Trang chủ Khám phá Trợ giúp
Đăng nhập
sebastian
/
androidMotionLogger
1
0
Fork 0
Các tập tin Các vấn đề 0 Yêu cầu khéo về 0 Wiki
Tree: e6463c07c2
Branches Tags
androidMotio...
/
julia
/
logs
/
log_29_09_2019.log

log_29_09_2019.log 40 KB
Lịch sử Raw

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650 
  1. 

  2. --------[29_09_2019 16:52:06]--------
    3. 

  3. second stage Hyperparameter Tuning with 1 net
    4. 

  4. 

  5. Configuration learning_rate=0.3, decay_step=20
    6. 

  6. [16:53:20] INIT Loss(val): 0.180713 Accuarcy: 0.083435
    7. 

  7. [16:55:33] Epoch   1: Loss(train): 0.106532 Loss(val): 0.109270
    8. 

  8. [16:56:20] Epoch   2: Loss(train): 0.095428 Loss(val): 0.097530
    9. 

  9. [16:57:06] Epoch   3: Loss(train): 0.090024 Loss(val): 0.091654
    10. 

  10. [16:57:52] Epoch   4: Loss(train): 0.086840 Loss(val): 0.088290
    11. 

  11. [16:58:38] Epoch   5: Loss(train): 0.085175 Loss(val): 0.086443
    12. 

  12. [16:59:24] Epoch   6: Loss(train): 0.084256 Loss(val): 0.085231
    13. 

  13. [17:00:09] Epoch   7: Loss(train): 0.083388 Loss(val): 0.084379
    14. 

  14. [17:00:54] Epoch   8: Loss(train): 0.082766 Loss(val): 0.083685
    15. 

  15. [17:01:40] Epoch   9: Loss(train): 0.082271 Loss(val): 0.083217
    16. 

  16. [17:02:25] Epoch  10: Loss(train): 0.081836 Loss(val): 0.082803
    17. 

  17. [17:03:10] Epoch  11: Loss(train): 0.081547 Loss(val): 0.082447
    18. 

  18. [17:03:56] Epoch  12: Loss(train): 0.081178 Loss(val): 0.082092
    19. 

  19. [17:04:42] Epoch  13: Loss(train): 0.080869 Loss(val): 0.081751
    20. 

  20. [17:05:27] Epoch  14: Loss(train): 0.080644 Loss(val): 0.081556
    21. 

  21. [17:06:12] Epoch  15: Loss(train): 0.080391 Loss(val): 0.081279
    22. 

  22. [17:06:57] Epoch  16: Loss(train): 0.080222 Loss(val): 0.081098
    23. 

  23. [17:07:43] Epoch  17: Loss(train): 0.080038 Loss(val): 0.080880
    24. 

  24. [17:08:28] Epoch  18: Loss(train): 0.079911 Loss(val): 0.080777
    25. 

  25. [17:09:14] Epoch  19: Loss(train): 0.079714 Loss(val): 0.080606
    26. 

  26. [17:09:59] Epoch  20: Loss(train): 0.079589 Loss(val): 0.080480
    27. 

  27. [17:10:45] Epoch  21: Loss(train): 0.079406 Loss(val): 0.080328
    28. 

  28. [17:11:31] Epoch  22: Loss(train): 0.079339 Loss(val): 0.080205
    29. 

  29. [17:12:16] Epoch  23: Loss(train): 0.079207 Loss(val): 0.080088
    30. 

  30. [17:13:02] Epoch  24: Loss(train): 0.079084 Loss(val): 0.079978
    31. 

  31. [17:13:48] Epoch  25: Loss(train): 0.079040 Loss(val): 0.079932
    32. 

  32. [17:14:35] Epoch  26: Loss(train): 0.078889 Loss(val): 0.079799
    33. 

  33. [17:15:26] Epoch  27: Loss(train): 0.078822 Loss(val): 0.079725
    34. 

  34. [17:16:19] Epoch  28: Loss(train): 0.078742 Loss(val): 0.079628
    35. 

  35. [17:17:12] Epoch  29: Loss(train): 0.078611 Loss(val): 0.079555
    36. 

  36. [17:18:01] Epoch  30: Loss(train): 0.078586 Loss(val): 0.079513
    37. 

  37. [17:18:51] Epoch  31: Loss(train): 0.078523 Loss(val): 0.079450
    38. 

  38. [17:19:43] Epoch  32: Loss(train): 0.078456 Loss(val): 0.079391
    39. 

  39. [17:20:29] Epoch  33: Loss(train): 0.078401 Loss(val): 0.079339
    40. 

  40. [17:21:16] Epoch  34: Loss(train): 0.078364 Loss(val): 0.079309
    41. 

  41. [17:22:03] Epoch  35: Loss(train): 0.078296 Loss(val): 0.079267
    42. 

  42. [17:22:49] Epoch  36: Loss(train): 0.078242 Loss(val): 0.079220
    43. 

  43. [17:23:36] Epoch  37: Loss(train): 0.078217 Loss(val): 0.079206
    44. 

  44. [17:24:27] Epoch  38: Loss(train): 0.078172 Loss(val): 0.079159
    45. 

  45. [17:25:13] Epoch  39: Loss(train): 0.078125 Loss(val): 0.079112
    46. 

  46. [17:25:59] Epoch  40: Loss(train): 0.078090 Loss(val): 0.079078
    47. 

  47. [17:26:47] Epoch  41: Loss(train): 0.078064 Loss(val): 0.079061
    48. 

  48. [17:27:34] Epoch  42: Loss(train): 0.078024 Loss(val): 0.079030
    49. 

  49. [17:28:21] Epoch  43: Loss(train): 0.077999 Loss(val): 0.079014
    50. 

  50. [17:29:07] Epoch  44: Loss(train): 0.077969 Loss(val): 0.079004
    51. 

  51. [17:29:54] Epoch  45: Loss(train): 0.077942 Loss(val): 0.078974
    52. 

  52. [17:30:40] Epoch  46: Loss(train): 0.077913 Loss(val): 0.078955
    53. 

  53. [17:31:26] Epoch  47: Loss(train): 0.077897 Loss(val): 0.078943
    54. 

  54. [17:32:14] Epoch  48: Loss(train): 0.077872 Loss(val): 0.078943
    55. 

  55. [17:33:00] Epoch  49: Loss(train): 0.077860 Loss(val): 0.078925
    56. 

  56. [17:33:46] Epoch  50: Loss(train): 0.077845 Loss(val): 0.078909
    57. 

  57. Converged at Loss(train): 0.082486, Loss(val): 0.083474 in epoch 50 with accuracy(val): 0.445493
    58. 

  58. 

  59. Configuration learning_rate=0.3, decay_step=40
    60. 

  60. [17:34:11] INIT Loss(val): 0.154576 Accuarcy: 0.089405
    61. 

  61. [17:34:58] Epoch   1: Loss(train): 0.106738 Loss(val): 0.110443
    62. 

  62. [17:35:46] Epoch   2: Loss(train): 0.094961 Loss(val): 0.097940
    63. 

  63. [17:36:33] Epoch   3: Loss(train): 0.089637 Loss(val): 0.092018
    64. 

  64. [17:37:20] Epoch   4: Loss(train): 0.086759 Loss(val): 0.088715
    65. 

  65. [17:38:07] Epoch   5: Loss(train): 0.085159 Loss(val): 0.086774
    66. 

  66. [17:38:54] Epoch   6: Loss(train): 0.084000 Loss(val): 0.085421
    67. 

  67. [17:39:40] Epoch   7: Loss(train): 0.083180 Loss(val): 0.084470
    68. 

  68. [17:40:26] Epoch   8: Loss(train): 0.082572 Loss(val): 0.083797
    69. 

  69. [17:41:13] Epoch   9: Loss(train): 0.082123 Loss(val): 0.083295
    70. 

  70. [17:41:59] Epoch  10: Loss(train): 0.081630 Loss(val): 0.082839
    71. 

  71. [17:42:45] Epoch  11: Loss(train): 0.081291 Loss(val): 0.082452
    72. 

  72. [17:43:32] Epoch  12: Loss(train): 0.080972 Loss(val): 0.082084
    73. 

  73. [17:44:18] Epoch  13: Loss(train): 0.080653 Loss(val): 0.081747
    74. 

  74. [17:45:04] Epoch  14: Loss(train): 0.080442 Loss(val): 0.081523
    75. 

  75. [17:45:50] Epoch  15: Loss(train): 0.080123 Loss(val): 0.081291
    76. 

  76. [17:46:37] Epoch  16: Loss(train): 0.079901 Loss(val): 0.081047
    77. 

  77. [17:47:24] Epoch  17: Loss(train): 0.079744 Loss(val): 0.080816
    78. 

  78. [17:48:12] Epoch  18: Loss(train): 0.079591 Loss(val): 0.080666
    79. 

  79. [17:48:58] Epoch  19: Loss(train): 0.079401 Loss(val): 0.080512
    80. 

  80. [17:49:45] Epoch  20: Loss(train): 0.079236 Loss(val): 0.080347
    81. 

  81. [17:50:33] Epoch  21: Loss(train): 0.079109 Loss(val): 0.080221
    82. 

  82. [17:51:24] Epoch  22: Loss(train): 0.078977 Loss(val): 0.080101
    83. 

  83. [17:52:12] Epoch  23: Loss(train): 0.078854 Loss(val): 0.080001
    84. 

  84. [17:52:59] Epoch  24: Loss(train): 0.078805 Loss(val): 0.079867
    85. 

  85. [17:53:46] Epoch  25: Loss(train): 0.078686 Loss(val): 0.079800
    86. 

  86. [17:54:36] Epoch  26: Loss(train): 0.078619 Loss(val): 0.079673
    87. 

  87. [17:55:28] Epoch  27: Loss(train): 0.078528 Loss(val): 0.079612
    88. 

  88. [17:56:15] Epoch  28: Loss(train): 0.078430 Loss(val): 0.079535
    89. 

  89. [17:57:02] Epoch  29: Loss(train): 0.078359 Loss(val): 0.079456
    90. 

  90. [17:57:50] Epoch  30: Loss(train): 0.078281 Loss(val): 0.079384
    91. 

  91. [17:58:44] Epoch  31: Loss(train): 0.078242 Loss(val): 0.079335
    92. 

  92. [17:59:33] Epoch  32: Loss(train): 0.078183 Loss(val): 0.079284
    93. 

  93. [18:00:24] Epoch  33: Loss(train): 0.078123 Loss(val): 0.079225
    94. 

  94. [18:01:13] Epoch  34: Loss(train): 0.078082 Loss(val): 0.079195
    95. 

  95. [18:02:00] Epoch  35: Loss(train): 0.078023 Loss(val): 0.079155
    96. 

  96. [18:02:49] Epoch  36: Loss(train): 0.077983 Loss(val): 0.079103
    97. 

  97. [18:03:38] Epoch  37: Loss(train): 0.077936 Loss(val): 0.079066
    98. 

  98. [18:04:26] Epoch  38: Loss(train): 0.077902 Loss(val): 0.079045
    99. 

  99. [18:05:14] Epoch  39: Loss(train): 0.077875 Loss(val): 0.079019
    100. 

  100. [18:06:03] Epoch  40: Loss(train): 0.077839 Loss(val): 0.078968
    101. 

  101. [18:06:51] Epoch  41: Loss(train): 0.077803 Loss(val): 0.078962
    102. 

  102. [18:07:39] Epoch  42: Loss(train): 0.077777 Loss(val): 0.078970
    103. 

  103. [18:08:27] Epoch  43: Loss(train): 0.077755 Loss(val): 0.078905
    104. 

  104. [18:09:15] Epoch  44: Loss(train): 0.077733 Loss(val): 0.078915
    105. 

  105. [18:10:03] Epoch  45: Loss(train): 0.077713 Loss(val): 0.078907
    106. 

  106. [18:10:52] Epoch  46: Loss(train): 0.077690 Loss(val): 0.078885
    107. 

  107. [18:11:39] Epoch  47: Loss(train): 0.077666 Loss(val): 0.078863
    108. 

  108. [18:12:27] Epoch  48: Loss(train): 0.077652 Loss(val): 0.078851
    109. 

  109. [18:13:15] Epoch  49: Loss(train): 0.077632 Loss(val): 0.078827
    110. 

  110. [18:14:03] Epoch  50: Loss(train): 0.077617 Loss(val): 0.078828
    111. 

  111. [18:14:50] Epoch  51: Loss(train): 0.077603 Loss(val): 0.078800
    112. 

  112. [18:15:38] Epoch  52: Loss(train): 0.077587 Loss(val): 0.078780
    113. 

  113. [18:16:25] Epoch  53: Loss(train): 0.077573 Loss(val): 0.078771
    114. 

  114. [18:17:13] Epoch  54: Loss(train): 0.077561 Loss(val): 0.078770
    115. 

  115. [18:18:00] Epoch  55: Loss(train): 0.077551 Loss(val): 0.078766
    116. 

  116. [18:18:48] Epoch  56: Loss(train): 0.077541 Loss(val): 0.078755
    117. 

  117. [18:19:35] Epoch  57: Loss(train): 0.077533 Loss(val): 0.078769
    118. 

  118. [18:20:22] Epoch  58: Loss(train): 0.077520 Loss(val): 0.078739
    119. 

  119. [18:21:09] Epoch  59: Loss(train): 0.077514 Loss(val): 0.078746
    120. 

  120. [18:21:57] Epoch  60: Loss(train): 0.077504 Loss(val): 0.078725
    121. 

  121. [18:22:44] Epoch  61: Loss(train): 0.077499 Loss(val): 0.078735
    122. 

  122. [18:23:32] Epoch  62: Loss(train): 0.077492 Loss(val): 0.078728
    123. 

  123. [18:24:20] Epoch  63: Loss(train): 0.077485 Loss(val): 0.078724
    124. 

  124. [18:25:08] Epoch  64: Loss(train): 0.077478 Loss(val): 0.078721
    125. 

  125. [18:25:56] Epoch  65: Loss(train): 0.077468 Loss(val): 0.078695
    126. 

  126. [18:26:45] Epoch  66: Loss(train): 0.077468 Loss(val): 0.078714
    127. 

  127. [18:27:40] Epoch  67: Loss(train): 0.077462 Loss(val): 0.078705
    128. 

  128. [18:28:28] Epoch  68: Loss(train): 0.077458 Loss(val): 0.078700
    129. 

  129. [18:29:17] Epoch  69: Loss(train): 0.077453 Loss(val): 0.078694
    130. 

  130. [18:30:11] Epoch  70: Loss(train): 0.077454 Loss(val): 0.078712
    131. 

  131. [18:31:04] Epoch  71: Loss(train): 0.077449 Loss(val): 0.078707
    132. 

  132. [18:32:01] Epoch  72: Loss(train): 0.077444 Loss(val): 0.078695
    133. 

  133. [18:32:56] Epoch  73: Loss(train): 0.077440 Loss(val): 0.078689
    134. 

  134. [18:33:45] Epoch  74: Loss(train): 0.077438 Loss(val): 0.078692
    135. 

  135. [18:34:34] Epoch  75: Loss(train): 0.077435 Loss(val): 0.078691
    136. 

  136. [18:35:26] Epoch  76: Loss(train): 0.077430 Loss(val): 0.078680
    137. 

  137. [18:36:16] Epoch  77: Loss(train): 0.077427 Loss(val): 0.078679
    138. 

  138. [18:37:05] Epoch  78: Loss(train): 0.077426 Loss(val): 0.078680
    139. 

  139. [18:37:54] Epoch  79: Loss(train): 0.077424 Loss(val): 0.078680
    140. 

  140. [18:38:48] Epoch  80: Loss(train): 0.077420 Loss(val): 0.078671
    141. 

  141. [18:39:36] Epoch  81: Loss(train): 0.077419 Loss(val): 0.078675
    142. 

  142. [18:40:28] Epoch  82: Loss(train): 0.077415 Loss(val): 0.078664
    143. 

  143. [18:41:17] Epoch  83: Loss(train): 0.077414 Loss(val): 0.078668
    144. 

  144. [18:42:06] Epoch  84: Loss(train): 0.077410 Loss(val): 0.078661
    145. 

  145. [18:42:55] Epoch  85: Loss(train): 0.077410 Loss(val): 0.078663
    146. 

  146. [18:43:45] Epoch  86: Loss(train): 0.077409 Loss(val): 0.078667
    147. 

  147. [18:44:33] Epoch  87: Loss(train): 0.077407 Loss(val): 0.078664
    148. 

  148. [18:45:23] Epoch  88: Loss(train): 0.077406 Loss(val): 0.078664
    149. 

  149. [18:46:12] Epoch  89: Loss(train): 0.077404 Loss(val): 0.078660
    150. 

  150. [18:47:02] Epoch  90: Loss(train): 0.077403 Loss(val): 0.078663
    151. 

  151. [18:47:51] Epoch  91: Loss(train): 0.077401 Loss(val): 0.078657
    152. 

  152. [18:48:41] Epoch  92: Loss(train): 0.077399 Loss(val): 0.078655
    153. 

  153. [18:49:30] Epoch  93: Loss(train): 0.077399 Loss(val): 0.078657
    154. 

  154. [18:50:19] Epoch  94: Loss(train): 0.077399 Loss(val): 0.078657
    155. 

  155. [18:51:08] Epoch  95: Loss(train): 0.077398 Loss(val): 0.078657
    156. 

  156. [18:51:57] Epoch  96: Loss(train): 0.077397 Loss(val): 0.078658
    157. 

  157. [18:52:46] Epoch  97: Loss(train): 0.077397 Loss(val): 0.078657
    158. 

  158. [18:53:36] Epoch  98: Loss(train): 0.077394 Loss(val): 0.078651
    159. 

  159. [18:54:25] Epoch  99: Loss(train): 0.077394 Loss(val): 0.078652
    160. 

  160. [18:55:14] Epoch 100: Loss(train): 0.077393 Loss(val): 0.078650
    161. 

  161. [18:56:04] Epoch 101: Loss(train): 0.077392 Loss(val): 0.078649
    162. 

  162. [18:56:53] Epoch 102: Loss(train): 0.077391 Loss(val): 0.078646
    163. 

  163. [18:57:42] Epoch 103: Loss(train): 0.077390 Loss(val): 0.078645
    164. 

  164. [18:58:32] Epoch 104: Loss(train): 0.077389 Loss(val): 0.078644
    165. 

  165. [18:59:22] Epoch 105: Loss(train): 0.077389 Loss(val): 0.078644
    166. 

  166. [19:00:12] Epoch 106: Loss(train): 0.077388 Loss(val): 0.078642
    167. 

  167. [19:01:09] Epoch 107: Loss(train): 0.077388 Loss(val): 0.078642
    168. 

  168. [19:01:59] Epoch 108: Loss(train): 0.077388 Loss(val): 0.078644
    169. 

  169. [19:02:49] Epoch 109: Loss(train): 0.077387 Loss(val): 0.078643
    170. 

  170. [19:03:39] Epoch 110: Loss(train): 0.077387 Loss(val): 0.078643
    171. 

  171. [19:04:32] Epoch 111: Loss(train): 0.077386 Loss(val): 0.078641
    172. 

  172. [19:05:30] Epoch 112: Loss(train): 0.077386 Loss(val): 0.078641
    173. 

  173. [19:06:25] Epoch 113: Loss(train): 0.077386 Loss(val): 0.078642
    174. 

  174. [19:07:16] Epoch 114: Loss(train): 0.077386 Loss(val): 0.078641
    175. 

  175. [19:08:06] Epoch 115: Loss(train): 0.077385 Loss(val): 0.078641
    176. 

  176. [19:08:55] Epoch 116: Loss(train): 0.077385 Loss(val): 0.078641
    177. 

  177. [19:09:46] Epoch 117: Loss(train): 0.077385 Loss(val): 0.078641
    178. 

  178. [19:10:37] Epoch 118: Loss(train): 0.077385 Loss(val): 0.078641
    179. 

  179. [19:11:27] Epoch 119: Loss(train): 0.077385 Loss(val): 0.078642
    180. 

  180. [19:12:21] Epoch 120: Loss(train): 0.077384 Loss(val): 0.078641
    181. 

  181. [19:13:14] Epoch 121: Loss(train): 0.077384 Loss(val): 0.078641
    182. 

  182. [19:14:03] Epoch 122: Loss(train): 0.077384 Loss(val): 0.078641
    183. 

  183. [19:14:54] Epoch 123: Loss(train): 0.077384 Loss(val): 0.078641
    184. 

  184. [19:15:46] Epoch 124: Loss(train): 0.077384 Loss(val): 0.078641
    185. 

  185. [19:16:37] Epoch 125: Loss(train): 0.077384 Loss(val): 0.078640
    186. 

  186. [19:17:28] Epoch 126: Loss(train): 0.077384 Loss(val): 0.078640
    187. 

  187. [19:18:20] Epoch 127: Loss(train): 0.077384 Loss(val): 0.078641
    188. 

  188. [19:19:11] Epoch 128: Loss(train): 0.077384 Loss(val): 0.078640
    189. 

  189. [19:20:01] Epoch 129: Loss(train): 0.077383 Loss(val): 0.078640
    190. 

  190. [19:20:52] Epoch 130: Loss(train): 0.077383 Loss(val): 0.078640
    191. 

  191. [19:21:44] Epoch 131: Loss(train): 0.077383 Loss(val): 0.078639
    192. 

  192. [19:22:36] Epoch 132: Loss(train): 0.077383 Loss(val): 0.078640
    193. 

  193. [19:23:26] Epoch 133: Loss(train): 0.077383 Loss(val): 0.078640
    194. 

  194. [19:24:17] Epoch 134: Loss(train): 0.077383 Loss(val): 0.078639
    195. 

  195. [19:25:07] Epoch 135: Loss(train): 0.077383 Loss(val): 0.078639
    196. 

  196. [19:25:58] Epoch 136: Loss(train): 0.077383 Loss(val): 0.078639
    197. 

  197. [19:26:48] Epoch 137: Loss(train): 0.077383 Loss(val): 0.078639
    198. 

  198. [19:27:40] Epoch 138: Loss(train): 0.077383 Loss(val): 0.078639
    199. 

  199. [19:28:31] Epoch 139: Loss(train): 0.077383 Loss(val): 0.078639
    200. 

  200. [19:29:22] Epoch 140: Loss(train): 0.077383 Loss(val): 0.078639
    201. 

  201. [19:30:13] Epoch 141: Loss(train): 0.077383 Loss(val): 0.078639
    202. 

  202. [19:31:04] Epoch 142: Loss(train): 0.077383 Loss(val): 0.078639
    203. 

  203. [19:31:55] Epoch 143: Loss(train): 0.077383 Loss(val): 0.078639
    204. 

  204. [19:32:46] Epoch 144: Loss(train): 0.077383 Loss(val): 0.078639
    205. 

  205. [19:33:36] Epoch 145: Loss(train): 0.077383 Loss(val): 0.078639
    206. 

  206. [19:34:26] Epoch 146: Loss(train): 0.077383 Loss(val): 0.078640
    207. 

  207. [19:35:22] Epoch 147: Loss(train): 0.077383 Loss(val): 0.078639
    208. 

  208. [19:36:17] Epoch 148: Loss(train): 0.077383 Loss(val): 0.078639
    209. 

  209. [19:37:15] Epoch 149: Loss(train): 0.077383 Loss(val): 0.078639
    210. 

  210. [19:38:16] Epoch 150: Loss(train): 0.077383 Loss(val): 0.078639
    211. 

  211. [19:39:07] Epoch 151: Loss(train): 0.077383 Loss(val): 0.078639
    212. 

  212. [19:39:59] Epoch 152: Loss(train): 0.077383 Loss(val): 0.078639
    213. 

  213. [19:40:50] Epoch 153: Loss(train): 0.077383 Loss(val): 0.078639
    214. 

  214. [19:41:45] Epoch 154: Loss(train): 0.077382 Loss(val): 0.078639
    215. 

  215. [19:42:36] Epoch 155: Loss(train): 0.077382 Loss(val): 0.078639
    216. 

  216. [19:43:33] Epoch 156: Loss(train): 0.077382 Loss(val): 0.078639
    217. 

  217. [19:44:26] Epoch 157: Loss(train): 0.077382 Loss(val): 0.078639
    218. 

  218. [19:45:19] Epoch 158: Loss(train): 0.077382 Loss(val): 0.078639
    219. 

  219. [19:46:10] Epoch 159: Loss(train): 0.077382 Loss(val): 0.078639
    220. 

  220. [19:47:03] Epoch 160: Loss(train): 0.077382 Loss(val): 0.078639
    221. 

  221. [19:47:57] Epoch 161: Loss(train): 0.077382 Loss(val): 0.078639
    222. 

  222. [19:48:48] Epoch 162: Loss(train): 0.077382 Loss(val): 0.078639
    223. 

  223. [19:49:40] Epoch 163: Loss(train): 0.077382 Loss(val): 0.078639
    224. 

  224. [19:50:32] Epoch 164: Loss(train): 0.077382 Loss(val): 0.078639
    225. 

  225. [19:51:23] Epoch 165: Loss(train): 0.077382 Loss(val): 0.078639
    226. 

  226. [19:52:18] Epoch 166: Loss(train): 0.077382 Loss(val): 0.078639
    227. 

  227. [19:53:09] Epoch 167: Loss(train): 0.077382 Loss(val): 0.078639
    228. 

  228. [19:54:00] Epoch 168: Loss(train): 0.077382 Loss(val): 0.078639
    229. 

  229. [19:54:52] Epoch 169: Loss(train): 0.077382 Loss(val): 0.078639
    230. 

  230. [19:55:44] Epoch 170: Loss(train): 0.077382 Loss(val): 0.078639
    231. 

  231. [19:56:35] Epoch 171: Loss(train): 0.077382 Loss(val): 0.078639
    232. 

  232. [19:57:27] Epoch 172: Loss(train): 0.077382 Loss(val): 0.078639
    233. 

  233. [19:58:18] Epoch 173: Loss(train): 0.077382 Loss(val): 0.078639
    234. 

  234. [19:59:09] Epoch 174: Loss(train): 0.077382 Loss(val): 0.078639
    235. 

  235. [20:00:01] Epoch 175: Loss(train): 0.077382 Loss(val): 0.078639
    236. 

  236. [20:00:54] Epoch 176: Loss(train): 0.077382 Loss(val): 0.078639
    237. 

  237. [20:01:47] Epoch 177: Loss(train): 0.077382 Loss(val): 0.078639
    238. 

  238. [20:02:39] Epoch 178: Loss(train): 0.077382 Loss(val): 0.078639
    239. 

  239. [20:03:32] Epoch 179: Loss(train): 0.077382 Loss(val): 0.078639
    240. 

  240. [20:04:24] Epoch 180: Loss(train): 0.077382 Loss(val): 0.078639
    241. 

  241. [20:05:17] Epoch 181: Loss(train): 0.077382 Loss(val): 0.078639
    242. 

  242. [20:06:10] Epoch 182: Loss(train): 0.077382 Loss(val): 0.078639
    243. 

  243. [20:07:11] Epoch 183: Loss(train): 0.077382 Loss(val): 0.078639
    244. 

  244. [20:08:09] Epoch 184: Loss(train): 0.077382 Loss(val): 0.078639
    245. 

  245. [20:09:13] Epoch 185: Loss(train): 0.077382 Loss(val): 0.078639
    246. 

  246. [20:10:11] Epoch 186: Loss(train): 0.077382 Loss(val): 0.078639
    247. 

  247. [20:11:09] Epoch 187: Loss(train): 0.077382 Loss(val): 0.078639
    248. 

  248. [20:12:05] Epoch 188: Loss(train): 0.077382 Loss(val): 0.078639
    249. 

  249. [20:13:04] Epoch 189: Loss(train): 0.077382 Loss(val): 0.078639
    250. 

  250. [20:14:00] Epoch 190: Loss(train): 0.077382 Loss(val): 0.078639
    251. 

  251. [20:14:52] Epoch 191: Loss(train): 0.077382 Loss(val): 0.078639
    252. 

  252. [20:15:49] Epoch 192: Loss(train): 0.077382 Loss(val): 0.078639
    253. 

  253. [20:16:44] Epoch 193: Loss(train): 0.077382 Loss(val): 0.078639
    254. 

  254. [20:17:37] Epoch 194: Loss(train): 0.077382 Loss(val): 0.078639
    255. 

  255. [20:18:30] Epoch 195: Loss(train): 0.077382 Loss(val): 0.078639
    256. 

  256. [20:19:27] Epoch 196: Loss(train): 0.077382 Loss(val): 0.078639
    257. 

  257. [20:20:21] Epoch 197: Loss(train): 0.077382 Loss(val): 0.078639
    258. 

  258. [20:21:13] Epoch 198: Loss(train): 0.077382 Loss(val): 0.078639
    259. 

  259. [20:22:06] Epoch 199: Loss(train): 0.077382 Loss(val): 0.078639
    260. 

  260. [20:23:01] Epoch 200: Loss(train): 0.077382 Loss(val): 0.078639
    261. 

  261. [20:23:56] Epoch 201: Loss(train): 0.077382 Loss(val): 0.078639
    262. 

  262. [20:24:49] Epoch 202: Loss(train): 0.077382 Loss(val): 0.078639
    263. 

  263. [20:25:42] Epoch 203: Loss(train): 0.077382 Loss(val): 0.078639
    264. 

  264. [20:26:36] Epoch 204: Loss(train): 0.077382 Loss(val): 0.078639
    265. 

  265. [20:27:28] Epoch 205: Loss(train): 0.077382 Loss(val): 0.078639
    266. 

  266. [20:28:22] Epoch 206: Loss(train): 0.077382 Loss(val): 0.078639
    267. 

  267. [20:29:16] Epoch 207: Loss(train): 0.077382 Loss(val): 0.078639
    268. 

  268. [20:30:10] Epoch 208: Loss(train): 0.077382 Loss(val): 0.078639
    269. 

  269. [20:31:03] Epoch 209: Loss(train): 0.077382 Loss(val): 0.078639
    270. 

  270. [20:31:55] Epoch 210: Loss(train): 0.077382 Loss(val): 0.078639
    271. 

  271. [20:32:49] Epoch 211: Loss(train): 0.077382 Loss(val): 0.078639
    272. 

  272. [20:33:42] Epoch 212: Loss(train): 0.077382 Loss(val): 0.078639
    273. 

  273. [20:34:35] Epoch 213: Loss(train): 0.077382 Loss(val): 0.078639
    274. 

  274. [20:35:28] Epoch 214: Loss(train): 0.077382 Loss(val): 0.078639
    275. 

  275. [20:37:07] Epoch 215: Loss(train): 0.077382 Loss(val): 0.078639
    276. 

  276. [20:39:39] Epoch 216: Loss(train): 0.077382 Loss(val): 0.078639
    277. 

  277. [20:42:07] Epoch 217: Loss(train): 0.077382 Loss(val): 0.078639
    278. 

  278. [20:44:41] Epoch 218: Loss(train): 0.077382 Loss(val): 0.078639
    279. 

  279. [20:47:12] Epoch 219: Loss(train): 0.077382 Loss(val): 0.078639
    280. 

  280. [20:49:23] Epoch 220: Loss(train): 0.077382 Loss(val): 0.078639
    281. 

  281. [20:51:26] Epoch 221: Loss(train): 0.077382 Loss(val): 0.078639
    282. 

  282. [20:53:30] Epoch 222: Loss(train): 0.077382 Loss(val): 0.078639
    283. 

  283. [20:55:28] Epoch 223: Loss(train): 0.077382 Loss(val): 0.078639
    284. 

  284. [20:57:30] Epoch 224: Loss(train): 0.077382 Loss(val): 0.078639
    285. 

  285. [20:59:28] Epoch 225: Loss(train): 0.077382 Loss(val): 0.078639
    286. 

  286. [21:01:23] Epoch 226: Loss(train): 0.077382 Loss(val): 0.078639
    287. 

  287. [21:03:18] Epoch 227: Loss(train): 0.077382 Loss(val): 0.078639
    288. 

  288. [21:05:13] Epoch 228: Loss(train): 0.077382 Loss(val): 0.078639
    289. 

  289. [21:07:08] Epoch 229: Loss(train): 0.077382 Loss(val): 0.078639
    290. 

  290. [21:09:06] Epoch 230: Loss(train): 0.077382 Loss(val): 0.078639
    291. 

  291. [21:11:45] Epoch 231: Loss(train): 0.077382 Loss(val): 0.078639
    292. 

  292. [21:14:17] Epoch 232: Loss(train): 0.077382 Loss(val): 0.078639
    293. 

  293. [21:16:50] Epoch 233: Loss(train): 0.077382 Loss(val): 0.078639
    294. 

  294. [21:18:55] Epoch 234: Loss(train): 0.077382 Loss(val): 0.078639
    295. 

  295. [21:21:14] Epoch 235: Loss(train): 0.077382 Loss(val): 0.078639
    296. 

  296. [21:23:20] Epoch 236: Loss(train): 0.077382 Loss(val): 0.078639
    297. 

  297. [21:25:19] Epoch 237: Loss(train): 0.077382 Loss(val): 0.078639
    298. 

  298. [21:27:31] Epoch 238: Loss(train): 0.077382 Loss(val): 0.078639
    299. 

  299. [21:29:41] Epoch 239: Loss(train): 0.077382 Loss(val): 0.078639
    300. 

  300. [21:31:41] Epoch 240: Loss(train): 0.077382 Loss(val): 0.078639
    301. 

  301. [21:33:47] Epoch 241: Loss(train): 0.077382 Loss(val): 0.078639
    302. 

  302. [21:35:49] Epoch 242: Loss(train): 0.077382 Loss(val): 0.078639
    303. 

  303. [21:37:51] Epoch 243: Loss(train): 0.077382 Loss(val): 0.078639
    304. 

  304. [21:39:51] Epoch 244: Loss(train): 0.077382 Loss(val): 0.078639
    305. 

  305. [21:42:19] Epoch 245: Loss(train): 0.077382 Loss(val): 0.078639
    306. 

  306. [21:44:40] Epoch 246: Loss(train): 0.077382 Loss(val): 0.078639
    307. 

  307. [21:46:46] Epoch 247: Loss(train): 0.077382 Loss(val): 0.078639
    308. 

  308. [21:49:01] Epoch 248: Loss(train): 0.077382 Loss(val): 0.078639
    309. 

  309. [21:51:19] Epoch 249: Loss(train): 0.077382 Loss(val): 0.078639
    310. 

  310. [21:53:26] Epoch 250: Loss(train): 0.077382 Loss(val): 0.078639
    311. 

  311. [21:55:35] Epoch 251: Loss(train): 0.077382 Loss(val): 0.078639
    312. 

  312. [21:57:42] Epoch 252: Loss(train): 0.077382 Loss(val): 0.078639
    313. 

  313. [21:59:52] Epoch 253: Loss(train): 0.077382 Loss(val): 0.078639
    314. 

  314. [22:01:59] Epoch 254: Loss(train): 0.077382 Loss(val): 0.078639
    315. 

  315. [22:04:11] Epoch 255: Loss(train): 0.077382 Loss(val): 0.078639
    316. 

  316. [22:06:13] Epoch 256: Loss(train): 0.077382 Loss(val): 0.078639
    317. 

  317. [22:08:20] Epoch 257: Loss(train): 0.077382 Loss(val): 0.078639
    318. 

  318. [22:10:22] Epoch 258: Loss(train): 0.077382 Loss(val): 0.078639
    319. 

  319. [22:12:25] Epoch 259: Loss(train): 0.077382 Loss(val): 0.078639
    320. 

  320. [22:14:28] Epoch 260: Loss(train): 0.077382 Loss(val): 0.078639
    321. 

  321. [22:16:51] Epoch 261: Loss(train): 0.077382 Loss(val): 0.078639
    322. 

  322. [22:19:19] Epoch 262: Loss(train): 0.077382 Loss(val): 0.078639
    323. 

  323. [22:21:37] Epoch 263: Loss(train): 0.077382 Loss(val): 0.078639
    324. 

  324. [22:24:16] Epoch 264: Loss(train): 0.077382 Loss(val): 0.078639
    325. 

  325. [22:26:31] Epoch 265: Loss(train): 0.077382 Loss(val): 0.078639
    326. 

  326. [22:28:42] Epoch 266: Loss(train): 0.077382 Loss(val): 0.078639
    327. 

  327. [22:30:51] Epoch 267: Loss(train): 0.077382 Loss(val): 0.078639
    328. 

  328. [22:33:05] Epoch 268: Loss(train): 0.077382 Loss(val): 0.078639
    329. 

  329. [22:35:15] Epoch 269: Loss(train): 0.077382 Loss(val): 0.078639
    330. 

  330. [22:37:29] Epoch 270: Loss(train): 0.077382 Loss(val): 0.078639
    331. 

  331. [22:39:37] Epoch 271: Loss(train): 0.077382 Loss(val): 0.078639
    332. 

  332. [22:41:48] Epoch 272: Loss(train): 0.077382 Loss(val): 0.078639
    333. 

  333. [22:43:53] Epoch 273: Loss(train): 0.077382 Loss(val): 0.078639
    334. 

  334. [22:45:58] Epoch 274: Loss(train): 0.077382 Loss(val): 0.078639
    335. 

  335. [22:48:04] Epoch 275: Loss(train): 0.077382 Loss(val): 0.078639
    336. 

  336. [22:50:17] Epoch 276: Loss(train): 0.077382 Loss(val): 0.078639
    337. 

  337. [22:52:25] Epoch 277: Loss(train): 0.077382 Loss(val): 0.078639
    338. 

  338. [22:54:34] Epoch 278: Loss(train): 0.077382 Loss(val): 0.078639
    339. 

  339. [22:56:40] Epoch 279: Loss(train): 0.077382 Loss(val): 0.078639
    340. 

  340. [22:58:46] Epoch 280: Loss(train): 0.077382 Loss(val): 0.078639
    341. 

  341. [23:01:05] Epoch 281: Loss(train): 0.077382 Loss(val): 0.078639
    342. 

  342. [23:03:23] Epoch 282: Loss(train): 0.077382 Loss(val): 0.078639
    343. 

  343. Converged at Loss(train): 0.082034, Loss(val): 0.083194 in epoch 282 with accuracy(val): 0.458486
    344. 

  344. 

  345. Configuration learning_rate=0.3, decay_step=60
    346. 

  346. [23:04:17] INIT Loss(val): 0.148113 Accuarcy: 0.088044
    347. 

  347. [23:06:34] Epoch   1: Loss(train): 0.107054 Loss(val): 0.109363
    348. 

  348. [23:08:56] Epoch   2: Loss(train): 0.095789 Loss(val): 0.097876
    349. 

  349. [23:11:04] Epoch   3: Loss(train): 0.090508 Loss(val): 0.092148
    350. 

  350. [23:13:35] Epoch   4: Loss(train): 0.087590 Loss(val): 0.088927
    351. 

  351. [23:17:01] Epoch   5: Loss(train): 0.085521 Loss(val): 0.086741
    352. 

  352. [23:20:12] Epoch   6: Loss(train): 0.084280 Loss(val): 0.085431
    353. 

  353. [23:22:24] Epoch   7: Loss(train): 0.083406 Loss(val): 0.084504
    354. 

  354. [23:24:58] Epoch   8: Loss(train): 0.082772 Loss(val): 0.083805
    355. 

  355. [23:27:22] Epoch   9: Loss(train): 0.082204 Loss(val): 0.083247
    356. 

  356. [23:29:41] Epoch  10: Loss(train): 0.081678 Loss(val): 0.082771
    357. 

  357. [23:32:03] Epoch  11: Loss(train): 0.081268 Loss(val): 0.082301
    358. 

  358. [23:34:12] Epoch  12: Loss(train): 0.080904 Loss(val): 0.081968
    359. 

  359. [23:36:27] Epoch  13: Loss(train): 0.080724 Loss(val): 0.081766
    360. 

  360. [23:38:41] Epoch  14: Loss(train): 0.080442 Loss(val): 0.081502
    361. 

  361. [23:40:53] Epoch  15: Loss(train): 0.080174 Loss(val): 0.081228
    362. 

  362. [23:43:06] Epoch  16: Loss(train): 0.079994 Loss(val): 0.081023
    363. 

  363. [23:46:08] Epoch  17: Loss(train): 0.079753 Loss(val): 0.080801
    364. 

  364. [23:48:40] Epoch  18: Loss(train): 0.079578 Loss(val): 0.080634
    365. 

  365. [23:51:34] Epoch  19: Loss(train): 0.079432 Loss(val): 0.080512
    366. 

  366. [23:54:06] Epoch  20: Loss(train): 0.079271 Loss(val): 0.080309
    367. 

  367. [23:56:30] Epoch  21: Loss(train): 0.079122 Loss(val): 0.080194
    368. 

  368. [23:58:59] Epoch  22: Loss(train): 0.079044 Loss(val): 0.080105
    369. 

  369. [00:01:16] Epoch  23: Loss(train): 0.078923 Loss(val): 0.079973
    370. 

  370. [00:03:34] Epoch  24: Loss(train): 0.078793 Loss(val): 0.079894
    371. 

  371. [00:05:51] Epoch  25: Loss(train): 0.078695 Loss(val): 0.079780
    372. 

  372. [00:08:07] Epoch  26: Loss(train): 0.078608 Loss(val): 0.079694
    373. 

  373. [00:10:24] Epoch  27: Loss(train): 0.078494 Loss(val): 0.079601
    374. 

  374. [00:12:39] Epoch  28: Loss(train): 0.078417 Loss(val): 0.079530
    375. 

  375. [00:15:31] Epoch  29: Loss(train): 0.078361 Loss(val): 0.079450
    376. 

  376. [00:18:25] Epoch  30: Loss(train): 0.078333 Loss(val): 0.079394
    377. 

  377. [00:21:07] Epoch  31: Loss(train): 0.078235 Loss(val): 0.079327
    378. 

  378. [00:24:01] Epoch  32: Loss(train): 0.078153 Loss(val): 0.079252
    379. 

  379. [00:26:34] Epoch  33: Loss(train): 0.078107 Loss(val): 0.079232
    380. 

  380. [00:29:00] Epoch  34: Loss(train): 0.078064 Loss(val): 0.079163
    381. 

  381. [00:31:28] Epoch  35: Loss(train): 0.078004 Loss(val): 0.079139
    382. 

  382. [00:33:49] Epoch  36: Loss(train): 0.077960 Loss(val): 0.079076
    383. 

  383. [00:36:08] Epoch  37: Loss(train): 0.077932 Loss(val): 0.079059
    384. 

  384. [00:38:29] Epoch  38: Loss(train): 0.077874 Loss(val): 0.079005
    385. 

  385. [00:40:51] Epoch  39: Loss(train): 0.077841 Loss(val): 0.078978
    386. 

  386. [00:43:11] Epoch  40: Loss(train): 0.077795 Loss(val): 0.078931
    387. 

  387. [00:45:57] Epoch  41: Loss(train): 0.077764 Loss(val): 0.078925
    388. 

  388. [00:49:17] Epoch  42: Loss(train): 0.077733 Loss(val): 0.078916
    389. 

  389. [00:52:09] Epoch  43: Loss(train): 0.077707 Loss(val): 0.078873
    390. 

  390. [00:54:39] Epoch  44: Loss(train): 0.077685 Loss(val): 0.078852
    391. 

  391. [00:57:11] Epoch  45: Loss(train): 0.077660 Loss(val): 0.078848
    392. 

  392. [00:59:43] Epoch  46: Loss(train): 0.077632 Loss(val): 0.078837
    393. 

  393. [01:02:15] Epoch  47: Loss(train): 0.077619 Loss(val): 0.078815
    394. 

  394. [01:04:43] Epoch  48: Loss(train): 0.077599 Loss(val): 0.078788
    395. 

  395. [01:07:05] Epoch  49: Loss(train): 0.077581 Loss(val): 0.078782
    396. 

  396. [01:09:26] Epoch  50: Loss(train): 0.077557 Loss(val): 0.078758
    397. 

  397. [01:11:49] Epoch  51: Loss(train): 0.077544 Loss(val): 0.078751
    398. 

  398. [01:14:11] Epoch  52: Loss(train): 0.077522 Loss(val): 0.078734
    399. 

  399. [01:17:36] Epoch  53: Loss(train): 0.077510 Loss(val): 0.078726
    400. 

  400. [01:20:53] Epoch  54: Loss(train): 0.077499 Loss(val): 0.078728
    401. 

  401. [01:23:46] Epoch  55: Loss(train): 0.077487 Loss(val): 0.078702
    402. 

  402. [01:26:28] Epoch  56: Loss(train): 0.077474 Loss(val): 0.078712
    403. 

  403. [01:29:08] Epoch  57: Loss(train): 0.077465 Loss(val): 0.078715
    404. 

  404. [01:31:37] Epoch  58: Loss(train): 0.077457 Loss(val): 0.078715
    405. 

  405. [01:34:07] Epoch  59: Loss(train): 0.077445 Loss(val): 0.078696
    406. 

  406. [01:36:30] Epoch  60: Loss(train): 0.077437 Loss(val): 0.078695
    407. 

  407. [01:38:58] Epoch  61: Loss(train): 0.077429 Loss(val): 0.078679
    408. 

  408. [01:41:20] Epoch  62: Loss(train): 0.077426 Loss(val): 0.078690
    409. 

  409. [01:43:42] Epoch  63: Loss(train): 0.077418 Loss(val): 0.078677
    410. 

  410. [01:46:26] Epoch  64: Loss(train): 0.077412 Loss(val): 0.078690
    411. 

  411. [01:49:45] Epoch  65: Loss(train): 0.077403 Loss(val): 0.078668
    412. 

  412. [01:52:30] Epoch  66: Loss(train): 0.077400 Loss(val): 0.078670
    413. 

  413. [01:55:42] Epoch  67: Loss(train): 0.077396 Loss(val): 0.078664
    414. 

  414. [01:58:18] Epoch  68: Loss(train): 0.077391 Loss(val): 0.078665
    415. 

  415. [02:00:59] Epoch  69: Loss(train): 0.077385 Loss(val): 0.078659
    416. 

  416. [02:03:31] Epoch  70: Loss(train): 0.077380 Loss(val): 0.078658
    417. 

  417. [02:06:07] Epoch  71: Loss(train): 0.077376 Loss(val): 0.078658
    418. 

  418. [02:08:35] Epoch  72: Loss(train): 0.077374 Loss(val): 0.078661
    419. 

  419. [02:11:04] Epoch  73: Loss(train): 0.077371 Loss(val): 0.078655
    420. 

  420. [02:13:32] Epoch  74: Loss(train): 0.077368 Loss(val): 0.078656
    421. 

  421. [02:15:59] Epoch  75: Loss(train): 0.077364 Loss(val): 0.078651
    422. 

  422. [02:18:26] Epoch  76: Loss(train): 0.077361 Loss(val): 0.078648
    423. 

  423. [02:21:38] Epoch  77: Loss(train): 0.077359 Loss(val): 0.078647
    424. 

  424. [02:24:25] Epoch  78: Loss(train): 0.077356 Loss(val): 0.078647
    425. 

  425. [02:27:14] Epoch  79: Loss(train): 0.077351 Loss(val): 0.078638
    426. 

  426. [02:30:05] Epoch  80: Loss(train): 0.077349 Loss(val): 0.078634
    427. 

  427. [02:32:44] Epoch  81: Loss(train): 0.077346 Loss(val): 0.078634
    428. 

  428. [02:35:28] Epoch  82: Loss(train): 0.077344 Loss(val): 0.078633
    429. 

  429. [02:38:05] Epoch  83: Loss(train): 0.077345 Loss(val): 0.078639
    430. 

  430. [02:40:38] Epoch  84: Loss(train): 0.077343 Loss(val): 0.078638
    431. 

  431. [02:43:12] Epoch  85: Loss(train): 0.077342 Loss(val): 0.078642
    432. 

  432. [02:45:45] Epoch  86: Loss(train): 0.077339 Loss(val): 0.078636
    433. 

  433. [02:48:13] Epoch  87: Loss(train): 0.077338 Loss(val): 0.078637
    434. 

  434. [02:50:41] Epoch  88: Loss(train): 0.077337 Loss(val): 0.078637
    435. 

  435. [02:53:51] Epoch  89: Loss(train): 0.077335 Loss(val): 0.078632
    436. 

  436. [02:56:53] Epoch  90: Loss(train): 0.077334 Loss(val): 0.078630
    437. 

  437. [02:59:47] Epoch  91: Loss(train): 0.077332 Loss(val): 0.078628
    438. 

  438. [03:02:24] Epoch  92: Loss(train): 0.077331 Loss(val): 0.078627
    439. 

  439. [03:05:12] Epoch  93: Loss(train): 0.077330 Loss(val): 0.078627
    440. 

  440. [03:07:58] Epoch  94: Loss(train): 0.077328 Loss(val): 0.078623
    441. 

  441. [03:10:35] Epoch  95: Loss(train): 0.077327 Loss(val): 0.078619
    442. 

  442. [03:13:15] Epoch  96: Loss(train): 0.077327 Loss(val): 0.078623
    443. 

  443. [03:15:50] Epoch  97: Loss(train): 0.077327 Loss(val): 0.078625
    444. 

  444. [03:18:21] Epoch  98: Loss(train): 0.077326 Loss(val): 0.078626
    445. 

  445. [03:20:56] Epoch  99: Loss(train): 0.077326 Loss(val): 0.078625
    446. 

  446. [03:23:41] Epoch 100: Loss(train): 0.077325 Loss(val): 0.078622
    447. 

  447. [03:26:50] Epoch 101: Loss(train): 0.077324 Loss(val): 0.078623
    448. 

  448. [03:30:06] Epoch 102: Loss(train): 0.077324 Loss(val): 0.078622
    449. 

  449. [03:33:21] Epoch 103: Loss(train): 0.077323 Loss(val): 0.078622
    450. 

  450. [03:36:06] Epoch 104: Loss(train): 0.077323 Loss(val): 0.078622
    451. 

  451. [03:38:49] Epoch 105: Loss(train): 0.077322 Loss(val): 0.078622
    452. 

  452. [03:41:29] Epoch 106: Loss(train): 0.077321 Loss(val): 0.078619
    453. 

  453. [03:44:10] Epoch 107: Loss(train): 0.077320 Loss(val): 0.078616
    454. 

  454. [03:46:47] Epoch 108: Loss(train): 0.077319 Loss(val): 0.078615
    455. 

  455. [03:49:22] Epoch 109: Loss(train): 0.077319 Loss(val): 0.078616
    456. 

  456. [03:51:55] Epoch 110: Loss(train): 0.077319 Loss(val): 0.078615
    457. 

  457. [03:54:28] Epoch 111: Loss(train): 0.077318 Loss(val): 0.078614
    458. 

  458. [03:57:08] Epoch 112: Loss(train): 0.077319 Loss(val): 0.078616
    459. 

  459. [04:00:37] Epoch 113: Loss(train): 0.077318 Loss(val): 0.078615
    460. 

  460. [04:03:24] Epoch 114: Loss(train): 0.077318 Loss(val): 0.078617
    461. 

  461. [04:06:31] Epoch 115: Loss(train): 0.077319 Loss(val): 0.078618
    462. 

  462. [04:09:20] Epoch 116: Loss(train): 0.077319 Loss(val): 0.078618
    463. 

  463. [04:12:07] Epoch 117: Loss(train): 0.077318 Loss(val): 0.078617
    464. 

  464. [04:14:48] Epoch 118: Loss(train): 0.077318 Loss(val): 0.078617
    465. 

  465. [04:17:30] Epoch 119: Loss(train): 0.077318 Loss(val): 0.078617
    466. 

  466. [04:20:10] Epoch 120: Loss(train): 0.077317 Loss(val): 0.078616
    467. 

  467. [04:22:50] Epoch 121: Loss(train): 0.077317 Loss(val): 0.078617
    468. 

  468. [04:25:30] Epoch 122: Loss(train): 0.077317 Loss(val): 0.078618
    469. 

  469. [04:28:06] Epoch 123: Loss(train): 0.077318 Loss(val): 0.078618
    470. 

  470. [04:30:57] Epoch 124: Loss(train): 0.077318 Loss(val): 0.078619
    471. 

  471. [04:33:55] Epoch 125: Loss(train): 0.077317 Loss(val): 0.078619
    472. 

  472. [04:36:46] Epoch 126: Loss(train): 0.077317 Loss(val): 0.078619
    473. 

  473. [04:39:57] Epoch 127: Loss(train): 0.077317 Loss(val): 0.078618
    474. 

  474. [04:42:50] Epoch 128: Loss(train): 0.077317 Loss(val): 0.078618
    475. 

  475. [04:45:43] Epoch 129: Loss(train): 0.077317 Loss(val): 0.078618
    476. 

  476. [04:48:25] Epoch 130: Loss(train): 0.077317 Loss(val): 0.078618
    477. 

  477. [04:51:16] Epoch 131: Loss(train): 0.077317 Loss(val): 0.078618
    478. 

  478. [04:53:57] Epoch 132: Loss(train): 0.077317 Loss(val): 0.078617
    479. 

  479. [04:56:34] Epoch 133: Loss(train): 0.077317 Loss(val): 0.078617
    480. 

  480. [04:59:17] Epoch 134: Loss(train): 0.077316 Loss(val): 0.078617
    481. 

  481. [05:01:58] Epoch 135: Loss(train): 0.077316 Loss(val): 0.078617
    482. 

  482. [05:05:03] Epoch 136: Loss(train): 0.077316 Loss(val): 0.078617
    483. 

  483. [05:08:22] Epoch 137: Loss(train): 0.077316 Loss(val): 0.078616
    484. 

  484. [05:11:24] Epoch 138: Loss(train): 0.077316 Loss(val): 0.078616
    485. 

  485. [05:14:25] Epoch 139: Loss(train): 0.077316 Loss(val): 0.078616
    486. 

  486. [05:17:22] Epoch 140: Loss(train): 0.077316 Loss(val): 0.078616
    487. 

  487. [05:20:16] Epoch 141: Loss(train): 0.077316 Loss(val): 0.078616
    488. 

  488. [05:23:04] Epoch 142: Loss(train): 0.077316 Loss(val): 0.078615
    489. 

  489. [05:25:53] Epoch 143: Loss(train): 0.077316 Loss(val): 0.078615
    490. 

  490. [05:28:38] Epoch 144: Loss(train): 0.077316 Loss(val): 0.078615
    491. 

  491. [05:31:22] Epoch 145: Loss(train): 0.077316 Loss(val): 0.078615
    492. 

  492. [05:34:02] Epoch 146: Loss(train): 0.077316 Loss(val): 0.078615
    493. 

  493. [05:36:46] Epoch 147: Loss(train): 0.077316 Loss(val): 0.078615
    494. 

  494. [05:40:11] Epoch 148: Loss(train): 0.077316 Loss(val): 0.078615
    495. 

  495. [05:43:43] Epoch 149: Loss(train): 0.077316 Loss(val): 0.078615
    496. 

  496. [05:46:56] Epoch 150: Loss(train): 0.077316 Loss(val): 0.078615
    497. 

  497. [05:49:55] Epoch 151: Loss(train): 0.077315 Loss(val): 0.078615
    498. 

  498. [05:52:53] Epoch 152: Loss(train): 0.077315 Loss(val): 0.078615
    499. 

  499. [05:55:52] Epoch 153: Loss(train): 0.077315 Loss(val): 0.078615
    500. 

  500. [05:58:45] Epoch 154: Loss(train): 0.077315 Loss(val): 0.078615
    501. 

  501. [06:01:39] Epoch 155: Loss(train): 0.077315 Loss(val): 0.078615
    502. 

  502. [06:04:25] Epoch 156: Loss(train): 0.077315 Loss(val): 0.078615
    503. 

  503. [06:07:07] Epoch 157: Loss(train): 0.077315 Loss(val): 0.078615
    504. 

  504. [06:09:52] Epoch 158: Loss(train): 0.077315 Loss(val): 0.078615
    505. 

  505. [06:12:41] Epoch 159: Loss(train): 0.077315 Loss(val): 0.078615
    506. 

  506. Converged at Loss(train): 0.081795, Loss(val): 0.082810 in epoch 159 with accuracy(val): 0.455119
    507. 

  507. 

  508. Configuration learning_rate=0.1, decay_step=20
    509. 

  509. [06:14:12] INIT Loss(val): 0.151758 Accuarcy: 0.109337
    510. 

  510. [06:17:00] Epoch   1: Loss(train): 0.108645 Loss(val): 0.110902
    511. 

  511. [06:19:52] Epoch   2: Loss(train): 0.097215 Loss(val): 0.099030
    512. 

  512. [06:22:40] Epoch   3: Loss(train): 0.091263 Loss(val): 0.092792
    513. 

  513. [06:25:30] Epoch   4: Loss(train): 0.088060 Loss(val): 0.089313
    514. 

  514. [06:28:30] Epoch   5: Loss(train): 0.086003 Loss(val): 0.087071
    515. 

  515. [06:31:27] Epoch   6: Loss(train): 0.084676 Loss(val): 0.085638
    516. 

  516. [06:34:27] Epoch   7: Loss(train): 0.083821 Loss(val): 0.084719
    517. 

  517. [06:37:15] Epoch   8: Loss(train): 0.083102 Loss(val): 0.083924
    518. 

  518. [06:40:03] Epoch   9: Loss(train): 0.082514 Loss(val): 0.083343
    519. 

  519. [06:42:51] Epoch  10: Loss(train): 0.082124 Loss(val): 0.082861
    520. 

  520. [06:45:39] Epoch  11: Loss(train): 0.081586 Loss(val): 0.082425
    521. 

  521. [06:48:23] Epoch  12: Loss(train): 0.081260 Loss(val): 0.082115
    522. 

  522. [06:51:13] Epoch  13: Loss(train): 0.080916 Loss(val): 0.081760
    523. 

  523. [06:53:59] Epoch  14: Loss(train): 0.080611 Loss(val): 0.081422
    524. 

  524. [06:56:45] Epoch  15: Loss(train): 0.080433 Loss(val): 0.081247
    525. 

  525. [06:59:36] Epoch  16: Loss(train): 0.080208 Loss(val): 0.081032
    526. 

  526. [07:02:22] Epoch  17: Loss(train): 0.080056 Loss(val): 0.080846
    527. 

  527. [07:05:14] Epoch  18: Loss(train): 0.079824 Loss(val): 0.080675
    528. 

  528. [07:08:09] Epoch  19: Loss(train): 0.079619 Loss(val): 0.080466
    529. 

  529. [07:11:07] Epoch  20: Loss(train): 0.079563 Loss(val): 0.080365
    530. 

  530. [07:14:22] Epoch  21: Loss(train): 0.079408 Loss(val): 0.080246
    531. 

  531. [07:17:29] Epoch  22: Loss(train): 0.079267 Loss(val): 0.080076
    532. 

  532. [07:22:07] Epoch  23: Loss(train): 0.079120 Loss(val): 0.079969
    533. 

  533. [07:25:39] Epoch  24: Loss(train): 0.079023 Loss(val): 0.079840
    534. 

  534. [07:28:35] Epoch  25: Loss(train): 0.078940 Loss(val): 0.079775
    535. 

  535. [07:31:55] Epoch  26: Loss(train): 0.078815 Loss(val): 0.079648
    536. 

  536. [07:35:01] Epoch  27: Loss(train): 0.078760 Loss(val): 0.079581
    537. 

  537. [07:37:52] Epoch  28: Loss(train): 0.078676 Loss(val): 0.079495
    538. 

  538. [07:40:47] Epoch  29: Loss(train): 0.078605 Loss(val): 0.079448
    539. 

  539. [07:43:38] Epoch  30: Loss(train): 0.078492 Loss(val): 0.079332
    540. 

  540. [07:46:29] Epoch  31: Loss(train): 0.078412 Loss(val): 0.079296
    541. 

  541. [07:50:38] Epoch  32: Loss(train): 0.078362 Loss(val): 0.079227
    542. 

  542. [07:53:58] Epoch  33: Loss(train): 0.078317 Loss(val): 0.079200
    543. 

  543. [07:57:46] Epoch  34: Loss(train): 0.078244 Loss(val): 0.079147
    544. 

  544. [08:00:52] Epoch  35: Loss(train): 0.078228 Loss(val): 0.079104
    545. 

  545. [08:04:06] Epoch  36: Loss(train): 0.078162 Loss(val): 0.079045
    546. 

  546. [08:07:02] Epoch  37: Loss(train): 0.078107 Loss(val): 0.079005
    547. 

  547. [08:10:00] Epoch  38: Loss(train): 0.078047 Loss(val): 0.078963
    548. 

  548. [08:12:51] Epoch  39: Loss(train): 0.078025 Loss(val): 0.078911
    549. 

  549. [08:15:43] Epoch  40: Loss(train): 0.077986 Loss(val): 0.078901
    550. 

  550. [08:18:48] Epoch  41: Loss(train): 0.077959 Loss(val): 0.078886
    551. 

  551. [08:23:08] Epoch  42: Loss(train): 0.077921 Loss(val): 0.078858
    552. 

  552. [08:26:57] Epoch  43: Loss(train): 0.077886 Loss(val): 0.078826
    553. 

  553. [08:30:10] Epoch  44: Loss(train): 0.077849 Loss(val): 0.078798
    554. 

  554. [08:33:24] Epoch  45: Loss(train): 0.077820 Loss(val): 0.078793
    555. 

  555. [08:36:39] Epoch  46: Loss(train): 0.077805 Loss(val): 0.078768
    556. 

  556. [08:39:38] Epoch  47: Loss(train): 0.077782 Loss(val): 0.078746
    557. 

  557. [08:42:37] Epoch  48: Loss(train): 0.077759 Loss(val): 0.078739
    558. 

  558. [08:45:34] Epoch  49: Loss(train): 0.077742 Loss(val): 0.078724
    559. 

  559. [08:48:30] Epoch  50: Loss(train): 0.077717 Loss(val): 0.078697
    560. 

  560. [08:52:57] Epoch  51: Loss(train): 0.077710 Loss(val): 0.078685
    561. 

  561. [08:56:15] Epoch  52: Loss(train): 0.077692 Loss(val): 0.078671
    562. 

  562. [09:00:01] Epoch  53: Loss(train): 0.077666 Loss(val): 0.078661
    563. 

  563. [09:03:12] Epoch  54: Loss(train): 0.077662 Loss(val): 0.078661
    564. 

  564. [09:06:28] Epoch  55: Loss(train): 0.077645 Loss(val): 0.078655
    565. 

  565. [09:09:45] Epoch  56: Loss(train): 0.077640 Loss(val): 0.078639
    566. 

  566. [09:12:53] Epoch  57: Loss(train): 0.077625 Loss(val): 0.078633
    567. 

  567. [09:16:00] Epoch  58: Loss(train): 0.077610 Loss(val): 0.078614
    568. 

  568. [09:19:11] Epoch  59: Loss(train): 0.077602 Loss(val): 0.078613
    569. 

  569. [09:22:16] Epoch  60: Loss(train): 0.077597 Loss(val): 0.078604
    570. 

  570. [09:26:11] Epoch  61: Loss(train): 0.077585 Loss(val): 0.078593
    571. 

  571. [09:29:35] Epoch  62: Loss(train): 0.077575 Loss(val): 0.078597
    572. 

  572. [09:33:11] Epoch  63: Loss(train): 0.077566 Loss(val): 0.078592
    573. 

  573. [09:36:24] Epoch  64: Loss(train): 0.077563 Loss(val): 0.078593
    574. 

  574. [09:39:33] Epoch  65: Loss(train): 0.077556 Loss(val): 0.078583
    575. 

  575. [09:42:46] Epoch  66: Loss(train): 0.077552 Loss(val): 0.078584
    576. 

  576. [09:45:59] Epoch  67: Loss(train): 0.077546 Loss(val): 0.078584
    577. 

  577. [09:49:01] Epoch  68: Loss(train): 0.077539 Loss(val): 0.078566
    578. 

  578. [09:52:04] Epoch  69: Loss(train): 0.077531 Loss(val): 0.078566
    579. 

  579. [09:55:07] Epoch  70: Loss(train): 0.077528 Loss(val): 0.078561
    580. 

  580. [09:58:32] Epoch  71: Loss(train): 0.077520 Loss(val): 0.078549
    581. 

  581. [10:02:30] Epoch  72: Loss(train): 0.077516 Loss(val): 0.078549
    582. 

  582. [10:06:15] Epoch  73: Loss(train): 0.077514 Loss(val): 0.078551
    583. 

  583. [10:09:43] Epoch  74: Loss(train): 0.077510 Loss(val): 0.078550
    584. 

  584. [10:13:03] Epoch  75: Loss(train): 0.077506 Loss(val): 0.078551
    585. 

  585. [10:16:21] Epoch  76: Loss(train): 0.077503 Loss(val): 0.078544
    586. 

  586. [10:19:34] Epoch  77: Loss(train): 0.077501 Loss(val): 0.078549
    587. 

  587. [10:22:42] Epoch  78: Loss(train): 0.077498 Loss(val): 0.078546
    588. 

  588. [10:25:45] Epoch  79: Loss(train): 0.077495 Loss(val): 0.078537
    589. 

  589. [10:29:12] Epoch  80: Loss(train): 0.077493 Loss(val): 0.078541
    590. 

  590. [10:33:03] Epoch  81: Loss(train): 0.077491 Loss(val): 0.078534
    591. 

  591. [10:36:51] Epoch  82: Loss(train): 0.077488 Loss(val): 0.078536
    592. 

  592. [10:40:24] Epoch  83: Loss(train): 0.077485 Loss(val): 0.078529
    593. 

  593. [10:43:48] Epoch  84: Loss(train): 0.077483 Loss(val): 0.078526
    594. 

  594. [10:47:08] Epoch  85: Loss(train): 0.077481 Loss(val): 0.078524
    595. 

  595. [10:50:22] Epoch  86: Loss(train): 0.077480 Loss(val): 0.078528
    596. 

  596. [10:53:34] Epoch  87: Loss(train): 0.077477 Loss(val): 0.078526
    597. 

  597. [10:56:41] Epoch  88: Loss(train): 0.077476 Loss(val): 0.078521
    598. 

  598. [10:59:47] Epoch  89: Loss(train): 0.077475 Loss(val): 0.078524
    599. 

  599. [11:03:39] Epoch  90: Loss(train): 0.077473 Loss(val): 0.078523
    600. 

  600. [11:07:25] Epoch  91: Loss(train): 0.077472 Loss(val): 0.078522
    601. 

  601. [11:10:54] Epoch  92: Loss(train): 0.077471 Loss(val): 0.078519
    602. 

  602. [11:14:24] Epoch  93: Loss(train): 0.077469 Loss(val): 0.078519
    603. 

  603. [11:17:48] Epoch  94: Loss(train): 0.077468 Loss(val): 0.078517
    604. 

  604. [11:21:07] Epoch  95: Loss(train): 0.077467 Loss(val): 0.078516
    605. 

  605. [11:24:20] Epoch  96: Loss(train): 0.077466 Loss(val): 0.078516
    606. 

  606. [11:27:26] Epoch  97: Loss(train): 0.077465 Loss(val): 0.078516
    607. 

  607. [11:30:32] Epoch  98: Loss(train): 0.077464 Loss(val): 0.078514
    608. 

  608. [11:34:32] Epoch  99: Loss(train): 0.077464 Loss(val): 0.078514
    609. 

  609. [11:38:37] Epoch 100: Loss(train): 0.077463 Loss(val): 0.078512
    610. 

  610. [11:42:36] Epoch 101: Loss(train): 0.077462 Loss(val): 0.078514
    611. 

  611. [11:46:02] Epoch 102: Loss(train): 0.077461 Loss(val): 0.078513
    612. 

  612. [11:49:25] Epoch 103: Loss(train): 0.077460 Loss(val): 0.078513
    613. 

  613. [11:52:52] Epoch 104: Loss(train): 0.077460 Loss(val): 0.078512
    614. 

  614. [11:56:09] Epoch 105: Loss(train): 0.077460 Loss(val): 0.078513
    615. 

  615. [11:59:18] Epoch 106: Loss(train): 0.077459 Loss(val): 0.078512
    616. 

  616. [12:02:33] Epoch 107: Loss(train): 0.077459 Loss(val): 0.078512
    617. 

  617. [12:06:57] Epoch 108: Loss(train): 0.077458 Loss(val): 0.078510
    618. 

  618. [12:11:14] Epoch 109: Loss(train): 0.077458 Loss(val): 0.078511
    619. 

  619. [12:14:53] Epoch 110: Loss(train): 0.077457 Loss(val): 0.078510
    620. 

  620. [12:18:21] Epoch 111: Loss(train): 0.077457 Loss(val): 0.078508
    621. 

  621. [12:21:48] Epoch 112: Loss(train): 0.077456 Loss(val): 0.078508
    622. 

  622. [12:24:58] Epoch 113: Loss(train): 0.077456 Loss(val): 0.078507
    623. 

  623. [12:28:09] Epoch 114: Loss(train): 0.077456 Loss(val): 0.078506
    624. 

  624. [12:31:19] Epoch 115: Loss(train): 0.077455 Loss(val): 0.078507
    625. 

  625. [12:34:43] Epoch 116: Loss(train): 0.077455 Loss(val): 0.078507
    626. 

  626. [12:38:17] Epoch 117: Loss(train): 0.077455 Loss(val): 0.078507
    627. 

  627. [12:41:56] Epoch 118: Loss(train): 0.077455 Loss(val): 0.078506
    628. 

  628. [12:45:28] Epoch 119: Loss(train): 0.077455 Loss(val): 0.078507
    629. 

  629. [12:48:53] Epoch 120: Loss(train): 0.077454 Loss(val): 0.078506
    630. 

  630. [12:52:25] Epoch 121: Loss(train): 0.077454 Loss(val): 0.078506
    631. 

  631. [12:55:46] Epoch 122: Loss(train): 0.077454 Loss(val): 0.078507
    632. 

  632. [12:59:20] Epoch 123: Loss(train): 0.077454 Loss(val): 0.078506
    633. 

  633. [13:02:35] Epoch 124: Loss(train): 0.077454 Loss(val): 0.078506
    634. 

  634. [13:05:54] Epoch 125: Loss(train): 0.077454 Loss(val): 0.078506
    635. 

  635. [13:09:08] Epoch 126: Loss(train): 0.077454 Loss(val): 0.078506
    636. 

  636. [13:12:19] Epoch 127: Loss(train): 0.077454 Loss(val): 0.078506
    637. 

  637. [13:16:39] Epoch 128: Loss(train): 0.077454 Loss(val): 0.078505
    638. 

  638. [13:20:46] Epoch 129: Loss(train): 0.077453 Loss(val): 0.078505
    639. 

  639. [13:24:20] Epoch 130: Loss(train): 0.077453 Loss(val): 0.078505
    640. 

  640. [13:27:57] Epoch 131: Loss(train): 0.077453 Loss(val): 0.078505
    641. 

  641. [13:31:29] Epoch 132: Loss(train): 0.077453 Loss(val): 0.078504
    642. 

  642. [13:34:57] Epoch 133: Loss(train): 0.077453 Loss(val): 0.078504
    643. 

  643. [13:38:20] Epoch 134: Loss(train): 0.077453 Loss(val): 0.078504
    644. 

  644. [13:41:36] Epoch 135: Loss(train): 0.077453 Loss(val): 0.078504
    645. 

  645. [13:44:56] Epoch 136: Loss(train): 0.077453 Loss(val): 0.078504
    646. 

  646. [13:48:43] Epoch 137: Loss(train): 0.077453 Loss(val): 0.078504
    647. 

  647. [13:52:31] Epoch 138: Loss(train): 0.077453 Loss(val): 0.078504
    648. 

  648. [13:56:19] Epoch 139: Loss(train): 0.077453 Loss(val): 0.078504
    649. 

  649. [13:59:54] Epoch 140: Loss(train): 0.077453 Loss(val): 0.078504
    650. 

  650. [14:03:34] Epoch 141: Loss(train): 0.077453 Loss(val): 0.078504
    651.