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log_27_09_2019.log

log_27_09_2019.log 35 KB
永久連結 文件歷史 原始文件

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  1. 

  2. --------[27_09_2019 16:51:25]--------
    3. 

  3. second stage Hyperparameter Tuning with 1 net
    4. 

  4. 

  5. Configuration learning_rate=0.03, decay_step=20
    6. 

  6. [16:52:47] INIT Loss(val): 0.148451 Accuarcy: 0.112245
    7. 

  7. [16:54:51] Epoch   1: Loss(train): 0.089663 Loss(val): 0.088737
    8. 

  8. [16:55:10] Epoch   2: Loss(train): 0.070667 Loss(val): 0.068928
    9. 

  9. [16:55:27] Epoch   3: Loss(train): 0.063542 Loss(val): 0.062128
    10. 

  10. [16:55:43] Epoch   4: Loss(train): 0.060417 Loss(val): 0.059723
    11. 

  11. [16:56:01] Epoch   5: Loss(train): 0.059493 Loss(val): 0.059026
    12. 

  12. [16:56:19] Epoch   6: Loss(train): 0.058942 Loss(val): 0.058448
    13. 

  13. [16:56:36] Epoch   7: Loss(train): 0.058415 Loss(val): 0.057891
    14. 

  14. [16:56:56] Epoch   8: Loss(train): 0.057964 Loss(val): 0.057296
    15. 

  15. [16:57:13] Epoch   9: Loss(train): 0.057504 Loss(val): 0.056888
    16. 

  16. [16:57:31] Epoch  10: Loss(train): 0.056998 Loss(val): 0.056621
    17. 

  17. [16:57:48] Epoch  11: Loss(train): 0.056863 Loss(val): 0.056618
    18. 

  18. [16:58:07] Epoch  12: Loss(train): 0.056772 Loss(val): 0.056483
    19. 

  19. [16:58:23] Epoch  13: Loss(train): 0.056706 Loss(val): 0.056282
    20. 

  20. [16:58:39] Epoch  14: Loss(train): 0.056430 Loss(val): 0.055929
    21. 

  21. [16:58:55] Epoch  15: Loss(train): 0.056260 Loss(val): 0.055614
    22. 

  22. [16:59:12] Epoch  16: Loss(train): 0.055839 Loss(val): 0.055432
    23. 

  23. [16:59:30] Epoch  17: Loss(train): 0.055648 Loss(val): 0.055394
    24. 

  24. [16:59:54] Epoch  18: Loss(train): 0.055889 Loss(val): 0.055650
    25. 

  25. [17:00:12] Epoch  19: Loss(train): 0.056102 Loss(val): 0.055804
    26. 

  26. [17:00:28] Epoch  20: Loss(train): 0.055990 Loss(val): 0.055733
    27. 

  27. [17:00:45] Epoch  21: Loss(train): 0.056002 Loss(val): 0.055650
    28. 

  28. [17:01:01] Epoch  22: Loss(train): 0.055604 Loss(val): 0.055251
    29. 

  29. [17:01:18] Epoch  23: Loss(train): 0.054814 Loss(val): 0.054507
    30. 

  30. [17:01:35] Epoch  24: Loss(train): 0.054289 Loss(val): 0.053961
    31. 

  31. [17:01:59] Epoch  25: Loss(train): 0.054040 Loss(val): 0.053693
    32. 

  32. [17:02:16] Epoch  26: Loss(train): 0.054076 Loss(val): 0.053621
    33. 

  33. [17:02:32] Epoch  27: Loss(train): 0.054120 Loss(val): 0.053601
    34. 

  34. [17:02:49] Epoch  28: Loss(train): 0.054555 Loss(val): 0.053849
    35. 

  35. [17:03:06] Epoch  29: Loss(train): 0.055195 Loss(val): 0.054283
    36. 

  36. [17:03:24] Epoch  30: Loss(train): 0.055647 Loss(val): 0.054590
    37. 

  37. [17:03:41] Epoch  31: Loss(train): 0.055832 Loss(val): 0.054655
    38. 

  38. [17:03:58] Epoch  32: Loss(train): 0.055375 Loss(val): 0.054319
    39. 

  39. [17:04:16] Epoch  33: Loss(train): 0.054487 Loss(val): 0.053729
    40. 

  40. [17:04:33] Epoch  34: Loss(train): 0.053432 Loss(val): 0.053076
    41. 

  41. [17:04:50] Epoch  35: Loss(train): 0.052729 Loss(val): 0.052693
    42. 

  42. [17:05:08] Epoch  36: Loss(train): 0.052390 Loss(val): 0.052524
    43. 

  43. [17:05:25] Epoch  37: Loss(train): 0.052178 Loss(val): 0.052437
    44. 

  44. [17:05:43] Epoch  38: Loss(train): 0.052099 Loss(val): 0.052385
    45. 

  45. [17:06:00] Epoch  39: Loss(train): 0.052070 Loss(val): 0.052320
    46. 

  46. [17:06:17] Epoch  40: Loss(train): 0.052017 Loss(val): 0.052243
    47. 

  47. [17:06:35] Epoch  41: Loss(train): 0.051925 Loss(val): 0.052159
    48. 

  48. [17:06:52] Epoch  42: Loss(train): 0.051824 Loss(val): 0.052056
    49. 

  49. [17:07:10] Epoch  43: Loss(train): 0.051765 Loss(val): 0.051970
    50. 

  50. [17:07:27] Epoch  44: Loss(train): 0.051683 Loss(val): 0.051933
    51. 

  51. [17:07:45] Epoch  45: Loss(train): 0.051606 Loss(val): 0.051889
    52. 

  52. [17:08:01] Epoch  46: Loss(train): 0.051533 Loss(val): 0.051864
    53. 

  53. [17:08:18] Epoch  47: Loss(train): 0.051464 Loss(val): 0.051846
    54. 

  54. [17:08:35] Epoch  48: Loss(train): 0.051401 Loss(val): 0.051838
    55. 

  55. [17:08:52] Epoch  49: Loss(train): 0.051325 Loss(val): 0.051843
    56. 

  56. [17:09:09] Epoch  50: Loss(train): 0.051266 Loss(val): 0.051860
    57. 

  57. [17:09:26] Epoch  51: Loss(train): 0.051222 Loss(val): 0.051899
    58. 

  58. [17:09:43] Epoch  52: Loss(train): 0.051193 Loss(val): 0.051918
    59. 

  59. [17:10:01] Epoch  53: Loss(train): 0.051176 Loss(val): 0.051968
    60. 

  60. [17:10:18] Epoch  54: Loss(train): 0.051162 Loss(val): 0.052006
    61. 

  61. [17:10:35] Epoch  55: Loss(train): 0.051134 Loss(val): 0.051998
    62. 

  62. [17:10:52] Epoch  56: Loss(train): 0.051111 Loss(val): 0.051992
    63. 

  63. [17:11:10] Epoch  57: Loss(train): 0.051075 Loss(val): 0.051958
    64. 

  64. [17:11:28] Epoch  58: Loss(train): 0.051050 Loss(val): 0.051941
    65. 

  65. [17:11:45] Epoch  59: Loss(train): 0.051010 Loss(val): 0.051883
    66. 

  66. [17:12:03] Epoch  60: Loss(train): 0.050980 Loss(val): 0.051837
    67. 

  67. [17:12:20] Epoch  61: Loss(train): 0.050952 Loss(val): 0.051785
    68. 

  68. [17:12:37] Epoch  62: Loss(train): 0.050934 Loss(val): 0.051770
    69. 

  69. [17:12:55] Epoch  63: Loss(train): 0.050916 Loss(val): 0.051740
    70. 

  70. [17:13:12] Epoch  64: Loss(train): 0.050899 Loss(val): 0.051707
    71. 

  71. [17:13:29] Epoch  65: Loss(train): 0.050887 Loss(val): 0.051699
    72. 

  72. [17:13:48] Epoch  66: Loss(train): 0.050875 Loss(val): 0.051674
    73. 

  73. [17:14:05] Epoch  67: Loss(train): 0.050866 Loss(val): 0.051669
    74. 

  74. [17:14:22] Epoch  68: Loss(train): 0.050856 Loss(val): 0.051652
    75. 

  75. [17:14:40] Epoch  69: Loss(train): 0.050847 Loss(val): 0.051641
    76. 

  76. [17:14:57] Epoch  70: Loss(train): 0.050839 Loss(val): 0.051635
    77. 

  77. Converged at Loss(train): 0.051801, Loss(val): 0.052561 in epoch 70 with accuracy(val): 0.654473
    78. 

  78. 

  79. Configuration learning_rate=0.03, decay_step=40
    80. 

  80. [17:15:12] INIT Loss(val): 0.149915 Accuarcy: 0.098707
    81. 

  81. [17:15:33] Epoch   1: Loss(train): 0.088789 Loss(val): 0.087691
    82. 

  82. [17:15:50] Epoch   2: Loss(train): 0.068032 Loss(val): 0.067004
    83. 

  83. [17:16:08] Epoch   3: Loss(train): 0.063522 Loss(val): 0.062396
    84. 

  84. [17:16:26] Epoch   4: Loss(train): 0.061677 Loss(val): 0.060596
    85. 

  85. [17:16:43] Epoch   5: Loss(train): 0.060235 Loss(val): 0.059540
    86. 

  86. [17:17:01] Epoch   6: Loss(train): 0.060055 Loss(val): 0.059479
    87. 

  87. [17:17:18] Epoch   7: Loss(train): 0.059696 Loss(val): 0.058983
    88. 

  88. [17:17:36] Epoch   8: Loss(train): 0.058420 Loss(val): 0.057931
    89. 

  89. [17:17:53] Epoch   9: Loss(train): 0.057890 Loss(val): 0.057784
    90. 

  90. [17:18:11] Epoch  10: Loss(train): 0.057887 Loss(val): 0.057939
    91. 

  91. [17:18:28] Epoch  11: Loss(train): 0.058021 Loss(val): 0.058248
    92. 

  92. [17:18:45] Epoch  12: Loss(train): 0.057937 Loss(val): 0.058021
    93. 

  93. [17:19:03] Epoch  13: Loss(train): 0.057402 Loss(val): 0.057456
    94. 

  94. [17:19:20] Epoch  14: Loss(train): 0.056914 Loss(val): 0.057007
    95. 

  95. [17:19:37] Epoch  15: Loss(train): 0.056709 Loss(val): 0.056965
    96. 

  96. [17:19:55] Epoch  16: Loss(train): 0.056852 Loss(val): 0.057157
    97. 

  97. [17:20:13] Epoch  17: Loss(train): 0.057367 Loss(val): 0.057542
    98. 

  98. [17:20:31] Epoch  18: Loss(train): 0.057601 Loss(val): 0.057665
    99. 

  99. [17:20:49] Epoch  19: Loss(train): 0.057731 Loss(val): 0.057718
    100. 

  100. [17:21:07] Epoch  20: Loss(train): 0.057218 Loss(val): 0.057077
    101. 

  101. [17:21:24] Epoch  21: Loss(train): 0.056135 Loss(val): 0.056025
    102. 

  102. [17:21:43] Epoch  22: Loss(train): 0.055024 Loss(val): 0.054987
    103. 

  103. [17:22:00] Epoch  23: Loss(train): 0.054295 Loss(val): 0.054281
    104. 

  104. [17:22:18] Epoch  24: Loss(train): 0.053959 Loss(val): 0.053951
    105. 

  105. [17:22:36] Epoch  25: Loss(train): 0.053691 Loss(val): 0.053717
    106. 

  106. [17:22:53] Epoch  26: Loss(train): 0.053627 Loss(val): 0.053622
    107. 

  107. [17:23:11] Epoch  27: Loss(train): 0.053773 Loss(val): 0.053724
    108. 

  108. [17:23:29] Epoch  28: Loss(train): 0.054236 Loss(val): 0.053973
    109. 

  109. [17:23:47] Epoch  29: Loss(train): 0.054522 Loss(val): 0.054070
    110. 

  110. [17:24:04] Epoch  30: Loss(train): 0.054943 Loss(val): 0.054246
    111. 

  111. [17:24:22] Epoch  31: Loss(train): 0.055004 Loss(val): 0.054189
    112. 

  112. [17:24:40] Epoch  32: Loss(train): 0.054876 Loss(val): 0.053994
    113. 

  113. [17:24:57] Epoch  33: Loss(train): 0.054486 Loss(val): 0.053667
    114. 

  114. [17:25:15] Epoch  34: Loss(train): 0.053800 Loss(val): 0.053187
    115. 

  115. [17:25:33] Epoch  35: Loss(train): 0.053172 Loss(val): 0.052797
    116. 

  116. [17:25:51] Epoch  36: Loss(train): 0.052741 Loss(val): 0.052563
    117. 

  117. [17:26:09] Epoch  37: Loss(train): 0.052461 Loss(val): 0.052420
    118. 

  118. [17:26:27] Epoch  38: Loss(train): 0.052257 Loss(val): 0.052302
    119. 

  119. [17:26:46] Epoch  39: Loss(train): 0.052129 Loss(val): 0.052214
    120. 

  120. [17:27:14] Epoch  40: Loss(train): 0.052035 Loss(val): 0.052163
    121. 

  121. [17:27:44] Epoch  41: Loss(train): 0.051944 Loss(val): 0.052115
    122. 

  122. [17:28:14] Epoch  42: Loss(train): 0.051880 Loss(val): 0.052084
    123. 

  123. [17:28:31] Epoch  43: Loss(train): 0.051804 Loss(val): 0.052055
    124. 

  124. [17:28:51] Epoch  44: Loss(train): 0.051738 Loss(val): 0.052032
    125. 

  125. [17:29:18] Epoch  45: Loss(train): 0.051650 Loss(val): 0.052006
    126. 

  126. [17:29:47] Epoch  46: Loss(train): 0.051562 Loss(val): 0.051986
    127. 

  127. [17:30:09] Epoch  47: Loss(train): 0.051479 Loss(val): 0.051978
    128. 

  128. [17:30:29] Epoch  48: Loss(train): 0.051412 Loss(val): 0.051979
    129. 

  129. [17:30:54] Epoch  49: Loss(train): 0.051361 Loss(val): 0.051999
    130. 

  130. [17:31:19] Epoch  50: Loss(train): 0.051323 Loss(val): 0.052038
    131. 

  131. [17:31:38] Epoch  51: Loss(train): 0.051302 Loss(val): 0.052081
    132. 

  132. [17:31:56] Epoch  52: Loss(train): 0.051278 Loss(val): 0.052096
    133. 

  133. [17:32:15] Epoch  53: Loss(train): 0.051261 Loss(val): 0.052118
    134. 

  134. [17:32:46] Epoch  54: Loss(train): 0.051239 Loss(val): 0.052120
    135. 

  135. [17:33:13] Epoch  55: Loss(train): 0.051207 Loss(val): 0.052095
    136. 

  136. Converged at Loss(train): 0.052161, Loss(val): 0.053005 in epoch 55 with accuracy(val): 0.645272
    137. 

  137. 

  138. Configuration learning_rate=0.03, decay_step=60
    139. 

  139. [17:33:32] INIT Loss(val): 0.165520 Accuarcy: 0.073197
    140. 

  140. [17:33:53] Epoch   1: Loss(train): 0.090615 Loss(val): 0.089994
    141. 

  141. [17:34:11] Epoch   2: Loss(train): 0.069812 Loss(val): 0.068600
    142. 

  142. [17:34:30] Epoch   3: Loss(train): 0.064214 Loss(val): 0.063470
    143. 

  143. [17:34:49] Epoch   4: Loss(train): 0.061411 Loss(val): 0.061059
    144. 

  144. [17:35:10] Epoch   5: Loss(train): 0.060100 Loss(val): 0.059814
    145. 

  145. [17:35:31] Epoch   6: Loss(train): 0.059087 Loss(val): 0.059043
    146. 

  146. [17:35:52] Epoch   7: Loss(train): 0.058686 Loss(val): 0.058661
    147. 

  147. [17:36:10] Epoch   8: Loss(train): 0.058388 Loss(val): 0.058214
    148. 

  148. [17:36:30] Epoch   9: Loss(train): 0.058066 Loss(val): 0.057698
    149. 

  149. [17:36:51] Epoch  10: Loss(train): 0.057511 Loss(val): 0.057332
    150. 

  150. [17:37:16] Epoch  11: Loss(train): 0.057068 Loss(val): 0.057097
    151. 

  151. [17:37:35] Epoch  12: Loss(train): 0.057114 Loss(val): 0.057160
    152. 

  152. [17:37:55] Epoch  13: Loss(train): 0.056885 Loss(val): 0.056944
    153. 

  153. [17:38:15] Epoch  14: Loss(train): 0.056694 Loss(val): 0.056566
    154. 

  154. [17:38:35] Epoch  15: Loss(train): 0.056233 Loss(val): 0.056134
    155. 

  155. [17:38:55] Epoch  16: Loss(train): 0.055359 Loss(val): 0.055511
    156. 

  156. [17:39:15] Epoch  17: Loss(train): 0.055256 Loss(val): 0.055431
    157. 

  157. [17:39:35] Epoch  18: Loss(train): 0.055643 Loss(val): 0.055726
    158. 

  158. [17:39:54] Epoch  19: Loss(train): 0.056205 Loss(val): 0.056171
    159. 

  159. [17:40:14] Epoch  20: Loss(train): 0.056926 Loss(val): 0.056773
    160. 

  160. [17:40:34] Epoch  21: Loss(train): 0.057006 Loss(val): 0.056768
    161. 

  161. [17:40:53] Epoch  22: Loss(train): 0.056528 Loss(val): 0.056344
    162. 

  162. [17:41:12] Epoch  23: Loss(train): 0.055433 Loss(val): 0.055290
    163. 

  163. [17:41:31] Epoch  24: Loss(train): 0.054533 Loss(val): 0.054371
    164. 

  164. [17:41:50] Epoch  25: Loss(train): 0.054282 Loss(val): 0.054007
    165. 

  165. [17:42:10] Epoch  26: Loss(train): 0.054135 Loss(val): 0.053829
    166. 

  166. [17:42:29] Epoch  27: Loss(train): 0.054173 Loss(val): 0.053806
    167. 

  167. [17:42:48] Epoch  28: Loss(train): 0.054269 Loss(val): 0.053833
    168. 

  168. [17:43:07] Epoch  29: Loss(train): 0.054556 Loss(val): 0.054030
    169. 

  169. [17:43:27] Epoch  30: Loss(train): 0.054662 Loss(val): 0.054104
    170. 

  170. [17:43:47] Epoch  31: Loss(train): 0.054619 Loss(val): 0.054089
    171. 

  171. [17:44:06] Epoch  32: Loss(train): 0.054174 Loss(val): 0.053796
    172. 

  172. [17:44:25] Epoch  33: Loss(train): 0.053649 Loss(val): 0.053463
    173. 

  173. [17:44:44] Epoch  34: Loss(train): 0.052997 Loss(val): 0.053043
    174. 

  174. [17:45:03] Epoch  35: Loss(train): 0.052595 Loss(val): 0.052792
    175. 

  175. [17:45:22] Epoch  36: Loss(train): 0.052346 Loss(val): 0.052621
    176. 

  176. [17:45:41] Epoch  37: Loss(train): 0.052151 Loss(val): 0.052500
    177. 

  177. [17:46:00] Epoch  38: Loss(train): 0.052059 Loss(val): 0.052406
    178. 

  178. [17:46:19] Epoch  39: Loss(train): 0.052001 Loss(val): 0.052335
    179. 

  179. [17:46:38] Epoch  40: Loss(train): 0.051941 Loss(val): 0.052277
    180. 

  180. [17:46:57] Epoch  41: Loss(train): 0.051894 Loss(val): 0.052226
    181. 

  181. [17:47:17] Epoch  42: Loss(train): 0.051868 Loss(val): 0.052181
    182. 

  182. [17:47:36] Epoch  43: Loss(train): 0.051789 Loss(val): 0.052130
    183. 

  183. [17:47:55] Epoch  44: Loss(train): 0.051722 Loss(val): 0.052078
    184. 

  184. [17:48:13] Epoch  45: Loss(train): 0.051639 Loss(val): 0.052052
    185. 

  185. [17:48:33] Epoch  46: Loss(train): 0.051541 Loss(val): 0.052007
    186. 

  186. [17:48:52] Epoch  47: Loss(train): 0.051465 Loss(val): 0.052000
    187. 

  187. [17:49:12] Epoch  48: Loss(train): 0.051404 Loss(val): 0.051979
    188. 

  188. [17:49:31] Epoch  49: Loss(train): 0.051339 Loss(val): 0.051980
    189. 

  189. [17:49:51] Epoch  50: Loss(train): 0.051292 Loss(val): 0.051991
    190. 

  190. [17:50:11] Epoch  51: Loss(train): 0.051249 Loss(val): 0.051991
    191. 

  191. [17:50:30] Epoch  52: Loss(train): 0.051222 Loss(val): 0.052008
    192. 

  192. [17:50:50] Epoch  53: Loss(train): 0.051198 Loss(val): 0.052018
    193. 

  193. [17:51:09] Epoch  54: Loss(train): 0.051173 Loss(val): 0.052017
    194. 

  194. [17:51:29] Epoch  55: Loss(train): 0.051146 Loss(val): 0.052003
    195. 

  195. [17:51:48] Epoch  56: Loss(train): 0.051118 Loss(val): 0.051979
    196. 

  196. [17:52:08] Epoch  57: Loss(train): 0.051087 Loss(val): 0.051948
    197. 

  197. [17:52:27] Epoch  58: Loss(train): 0.051059 Loss(val): 0.051916
    198. 

  198. [17:52:48] Epoch  59: Loss(train): 0.051032 Loss(val): 0.051880
    199. 

  199. [17:53:08] Epoch  60: Loss(train): 0.051009 Loss(val): 0.051852
    200. 

  200. Converged at Loss(train): 0.051977, Loss(val): 0.052759 in epoch 60 with accuracy(val): 0.642109
    201. 

  201. 

  202. Configuration learning_rate=0.01, decay_step=20
    203. 

  203. [17:53:25] INIT Loss(val): 0.187323 Accuarcy: 0.077789
    204. 

  204. [17:53:47] Epoch   1: Loss(train): 0.087576 Loss(val): 0.086033
    205. 

  205. [17:54:07] Epoch   2: Loss(train): 0.068133 Loss(val): 0.066643
    206. 

  206. [17:54:26] Epoch   3: Loss(train): 0.062122 Loss(val): 0.060931
    207. 

  207. [17:54:46] Epoch   4: Loss(train): 0.058892 Loss(val): 0.058234
    208. 

  208. [17:55:05] Epoch   5: Loss(train): 0.057786 Loss(val): 0.057269
    209. 

  209. [17:55:24] Epoch   6: Loss(train): 0.057601 Loss(val): 0.057197
    210. 

  210. [17:55:44] Epoch   7: Loss(train): 0.056949 Loss(val): 0.056568
    211. 

  211. [17:56:03] Epoch   8: Loss(train): 0.056481 Loss(val): 0.056139
    212. 

  212. [17:56:23] Epoch   9: Loss(train): 0.056194 Loss(val): 0.055886
    213. 

  213. [17:56:43] Epoch  10: Loss(train): 0.055974 Loss(val): 0.055750
    214. 

  214. [17:57:03] Epoch  11: Loss(train): 0.055800 Loss(val): 0.055777
    215. 

  215. [17:57:22] Epoch  12: Loss(train): 0.055692 Loss(val): 0.055731
    216. 

  216. [17:57:42] Epoch  13: Loss(train): 0.055691 Loss(val): 0.055779
    217. 

  217. [17:58:02] Epoch  14: Loss(train): 0.055478 Loss(val): 0.055565
    218. 

  218. [17:58:22] Epoch  15: Loss(train): 0.055101 Loss(val): 0.055146
    219. 

  219. [17:58:42] Epoch  16: Loss(train): 0.054910 Loss(val): 0.054922
    220. 

  220. [17:59:01] Epoch  17: Loss(train): 0.054790 Loss(val): 0.054875
    221. 

  221. [17:59:21] Epoch  18: Loss(train): 0.054960 Loss(val): 0.055119
    222. 

  222. [17:59:41] Epoch  19: Loss(train): 0.055063 Loss(val): 0.055270
    223. 

  223. [18:00:02] Epoch  20: Loss(train): 0.054944 Loss(val): 0.055221
    224. 

  224. [18:00:23] Epoch  21: Loss(train): 0.054798 Loss(val): 0.055048
    225. 

  225. [18:00:44] Epoch  22: Loss(train): 0.054379 Loss(val): 0.054582
    226. 

  226. [18:01:05] Epoch  23: Loss(train): 0.053794 Loss(val): 0.053956
    227. 

  227. [18:01:31] Epoch  24: Loss(train): 0.053113 Loss(val): 0.053302
    228. 

  228. [18:01:56] Epoch  25: Loss(train): 0.052780 Loss(val): 0.052874
    229. 

  229. [18:02:22] Epoch  26: Loss(train): 0.052543 Loss(val): 0.052660
    230. 

  230. [18:02:56] Epoch  27: Loss(train): 0.052278 Loss(val): 0.052454
    231. 

  231. [18:03:31] Epoch  28: Loss(train): 0.052146 Loss(val): 0.052352
    232. 

  232. [18:03:55] Epoch  29: Loss(train): 0.052045 Loss(val): 0.052292
    233. 

  233. [18:04:17] Epoch  30: Loss(train): 0.051909 Loss(val): 0.052243
    234. 

  234. [18:04:38] Epoch  31: Loss(train): 0.051838 Loss(val): 0.052235
    235. 

  235. [18:05:01] Epoch  32: Loss(train): 0.051793 Loss(val): 0.052183
    236. 

  236. [18:05:34] Epoch  33: Loss(train): 0.051737 Loss(val): 0.052164
    237. 

  237. [18:06:03] Epoch  34: Loss(train): 0.051672 Loss(val): 0.052101
    238. 

  238. [18:06:27] Epoch  35: Loss(train): 0.051629 Loss(val): 0.052036
    239. 

  239. [18:07:02] Epoch  36: Loss(train): 0.051564 Loss(val): 0.051960
    240. 

  240. [18:07:36] Epoch  37: Loss(train): 0.051538 Loss(val): 0.051894
    241. 

  241. [18:08:09] Epoch  38: Loss(train): 0.051462 Loss(val): 0.051794
    242. 

  242. [18:08:38] Epoch  39: Loss(train): 0.051404 Loss(val): 0.051718
    243. 

  243. [18:09:02] Epoch  40: Loss(train): 0.051335 Loss(val): 0.051661
    244. 

  244. [18:09:22] Epoch  41: Loss(train): 0.051274 Loss(val): 0.051597
    245. 

  245. [18:09:44] Epoch  42: Loss(train): 0.051173 Loss(val): 0.051555
    246. 

  246. [18:10:09] Epoch  43: Loss(train): 0.051112 Loss(val): 0.051520
    247. 

  247. [18:10:30] Epoch  44: Loss(train): 0.051043 Loss(val): 0.051497
    248. 

  248. [18:10:52] Epoch  45: Loss(train): 0.050990 Loss(val): 0.051464
    249. 

  249. [18:11:13] Epoch  46: Loss(train): 0.050939 Loss(val): 0.051442
    250. 

  250. [18:11:34] Epoch  47: Loss(train): 0.050889 Loss(val): 0.051419
    251. 

  251. [18:11:55] Epoch  48: Loss(train): 0.050855 Loss(val): 0.051395
    252. 

  252. [18:12:22] Epoch  49: Loss(train): 0.050825 Loss(val): 0.051377
    253. 

  253. [18:12:44] Epoch  50: Loss(train): 0.050809 Loss(val): 0.051350
    254. 

  254. [18:13:13] Epoch  51: Loss(train): 0.050787 Loss(val): 0.051347
    255. 

  255. [18:13:35] Epoch  52: Loss(train): 0.050774 Loss(val): 0.051326
    256. 

  256. [18:13:57] Epoch  53: Loss(train): 0.050761 Loss(val): 0.051317
    257. 

  257. [18:14:18] Epoch  54: Loss(train): 0.050748 Loss(val): 0.051308
    258. 

  258. [18:14:40] Epoch  55: Loss(train): 0.050737 Loss(val): 0.051294
    259. 

  259. Converged at Loss(train): 0.051697, Loss(val): 0.052241 in epoch 55 with accuracy(val): 0.656922
    260. 

  260. 

  261. Configuration learning_rate=0.01, decay_step=40
    262. 

  262. [18:15:00] INIT Loss(val): 0.177921 Accuarcy: 0.078452
    263. 

  263. [18:15:25] Epoch   1: Loss(train): 0.085913 Loss(val): 0.084972
    264. 

  264. [18:15:47] Epoch   2: Loss(train): 0.067994 Loss(val): 0.067212
    265. 

  265. [18:16:08] Epoch   3: Loss(train): 0.064593 Loss(val): 0.063531
    266. 

  266. [18:16:29] Epoch   4: Loss(train): 0.062117 Loss(val): 0.061084
    267. 

  267. [18:16:49] Epoch   5: Loss(train): 0.060363 Loss(val): 0.060003
    268. 

  268. [18:17:10] Epoch   6: Loss(train): 0.059131 Loss(val): 0.058921
    269. 

  269. [18:17:31] Epoch   7: Loss(train): 0.058338 Loss(val): 0.058271
    270. 

  270. [18:17:52] Epoch   8: Loss(train): 0.057717 Loss(val): 0.057788
    271. 

  271. [18:18:15] Epoch   9: Loss(train): 0.057529 Loss(val): 0.057686
    272. 

  272. [18:18:35] Epoch  10: Loss(train): 0.057496 Loss(val): 0.057720
    273. 

  273. [18:18:56] Epoch  11: Loss(train): 0.057429 Loss(val): 0.057801
    274. 

  274. [18:19:17] Epoch  12: Loss(train): 0.057253 Loss(val): 0.057779
    275. 

  275. [18:19:40] Epoch  13: Loss(train): 0.056893 Loss(val): 0.057320
    276. 

  276. [18:20:02] Epoch  14: Loss(train): 0.056243 Loss(val): 0.056562
    277. 

  277. [18:20:22] Epoch  15: Loss(train): 0.056184 Loss(val): 0.056340
    278. 

  278. [18:20:45] Epoch  16: Loss(train): 0.056221 Loss(val): 0.056419
    279. 

  279. [18:21:06] Epoch  17: Loss(train): 0.056585 Loss(val): 0.056762
    280. 

  280. [18:21:28] Epoch  18: Loss(train): 0.056598 Loss(val): 0.056918
    281. 

  281. [18:21:49] Epoch  19: Loss(train): 0.056542 Loss(val): 0.056837
    282. 

  282. [18:22:10] Epoch  20: Loss(train): 0.056023 Loss(val): 0.056362
    283. 

  283. [18:22:34] Epoch  21: Loss(train): 0.055122 Loss(val): 0.055430
    284. 

  284. [18:22:55] Epoch  22: Loss(train): 0.054322 Loss(val): 0.054565
    285. 

  285. [18:23:15] Epoch  23: Loss(train): 0.053714 Loss(val): 0.053932
    286. 

  286. [18:24:01] Epoch  24: Loss(train): 0.053428 Loss(val): 0.053591
    287. 

  287. [18:24:44] Epoch  25: Loss(train): 0.053074 Loss(val): 0.053378
    288. 

  288. [18:25:28] Epoch  26: Loss(train): 0.052950 Loss(val): 0.053381
    289. 

  289. [18:26:12] Epoch  27: Loss(train): 0.052878 Loss(val): 0.053369
    290. 

  290. [18:26:55] Epoch  28: Loss(train): 0.052749 Loss(val): 0.053334
    291. 

  291. [18:27:39] Epoch  29: Loss(train): 0.052654 Loss(val): 0.053279
    292. 

  292. [18:28:23] Epoch  30: Loss(train): 0.052551 Loss(val): 0.053160
    293. 

  293. [18:29:07] Epoch  31: Loss(train): 0.052434 Loss(val): 0.052998
    294. 

  294. [18:29:51] Epoch  32: Loss(train): 0.052321 Loss(val): 0.052879
    295. 

  295. [18:30:34] Epoch  33: Loss(train): 0.052148 Loss(val): 0.052693
    296. 

  296. [18:31:20] Epoch  34: Loss(train): 0.052083 Loss(val): 0.052561
    297. 

  297. [18:32:09] Epoch  35: Loss(train): 0.051987 Loss(val): 0.052415
    298. 

  298. [18:33:10] Epoch  36: Loss(train): 0.051942 Loss(val): 0.052319
    299. 

  299. [18:34:01] Epoch  37: Loss(train): 0.051942 Loss(val): 0.052249
    300. 

  300. [18:35:10] Epoch  38: Loss(train): 0.051861 Loss(val): 0.052164
    301. 

  301. [18:36:23] Epoch  39: Loss(train): 0.051847 Loss(val): 0.052113
    302. 

  302. [18:37:32] Epoch  40: Loss(train): 0.051747 Loss(val): 0.052048
    303. 

  303. [18:38:32] Epoch  41: Loss(train): 0.051614 Loss(val): 0.051969
    304. 

  304. [18:39:30] Epoch  42: Loss(train): 0.051501 Loss(val): 0.051891
    305. 

  305. [18:40:41] Epoch  43: Loss(train): 0.051425 Loss(val): 0.051849
    306. 

  306. [18:41:25] Epoch  44: Loss(train): 0.051336 Loss(val): 0.051807
    307. 

  307. [18:42:13] Epoch  45: Loss(train): 0.051292 Loss(val): 0.051769
    308. 

  308. [18:43:09] Epoch  46: Loss(train): 0.051229 Loss(val): 0.051732
    309. 

  309. [18:43:55] Epoch  47: Loss(train): 0.051206 Loss(val): 0.051705
    310. 

  310. [18:44:47] Epoch  48: Loss(train): 0.051182 Loss(val): 0.051671
    311. 

  311. [18:45:34] Epoch  49: Loss(train): 0.051149 Loss(val): 0.051648
    312. 

  312. [18:46:25] Epoch  50: Loss(train): 0.051119 Loss(val): 0.051630
    313. 

  313. [18:47:15] Epoch  51: Loss(train): 0.051105 Loss(val): 0.051612
    314. 

  314. [18:48:02] Epoch  52: Loss(train): 0.051082 Loss(val): 0.051598
    315. 

  315. [18:48:48] Epoch  53: Loss(train): 0.051052 Loss(val): 0.051591
    316. 

  316. [18:49:37] Epoch  54: Loss(train): 0.051018 Loss(val): 0.051580
    317. 

  317. Converged at Loss(train): 0.051932, Loss(val): 0.052555 in epoch 54 with accuracy(val): 0.645799
    318. 

  318. 

  319. Configuration learning_rate=0.01, decay_step=60
    320. 

  320. [18:50:03] INIT Loss(val): 0.131412 Accuarcy: 0.101854
    321. 

  321. [18:50:48] Epoch   1: Loss(train): 0.080744 Loss(val): 0.079622
    322. 

  322. [18:51:38] Epoch   2: Loss(train): 0.066512 Loss(val): 0.064695
    323. 

  323. [18:52:26] Epoch   3: Loss(train): 0.062622 Loss(val): 0.061448
    324. 

  324. [18:53:13] Epoch   4: Loss(train): 0.060374 Loss(val): 0.059260
    325. 

  325. [18:54:00] Epoch   5: Loss(train): 0.058809 Loss(val): 0.058090
    326. 

  326. [18:54:47] Epoch   6: Loss(train): 0.057931 Loss(val): 0.057646
    327. 

  327. [18:55:35] Epoch   7: Loss(train): 0.057441 Loss(val): 0.057309
    328. 

  328. [18:56:22] Epoch   8: Loss(train): 0.057106 Loss(val): 0.056946
    329. 

  329. [18:57:08] Epoch   9: Loss(train): 0.056751 Loss(val): 0.056449
    330. 

  330. [18:57:55] Epoch  10: Loss(train): 0.056720 Loss(val): 0.056406
    331. 

  331. [18:58:42] Epoch  11: Loss(train): 0.056405 Loss(val): 0.056351
    332. 

  332. [18:59:30] Epoch  12: Loss(train): 0.056383 Loss(val): 0.056457
    333. 

  333. [19:00:17] Epoch  13: Loss(train): 0.056200 Loss(val): 0.056471
    334. 

  334. [19:01:04] Epoch  14: Loss(train): 0.055845 Loss(val): 0.056025
    335. 

  335. [19:01:52] Epoch  15: Loss(train): 0.055829 Loss(val): 0.055832
    336. 

  336. [19:02:39] Epoch  16: Loss(train): 0.055686 Loss(val): 0.055626
    337. 

  337. [19:03:25] Epoch  17: Loss(train): 0.055622 Loss(val): 0.055737
    338. 

  338. [19:04:14] Epoch  18: Loss(train): 0.055771 Loss(val): 0.055958
    339. 

  339. [19:05:05] Epoch  19: Loss(train): 0.055711 Loss(val): 0.056073
    340. 

  340. [19:06:15] Epoch  20: Loss(train): 0.055973 Loss(val): 0.056337
    341. 

  341. [19:07:04] Epoch  21: Loss(train): 0.055825 Loss(val): 0.056190
    342. 

  342. [19:07:52] Epoch  22: Loss(train): 0.055313 Loss(val): 0.055599
    343. 

  343. [19:08:41] Epoch  23: Loss(train): 0.054893 Loss(val): 0.055099
    344. 

  344. [19:09:42] Epoch  24: Loss(train): 0.054391 Loss(val): 0.054471
    345. 

  345. [19:10:44] Epoch  25: Loss(train): 0.053839 Loss(val): 0.053866
    346. 

  346. [19:11:36] Epoch  26: Loss(train): 0.053508 Loss(val): 0.053500
    347. 

  347. [19:12:40] Epoch  27: Loss(train): 0.053171 Loss(val): 0.053214
    348. 

  348. [19:13:37] Epoch  28: Loss(train): 0.052970 Loss(val): 0.053028
    349. 

  349. [19:14:26] Epoch  29: Loss(train): 0.052803 Loss(val): 0.052932
    350. 

  350. [19:15:16] Epoch  30: Loss(train): 0.052774 Loss(val): 0.052891
    351. 

  351. [19:16:07] Epoch  31: Loss(train): 0.052981 Loss(val): 0.052950
    352. 

  352. [19:17:00] Epoch  32: Loss(train): 0.053209 Loss(val): 0.053051
    353. 

  353. [19:18:01] Epoch  33: Loss(train): 0.053366 Loss(val): 0.053110
    354. 

  354. [19:19:03] Epoch  34: Loss(train): 0.053671 Loss(val): 0.053226
    355. 

  355. [19:19:51] Epoch  35: Loss(train): 0.053941 Loss(val): 0.053339
    356. 

  356. [19:20:42] Epoch  36: Loss(train): 0.053890 Loss(val): 0.053279
    357. 

  357. [19:21:33] Epoch  37: Loss(train): 0.053691 Loss(val): 0.053105
    358. 

  358. [19:22:22] Epoch  38: Loss(train): 0.053180 Loss(val): 0.052768
    359. 

  359. [19:23:23] Epoch  39: Loss(train): 0.052641 Loss(val): 0.052439
    360. 

  360. [19:24:15] Epoch  40: Loss(train): 0.052294 Loss(val): 0.052223
    361. 

  361. [19:25:05] Epoch  41: Loss(train): 0.052021 Loss(val): 0.052078
    362. 

  362. [19:25:57] Epoch  42: Loss(train): 0.051852 Loss(val): 0.051986
    363. 

  363. [19:26:46] Epoch  43: Loss(train): 0.051764 Loss(val): 0.051918
    364. 

  364. [19:27:36] Epoch  44: Loss(train): 0.051702 Loss(val): 0.051857
    365. 

  365. [19:28:29] Epoch  45: Loss(train): 0.051609 Loss(val): 0.051782
    366. 

  366. [19:29:18] Epoch  46: Loss(train): 0.051520 Loss(val): 0.051719
    367. 

  367. [19:30:08] Epoch  47: Loss(train): 0.051464 Loss(val): 0.051675
    368. 

  368. [19:30:57] Epoch  48: Loss(train): 0.051390 Loss(val): 0.051640
    369. 

  369. [19:31:47] Epoch  49: Loss(train): 0.051337 Loss(val): 0.051621
    370. 

  370. [19:32:37] Epoch  50: Loss(train): 0.051246 Loss(val): 0.051601
    371. 

  371. [19:33:26] Epoch  51: Loss(train): 0.051194 Loss(val): 0.051589
    372. 

  372. [19:34:16] Epoch  52: Loss(train): 0.051123 Loss(val): 0.051589
    373. 

  373. [19:35:06] Epoch  53: Loss(train): 0.051061 Loss(val): 0.051593
    374. 

  374. [19:35:56] Epoch  54: Loss(train): 0.051009 Loss(val): 0.051610
    375. 

  375. [19:37:03] Epoch  55: Loss(train): 0.050970 Loss(val): 0.051631
    376. 

  376. [19:38:13] Epoch  56: Loss(train): 0.050941 Loss(val): 0.051682
    377. 

  377. [19:39:31] Epoch  57: Loss(train): 0.050926 Loss(val): 0.051712
    378. 

  378. [19:40:44] Epoch  58: Loss(train): 0.050924 Loss(val): 0.051774
    379. 

  379. [19:41:48] Epoch  59: Loss(train): 0.050913 Loss(val): 0.051794
    380. 

  380. [19:42:38] Epoch  60: Loss(train): 0.050901 Loss(val): 0.051803
    381. 

  381. [19:43:59] Epoch  61: Loss(train): 0.050880 Loss(val): 0.051795
    382. 

  382. [19:45:01] Epoch  62: Loss(train): 0.050853 Loss(val): 0.051767
    383. 

  383. [19:46:02] Epoch  63: Loss(train): 0.050823 Loss(val): 0.051729
    384. 

  384. Converged at Loss(train): 0.051745, Loss(val): 0.052544 in epoch 63 with accuracy(val): 0.647007
    385. 

  385. 

  386. Configuration learning_rate=0.003, decay_step=20
    387. 

  387. [19:46:27] INIT Loss(val): 0.197653 Accuarcy: 0.091241
    388. 

  388. [19:47:20] Epoch   1: Loss(train): 0.079464 Loss(val): 0.077783
    389. 

  389. [19:48:20] Epoch   2: Loss(train): 0.067149 Loss(val): 0.066139
    390. 

  390. [19:49:25] Epoch   3: Loss(train): 0.062640 Loss(val): 0.061764
    391. 

  391. [19:50:18] Epoch   4: Loss(train): 0.060908 Loss(val): 0.059928
    392. 

  392. [19:51:14] Epoch   5: Loss(train): 0.058845 Loss(val): 0.058380
    393. 

  393. [19:52:13] Epoch   6: Loss(train): 0.058177 Loss(val): 0.057776
    394. 

  394. [19:53:06] Epoch   7: Loss(train): 0.057540 Loss(val): 0.057122
    395. 

  395. [19:53:59] Epoch   8: Loss(train): 0.056814 Loss(val): 0.056477
    396. 

  396. [19:54:56] Epoch   9: Loss(train): 0.056360 Loss(val): 0.056050
    397. 

  397. [19:55:52] Epoch  10: Loss(train): 0.056028 Loss(val): 0.055813
    398. 

  398. [19:56:48] Epoch  11: Loss(train): 0.055597 Loss(val): 0.055646
    399. 

  399. [19:57:44] Epoch  12: Loss(train): 0.055225 Loss(val): 0.055342
    400. 

  400. [19:58:37] Epoch  13: Loss(train): 0.055302 Loss(val): 0.055394
    401. 

  401. [19:59:31] Epoch  14: Loss(train): 0.055238 Loss(val): 0.055283
    402. 

  402. [20:00:26] Epoch  15: Loss(train): 0.055095 Loss(val): 0.055047
    403. 

  403. [20:01:19] Epoch  16: Loss(train): 0.054809 Loss(val): 0.054673
    404. 

  404. [20:02:12] Epoch  17: Loss(train): 0.054337 Loss(val): 0.054301
    405. 

  405. [20:03:06] Epoch  18: Loss(train): 0.053958 Loss(val): 0.054079
    406. 

  406. [20:03:59] Epoch  19: Loss(train): 0.053873 Loss(val): 0.054106
    407. 

  407. [20:04:51] Epoch  20: Loss(train): 0.054026 Loss(val): 0.054221
    408. 

  408. [20:05:45] Epoch  21: Loss(train): 0.054201 Loss(val): 0.054455
    409. 

  409. [20:06:38] Epoch  22: Loss(train): 0.054305 Loss(val): 0.054560
    410. 

  410. [20:07:32] Epoch  23: Loss(train): 0.054394 Loss(val): 0.054562
    411. 

  411. [20:08:25] Epoch  24: Loss(train): 0.054172 Loss(val): 0.054326
    412. 

  412. [20:09:19] Epoch  25: Loss(train): 0.053743 Loss(val): 0.053848
    413. 

  413. [20:10:12] Epoch  26: Loss(train): 0.053142 Loss(val): 0.053302
    414. 

  414. [20:11:06] Epoch  27: Loss(train): 0.052741 Loss(val): 0.052896
    415. 

  415. [20:11:59] Epoch  28: Loss(train): 0.052561 Loss(val): 0.052737
    416. 

  416. [20:13:15] Epoch  29: Loss(train): 0.052489 Loss(val): 0.052637
    417. 

  417. [20:14:12] Epoch  30: Loss(train): 0.052407 Loss(val): 0.052581
    418. 

  418. [20:15:14] Epoch  31: Loss(train): 0.052487 Loss(val): 0.052619
    419. 

  419. [20:16:17] Epoch  32: Loss(train): 0.052642 Loss(val): 0.052717
    420. 

  420. [20:17:31] Epoch  33: Loss(train): 0.052745 Loss(val): 0.052786
    421. 

  421. [20:18:41] Epoch  34: Loss(train): 0.052957 Loss(val): 0.052931
    422. 

  422. [20:19:53] Epoch  35: Loss(train): 0.053042 Loss(val): 0.052976
    423. 

  423. [20:20:52] Epoch  36: Loss(train): 0.053068 Loss(val): 0.052970
    424. 

  424. [20:21:49] Epoch  37: Loss(train): 0.052881 Loss(val): 0.052853
    425. 

  425. [20:22:59] Epoch  38: Loss(train): 0.052606 Loss(val): 0.052675
    426. 

  426. [20:24:06] Epoch  39: Loss(train): 0.052210 Loss(val): 0.052439
    427. 

  427. [20:25:05] Epoch  40: Loss(train): 0.051897 Loss(val): 0.052271
    428. 

  428. [20:26:08] Epoch  41: Loss(train): 0.051670 Loss(val): 0.052125
    429. 

  429. [20:27:04] Epoch  42: Loss(train): 0.051492 Loss(val): 0.052033
    430. 

  430. [20:28:03] Epoch  43: Loss(train): 0.051401 Loss(val): 0.051973
    431. 

  431. [20:28:59] Epoch  44: Loss(train): 0.051340 Loss(val): 0.051914
    432. 

  432. [20:30:00] Epoch  45: Loss(train): 0.051286 Loss(val): 0.051879
    433. 

  433. [20:31:02] Epoch  46: Loss(train): 0.051234 Loss(val): 0.051821
    434. 

  434. [20:31:59] Epoch  47: Loss(train): 0.051182 Loss(val): 0.051773
    435. 

  435. [20:32:55] Epoch  48: Loss(train): 0.051136 Loss(val): 0.051725
    436. 

  436. [20:33:51] Epoch  49: Loss(train): 0.051084 Loss(val): 0.051684
    437. 

  437. [20:34:47] Epoch  50: Loss(train): 0.051037 Loss(val): 0.051649
    438. 

  438. [20:35:43] Epoch  51: Loss(train): 0.050997 Loss(val): 0.051624
    439. 

  439. [20:36:43] Epoch  52: Loss(train): 0.050962 Loss(val): 0.051611
    440. 

  440. [20:37:39] Epoch  53: Loss(train): 0.050926 Loss(val): 0.051608
    441. 

  441. [20:38:34] Epoch  54: Loss(train): 0.050892 Loss(val): 0.051609
    442. 

  442. [20:39:31] Epoch  55: Loss(train): 0.050861 Loss(val): 0.051612
    443. 

  443. [20:40:26] Epoch  56: Loss(train): 0.050835 Loss(val): 0.051619
    444. 

  444. [20:41:23] Epoch  57: Loss(train): 0.050810 Loss(val): 0.051647
    445. 

  445. [20:42:18] Epoch  58: Loss(train): 0.050793 Loss(val): 0.051664
    446. 

  446. [20:43:13] Epoch  59: Loss(train): 0.050780 Loss(val): 0.051697
    447. 

  447. [20:44:09] Epoch  60: Loss(train): 0.050769 Loss(val): 0.051714
    448. 

  448. [20:45:06] Epoch  61: Loss(train): 0.050759 Loss(val): 0.051723
    449. 

  449. [20:46:02] Epoch  62: Loss(train): 0.050747 Loss(val): 0.051723
    450. 

  450. [20:47:04] Epoch  63: Loss(train): 0.050739 Loss(val): 0.051730
    451. 

  451. [20:48:21] Epoch  64: Loss(train): 0.050730 Loss(val): 0.051738
    452. 

  452. [20:49:22] Epoch  65: Loss(train): 0.050708 Loss(val): 0.051697
    453. 

  453. Converged at Loss(train): 0.051663, Loss(val): 0.052587 in epoch 65 with accuracy(val): 0.643129
    454. 

  454. 

  455. Configuration learning_rate=0.003, decay_step=40
    456. 

  456. [20:49:52] INIT Loss(val): 0.131267 Accuarcy: 0.083214
    457. 

  457. [20:50:53] Epoch   1: Loss(train): 0.085294 Loss(val): 0.084576
    458. 

  458. [20:52:03] Epoch   2: Loss(train): 0.067979 Loss(val): 0.066454
    459. 

  459. [20:53:25] Epoch   3: Loss(train): 0.064692 Loss(val): 0.063468
    460. 

  460. [20:54:50] Epoch   4: Loss(train): 0.060204 Loss(val): 0.059563
    461. 

  461. [20:55:54] Epoch   5: Loss(train): 0.059113 Loss(val): 0.058688
    462. 

  462. [20:56:51] Epoch   6: Loss(train): 0.058466 Loss(val): 0.058163
    463. 

  463. [20:57:49] Epoch   7: Loss(train): 0.058073 Loss(val): 0.057866
    464. 

  464. [20:58:55] Epoch   8: Loss(train): 0.057293 Loss(val): 0.057096
    465. 

  465. [20:59:57] Epoch   9: Loss(train): 0.056591 Loss(val): 0.056324
    466. 

  466. [21:01:01] Epoch  10: Loss(train): 0.056213 Loss(val): 0.056077
    467. 

  467. [21:02:04] Epoch  11: Loss(train): 0.055718 Loss(val): 0.055704
    468. 

  468. [21:03:08] Epoch  12: Loss(train): 0.055250 Loss(val): 0.055418
    469. 

  469. [21:04:14] Epoch  13: Loss(train): 0.055137 Loss(val): 0.055309
    470. 

  470. [21:05:16] Epoch  14: Loss(train): 0.055121 Loss(val): 0.055232
    471. 

  471. [21:06:19] Epoch  15: Loss(train): 0.055151 Loss(val): 0.055145
    472. 

  472. [21:07:20] Epoch  16: Loss(train): 0.055220 Loss(val): 0.055140
    473. 

  473. [21:08:20] Epoch  17: Loss(train): 0.054638 Loss(val): 0.054753
    474. 

  474. [21:09:19] Epoch  18: Loss(train): 0.054304 Loss(val): 0.054612
    475. 

  475. [21:10:20] Epoch  19: Loss(train): 0.054102 Loss(val): 0.054496
    476. 

  476. [21:11:21] Epoch  20: Loss(train): 0.054121 Loss(val): 0.054624
    477. 

  477. [21:12:20] Epoch  21: Loss(train): 0.054457 Loss(val): 0.054914
    478. 

  478. [21:13:20] Epoch  22: Loss(train): 0.054731 Loss(val): 0.055191
    479. 

  479. [21:14:20] Epoch  23: Loss(train): 0.054807 Loss(val): 0.055170
    480. 

  480. [21:15:19] Epoch  24: Loss(train): 0.054750 Loss(val): 0.055053
    481. 

  481. [21:16:20] Epoch  25: Loss(train): 0.054323 Loss(val): 0.054540
    482. 

  482. [21:17:19] Epoch  26: Loss(train): 0.053600 Loss(val): 0.053798
    483. 

  483. [21:18:17] Epoch  27: Loss(train): 0.053148 Loss(val): 0.053314
    484. 

  484. [21:19:16] Epoch  28: Loss(train): 0.052852 Loss(val): 0.053044
    485. 

  485. [21:20:15] Epoch  29: Loss(train): 0.052607 Loss(val): 0.052848
    486. 

  486. [21:21:39] Epoch  30: Loss(train): 0.052449 Loss(val): 0.052746
    487. 

  487. [21:23:03] Epoch  31: Loss(train): 0.052500 Loss(val): 0.052788
    488. 

  488. [21:24:22] Epoch  32: Loss(train): 0.052512 Loss(val): 0.052785
    489. 

  489. [21:25:49] Epoch  33: Loss(train): 0.052571 Loss(val): 0.052789
    490. 

  490. [21:26:54] Epoch  34: Loss(train): 0.052684 Loss(val): 0.052806
    491. 

  491. [21:28:20] Epoch  35: Loss(train): 0.052755 Loss(val): 0.052841
    492. 

  492. [21:29:30] Epoch  36: Loss(train): 0.052775 Loss(val): 0.052819
    493. 

  493. [21:30:48] Epoch  37: Loss(train): 0.052677 Loss(val): 0.052722
    494. 

  494. [21:32:02] Epoch  38: Loss(train): 0.052491 Loss(val): 0.052566
    495. 

  495. [21:33:11] Epoch  39: Loss(train): 0.052202 Loss(val): 0.052362
    496. 

  496. [21:34:15] Epoch  40: Loss(train): 0.051935 Loss(val): 0.052191
    497. 

  497. [21:35:18] Epoch  41: Loss(train): 0.051739 Loss(val): 0.052059
    498. 

  498. [21:36:22] Epoch  42: Loss(train): 0.051572 Loss(val): 0.051957
    499. 

  499. [21:37:25] Epoch  43: Loss(train): 0.051486 Loss(val): 0.051892
    500. 

  500. [21:38:29] Epoch  44: Loss(train): 0.051410 Loss(val): 0.051844
    501. 

  501. [21:39:35] Epoch  45: Loss(train): 0.051352 Loss(val): 0.051801
    502. 

  502. [21:40:38] Epoch  46: Loss(train): 0.051312 Loss(val): 0.051755
    503. 

  503. [21:41:45] Epoch  47: Loss(train): 0.051259 Loss(val): 0.051708
    504. 

  504. [21:42:48] Epoch  48: Loss(train): 0.051191 Loss(val): 0.051682
    505. 

  505. [21:43:52] Epoch  49: Loss(train): 0.051153 Loss(val): 0.051656
    506. 

  506. [21:44:55] Epoch  50: Loss(train): 0.051126 Loss(val): 0.051639
    507. 

  507. [21:45:56] Epoch  51: Loss(train): 0.051106 Loss(val): 0.051628
    508. 

  508. [21:46:59] Epoch  52: Loss(train): 0.051067 Loss(val): 0.051622
    509. 

  509. [21:48:02] Epoch  53: Loss(train): 0.051033 Loss(val): 0.051616
    510. 

  510. [21:49:05] Epoch  54: Loss(train): 0.051009 Loss(val): 0.051614
    511. 

  511. [21:50:07] Epoch  55: Loss(train): 0.050979 Loss(val): 0.051613
    512. 

  512. [21:51:08] Epoch  56: Loss(train): 0.050938 Loss(val): 0.051623
    513. 

  513. [21:52:11] Epoch  57: Loss(train): 0.050908 Loss(val): 0.051627
    514. 

  514. [21:53:14] Epoch  58: Loss(train): 0.050878 Loss(val): 0.051646
    515. 

  515. [21:54:23] Epoch  59: Loss(train): 0.050853 Loss(val): 0.051660
    516. 

  516. [21:55:29] Epoch  60: Loss(train): 0.050829 Loss(val): 0.051672
    517. 

  517. Converged at Loss(train): 0.051769, Loss(val): 0.052593 in epoch 60 with accuracy(val): 0.647092
    518. 

  518. 

  519. Configuration learning_rate=0.003, decay_step=60
    520. 

  520. [21:56:01] INIT Loss(val): 0.128400 Accuarcy: 0.091173
    521. 

  521. [21:57:04] Epoch   1: Loss(train): 0.087630 Loss(val): 0.086648
    522. 

  522. [21:58:09] Epoch   2: Loss(train): 0.068650 Loss(val): 0.067234
    523. 

  523. [21:59:13] Epoch   3: Loss(train): 0.064484 Loss(val): 0.062984
    524. 

  524. [22:00:15] Epoch   4: Loss(train): 0.062109 Loss(val): 0.060795
    525. 

  525. [22:01:20] Epoch   5: Loss(train): 0.059852 Loss(val): 0.059078
    526. 

  526. [22:02:26] Epoch   6: Loss(train): 0.059096 Loss(val): 0.058652
    527. 

  527. [22:03:28] Epoch   7: Loss(train): 0.058634 Loss(val): 0.058505
    528. 

  528. [22:04:30] Epoch   8: Loss(train): 0.058904 Loss(val): 0.058879
    529. 

  529. [22:05:33] Epoch   9: Loss(train): 0.058865 Loss(val): 0.058817
    530. 

  530. [22:06:40] Epoch  10: Loss(train): 0.058145 Loss(val): 0.058074
    531. 

  531. [22:07:45] Epoch  11: Loss(train): 0.057094 Loss(val): 0.057320
    532. 

  532. [22:08:57] Epoch  12: Loss(train): 0.056686 Loss(val): 0.057000
    533. 

  533. [22:10:03] Epoch  13: Loss(train): 0.056440 Loss(val): 0.056806
    534. 

  534. [22:11:07] Epoch  14: Loss(train): 0.056780 Loss(val): 0.056969
    535. 

  535. [22:12:15] Epoch  15: Loss(train): 0.057121 Loss(val): 0.057249
    536. 

  536. [22:13:19] Epoch  16: Loss(train): 0.057089 Loss(val): 0.057170
    537. 

  537. [22:14:23] Epoch  17: Loss(train): 0.056695 Loss(val): 0.056844
    538. 

  538. [22:15:28] Epoch  18: Loss(train): 0.056033 Loss(val): 0.056204
    539. 

  539. [22:16:41] Epoch  19: Loss(train): 0.055199 Loss(val): 0.055399
    540. 

  540. [22:17:51] Epoch  20: Loss(train): 0.054661 Loss(val): 0.054834
    541. 

  541. [22:18:59] Epoch  21: Loss(train): 0.054271 Loss(val): 0.054396
    542. 

  542. [22:20:05] Epoch  22: Loss(train): 0.054036 Loss(val): 0.054124
    543. 

  543. [22:21:12] Epoch  23: Loss(train): 0.054021 Loss(val): 0.054075
    544. 

  544. [22:22:18] Epoch  24: Loss(train): 0.053884 Loss(val): 0.054009
    545. 

  545. [22:23:23] Epoch  25: Loss(train): 0.053809 Loss(val): 0.053988
    546. 

  546. [22:24:28] Epoch  26: Loss(train): 0.053688 Loss(val): 0.053978
    547. 

  547. [22:25:32] Epoch  27: Loss(train): 0.053466 Loss(val): 0.053863
    548. 

  548. [22:26:35] Epoch  28: Loss(train): 0.053356 Loss(val): 0.053755
    549. 

  549. [22:27:42] Epoch  29: Loss(train): 0.053260 Loss(val): 0.053652
    550. 

  550. [22:28:47] Epoch  30: Loss(train): 0.053242 Loss(val): 0.053502
    551. 

  551. [22:29:52] Epoch  31: Loss(train): 0.053209 Loss(val): 0.053376
    552. 

  552. [22:30:55] Epoch  32: Loss(train): 0.053310 Loss(val): 0.053301
    553. 

  553. [22:32:02] Epoch  33: Loss(train): 0.053360 Loss(val): 0.053194
    554. 

  554. [22:33:11] Epoch  34: Loss(train): 0.053411 Loss(val): 0.053129
    555. 

  555. [22:34:18] Epoch  35: Loss(train): 0.053490 Loss(val): 0.053088
    556. 

  556. [22:35:25] Epoch  36: Loss(train): 0.053329 Loss(val): 0.052931
    557. 

  557. [22:36:35] Epoch  37: Loss(train): 0.053057 Loss(val): 0.052750
    558. 

  558. [22:37:46] Epoch  38: Loss(train): 0.052699 Loss(val): 0.052533
    559. 

  559. [22:38:57] Epoch  39: Loss(train): 0.052314 Loss(val): 0.052344
    560. 

  560. [22:40:06] Epoch  40: Loss(train): 0.052103 Loss(val): 0.052243
    561. 

  561. [22:41:26] Epoch  41: Loss(train): 0.051960 Loss(val): 0.052185
    562. 

  562. [22:42:35] Epoch  42: Loss(train): 0.051858 Loss(val): 0.052141
    563. 

  563. [22:43:43] Epoch  43: Loss(train): 0.051790 Loss(val): 0.052123
    564. 

  564. [22:44:48] Epoch  44: Loss(train): 0.051754 Loss(val): 0.052102
    565. 

  565. [22:45:55] Epoch  45: Loss(train): 0.051746 Loss(val): 0.052096
    566. 

  566. [22:47:15] Epoch  46: Loss(train): 0.051733 Loss(val): 0.052079
    567. 

  567. [22:48:27] Epoch  47: Loss(train): 0.051714 Loss(val): 0.052058
    568. 

  568. [22:49:37] Epoch  48: Loss(train): 0.051704 Loss(val): 0.052038
    569. 

  569. [22:50:50] Epoch  49: Loss(train): 0.051654 Loss(val): 0.052020
    570. 

  570. [22:51:58] Epoch  50: Loss(train): 0.051591 Loss(val): 0.051994
    571. 

  571. [22:53:08] Epoch  51: Loss(train): 0.051546 Loss(val): 0.051980
    572. 

  572. Converged at Loss(train): 0.052474, Loss(val): 0.052877 in epoch 51 with accuracy(val): 0.638793
    573.