] loss: 0.883
[14, 81] loss: 0.772
[14, 101] loss: 0.867
[14, 121] loss: 0.933
[14, 141] loss: 0.804
[14, 161] loss: 0.859
Epoch: 13, Loss: 0.86067, Accuracy: 0.69
Accuracy: 0.50, Total items: 690
[[39. 6. 6. 4. 6. 7. 2. 8. 1. 10.]
[10. 37. 2. 1. 1. 4. 2. 2. 11. 3.]
[13. 10. 20. 6. 9. 2. 4. 0. 3. 0.]
[ 8. 7. 7. 15. 15. 3. 4. 4. 1. 3.]
[ 5. 3. 5. 10. 28. 5. 3. 1. 2. 0.]
[ 7. 5. 4. 4. 11. 19. 3. 5. 3. 3.]
[ 1. 2. 1. 0. 1. 1. 50. 6. 5. 1.]
[12. 1. 1. 0. 3. 0. 10. 44. 0. 3.]
[ 1. 6. 1. 0. 1. 2. 3. 0. 45. 0.]
[ 9. 1. 0. 0. 0. 2. 1. 4. 1. 49.]]
[15, 1] loss: 0.050
[15, 21] loss: 0.785
[15, 41] loss: 0.905
[15, 61] loss: 0.779
[15, 81] loss: 0.709
[15, 101] loss: 0.699
[15, 121] loss: 0.747
[15, 141] loss: 0.700
[15, 161] loss: 0.800
Epoch: 14, Loss: 0.75664, Accuracy: 0.73
Accuracy: 0.50, Total items: 690
[[39. 8. 5. 7. 5. 6. 2. 7. 1. 9.]
[10. 38. 1. 2. 0. 4. 2. 1. 12. 3.]
[12. 10. 19. 8. 7. 3. 3. 1. 4. 0.]
[ 8. 7. 7. 16. 13. 5. 5. 3. 1. 2.]
[ 5. 4. 5. 10. 25. 7. 3. 1. 2. 0.]
[ 6. 6. 3. 6. 6. 24. 4. 3. 3. 3.]
[ 1. 2. 1. 0. 1. 2. 48. 8. 5. 0.]
[12. 1. 2. 1. 4. 0. 8. 43. 0. 3.]
[ 0. 9. 0. 1. 0. 1. 2. 0. 46. 0.]
[11. 1. 0. 0. 0. 2. 1. 3. 1. 48.]]
Finished Training
Accuracy: 0.50, Total items: 690
[[39. 8. 5. 7. 5. 6. 2. 7. 1. 9.]
[10. 38. 1. 2. 0. 4. 2. 1. 12. 3.]
[12. 10. 19. 8. 7. 3. 3. 1. 4. 0.]
[ 8. 7. 7. 16. 13. 5. 5. 3. 1. 2.]
[ 5. 4. 5. 10. 25. 7. 3. 1. 2. 0.]
[ 6. 6. 3. 6. 6. 24. 4. 3. 3. 3.]
[ 1. 2. 1. 0. 1. 2. 48. 8. 5. 0.]
[12. 1. 2. 1. 4. 0. 8. 43. 0. 3.]
[ 0. 9. 0. 1. 0. 1. 2. 0. 46. 0.]
[11. 1. 0. 0. 0. 2. 1. 3. 1. 48.]]