out.log

                                       relative_path  classID
0  files_train/signal_data_new/files_0/signal_1_0...        0
1  files_train/signal_data_new/files_0/signal_1_0...        0
2  files_train/signal_data_new/files_0/signal_1_0...        0
3  files_train/signal_data_new/files_0/signal_1_0...        0
4  files_train/signal_data_new/files_0/signal_1_0...        0
cuda:0
start training
[1,     1] loss: 0.115
[1,    21] loss: 2.303
[1,    41] loss: 2.302
[1,    61] loss: 2.301
[1,    81] loss: 2.299
[1,   101] loss: 2.295
[1,   121] loss: 2.298
[1,   141] loss: 2.297
[1,   161] loss: 2.294
[1,   181] loss: 2.282
[1,   201] loss: 2.286
Epoch: 0, Loss: 2.29530, Accuracy: 0.14
Accuracy: 0.18, Total items: 826
[[58. 12.  0.  0.  0.  0.  0.  0.  7.  0.]
 [28. 21.  0.  0.  0.  0.  0.  0. 61.  0.]
 [13. 18.  0.  0.  0.  0.  0.  0. 46.  0.]
 [25. 26.  0.  0.  0.  0.  0.  0. 40.  0.]
 [24. 14.  0.  0.  0.  0.  0.  0. 28.  0.]
 [27. 21.  0.  0.  0.  0.  0.  0. 35.  0.]
 [57.  7.  0.  0.  0.  0.  0.  0. 24.  0.]
 [61.  5.  0.  0.  0.  0.  0.  0. 14.  0.]
 [ 4. 10.  0.  0.  0.  0.  0.  0. 69.  0.]
 [42.  8.  0.  0.  0.  0.  0.  0. 21.  0.]]
[2,     1] loss: 0.112
[2,    21] loss: 2.247
[2,    41] loss: 2.239
[2,    61] loss: 2.251
[2,    81] loss: 2.221
[2,   101] loss: 2.125
[2,   121] loss: 2.272
[2,   141] loss: 2.216
[2,   161] loss: 2.210
[2,   181] loss: 2.180
[2,   201] loss: 2.224
Epoch: 1, Loss: 2.21776, Accuracy: 0.18
Accuracy: 0.19, Total items: 826
[[50. 14.  0.  0.  0.  0.  0. 13.  0.  0.]
 [20. 71.  0.  1.  0.  0.  0. 11.  7.  0.]
 [11. 54.  0.  2.  0.  0.  0.  5.  5.  0.]
 [25. 58.  0.  1.  0.  0.  0.  7.  0.  0.]
 [18. 31.  0.  1.  0.  0.  0. 10.  6.  0.]
 [19. 44.  0.  2.  0.  0.  0. 12.  6.  0.]
 [34. 22.  0.  0.  0.  0.  0. 28.  4.  0.]
 [37. 12.  0.  1.  0.  0.  0. 28.  2.  0.]
 [ 8. 70.  0.  0.  0.  0.  0.  1.  4.  0.]
 [22. 20.  0.  0.  0.  0.  0. 27.  2.  0.]]
[3,     1] loss: 0.115
[3,    21] loss: 2.156
[3,    41] loss: 2.141
[3,    61] loss: 2.140
[3,    81] loss: 2.156
[3,   101] loss: 2.166
[3,   121] loss: 2.171
[3,   141] loss: 2.164
[3,   161] loss: 2.178
[3,   181] loss: 2.177
[3,   201] loss: 2.112
Epoch: 2, Loss: 2.15583, Accuracy: 0.19
Accuracy: 0.19, Total items: 826
[[63.  8.  1.  1.  0.  0.  0.  1.  3.  0.]
 [26. 35.  0.  0.  0.  0.  0.  5. 44.  0.]
 [19. 28.  4.  0.  0.  0.  0.  0. 26.  0.]
 [32. 33.  4.  0.  0.  0.  0.  2. 20.  0.]
 [26. 21.  2.  0.  0.  0.  0.  3. 14.  0.]
 [30. 28.  3.  0.  0.  0.  1.  2. 19.  0.]
 [61.  8.  1.  1.  0.  0.  0.  2. 15.  0.]
 [60.  5.  1.  0.  0.  0.  0.  5.  9.  0.]
 [ 8. 25.  3.  0.  0.  0.  1.  0. 46.  0.]
 [45.  7.  3.  0.  0.  0.  0.  4. 12.  0.]]
[4,     1] loss: 0.122
[4,    21] loss: 2.071
[4,    41] loss: 2.158
[4,    61] loss: 2.160
[4,    81] loss: 2.097
[4,   101] loss: 2.137
[4,   121] loss: 2.151
[4,   141] loss: 2.185
[4,   161] loss: 2.146
[4,   181] loss: 2.073
[4,   201] loss: 2.143
Epoch: 3, Loss: 2.13538, Accuracy: 0.22
Accuracy: 0.21, Total items: 826
[[50.  9.  0.  5.  1.  0.  0. 11.  1.  0.]
 [18. 63.  0.  4.  0.  0.  0.  4. 21.  0.]
 [ 9. 40.  0.  8.  0.  0.  0.  4. 16.  0.]
 [19. 31.  0. 17.  1.  0.  0. 10. 12.  1.]
 [10. 32.  1.  4.  1.  1.  0.  6. 11.  0.]
 [19. 35.  0.  7.  1.  1.  1.  7. 12.  0.]
 [40. 13.  0.  5.  0.  0.  0. 19. 11.  0.]
 [34. 17.  0.  3.  0.  0.  0. 22.  4.  0.]
 [ 1. 50.  1.  6.  0.  0.  0.  2. 22.  1.]
 [27. 18.  0.  6.  0.  0.  0. 18.  2.  0.]]
[5,     1] loss: 0.104
[5,    21] loss: 2.155
[5,    41] loss: 2.138
[5,    61] loss: 2.158
[5,    81] loss: 2.099
[5,   101] loss: 2.139
[5,   121] loss: 2.128
[5,   141] loss: 2.125
[5,   161] loss: 2.098
[5,   181] loss: 2.077
[5,   201] loss: 2.091
Epoch: 4, Loss: 2.12136, Accuracy: 0.22
Accuracy: 0.22, Total items: 826
[[49.  1.  8.  0.  0.  0.  0. 14.  3.  2.]
 [16. 23. 16.  0.  0.  1.  0. 11. 42.  1.]
 [11. 11. 23.  0.  0.  0.  0.  6. 24.  2.]
 [21.  8. 28.  0.  0.  1.  0. 13. 18.  2.]
 [11. 10. 10.  0.  0.  1.  0. 14. 16.  4.]
 [18. 12. 15.  0.  0.  2.  0. 15. 20.  1.]
 [36.  5.  5.  0.  0.  0.  0. 28. 14.  0.]
 [28.  2.  4.  0.  0.  0.  0. 36. 10.  0.]
 [ 3. 19. 12.  0.  0.  0.  0.  5. 43.  1.]
 [22. 12.  6.  0.  0.  0.  0. 25.  4.  2.]]
[6,     1] loss: 0.100
[6,    21] loss: 2.128
[6,    41] loss: 2.114
[6,    61] loss: 2.057
[6,    81] loss: 2.089
[6,   101] loss: 2.128
[6,   121] loss: 2.146
[6,   141] loss: 2.146
[6,   161] loss: 2.083
[6,   181] loss: 2.113
[6,   201] loss: 2.115
Epoch: 5, Loss: 2.10992, Accuracy: 0.22
Accuracy: 0.21, Total items: 826
[[43. 11.  0.  0.  4.  0.  0. 19.  0.  0.]
 [12. 73.  0.  0.  4.  1.  1. 15.  4.  0.]
 [ 6. 49.  0.  0.  8.  0.  1.  8.  5.  0.]
 [19. 40.  0.  0. 15.  2.  1. 13.  1.  0.]
 [11. 28.  0.  0.  5.  2.  1. 12.  7.  0.]
 [16. 35.  0.  0. 11.  2.  1. 12.  6.  0.]
 [23. 17.  0.  0.  3.  0.  0. 39.  6.  0.]
 [20. 12.  0.  0.  1.  1.  0. 44.  2.  0.]
 [ 1. 64.  0.  0.  5.  1.  4.  4.  4.  0.]
 [16. 17.  0.  0.  6.  0.  0. 31.  1.  0.]]
[7,     1] loss: 0.112
[7,    21] loss: 2.025
[7,    41] loss: 2.110
[7,    61] loss: 2.123
[7,    81] loss: 2.089
[7,   101] loss: 2.144
[7,   121] loss: 2.136
[7,   141] loss: 2.101
[7,   161] loss: 2.037
[7,   181] loss: 2.167
[7,   201] loss: 2.040
Epoch: 6, Loss: 2.09591, Accuracy: 0.23
Accuracy: 0.22, Total items: 826
[[39.  9.  0.  6.  0.  0.  0.  3.  1. 19.]
 [ 6. 49.  1.  6.  0.  0.  0.  4. 27. 17.]
 [ 6. 34.  4.  6.  0.  0.  0.  0. 16. 11.]
 [13. 30.  4. 14.  0.  1.  0.  1. 11. 17.]
 [ 8. 24.  0.  4.  2.  0.  0.  4. 13. 11.]
 [18. 20.  1.  8.  2.  2.  0. 10. 19.  3.]
 [22. 10.  0.  4.  0.  0.  0. 10. 12. 30.]
 [23.  8.  0.  3.  0.  0.  0. 12.  5. 29.]
 [ 0. 41.  4.  3.  2.  0.  0.  0. 30.  3.]
 [11. 14.  1.  5.  0.  0.  0.  6.  3. 31.]]
[8,     1] loss: 0.123
[8,    21] loss: 2.042
[8,    41] loss: 2.036
[8,    61] loss: 2.112
[8,    81] loss: 2.065
[8,   101] loss: 2.074
[8,   121] loss: 2.092
[8,   141] loss: 2.086
[8,   161] loss: 2.038
[8,   181] loss: 2.050
[8,   201] loss: 2.096
Epoch: 7, Loss: 2.07547, Accuracy: 0.24
Accuracy: 0.25, Total items: 826
[[31.  4.  0.  5.  0.  1.  7. 16.  5.  8.]
 [ 3. 17.  0.  4.  2.  4.  8.  7. 55. 10.]
 [ 4. 13.  1. 10.  1.  5.  1.  5. 29.  8.]
 [11.  7.  2. 17.  0.  7.  3. 10. 27.  7.]
 [ 5.  7.  0.  4.  2.  6.  1.  9. 26.  6.]
 [11.  4.  0.  7.  3. 16.  3.  9. 29.  1.]
 [16.  1.  0.  4.  1.  1. 10. 29. 20.  6.]
 [ 8.  2.  0.  1.  2.  1. 16. 34. 11.  5.]
 [ 0.  9.  2.  2.  1.  1.  1.  2. 64.  1.]
 [ 5.  7.  0.  9.  1.  2.  7. 20.  9. 11.]]
[9,     1] loss: 0.119
[9,    21] loss: 2.056
[9,    41] loss: 2.006
[9,    61] loss: 2.055
[9,    81] loss: 2.050
[9,   101] loss: 2.014
[9,   121] loss: 2.099
[9,   141] loss: 2.064
[9,   161] loss: 2.083
[9,   181] loss: 2.113
[9,   201] loss: 1.990
Epoch: 8, Loss: 2.05389, Accuracy: 0.24
Accuracy: 0.24, Total items: 826
[[44.  1.  7.  5.  0.  0.  0.  5.  3. 12.]
 [ 9.  7. 17.  3.  0.  6.  0.  6. 50. 12.]
 [ 8.  4. 22.  4.  0.  5.  0.  1. 27.  6.]
 [15.  1. 27.  8.  0.  5.  0.  5. 20. 10.]
 [ 6.  5.  9.  3.  1. 10.  0.  4. 20.  8.]
 [14.  2. 12.  4.  3. 20.  1.  3. 23.  1.]
 [35.  1.  4.  4.  1.  5.  7. 10. 16.  5.]
 [27.  2.  3.  1.  0.  3.  2. 16. 10. 16.]
 [ 1.  3.  9.  3.  2.  6.  0.  0. 58.  1.]
 [13.  7.  3.  6.  0.  3.  2. 10. 12. 15.]]
[10,     1] loss: 0.101
[10,    21] loss: 2.042
[10,    41] loss: 2.014
[10,    61] loss: 2.031
[10,    81] loss: 2.044
[10,   101] loss: 2.006
[10,   121] loss: 2.042
[10,   141] loss: 1.980
[10,   161] loss: 2.006
[10,   181] loss: 2.049
[10,   201] loss: 2.031
Epoch: 9, Loss: 2.02307, Accuracy: 0.26
Accuracy: 0.25, Total items: 826
[[41.  6.  6.  0.  0.  0.  2. 12.  2.  8.]
 [ 7. 40.  6.  0.  0.  3.  1.  7. 34. 12.]
 [13. 28. 10.  0.  0.  2.  2.  3. 17.  2.]
 [17. 30. 15.  0.  0.  2.  1.  9. 10.  7.]
 [10. 16.  6.  0.  0.  2.  2. 12. 14.  4.]
 [22. 16.  5.  0.  0. 17.  1.  6. 15.  1.]
 [30.  8.  1.  0.  0.  0. 13. 17. 14.  5.]
 [22.  4.  1.  0.  1.  0.  9. 25. 10.  8.]
 [ 1. 23.  6.  0.  2.  1.  0.  1. 46.  3.]
 [13. 12.  5.  0.  0.  0.  5. 21.  4. 11.]]
[11,     1] loss: 0.099
[11,    21] loss: 1.945
[11,    41] loss: 1.961
[11,    61] loss: 1.956
[11,    81] loss: 2.031
[11,   101] loss: 2.049
[11,   121] loss: 1.970
[11,   141] loss: 2.002
[11,   161] loss: 1.890
[11,   181] loss: 1.968
[11,   201] loss: 2.023
Epoch: 10, Loss: 1.97971, Accuracy: 0.27
Accuracy: 0.25, Total items: 826
[[14.  5.  3.  4.  0.  6.  6. 34.  0.  5.]
 [ 1. 30.  3.  2.  2.  3.  5. 12. 44.  8.]
 [ 0. 24.  5.  6.  2.  3.  2.  7. 23.  5.]
 [ 6. 22.  5. 13.  1.  6.  1. 14. 15.  8.]
 [ 1. 15.  6.  2.  4.  3.  2. 13. 17.  3.]
 [ 5.  9.  5.  5.  3. 20.  2. 13. 20.  1.]
 [ 7.  9.  1.  2.  0.  1. 16. 35. 14.  3.]
 [ 3.  4.  0.  0.  3.  2. 15. 38. 11.  4.]
 [ 0. 22.  3.  2.  2.  1.  1.  2. 49.  1.]
 [ 3.  8.  1.  4.  1.  0.  6. 22. 10. 16.]]
[12,     1] loss: 0.094
[12,    21] loss: 1.876
[12,    41] loss: 1.854
[12,    61] loss: 1.942
[12,    81] loss: 1.941
[12,   101] loss: 1.873
[12,   121] loss: 1.911
[12,   141] loss: 1.923
[12,   161] loss: 1.890
[12,   181] loss: 1.926
[12,   201] loss: 2.011
Epoch: 11, Loss: 1.91205, Accuracy: 0.31
Accuracy: 0.26, Total items: 826
[[37.  1.  9.  0.  2.  0.  1.  9.  3. 15.]
 [ 5. 17. 16.  4.  5.  1.  7.  3. 40. 12.]
 [ 3.  8. 25.  0.  3.  1.  1.  2. 23. 11.]
 [10.  7. 32.  1.  4.  4.  2.  3. 13. 15.]
 [ 3.  5. 14.  0. 10.  0.  4.  4. 17.  9.]
 [10.  3. 18.  0. 13. 11.  3.  5. 18.  2.]
 [21.  4.  5.  3.  1.  1. 20. 12. 14.  7.]
 [17.  2.  4.  0.  4.  2.  9. 20.  9. 13.]
 [ 0. 10. 17.  1.  4.  1.  2.  2. 46.  0.]
 [ 2.  3.  7.  0.  1.  1.  5. 12. 10. 30.]]
[13,     1] loss: 0.088
[13,    21] loss: 1.809
[13,    41] loss: 1.861
[13,    61] loss: 1.821
[13,    81] loss: 1.815
[13,   101] loss: 1.843
[13,   121] loss: 1.864
[13,   141] loss: 1.951
[13,   161] loss: 1.862
[13,   181] loss: 1.814
[13,   201] loss: 1.841
Epoch: 12, Loss: 1.84873, Accuracy: 0.33
Accuracy: 0.26, Total items: 826
[[48.  3.  5.  1.  1.  1.  2.  2.  0. 14.]
 [ 8. 33.  6.  7.  2.  1.  4.  6. 32. 11.]
 [14. 13. 13.  5.  6.  1.  0.  1. 19.  5.]
 [22. 14. 11. 12.  2.  1.  3.  1. 16.  9.]
 [ 7. 11.  5.  5.  6.  0.  3.  6. 14.  9.]
 [19.  8.  9.  7.  6.  6.  5.  3. 15.  5.]
 [35.  4.  2.  5.  1.  0. 14.  5. 15.  7.]
 [37.  6.  2.  2.  0.  0.  2.  8.  7. 16.]
 [ 2. 15.  8.  5.  3.  2.  0.  0. 47.  1.]
 [16.  5.  4.  3.  1.  0.  3.  5.  8. 26.]]
[14,     1] loss: 0.091
[14,    21] loss: 1.707
[14,    41] loss: 1.833
[14,    61] loss: 1.663
[14,    81] loss: 1.741
[14,   101] loss: 1.749
[14,   121] loss: 1.734
[14,   141] loss: 1.758
[14,   161] loss: 1.707
[14,   181] loss: 1.815
[14,   201] loss: 1.838
Epoch: 13, Loss: 1.75597, Accuracy: 0.35
Accuracy: 0.28, Total items: 826
[[45.  0.  1.  6.  1.  9.  1.  5.  0.  9.]
 [ 8. 22. 16. 16. 10.  8.  3.  5. 13.  9.]
 [12.  8. 12. 16.  8. 10.  1.  2.  5.  3.]
 [13.  3.  5. 23.  9. 12.  3.  1. 11. 11.]
 [ 6.  2.  7. 11.  8.  7.  4.  7.  9.  5.]
 [11.  1. 11.  7. 12. 31.  0.  4.  6.  0.]
 [23.  0.  6.  6.  6.  7. 21.  6.  8.  5.]
 [31.  2.  4.  3.  3.  5.  4. 15.  4.  9.]
 [ 1.  2. 10. 14. 10.  4.  1.  1. 39.  1.]
 [12.  2.  1. 11.  3.  6.  2. 12.  3. 19.]]
[15,     1] loss: 0.088
[15,    21] loss: 1.624
[15,    41] loss: 1.662
[15,    61] loss: 1.645
[15,    81] loss: 1.681
[15,   101] loss: 1.607
[15,   121] loss: 1.619
[15,   141] loss: 1.664
[15,   161] loss: 1.696
[15,   181] loss: 1.671
[15,   201] loss: 1.557
Epoch: 14, Loss: 1.64491, Accuracy: 0.41
Accuracy: 0.30, Total items: 826
[[18.  1.  3.  4.  2. 13.  8. 11.  1. 16.]
 [ 1. 33. 15.  3.  2.  7.  6. 12. 18. 13.]
 [ 4.  8. 21.  6.  2.  9.  0.  6. 14.  7.]
 [ 7.  7. 19.  8.  4.  7.  1. 11. 11. 16.]
 [ 3.  6.  8.  4.  4.  6.  1. 19.  9.  6.]
 [ 7.  5. 13.  4.  4. 24.  7. 10.  8.  1.]
 [ 3.  4.  6.  3.  2.  8. 35. 15.  6.  6.]
 [ 8.  4.  4.  1.  1.  4.  8. 43.  2.  5.]
 [ 0.  9. 13.  4.  3. 13.  1.  2. 36.  2.]
 [ 2.  1.  6.  0.  3.  4.  6. 18.  4. 27.]]
[16,     1] loss: 0.090
[16,    21] loss: 1.568
[16,    41] loss: 1.536
[16,    61] loss: 1.568
[16,    81] loss: 1.500
[16,   101] loss: 1.447
[16,   121] loss: 1.492
[16,   141] loss: 1.512
[16,   161] loss: 1.538
[16,   181] loss: 1.494
[16,   201] loss: 1.747
Epoch: 15, Loss: 1.54722, Accuracy: 0.45
Accuracy: 0.31, Total items: 826
[[29.  2.  8.  7.  2.  0. 12.  6.  0. 11.]
 [ 2. 33. 19.  3.  6.  4.  8.  4. 22.  9.]
 [ 3. 14. 23.  4.  9.  2.  1.  1. 11.  9.]
 [ 9.  6. 17. 17.  6.  4.  3.  3. 13. 13.]
 [ 4.  6. 12.  0. 14.  2.  1.  8. 12.  7.]
 [ 5.  4. 16.  6. 10. 16. 11.  3. 11.  1.]
 [ 8.  5.  6.  2.  6.  0. 40.  4. 11.  6.]
 [16.  4.  3.  2.  8.  1. 13. 21.  4.  8.]
 [ 1.  8. 18.  4.  2.  1.  2.  1. 44.  2.]
 [ 2.  2.  5.  7.  4.  3.  7. 11.  7. 23.]]
[17,     1] loss: 0.077
[17,    21] loss: 1.337
[17,    41] loss: 1.455
[17,    61] loss: 1.339
[17,    81] loss: 1.371
[17,   101] loss: 1.372
[17,   121] loss: 1.364
[17,   141] loss: 1.430
[17,   161] loss: 1.362
[17,   181] loss: 1.320
[17,   201] loss: 1.442
Epoch: 16, Loss: 1.38715, Accuracy: 0.50
Accuracy: 0.30, Total items: 826
[[23.  0.  4.  7.  8. 16.  4.  9.  0.  6.]
 [ 3. 22. 28. 10.  7.  7.  4.  6. 21.  2.]
 [ 6.  8. 21.  7. 12.  6.  2.  2. 13.  0.]
 [ 9.  5. 17. 23. 10.  8.  1.  6.  9.  3.]
 [ 1.  3. 10.  9. 12.  7.  3. 10.  8.  3.]
 [ 6.  2. 13.  9.  9. 28.  4.  4.  8.  0.]
 [ 2.  1.  9.  7.  6.  6. 42.  5.  8.  2.]
 [12.  2.  5.  4.  5.  7.  9. 28.  8.  0.]
 [ 0.  4. 20.  9.  4.  1.  2.  1. 40.  2.]
 [ 5.  0.  3. 20.  3.  3.  8. 11.  7. 11.]]
[18,     1] loss: 0.070
[18,    21] loss: 1.217
[18,    41] loss: 1.230
[18,    61] loss: 1.246
[18,    81] loss: 1.400
[18,   101] loss: 1.315
[18,   121] loss: 1.222
[18,   141] loss: 1.264
[18,   161] loss: 1.216
[18,   181] loss: 1.334
[18,   201] loss: 1.198
Epoch: 17, Loss: 1.26331, Accuracy: 0.54
Accuracy: 0.30, Total items: 826
[[44.  1.  3.  4.  5.  2.  1.  2.  0. 15.]
 [ 9. 25. 11.  8. 10. 13.  1.  5. 21.  7.]
 [11.  9.  7.  8. 14. 10.  3.  1. 10.  4.]
 [15.  5.  6. 20.  7.  7.  2.  5. 13. 11.]
 [ 9.  5.  3.  6.  8.  5.  1. 14. 10.  5.]
 [15.  1.  5.  9.  7. 25.  1.  9. 10.  1.]
 [23.  2.  4.  5.  6.  0. 28.  7. 10.  3.]
 [29.  0.  4.  3.  1.  2.  2. 27.  4.  8.]
 [ 1.  4.  8. 12. 10.  3.  0.  1. 43.  1.]
 [12.  4.  2.  3.  4.  1.  5. 10.  6. 24.]]
[19,     1] loss: 0.059
[19,    21] loss: 1.051
[19,    41] loss: 1.042
[19,    61] loss: 1.057
[19,    81] loss: 0.997
[19,   101] loss: 1.056
[19,   121] loss: 1.081
[19,   141] loss: 1.120
[19,   161] loss: 1.200
[19,   181] loss: 1.067
[19,   201] loss: 1.079
Epoch: 18, Loss: 1.07639, Accuracy: 0.61
Accuracy: 0.31, Total items: 826
[[23.  4. 11.  5.  6.  5.  2. 12.  1.  8.]
 [ 2. 36. 19.  1. 16.  4.  7.  5. 15.  5.]
 [ 2. 14. 26.  6.  7.  8.  3.  4.  6.  1.]
 [ 7.  7. 23. 15. 10.  5.  2.  7. 10.  5.]
 [ 1.  8.  7.  1. 19.  5.  4. 10. 10.  1.]
 [ 4.  2. 14.  3. 12. 24.  6.  9.  9.  0.]
 [ 8.  2. 10.  3.  8.  3. 27. 19.  5.  3.]
 [ 9.  2.  6.  0. 11.  0.  6. 39.  1.  6.]
 [ 0.  7. 15.  3.  9.  8.  7.  0. 34.  0.]
 [ 2.  7.  7.  2. 11.  1.  8. 17.  4. 12.]]
[20,     1] loss: 0.053
[20,    21] loss: 1.069
[20,    41] loss: 1.123
[20,    61] loss: 0.863
[20,    81] loss: 0.877
[20,   101] loss: 1.051
[20,   121] loss: 0.862
[20,   141] loss: 0.942
[20,   161] loss: 0.993
[20,   181] loss: 0.856
[20,   201] loss: 0.887
Epoch: 19, Loss: 0.95922, Accuracy: 0.65
Accuracy: 0.33, Total items: 826
[[33.  3.  4. 10.  6.  3.  4.  3.  1. 10.]
 [ 2. 45. 13.  3.  4.  7.  9.  7. 16.  4.]
 [ 4. 15. 14.  9. 10.  6.  6.  2.  9.  2.]
 [10.  8. 16. 18. 10.  6.  3.  4. 10.  6.]
 [ 5.  7.  9.  6. 13.  5.  1. 13.  6.  1.]
 [ 6.  5. 11.  9.  6. 26.  5.  6.  8.  1.]
 [ 9.  5.  2.  6.  5.  2. 41.  6. 10.  2.]
 [18.  6.  3.  3.  2.  4.  7. 27.  3.  7.]
 [ 0.  9. 14.  8.  5.  4.  3.  0. 38.  2.]
 [ 8.  4.  3. 10.  5.  2.  7. 11.  5. 16.]]
[21,     1] loss: 0.030
[21,    21] loss: 0.780
[21,    41] loss: 0.734
[21,    61] loss: 0.839
[21,    81] loss: 0.784
[21,   101] loss: 0.756
[21,   121] loss: 0.779
[21,   141] loss: 0.750
[21,   161] loss: 0.841
[21,   181] loss: 0.750
[21,   201] loss: 0.675
Epoch: 20, Loss: 0.76783, Accuracy: 0.73
Accuracy: 0.33, Total items: 826
[[35.  5.  8.  3.  5.  2.  3.  3.  2. 11.]
 [ 5. 50. 15.  3.  5.  6.  5.  6. 11.  4.]
 [ 8. 14. 23.  3.  9.  7.  5.  2.  4.  2.]
 [12.  5. 21. 10. 10.  6.  1.  5. 11. 10.]
 [ 6.  9. 11.  5. 11.  5.  1.  9.  6.  3.]
 [ 5.  7. 13.  6.  8. 26.  4.  6.  6.  2.]
 [ 7.  5.  8.  3.  9.  1. 35.  8.  6.  6.]
 [18.  3.  4.  1.  5.  5.  3. 23.  5. 13.]
 [ 1.  8. 23.  5.  3.  4.  2.  0. 33.  4.]
 [ 3.  7.  6.  3.  5.  2.  7. 11.  4. 23.]]
[22,     1] loss: 0.024
[22,    21] loss: 0.581
[22,    41] loss: 0.541
[22,    61] loss: 0.603
[22,    81] loss: 0.643
[22,   101] loss: 0.580
[22,   121] loss: 0.711
[22,   141] loss: 0.667
[22,   161] loss: 0.730
[22,   181] loss: 0.676
[22,   201] loss: 0.666
Epoch: 21, Loss: 0.63990, Accuracy: 0.77
Accuracy: 0.31, Total items: 826
[[32.  4.  1. 20.  1.  1.  1.  5.  1. 11.]
 [ 4. 36. 11. 18.  8.  2.  4.  8. 15.  4.]
 [ 8. 12.  6. 17. 11.  4.  3.  2. 13.  1.]
 [ 8.  7.  8. 38.  3.  5.  1.  4. 10.  7.]
 [ 3.  4.  2. 20. 14.  1.  2. 10.  7.  3.]
 [ 4.  6.  5. 14. 11. 24.  2. 10.  6.  1.]
 [ 9.  2.  4. 14.  5.  0. 25. 14. 10.  5.]
 [18.  4.  1.  8.  3.  2.  2. 31.  4.  7.]
 [ 0.  5. 12. 19.  6.  2.  1.  1. 35.  2.]
 [ 3.  6.  2. 18.  2.  2.  4.  9.  6. 19.]]
[23,     1] loss: 0.034
[23,    21] loss: 0.480
[23,    41] loss: 0.471
[23,    61] loss: 0.520
[23,    81] loss: 0.541
[23,   101] loss: 0.509
[23,   121] loss: 0.598
[23,   141] loss: 0.489
[23,   161] loss: 0.486
[23,   181] loss: 0.598
[23,   201] loss: 0.488
Epoch: 22, Loss: 0.52053, Accuracy: 0.82
Accuracy: 0.32, Total items: 826
[[28.  2.  3. 10.  5.  4.  3.  3.  2. 17.]
 [ 0. 35. 15. 10.  6.  6.  9.  5. 18.  6.]
 [ 2. 12. 15. 12. 10.  6.  6.  1. 10.  3.]
 [ 7.  4. 15. 23.  9.  8.  2.  4. 11.  8.]
 [ 2.  7.  6.  9. 13.  4.  3. 12.  7.  3.]
 [ 3.  4.  9.  8. 12. 30.  3.  5.  7.  2.]
 [ 7.  3.  5.  8.  8.  3. 37.  5.  8.  4.]
 [14.  1.  3.  3.  5.  4.  7. 26.  7. 10.]
 [ 0.  6. 16. 11.  3.  3.  3.  0. 39.  2.]
 [ 4.  5.  2. 14.  4.  3.  5. 11.  7. 16.]]
[24,     1] loss: 0.024
[24,    21] loss: 0.379
[24,    41] loss: 0.353
[24,    61] loss: 0.353
[24,    81] loss: 0.449
[24,   101] loss: 0.499
[24,   121] loss: 0.469
[24,   141] loss: 0.350
[24,   161] loss: 0.517
[24,   181] loss: 0.418
[24,   201] loss: 0.414
Epoch: 23, Loss: 0.42029, Accuracy: 0.85
Accuracy: 0.33, Total items: 826
[[26.  6.  6.  6.  9.  2.  2.  6.  2. 12.]
 [ 0. 42. 13.  5.  8. 10.  2.  8. 16.  6.]
 [ 3. 14. 19.  5.  9.  9.  2.  4.  9.  3.]
 [ 7.  6. 18. 23.  9.  7.  2.  3. 12.  4.]
 [ 2.  9.  8.  1. 19.  5.  2. 10.  6.  4.]
 [ 3.  4. 15.  9. 11. 24.  3.  7.  6.  1.]
 [ 5.  4. 10.  4.  8.  2. 31. 12.  7.  5.]
 [ 9.  5.  2.  0.  9.  4.  3. 34.  5.  9.]
 [ 0. 10. 16.  6.  5.  4.  1.  1. 38.  2.]
 [ 1.  8.  4.  8.  4.  2.  4. 15.  6. 19.]]
[25,     1] loss: 0.025
[25,    21] loss: 0.323
[25,    41] loss: 0.313
[25,    61] loss: 0.315
[25,    81] loss: 0.319
[25,   101] loss: 0.290
[25,   121] loss: 0.316
[25,   141] loss: 0.286
[25,   161] loss: 0.331
[25,   181] loss: 0.339
[25,   201] loss: 0.285
Epoch: 24, Loss: 0.31068, Accuracy: 0.89
Accuracy: 0.32, Total items: 826
[[25.  8.  7.  6.  5.  5.  2. 10.  1.  8.]
 [ 1. 43. 10.  5. 12.  6.  8.  4. 15.  6.]
 [ 6. 16. 12.  5. 14.  7.  4.  2. 10.  1.]
 [ 8.  6. 14. 19. 14.  7.  4.  3.  9.  7.]
 [ 2.  9.  6.  6. 14.  4.  2. 14.  7.  2.]
 [ 4.  6.  8.  5. 14. 28.  4.  8.  6.  0.]
 [ 6.  3.  4.  3.  7.  7. 39.  7.  5.  7.]
 [12.  3.  1.  2.  8.  6.  5. 30.  4.  9.]
 [ 0. 10. 14.  4. 12.  2.  6.  0. 34.  1.]
 [ 2.  7.  4.  7.  5.  2.  5. 12.  6. 21.]]
[26,     1] loss: 0.005
[26,    21] loss: 0.282
[26,    41] loss: 0.253
[26,    61] loss: 0.249
[26,    81] loss: 0.204
[26,   101] loss: 0.245
[26,   121] loss: 0.251
[26,   141] loss: 0.225
[26,   161] loss: 0.228
[26,   181] loss: 0.208
[26,   201] loss: 0.288
Epoch: 25, Loss: 0.24129, Accuracy: 0.92
Accuracy: 0.34, Total items: 826
[[38.  2.  1. 12.  4.  1.  1.  6.  2. 10.]
 [ 2. 49. 10. 12.  5.  6.  3.  4. 12.  7.]
 [ 7. 16. 16.  6. 10.  4.  3.  2.  9.  4.]
 [13.  8.  9. 27.  9.  5.  1.  4.  9.  6.]
 [ 3.  7.  6. 11. 12.  2.  2. 11.  7.  5.]
 [ 7.  6. 12. 10. 10. 22.  5.  4.  4.  3.]
 [10.  5.  5.  9.  4.  2. 34.  6.  7.  6.]
 [17.  4.  2.  6.  5.  3.  3. 26.  3. 11.]
 [ 1.  7. 11. 17.  4.  3.  4.  1. 33.  2.]
 [ 7.  4.  2. 12.  2.  3.  4. 10.  5. 22.]]
[27,     1] loss: 0.012
[27,    21] loss: 0.145
[27,    41] loss: 0.111
[27,    61] loss: 0.166
[27,    81] loss: 0.191
[27,   101] loss: 0.134
[27,   121] loss: 0.187
[27,   141] loss: 0.167
[27,   161] loss: 0.190
[27,   181] loss: 0.125
[27,   201] loss: 0.266
Epoch: 26, Loss: 0.16804, Accuracy: 0.95
Accuracy: 0.32, Total items: 826
[[33.  1.  6. 12.  4.  2.  1.  7.  1. 10.]
 [ 4. 28. 21.  8.  6.  7.  6.  6. 18.  6.]
 [ 6.  7. 21. 10. 12.  4.  5.  3.  8.  1.]
 [13.  3. 12. 27.  6.  6.  3.  7.  9.  5.]
 [ 2.  3. 10.  8. 12.  3.  3. 13.  7.  5.]
 [ 6.  4. 11.  8.  9. 23.  4.  8.  9.  1.]
 [ 8.  1.  3. 10.  6.  3. 32. 11. 10.  4.]
 [17.  2.  1.  6.  3.  2.  5. 30.  5.  9.]
 [ 0.  4. 12. 15.  2.  3.  3.  1. 42.  1.]
 [ 6.  2.  4.  9.  4.  0.  5. 15.  6. 20.]]
[28,     1] loss: 0.002
[28,    21] loss: 0.136
[28,    41] loss: 0.177
[28,    61] loss: 0.199
[28,    81] loss: 0.171
[28,   101] loss: 0.173
[28,   121] loss: 0.085
[28,   141] loss: 0.124
[28,   161] loss: 0.124
[28,   181] loss: 0.100
[28,   201] loss: 0.103
Epoch: 27, Loss: 0.14209, Accuracy: 0.96
Accuracy: 0.33, Total items: 826
[[31.  1.  7.  8.  6.  3.  1.  8.  2. 10.]
 [ 2. 46. 11.  9.  8.  6.  1.  7. 13.  7.]
 [ 5. 12. 15.  6. 12.  8.  3.  3.  7.  6.]
 [ 9.  7. 14. 21. 10.  7.  1.  8.  7.  7.]
 [ 1.  6.  7.  7. 13.  4.  2. 14.  7.  5.]
 [ 4.  5.  8.  9. 13. 29.  2.  7.  4.  2.]
 [ 8.  2.  6.  8.  9.  3. 33.  9.  6.  4.]
 [14.  2.  1.  3.  8.  6.  4. 26.  4. 12.]
 [ 0.  7. 12. 14.  8.  2.  2.  2. 32.  4.]
 [ 3.  3.  5.  6.  6.  2.  3. 15.  5. 23.]]
[29,     1] loss: 0.003
[29,    21] loss: 0.112
[29,    41] loss: 0.116
[29,    61] loss: 0.085
[29,    81] loss: 0.081
[29,   101] loss: 0.082
[29,   121] loss: 0.084
[29,   141] loss: 0.086
[29,   161] loss: 0.073
[29,   181] loss: 0.079
[29,   201] loss: 0.108
Epoch: 28, Loss: 0.08876, Accuracy: 0.98
Accuracy: 0.33, Total items: 826
[[38.  1.  3.  8.  6.  2.  1.  6.  2. 10.]
 [ 3. 40. 13.  8.  8.  6.  6.  6. 12.  8.]
 [ 6.  9. 17.  7. 10.  8.  3.  3.  8.  6.]
 [13.  6. 12. 22.  9.  7.  2.  6.  8.  6.]
 [ 2.  5.  7.  8. 14.  3.  2. 15.  7.  3.]
 [ 6.  5. 10.  7. 12. 25.  4.  9.  4.  1.]
 [11.  3.  5.  6.  8.  3. 35.  9.  6.  2.]
 [17.  2.  1.  2.  8.  2.  4. 30.  4. 10.]
 [ 0.  6. 12. 16.  6.  2.  4.  1. 33.  3.]
 [ 8.  1.  4. 10.  4.  3.  2. 14.  6. 19.]]
[30,     1] loss: 0.005
[30,    21] loss: 0.096
[30,    41] loss: 0.068
[30,    61] loss: 0.066
[30,    81] loss: 0.076
[30,   101] loss: 0.078
[30,   121] loss: 0.046
[30,   141] loss: 0.073
[30,   161] loss: 0.098
[30,   181] loss: 0.097
[30,   201] loss: 0.057
Epoch: 29, Loss: 0.07589, Accuracy: 0.98
Accuracy: 0.33, Total items: 826
[[36.  1.  3.  9.  6.  3.  2.  6.  2.  9.]
 [ 3. 41. 13.  7.  7.  6.  8.  6. 13.  6.]
 [ 6. 11. 17.  9. 11.  8.  4.  3.  5.  3.]
 [11.  7. 14. 23.  9.  7.  2.  6.  8.  4.]
 [ 2.  5.  8.  8. 14.  3.  2. 14.  7.  3.]
 [ 8.  5.  9.  7. 12. 25.  4.  7.  4.  2.]
 [10.  3.  6.  7.  8.  2. 39.  6.  4.  3.]
 [15.  3.  2.  3.  7.  4.  5. 26.  4. 11.]
 [ 0.  6. 13. 15.  6.  2.  4.  1. 33.  3.]
 [ 7.  4.  4. 11.  4.  2.  3. 13.  5. 18.]]
Finished Training
Accuracy: 0.33, Total items: 826
[[36.  1.  3.  9.  6.  3.  2.  6.  2.  9.]
 [ 3. 41. 13.  7.  7.  6.  8.  6. 13.  6.]
 [ 6. 11. 17.  9. 11.  8.  4.  3.  5.  3.]
 [11.  7. 14. 23.  9.  7.  2.  6.  8.  4.]
 [ 2.  5.  8.  8. 14.  3.  2. 14.  7.  3.]
 [ 8.  5.  9.  7. 12. 25.  4.  7.  4.  2.]
 [10.  3.  6.  7.  8.  2. 39.  6.  4.  3.]
 [15.  3.  2.  3.  7.  4.  5. 26.  4. 11.]
 [ 0.  6. 13. 15.  6.  2.  4.  1. 33.  3.]
 [ 7.  4.  4. 11.  4.  2.  3. 13.  5. 18.]]