API tools faq
paste
Advertisement
lamiastella

different_filters_epoch50

lamiastella
Oct 22nd, 2018
251
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 209.38 KB | None | 0 0
raw download clone embed print report
  1. [jalal@scc-x04 pset3_CNN]$ python different_filters.py
  2. 2018-10-22 22:36:25.320600: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
  3. 2018-10-22 22:36:25.489973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
  4. name: Tesla P100-PCIE-12GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
  5. pciBusID: 0000:02:00.0
  6. totalMemory: 11.91GiB freeMemory: 11.63GiB
  7. 2018-10-22 22:36:25.490007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  8. 2018-10-22 22:36:25.780948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  9. 2018-10-22 22:36:25.780981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  10. 2018-10-22 22:36:25.780987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  11. 2018-10-22 22:36:25.781268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11251 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-12GB, pci bus id: 0000:02:00.0, compute capability: 6.0)
  12. 2018-10-22 22:36:25.993479: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 28. Tune using inter_op_parallelism_threads for best performance.
  13. current filter size is: 16
  14. 2018-10-22 22:36:29.191279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  15. 2018-10-22 22:36:29.191316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  16. 2018-10-22 22:36:29.191322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  17. 2018-10-22 22:36:29.191327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  18. 2018-10-22 22:36:29.191452: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11251 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-12GB, pci bus id: 0000:02:00.0, compute capability: 6.0)
  19. epoch 0 iter 0, loss: 83.420, accuracy: 0.186
  20. epoch 0 iter 10, loss: 4.640, accuracy: 0.141
  21. epoch 0 iter 20, loss: 2.353, accuracy: 0.138
  22. epoch 0 iter 30, loss: 2.260, accuracy: 0.182
  23. epoch 0 iter 40, loss: 2.256, accuracy: 0.189
  24. epoch 0 iter 50, loss: 2.248, accuracy: 0.190
  25. epoch 0 iter 60, loss: 2.244, accuracy: 0.191
  26. epoch 0 iter 70, loss: 2.241, accuracy: 0.192
  27. epoch 0 iter 80, loss: 2.242, accuracy: 0.190
  28. epoch 0 iter 90, loss: 2.238, accuracy: 0.192
  29. epoch 0 iter 100, loss: 2.233, accuracy: 0.193
  30. epoch 0 iter 110, loss: 2.234, accuracy: 0.192
  31. epoch 0 iter 120, loss: 2.234, accuracy: 0.189
  32. epoch 0 iter 130, loss: 2.228, accuracy: 0.190
  33. epoch 0 iter 140, loss: 2.227, accuracy: 0.190
  34. epoch 0 iter 150, loss: 2.227, accuracy: 0.190
  35. epoch 0 iter 160, loss: 2.228, accuracy: 0.193
  36. epoch 0 iter 170, loss: 2.229, accuracy: 0.190
  37. epoch 0 iter 180, loss: 2.228, accuracy: 0.192
  38. epoch 0 iter 190, loss: 2.227, accuracy: 0.194
  39. epoch 0 iter 200, loss: 2.224, accuracy: 0.193
  40. epoch 0 iter 210, loss: 2.225, accuracy: 0.194
  41. epoch 0 iter 220, loss: 2.224, accuracy: 0.192
  42. epoch 0 iter 230, loss: 2.220, accuracy: 0.193
  43. epoch 0 iter 240, loss: 2.220, accuracy: 0.194
  44. epoch 0 iter 250, loss: 2.220, accuracy: 0.196
  45. epoch 0 iter 260, loss: 2.216, accuracy: 0.194
  46. epoch 0 iter 270, loss: 2.217, accuracy: 0.194
  47. epoch 0 iter 280, loss: 2.213, accuracy: 0.194
  48. epoch 1 iter 0, loss: 2.211, accuracy: 0.196
  49. epoch 1 iter 10, loss: 2.209, accuracy: 0.199
  50. epoch 1 iter 20, loss: 2.203, accuracy: 0.204
  51. epoch 1 iter 30, loss: 2.204, accuracy: 0.193
  52. epoch 1 iter 40, loss: 2.192, accuracy: 0.205
  53. epoch 1 iter 50, loss: 2.198, accuracy: 0.202
  54. epoch 1 iter 60, loss: 2.198, accuracy: 0.201
  55. epoch 1 iter 70, loss: 2.187, accuracy: 0.207
  56. epoch 1 iter 80, loss: 2.196, accuracy: 0.202
  57. epoch 1 iter 90, loss: 2.158, accuracy: 0.226
  58. epoch 1 iter 100, loss: 2.116, accuracy: 0.250
  59. epoch 1 iter 110, loss: 2.092, accuracy: 0.249
  60. epoch 1 iter 120, loss: 2.019, accuracy: 0.326
  61. epoch 1 iter 130, loss: 1.855, accuracy: 0.370
  62. epoch 1 iter 140, loss: 1.729, accuracy: 0.428
  63. epoch 1 iter 150, loss: 1.617, accuracy: 0.463
  64. epoch 1 iter 160, loss: 1.540, accuracy: 0.495
  65. epoch 1 iter 170, loss: 1.486, accuracy: 0.527
  66. epoch 1 iter 180, loss: 1.411, accuracy: 0.543
  67. epoch 1 iter 190, loss: 1.318, accuracy: 0.591
  68. epoch 1 iter 200, loss: 1.316, accuracy: 0.584
  69. epoch 1 iter 210, loss: 1.298, accuracy: 0.594
  70. epoch 1 iter 220, loss: 1.261, accuracy: 0.610
  71. epoch 1 iter 230, loss: 1.207, accuracy: 0.625
  72. epoch 1 iter 240, loss: 1.185, accuracy: 0.632
  73. epoch 1 iter 250, loss: 1.158, accuracy: 0.646
  74. epoch 1 iter 260, loss: 1.134, accuracy: 0.650
  75. epoch 1 iter 270, loss: 1.155, accuracy: 0.644
  76. epoch 1 iter 280, loss: 1.107, accuracy: 0.660
  77. epoch 2 iter 0, loss: 1.122, accuracy: 0.657
  78. epoch 2 iter 10, loss: 1.159, accuracy: 0.652
  79. epoch 2 iter 20, loss: 1.090, accuracy: 0.671
  80. epoch 2 iter 30, loss: 1.092, accuracy: 0.673
  81. epoch 2 iter 40, loss: 1.080, accuracy: 0.672
  82. epoch 2 iter 50, loss: 1.088, accuracy: 0.669
  83. epoch 2 iter 60, loss: 1.074, accuracy: 0.678
  84. epoch 2 iter 70, loss: 1.076, accuracy: 0.675
  85. epoch 2 iter 80, loss: 1.032, accuracy: 0.694
  86. epoch 2 iter 90, loss: 1.015, accuracy: 0.697
  87. epoch 2 iter 100, loss: 1.012, accuracy: 0.697
  88. epoch 2 iter 110, loss: 1.031, accuracy: 0.688
  89. epoch 2 iter 120, loss: 1.006, accuracy: 0.698
  90. epoch 2 iter 130, loss: 0.996, accuracy: 0.704
  91. epoch 2 iter 140, loss: 0.979, accuracy: 0.710
  92. epoch 2 iter 150, loss: 1.031, accuracy: 0.691
  93. epoch 2 iter 160, loss: 1.029, accuracy: 0.693
  94. epoch 2 iter 170, loss: 0.972, accuracy: 0.710
  95. epoch 2 iter 180, loss: 0.958, accuracy: 0.712
  96. epoch 2 iter 190, loss: 0.976, accuracy: 0.708
  97. epoch 2 iter 200, loss: 0.971, accuracy: 0.711
  98. epoch 2 iter 210, loss: 1.007, accuracy: 0.698
  99. epoch 2 iter 220, loss: 0.967, accuracy: 0.713
  100. epoch 2 iter 230, loss: 0.946, accuracy: 0.721
  101. epoch 2 iter 240, loss: 0.922, accuracy: 0.729
  102. epoch 2 iter 250, loss: 0.933, accuracy: 0.728
  103. epoch 2 iter 260, loss: 0.914, accuracy: 0.732
  104. epoch 2 iter 270, loss: 0.932, accuracy: 0.729
  105. epoch 2 iter 280, loss: 0.934, accuracy: 0.729
  106. epoch 3 iter 0, loss: 0.960, accuracy: 0.721
  107. epoch 3 iter 10, loss: 0.944, accuracy: 0.725
  108. epoch 3 iter 20, loss: 0.923, accuracy: 0.732
  109. epoch 3 iter 30, loss: 0.925, accuracy: 0.733
  110. epoch 3 iter 40, loss: 0.949, accuracy: 0.724
  111. epoch 3 iter 50, loss: 0.958, accuracy: 0.720
  112. epoch 3 iter 60, loss: 0.971, accuracy: 0.716
  113. epoch 3 iter 70, loss: 0.960, accuracy: 0.716
  114. epoch 3 iter 80, loss: 0.915, accuracy: 0.736
  115. epoch 3 iter 90, loss: 0.912, accuracy: 0.735
  116. epoch 3 iter 100, loss: 0.892, accuracy: 0.738
  117. epoch 3 iter 110, loss: 0.920, accuracy: 0.728
  118. epoch 3 iter 120, loss: 0.884, accuracy: 0.743
  119. epoch 3 iter 130, loss: 0.867, accuracy: 0.750
  120. epoch 3 iter 140, loss: 0.915, accuracy: 0.742
  121. epoch 3 iter 150, loss: 0.932, accuracy: 0.728
  122. epoch 3 iter 160, loss: 0.960, accuracy: 0.713
  123. epoch 3 iter 170, loss: 0.885, accuracy: 0.743
  124. epoch 3 iter 180, loss: 0.892, accuracy: 0.741
  125. epoch 3 iter 190, loss: 0.894, accuracy: 0.740
  126. epoch 3 iter 200, loss: 0.942, accuracy: 0.731
  127. epoch 3 iter 210, loss: 0.912, accuracy: 0.733
  128. epoch 3 iter 220, loss: 0.923, accuracy: 0.738
  129. epoch 3 iter 230, loss: 0.883, accuracy: 0.749
  130. epoch 3 iter 240, loss: 0.883, accuracy: 0.745
  131. epoch 3 iter 250, loss: 0.856, accuracy: 0.754
  132. epoch 3 iter 260, loss: 0.855, accuracy: 0.756
  133. epoch 3 iter 270, loss: 0.876, accuracy: 0.749
  134. epoch 3 iter 280, loss: 0.838, accuracy: 0.763
  135. epoch 4 iter 0, loss: 0.883, accuracy: 0.748
  136. epoch 4 iter 10, loss: 0.859, accuracy: 0.761
  137. epoch 4 iter 20, loss: 0.833, accuracy: 0.764
  138. epoch 4 iter 30, loss: 0.896, accuracy: 0.739
  139. epoch 4 iter 40, loss: 0.845, accuracy: 0.757
  140. epoch 4 iter 50, loss: 0.843, accuracy: 0.759
  141. epoch 4 iter 60, loss: 0.894, accuracy: 0.747
  142. epoch 4 iter 70, loss: 0.845, accuracy: 0.762
  143. epoch 4 iter 80, loss: 0.838, accuracy: 0.767
  144. epoch 4 iter 90, loss: 0.851, accuracy: 0.763
  145. epoch 4 iter 100, loss: 0.856, accuracy: 0.760
  146. epoch 4 iter 110, loss: 0.858, accuracy: 0.754
  147. epoch 4 iter 120, loss: 0.845, accuracy: 0.759
  148. epoch 4 iter 130, loss: 0.808, accuracy: 0.771
  149. epoch 4 iter 140, loss: 0.853, accuracy: 0.763
  150. epoch 4 iter 150, loss: 0.840, accuracy: 0.766
  151. epoch 4 iter 160, loss: 0.819, accuracy: 0.765
  152. epoch 4 iter 170, loss: 0.812, accuracy: 0.769
  153. epoch 4 iter 180, loss: 0.857, accuracy: 0.756
  154. epoch 4 iter 190, loss: 0.829, accuracy: 0.768
  155. epoch 4 iter 200, loss: 0.884, accuracy: 0.751
  156. epoch 4 iter 210, loss: 0.836, accuracy: 0.761
  157. epoch 4 iter 220, loss: 0.811, accuracy: 0.773
  158. epoch 4 iter 230, loss: 0.833, accuracy: 0.768
  159. epoch 4 iter 240, loss: 0.814, accuracy: 0.774
  160. epoch 4 iter 250, loss: 0.792, accuracy: 0.781
  161. epoch 4 iter 260, loss: 0.811, accuracy: 0.770
  162. epoch 4 iter 270, loss: 0.813, accuracy: 0.775
  163. epoch 4 iter 280, loss: 0.821, accuracy: 0.770
  164. epoch 5 iter 0, loss: 0.828, accuracy: 0.767
  165. epoch 5 iter 10, loss: 0.794, accuracy: 0.782
  166. epoch 5 iter 20, loss: 0.802, accuracy: 0.780
  167. epoch 5 iter 30, loss: 0.852, accuracy: 0.762
  168. epoch 5 iter 40, loss: 0.822, accuracy: 0.773
  169. epoch 5 iter 50, loss: 0.861, accuracy: 0.758
  170. epoch 5 iter 60, loss: 0.908, accuracy: 0.745
  171. epoch 5 iter 70, loss: 0.829, accuracy: 0.773
  172. epoch 5 iter 80, loss: 0.789, accuracy: 0.780
  173. epoch 5 iter 90, loss: 0.810, accuracy: 0.777
  174. epoch 5 iter 100, loss: 0.794, accuracy: 0.781
  175. epoch 5 iter 110, loss: 0.807, accuracy: 0.777
  176. epoch 5 iter 120, loss: 0.815, accuracy: 0.778
  177. epoch 5 iter 130, loss: 0.800, accuracy: 0.776
  178. epoch 5 iter 140, loss: 0.801, accuracy: 0.781
  179. epoch 5 iter 150, loss: 0.830, accuracy: 0.779
  180. epoch 5 iter 160, loss: 0.809, accuracy: 0.779
  181. epoch 5 iter 170, loss: 0.796, accuracy: 0.780
  182. epoch 5 iter 180, loss: 0.805, accuracy: 0.778
  183. epoch 5 iter 190, loss: 0.783, accuracy: 0.790
  184. epoch 5 iter 200, loss: 0.813, accuracy: 0.777
  185. epoch 5 iter 210, loss: 0.820, accuracy: 0.773
  186. epoch 5 iter 220, loss: 0.765, accuracy: 0.789
  187. epoch 5 iter 230, loss: 0.803, accuracy: 0.783
  188. epoch 5 iter 240, loss: 0.784, accuracy: 0.790
  189. epoch 5 iter 250, loss: 0.773, accuracy: 0.790
  190. epoch 5 iter 260, loss: 0.758, accuracy: 0.789
  191. epoch 5 iter 270, loss: 0.831, accuracy: 0.776
  192. epoch 5 iter 280, loss: 0.853, accuracy: 0.764
  193. epoch 6 iter 0, loss: 0.818, accuracy: 0.778
  194. epoch 6 iter 10, loss: 0.819, accuracy: 0.777
  195. epoch 6 iter 20, loss: 0.806, accuracy: 0.783
  196. epoch 6 iter 30, loss: 0.817, accuracy: 0.779
  197. epoch 6 iter 40, loss: 0.821, accuracy: 0.779
  198. epoch 6 iter 50, loss: 0.843, accuracy: 0.771
  199. epoch 6 iter 60, loss: 0.855, accuracy: 0.766
  200. epoch 6 iter 70, loss: 0.819, accuracy: 0.778
  201. epoch 6 iter 80, loss: 0.793, accuracy: 0.785
  202. epoch 6 iter 90, loss: 0.768, accuracy: 0.793
  203. epoch 6 iter 100, loss: 0.784, accuracy: 0.786
  204. epoch 6 iter 110, loss: 0.794, accuracy: 0.790
  205. epoch 6 iter 120, loss: 0.794, accuracy: 0.790
  206. epoch 6 iter 130, loss: 0.787, accuracy: 0.785
  207. epoch 6 iter 140, loss: 0.816, accuracy: 0.776
  208. epoch 6 iter 150, loss: 0.789, accuracy: 0.797
  209. epoch 6 iter 160, loss: 0.795, accuracy: 0.787
  210. epoch 6 iter 170, loss: 0.785, accuracy: 0.788
  211. epoch 6 iter 180, loss: 0.780, accuracy: 0.790
  212. epoch 6 iter 190, loss: 0.779, accuracy: 0.796
  213. epoch 6 iter 200, loss: 0.795, accuracy: 0.791
  214. epoch 6 iter 210, loss: 0.840, accuracy: 0.775
  215. epoch 6 iter 220, loss: 0.779, accuracy: 0.791
  216. epoch 6 iter 230, loss: 0.801, accuracy: 0.787
  217. epoch 6 iter 240, loss: 0.822, accuracy: 0.783
  218. epoch 6 iter 250, loss: 0.801, accuracy: 0.786
  219. epoch 6 iter 260, loss: 0.809, accuracy: 0.785
  220. epoch 6 iter 270, loss: 0.800, accuracy: 0.794
  221. epoch 6 iter 280, loss: 0.795, accuracy: 0.793
  222. epoch 7 iter 0, loss: 0.806, accuracy: 0.787
  223. epoch 7 iter 10, loss: 0.779, accuracy: 0.797
  224. epoch 7 iter 20, loss: 0.866, accuracy: 0.775
  225. epoch 7 iter 30, loss: 0.828, accuracy: 0.788
  226. epoch 7 iter 40, loss: 0.836, accuracy: 0.789
  227. epoch 7 iter 50, loss: 0.817, accuracy: 0.786
  228. epoch 7 iter 60, loss: 0.849, accuracy: 0.779
  229. epoch 7 iter 70, loss: 0.840, accuracy: 0.785
  230. epoch 7 iter 80, loss: 0.794, accuracy: 0.795
  231. epoch 7 iter 90, loss: 0.767, accuracy: 0.798
  232. epoch 7 iter 100, loss: 0.797, accuracy: 0.792
  233. epoch 7 iter 110, loss: 0.802, accuracy: 0.789
  234. epoch 7 iter 120, loss: 0.808, accuracy: 0.795
  235. epoch 7 iter 130, loss: 0.823, accuracy: 0.788
  236. epoch 7 iter 140, loss: 0.811, accuracy: 0.791
  237. epoch 7 iter 150, loss: 0.794, accuracy: 0.800
  238. epoch 7 iter 160, loss: 0.797, accuracy: 0.793
  239. epoch 7 iter 170, loss: 0.807, accuracy: 0.794
  240. epoch 7 iter 180, loss: 0.800, accuracy: 0.795
  241. epoch 7 iter 190, loss: 0.801, accuracy: 0.798
  242. epoch 7 iter 200, loss: 0.826, accuracy: 0.791
  243. epoch 7 iter 210, loss: 0.807, accuracy: 0.792
  244. epoch 7 iter 220, loss: 0.802, accuracy: 0.793
  245. epoch 7 iter 230, loss: 0.801, accuracy: 0.796
  246. epoch 7 iter 240, loss: 0.853, accuracy: 0.784
  247. epoch 7 iter 250, loss: 0.808, accuracy: 0.792
  248. epoch 7 iter 260, loss: 0.823, accuracy: 0.790
  249. epoch 7 iter 270, loss: 0.835, accuracy: 0.786
  250. epoch 7 iter 280, loss: 0.814, accuracy: 0.791
  251. epoch 8 iter 0, loss: 0.828, accuracy: 0.789
  252. epoch 8 iter 10, loss: 0.801, accuracy: 0.802
  253. epoch 8 iter 20, loss: 0.843, accuracy: 0.790
  254. epoch 8 iter 30, loss: 0.829, accuracy: 0.791
  255. epoch 8 iter 40, loss: 0.860, accuracy: 0.786
  256. epoch 8 iter 50, loss: 0.839, accuracy: 0.786
  257. epoch 8 iter 60, loss: 0.898, accuracy: 0.777
  258. epoch 8 iter 70, loss: 0.846, accuracy: 0.794
  259. epoch 8 iter 80, loss: 0.843, accuracy: 0.788
  260. epoch 8 iter 90, loss: 0.781, accuracy: 0.800
  261. epoch 8 iter 100, loss: 0.819, accuracy: 0.791
  262. epoch 8 iter 110, loss: 0.819, accuracy: 0.788
  263. epoch 8 iter 120, loss: 0.826, accuracy: 0.794
  264. epoch 8 iter 130, loss: 0.840, accuracy: 0.790
  265. epoch 8 iter 140, loss: 0.826, accuracy: 0.795
  266. epoch 8 iter 150, loss: 0.820, accuracy: 0.797
  267. epoch 8 iter 160, loss: 0.816, accuracy: 0.795
  268. epoch 8 iter 170, loss: 0.826, accuracy: 0.800
  269. epoch 8 iter 180, loss: 0.907, accuracy: 0.775
  270. epoch 8 iter 190, loss: 0.801, accuracy: 0.802
  271. epoch 8 iter 200, loss: 0.837, accuracy: 0.795
  272. epoch 8 iter 210, loss: 0.852, accuracy: 0.792
  273. epoch 8 iter 220, loss: 0.837, accuracy: 0.786
  274. epoch 8 iter 230, loss: 0.857, accuracy: 0.794
  275. epoch 8 iter 240, loss: 0.847, accuracy: 0.794
  276. epoch 8 iter 250, loss: 0.835, accuracy: 0.792
  277. epoch 8 iter 260, loss: 0.811, accuracy: 0.800
  278. epoch 8 iter 270, loss: 0.917, accuracy: 0.772
  279. epoch 8 iter 280, loss: 0.876, accuracy: 0.784
  280. epoch 9 iter 0, loss: 0.867, accuracy: 0.783
  281. epoch 9 iter 10, loss: 0.865, accuracy: 0.787
  282. epoch 9 iter 20, loss: 0.881, accuracy: 0.789
  283. epoch 9 iter 30, loss: 0.924, accuracy: 0.781
  284. epoch 9 iter 40, loss: 0.887, accuracy: 0.789
  285. epoch 9 iter 50, loss: 0.913, accuracy: 0.781
  286. epoch 9 iter 60, loss: 0.849, accuracy: 0.796
  287. epoch 9 iter 70, loss: 0.887, accuracy: 0.787
  288. epoch 9 iter 80, loss: 0.852, accuracy: 0.798
  289. epoch 9 iter 90, loss: 0.821, accuracy: 0.798
  290. epoch 9 iter 100, loss: 0.862, accuracy: 0.795
  291. epoch 9 iter 110, loss: 0.838, accuracy: 0.790
  292. epoch 9 iter 120, loss: 0.834, accuracy: 0.801
  293. epoch 9 iter 130, loss: 0.874, accuracy: 0.798
  294. epoch 9 iter 140, loss: 0.856, accuracy: 0.798
  295. epoch 9 iter 150, loss: 0.850, accuracy: 0.798
  296. epoch 9 iter 160, loss: 0.847, accuracy: 0.795
  297. epoch 9 iter 170, loss: 0.847, accuracy: 0.801
  298. epoch 9 iter 180, loss: 0.913, accuracy: 0.786
  299. epoch 9 iter 190, loss: 0.854, accuracy: 0.794
  300. epoch 9 iter 200, loss: 0.871, accuracy: 0.790
  301. epoch 9 iter 210, loss: 0.917, accuracy: 0.792
  302. epoch 9 iter 220, loss: 0.856, accuracy: 0.795
  303. epoch 9 iter 230, loss: 0.869, accuracy: 0.790
  304. epoch 9 iter 240, loss: 0.898, accuracy: 0.796
  305. epoch 9 iter 250, loss: 0.848, accuracy: 0.799
  306. epoch 9 iter 260, loss: 0.849, accuracy: 0.800
  307. epoch 9 iter 270, loss: 0.924, accuracy: 0.777
  308. epoch 9 iter 280, loss: 0.930, accuracy: 0.785
  309. epoch 10 iter 0, loss: 0.880, accuracy: 0.787
  310. epoch 10 iter 10, loss: 0.850, accuracy: 0.795
  311. epoch 10 iter 20, loss: 0.852, accuracy: 0.805
  312. epoch 10 iter 30, loss: 0.933, accuracy: 0.790
  313. epoch 10 iter 40, loss: 0.900, accuracy: 0.800
  314. epoch 10 iter 50, loss: 0.895, accuracy: 0.792
  315. epoch 10 iter 60, loss: 0.917, accuracy: 0.788
  316. epoch 10 iter 70, loss: 0.915, accuracy: 0.787
  317. epoch 10 iter 80, loss: 0.909, accuracy: 0.796
  318. epoch 10 iter 90, loss: 0.875, accuracy: 0.794
  319. epoch 10 iter 100, loss: 0.861, accuracy: 0.797
  320. epoch 10 iter 110, loss: 0.835, accuracy: 0.799
  321. epoch 10 iter 120, loss: 0.880, accuracy: 0.796
  322. epoch 10 iter 130, loss: 0.888, accuracy: 0.803
  323. epoch 10 iter 140, loss: 0.903, accuracy: 0.796
  324. epoch 10 iter 150, loss: 0.893, accuracy: 0.801
  325. epoch 10 iter 160, loss: 0.912, accuracy: 0.791
  326. epoch 10 iter 170, loss: 0.908, accuracy: 0.803
  327. epoch 10 iter 180, loss: 0.963, accuracy: 0.785
  328. epoch 10 iter 190, loss: 0.956, accuracy: 0.791
  329. epoch 10 iter 200, loss: 0.988, accuracy: 0.775
  330. epoch 10 iter 210, loss: 0.961, accuracy: 0.793
  331. epoch 10 iter 220, loss: 0.880, accuracy: 0.801
  332. epoch 10 iter 230, loss: 0.847, accuracy: 0.804
  333. epoch 10 iter 240, loss: 0.888, accuracy: 0.804
  334. epoch 10 iter 250, loss: 0.925, accuracy: 0.803
  335. epoch 10 iter 260, loss: 0.859, accuracy: 0.807
  336. epoch 10 iter 270, loss: 0.968, accuracy: 0.791
  337. epoch 10 iter 280, loss: 0.948, accuracy: 0.790
  338. epoch 11 iter 0, loss: 0.900, accuracy: 0.798
  339. epoch 11 iter 10, loss: 0.881, accuracy: 0.793
  340. epoch 11 iter 20, loss: 0.843, accuracy: 0.809
  341. epoch 11 iter 30, loss: 0.885, accuracy: 0.803
  342. epoch 11 iter 40, loss: 1.096, accuracy: 0.789
  343. epoch 11 iter 50, loss: 0.877, accuracy: 0.801
  344. epoch 11 iter 60, loss: 0.933, accuracy: 0.792
  345. epoch 11 iter 70, loss: 0.938, accuracy: 0.794
  346. epoch 11 iter 80, loss: 0.964, accuracy: 0.797
  347. epoch 11 iter 90, loss: 0.899, accuracy: 0.799
  348. epoch 11 iter 100, loss: 0.888, accuracy: 0.796
  349. epoch 11 iter 110, loss: 0.922, accuracy: 0.792
  350. epoch 11 iter 120, loss: 0.894, accuracy: 0.796
  351. epoch 11 iter 130, loss: 0.916, accuracy: 0.809
  352. epoch 11 iter 140, loss: 0.931, accuracy: 0.803
  353. epoch 11 iter 150, loss: 0.906, accuracy: 0.805
  354. epoch 11 iter 160, loss: 0.942, accuracy: 0.797
  355. epoch 11 iter 170, loss: 0.927, accuracy: 0.803
  356. epoch 11 iter 180, loss: 0.924, accuracy: 0.789
  357. epoch 11 iter 190, loss: 1.088, accuracy: 0.791
  358. epoch 11 iter 200, loss: 0.982, accuracy: 0.795
  359. epoch 11 iter 210, loss: 0.904, accuracy: 0.802
  360. epoch 11 iter 220, loss: 0.948, accuracy: 0.800
  361. epoch 11 iter 230, loss: 0.900, accuracy: 0.801
  362. epoch 11 iter 240, loss: 0.981, accuracy: 0.800
  363. epoch 11 iter 250, loss: 0.995, accuracy: 0.793
  364. epoch 11 iter 260, loss: 0.901, accuracy: 0.809
  365. epoch 11 iter 270, loss: 1.031, accuracy: 0.795
  366. epoch 11 iter 280, loss: 1.033, accuracy: 0.789
  367. epoch 12 iter 0, loss: 0.984, accuracy: 0.784
  368. epoch 12 iter 10, loss: 0.942, accuracy: 0.789
  369. epoch 12 iter 20, loss: 0.940, accuracy: 0.803
  370. epoch 12 iter 30, loss: 0.937, accuracy: 0.803
  371. epoch 12 iter 40, loss: 1.240, accuracy: 0.785
  372. epoch 12 iter 50, loss: 0.954, accuracy: 0.803
  373. epoch 12 iter 60, loss: 0.923, accuracy: 0.802
  374. epoch 12 iter 70, loss: 1.034, accuracy: 0.793
  375. epoch 12 iter 80, loss: 0.996, accuracy: 0.797
  376. epoch 12 iter 90, loss: 0.938, accuracy: 0.796
  377. epoch 12 iter 100, loss: 0.949, accuracy: 0.804
  378. epoch 12 iter 110, loss: 0.930, accuracy: 0.808
  379. epoch 12 iter 120, loss: 0.949, accuracy: 0.794
  380. epoch 12 iter 130, loss: 1.001, accuracy: 0.806
  381. epoch 12 iter 140, loss: 1.000, accuracy: 0.802
  382. epoch 12 iter 150, loss: 1.016, accuracy: 0.800
  383. epoch 12 iter 160, loss: 0.948, accuracy: 0.807
  384. epoch 12 iter 170, loss: 0.999, accuracy: 0.795
  385. epoch 12 iter 180, loss: 0.994, accuracy: 0.799
  386. epoch 12 iter 190, loss: 1.050, accuracy: 0.800
  387. epoch 12 iter 200, loss: 1.006, accuracy: 0.797
  388. epoch 12 iter 210, loss: 0.977, accuracy: 0.792
  389. epoch 12 iter 220, loss: 1.000, accuracy: 0.799
  390. epoch 12 iter 230, loss: 0.911, accuracy: 0.798
  391. epoch 12 iter 240, loss: 1.013, accuracy: 0.799
  392. epoch 12 iter 250, loss: 1.035, accuracy: 0.800
  393. epoch 12 iter 260, loss: 0.932, accuracy: 0.802
  394. epoch 12 iter 270, loss: 0.979, accuracy: 0.804
  395. epoch 12 iter 280, loss: 1.024, accuracy: 0.804
  396. epoch 13 iter 0, loss: 1.011, accuracy: 0.796
  397. epoch 13 iter 10, loss: 0.955, accuracy: 0.794
  398. epoch 13 iter 20, loss: 0.940, accuracy: 0.802
  399. epoch 13 iter 30, loss: 1.012, accuracy: 0.805
  400. epoch 13 iter 40, loss: 1.064, accuracy: 0.800
  401. epoch 13 iter 50, loss: 1.035, accuracy: 0.801
  402. epoch 13 iter 60, loss: 0.961, accuracy: 0.801
  403. epoch 13 iter 70, loss: 1.120, accuracy: 0.788
  404. epoch 13 iter 80, loss: 1.024, accuracy: 0.793
  405. epoch 13 iter 90, loss: 0.992, accuracy: 0.787
  406. epoch 13 iter 100, loss: 0.930, accuracy: 0.804
  407. epoch 13 iter 110, loss: 1.004, accuracy: 0.807
  408. epoch 13 iter 120, loss: 1.024, accuracy: 0.793
  409. epoch 13 iter 130, loss: 0.985, accuracy: 0.805
  410. epoch 13 iter 140, loss: 1.035, accuracy: 0.807
  411. epoch 13 iter 150, loss: 1.180, accuracy: 0.797
  412. epoch 13 iter 160, loss: 1.026, accuracy: 0.803
  413. epoch 13 iter 170, loss: 1.047, accuracy: 0.801
  414. epoch 13 iter 180, loss: 1.069, accuracy: 0.797
  415. epoch 13 iter 190, loss: 1.058, accuracy: 0.803
  416. epoch 13 iter 200, loss: 1.138, accuracy: 0.785
  417. epoch 13 iter 210, loss: 1.091, accuracy: 0.793
  418. epoch 13 iter 220, loss: 1.073, accuracy: 0.801
  419. epoch 13 iter 230, loss: 0.997, accuracy: 0.788
  420. epoch 13 iter 240, loss: 1.157, accuracy: 0.787
  421. epoch 13 iter 250, loss: 1.082, accuracy: 0.798
  422. epoch 13 iter 260, loss: 1.022, accuracy: 0.801
  423. epoch 13 iter 270, loss: 1.032, accuracy: 0.808
  424. epoch 13 iter 280, loss: 1.104, accuracy: 0.793
  425. epoch 14 iter 0, loss: 1.059, accuracy: 0.808
  426. epoch 14 iter 10, loss: 0.976, accuracy: 0.814
  427. epoch 14 iter 20, loss: 0.982, accuracy: 0.802
  428. epoch 14 iter 30, loss: 1.099, accuracy: 0.794
  429. epoch 14 iter 40, loss: 1.156, accuracy: 0.793
  430. epoch 14 iter 50, loss: 1.006, accuracy: 0.810
  431. epoch 14 iter 60, loss: 1.001, accuracy: 0.797
  432. epoch 14 iter 70, loss: 1.121, accuracy: 0.798
  433. epoch 14 iter 80, loss: 1.159, accuracy: 0.766
  434. epoch 14 iter 90, loss: 1.039, accuracy: 0.795
  435. epoch 14 iter 100, loss: 0.960, accuracy: 0.798
  436. epoch 14 iter 110, loss: 1.052, accuracy: 0.804
  437. epoch 14 iter 120, loss: 0.991, accuracy: 0.803
  438. epoch 14 iter 130, loss: 1.037, accuracy: 0.803
  439. epoch 14 iter 140, loss: 1.075, accuracy: 0.809
  440. epoch 14 iter 150, loss: 1.142, accuracy: 0.799
  441. epoch 14 iter 160, loss: 1.061, accuracy: 0.789
  442. epoch 14 iter 170, loss: 1.107, accuracy: 0.794
  443. epoch 14 iter 180, loss: 1.058, accuracy: 0.801
  444. epoch 14 iter 190, loss: 1.036, accuracy: 0.813
  445. epoch 14 iter 200, loss: 1.200, accuracy: 0.791
  446. epoch 14 iter 210, loss: 1.219, accuracy: 0.788
  447. epoch 14 iter 220, loss: 1.026, accuracy: 0.804
  448. epoch 14 iter 230, loss: 1.081, accuracy: 0.803
  449. epoch 14 iter 240, loss: 1.085, accuracy: 0.800
  450. epoch 14 iter 250, loss: 1.068, accuracy: 0.806
  451. epoch 14 iter 260, loss: 1.025, accuracy: 0.805
  452. epoch 14 iter 270, loss: 1.054, accuracy: 0.809
  453. epoch 14 iter 280, loss: 1.046, accuracy: 0.805
  454. epoch 15 iter 0, loss: 1.082, accuracy: 0.799
  455. epoch 15 iter 10, loss: 1.026, accuracy: 0.807
  456. epoch 15 iter 20, loss: 0.985, accuracy: 0.808
  457. epoch 15 iter 30, loss: 1.145, accuracy: 0.789
  458. epoch 15 iter 40, loss: 1.110, accuracy: 0.797
  459. epoch 15 iter 50, loss: 1.139, accuracy: 0.801
  460. epoch 15 iter 60, loss: 1.038, accuracy: 0.798
  461. epoch 15 iter 70, loss: 1.232, accuracy: 0.790
  462. epoch 15 iter 80, loss: 1.207, accuracy: 0.784
  463. epoch 15 iter 90, loss: 1.076, accuracy: 0.801
  464. epoch 15 iter 100, loss: 1.008, accuracy: 0.803
  465. epoch 15 iter 110, loss: 1.098, accuracy: 0.804
  466. epoch 15 iter 120, loss: 1.080, accuracy: 0.798
  467. epoch 15 iter 130, loss: 1.041, accuracy: 0.807
  468. epoch 15 iter 140, loss: 1.127, accuracy: 0.805
  469. epoch 15 iter 150, loss: 1.117, accuracy: 0.805
  470. epoch 15 iter 160, loss: 1.130, accuracy: 0.795
  471. epoch 15 iter 170, loss: 1.214, accuracy: 0.798
  472. epoch 15 iter 180, loss: 1.113, accuracy: 0.804
  473. epoch 15 iter 190, loss: 1.035, accuracy: 0.815
  474. epoch 15 iter 200, loss: 1.157, accuracy: 0.807
  475. epoch 15 iter 210, loss: 1.243, accuracy: 0.794
  476. epoch 15 iter 220, loss: 1.109, accuracy: 0.802
  477. epoch 15 iter 230, loss: 1.156, accuracy: 0.800
  478. epoch 15 iter 240, loss: 1.046, accuracy: 0.809
  479. epoch 15 iter 250, loss: 1.145, accuracy: 0.794
  480. epoch 15 iter 260, loss: 1.151, accuracy: 0.799
  481. epoch 15 iter 270, loss: 1.135, accuracy: 0.810
  482. epoch 15 iter 280, loss: 1.146, accuracy: 0.808
  483. epoch 16 iter 0, loss: 1.194, accuracy: 0.797
  484. epoch 16 iter 10, loss: 1.072, accuracy: 0.809
  485. epoch 16 iter 20, loss: 1.057, accuracy: 0.813
  486. epoch 16 iter 30, loss: 1.158, accuracy: 0.804
  487. epoch 16 iter 40, loss: 1.122, accuracy: 0.804
  488. epoch 16 iter 50, loss: 1.244, accuracy: 0.806
  489. epoch 16 iter 60, loss: 1.130, accuracy: 0.805
  490. epoch 16 iter 70, loss: 1.115, accuracy: 0.807
  491. epoch 16 iter 80, loss: 1.237, accuracy: 0.796
  492. epoch 16 iter 90, loss: 1.204, accuracy: 0.797
  493. epoch 16 iter 100, loss: 1.066, accuracy: 0.803
  494. epoch 16 iter 110, loss: 1.089, accuracy: 0.811
  495. epoch 16 iter 120, loss: 1.148, accuracy: 0.802
  496. epoch 16 iter 130, loss: 1.138, accuracy: 0.797
  497. epoch 16 iter 140, loss: 1.036, accuracy: 0.811
  498. epoch 16 iter 150, loss: 1.133, accuracy: 0.812
  499. epoch 16 iter 160, loss: 1.223, accuracy: 0.796
  500. epoch 16 iter 170, loss: 1.257, accuracy: 0.800
  501. epoch 16 iter 180, loss: 1.217, accuracy: 0.801
  502. epoch 16 iter 190, loss: 1.148, accuracy: 0.810
  503. epoch 16 iter 200, loss: 1.195, accuracy: 0.805
  504. epoch 16 iter 210, loss: 1.323, accuracy: 0.799
  505. epoch 16 iter 220, loss: 1.225, accuracy: 0.806
  506. epoch 16 iter 230, loss: 1.181, accuracy: 0.808
  507. epoch 16 iter 240, loss: 1.109, accuracy: 0.807
  508. epoch 16 iter 250, loss: 1.190, accuracy: 0.798
  509. epoch 16 iter 260, loss: 1.126, accuracy: 0.812
  510. epoch 16 iter 270, loss: 1.148, accuracy: 0.809
  511. epoch 16 iter 280, loss: 1.182, accuracy: 0.813
  512. epoch 17 iter 0, loss: 1.170, accuracy: 0.809
  513. epoch 17 iter 10, loss: 1.148, accuracy: 0.810
  514. epoch 17 iter 20, loss: 1.158, accuracy: 0.813
  515. epoch 17 iter 30, loss: 1.154, accuracy: 0.802
  516. epoch 17 iter 40, loss: 1.223, accuracy: 0.804
  517. epoch 17 iter 50, loss: 1.338, accuracy: 0.806
  518. epoch 17 iter 60, loss: 1.259, accuracy: 0.787
  519. epoch 17 iter 70, loss: 1.323, accuracy: 0.804
  520. epoch 17 iter 80, loss: 1.216, accuracy: 0.802
  521. epoch 17 iter 90, loss: 1.324, accuracy: 0.790
  522. epoch 17 iter 100, loss: 1.146, accuracy: 0.796
  523. epoch 17 iter 110, loss: 1.156, accuracy: 0.805
  524. epoch 17 iter 120, loss: 1.320, accuracy: 0.787
  525. epoch 17 iter 130, loss: 1.191, accuracy: 0.804
  526. epoch 17 iter 140, loss: 1.180, accuracy: 0.809
  527. epoch 17 iter 150, loss: 1.170, accuracy: 0.815
  528. epoch 17 iter 160, loss: 1.224, accuracy: 0.807
  529. epoch 17 iter 170, loss: 1.261, accuracy: 0.807
  530. epoch 17 iter 180, loss: 1.229, accuracy: 0.796
  531. epoch 17 iter 190, loss: 1.223, accuracy: 0.808
  532. epoch 17 iter 200, loss: 1.326, accuracy: 0.794
  533. epoch 17 iter 210, loss: 1.351, accuracy: 0.796
  534. epoch 17 iter 220, loss: 1.335, accuracy: 0.799
  535. epoch 17 iter 230, loss: 1.252, accuracy: 0.799
  536. epoch 17 iter 240, loss: 1.221, accuracy: 0.804
  537. epoch 17 iter 250, loss: 1.248, accuracy: 0.804
  538. epoch 17 iter 260, loss: 1.213, accuracy: 0.809
  539. epoch 17 iter 270, loss: 1.230, accuracy: 0.804
  540. epoch 17 iter 280, loss: 1.275, accuracy: 0.805
  541. epoch 18 iter 0, loss: 1.222, accuracy: 0.810
  542. epoch 18 iter 10, loss: 1.210, accuracy: 0.813
  543. epoch 18 iter 20, loss: 1.227, accuracy: 0.808
  544. epoch 18 iter 30, loss: 1.315, accuracy: 0.795
  545. epoch 18 iter 40, loss: 1.272, accuracy: 0.809
  546. epoch 18 iter 50, loss: 1.322, accuracy: 0.810
  547. epoch 18 iter 60, loss: 1.237, accuracy: 0.801
  548. epoch 18 iter 70, loss: 1.359, accuracy: 0.802
  549. epoch 18 iter 80, loss: 1.297, accuracy: 0.799
  550. epoch 18 iter 90, loss: 1.218, accuracy: 0.804
  551. epoch 18 iter 100, loss: 1.218, accuracy: 0.799
  552. epoch 18 iter 110, loss: 1.268, accuracy: 0.804
  553. epoch 18 iter 120, loss: 1.318, accuracy: 0.793
  554. epoch 18 iter 130, loss: 1.349, accuracy: 0.788
  555. epoch 18 iter 140, loss: 1.240, accuracy: 0.804
  556. epoch 18 iter 150, loss: 1.342, accuracy: 0.803
  557. epoch 18 iter 160, loss: 1.293, accuracy: 0.807
  558. epoch 18 iter 170, loss: 1.331, accuracy: 0.805
  559. epoch 18 iter 180, loss: 1.212, accuracy: 0.809
  560. epoch 18 iter 190, loss: 1.364, accuracy: 0.807
  561. epoch 18 iter 200, loss: 1.376, accuracy: 0.803
  562. epoch 18 iter 210, loss: 1.441, accuracy: 0.786
  563. epoch 18 iter 220, loss: 1.520, accuracy: 0.795
  564. epoch 18 iter 230, loss: 1.321, accuracy: 0.796
  565. epoch 18 iter 240, loss: 1.242, accuracy: 0.802
  566. epoch 18 iter 250, loss: 1.409, accuracy: 0.799
  567. epoch 18 iter 260, loss: 1.300, accuracy: 0.805
  568. epoch 18 iter 270, loss: 1.311, accuracy: 0.802
  569. epoch 18 iter 280, loss: 1.462, accuracy: 0.799
  570. epoch 19 iter 0, loss: 1.343, accuracy: 0.805
  571. epoch 19 iter 10, loss: 1.309, accuracy: 0.810
  572. epoch 19 iter 20, loss: 1.276, accuracy: 0.810
  573. epoch 19 iter 30, loss: 1.348, accuracy: 0.802
  574. epoch 19 iter 40, loss: 1.372, accuracy: 0.807
  575. epoch 19 iter 50, loss: 1.390, accuracy: 0.811
  576. epoch 19 iter 60, loss: 1.356, accuracy: 0.800
  577. epoch 19 iter 70, loss: 1.401, accuracy: 0.802
  578. epoch 19 iter 80, loss: 1.355, accuracy: 0.806
  579. epoch 19 iter 90, loss: 1.292, accuracy: 0.805
  580. epoch 19 iter 100, loss: 1.213, accuracy: 0.809
  581. epoch 19 iter 110, loss: 1.313, accuracy: 0.808
  582. epoch 19 iter 120, loss: 1.307, accuracy: 0.797
  583. epoch 19 iter 130, loss: 1.429, accuracy: 0.797
  584. epoch 19 iter 140, loss: 1.422, accuracy: 0.808
  585. epoch 19 iter 150, loss: 1.317, accuracy: 0.812
  586. epoch 19 iter 160, loss: 1.375, accuracy: 0.802
  587. epoch 19 iter 170, loss: 1.507, accuracy: 0.801
  588. epoch 19 iter 180, loss: 1.325, accuracy: 0.807
  589. epoch 19 iter 190, loss: 1.553, accuracy: 0.805
  590. epoch 19 iter 200, loss: 1.477, accuracy: 0.812
  591. epoch 19 iter 210, loss: 1.356, accuracy: 0.806
  592. epoch 19 iter 220, loss: 1.559, accuracy: 0.788
  593. epoch 19 iter 230, loss: 1.358, accuracy: 0.802
  594. epoch 19 iter 240, loss: 1.347, accuracy: 0.812
  595. epoch 19 iter 250, loss: 1.525, accuracy: 0.799
  596. epoch 19 iter 260, loss: 1.511, accuracy: 0.796
  597. epoch 19 iter 270, loss: 1.455, accuracy: 0.795
  598. epoch 19 iter 280, loss: 1.624, accuracy: 0.796
  599. epoch 20 iter 0, loss: 1.511, accuracy: 0.795
  600. epoch 20 iter 10, loss: 1.383, accuracy: 0.808
  601. epoch 20 iter 20, loss: 1.440, accuracy: 0.807
  602. epoch 20 iter 30, loss: 1.441, accuracy: 0.801
  603. epoch 20 iter 40, loss: 1.488, accuracy: 0.804
  604. epoch 20 iter 50, loss: 1.647, accuracy: 0.804
  605. epoch 20 iter 60, loss: 1.382, accuracy: 0.805
  606. epoch 20 iter 70, loss: 1.444, accuracy: 0.801
  607. epoch 20 iter 80, loss: 1.436, accuracy: 0.806
  608. epoch 20 iter 90, loss: 1.339, accuracy: 0.809
  609. epoch 20 iter 100, loss: 1.290, accuracy: 0.805
  610. epoch 20 iter 110, loss: 1.372, accuracy: 0.804
  611. epoch 20 iter 120, loss: 1.410, accuracy: 0.806
  612. epoch 20 iter 130, loss: 1.504, accuracy: 0.800
  613. epoch 20 iter 140, loss: 1.516, accuracy: 0.802
  614. epoch 20 iter 150, loss: 1.384, accuracy: 0.811
  615. epoch 20 iter 160, loss: 1.427, accuracy: 0.799
  616. epoch 20 iter 170, loss: 1.581, accuracy: 0.804
  617. epoch 20 iter 180, loss: 1.447, accuracy: 0.808
  618. epoch 20 iter 190, loss: 1.668, accuracy: 0.800
  619. epoch 20 iter 200, loss: 1.632, accuracy: 0.806
  620. epoch 20 iter 210, loss: 1.449, accuracy: 0.811
  621. epoch 20 iter 220, loss: 1.600, accuracy: 0.798
  622. epoch 20 iter 230, loss: 1.440, accuracy: 0.798
  623. epoch 20 iter 240, loss: 1.347, accuracy: 0.797
  624. epoch 20 iter 250, loss: 1.604, accuracy: 0.800
  625. epoch 20 iter 260, loss: 1.447, accuracy: 0.792
  626. epoch 20 iter 270, loss: 1.390, accuracy: 0.803
  627. epoch 20 iter 280, loss: 1.450, accuracy: 0.795
  628. epoch 21 iter 0, loss: 1.556, accuracy: 0.800
  629. epoch 21 iter 10, loss: 1.380, accuracy: 0.807
  630. epoch 21 iter 20, loss: 1.432, accuracy: 0.813
  631. epoch 21 iter 30, loss: 1.552, accuracy: 0.793
  632. epoch 21 iter 40, loss: 1.520, accuracy: 0.805
  633. epoch 21 iter 50, loss: 1.559, accuracy: 0.810
  634. epoch 21 iter 60, loss: 1.386, accuracy: 0.807
  635. epoch 21 iter 70, loss: 1.513, accuracy: 0.803
  636. epoch 21 iter 80, loss: 1.446, accuracy: 0.813
  637. epoch 21 iter 90, loss: 1.439, accuracy: 0.811
  638. epoch 21 iter 100, loss: 1.308, accuracy: 0.810
  639. epoch 21 iter 110, loss: 1.616, accuracy: 0.791
  640. epoch 21 iter 120, loss: 1.485, accuracy: 0.797
  641. epoch 21 iter 130, loss: 1.697, accuracy: 0.797
  642. epoch 21 iter 140, loss: 1.635, accuracy: 0.792
  643. epoch 21 iter 150, loss: 1.456, accuracy: 0.811
  644. epoch 21 iter 160, loss: 1.401, accuracy: 0.804
  645. epoch 21 iter 170, loss: 1.550, accuracy: 0.809
  646. epoch 21 iter 180, loss: 1.482, accuracy: 0.812
  647. epoch 21 iter 190, loss: 1.523, accuracy: 0.807
  648. epoch 21 iter 200, loss: 1.511, accuracy: 0.811
  649. epoch 21 iter 210, loss: 1.576, accuracy: 0.803
  650. epoch 21 iter 220, loss: 1.460, accuracy: 0.802
  651. epoch 21 iter 230, loss: 1.448, accuracy: 0.802
  652. epoch 21 iter 240, loss: 1.319, accuracy: 0.804
  653. epoch 21 iter 250, loss: 1.436, accuracy: 0.815
  654. epoch 21 iter 260, loss: 1.446, accuracy: 0.798
  655. epoch 21 iter 270, loss: 1.554, accuracy: 0.804
  656. epoch 21 iter 280, loss: 1.540, accuracy: 0.787
  657. epoch 22 iter 0, loss: 1.731, accuracy: 0.793
  658. epoch 22 iter 10, loss: 1.429, accuracy: 0.806
  659. epoch 22 iter 20, loss: 1.411, accuracy: 0.819
  660. epoch 22 iter 30, loss: 1.577, accuracy: 0.801
  661. epoch 22 iter 40, loss: 1.513, accuracy: 0.804
  662. epoch 22 iter 50, loss: 1.662, accuracy: 0.806
  663. epoch 22 iter 60, loss: 1.552, accuracy: 0.802
  664. epoch 22 iter 70, loss: 1.562, accuracy: 0.804
  665. epoch 22 iter 80, loss: 1.569, accuracy: 0.812
  666. epoch 22 iter 90, loss: 1.575, accuracy: 0.799
  667. epoch 22 iter 100, loss: 1.440, accuracy: 0.798
  668. epoch 22 iter 110, loss: 1.497, accuracy: 0.795
  669. epoch 22 iter 120, loss: 1.479, accuracy: 0.794
  670. epoch 22 iter 130, loss: 1.605, accuracy: 0.800
  671. epoch 22 iter 140, loss: 1.590, accuracy: 0.782
  672. epoch 22 iter 150, loss: 1.615, accuracy: 0.800
  673. epoch 22 iter 160, loss: 1.495, accuracy: 0.798
  674. epoch 22 iter 170, loss: 1.658, accuracy: 0.805
  675. epoch 22 iter 180, loss: 1.603, accuracy: 0.808
  676. epoch 22 iter 190, loss: 1.624, accuracy: 0.809
  677. epoch 22 iter 200, loss: 1.658, accuracy: 0.809
  678. epoch 22 iter 210, loss: 1.735, accuracy: 0.803
  679. epoch 22 iter 220, loss: 1.599, accuracy: 0.802
  680. epoch 22 iter 230, loss: 1.491, accuracy: 0.812
  681. epoch 22 iter 240, loss: 1.452, accuracy: 0.808
  682. epoch 22 iter 250, loss: 1.438, accuracy: 0.818
  683. epoch 22 iter 260, loss: 1.428, accuracy: 0.805
  684. epoch 22 iter 270, loss: 1.703, accuracy: 0.793
  685. epoch 22 iter 280, loss: 1.640, accuracy: 0.784
  686. epoch 23 iter 0, loss: 1.818, accuracy: 0.790
  687. epoch 23 iter 10, loss: 1.495, accuracy: 0.798
  688. epoch 23 iter 20, loss: 1.489, accuracy: 0.804
  689. epoch 23 iter 30, loss: 1.515, accuracy: 0.811
  690. epoch 23 iter 40, loss: 1.515, accuracy: 0.816
  691. epoch 23 iter 50, loss: 1.917, accuracy: 0.798
  692. epoch 23 iter 60, loss: 1.560, accuracy: 0.807
  693. epoch 23 iter 70, loss: 1.527, accuracy: 0.805
  694. epoch 23 iter 80, loss: 1.571, accuracy: 0.817
  695. epoch 23 iter 90, loss: 1.735, accuracy: 0.797
  696. epoch 23 iter 100, loss: 1.557, accuracy: 0.799
  697. epoch 23 iter 110, loss: 1.546, accuracy: 0.785
  698. epoch 23 iter 120, loss: 1.583, accuracy: 0.776
  699. epoch 23 iter 130, loss: 1.691, accuracy: 0.792
  700. epoch 23 iter 140, loss: 1.552, accuracy: 0.795
  701. epoch 23 iter 150, loss: 1.660, accuracy: 0.808
  702. epoch 23 iter 160, loss: 1.339, accuracy: 0.802
  703. epoch 23 iter 170, loss: 1.568, accuracy: 0.798
  704. epoch 23 iter 180, loss: 1.463, accuracy: 0.817
  705. epoch 23 iter 190, loss: 1.668, accuracy: 0.808
  706. epoch 23 iter 200, loss: 1.572, accuracy: 0.817
  707. epoch 23 iter 210, loss: 1.598, accuracy: 0.810
  708. epoch 23 iter 220, loss: 1.631, accuracy: 0.809
  709. epoch 23 iter 230, loss: 1.560, accuracy: 0.800
  710. epoch 23 iter 240, loss: 1.514, accuracy: 0.808
  711. epoch 23 iter 250, loss: 1.537, accuracy: 0.812
  712. epoch 23 iter 260, loss: 1.513, accuracy: 0.805
  713. epoch 23 iter 270, loss: 1.642, accuracy: 0.804
  714. epoch 23 iter 280, loss: 1.525, accuracy: 0.802
  715. epoch 24 iter 0, loss: 1.850, accuracy: 0.798
  716. epoch 24 iter 10, loss: 1.608, accuracy: 0.807
  717. epoch 24 iter 20, loss: 1.546, accuracy: 0.812
  718. epoch 24 iter 30, loss: 1.638, accuracy: 0.814
  719. epoch 24 iter 40, loss: 1.626, accuracy: 0.807
  720. epoch 24 iter 50, loss: 2.021, accuracy: 0.801
  721. epoch 24 iter 60, loss: 1.708, accuracy: 0.800
  722. epoch 24 iter 70, loss: 1.489, accuracy: 0.813
  723. epoch 24 iter 80, loss: 1.724, accuracy: 0.815
  724. epoch 24 iter 90, loss: 1.738, accuracy: 0.794
  725. epoch 24 iter 100, loss: 1.702, accuracy: 0.805
  726. epoch 24 iter 110, loss: 1.499, accuracy: 0.798
  727. epoch 24 iter 120, loss: 1.519, accuracy: 0.797
  728. epoch 24 iter 130, loss: 1.730, accuracy: 0.807
  729. epoch 24 iter 140, loss: 1.782, accuracy: 0.798
  730. epoch 24 iter 150, loss: 1.986, accuracy: 0.801
  731. epoch 24 iter 160, loss: 1.497, accuracy: 0.795
  732. epoch 24 iter 170, loss: 1.783, accuracy: 0.791
  733. epoch 24 iter 180, loss: 1.652, accuracy: 0.807
  734. epoch 24 iter 190, loss: 1.694, accuracy: 0.814
  735. epoch 24 iter 200, loss: 1.793, accuracy: 0.807
  736. epoch 24 iter 210, loss: 1.850, accuracy: 0.798
  737. epoch 24 iter 220, loss: 1.803, accuracy: 0.798
  738. epoch 24 iter 230, loss: 1.668, accuracy: 0.811
  739. epoch 24 iter 240, loss: 1.608, accuracy: 0.810
  740. epoch 24 iter 250, loss: 1.653, accuracy: 0.817
  741. epoch 24 iter 260, loss: 1.528, accuracy: 0.807
  742. epoch 24 iter 270, loss: 1.769, accuracy: 0.804
  743. epoch 24 iter 280, loss: 1.679, accuracy: 0.806
  744. epoch 25 iter 0, loss: 1.805, accuracy: 0.796
  745. epoch 25 iter 10, loss: 1.674, accuracy: 0.811
  746. epoch 25 iter 20, loss: 1.438, accuracy: 0.809
  747. epoch 25 iter 30, loss: 1.814, accuracy: 0.806
  748. epoch 25 iter 40, loss: 1.642, accuracy: 0.803
  749. epoch 25 iter 50, loss: 1.981, accuracy: 0.806
  750. epoch 25 iter 60, loss: 1.944, accuracy: 0.796
  751. epoch 25 iter 70, loss: 1.769, accuracy: 0.805
  752. epoch 25 iter 80, loss: 1.868, accuracy: 0.808
  753. epoch 25 iter 90, loss: 1.974, accuracy: 0.787
  754. epoch 25 iter 100, loss: 1.874, accuracy: 0.801
  755. epoch 25 iter 110, loss: 1.456, accuracy: 0.806
  756. epoch 25 iter 120, loss: 1.601, accuracy: 0.805
  757. epoch 25 iter 130, loss: 1.631, accuracy: 0.813
  758. epoch 25 iter 140, loss: 1.660, accuracy: 0.809
  759. epoch 25 iter 150, loss: 1.929, accuracy: 0.810
  760. epoch 25 iter 160, loss: 1.458, accuracy: 0.805
  761. epoch 25 iter 170, loss: 1.815, accuracy: 0.786
  762. epoch 25 iter 180, loss: 1.650, accuracy: 0.797
  763. epoch 25 iter 190, loss: 1.857, accuracy: 0.809
  764. epoch 25 iter 200, loss: 1.815, accuracy: 0.817
  765. epoch 25 iter 210, loss: 1.905, accuracy: 0.804
  766. epoch 25 iter 220, loss: 1.777, accuracy: 0.802
  767. epoch 25 iter 230, loss: 1.658, accuracy: 0.811
  768. epoch 25 iter 240, loss: 1.598, accuracy: 0.803
  769. epoch 25 iter 250, loss: 1.538, accuracy: 0.820
  770. epoch 25 iter 260, loss: 1.738, accuracy: 0.803
  771. epoch 25 iter 270, loss: 1.717, accuracy: 0.804
  772. epoch 25 iter 280, loss: 1.587, accuracy: 0.803
  773. epoch 26 iter 0, loss: 1.903, accuracy: 0.794
  774. epoch 26 iter 10, loss: 1.881, accuracy: 0.808
  775. epoch 26 iter 20, loss: 1.529, accuracy: 0.799
  776. epoch 26 iter 30, loss: 1.615, accuracy: 0.813
  777. epoch 26 iter 40, loss: 1.797, accuracy: 0.794
  778. epoch 26 iter 50, loss: 2.043, accuracy: 0.801
  779. epoch 26 iter 60, loss: 1.955, accuracy: 0.796
  780. epoch 26 iter 70, loss: 1.641, accuracy: 0.806
  781. epoch 26 iter 80, loss: 1.976, accuracy: 0.801
  782. epoch 26 iter 90, loss: 1.798, accuracy: 0.796
  783. epoch 26 iter 100, loss: 1.852, accuracy: 0.800
  784. epoch 26 iter 110, loss: 1.499, accuracy: 0.805
  785. epoch 26 iter 120, loss: 1.412, accuracy: 0.806
  786. epoch 26 iter 130, loss: 1.691, accuracy: 0.805
  787. epoch 26 iter 140, loss: 1.700, accuracy: 0.806
  788. epoch 26 iter 150, loss: 1.797, accuracy: 0.815
  789. epoch 26 iter 160, loss: 1.590, accuracy: 0.816
  790. epoch 26 iter 170, loss: 1.720, accuracy: 0.798
  791. epoch 26 iter 180, loss: 1.582, accuracy: 0.798
  792. epoch 26 iter 190, loss: 1.858, accuracy: 0.812
  793. epoch 26 iter 200, loss: 1.771, accuracy: 0.817
  794. epoch 26 iter 210, loss: 1.708, accuracy: 0.815
  795. epoch 26 iter 220, loss: 1.803, accuracy: 0.815
  796. epoch 26 iter 230, loss: 1.690, accuracy: 0.817
  797. epoch 26 iter 240, loss: 1.525, accuracy: 0.817
  798. epoch 26 iter 250, loss: 1.681, accuracy: 0.813
  799. epoch 26 iter 260, loss: 1.609, accuracy: 0.806
  800. epoch 26 iter 270, loss: 1.778, accuracy: 0.807
  801. epoch 26 iter 280, loss: 1.722, accuracy: 0.811
  802. epoch 27 iter 0, loss: 1.728, accuracy: 0.802
  803. epoch 27 iter 10, loss: 2.074, accuracy: 0.801
  804. epoch 27 iter 20, loss: 1.587, accuracy: 0.803
  805. epoch 27 iter 30, loss: 1.572, accuracy: 0.815
  806. epoch 27 iter 40, loss: 1.796, accuracy: 0.814
  807. epoch 27 iter 50, loss: 2.044, accuracy: 0.804
  808. epoch 27 iter 60, loss: 2.217, accuracy: 0.792
  809. epoch 27 iter 70, loss: 1.726, accuracy: 0.792
  810. epoch 27 iter 80, loss: 1.955, accuracy: 0.802
  811. epoch 27 iter 90, loss: 1.883, accuracy: 0.804
  812. epoch 27 iter 100, loss: 1.897, accuracy: 0.804
  813. epoch 27 iter 110, loss: 1.604, accuracy: 0.807
  814. epoch 27 iter 120, loss: 1.674, accuracy: 0.791
  815. epoch 27 iter 130, loss: 1.781, accuracy: 0.804
  816. epoch 27 iter 140, loss: 1.722, accuracy: 0.804
  817. epoch 27 iter 150, loss: 1.829, accuracy: 0.813
  818. epoch 27 iter 160, loss: 1.825, accuracy: 0.812
  819. epoch 27 iter 170, loss: 1.768, accuracy: 0.803
  820. epoch 27 iter 180, loss: 1.546, accuracy: 0.811
  821. epoch 27 iter 190, loss: 1.656, accuracy: 0.811
  822. epoch 27 iter 200, loss: 1.910, accuracy: 0.811
  823. epoch 27 iter 210, loss: 1.835, accuracy: 0.811
  824. epoch 27 iter 220, loss: 1.761, accuracy: 0.815
  825. epoch 27 iter 230, loss: 1.810, accuracy: 0.814
  826. epoch 27 iter 240, loss: 1.583, accuracy: 0.814
  827. epoch 27 iter 250, loss: 1.626, accuracy: 0.814
  828. epoch 27 iter 260, loss: 1.578, accuracy: 0.817
  829. epoch 27 iter 270, loss: 1.648, accuracy: 0.818
  830. epoch 27 iter 280, loss: 1.774, accuracy: 0.808
  831. epoch 28 iter 0, loss: 1.754, accuracy: 0.810
  832. epoch 28 iter 10, loss: 1.985, accuracy: 0.809
  833. epoch 28 iter 20, loss: 1.609, accuracy: 0.813
  834. epoch 28 iter 30, loss: 1.691, accuracy: 0.818
  835. epoch 28 iter 40, loss: 1.858, accuracy: 0.813
  836. epoch 28 iter 50, loss: 1.844, accuracy: 0.810
  837. epoch 28 iter 60, loss: 2.432, accuracy: 0.780
  838. epoch 28 iter 70, loss: 1.791, accuracy: 0.799
  839. epoch 28 iter 80, loss: 1.991, accuracy: 0.803
  840. epoch 28 iter 90, loss: 1.738, accuracy: 0.813
  841. epoch 28 iter 100, loss: 1.879, accuracy: 0.802
  842. epoch 28 iter 110, loss: 1.690, accuracy: 0.807
  843. epoch 28 iter 120, loss: 1.594, accuracy: 0.800
  844. epoch 28 iter 130, loss: 1.717, accuracy: 0.814
  845. epoch 28 iter 140, loss: 1.619, accuracy: 0.812
  846. epoch 28 iter 150, loss: 1.763, accuracy: 0.816
  847. epoch 28 iter 160, loss: 1.775, accuracy: 0.814
  848. epoch 28 iter 170, loss: 1.715, accuracy: 0.821
  849. epoch 28 iter 180, loss: 1.847, accuracy: 0.818
  850. epoch 28 iter 190, loss: 1.822, accuracy: 0.790
  851. epoch 28 iter 200, loss: 2.003, accuracy: 0.810
  852. epoch 28 iter 210, loss: 1.885, accuracy: 0.811
  853. epoch 28 iter 220, loss: 1.844, accuracy: 0.810
  854. epoch 28 iter 230, loss: 1.893, accuracy: 0.816
  855. epoch 28 iter 240, loss: 1.703, accuracy: 0.815
  856. epoch 28 iter 250, loss: 1.755, accuracy: 0.818
  857. epoch 28 iter 260, loss: 1.696, accuracy: 0.824
  858. epoch 28 iter 270, loss: 1.795, accuracy: 0.815
  859. epoch 28 iter 280, loss: 1.784, accuracy: 0.822
  860. epoch 29 iter 0, loss: 1.920, accuracy: 0.800
  861. epoch 29 iter 10, loss: 1.998, accuracy: 0.815
  862. epoch 29 iter 20, loss: 1.826, accuracy: 0.819
  863. epoch 29 iter 30, loss: 1.805, accuracy: 0.812
  864. epoch 29 iter 40, loss: 1.889, accuracy: 0.824
  865. epoch 29 iter 50, loss: 2.058, accuracy: 0.816
  866. epoch 29 iter 60, loss: 2.501, accuracy: 0.795
  867. epoch 29 iter 70, loss: 1.812, accuracy: 0.792
  868. epoch 29 iter 80, loss: 2.206, accuracy: 0.801
  869. epoch 29 iter 90, loss: 1.898, accuracy: 0.816
  870. epoch 29 iter 100, loss: 2.127, accuracy: 0.804
  871. epoch 29 iter 110, loss: 1.957, accuracy: 0.815
  872. epoch 29 iter 120, loss: 1.768, accuracy: 0.809
  873. epoch 29 iter 130, loss: 1.927, accuracy: 0.817
  874. epoch 29 iter 140, loss: 1.764, accuracy: 0.819
  875. epoch 29 iter 150, loss: 1.867, accuracy: 0.816
  876. epoch 29 iter 160, loss: 1.844, accuracy: 0.815
  877. epoch 29 iter 170, loss: 1.943, accuracy: 0.820
  878. epoch 29 iter 180, loss: 1.953, accuracy: 0.813
  879. epoch 29 iter 190, loss: 1.895, accuracy: 0.805
  880. epoch 29 iter 200, loss: 2.081, accuracy: 0.811
  881. epoch 29 iter 210, loss: 2.013, accuracy: 0.811
  882. epoch 29 iter 220, loss: 1.930, accuracy: 0.808
  883. epoch 29 iter 230, loss: 1.995, accuracy: 0.813
  884. epoch 29 iter 240, loss: 1.802, accuracy: 0.818
  885. epoch 29 iter 250, loss: 1.820, accuracy: 0.814
  886. epoch 29 iter 260, loss: 1.804, accuracy: 0.821
  887. epoch 29 iter 270, loss: 1.883, accuracy: 0.812
  888. epoch 29 iter 280, loss: 1.899, accuracy: 0.819
  889. epoch 30 iter 0, loss: 1.876, accuracy: 0.816
  890. epoch 30 iter 10, loss: 2.161, accuracy: 0.802
  891. epoch 30 iter 20, loss: 1.842, accuracy: 0.818
  892. epoch 30 iter 30, loss: 1.759, accuracy: 0.815
  893. epoch 30 iter 40, loss: 1.930, accuracy: 0.822
  894. epoch 30 iter 50, loss: 2.045, accuracy: 0.811
  895. epoch 30 iter 60, loss: 2.362, accuracy: 0.804
  896. epoch 30 iter 70, loss: 2.031, accuracy: 0.810
  897. epoch 30 iter 80, loss: 2.009, accuracy: 0.808
  898. epoch 30 iter 90, loss: 2.086, accuracy: 0.803
  899. epoch 30 iter 100, loss: 1.937, accuracy: 0.802
  900. epoch 30 iter 110, loss: 1.926, accuracy: 0.806
  901. epoch 30 iter 120, loss: 1.873, accuracy: 0.804
  902. epoch 30 iter 130, loss: 1.769, accuracy: 0.823
  903. epoch 30 iter 140, loss: 1.854, accuracy: 0.821
  904. epoch 30 iter 150, loss: 1.849, accuracy: 0.820
  905. epoch 30 iter 160, loss: 1.876, accuracy: 0.821
  906. epoch 30 iter 170, loss: 1.806, accuracy: 0.821
  907. epoch 30 iter 180, loss: 2.108, accuracy: 0.806
  908. epoch 30 iter 190, loss: 2.070, accuracy: 0.798
  909. epoch 30 iter 200, loss: 2.082, accuracy: 0.815
  910. epoch 30 iter 210, loss: 2.076, accuracy: 0.813
  911. epoch 30 iter 220, loss: 2.146, accuracy: 0.802
  912. epoch 30 iter 230, loss: 2.020, accuracy: 0.815
  913. epoch 30 iter 240, loss: 1.903, accuracy: 0.813
  914. epoch 30 iter 250, loss: 1.887, accuracy: 0.817
  915. epoch 30 iter 260, loss: 1.884, accuracy: 0.819
  916. epoch 30 iter 270, loss: 1.871, accuracy: 0.814
  917. epoch 30 iter 280, loss: 2.108, accuracy: 0.818
  918. epoch 31 iter 0, loss: 2.196, accuracy: 0.800
  919. epoch 31 iter 10, loss: 2.124, accuracy: 0.805
  920. epoch 31 iter 20, loss: 2.027, accuracy: 0.817
  921. epoch 31 iter 30, loss: 1.872, accuracy: 0.817
  922. epoch 31 iter 40, loss: 1.951, accuracy: 0.823
  923. epoch 31 iter 50, loss: 2.112, accuracy: 0.810
  924. epoch 31 iter 60, loss: 2.377, accuracy: 0.802
  925. epoch 31 iter 70, loss: 2.110, accuracy: 0.813
  926. epoch 31 iter 80, loss: 2.211, accuracy: 0.809
  927. epoch 31 iter 90, loss: 2.155, accuracy: 0.813
  928. epoch 31 iter 100, loss: 2.134, accuracy: 0.792
  929. epoch 31 iter 110, loss: 1.975, accuracy: 0.811
  930. epoch 31 iter 120, loss: 1.810, accuracy: 0.808
  931. epoch 31 iter 130, loss: 1.903, accuracy: 0.818
  932. epoch 31 iter 140, loss: 1.909, accuracy: 0.819
  933. epoch 31 iter 150, loss: 1.898, accuracy: 0.817
  934. epoch 31 iter 160, loss: 1.900, accuracy: 0.827
  935. epoch 31 iter 170, loss: 1.930, accuracy: 0.820
  936. epoch 31 iter 180, loss: 1.992, accuracy: 0.814
  937. epoch 31 iter 190, loss: 1.936, accuracy: 0.823
  938. epoch 31 iter 200, loss: 2.004, accuracy: 0.822
  939. epoch 31 iter 210, loss: 2.099, accuracy: 0.823
  940. epoch 31 iter 220, loss: 2.001, accuracy: 0.815
  941. epoch 31 iter 230, loss: 2.020, accuracy: 0.822
  942. epoch 31 iter 240, loss: 1.905, accuracy: 0.823
  943. epoch 31 iter 250, loss: 1.756, accuracy: 0.821
  944. epoch 31 iter 260, loss: 2.032, accuracy: 0.813
  945. epoch 31 iter 270, loss: 1.945, accuracy: 0.818
  946. epoch 31 iter 280, loss: 2.017, accuracy: 0.820
  947. epoch 32 iter 0, loss: 1.999, accuracy: 0.819
  948. epoch 32 iter 10, loss: 2.215, accuracy: 0.793
  949. epoch 32 iter 20, loss: 2.064, accuracy: 0.819
  950. epoch 32 iter 30, loss: 1.935, accuracy: 0.810
  951. epoch 32 iter 40, loss: 1.932, accuracy: 0.824
  952. epoch 32 iter 50, loss: 2.150, accuracy: 0.819
  953. epoch 32 iter 60, loss: 2.513, accuracy: 0.800
  954. epoch 32 iter 70, loss: 2.245, accuracy: 0.813
  955. epoch 32 iter 80, loss: 2.175, accuracy: 0.806
  956. epoch 32 iter 90, loss: 2.178, accuracy: 0.815
  957. epoch 32 iter 100, loss: 2.144, accuracy: 0.804
  958. epoch 32 iter 110, loss: 2.198, accuracy: 0.812
  959. epoch 32 iter 120, loss: 2.069, accuracy: 0.808
  960. epoch 32 iter 130, loss: 1.846, accuracy: 0.818
  961. epoch 32 iter 140, loss: 1.909, accuracy: 0.825
  962. epoch 32 iter 150, loss: 1.936, accuracy: 0.816
  963. epoch 32 iter 160, loss: 2.082, accuracy: 0.815
  964. epoch 32 iter 170, loss: 2.009, accuracy: 0.818
  965. epoch 32 iter 180, loss: 1.964, accuracy: 0.822
  966. epoch 32 iter 190, loss: 2.089, accuracy: 0.819
  967. epoch 32 iter 200, loss: 2.033, accuracy: 0.818
  968. epoch 32 iter 210, loss: 2.247, accuracy: 0.818
  969. epoch 32 iter 220, loss: 2.190, accuracy: 0.806
  970. epoch 32 iter 230, loss: 2.102, accuracy: 0.814
  971. epoch 32 iter 240, loss: 2.073, accuracy: 0.815
  972. epoch 32 iter 250, loss: 1.826, accuracy: 0.822
  973. epoch 32 iter 260, loss: 2.054, accuracy: 0.808
  974. epoch 32 iter 270, loss: 2.014, accuracy: 0.815
  975. epoch 32 iter 280, loss: 2.133, accuracy: 0.815
  976. epoch 33 iter 0, loss: 2.087, accuracy: 0.817
  977. epoch 33 iter 10, loss: 2.200, accuracy: 0.805
  978. epoch 33 iter 20, loss: 2.124, accuracy: 0.817
  979. epoch 33 iter 30, loss: 2.001, accuracy: 0.814
  980. epoch 33 iter 40, loss: 2.076, accuracy: 0.825
  981. epoch 33 iter 50, loss: 2.139, accuracy: 0.822
  982. epoch 33 iter 60, loss: 2.333, accuracy: 0.807
  983. epoch 33 iter 70, loss: 2.235, accuracy: 0.815
  984. epoch 33 iter 80, loss: 2.010, accuracy: 0.809
  985. epoch 33 iter 90, loss: 2.338, accuracy: 0.812
  986. epoch 33 iter 100, loss: 2.125, accuracy: 0.803
  987. epoch 33 iter 110, loss: 2.231, accuracy: 0.819
  988. epoch 33 iter 120, loss: 2.197, accuracy: 0.809
  989. epoch 33 iter 130, loss: 1.987, accuracy: 0.816
  990. epoch 33 iter 140, loss: 2.044, accuracy: 0.822
  991. epoch 33 iter 150, loss: 1.953, accuracy: 0.820
  992. epoch 33 iter 160, loss: 2.217, accuracy: 0.818
  993. epoch 33 iter 170, loss: 2.145, accuracy: 0.811
  994. epoch 33 iter 180, loss: 2.018, accuracy: 0.817
  995. epoch 33 iter 190, loss: 2.053, accuracy: 0.825
  996. epoch 33 iter 200, loss: 2.227, accuracy: 0.819
  997. epoch 33 iter 210, loss: 2.147, accuracy: 0.826
  998. epoch 33 iter 220, loss: 2.289, accuracy: 0.801
  999. epoch 33 iter 230, loss: 2.168, accuracy: 0.820
  1000. epoch 33 iter 240, loss: 2.129, accuracy: 0.823
  1001. epoch 33 iter 250, loss: 2.020, accuracy: 0.813
  1002. epoch 33 iter 260, loss: 2.168, accuracy: 0.812
  1003. epoch 33 iter 270, loss: 2.094, accuracy: 0.816
  1004. epoch 33 iter 280, loss: 2.280, accuracy: 0.808
  1005. epoch 34 iter 0, loss: 2.273, accuracy: 0.811
  1006. epoch 34 iter 10, loss: 2.283, accuracy: 0.810
  1007. epoch 34 iter 20, loss: 2.308, accuracy: 0.813
  1008. epoch 34 iter 30, loss: 2.192, accuracy: 0.801
  1009. epoch 34 iter 40, loss: 2.117, accuracy: 0.820
  1010. epoch 34 iter 50, loss: 2.174, accuracy: 0.823
  1011. epoch 34 iter 60, loss: 2.439, accuracy: 0.820
  1012. epoch 34 iter 70, loss: 2.349, accuracy: 0.807
  1013. epoch 34 iter 80, loss: 2.324, accuracy: 0.816
  1014. epoch 34 iter 90, loss: 2.319, accuracy: 0.819
  1015. epoch 34 iter 100, loss: 2.291, accuracy: 0.808
  1016. epoch 34 iter 110, loss: 2.375, accuracy: 0.811
  1017. epoch 34 iter 120, loss: 2.328, accuracy: 0.804
  1018. epoch 34 iter 130, loss: 2.046, accuracy: 0.804
  1019. epoch 34 iter 140, loss: 2.202, accuracy: 0.817
  1020. epoch 34 iter 150, loss: 2.061, accuracy: 0.821
  1021. epoch 34 iter 160, loss: 2.367, accuracy: 0.815
  1022. epoch 34 iter 170, loss: 2.235, accuracy: 0.819
  1023. epoch 34 iter 180, loss: 2.006, accuracy: 0.829
  1024. epoch 34 iter 190, loss: 2.283, accuracy: 0.817
  1025. epoch 34 iter 200, loss: 2.367, accuracy: 0.817
  1026. epoch 34 iter 210, loss: 2.308, accuracy: 0.820
  1027. epoch 34 iter 220, loss: 2.324, accuracy: 0.814
  1028. epoch 34 iter 230, loss: 2.332, accuracy: 0.821
  1029. epoch 34 iter 240, loss: 2.109, accuracy: 0.824
  1030. epoch 34 iter 250, loss: 2.014, accuracy: 0.827
  1031. epoch 34 iter 260, loss: 2.254, accuracy: 0.810
  1032. epoch 34 iter 270, loss: 1.985, accuracy: 0.823
  1033. epoch 34 iter 280, loss: 2.256, accuracy: 0.826
  1034. epoch 35 iter 0, loss: 2.264, accuracy: 0.824
  1035. epoch 35 iter 10, loss: 2.528, accuracy: 0.802
  1036. epoch 35 iter 20, loss: 2.328, accuracy: 0.807
  1037. epoch 35 iter 30, loss: 2.277, accuracy: 0.808
  1038. epoch 35 iter 40, loss: 2.243, accuracy: 0.817
  1039. epoch 35 iter 50, loss: 2.184, accuracy: 0.826
  1040. epoch 35 iter 60, loss: 2.301, accuracy: 0.818
  1041. epoch 35 iter 70, loss: 2.394, accuracy: 0.811
  1042. epoch 35 iter 80, loss: 2.489, accuracy: 0.816
  1043. epoch 35 iter 90, loss: 2.498, accuracy: 0.813
  1044. epoch 35 iter 100, loss: 2.424, accuracy: 0.803
  1045. epoch 35 iter 110, loss: 2.480, accuracy: 0.815
  1046. epoch 35 iter 120, loss: 2.450, accuracy: 0.795
  1047. epoch 35 iter 130, loss: 2.283, accuracy: 0.807
  1048. epoch 35 iter 140, loss: 2.281, accuracy: 0.822
  1049. epoch 35 iter 150, loss: 2.178, accuracy: 0.824
  1050. epoch 35 iter 160, loss: 2.353, accuracy: 0.814
  1051. epoch 35 iter 170, loss: 2.597, accuracy: 0.814
  1052. epoch 35 iter 180, loss: 2.090, accuracy: 0.819
  1053. epoch 35 iter 190, loss: 2.403, accuracy: 0.814
  1054. epoch 35 iter 200, loss: 2.468, accuracy: 0.801
  1055. epoch 35 iter 210, loss: 2.367, accuracy: 0.813
  1056. epoch 35 iter 220, loss: 2.590, accuracy: 0.798
  1057. epoch 35 iter 230, loss: 2.392, accuracy: 0.820
  1058. epoch 35 iter 240, loss: 2.155, accuracy: 0.819
  1059. epoch 35 iter 250, loss: 1.984, accuracy: 0.823
  1060. epoch 35 iter 260, loss: 2.149, accuracy: 0.820
  1061. epoch 35 iter 270, loss: 2.052, accuracy: 0.822
  1062. epoch 35 iter 280, loss: 2.197, accuracy: 0.818
  1063. epoch 36 iter 0, loss: 2.300, accuracy: 0.821
  1064. epoch 36 iter 10, loss: 2.399, accuracy: 0.811
  1065. epoch 36 iter 20, loss: 2.350, accuracy: 0.811
  1066. epoch 36 iter 30, loss: 2.335, accuracy: 0.815
  1067. epoch 36 iter 40, loss: 2.241, accuracy: 0.814
  1068. epoch 36 iter 50, loss: 2.256, accuracy: 0.822
  1069. epoch 36 iter 60, loss: 2.508, accuracy: 0.815
  1070. epoch 36 iter 70, loss: 2.502, accuracy: 0.813
  1071. epoch 36 iter 80, loss: 2.299, accuracy: 0.819
  1072. epoch 36 iter 90, loss: 2.482, accuracy: 0.811
  1073. epoch 36 iter 100, loss: 2.327, accuracy: 0.807
  1074. epoch 36 iter 110, loss: 2.291, accuracy: 0.812
  1075. epoch 36 iter 120, loss: 2.329, accuracy: 0.803
  1076. epoch 36 iter 130, loss: 2.224, accuracy: 0.806
  1077. epoch 36 iter 140, loss: 2.271, accuracy: 0.822
  1078. epoch 36 iter 150, loss: 2.319, accuracy: 0.815
  1079. epoch 36 iter 160, loss: 2.368, accuracy: 0.816
  1080. epoch 36 iter 170, loss: 2.433, accuracy: 0.816
  1081. epoch 36 iter 180, loss: 2.259, accuracy: 0.817
  1082. epoch 36 iter 190, loss: 2.128, accuracy: 0.819
  1083. epoch 36 iter 200, loss: 2.505, accuracy: 0.811
  1084. epoch 36 iter 210, loss: 2.311, accuracy: 0.822
  1085. epoch 36 iter 220, loss: 2.397, accuracy: 0.801
  1086. epoch 36 iter 230, loss: 2.433, accuracy: 0.820
  1087. epoch 36 iter 240, loss: 2.342, accuracy: 0.815
  1088. epoch 36 iter 250, loss: 2.142, accuracy: 0.815
  1089. epoch 36 iter 260, loss: 2.243, accuracy: 0.815
  1090. epoch 36 iter 270, loss: 2.101, accuracy: 0.810
  1091. epoch 36 iter 280, loss: 2.254, accuracy: 0.812
  1092. epoch 37 iter 0, loss: 2.409, accuracy: 0.815
  1093. epoch 37 iter 10, loss: 2.272, accuracy: 0.819
  1094. epoch 37 iter 20, loss: 2.365, accuracy: 0.817
  1095. epoch 37 iter 30, loss: 2.281, accuracy: 0.808
  1096. epoch 37 iter 40, loss: 2.308, accuracy: 0.811
  1097. epoch 37 iter 50, loss: 2.400, accuracy: 0.821
  1098. epoch 37 iter 60, loss: 2.436, accuracy: 0.807
  1099. epoch 37 iter 70, loss: 2.641, accuracy: 0.809
  1100. epoch 37 iter 80, loss: 2.383, accuracy: 0.809
  1101. epoch 37 iter 90, loss: 2.581, accuracy: 0.808
  1102. epoch 37 iter 100, loss: 2.335, accuracy: 0.816
  1103. epoch 37 iter 110, loss: 2.480, accuracy: 0.813
  1104. epoch 37 iter 120, loss: 2.390, accuracy: 0.804
  1105. epoch 37 iter 130, loss: 2.278, accuracy: 0.810
  1106. epoch 37 iter 140, loss: 2.383, accuracy: 0.826
  1107. epoch 37 iter 150, loss: 2.296, accuracy: 0.820
  1108. epoch 37 iter 160, loss: 2.308, accuracy: 0.824
  1109. epoch 37 iter 170, loss: 2.641, accuracy: 0.814
  1110. epoch 37 iter 180, loss: 2.314, accuracy: 0.819
  1111. epoch 37 iter 190, loss: 2.215, accuracy: 0.820
  1112. epoch 37 iter 200, loss: 2.631, accuracy: 0.803
  1113. epoch 37 iter 210, loss: 2.331, accuracy: 0.818
  1114. epoch 37 iter 220, loss: 2.451, accuracy: 0.813
  1115. epoch 37 iter 230, loss: 2.462, accuracy: 0.818
  1116. epoch 37 iter 240, loss: 2.360, accuracy: 0.816
  1117. epoch 37 iter 250, loss: 2.122, accuracy: 0.818
  1118. epoch 37 iter 260, loss: 2.289, accuracy: 0.817
  1119. epoch 37 iter 270, loss: 2.099, accuracy: 0.804
  1120. epoch 37 iter 280, loss: 2.223, accuracy: 0.814
  1121. epoch 38 iter 0, loss: 2.479, accuracy: 0.815
  1122. epoch 38 iter 10, loss: 2.445, accuracy: 0.818
  1123. epoch 38 iter 20, loss: 2.406, accuracy: 0.815
  1124. epoch 38 iter 30, loss: 2.283, accuracy: 0.819
  1125. epoch 38 iter 40, loss: 2.268, accuracy: 0.819
  1126. epoch 38 iter 50, loss: 2.466, accuracy: 0.824
  1127. epoch 38 iter 60, loss: 2.769, accuracy: 0.809
  1128. epoch 38 iter 70, loss: 2.417, accuracy: 0.819
  1129. epoch 38 iter 80, loss: 2.504, accuracy: 0.808
  1130. epoch 38 iter 90, loss: 2.545, accuracy: 0.814
  1131. epoch 38 iter 100, loss: 2.356, accuracy: 0.815
  1132. epoch 38 iter 110, loss: 2.537, accuracy: 0.808
  1133. epoch 38 iter 120, loss: 2.623, accuracy: 0.786
  1134. epoch 38 iter 130, loss: 2.223, accuracy: 0.806
  1135. epoch 38 iter 140, loss: 2.540, accuracy: 0.826
  1136. epoch 38 iter 150, loss: 2.474, accuracy: 0.819
  1137. epoch 38 iter 160, loss: 2.463, accuracy: 0.814
  1138. epoch 38 iter 170, loss: 2.591, accuracy: 0.818
  1139. epoch 38 iter 180, loss: 2.548, accuracy: 0.813
  1140. epoch 38 iter 190, loss: 2.382, accuracy: 0.812
  1141. epoch 38 iter 200, loss: 2.737, accuracy: 0.813
  1142. epoch 38 iter 210, loss: 2.664, accuracy: 0.820
  1143. epoch 38 iter 220, loss: 2.605, accuracy: 0.811
  1144. epoch 38 iter 230, loss: 2.537, accuracy: 0.821
  1145. epoch 38 iter 240, loss: 2.535, accuracy: 0.822
  1146. epoch 38 iter 250, loss: 2.225, accuracy: 0.822
  1147. epoch 38 iter 260, loss: 2.451, accuracy: 0.825
  1148. epoch 38 iter 270, loss: 2.216, accuracy: 0.822
  1149. epoch 38 iter 280, loss: 2.184, accuracy: 0.816
  1150. epoch 39 iter 0, loss: 2.454, accuracy: 0.821
  1151. epoch 39 iter 10, loss: 2.724, accuracy: 0.819
  1152. epoch 39 iter 20, loss: 2.573, accuracy: 0.817
  1153. epoch 39 iter 30, loss: 2.395, accuracy: 0.822
  1154. epoch 39 iter 40, loss: 2.343, accuracy: 0.813
  1155. epoch 39 iter 50, loss: 2.642, accuracy: 0.819
  1156. epoch 39 iter 60, loss: 2.695, accuracy: 0.827
  1157. epoch 39 iter 70, loss: 2.615, accuracy: 0.817
  1158. epoch 39 iter 80, loss: 2.554, accuracy: 0.815
  1159. epoch 39 iter 90, loss: 2.743, accuracy: 0.813
  1160. epoch 39 iter 100, loss: 2.593, accuracy: 0.817
  1161. epoch 39 iter 110, loss: 2.628, accuracy: 0.811
  1162. epoch 39 iter 120, loss: 2.833, accuracy: 0.804
  1163. epoch 39 iter 130, loss: 2.413, accuracy: 0.800
  1164. epoch 39 iter 140, loss: 2.568, accuracy: 0.825
  1165. epoch 39 iter 150, loss: 2.494, accuracy: 0.823
  1166. epoch 39 iter 160, loss: 2.442, accuracy: 0.819
  1167. epoch 39 iter 170, loss: 2.972, accuracy: 0.817
  1168. epoch 39 iter 180, loss: 2.739, accuracy: 0.820
  1169. epoch 39 iter 190, loss: 2.248, accuracy: 0.821
  1170. epoch 39 iter 200, loss: 2.711, accuracy: 0.812
  1171. epoch 39 iter 210, loss: 2.656, accuracy: 0.819
  1172. epoch 39 iter 220, loss: 2.614, accuracy: 0.819
  1173. epoch 39 iter 230, loss: 2.595, accuracy: 0.811
  1174. epoch 39 iter 240, loss: 2.648, accuracy: 0.815
  1175. epoch 39 iter 250, loss: 2.249, accuracy: 0.830
  1176. epoch 39 iter 260, loss: 2.229, accuracy: 0.825
  1177. epoch 39 iter 270, loss: 2.462, accuracy: 0.818
  1178. epoch 39 iter 280, loss: 2.287, accuracy: 0.810
  1179. epoch 40 iter 0, loss: 2.517, accuracy: 0.822
  1180. epoch 40 iter 10, loss: 2.783, accuracy: 0.819
  1181. epoch 40 iter 20, loss: 2.658, accuracy: 0.819
  1182. epoch 40 iter 30, loss: 2.566, accuracy: 0.817
  1183. epoch 40 iter 40, loss: 2.471, accuracy: 0.816
  1184. epoch 40 iter 50, loss: 2.630, accuracy: 0.824
  1185. epoch 40 iter 60, loss: 2.469, accuracy: 0.825
  1186. epoch 40 iter 70, loss: 2.712, accuracy: 0.818
  1187. epoch 40 iter 80, loss: 2.675, accuracy: 0.820
  1188. epoch 40 iter 90, loss: 2.910, accuracy: 0.813
  1189. epoch 40 iter 100, loss: 2.539, accuracy: 0.821
  1190. epoch 40 iter 110, loss: 2.630, accuracy: 0.807
  1191. epoch 40 iter 120, loss: 2.569, accuracy: 0.813
  1192. epoch 40 iter 130, loss: 2.380, accuracy: 0.818
  1193. epoch 40 iter 140, loss: 2.474, accuracy: 0.822
  1194. epoch 40 iter 150, loss: 2.769, accuracy: 0.821
  1195. epoch 40 iter 160, loss: 2.473, accuracy: 0.819
  1196. epoch 40 iter 170, loss: 2.876, accuracy: 0.824
  1197. epoch 40 iter 180, loss: 2.718, accuracy: 0.825
  1198. epoch 40 iter 190, loss: 2.188, accuracy: 0.821
  1199. epoch 40 iter 200, loss: 2.737, accuracy: 0.805
  1200. epoch 40 iter 210, loss: 2.857, accuracy: 0.817
  1201. epoch 40 iter 220, loss: 2.645, accuracy: 0.811
  1202. epoch 40 iter 230, loss: 2.728, accuracy: 0.817
  1203. epoch 40 iter 240, loss: 2.827, accuracy: 0.811
  1204. epoch 40 iter 250, loss: 2.269, accuracy: 0.826
  1205. epoch 40 iter 260, loss: 2.359, accuracy: 0.825
  1206. epoch 40 iter 270, loss: 2.373, accuracy: 0.822
  1207. epoch 40 iter 280, loss: 2.258, accuracy: 0.820
  1208. epoch 41 iter 0, loss: 2.391, accuracy: 0.821
  1209. epoch 41 iter 10, loss: 2.751, accuracy: 0.818
  1210. epoch 41 iter 20, loss: 2.720, accuracy: 0.817
  1211. epoch 41 iter 30, loss: 2.425, accuracy: 0.818
  1212. epoch 41 iter 40, loss: 2.383, accuracy: 0.826
  1213. epoch 41 iter 50, loss: 2.656, accuracy: 0.823
  1214. epoch 41 iter 60, loss: 2.691, accuracy: 0.828
  1215. epoch 41 iter 70, loss: 2.666, accuracy: 0.813
  1216. epoch 41 iter 80, loss: 2.638, accuracy: 0.819
  1217. epoch 41 iter 90, loss: 2.693, accuracy: 0.819
  1218. epoch 41 iter 100, loss: 2.642, accuracy: 0.826
  1219. epoch 41 iter 110, loss: 2.619, accuracy: 0.811
  1220. epoch 41 iter 120, loss: 2.857, accuracy: 0.811
  1221. epoch 41 iter 130, loss: 2.524, accuracy: 0.797
  1222. epoch 41 iter 140, loss: 2.841, accuracy: 0.820
  1223. epoch 41 iter 150, loss: 2.676, accuracy: 0.824
  1224. epoch 41 iter 160, loss: 2.562, accuracy: 0.827
  1225. epoch 41 iter 170, loss: 2.785, accuracy: 0.824
  1226. epoch 41 iter 180, loss: 2.975, accuracy: 0.818
  1227. epoch 41 iter 190, loss: 2.445, accuracy: 0.829
  1228. epoch 41 iter 200, loss: 2.664, accuracy: 0.821
  1229. epoch 41 iter 210, loss: 2.711, accuracy: 0.820
  1230. epoch 41 iter 220, loss: 2.668, accuracy: 0.821
  1231. epoch 41 iter 230, loss: 2.724, accuracy: 0.814
  1232. epoch 41 iter 240, loss: 2.680, accuracy: 0.823
  1233. epoch 41 iter 250, loss: 2.359, accuracy: 0.828
  1234. epoch 41 iter 260, loss: 2.436, accuracy: 0.828
  1235. epoch 41 iter 270, loss: 2.639, accuracy: 0.820
  1236. epoch 41 iter 280, loss: 2.451, accuracy: 0.811
  1237. epoch 42 iter 0, loss: 2.528, accuracy: 0.811
  1238. epoch 42 iter 10, loss: 2.815, accuracy: 0.816
  1239. epoch 42 iter 20, loss: 2.773, accuracy: 0.817
  1240. epoch 42 iter 30, loss: 2.592, accuracy: 0.822
  1241. epoch 42 iter 40, loss: 2.446, accuracy: 0.824
  1242. epoch 42 iter 50, loss: 2.670, accuracy: 0.822
  1243. epoch 42 iter 60, loss: 2.789, accuracy: 0.823
  1244. epoch 42 iter 70, loss: 2.733, accuracy: 0.813
  1245. epoch 42 iter 80, loss: 2.821, accuracy: 0.821
  1246. epoch 42 iter 90, loss: 2.733, accuracy: 0.825
  1247. epoch 42 iter 100, loss: 2.740, accuracy: 0.825
  1248. epoch 42 iter 110, loss: 2.688, accuracy: 0.816
  1249. epoch 42 iter 120, loss: 2.858, accuracy: 0.814
  1250. epoch 42 iter 130, loss: 2.628, accuracy: 0.813
  1251. epoch 42 iter 140, loss: 3.090, accuracy: 0.804
  1252. epoch 42 iter 150, loss: 3.234, accuracy: 0.811
  1253. epoch 42 iter 160, loss: 2.643, accuracy: 0.817
  1254. epoch 42 iter 170, loss: 2.670, accuracy: 0.821
  1255. epoch 42 iter 180, loss: 2.916, accuracy: 0.819
  1256. epoch 42 iter 190, loss: 2.481, accuracy: 0.822
  1257. epoch 42 iter 200, loss: 2.882, accuracy: 0.806
  1258. epoch 42 iter 210, loss: 2.932, accuracy: 0.813
  1259. epoch 42 iter 220, loss: 2.843, accuracy: 0.819
  1260. epoch 42 iter 230, loss: 2.918, accuracy: 0.813
  1261. epoch 42 iter 240, loss: 2.686, accuracy: 0.821
  1262. epoch 42 iter 250, loss: 2.431, accuracy: 0.819
  1263. epoch 42 iter 260, loss: 2.554, accuracy: 0.826
  1264. epoch 42 iter 270, loss: 2.778, accuracy: 0.821
  1265. epoch 42 iter 280, loss: 2.448, accuracy: 0.826
  1266. epoch 43 iter 0, loss: 2.615, accuracy: 0.818
  1267. epoch 43 iter 10, loss: 2.891, accuracy: 0.815
  1268. epoch 43 iter 20, loss: 3.010, accuracy: 0.814
  1269. epoch 43 iter 30, loss: 2.683, accuracy: 0.819
  1270. epoch 43 iter 40, loss: 2.409, accuracy: 0.819
  1271. epoch 43 iter 50, loss: 2.705, accuracy: 0.817
  1272. epoch 43 iter 60, loss: 2.960, accuracy: 0.824
  1273. epoch 43 iter 70, loss: 2.788, accuracy: 0.819
  1274. epoch 43 iter 80, loss: 2.733, accuracy: 0.818
  1275. epoch 43 iter 90, loss: 2.969, accuracy: 0.815
  1276. epoch 43 iter 100, loss: 2.617, accuracy: 0.827
  1277. epoch 43 iter 110, loss: 2.587, accuracy: 0.820
  1278. epoch 43 iter 120, loss: 3.083, accuracy: 0.811
  1279. epoch 43 iter 130, loss: 2.710, accuracy: 0.809
  1280. epoch 43 iter 140, loss: 2.599, accuracy: 0.821
  1281. epoch 43 iter 150, loss: 2.847, accuracy: 0.825
  1282. epoch 43 iter 160, loss: 2.667, accuracy: 0.827
  1283. epoch 43 iter 170, loss: 2.867, accuracy: 0.823
  1284. epoch 43 iter 180, loss: 2.791, accuracy: 0.829
  1285. epoch 43 iter 190, loss: 2.458, accuracy: 0.834
  1286. epoch 43 iter 200, loss: 2.594, accuracy: 0.817
  1287. epoch 43 iter 210, loss: 2.792, accuracy: 0.822
  1288. epoch 43 iter 220, loss: 2.814, accuracy: 0.820
  1289. epoch 43 iter 230, loss: 2.867, accuracy: 0.818
  1290. epoch 43 iter 240, loss: 2.802, accuracy: 0.826
  1291. epoch 43 iter 250, loss: 2.631, accuracy: 0.823
  1292. epoch 43 iter 260, loss: 2.718, accuracy: 0.829
  1293. epoch 43 iter 270, loss: 2.674, accuracy: 0.826
  1294. epoch 43 iter 280, loss: 2.497, accuracy: 0.828
  1295. epoch 44 iter 0, loss: 2.638, accuracy: 0.822
  1296. epoch 44 iter 10, loss: 2.933, accuracy: 0.820
  1297. epoch 44 iter 20, loss: 2.994, accuracy: 0.823
  1298. epoch 44 iter 30, loss: 2.726, accuracy: 0.824
  1299. epoch 44 iter 40, loss: 2.582, accuracy: 0.820
  1300. epoch 44 iter 50, loss: 2.684, accuracy: 0.828
  1301. epoch 44 iter 60, loss: 3.147, accuracy: 0.826
  1302. epoch 44 iter 70, loss: 3.202, accuracy: 0.820
  1303. epoch 44 iter 80, loss: 2.925, accuracy: 0.825
  1304. epoch 44 iter 90, loss: 3.072, accuracy: 0.820
  1305. epoch 44 iter 100, loss: 2.760, accuracy: 0.821
  1306. epoch 44 iter 110, loss: 2.803, accuracy: 0.822
  1307. epoch 44 iter 120, loss: 3.135, accuracy: 0.817
  1308. epoch 44 iter 130, loss: 2.880, accuracy: 0.812
  1309. epoch 44 iter 140, loss: 2.818, accuracy: 0.816
  1310. epoch 44 iter 150, loss: 2.950, accuracy: 0.825
  1311. epoch 44 iter 160, loss: 2.651, accuracy: 0.825
  1312. epoch 44 iter 170, loss: 2.973, accuracy: 0.822
  1313. epoch 44 iter 180, loss: 3.126, accuracy: 0.822
  1314. epoch 44 iter 190, loss: 2.596, accuracy: 0.826
  1315. epoch 44 iter 200, loss: 2.698, accuracy: 0.816
  1316. epoch 44 iter 210, loss: 3.044, accuracy: 0.823
  1317. epoch 44 iter 220, loss: 2.822, accuracy: 0.821
  1318. epoch 44 iter 230, loss: 2.967, accuracy: 0.819
  1319. epoch 44 iter 240, loss: 2.926, accuracy: 0.824
  1320. epoch 44 iter 250, loss: 2.746, accuracy: 0.829
  1321. epoch 44 iter 260, loss: 2.622, accuracy: 0.826
  1322. epoch 44 iter 270, loss: 2.866, accuracy: 0.824
  1323. epoch 44 iter 280, loss: 2.622, accuracy: 0.820
  1324. epoch 45 iter 0, loss: 2.654, accuracy: 0.817
  1325. epoch 45 iter 10, loss: 3.017, accuracy: 0.817
  1326. epoch 45 iter 20, loss: 3.259, accuracy: 0.810
  1327. epoch 45 iter 30, loss: 2.757, accuracy: 0.815
  1328. epoch 45 iter 40, loss: 2.647, accuracy: 0.821
  1329. epoch 45 iter 50, loss: 2.747, accuracy: 0.822
  1330. epoch 45 iter 60, loss: 2.866, accuracy: 0.821
  1331. epoch 45 iter 70, loss: 3.140, accuracy: 0.820
  1332. epoch 45 iter 80, loss: 3.010, accuracy: 0.818
  1333. epoch 45 iter 90, loss: 3.174, accuracy: 0.821
  1334. epoch 45 iter 100, loss: 2.908, accuracy: 0.815
  1335. epoch 45 iter 110, loss: 2.714, accuracy: 0.820
  1336. epoch 45 iter 120, loss: 2.936, accuracy: 0.823
  1337. epoch 45 iter 130, loss: 2.872, accuracy: 0.814
  1338. epoch 45 iter 140, loss: 2.595, accuracy: 0.811
  1339. epoch 45 iter 150, loss: 3.234, accuracy: 0.816
  1340. epoch 45 iter 160, loss: 2.629, accuracy: 0.823
  1341. epoch 45 iter 170, loss: 2.878, accuracy: 0.821
  1342. epoch 45 iter 180, loss: 3.151, accuracy: 0.819
  1343. epoch 45 iter 190, loss: 2.619, accuracy: 0.821
  1344. epoch 45 iter 200, loss: 2.752, accuracy: 0.825
  1345. epoch 45 iter 210, loss: 3.064, accuracy: 0.812
  1346. epoch 45 iter 220, loss: 3.126, accuracy: 0.815
  1347. epoch 45 iter 230, loss: 2.960, accuracy: 0.808
  1348. epoch 45 iter 240, loss: 3.100, accuracy: 0.822
  1349. epoch 45 iter 250, loss: 2.862, accuracy: 0.821
  1350. epoch 45 iter 260, loss: 2.752, accuracy: 0.819
  1351. epoch 45 iter 270, loss: 2.947, accuracy: 0.825
  1352. epoch 45 iter 280, loss: 2.769, accuracy: 0.817
  1353. epoch 46 iter 0, loss: 2.896, accuracy: 0.817
  1354. epoch 46 iter 10, loss: 3.210, accuracy: 0.821
  1355. epoch 46 iter 20, loss: 3.177, accuracy: 0.818
  1356. epoch 46 iter 30, loss: 2.915, accuracy: 0.813
  1357. epoch 46 iter 40, loss: 3.015, accuracy: 0.822
  1358. epoch 46 iter 50, loss: 2.762, accuracy: 0.823
  1359. epoch 46 iter 60, loss: 3.159, accuracy: 0.823
  1360. epoch 46 iter 70, loss: 3.153, accuracy: 0.813
  1361. epoch 46 iter 80, loss: 3.214, accuracy: 0.814
  1362. epoch 46 iter 90, loss: 3.214, accuracy: 0.822
  1363. epoch 46 iter 100, loss: 3.085, accuracy: 0.808
  1364. epoch 46 iter 110, loss: 3.031, accuracy: 0.818
  1365. epoch 46 iter 120, loss: 3.245, accuracy: 0.816
  1366. epoch 46 iter 130, loss: 3.154, accuracy: 0.815
  1367. epoch 46 iter 140, loss: 2.825, accuracy: 0.813
  1368. epoch 46 iter 150, loss: 3.019, accuracy: 0.824
  1369. epoch 46 iter 160, loss: 2.818, accuracy: 0.823
  1370. epoch 46 iter 170, loss: 3.079, accuracy: 0.823
  1371. epoch 46 iter 180, loss: 3.266, accuracy: 0.821
  1372. epoch 46 iter 190, loss: 2.798, accuracy: 0.831
  1373. epoch 46 iter 200, loss: 2.762, accuracy: 0.810
  1374. epoch 46 iter 210, loss: 2.860, accuracy: 0.829
  1375. epoch 46 iter 220, loss: 2.935, accuracy: 0.825
  1376. epoch 46 iter 230, loss: 3.212, accuracy: 0.822
  1377. epoch 46 iter 240, loss: 3.132, accuracy: 0.824
  1378. epoch 46 iter 250, loss: 2.853, accuracy: 0.826
  1379. epoch 46 iter 260, loss: 2.808, accuracy: 0.821
  1380. epoch 46 iter 270, loss: 2.911, accuracy: 0.823
  1381. epoch 46 iter 280, loss: 2.774, accuracy: 0.827
  1382. epoch 47 iter 0, loss: 2.911, accuracy: 0.822
  1383. epoch 47 iter 10, loss: 3.273, accuracy: 0.821
  1384. epoch 47 iter 20, loss: 3.310, accuracy: 0.810
  1385. epoch 47 iter 30, loss: 2.929, accuracy: 0.816
  1386. epoch 47 iter 40, loss: 2.895, accuracy: 0.827
  1387. epoch 47 iter 50, loss: 2.918, accuracy: 0.827
  1388. epoch 47 iter 60, loss: 3.008, accuracy: 0.825
  1389. epoch 47 iter 70, loss: 3.194, accuracy: 0.820
  1390. epoch 47 iter 80, loss: 3.090, accuracy: 0.812
  1391. epoch 47 iter 90, loss: 3.328, accuracy: 0.818
  1392. epoch 47 iter 100, loss: 3.086, accuracy: 0.817
  1393. epoch 47 iter 110, loss: 2.852, accuracy: 0.811
  1394. epoch 47 iter 120, loss: 3.140, accuracy: 0.818
  1395. epoch 47 iter 130, loss: 3.043, accuracy: 0.811
  1396. epoch 47 iter 140, loss: 2.926, accuracy: 0.813
  1397. epoch 47 iter 150, loss: 3.027, accuracy: 0.821
  1398. epoch 47 iter 160, loss: 3.151, accuracy: 0.826
  1399. epoch 47 iter 170, loss: 3.006, accuracy: 0.826
  1400. epoch 47 iter 180, loss: 3.404, accuracy: 0.819
  1401. epoch 47 iter 190, loss: 3.048, accuracy: 0.828
  1402. epoch 47 iter 200, loss: 2.726, accuracy: 0.825
  1403. epoch 47 iter 210, loss: 3.097, accuracy: 0.822
  1404. epoch 47 iter 220, loss: 2.889, accuracy: 0.821
  1405. epoch 47 iter 230, loss: 2.995, accuracy: 0.820
  1406. epoch 47 iter 240, loss: 3.225, accuracy: 0.824
  1407. epoch 47 iter 250, loss: 2.946, accuracy: 0.821
  1408. epoch 47 iter 260, loss: 2.771, accuracy: 0.828
  1409. epoch 47 iter 270, loss: 2.998, accuracy: 0.827
  1410. epoch 47 iter 280, loss: 2.859, accuracy: 0.830
  1411. epoch 48 iter 0, loss: 2.847, accuracy: 0.828
  1412. epoch 48 iter 10, loss: 3.058, accuracy: 0.826
  1413. epoch 48 iter 20, loss: 3.316, accuracy: 0.811
  1414. epoch 48 iter 30, loss: 3.267, accuracy: 0.815
  1415. epoch 48 iter 40, loss: 2.941, accuracy: 0.820
  1416. epoch 48 iter 50, loss: 3.221, accuracy: 0.823
  1417. epoch 48 iter 60, loss: 3.018, accuracy: 0.825
  1418. epoch 48 iter 70, loss: 3.108, accuracy: 0.828
  1419. epoch 48 iter 80, loss: 3.175, accuracy: 0.824
  1420. epoch 48 iter 90, loss: 3.306, accuracy: 0.823
  1421. epoch 48 iter 100, loss: 3.324, accuracy: 0.828
  1422. epoch 48 iter 110, loss: 2.880, accuracy: 0.820
  1423. epoch 48 iter 120, loss: 3.056, accuracy: 0.819
  1424. epoch 48 iter 130, loss: 3.153, accuracy: 0.820
  1425. epoch 48 iter 140, loss: 3.081, accuracy: 0.814
  1426. epoch 48 iter 150, loss: 3.008, accuracy: 0.823
  1427. epoch 48 iter 160, loss: 3.074, accuracy: 0.825
  1428. epoch 48 iter 170, loss: 2.959, accuracy: 0.826
  1429. epoch 48 iter 180, loss: 3.337, accuracy: 0.824
  1430. epoch 48 iter 190, loss: 2.991, accuracy: 0.829
  1431. epoch 48 iter 200, loss: 2.770, accuracy: 0.813
  1432. epoch 48 iter 210, loss: 3.302, accuracy: 0.816
  1433. epoch 48 iter 220, loss: 3.197, accuracy: 0.820
  1434. epoch 48 iter 230, loss: 2.986, accuracy: 0.820
  1435. epoch 48 iter 240, loss: 3.121, accuracy: 0.824
  1436. epoch 48 iter 250, loss: 2.958, accuracy: 0.823
  1437. epoch 48 iter 260, loss: 2.669, accuracy: 0.829
  1438. epoch 48 iter 270, loss: 2.960, accuracy: 0.823
  1439. epoch 48 iter 280, loss: 3.033, accuracy: 0.830
  1440. epoch 49 iter 0, loss: 3.041, accuracy: 0.825
  1441. epoch 49 iter 10, loss: 3.207, accuracy: 0.819
  1442. epoch 49 iter 20, loss: 3.550, accuracy: 0.815
  1443. epoch 49 iter 30, loss: 3.269, accuracy: 0.819
  1444. epoch 49 iter 40, loss: 3.067, accuracy: 0.819
  1445. epoch 49 iter 50, loss: 3.106, accuracy: 0.812
  1446. epoch 49 iter 60, loss: 3.291, accuracy: 0.821
  1447. epoch 49 iter 70, loss: 3.158, accuracy: 0.822
  1448. epoch 49 iter 80, loss: 3.061, accuracy: 0.823
  1449. epoch 49 iter 90, loss: 3.103, accuracy: 0.821
  1450. epoch 49 iter 100, loss: 3.133, accuracy: 0.828
  1451. epoch 49 iter 110, loss: 2.883, accuracy: 0.820
  1452. epoch 49 iter 120, loss: 3.276, accuracy: 0.825
  1453. epoch 49 iter 130, loss: 3.163, accuracy: 0.817
  1454. epoch 49 iter 140, loss: 3.007, accuracy: 0.819
  1455. epoch 49 iter 150, loss: 3.073, accuracy: 0.825
  1456. epoch 49 iter 160, loss: 3.234, accuracy: 0.822
  1457. epoch 49 iter 170, loss: 3.134, accuracy: 0.827
  1458. epoch 49 iter 180, loss: 3.229, accuracy: 0.822
  1459. epoch 49 iter 190, loss: 3.205, accuracy: 0.826
  1460. epoch 49 iter 200, loss: 2.819, accuracy: 0.827
  1461. epoch 49 iter 210, loss: 3.418, accuracy: 0.811
  1462. epoch 49 iter 220, loss: 3.228, accuracy: 0.817
  1463. epoch 49 iter 230, loss: 3.088, accuracy: 0.819
  1464. epoch 49 iter 240, loss: 3.149, accuracy: 0.826
  1465. epoch 49 iter 250, loss: 3.115, accuracy: 0.821
  1466. epoch 49 iter 260, loss: 2.733, accuracy: 0.823
  1467. epoch 49 iter 270, loss: 3.027, accuracy: 0.820
  1468. epoch 49 iter 280, loss: 3.064, accuracy: 0.826
  1469. current filter size is: 32
  1470. 2018-10-22 23:04:16.937197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  1471. 2018-10-22 23:04:16.937237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  1472. 2018-10-22 23:04:16.937244: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  1473. 2018-10-22 23:04:16.937249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  1474. 2018-10-22 23:04:16.937362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11251 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-12GB, pci bus id: 0000:02:00.0, compute capability: 6.0)
  1475. epoch 0 iter 0, loss: 141.425, accuracy: 0.076
  1476. epoch 0 iter 10, loss: 2.615, accuracy: 0.127
  1477. epoch 0 iter 20, loss: 2.301, accuracy: 0.170
  1478. epoch 0 iter 30, loss: 2.262, accuracy: 0.190
  1479. epoch 0 iter 40, loss: 2.245, accuracy: 0.196
  1480. epoch 0 iter 50, loss: 2.248, accuracy: 0.198
  1481. epoch 0 iter 60, loss: 2.240, accuracy: 0.193
  1482. epoch 0 iter 70, loss: 2.239, accuracy: 0.196
  1483. epoch 0 iter 80, loss: 2.239, accuracy: 0.196
  1484. epoch 0 iter 90, loss: 2.238, accuracy: 0.196
  1485. epoch 0 iter 100, loss: 2.243, accuracy: 0.196
  1486. epoch 0 iter 110, loss: 2.236, accuracy: 0.196
  1487. epoch 0 iter 120, loss: 2.231, accuracy: 0.196
  1488. epoch 0 iter 130, loss: 2.231, accuracy: 0.196
  1489. epoch 0 iter 140, loss: 2.227, accuracy: 0.200
  1490. epoch 0 iter 150, loss: 2.226, accuracy: 0.198
  1491. epoch 0 iter 160, loss: 2.209, accuracy: 0.203
  1492. epoch 0 iter 170, loss: 2.205, accuracy: 0.205
  1493. epoch 0 iter 180, loss: 2.207, accuracy: 0.197
  1494. epoch 0 iter 190, loss: 2.200, accuracy: 0.209
  1495. epoch 0 iter 200, loss: 2.196, accuracy: 0.206
  1496. epoch 0 iter 210, loss: 2.177, accuracy: 0.208
  1497. epoch 0 iter 220, loss: 2.152, accuracy: 0.239
  1498. epoch 0 iter 230, loss: 2.153, accuracy: 0.220
  1499. epoch 0 iter 240, loss: 2.147, accuracy: 0.236
  1500. epoch 0 iter 250, loss: 2.124, accuracy: 0.249
  1501. epoch 0 iter 260, loss: 2.134, accuracy: 0.247
  1502. epoch 0 iter 270, loss: 2.118, accuracy: 0.249
  1503. epoch 0 iter 280, loss: 2.169, accuracy: 0.232
  1504. epoch 1 iter 0, loss: 2.098, accuracy: 0.240
  1505. epoch 1 iter 10, loss: 2.114, accuracy: 0.231
  1506. epoch 1 iter 20, loss: 2.094, accuracy: 0.264
  1507. epoch 1 iter 30, loss: 2.127, accuracy: 0.235
  1508. epoch 1 iter 40, loss: 2.150, accuracy: 0.239
  1509. epoch 1 iter 50, loss: 2.167, accuracy: 0.225
  1510. epoch 1 iter 60, loss: 2.109, accuracy: 0.252
  1511. epoch 1 iter 70, loss: 2.083, accuracy: 0.253
  1512. epoch 1 iter 80, loss: 2.085, accuracy: 0.246
  1513. epoch 1 iter 90, loss: 2.072, accuracy: 0.266
  1514. epoch 1 iter 100, loss: 2.040, accuracy: 0.277
  1515. epoch 1 iter 110, loss: 2.043, accuracy: 0.258
  1516. epoch 1 iter 120, loss: 2.039, accuracy: 0.274
  1517. epoch 1 iter 130, loss: 2.097, accuracy: 0.252
  1518. epoch 1 iter 140, loss: 2.052, accuracy: 0.273
  1519. epoch 1 iter 150, loss: 2.045, accuracy: 0.271
  1520. epoch 1 iter 160, loss: 2.123, accuracy: 0.230
  1521. epoch 1 iter 170, loss: 2.111, accuracy: 0.248
  1522. epoch 1 iter 180, loss: 2.041, accuracy: 0.274
  1523. epoch 1 iter 190, loss: 2.021, accuracy: 0.280
  1524. epoch 1 iter 200, loss: 2.017, accuracy: 0.278
  1525. epoch 1 iter 210, loss: 2.089, accuracy: 0.252
  1526. epoch 1 iter 220, loss: 2.058, accuracy: 0.276
  1527. epoch 1 iter 230, loss: 2.052, accuracy: 0.276
  1528. epoch 1 iter 240, loss: 2.016, accuracy: 0.281
  1529. epoch 1 iter 250, loss: 2.002, accuracy: 0.294
  1530. epoch 1 iter 260, loss: 2.019, accuracy: 0.276
  1531. epoch 1 iter 270, loss: 2.005, accuracy: 0.289
  1532. epoch 1 iter 280, loss: 2.024, accuracy: 0.310
  1533. epoch 2 iter 0, loss: 1.888, accuracy: 0.367
  1534. epoch 2 iter 10, loss: 2.227, accuracy: 0.218
  1535. epoch 2 iter 20, loss: 2.251, accuracy: 0.244
  1536. epoch 2 iter 30, loss: 2.341, accuracy: 0.208
  1537. epoch 2 iter 40, loss: 2.189, accuracy: 0.261
  1538. epoch 2 iter 50, loss: 2.108, accuracy: 0.274
  1539. epoch 2 iter 60, loss: 2.120, accuracy: 0.278
  1540. epoch 2 iter 70, loss: 2.088, accuracy: 0.298
  1541. epoch 2 iter 80, loss: 2.045, accuracy: 0.322
  1542. epoch 2 iter 90, loss: 1.969, accuracy: 0.349
  1543. epoch 2 iter 100, loss: 1.875, accuracy: 0.393
  1544. epoch 2 iter 110, loss: 1.688, accuracy: 0.454
  1545. epoch 2 iter 120, loss: 1.648, accuracy: 0.475
  1546. epoch 2 iter 130, loss: 1.690, accuracy: 0.496
  1547. epoch 2 iter 140, loss: 1.510, accuracy: 0.527
  1548. epoch 2 iter 150, loss: 1.531, accuracy: 0.533
  1549. epoch 2 iter 160, loss: 1.403, accuracy: 0.568
  1550. epoch 2 iter 170, loss: 1.326, accuracy: 0.586
  1551. epoch 2 iter 180, loss: 1.284, accuracy: 0.609
  1552. epoch 2 iter 190, loss: 1.283, accuracy: 0.611
  1553. epoch 2 iter 200, loss: 1.297, accuracy: 0.607
  1554. epoch 2 iter 210, loss: 1.264, accuracy: 0.618
  1555. epoch 2 iter 220, loss: 1.236, accuracy: 0.630
  1556. epoch 2 iter 230, loss: 1.325, accuracy: 0.593
  1557. epoch 2 iter 240, loss: 1.207, accuracy: 0.640
  1558. epoch 2 iter 250, loss: 1.187, accuracy: 0.641
  1559. epoch 2 iter 260, loss: 1.130, accuracy: 0.657
  1560. epoch 2 iter 270, loss: 1.179, accuracy: 0.643
  1561. epoch 2 iter 280, loss: 1.200, accuracy: 0.635
  1562. epoch 3 iter 0, loss: 1.144, accuracy: 0.656
  1563. epoch 3 iter 10, loss: 1.126, accuracy: 0.660
  1564. epoch 3 iter 20, loss: 1.159, accuracy: 0.650
  1565. epoch 3 iter 30, loss: 1.115, accuracy: 0.659
  1566. epoch 3 iter 40, loss: 1.107, accuracy: 0.673
  1567. epoch 3 iter 50, loss: 1.078, accuracy: 0.673
  1568. epoch 3 iter 60, loss: 1.134, accuracy: 0.657
  1569. epoch 3 iter 70, loss: 1.051, accuracy: 0.684
  1570. epoch 3 iter 80, loss: 1.099, accuracy: 0.661
  1571. epoch 3 iter 90, loss: 1.085, accuracy: 0.672
  1572. epoch 3 iter 100, loss: 1.089, accuracy: 0.672
  1573. epoch 3 iter 110, loss: 1.074, accuracy: 0.677
  1574. epoch 3 iter 120, loss: 1.033, accuracy: 0.680
  1575. epoch 3 iter 130, loss: 1.011, accuracy: 0.701
  1576. epoch 3 iter 140, loss: 1.023, accuracy: 0.698
  1577. epoch 3 iter 150, loss: 1.014, accuracy: 0.695
  1578. epoch 3 iter 160, loss: 0.999, accuracy: 0.697
  1579. epoch 3 iter 170, loss: 1.002, accuracy: 0.696
  1580. epoch 3 iter 180, loss: 1.025, accuracy: 0.694
  1581. epoch 3 iter 190, loss: 1.013, accuracy: 0.695
  1582. epoch 3 iter 200, loss: 1.060, accuracy: 0.676
  1583. epoch 3 iter 210, loss: 1.037, accuracy: 0.696
  1584. epoch 3 iter 220, loss: 1.016, accuracy: 0.704
  1585. epoch 3 iter 230, loss: 0.996, accuracy: 0.706
  1586. epoch 3 iter 240, loss: 0.967, accuracy: 0.711
  1587. epoch 3 iter 250, loss: 0.945, accuracy: 0.712
  1588. epoch 3 iter 260, loss: 0.938, accuracy: 0.722
  1589. epoch 3 iter 270, loss: 0.951, accuracy: 0.716
  1590. epoch 3 iter 280, loss: 1.006, accuracy: 0.696
  1591. epoch 4 iter 0, loss: 0.973, accuracy: 0.709
  1592. epoch 4 iter 10, loss: 0.952, accuracy: 0.719
  1593. epoch 4 iter 20, loss: 0.993, accuracy: 0.709
  1594. epoch 4 iter 30, loss: 0.967, accuracy: 0.712
  1595. epoch 4 iter 40, loss: 0.963, accuracy: 0.714
  1596. epoch 4 iter 50, loss: 0.935, accuracy: 0.722
  1597. epoch 4 iter 60, loss: 0.952, accuracy: 0.714
  1598. epoch 4 iter 70, loss: 0.956, accuracy: 0.716
  1599. epoch 4 iter 80, loss: 0.947, accuracy: 0.721
  1600. epoch 4 iter 90, loss: 0.946, accuracy: 0.719
  1601. epoch 4 iter 100, loss: 0.928, accuracy: 0.721
  1602. epoch 4 iter 110, loss: 0.936, accuracy: 0.725
  1603. epoch 4 iter 120, loss: 0.906, accuracy: 0.732
  1604. epoch 4 iter 130, loss: 0.928, accuracy: 0.726
  1605. epoch 4 iter 140, loss: 0.920, accuracy: 0.727
  1606. epoch 4 iter 150, loss: 0.920, accuracy: 0.723
  1607. epoch 4 iter 160, loss: 0.924, accuracy: 0.726
  1608. epoch 4 iter 170, loss: 0.893, accuracy: 0.735
  1609. epoch 4 iter 180, loss: 0.931, accuracy: 0.727
  1610. epoch 4 iter 190, loss: 0.934, accuracy: 0.719
  1611. epoch 4 iter 200, loss: 0.940, accuracy: 0.722
  1612. epoch 4 iter 210, loss: 0.870, accuracy: 0.744
  1613. epoch 4 iter 220, loss: 0.946, accuracy: 0.725
  1614. epoch 4 iter 230, loss: 0.921, accuracy: 0.730
  1615. epoch 4 iter 240, loss: 0.890, accuracy: 0.736
  1616. epoch 4 iter 250, loss: 0.896, accuracy: 0.731
  1617. epoch 4 iter 260, loss: 0.889, accuracy: 0.743
  1618. epoch 4 iter 270, loss: 0.908, accuracy: 0.735
  1619. epoch 4 iter 280, loss: 0.931, accuracy: 0.723
  1620. epoch 5 iter 0, loss: 0.885, accuracy: 0.739
  1621. epoch 5 iter 10, loss: 0.927, accuracy: 0.735
  1622. epoch 5 iter 20, loss: 0.907, accuracy: 0.738
  1623. epoch 5 iter 30, loss: 0.888, accuracy: 0.739
  1624. epoch 5 iter 40, loss: 0.890, accuracy: 0.743
  1625. epoch 5 iter 50, loss: 0.858, accuracy: 0.748
  1626. epoch 5 iter 60, loss: 0.875, accuracy: 0.743
  1627. epoch 5 iter 70, loss: 0.906, accuracy: 0.733
  1628. epoch 5 iter 80, loss: 0.854, accuracy: 0.750
  1629. epoch 5 iter 90, loss: 0.873, accuracy: 0.742
  1630. epoch 5 iter 100, loss: 0.844, accuracy: 0.752
  1631. epoch 5 iter 110, loss: 0.897, accuracy: 0.735
  1632. epoch 5 iter 120, loss: 0.879, accuracy: 0.746
  1633. epoch 5 iter 130, loss: 0.859, accuracy: 0.748
  1634. epoch 5 iter 140, loss: 0.854, accuracy: 0.753
  1635. epoch 5 iter 150, loss: 0.878, accuracy: 0.749
  1636. epoch 5 iter 160, loss: 0.885, accuracy: 0.743
  1637. epoch 5 iter 170, loss: 0.846, accuracy: 0.754
  1638. epoch 5 iter 180, loss: 0.861, accuracy: 0.749
  1639. epoch 5 iter 190, loss: 0.899, accuracy: 0.741
  1640. epoch 5 iter 200, loss: 0.856, accuracy: 0.748
  1641. epoch 5 iter 210, loss: 0.840, accuracy: 0.754
  1642. epoch 5 iter 220, loss: 0.897, accuracy: 0.744
  1643. epoch 5 iter 230, loss: 0.908, accuracy: 0.736
  1644. epoch 5 iter 240, loss: 0.869, accuracy: 0.750
  1645. epoch 5 iter 250, loss: 0.861, accuracy: 0.743
  1646. epoch 5 iter 260, loss: 0.857, accuracy: 0.755
  1647. epoch 5 iter 270, loss: 0.869, accuracy: 0.751
  1648. epoch 5 iter 280, loss: 0.889, accuracy: 0.735
  1649. epoch 6 iter 0, loss: 0.864, accuracy: 0.748
  1650. epoch 6 iter 10, loss: 0.869, accuracy: 0.750
  1651. epoch 6 iter 20, loss: 0.856, accuracy: 0.752
  1652. epoch 6 iter 30, loss: 0.888, accuracy: 0.742
  1653. epoch 6 iter 40, loss: 0.882, accuracy: 0.746
  1654. epoch 6 iter 50, loss: 0.831, accuracy: 0.756
  1655. epoch 6 iter 60, loss: 0.843, accuracy: 0.752
  1656. epoch 6 iter 70, loss: 0.869, accuracy: 0.749
  1657. epoch 6 iter 80, loss: 0.845, accuracy: 0.757
  1658. epoch 6 iter 90, loss: 0.821, accuracy: 0.761
  1659. epoch 6 iter 100, loss: 0.810, accuracy: 0.763
  1660. epoch 6 iter 110, loss: 0.872, accuracy: 0.748
  1661. epoch 6 iter 120, loss: 0.837, accuracy: 0.757
  1662. epoch 6 iter 130, loss: 0.822, accuracy: 0.761
  1663. epoch 6 iter 140, loss: 0.816, accuracy: 0.767
  1664. epoch 6 iter 150, loss: 0.834, accuracy: 0.767
  1665. epoch 6 iter 160, loss: 0.843, accuracy: 0.758
  1666. epoch 6 iter 170, loss: 0.817, accuracy: 0.764
  1667. epoch 6 iter 180, loss: 0.843, accuracy: 0.759
  1668. epoch 6 iter 190, loss: 0.844, accuracy: 0.760
  1669. epoch 6 iter 200, loss: 0.810, accuracy: 0.766
  1670. epoch 6 iter 210, loss: 0.828, accuracy: 0.761
  1671. epoch 6 iter 220, loss: 0.867, accuracy: 0.754
  1672. epoch 6 iter 230, loss: 0.878, accuracy: 0.746
  1673. epoch 6 iter 240, loss: 0.849, accuracy: 0.757
  1674. epoch 6 iter 250, loss: 0.831, accuracy: 0.761
  1675. epoch 6 iter 260, loss: 0.829, accuracy: 0.764
  1676. epoch 6 iter 270, loss: 0.856, accuracy: 0.757
  1677. epoch 6 iter 280, loss: 0.870, accuracy: 0.742
  1678. epoch 7 iter 0, loss: 0.871, accuracy: 0.752
  1679. epoch 7 iter 10, loss: 0.916, accuracy: 0.748
  1680. epoch 7 iter 20, loss: 0.844, accuracy: 0.762
  1681. epoch 7 iter 30, loss: 0.864, accuracy: 0.752
  1682. epoch 7 iter 40, loss: 0.859, accuracy: 0.757
  1683. epoch 7 iter 50, loss: 0.807, accuracy: 0.767
  1684. epoch 7 iter 60, loss: 0.834, accuracy: 0.755
  1685. epoch 7 iter 70, loss: 0.811, accuracy: 0.768
  1686. epoch 7 iter 80, loss: 0.823, accuracy: 0.763
  1687. epoch 7 iter 90, loss: 0.783, accuracy: 0.774
  1688. epoch 7 iter 100, loss: 0.781, accuracy: 0.773
  1689. epoch 7 iter 110, loss: 0.825, accuracy: 0.765
  1690. epoch 7 iter 120, loss: 0.818, accuracy: 0.765
  1691. epoch 7 iter 130, loss: 0.789, accuracy: 0.776
  1692. epoch 7 iter 140, loss: 0.802, accuracy: 0.774
  1693. epoch 7 iter 150, loss: 0.808, accuracy: 0.777
  1694. epoch 7 iter 160, loss: 0.806, accuracy: 0.777
  1695. epoch 7 iter 170, loss: 0.785, accuracy: 0.776
  1696. epoch 7 iter 180, loss: 0.821, accuracy: 0.769
  1697. epoch 7 iter 190, loss: 0.808, accuracy: 0.774
  1698. epoch 7 iter 200, loss: 0.808, accuracy: 0.769
  1699. epoch 7 iter 210, loss: 0.790, accuracy: 0.777
  1700. epoch 7 iter 220, loss: 0.828, accuracy: 0.774
  1701. epoch 7 iter 230, loss: 0.831, accuracy: 0.768
  1702. epoch 7 iter 240, loss: 0.810, accuracy: 0.773
  1703. epoch 7 iter 250, loss: 0.829, accuracy: 0.772
  1704. epoch 7 iter 260, loss: 0.825, accuracy: 0.773
  1705. epoch 7 iter 270, loss: 0.808, accuracy: 0.779
  1706. epoch 7 iter 280, loss: 0.820, accuracy: 0.766
  1707. epoch 8 iter 0, loss: 0.819, accuracy: 0.774
  1708. epoch 8 iter 10, loss: 0.822, accuracy: 0.773
  1709. epoch 8 iter 20, loss: 0.822, accuracy: 0.775
  1710. epoch 8 iter 30, loss: 0.804, accuracy: 0.776
  1711. epoch 8 iter 40, loss: 0.812, accuracy: 0.778
  1712. epoch 8 iter 50, loss: 0.770, accuracy: 0.785
  1713. epoch 8 iter 60, loss: 0.777, accuracy: 0.782
  1714. epoch 8 iter 70, loss: 0.761, accuracy: 0.781
  1715. epoch 8 iter 80, loss: 0.777, accuracy: 0.785
  1716. epoch 8 iter 90, loss: 0.747, accuracy: 0.789
  1717. epoch 8 iter 100, loss: 0.768, accuracy: 0.780
  1718. epoch 8 iter 110, loss: 0.787, accuracy: 0.780
  1719. epoch 8 iter 120, loss: 0.817, accuracy: 0.774
  1720. epoch 8 iter 130, loss: 0.760, accuracy: 0.786
  1721. epoch 8 iter 140, loss: 0.746, accuracy: 0.796
  1722. epoch 8 iter 150, loss: 0.746, accuracy: 0.797
  1723. epoch 8 iter 160, loss: 0.763, accuracy: 0.790
  1724. epoch 8 iter 170, loss: 0.739, accuracy: 0.795
  1725. epoch 8 iter 180, loss: 0.754, accuracy: 0.787
  1726. epoch 8 iter 190, loss: 0.768, accuracy: 0.787
  1727. epoch 8 iter 200, loss: 0.756, accuracy: 0.792
  1728. epoch 8 iter 210, loss: 0.760, accuracy: 0.792
  1729. epoch 8 iter 220, loss: 0.758, accuracy: 0.794
  1730. epoch 8 iter 230, loss: 0.762, accuracy: 0.790
  1731. epoch 8 iter 240, loss: 0.757, accuracy: 0.797
  1732. epoch 8 iter 250, loss: 0.756, accuracy: 0.801
  1733. epoch 8 iter 260, loss: 0.770, accuracy: 0.786
  1734. epoch 8 iter 270, loss: 0.741, accuracy: 0.802
  1735. epoch 8 iter 280, loss: 0.736, accuracy: 0.798
  1736. epoch 9 iter 0, loss: 0.769, accuracy: 0.794
  1737. epoch 9 iter 10, loss: 0.794, accuracy: 0.789
  1738. epoch 9 iter 20, loss: 0.743, accuracy: 0.805
  1739. epoch 9 iter 30, loss: 0.778, accuracy: 0.781
  1740. epoch 9 iter 40, loss: 0.836, accuracy: 0.778
  1741. epoch 9 iter 50, loss: 0.769, accuracy: 0.791
  1742. epoch 9 iter 60, loss: 0.757, accuracy: 0.796
  1743. epoch 9 iter 70, loss: 0.749, accuracy: 0.789
  1744. epoch 9 iter 80, loss: 0.733, accuracy: 0.810
  1745. epoch 9 iter 90, loss: 0.710, accuracy: 0.804
  1746. epoch 9 iter 100, loss: 0.714, accuracy: 0.801
  1747. epoch 9 iter 110, loss: 0.741, accuracy: 0.796
  1748. epoch 9 iter 120, loss: 0.790, accuracy: 0.787
  1749. epoch 9 iter 130, loss: 0.702, accuracy: 0.803
  1750. epoch 9 iter 140, loss: 0.732, accuracy: 0.804
  1751. epoch 9 iter 150, loss: 0.734, accuracy: 0.803
  1752. epoch 9 iter 160, loss: 0.726, accuracy: 0.808
  1753. epoch 9 iter 170, loss: 0.743, accuracy: 0.809
  1754. epoch 9 iter 180, loss: 0.739, accuracy: 0.802
  1755. epoch 9 iter 190, loss: 0.719, accuracy: 0.811
  1756. epoch 9 iter 200, loss: 0.724, accuracy: 0.805
  1757. epoch 9 iter 210, loss: 0.747, accuracy: 0.803
  1758. epoch 9 iter 220, loss: 0.728, accuracy: 0.801
  1759. epoch 9 iter 230, loss: 0.738, accuracy: 0.808
  1760. epoch 9 iter 240, loss: 0.742, accuracy: 0.800
  1761. epoch 9 iter 250, loss: 0.763, accuracy: 0.807
  1762. epoch 9 iter 260, loss: 0.766, accuracy: 0.799
  1763. epoch 9 iter 270, loss: 0.760, accuracy: 0.808
  1764. epoch 9 iter 280, loss: 0.752, accuracy: 0.798
  1765. epoch 10 iter 0, loss: 0.788, accuracy: 0.792
  1766. epoch 10 iter 10, loss: 0.728, accuracy: 0.809
  1767. epoch 10 iter 20, loss: 0.738, accuracy: 0.805
  1768. epoch 10 iter 30, loss: 0.732, accuracy: 0.806
  1769. epoch 10 iter 40, loss: 0.775, accuracy: 0.800
  1770. epoch 10 iter 50, loss: 0.762, accuracy: 0.801
  1771. epoch 10 iter 60, loss: 0.736, accuracy: 0.802
  1772. epoch 10 iter 70, loss: 0.740, accuracy: 0.798
  1773. epoch 10 iter 80, loss: 0.759, accuracy: 0.807
  1774. epoch 10 iter 90, loss: 0.686, accuracy: 0.817
  1775. epoch 10 iter 100, loss: 0.697, accuracy: 0.810
  1776. epoch 10 iter 110, loss: 0.724, accuracy: 0.801
  1777. epoch 10 iter 120, loss: 0.760, accuracy: 0.807
  1778. epoch 10 iter 130, loss: 0.704, accuracy: 0.814
  1779. epoch 10 iter 140, loss: 0.718, accuracy: 0.806
  1780. epoch 10 iter 150, loss: 0.699, accuracy: 0.820
  1781. epoch 10 iter 160, loss: 0.689, accuracy: 0.816
  1782. epoch 10 iter 170, loss: 0.789, accuracy: 0.800
  1783. epoch 10 iter 180, loss: 0.785, accuracy: 0.791
  1784. epoch 10 iter 190, loss: 0.723, accuracy: 0.818
  1785. epoch 10 iter 200, loss: 0.752, accuracy: 0.805
  1786. epoch 10 iter 210, loss: 0.724, accuracy: 0.815
  1787. epoch 10 iter 220, loss: 0.731, accuracy: 0.808
  1788. epoch 10 iter 230, loss: 0.764, accuracy: 0.812
  1789. epoch 10 iter 240, loss: 0.765, accuracy: 0.804
  1790. epoch 10 iter 250, loss: 0.808, accuracy: 0.806
  1791. epoch 10 iter 260, loss: 0.735, accuracy: 0.817
  1792. epoch 10 iter 270, loss: 0.790, accuracy: 0.802
  1793. epoch 10 iter 280, loss: 0.786, accuracy: 0.810
  1794. epoch 11 iter 0, loss: 0.834, accuracy: 0.789
  1795. epoch 11 iter 10, loss: 0.761, accuracy: 0.810
  1796. epoch 11 iter 20, loss: 0.741, accuracy: 0.809
  1797. epoch 11 iter 30, loss: 0.798, accuracy: 0.797
  1798. epoch 11 iter 40, loss: 0.797, accuracy: 0.797
  1799. epoch 11 iter 50, loss: 0.791, accuracy: 0.807
  1800. epoch 11 iter 60, loss: 0.770, accuracy: 0.808
  1801. epoch 11 iter 70, loss: 0.742, accuracy: 0.814
  1802. epoch 11 iter 80, loss: 0.740, accuracy: 0.811
  1803. epoch 11 iter 90, loss: 0.696, accuracy: 0.821
  1804. epoch 11 iter 100, loss: 0.713, accuracy: 0.810
  1805. epoch 11 iter 110, loss: 0.738, accuracy: 0.805
  1806. epoch 11 iter 120, loss: 0.787, accuracy: 0.805
  1807. epoch 11 iter 130, loss: 0.725, accuracy: 0.818
  1808. epoch 11 iter 140, loss: 0.725, accuracy: 0.817
  1809. epoch 11 iter 150, loss: 0.725, accuracy: 0.824
  1810. epoch 11 iter 160, loss: 0.722, accuracy: 0.815
  1811. epoch 11 iter 170, loss: 0.707, accuracy: 0.824
  1812. epoch 11 iter 180, loss: 0.736, accuracy: 0.814
  1813. epoch 11 iter 190, loss: 0.732, accuracy: 0.823
  1814. epoch 11 iter 200, loss: 0.819, accuracy: 0.806
  1815. epoch 11 iter 210, loss: 0.732, accuracy: 0.815
  1816. epoch 11 iter 220, loss: 0.745, accuracy: 0.822
  1817. epoch 11 iter 230, loss: 0.722, accuracy: 0.818
  1818. epoch 11 iter 240, loss: 0.738, accuracy: 0.813
  1819. epoch 11 iter 250, loss: 0.786, accuracy: 0.811
  1820. epoch 11 iter 260, loss: 0.776, accuracy: 0.815
  1821. epoch 11 iter 270, loss: 0.787, accuracy: 0.807
  1822. epoch 11 iter 280, loss: 0.774, accuracy: 0.813
  1823. epoch 12 iter 0, loss: 0.768, accuracy: 0.810
  1824. epoch 12 iter 10, loss: 0.786, accuracy: 0.809
  1825. epoch 12 iter 20, loss: 0.767, accuracy: 0.814
  1826. epoch 12 iter 30, loss: 0.780, accuracy: 0.814
  1827. epoch 12 iter 40, loss: 0.756, accuracy: 0.810
  1828. epoch 12 iter 50, loss: 0.779, accuracy: 0.817
  1829. epoch 12 iter 60, loss: 0.807, accuracy: 0.812
  1830. epoch 12 iter 70, loss: 0.731, accuracy: 0.819
  1831. epoch 12 iter 80, loss: 0.792, accuracy: 0.812
  1832. epoch 12 iter 90, loss: 0.726, accuracy: 0.825
  1833. epoch 12 iter 100, loss: 0.715, accuracy: 0.821
  1834. epoch 12 iter 110, loss: 0.748, accuracy: 0.816
  1835. epoch 12 iter 120, loss: 0.772, accuracy: 0.808
  1836. epoch 12 iter 130, loss: 0.770, accuracy: 0.819
  1837. epoch 12 iter 140, loss: 0.757, accuracy: 0.820
  1838. epoch 12 iter 150, loss: 0.784, accuracy: 0.825
  1839. epoch 12 iter 160, loss: 0.748, accuracy: 0.816
  1840. epoch 12 iter 170, loss: 0.780, accuracy: 0.816
  1841. epoch 12 iter 180, loss: 0.794, accuracy: 0.811
  1842. epoch 12 iter 190, loss: 0.812, accuracy: 0.812
  1843. epoch 12 iter 200, loss: 0.892, accuracy: 0.803
  1844. epoch 12 iter 210, loss: 0.784, accuracy: 0.812
  1845. epoch 12 iter 220, loss: 0.762, accuracy: 0.825
  1846. epoch 12 iter 230, loss: 0.783, accuracy: 0.802
  1847. epoch 12 iter 240, loss: 0.752, accuracy: 0.822
  1848. epoch 12 iter 250, loss: 0.798, accuracy: 0.815
  1849. epoch 12 iter 260, loss: 0.784, accuracy: 0.826
  1850. epoch 12 iter 270, loss: 0.863, accuracy: 0.810
  1851. epoch 12 iter 280, loss: 0.874, accuracy: 0.800
  1852. epoch 13 iter 0, loss: 0.829, accuracy: 0.795
  1853. epoch 13 iter 10, loss: 0.858, accuracy: 0.802
  1854. epoch 13 iter 20, loss: 0.790, accuracy: 0.819
  1855. epoch 13 iter 30, loss: 0.782, accuracy: 0.820
  1856. epoch 13 iter 40, loss: 0.842, accuracy: 0.813
  1857. epoch 13 iter 50, loss: 0.788, accuracy: 0.819
  1858. epoch 13 iter 60, loss: 0.813, accuracy: 0.816
  1859. epoch 13 iter 70, loss: 0.823, accuracy: 0.808
  1860. epoch 13 iter 80, loss: 0.817, accuracy: 0.813
  1861. epoch 13 iter 90, loss: 0.780, accuracy: 0.824
  1862. epoch 13 iter 100, loss: 0.753, accuracy: 0.827
  1863. epoch 13 iter 110, loss: 0.750, accuracy: 0.820
  1864. epoch 13 iter 120, loss: 0.746, accuracy: 0.827
  1865. epoch 13 iter 130, loss: 0.818, accuracy: 0.816
  1866. epoch 13 iter 140, loss: 0.817, accuracy: 0.814
  1867. epoch 13 iter 150, loss: 0.822, accuracy: 0.826
  1868. epoch 13 iter 160, loss: 0.823, accuracy: 0.812
  1869. epoch 13 iter 170, loss: 0.838, accuracy: 0.810
  1870. epoch 13 iter 180, loss: 0.825, accuracy: 0.822
  1871. epoch 13 iter 190, loss: 0.858, accuracy: 0.811
  1872. epoch 13 iter 200, loss: 0.838, accuracy: 0.818
  1873. epoch 13 iter 210, loss: 0.813, accuracy: 0.816
  1874. epoch 13 iter 220, loss: 0.879, accuracy: 0.807
  1875. epoch 13 iter 230, loss: 0.754, accuracy: 0.820
  1876. epoch 13 iter 240, loss: 0.777, accuracy: 0.820
  1877. epoch 13 iter 250, loss: 0.825, accuracy: 0.824
  1878. epoch 13 iter 260, loss: 0.912, accuracy: 0.810
  1879. epoch 13 iter 270, loss: 0.996, accuracy: 0.797
  1880. epoch 13 iter 280, loss: 0.929, accuracy: 0.816
  1881. epoch 14 iter 0, loss: 0.860, accuracy: 0.810
  1882. epoch 14 iter 10, loss: 0.845, accuracy: 0.819
  1883. epoch 14 iter 20, loss: 0.830, accuracy: 0.816
  1884. epoch 14 iter 30, loss: 0.845, accuracy: 0.812
  1885. epoch 14 iter 40, loss: 0.836, accuracy: 0.820
  1886. epoch 14 iter 50, loss: 0.876, accuracy: 0.809
  1887. epoch 14 iter 60, loss: 0.861, accuracy: 0.813
  1888. epoch 14 iter 70, loss: 0.831, accuracy: 0.817
  1889. epoch 14 iter 80, loss: 0.854, accuracy: 0.811
  1890. epoch 14 iter 90, loss: 0.839, accuracy: 0.822
  1891. epoch 14 iter 100, loss: 0.825, accuracy: 0.817
  1892. epoch 14 iter 110, loss: 0.782, accuracy: 0.826
  1893. epoch 14 iter 120, loss: 0.821, accuracy: 0.816
  1894. epoch 14 iter 130, loss: 0.876, accuracy: 0.806
  1895. epoch 14 iter 140, loss: 0.836, accuracy: 0.822
  1896. epoch 14 iter 150, loss: 0.872, accuracy: 0.812
  1897. epoch 14 iter 160, loss: 0.813, accuracy: 0.814
  1898. epoch 14 iter 170, loss: 0.847, accuracy: 0.821
  1899. epoch 14 iter 180, loss: 0.842, accuracy: 0.826
  1900. epoch 14 iter 190, loss: 0.853, accuracy: 0.812
  1901. epoch 14 iter 200, loss: 0.893, accuracy: 0.813
  1902. epoch 14 iter 210, loss: 0.839, accuracy: 0.820
  1903. epoch 14 iter 220, loss: 0.863, accuracy: 0.820
  1904. epoch 14 iter 230, loss: 0.797, accuracy: 0.821
  1905. epoch 14 iter 240, loss: 0.787, accuracy: 0.823
  1906. epoch 14 iter 250, loss: 0.846, accuracy: 0.821
  1907. epoch 14 iter 260, loss: 0.897, accuracy: 0.819
  1908. epoch 14 iter 270, loss: 0.911, accuracy: 0.815
  1909. epoch 14 iter 280, loss: 0.917, accuracy: 0.820
  1910. epoch 15 iter 0, loss: 0.925, accuracy: 0.809
  1911. epoch 15 iter 10, loss: 0.846, accuracy: 0.821
  1912. epoch 15 iter 20, loss: 0.885, accuracy: 0.828
  1913. epoch 15 iter 30, loss: 0.858, accuracy: 0.820
  1914. epoch 15 iter 40, loss: 0.882, accuracy: 0.823
  1915. epoch 15 iter 50, loss: 0.892, accuracy: 0.822
  1916. epoch 15 iter 60, loss: 0.866, accuracy: 0.825
  1917. epoch 15 iter 70, loss: 0.857, accuracy: 0.820
  1918. epoch 15 iter 80, loss: 0.867, accuracy: 0.812
  1919. epoch 15 iter 90, loss: 0.920, accuracy: 0.817
  1920. epoch 15 iter 100, loss: 0.824, accuracy: 0.831
  1921. epoch 15 iter 110, loss: 0.821, accuracy: 0.818
  1922. epoch 15 iter 120, loss: 0.838, accuracy: 0.821
  1923. epoch 15 iter 130, loss: 0.894, accuracy: 0.818
  1924. epoch 15 iter 140, loss: 0.941, accuracy: 0.809
  1925. epoch 15 iter 150, loss: 0.902, accuracy: 0.820
  1926. epoch 15 iter 160, loss: 0.866, accuracy: 0.814
  1927. epoch 15 iter 170, loss: 0.854, accuracy: 0.826
  1928. epoch 15 iter 180, loss: 0.866, accuracy: 0.832
  1929. epoch 15 iter 190, loss: 0.872, accuracy: 0.824
  1930. epoch 15 iter 200, loss: 0.939, accuracy: 0.818
  1931. epoch 15 iter 210, loss: 0.950, accuracy: 0.817
  1932. epoch 15 iter 220, loss: 0.871, accuracy: 0.822
  1933. epoch 15 iter 230, loss: 0.851, accuracy: 0.822
  1934. epoch 15 iter 240, loss: 0.844, accuracy: 0.822
  1935. epoch 15 iter 250, loss: 0.882, accuracy: 0.819
  1936. epoch 15 iter 260, loss: 0.966, accuracy: 0.814
  1937. epoch 15 iter 270, loss: 0.924, accuracy: 0.823
  1938. epoch 15 iter 280, loss: 0.959, accuracy: 0.815
  1939. epoch 16 iter 0, loss: 1.016, accuracy: 0.804
  1940. epoch 16 iter 10, loss: 0.900, accuracy: 0.821
  1941. epoch 16 iter 20, loss: 0.940, accuracy: 0.821
  1942. epoch 16 iter 30, loss: 0.889, accuracy: 0.824
  1943. epoch 16 iter 40, loss: 0.884, accuracy: 0.828
  1944. epoch 16 iter 50, loss: 0.935, accuracy: 0.817
  1945. epoch 16 iter 60, loss: 0.933, accuracy: 0.823
  1946. epoch 16 iter 70, loss: 0.917, accuracy: 0.818
  1947. epoch 16 iter 80, loss: 0.930, accuracy: 0.818
  1948. epoch 16 iter 90, loss: 1.002, accuracy: 0.807
  1949. epoch 16 iter 100, loss: 0.923, accuracy: 0.829
  1950. epoch 16 iter 110, loss: 0.843, accuracy: 0.826
  1951. epoch 16 iter 120, loss: 0.877, accuracy: 0.822
  1952. epoch 16 iter 130, loss: 0.946, accuracy: 0.814
  1953. epoch 16 iter 140, loss: 0.962, accuracy: 0.806
  1954. epoch 16 iter 150, loss: 0.998, accuracy: 0.808
  1955. epoch 16 iter 160, loss: 0.927, accuracy: 0.809
  1956. epoch 16 iter 170, loss: 0.928, accuracy: 0.822
  1957. epoch 16 iter 180, loss: 0.913, accuracy: 0.827
  1958. epoch 16 iter 190, loss: 0.897, accuracy: 0.829
  1959. epoch 16 iter 200, loss: 0.957, accuracy: 0.828
  1960. epoch 16 iter 210, loss: 0.995, accuracy: 0.821
  1961. epoch 16 iter 220, loss: 0.941, accuracy: 0.822
  1962. epoch 16 iter 230, loss: 0.888, accuracy: 0.831
  1963. epoch 16 iter 240, loss: 0.846, accuracy: 0.834
  1964. epoch 16 iter 250, loss: 0.852, accuracy: 0.828
  1965. epoch 16 iter 260, loss: 1.000, accuracy: 0.822
  1966. epoch 16 iter 270, loss: 1.023, accuracy: 0.816
  1967. epoch 16 iter 280, loss: 1.053, accuracy: 0.820
  1968. epoch 17 iter 0, loss: 1.061, accuracy: 0.813
  1969. epoch 17 iter 10, loss: 0.912, accuracy: 0.832
  1970. epoch 17 iter 20, loss: 0.959, accuracy: 0.825
  1971. epoch 17 iter 30, loss: 0.972, accuracy: 0.820
  1972. epoch 17 iter 40, loss: 0.955, accuracy: 0.821
  1973. epoch 17 iter 50, loss: 1.010, accuracy: 0.821
  1974. epoch 17 iter 60, loss: 1.043, accuracy: 0.828
  1975. epoch 17 iter 70, loss: 0.999, accuracy: 0.824
  1976. epoch 17 iter 80, loss: 0.947, accuracy: 0.823
  1977. epoch 17 iter 90, loss: 1.027, accuracy: 0.822
  1978. epoch 17 iter 100, loss: 1.009, accuracy: 0.820
  1979. epoch 17 iter 110, loss: 0.932, accuracy: 0.814
  1980. epoch 17 iter 120, loss: 0.972, accuracy: 0.826
  1981. epoch 17 iter 130, loss: 0.953, accuracy: 0.825
  1982. epoch 17 iter 140, loss: 0.965, accuracy: 0.825
  1983. epoch 17 iter 150, loss: 0.979, accuracy: 0.824
  1984. epoch 17 iter 160, loss: 1.013, accuracy: 0.805
  1985. epoch 17 iter 170, loss: 0.997, accuracy: 0.824
  1986. epoch 17 iter 180, loss: 0.988, accuracy: 0.820
  1987. epoch 17 iter 190, loss: 1.048, accuracy: 0.823
  1988. epoch 17 iter 200, loss: 1.050, accuracy: 0.820
  1989. epoch 17 iter 210, loss: 1.078, accuracy: 0.823
  1990. epoch 17 iter 220, loss: 0.949, accuracy: 0.830
  1991. epoch 17 iter 230, loss: 0.968, accuracy: 0.831
  1992. epoch 17 iter 240, loss: 0.929, accuracy: 0.825
  1993. epoch 17 iter 250, loss: 0.929, accuracy: 0.832
  1994. epoch 17 iter 260, loss: 1.109, accuracy: 0.821
  1995. epoch 17 iter 270, loss: 1.130, accuracy: 0.824
  1996. epoch 17 iter 280, loss: 1.067, accuracy: 0.816
  1997. epoch 18 iter 0, loss: 1.141, accuracy: 0.815
  1998. epoch 18 iter 10, loss: 1.048, accuracy: 0.823
  1999. epoch 18 iter 20, loss: 1.023, accuracy: 0.830
  2000. epoch 18 iter 30, loss: 1.031, accuracy: 0.820
  2001. epoch 18 iter 40, loss: 1.008, accuracy: 0.825
  2002. epoch 18 iter 50, loss: 1.134, accuracy: 0.811
  2003. epoch 18 iter 60, loss: 1.004, accuracy: 0.830
  2004. epoch 18 iter 70, loss: 1.064, accuracy: 0.831
  2005. epoch 18 iter 80, loss: 1.046, accuracy: 0.815
  2006. epoch 18 iter 90, loss: 1.062, accuracy: 0.827
  2007. epoch 18 iter 100, loss: 1.067, accuracy: 0.817
  2008. epoch 18 iter 110, loss: 1.000, accuracy: 0.827
  2009. epoch 18 iter 120, loss: 1.000, accuracy: 0.822
  2010. epoch 18 iter 130, loss: 1.040, accuracy: 0.821
  2011. epoch 18 iter 140, loss: 1.062, accuracy: 0.823
  2012. epoch 18 iter 150, loss: 1.023, accuracy: 0.825
  2013. epoch 18 iter 160, loss: 1.053, accuracy: 0.812
  2014. epoch 18 iter 170, loss: 1.105, accuracy: 0.821
  2015. epoch 18 iter 180, loss: 0.981, accuracy: 0.830
  2016. epoch 18 iter 190, loss: 1.039, accuracy: 0.829
  2017. epoch 18 iter 200, loss: 1.109, accuracy: 0.821
  2018. epoch 18 iter 210, loss: 1.155, accuracy: 0.825
  2019. epoch 18 iter 220, loss: 1.090, accuracy: 0.829
  2020. epoch 18 iter 230, loss: 1.028, accuracy: 0.830
  2021. epoch 18 iter 240, loss: 0.968, accuracy: 0.827
  2022. epoch 18 iter 250, loss: 1.033, accuracy: 0.832
  2023. epoch 18 iter 260, loss: 1.148, accuracy: 0.819
  2024. epoch 18 iter 270, loss: 1.096, accuracy: 0.830
  2025. epoch 18 iter 280, loss: 1.186, accuracy: 0.799
  2026. epoch 19 iter 0, loss: 1.209, accuracy: 0.813
  2027. epoch 19 iter 10, loss: 1.097, accuracy: 0.830
  2028. epoch 19 iter 20, loss: 1.072, accuracy: 0.829
  2029. epoch 19 iter 30, loss: 1.125, accuracy: 0.815
  2030. epoch 19 iter 40, loss: 1.113, accuracy: 0.818
  2031. epoch 19 iter 50, loss: 1.179, accuracy: 0.817
  2032. epoch 19 iter 60, loss: 1.073, accuracy: 0.820
  2033. epoch 19 iter 70, loss: 1.127, accuracy: 0.828
  2034. epoch 19 iter 80, loss: 1.144, accuracy: 0.813
  2035. epoch 19 iter 90, loss: 1.223, accuracy: 0.814
  2036. epoch 19 iter 100, loss: 1.255, accuracy: 0.812
  2037. epoch 19 iter 110, loss: 1.166, accuracy: 0.817
  2038. epoch 19 iter 120, loss: 1.073, accuracy: 0.824
  2039. epoch 19 iter 130, loss: 1.034, accuracy: 0.834
  2040. epoch 19 iter 140, loss: 1.132, accuracy: 0.829
  2041. epoch 19 iter 150, loss: 1.174, accuracy: 0.816
  2042. epoch 19 iter 160, loss: 1.140, accuracy: 0.808
  2043. epoch 19 iter 170, loss: 1.209, accuracy: 0.817
  2044. epoch 19 iter 180, loss: 1.062, accuracy: 0.830
  2045. epoch 19 iter 190, loss: 1.123, accuracy: 0.830
  2046. epoch 19 iter 200, loss: 1.140, accuracy: 0.821
  2047. epoch 19 iter 210, loss: 1.245, accuracy: 0.818
  2048. epoch 19 iter 220, loss: 1.140, accuracy: 0.828
  2049. epoch 19 iter 230, loss: 1.096, accuracy: 0.831
  2050. epoch 19 iter 240, loss: 1.040, accuracy: 0.830
  2051. epoch 19 iter 250, loss: 1.120, accuracy: 0.833
  2052. epoch 19 iter 260, loss: 1.317, accuracy: 0.810
  2053. epoch 19 iter 270, loss: 1.205, accuracy: 0.820
  2054. epoch 19 iter 280, loss: 1.149, accuracy: 0.829
  2055. epoch 20 iter 0, loss: 1.206, accuracy: 0.826
  2056. epoch 20 iter 10, loss: 1.178, accuracy: 0.825
  2057. epoch 20 iter 20, loss: 1.099, accuracy: 0.836
  2058. epoch 20 iter 30, loss: 1.238, accuracy: 0.813
  2059. epoch 20 iter 40, loss: 1.273, accuracy: 0.823
  2060. epoch 20 iter 50, loss: 1.196, accuracy: 0.825
  2061. epoch 20 iter 60, loss: 1.285, accuracy: 0.814
  2062. epoch 20 iter 70, loss: 1.244, accuracy: 0.822
  2063. epoch 20 iter 80, loss: 1.334, accuracy: 0.806
  2064. epoch 20 iter 90, loss: 1.265, accuracy: 0.796
  2065. epoch 20 iter 100, loss: 1.247, accuracy: 0.812
  2066. epoch 20 iter 110, loss: 1.240, accuracy: 0.818
  2067. epoch 20 iter 120, loss: 1.249, accuracy: 0.803
  2068. epoch 20 iter 130, loss: 1.221, accuracy: 0.812
  2069. epoch 20 iter 140, loss: 1.161, accuracy: 0.820
  2070. epoch 20 iter 150, loss: 1.225, accuracy: 0.818
  2071. epoch 20 iter 160, loss: 1.200, accuracy: 0.809
  2072. epoch 20 iter 170, loss: 1.267, accuracy: 0.820
  2073. epoch 20 iter 180, loss: 1.142, accuracy: 0.820
  2074. epoch 20 iter 190, loss: 1.156, accuracy: 0.824
  2075. epoch 20 iter 200, loss: 1.169, accuracy: 0.824
  2076. epoch 20 iter 210, loss: 1.275, accuracy: 0.822
  2077. epoch 20 iter 220, loss: 1.259, accuracy: 0.815
  2078. epoch 20 iter 230, loss: 1.202, accuracy: 0.829
  2079. epoch 20 iter 240, loss: 1.111, accuracy: 0.824
  2080. epoch 20 iter 250, loss: 1.208, accuracy: 0.826
  2081. epoch 20 iter 260, loss: 1.525, accuracy: 0.793
  2082. epoch 20 iter 270, loss: 1.279, accuracy: 0.818
  2083. epoch 20 iter 280, loss: 1.279, accuracy: 0.819
  2084. epoch 21 iter 0, loss: 1.377, accuracy: 0.818
  2085. epoch 21 iter 10, loss: 1.279, accuracy: 0.822
  2086. epoch 21 iter 20, loss: 1.228, accuracy: 0.817
  2087. epoch 21 iter 30, loss: 1.285, accuracy: 0.820
  2088. epoch 21 iter 40, loss: 1.253, accuracy: 0.820
  2089. epoch 21 iter 50, loss: 1.351, accuracy: 0.819
  2090. epoch 21 iter 60, loss: 1.225, accuracy: 0.822
  2091. epoch 21 iter 70, loss: 1.277, accuracy: 0.823
  2092. epoch 21 iter 80, loss: 1.301, accuracy: 0.818
  2093. epoch 21 iter 90, loss: 1.253, accuracy: 0.819
  2094. epoch 21 iter 100, loss: 1.481, accuracy: 0.811
  2095. epoch 21 iter 110, loss: 1.337, accuracy: 0.818
  2096. epoch 21 iter 120, loss: 1.258, accuracy: 0.809
  2097. epoch 21 iter 130, loss: 1.325, accuracy: 0.808
  2098. epoch 21 iter 140, loss: 1.216, accuracy: 0.823
  2099. epoch 21 iter 150, loss: 1.345, accuracy: 0.808
  2100. epoch 21 iter 160, loss: 1.359, accuracy: 0.801
  2101. epoch 21 iter 170, loss: 1.237, accuracy: 0.821
  2102. epoch 21 iter 180, loss: 1.282, accuracy: 0.820
  2103. epoch 21 iter 190, loss: 1.207, accuracy: 0.827
  2104. epoch 21 iter 200, loss: 1.318, accuracy: 0.820
  2105. epoch 21 iter 210, loss: 1.360, accuracy: 0.822
  2106. epoch 21 iter 220, loss: 1.331, accuracy: 0.819
  2107. epoch 21 iter 230, loss: 1.213, accuracy: 0.822
  2108. epoch 21 iter 240, loss: 1.101, accuracy: 0.831
  2109. epoch 21 iter 250, loss: 1.199, accuracy: 0.828
  2110. epoch 21 iter 260, loss: 1.378, accuracy: 0.813
  2111. epoch 21 iter 270, loss: 1.319, accuracy: 0.821
  2112. epoch 21 iter 280, loss: 1.360, accuracy: 0.824
  2113. epoch 22 iter 0, loss: 1.423, accuracy: 0.817
  2114. epoch 22 iter 10, loss: 1.341, accuracy: 0.809
  2115. epoch 22 iter 20, loss: 1.327, accuracy: 0.810
  2116. epoch 22 iter 30, loss: 1.409, accuracy: 0.805
  2117. epoch 22 iter 40, loss: 1.412, accuracy: 0.813
  2118. epoch 22 iter 50, loss: 1.295, accuracy: 0.819
  2119. epoch 22 iter 60, loss: 1.295, accuracy: 0.825
  2120. epoch 22 iter 70, loss: 1.333, accuracy: 0.823
  2121. epoch 22 iter 80, loss: 1.337, accuracy: 0.818
  2122. epoch 22 iter 90, loss: 1.270, accuracy: 0.821
  2123. epoch 22 iter 100, loss: 1.442, accuracy: 0.821
  2124. epoch 22 iter 110, loss: 1.336, accuracy: 0.821
  2125. epoch 22 iter 120, loss: 1.243, accuracy: 0.820
  2126. epoch 22 iter 130, loss: 1.219, accuracy: 0.827
  2127. epoch 22 iter 140, loss: 1.223, accuracy: 0.829
  2128. epoch 22 iter 150, loss: 1.320, accuracy: 0.813
  2129. epoch 22 iter 160, loss: 1.287, accuracy: 0.810
  2130. epoch 22 iter 170, loss: 1.252, accuracy: 0.810
  2131. epoch 22 iter 180, loss: 1.320, accuracy: 0.824
  2132. epoch 22 iter 190, loss: 1.320, accuracy: 0.826
  2133. epoch 22 iter 200, loss: 1.416, accuracy: 0.812
  2134. epoch 22 iter 210, loss: 1.425, accuracy: 0.818
  2135. epoch 22 iter 220, loss: 1.460, accuracy: 0.821
  2136. epoch 22 iter 230, loss: 1.355, accuracy: 0.817
  2137. epoch 22 iter 240, loss: 1.218, accuracy: 0.830
  2138. epoch 22 iter 250, loss: 1.348, accuracy: 0.825
  2139. epoch 22 iter 260, loss: 1.351, accuracy: 0.815
  2140. epoch 22 iter 270, loss: 1.405, accuracy: 0.818
  2141. epoch 22 iter 280, loss: 1.437, accuracy: 0.817
  2142. epoch 23 iter 0, loss: 1.660, accuracy: 0.809
  2143. epoch 23 iter 10, loss: 1.421, accuracy: 0.821
  2144. epoch 23 iter 20, loss: 1.437, accuracy: 0.821
  2145. epoch 23 iter 30, loss: 1.295, accuracy: 0.825
  2146. epoch 23 iter 40, loss: 1.483, accuracy: 0.820
  2147. epoch 23 iter 50, loss: 1.370, accuracy: 0.824
  2148. epoch 23 iter 60, loss: 1.367, accuracy: 0.829
  2149. epoch 23 iter 70, loss: 1.339, accuracy: 0.822
  2150. epoch 23 iter 80, loss: 1.377, accuracy: 0.820
  2151. epoch 23 iter 90, loss: 1.304, accuracy: 0.825
  2152. epoch 23 iter 100, loss: 1.333, accuracy: 0.828
  2153. epoch 23 iter 110, loss: 1.404, accuracy: 0.827
  2154. epoch 23 iter 120, loss: 1.255, accuracy: 0.826
  2155. epoch 23 iter 130, loss: 1.277, accuracy: 0.830
  2156. epoch 23 iter 140, loss: 1.252, accuracy: 0.829
  2157. epoch 23 iter 150, loss: 1.396, accuracy: 0.814
  2158. epoch 23 iter 160, loss: 1.268, accuracy: 0.808
  2159. epoch 23 iter 170, loss: 1.282, accuracy: 0.815
  2160. epoch 23 iter 180, loss: 1.303, accuracy: 0.827
  2161. epoch 23 iter 190, loss: 1.279, accuracy: 0.831
  2162. epoch 23 iter 200, loss: 1.331, accuracy: 0.824
  2163. epoch 23 iter 210, loss: 1.413, accuracy: 0.814
  2164. epoch 23 iter 220, loss: 1.483, accuracy: 0.814
  2165. epoch 23 iter 230, loss: 1.415, accuracy: 0.813
  2166. epoch 23 iter 240, loss: 1.308, accuracy: 0.830
  2167. epoch 23 iter 250, loss: 1.314, accuracy: 0.830
  2168. epoch 23 iter 260, loss: 1.461, accuracy: 0.810
  2169. epoch 23 iter 270, loss: 1.473, accuracy: 0.817
  2170. epoch 23 iter 280, loss: 1.512, accuracy: 0.812
  2171. epoch 24 iter 0, loss: 1.464, accuracy: 0.824
  2172. epoch 24 iter 10, loss: 1.373, accuracy: 0.812
  2173. epoch 24 iter 20, loss: 1.364, accuracy: 0.822
  2174. epoch 24 iter 30, loss: 1.384, accuracy: 0.832
  2175. epoch 24 iter 40, loss: 1.430, accuracy: 0.825
  2176. epoch 24 iter 50, loss: 1.342, accuracy: 0.827
  2177. epoch 24 iter 60, loss: 1.420, accuracy: 0.830
  2178. epoch 24 iter 70, loss: 1.328, accuracy: 0.830
  2179. epoch 24 iter 80, loss: 1.491, accuracy: 0.827
  2180. epoch 24 iter 90, loss: 1.506, accuracy: 0.826
  2181. epoch 24 iter 100, loss: 1.394, accuracy: 0.825
  2182. epoch 24 iter 110, loss: 1.469, accuracy: 0.827
  2183. epoch 24 iter 120, loss: 1.408, accuracy: 0.816
  2184. epoch 24 iter 130, loss: 1.356, accuracy: 0.828
  2185. epoch 24 iter 140, loss: 1.402, accuracy: 0.825
  2186. epoch 24 iter 150, loss: 1.447, accuracy: 0.825
  2187. epoch 24 iter 160, loss: 1.290, accuracy: 0.809
  2188. epoch 24 iter 170, loss: 1.426, accuracy: 0.815
  2189. epoch 24 iter 180, loss: 1.428, accuracy: 0.815
  2190. epoch 24 iter 190, loss: 1.382, accuracy: 0.821
  2191. epoch 24 iter 200, loss: 1.432, accuracy: 0.825
  2192. epoch 24 iter 210, loss: 1.552, accuracy: 0.803
  2193. epoch 24 iter 220, loss: 1.427, accuracy: 0.826
  2194. epoch 24 iter 230, loss: 1.493, accuracy: 0.822
  2195. epoch 24 iter 240, loss: 1.429, accuracy: 0.823
  2196. epoch 24 iter 250, loss: 1.370, accuracy: 0.830
  2197. epoch 24 iter 260, loss: 1.637, accuracy: 0.807
  2198. epoch 24 iter 270, loss: 1.518, accuracy: 0.823
  2199. epoch 24 iter 280, loss: 1.522, accuracy: 0.814
  2200. epoch 25 iter 0, loss: 1.758, accuracy: 0.809
  2201. epoch 25 iter 10, loss: 1.410, accuracy: 0.822
  2202. epoch 25 iter 20, loss: 1.443, accuracy: 0.834
  2203. epoch 25 iter 30, loss: 1.423, accuracy: 0.827
  2204. epoch 25 iter 40, loss: 1.467, accuracy: 0.814
  2205. epoch 25 iter 50, loss: 1.440, accuracy: 0.830
  2206. epoch 25 iter 60, loss: 1.547, accuracy: 0.832
  2207. epoch 25 iter 70, loss: 1.423, accuracy: 0.823
  2208. epoch 25 iter 80, loss: 1.393, accuracy: 0.827
  2209. epoch 25 iter 90, loss: 1.496, accuracy: 0.834
  2210. epoch 25 iter 100, loss: 1.378, accuracy: 0.832
  2211. epoch 25 iter 110, loss: 1.482, accuracy: 0.820
  2212. epoch 25 iter 120, loss: 1.425, accuracy: 0.836
  2213. epoch 25 iter 130, loss: 1.396, accuracy: 0.829
  2214. epoch 25 iter 140, loss: 1.437, accuracy: 0.827
  2215. epoch 25 iter 150, loss: 1.425, accuracy: 0.831
  2216. epoch 25 iter 160, loss: 1.429, accuracy: 0.827
  2217. epoch 25 iter 170, loss: 1.477, accuracy: 0.822
  2218. epoch 25 iter 180, loss: 1.468, accuracy: 0.816
  2219. epoch 25 iter 190, loss: 1.495, accuracy: 0.819
  2220. epoch 25 iter 200, loss: 1.509, accuracy: 0.825
  2221. epoch 25 iter 210, loss: 1.487, accuracy: 0.821
  2222. epoch 25 iter 220, loss: 1.512, accuracy: 0.819
  2223. epoch 25 iter 230, loss: 1.498, accuracy: 0.820
  2224. epoch 25 iter 240, loss: 1.390, accuracy: 0.830
  2225. epoch 25 iter 250, loss: 1.412, accuracy: 0.829
  2226. epoch 25 iter 260, loss: 1.552, accuracy: 0.824
  2227. epoch 25 iter 270, loss: 1.518, accuracy: 0.826
  2228. epoch 25 iter 280, loss: 1.395, accuracy: 0.828
  2229. epoch 26 iter 0, loss: 1.716, accuracy: 0.822
  2230. epoch 26 iter 10, loss: 1.511, accuracy: 0.821
  2231. epoch 26 iter 20, loss: 1.557, accuracy: 0.831
  2232. epoch 26 iter 30, loss: 1.605, accuracy: 0.818
  2233. epoch 26 iter 40, loss: 1.621, accuracy: 0.799
  2234. epoch 26 iter 50, loss: 1.463, accuracy: 0.818
  2235. epoch 26 iter 60, loss: 1.563, accuracy: 0.828
  2236. epoch 26 iter 70, loss: 1.483, accuracy: 0.815
  2237. epoch 26 iter 80, loss: 1.446, accuracy: 0.822
  2238. epoch 26 iter 90, loss: 1.687, accuracy: 0.820
  2239. epoch 26 iter 100, loss: 1.430, accuracy: 0.830
  2240. epoch 26 iter 110, loss: 1.459, accuracy: 0.823
  2241. epoch 26 iter 120, loss: 1.478, accuracy: 0.835
  2242. epoch 26 iter 130, loss: 1.346, accuracy: 0.832
  2243. epoch 26 iter 140, loss: 1.472, accuracy: 0.833
  2244. epoch 26 iter 150, loss: 1.482, accuracy: 0.833
  2245. epoch 26 iter 160, loss: 1.504, accuracy: 0.824
  2246. epoch 26 iter 170, loss: 1.457, accuracy: 0.814
  2247. epoch 26 iter 180, loss: 1.485, accuracy: 0.817
  2248. epoch 26 iter 190, loss: 1.576, accuracy: 0.823
  2249. epoch 26 iter 200, loss: 1.578, accuracy: 0.823
  2250. epoch 26 iter 210, loss: 1.646, accuracy: 0.821
  2251. epoch 26 iter 220, loss: 1.648, accuracy: 0.825
  2252. epoch 26 iter 230, loss: 1.661, accuracy: 0.818
  2253. epoch 26 iter 240, loss: 1.496, accuracy: 0.822
  2254. epoch 26 iter 250, loss: 1.547, accuracy: 0.828
  2255. epoch 26 iter 260, loss: 1.852, accuracy: 0.814
  2256. epoch 26 iter 270, loss: 1.791, accuracy: 0.827
  2257. epoch 26 iter 280, loss: 1.503, accuracy: 0.824
  2258. epoch 27 iter 0, loss: 1.807, accuracy: 0.808
  2259. epoch 27 iter 10, loss: 1.710, accuracy: 0.824
  2260. epoch 27 iter 20, loss: 1.687, accuracy: 0.822
  2261. epoch 27 iter 30, loss: 1.669, accuracy: 0.821
  2262. epoch 27 iter 40, loss: 1.553, accuracy: 0.821
  2263. epoch 27 iter 50, loss: 1.659, accuracy: 0.823
  2264. epoch 27 iter 60, loss: 1.686, accuracy: 0.830
  2265. epoch 27 iter 70, loss: 1.570, accuracy: 0.829
  2266. epoch 27 iter 80, loss: 1.480, accuracy: 0.826
  2267. epoch 27 iter 90, loss: 1.695, accuracy: 0.826
  2268. epoch 27 iter 100, loss: 1.528, accuracy: 0.825
  2269. epoch 27 iter 110, loss: 1.561, accuracy: 0.829
  2270. epoch 27 iter 120, loss: 1.581, accuracy: 0.830
  2271. epoch 27 iter 130, loss: 1.417, accuracy: 0.819
  2272. epoch 27 iter 140, loss: 1.546, accuracy: 0.828
  2273. epoch 27 iter 150, loss: 1.658, accuracy: 0.825
  2274. epoch 27 iter 160, loss: 1.595, accuracy: 0.821
  2275. epoch 27 iter 170, loss: 1.530, accuracy: 0.815
  2276. epoch 27 iter 180, loss: 1.578, accuracy: 0.824
  2277. epoch 27 iter 190, loss: 1.581, accuracy: 0.811
  2278. epoch 27 iter 200, loss: 1.900, accuracy: 0.819
  2279. epoch 27 iter 210, loss: 1.776, accuracy: 0.827
  2280. epoch 27 iter 220, loss: 1.641, accuracy: 0.831
  2281. epoch 27 iter 230, loss: 1.713, accuracy: 0.831
  2282. epoch 27 iter 240, loss: 1.514, accuracy: 0.822
  2283. epoch 27 iter 250, loss: 1.583, accuracy: 0.835
  2284. epoch 27 iter 260, loss: 1.788, accuracy: 0.828
  2285. epoch 27 iter 270, loss: 1.711, accuracy: 0.825
  2286. epoch 27 iter 280, loss: 1.586, accuracy: 0.827
  2287. epoch 28 iter 0, loss: 1.579, accuracy: 0.833
  2288. epoch 28 iter 10, loss: 1.746, accuracy: 0.826
  2289. epoch 28 iter 20, loss: 1.608, accuracy: 0.814
  2290. epoch 28 iter 30, loss: 1.699, accuracy: 0.823
  2291. epoch 28 iter 40, loss: 1.707, accuracy: 0.815
  2292. epoch 28 iter 50, loss: 1.644, accuracy: 0.818
  2293. epoch 28 iter 60, loss: 1.698, accuracy: 0.822
  2294. epoch 28 iter 70, loss: 1.668, accuracy: 0.825
  2295. epoch 28 iter 80, loss: 1.684, accuracy: 0.831
  2296. epoch 28 iter 90, loss: 1.871, accuracy: 0.824
  2297. epoch 28 iter 100, loss: 1.644, accuracy: 0.832
  2298. epoch 28 iter 110, loss: 1.669, accuracy: 0.819
  2299. epoch 28 iter 120, loss: 1.695, accuracy: 0.830
  2300. epoch 28 iter 130, loss: 1.497, accuracy: 0.815
  2301. epoch 28 iter 140, loss: 1.600, accuracy: 0.830
  2302. epoch 28 iter 150, loss: 1.666, accuracy: 0.831
  2303. epoch 28 iter 160, loss: 1.507, accuracy: 0.824
  2304. epoch 28 iter 170, loss: 1.499, accuracy: 0.827
  2305. epoch 28 iter 180, loss: 1.629, accuracy: 0.831
  2306. epoch 28 iter 190, loss: 1.678, accuracy: 0.827
  2307. epoch 28 iter 200, loss: 1.807, accuracy: 0.823
  2308. epoch 28 iter 210, loss: 1.765, accuracy: 0.824
  2309. epoch 28 iter 220, loss: 1.673, accuracy: 0.831
  2310. epoch 28 iter 230, loss: 1.745, accuracy: 0.835
  2311. epoch 28 iter 240, loss: 1.575, accuracy: 0.824
  2312. epoch 28 iter 250, loss: 1.610, accuracy: 0.831
  2313. epoch 28 iter 260, loss: 1.634, accuracy: 0.833
  2314. epoch 28 iter 270, loss: 1.970, accuracy: 0.808
  2315. epoch 28 iter 280, loss: 1.700, accuracy: 0.813
  2316. epoch 29 iter 0, loss: 1.636, accuracy: 0.831
  2317. epoch 29 iter 10, loss: 1.653, accuracy: 0.822
  2318. epoch 29 iter 20, loss: 1.627, accuracy: 0.829
  2319. epoch 29 iter 30, loss: 1.673, accuracy: 0.823
  2320. epoch 29 iter 40, loss: 1.841, accuracy: 0.809
  2321. epoch 29 iter 50, loss: 1.634, accuracy: 0.821
  2322. epoch 29 iter 60, loss: 1.620, accuracy: 0.823
  2323. epoch 29 iter 70, loss: 1.642, accuracy: 0.820
  2324. epoch 29 iter 80, loss: 1.701, accuracy: 0.832
  2325. epoch 29 iter 90, loss: 1.697, accuracy: 0.827
  2326. epoch 29 iter 100, loss: 1.639, accuracy: 0.826
  2327. epoch 29 iter 110, loss: 1.738, accuracy: 0.824
  2328. epoch 29 iter 120, loss: 1.749, accuracy: 0.831
  2329. epoch 29 iter 130, loss: 1.674, accuracy: 0.835
  2330. epoch 29 iter 140, loss: 1.685, accuracy: 0.831
  2331. epoch 29 iter 150, loss: 1.807, accuracy: 0.826
  2332. epoch 29 iter 160, loss: 1.605, accuracy: 0.827
  2333. epoch 29 iter 170, loss: 1.682, accuracy: 0.830
  2334. epoch 29 iter 180, loss: 1.686, accuracy: 0.832
  2335. epoch 29 iter 190, loss: 1.652, accuracy: 0.830
  2336. epoch 29 iter 200, loss: 1.775, accuracy: 0.825
  2337. epoch 29 iter 210, loss: 1.782, accuracy: 0.830
  2338. epoch 29 iter 220, loss: 1.746, accuracy: 0.829
  2339. epoch 29 iter 230, loss: 1.787, accuracy: 0.832
  2340. epoch 29 iter 240, loss: 1.600, accuracy: 0.830
  2341. epoch 29 iter 250, loss: 1.676, accuracy: 0.839
  2342. epoch 29 iter 260, loss: 1.702, accuracy: 0.839
  2343. epoch 29 iter 270, loss: 2.054, accuracy: 0.829
  2344. epoch 29 iter 280, loss: 1.738, accuracy: 0.825
  2345. epoch 30 iter 0, loss: 1.772, accuracy: 0.824
  2346. epoch 30 iter 10, loss: 1.768, accuracy: 0.831
  2347. epoch 30 iter 20, loss: 1.682, accuracy: 0.828
  2348. epoch 30 iter 30, loss: 1.797, accuracy: 0.829
  2349. epoch 30 iter 40, loss: 1.963, accuracy: 0.826
  2350. epoch 30 iter 50, loss: 1.720, accuracy: 0.818
  2351. epoch 30 iter 60, loss: 1.732, accuracy: 0.823
  2352. epoch 30 iter 70, loss: 1.734, accuracy: 0.830
  2353. epoch 30 iter 80, loss: 1.740, accuracy: 0.832
  2354. epoch 30 iter 90, loss: 1.671, accuracy: 0.826
  2355. epoch 30 iter 100, loss: 1.737, accuracy: 0.838
  2356. epoch 30 iter 110, loss: 1.742, accuracy: 0.825
  2357. epoch 30 iter 120, loss: 1.845, accuracy: 0.818
  2358. epoch 30 iter 130, loss: 1.817, accuracy: 0.827
  2359. epoch 30 iter 140, loss: 1.732, accuracy: 0.832
  2360. epoch 30 iter 150, loss: 1.886, accuracy: 0.823
  2361. epoch 30 iter 160, loss: 1.697, accuracy: 0.829
  2362. epoch 30 iter 170, loss: 1.807, accuracy: 0.826
  2363. epoch 30 iter 180, loss: 1.797, accuracy: 0.828
  2364. epoch 30 iter 190, loss: 1.939, accuracy: 0.822
  2365. epoch 30 iter 200, loss: 1.980, accuracy: 0.825
  2366. epoch 30 iter 210, loss: 2.039, accuracy: 0.817
  2367. epoch 30 iter 220, loss: 1.931, accuracy: 0.822
  2368. epoch 30 iter 230, loss: 1.985, accuracy: 0.828
  2369. epoch 30 iter 240, loss: 1.724, accuracy: 0.822
  2370. epoch 30 iter 250, loss: 1.693, accuracy: 0.828
  2371. epoch 30 iter 260, loss: 1.696, accuracy: 0.837
  2372. epoch 30 iter 270, loss: 2.056, accuracy: 0.823
  2373. epoch 30 iter 280, loss: 1.883, accuracy: 0.831
  2374. epoch 31 iter 0, loss: 1.816, accuracy: 0.820
  2375. epoch 31 iter 10, loss: 1.990, accuracy: 0.835
  2376. epoch 31 iter 20, loss: 1.749, accuracy: 0.826
  2377. epoch 31 iter 30, loss: 1.946, accuracy: 0.824
  2378. epoch 31 iter 40, loss: 1.940, accuracy: 0.825
  2379. epoch 31 iter 50, loss: 1.832, accuracy: 0.828
  2380. epoch 31 iter 60, loss: 1.745, accuracy: 0.829
  2381. epoch 31 iter 70, loss: 1.933, accuracy: 0.832
  2382. epoch 31 iter 80, loss: 1.838, accuracy: 0.825
  2383. epoch 31 iter 90, loss: 1.797, accuracy: 0.828
  2384. epoch 31 iter 100, loss: 1.862, accuracy: 0.830
  2385. epoch 31 iter 110, loss: 1.865, accuracy: 0.821
  2386. epoch 31 iter 120, loss: 1.927, accuracy: 0.819
  2387. epoch 31 iter 130, loss: 1.923, accuracy: 0.818
  2388. epoch 31 iter 140, loss: 1.876, accuracy: 0.829
  2389. epoch 31 iter 150, loss: 1.849, accuracy: 0.826
  2390. epoch 31 iter 160, loss: 1.790, accuracy: 0.823
  2391. epoch 31 iter 170, loss: 1.799, accuracy: 0.825
  2392. epoch 31 iter 180, loss: 1.762, accuracy: 0.823
  2393. epoch 31 iter 190, loss: 1.922, accuracy: 0.822
  2394. epoch 31 iter 200, loss: 2.055, accuracy: 0.818
  2395. epoch 31 iter 210, loss: 2.026, accuracy: 0.817
  2396. epoch 31 iter 220, loss: 1.862, accuracy: 0.827
  2397. epoch 31 iter 230, loss: 1.857, accuracy: 0.835
  2398. epoch 31 iter 240, loss: 1.806, accuracy: 0.823
  2399. epoch 31 iter 250, loss: 1.818, accuracy: 0.834
  2400. epoch 31 iter 260, loss: 1.694, accuracy: 0.839
  2401. epoch 31 iter 270, loss: 1.993, accuracy: 0.824
  2402. epoch 31 iter 280, loss: 1.908, accuracy: 0.831
  2403. epoch 32 iter 0, loss: 1.844, accuracy: 0.826
  2404. epoch 32 iter 10, loss: 2.152, accuracy: 0.823
  2405. epoch 32 iter 20, loss: 1.821, accuracy: 0.823
  2406. epoch 32 iter 30, loss: 1.938, accuracy: 0.832
  2407. epoch 32 iter 40, loss: 1.905, accuracy: 0.833
  2408. epoch 32 iter 50, loss: 1.985, accuracy: 0.826
  2409. epoch 32 iter 60, loss: 1.975, accuracy: 0.821
  2410. epoch 32 iter 70, loss: 2.029, accuracy: 0.830
  2411. epoch 32 iter 80, loss: 1.870, accuracy: 0.817
  2412. epoch 32 iter 90, loss: 1.843, accuracy: 0.830
  2413. epoch 32 iter 100, loss: 1.932, accuracy: 0.834
  2414. epoch 32 iter 110, loss: 1.734, accuracy: 0.824
  2415. epoch 32 iter 120, loss: 1.880, accuracy: 0.829
  2416. epoch 32 iter 130, loss: 1.856, accuracy: 0.830
  2417. epoch 32 iter 140, loss: 1.918, accuracy: 0.828
  2418. epoch 32 iter 150, loss: 1.876, accuracy: 0.827
  2419. epoch 32 iter 160, loss: 1.736, accuracy: 0.825
  2420. epoch 32 iter 170, loss: 1.841, accuracy: 0.821
  2421. epoch 32 iter 180, loss: 1.812, accuracy: 0.825
  2422. epoch 32 iter 190, loss: 1.894, accuracy: 0.826
  2423. epoch 32 iter 200, loss: 2.091, accuracy: 0.821
  2424. epoch 32 iter 210, loss: 2.231, accuracy: 0.826
  2425. epoch 32 iter 220, loss: 2.032, accuracy: 0.819
  2426. epoch 32 iter 230, loss: 2.209, accuracy: 0.826
  2427. epoch 32 iter 240, loss: 1.911, accuracy: 0.827
  2428. epoch 32 iter 250, loss: 2.052, accuracy: 0.835
  2429. epoch 32 iter 260, loss: 1.982, accuracy: 0.827
  2430. epoch 32 iter 270, loss: 2.017, accuracy: 0.831
  2431. epoch 32 iter 280, loss: 1.902, accuracy: 0.830
  2432. epoch 33 iter 0, loss: 1.795, accuracy: 0.827
  2433. epoch 33 iter 10, loss: 2.030, accuracy: 0.837
  2434. epoch 33 iter 20, loss: 1.921, accuracy: 0.832
  2435. epoch 33 iter 30, loss: 1.891, accuracy: 0.829
  2436. epoch 33 iter 40, loss: 1.962, accuracy: 0.837
  2437. epoch 33 iter 50, loss: 1.947, accuracy: 0.830
  2438. epoch 33 iter 60, loss: 1.962, accuracy: 0.824
  2439. epoch 33 iter 70, loss: 2.052, accuracy: 0.828
  2440. epoch 33 iter 80, loss: 1.909, accuracy: 0.825
  2441. epoch 33 iter 90, loss: 1.925, accuracy: 0.834
  2442. epoch 33 iter 100, loss: 2.162, accuracy: 0.826
  2443. epoch 33 iter 110, loss: 1.926, accuracy: 0.815
  2444. epoch 33 iter 120, loss: 2.014, accuracy: 0.826
  2445. epoch 33 iter 130, loss: 1.909, accuracy: 0.819
  2446. epoch 33 iter 140, loss: 2.062, accuracy: 0.830
  2447. epoch 33 iter 150, loss: 1.954, accuracy: 0.835
  2448. epoch 33 iter 160, loss: 1.888, accuracy: 0.829
  2449. epoch 33 iter 170, loss: 1.832, accuracy: 0.818
  2450. epoch 33 iter 180, loss: 1.834, accuracy: 0.830
  2451. epoch 33 iter 190, loss: 1.770, accuracy: 0.829
  2452. epoch 33 iter 200, loss: 1.985, accuracy: 0.830
  2453. epoch 33 iter 210, loss: 2.126, accuracy: 0.832
  2454. epoch 33 iter 220, loss: 2.077, accuracy: 0.820
  2455. epoch 33 iter 230, loss: 1.943, accuracy: 0.823
  2456. epoch 33 iter 240, loss: 1.875, accuracy: 0.825
  2457. epoch 33 iter 250, loss: 1.836, accuracy: 0.830
  2458. epoch 33 iter 260, loss: 2.007, accuracy: 0.834
  2459. epoch 33 iter 270, loss: 2.109, accuracy: 0.822
  2460. epoch 33 iter 280, loss: 2.027, accuracy: 0.839
  2461. epoch 34 iter 0, loss: 1.979, accuracy: 0.836
  2462. epoch 34 iter 10, loss: 1.972, accuracy: 0.834
  2463. epoch 34 iter 20, loss: 1.847, accuracy: 0.836
  2464. epoch 34 iter 30, loss: 1.888, accuracy: 0.832
  2465. epoch 34 iter 40, loss: 2.094, accuracy: 0.834
  2466. epoch 34 iter 50, loss: 2.068, accuracy: 0.829
  2467. epoch 34 iter 60, loss: 2.101, accuracy: 0.827
  2468. epoch 34 iter 70, loss: 2.138, accuracy: 0.833
  2469. epoch 34 iter 80, loss: 2.003, accuracy: 0.830
  2470. epoch 34 iter 90, loss: 2.107, accuracy: 0.827
  2471. epoch 34 iter 100, loss: 2.125, accuracy: 0.837
  2472. epoch 34 iter 110, loss: 1.926, accuracy: 0.828
  2473. epoch 34 iter 120, loss: 2.010, accuracy: 0.826
  2474. epoch 34 iter 130, loss: 1.924, accuracy: 0.830
  2475. epoch 34 iter 140, loss: 2.162, accuracy: 0.838
  2476. epoch 34 iter 150, loss: 2.000, accuracy: 0.836
  2477. epoch 34 iter 160, loss: 1.964, accuracy: 0.828
  2478. epoch 34 iter 170, loss: 1.978, accuracy: 0.821
  2479. epoch 34 iter 180, loss: 2.137, accuracy: 0.822
  2480. epoch 34 iter 190, loss: 1.941, accuracy: 0.825
  2481. epoch 34 iter 200, loss: 2.107, accuracy: 0.832
  2482. epoch 34 iter 210, loss: 2.210, accuracy: 0.836
  2483. epoch 34 iter 220, loss: 2.125, accuracy: 0.832
  2484. epoch 34 iter 230, loss: 2.268, accuracy: 0.820
  2485. epoch 34 iter 240, loss: 2.222, accuracy: 0.829
  2486. epoch 34 iter 250, loss: 2.176, accuracy: 0.827
  2487. epoch 34 iter 260, loss: 2.292, accuracy: 0.824
  2488. epoch 34 iter 270, loss: 2.047, accuracy: 0.828
  2489. epoch 34 iter 280, loss: 2.241, accuracy: 0.837
  2490. epoch 35 iter 0, loss: 2.035, accuracy: 0.829
  2491. epoch 35 iter 10, loss: 2.174, accuracy: 0.836
  2492. epoch 35 iter 20, loss: 2.021, accuracy: 0.837
  2493. epoch 35 iter 30, loss: 2.088, accuracy: 0.829
  2494. epoch 35 iter 40, loss: 2.274, accuracy: 0.831
  2495. epoch 35 iter 50, loss: 2.064, accuracy: 0.826
  2496. epoch 35 iter 60, loss: 2.208, accuracy: 0.832
  2497. epoch 35 iter 70, loss: 2.210, accuracy: 0.831
  2498. epoch 35 iter 80, loss: 1.992, accuracy: 0.825
  2499. epoch 35 iter 90, loss: 2.364, accuracy: 0.818
  2500. epoch 35 iter 100, loss: 2.068, accuracy: 0.826
  2501. epoch 35 iter 110, loss: 1.953, accuracy: 0.830
  2502. epoch 35 iter 120, loss: 2.069, accuracy: 0.831
  2503. epoch 35 iter 130, loss: 2.086, accuracy: 0.825
  2504. epoch 35 iter 140, loss: 2.196, accuracy: 0.832
  2505. epoch 35 iter 150, loss: 2.134, accuracy: 0.837
  2506. epoch 35 iter 160, loss: 2.250, accuracy: 0.824
  2507. epoch 35 iter 170, loss: 1.883, accuracy: 0.818
  2508. epoch 35 iter 180, loss: 2.137, accuracy: 0.822
  2509. epoch 35 iter 190, loss: 2.018, accuracy: 0.819
  2510. epoch 35 iter 200, loss: 2.146, accuracy: 0.828
  2511. epoch 35 iter 210, loss: 2.381, accuracy: 0.821
  2512. epoch 35 iter 220, loss: 2.258, accuracy: 0.816
  2513. epoch 35 iter 230, loss: 2.190, accuracy: 0.824
  2514. epoch 35 iter 240, loss: 2.166, accuracy: 0.825
  2515. epoch 35 iter 250, loss: 2.080, accuracy: 0.825
  2516. epoch 35 iter 260, loss: 2.143, accuracy: 0.834
  2517. epoch 35 iter 270, loss: 2.109, accuracy: 0.837
  2518. epoch 35 iter 280, loss: 2.243, accuracy: 0.837
  2519. epoch 36 iter 0, loss: 2.168, accuracy: 0.831
  2520. epoch 36 iter 10, loss: 2.265, accuracy: 0.834
  2521. epoch 36 iter 20, loss: 2.016, accuracy: 0.836
  2522. epoch 36 iter 30, loss: 2.205, accuracy: 0.829
  2523. epoch 36 iter 40, loss: 2.123, accuracy: 0.830
  2524. epoch 36 iter 50, loss: 2.220, accuracy: 0.824
  2525. epoch 36 iter 60, loss: 2.266, accuracy: 0.832
  2526. epoch 36 iter 70, loss: 2.317, accuracy: 0.827
  2527. epoch 36 iter 80, loss: 2.076, accuracy: 0.821
  2528. epoch 36 iter 90, loss: 2.383, accuracy: 0.822
  2529. epoch 36 iter 100, loss: 2.143, accuracy: 0.828
  2530. epoch 36 iter 110, loss: 2.157, accuracy: 0.834
  2531. epoch 36 iter 120, loss: 1.930, accuracy: 0.826
  2532. epoch 36 iter 130, loss: 2.089, accuracy: 0.827
  2533. epoch 36 iter 140, loss: 2.093, accuracy: 0.839
  2534. epoch 36 iter 150, loss: 2.161, accuracy: 0.834
  2535. epoch 36 iter 160, loss: 2.170, accuracy: 0.822
  2536. epoch 36 iter 170, loss: 2.008, accuracy: 0.820
  2537. epoch 36 iter 180, loss: 2.136, accuracy: 0.830
  2538. epoch 36 iter 190, loss: 2.106, accuracy: 0.823
  2539. epoch 36 iter 200, loss: 2.237, accuracy: 0.826
  2540. epoch 36 iter 210, loss: 2.501, accuracy: 0.828
  2541. epoch 36 iter 220, loss: 2.642, accuracy: 0.812
  2542. epoch 36 iter 230, loss: 2.018, accuracy: 0.818
  2543. epoch 36 iter 240, loss: 2.270, accuracy: 0.824
  2544. epoch 36 iter 250, loss: 2.060, accuracy: 0.831
  2545. epoch 36 iter 260, loss: 2.499, accuracy: 0.828
  2546. epoch 36 iter 270, loss: 2.174, accuracy: 0.827
  2547. epoch 36 iter 280, loss: 2.289, accuracy: 0.837
  2548. epoch 37 iter 0, loss: 2.263, accuracy: 0.831
  2549. epoch 37 iter 10, loss: 2.082, accuracy: 0.836
  2550. epoch 37 iter 20, loss: 2.124, accuracy: 0.829
  2551. epoch 37 iter 30, loss: 2.139, accuracy: 0.830
  2552. epoch 37 iter 40, loss: 2.205, accuracy: 0.830
  2553. epoch 37 iter 50, loss: 2.300, accuracy: 0.831
  2554. epoch 37 iter 60, loss: 2.171, accuracy: 0.821
  2555. epoch 37 iter 70, loss: 2.330, accuracy: 0.825
  2556. epoch 37 iter 80, loss: 2.124, accuracy: 0.831
  2557. epoch 37 iter 90, loss: 2.483, accuracy: 0.820
  2558. epoch 37 iter 100, loss: 2.134, accuracy: 0.834
  2559. epoch 37 iter 110, loss: 2.181, accuracy: 0.832
  2560. epoch 37 iter 120, loss: 2.042, accuracy: 0.831
  2561. epoch 37 iter 130, loss: 1.948, accuracy: 0.825
  2562. epoch 37 iter 140, loss: 2.156, accuracy: 0.838
  2563. epoch 37 iter 150, loss: 2.314, accuracy: 0.835
  2564. epoch 37 iter 160, loss: 2.178, accuracy: 0.832
  2565. epoch 37 iter 170, loss: 2.106, accuracy: 0.818
  2566. epoch 37 iter 180, loss: 2.239, accuracy: 0.828
  2567. epoch 37 iter 190, loss: 2.021, accuracy: 0.825
  2568. epoch 37 iter 200, loss: 2.091, accuracy: 0.823
  2569. epoch 37 iter 210, loss: 2.425, accuracy: 0.826
  2570. epoch 37 iter 220, loss: 2.376, accuracy: 0.815
  2571. epoch 37 iter 230, loss: 2.250, accuracy: 0.817
  2572. epoch 37 iter 240, loss: 2.281, accuracy: 0.822
  2573. epoch 37 iter 250, loss: 2.006, accuracy: 0.838
  2574. epoch 37 iter 260, loss: 2.357, accuracy: 0.828
  2575. epoch 37 iter 270, loss: 2.456, accuracy: 0.819
  2576. epoch 37 iter 280, loss: 2.396, accuracy: 0.838
  2577. epoch 38 iter 0, loss: 2.175, accuracy: 0.831
  2578. epoch 38 iter 10, loss: 2.177, accuracy: 0.834
  2579. epoch 38 iter 20, loss: 2.261, accuracy: 0.832
  2580. epoch 38 iter 30, loss: 2.221, accuracy: 0.834
  2581. epoch 38 iter 40, loss: 2.218, accuracy: 0.831
  2582. epoch 38 iter 50, loss: 2.386, accuracy: 0.825
  2583. epoch 38 iter 60, loss: 2.126, accuracy: 0.825
  2584. epoch 38 iter 70, loss: 2.243, accuracy: 0.831
  2585. epoch 38 iter 80, loss: 2.209, accuracy: 0.831
  2586. epoch 38 iter 90, loss: 2.149, accuracy: 0.827
  2587. epoch 38 iter 100, loss: 2.263, accuracy: 0.829
  2588. epoch 38 iter 110, loss: 2.207, accuracy: 0.819
  2589. epoch 38 iter 120, loss: 2.106, accuracy: 0.823
  2590. epoch 38 iter 130, loss: 2.047, accuracy: 0.825
  2591. epoch 38 iter 140, loss: 2.164, accuracy: 0.825
  2592. epoch 38 iter 150, loss: 2.321, accuracy: 0.823
  2593. epoch 38 iter 160, loss: 2.135, accuracy: 0.835
  2594. epoch 38 iter 170, loss: 2.131, accuracy: 0.821
  2595. epoch 38 iter 180, loss: 2.330, accuracy: 0.823
  2596. epoch 38 iter 190, loss: 2.216, accuracy: 0.832
  2597. epoch 38 iter 200, loss: 2.261, accuracy: 0.835
  2598. epoch 38 iter 210, loss: 2.760, accuracy: 0.822
  2599. epoch 38 iter 220, loss: 2.315, accuracy: 0.827
  2600. epoch 38 iter 230, loss: 2.338, accuracy: 0.828
  2601. epoch 38 iter 240, loss: 2.383, accuracy: 0.826
  2602. epoch 38 iter 250, loss: 2.109, accuracy: 0.831
  2603. epoch 38 iter 260, loss: 2.224, accuracy: 0.830
  2604. epoch 38 iter 270, loss: 2.422, accuracy: 0.825
  2605. epoch 38 iter 280, loss: 2.445, accuracy: 0.835
  2606. epoch 39 iter 0, loss: 2.324, accuracy: 0.831
  2607. epoch 39 iter 10, loss: 2.182, accuracy: 0.830
  2608. epoch 39 iter 20, loss: 2.246, accuracy: 0.834
  2609. epoch 39 iter 30, loss: 2.418, accuracy: 0.832
  2610. epoch 39 iter 40, loss: 2.190, accuracy: 0.830
  2611. epoch 39 iter 50, loss: 2.238, accuracy: 0.835
  2612. epoch 39 iter 60, loss: 2.270, accuracy: 0.826
  2613. epoch 39 iter 70, loss: 2.282, accuracy: 0.833
  2614. epoch 39 iter 80, loss: 2.343, accuracy: 0.828
  2615. epoch 39 iter 90, loss: 2.294, accuracy: 0.825
  2616. epoch 39 iter 100, loss: 2.454, accuracy: 0.832
  2617. epoch 39 iter 110, loss: 2.115, accuracy: 0.831
  2618. epoch 39 iter 120, loss: 2.273, accuracy: 0.835
  2619. epoch 39 iter 130, loss: 2.060, accuracy: 0.834
  2620. epoch 39 iter 140, loss: 2.140, accuracy: 0.833
  2621. epoch 39 iter 150, loss: 2.513, accuracy: 0.824
  2622. epoch 39 iter 160, loss: 2.116, accuracy: 0.835
  2623. epoch 39 iter 170, loss: 2.108, accuracy: 0.830
  2624. epoch 39 iter 180, loss: 2.380, accuracy: 0.831
  2625. epoch 39 iter 190, loss: 2.286, accuracy: 0.826
  2626. epoch 39 iter 200, loss: 2.276, accuracy: 0.834
  2627. epoch 39 iter 210, loss: 2.597, accuracy: 0.826
  2628. epoch 39 iter 220, loss: 2.420, accuracy: 0.824
  2629. epoch 39 iter 230, loss: 2.367, accuracy: 0.834
  2630. epoch 39 iter 240, loss: 2.391, accuracy: 0.830
  2631. epoch 39 iter 250, loss: 2.240, accuracy: 0.831
  2632. epoch 39 iter 260, loss: 2.382, accuracy: 0.832
  2633. epoch 39 iter 270, loss: 2.435, accuracy: 0.830
  2634. epoch 39 iter 280, loss: 2.424, accuracy: 0.839
  2635. epoch 40 iter 0, loss: 2.459, accuracy: 0.839
  2636. epoch 40 iter 10, loss: 2.286, accuracy: 0.842
  2637. epoch 40 iter 20, loss: 2.330, accuracy: 0.841
  2638. epoch 40 iter 30, loss: 2.113, accuracy: 0.838
  2639. epoch 40 iter 40, loss: 2.405, accuracy: 0.836
  2640. epoch 40 iter 50, loss: 2.402, accuracy: 0.835
  2641. epoch 40 iter 60, loss: 2.328, accuracy: 0.836
  2642. epoch 40 iter 70, loss: 2.423, accuracy: 0.840
  2643. epoch 40 iter 80, loss: 2.481, accuracy: 0.828
  2644. epoch 40 iter 90, loss: 2.229, accuracy: 0.828
  2645. epoch 40 iter 100, loss: 2.553, accuracy: 0.838
  2646. epoch 40 iter 110, loss: 2.352, accuracy: 0.835
  2647. epoch 40 iter 120, loss: 2.250, accuracy: 0.836
  2648. epoch 40 iter 130, loss: 2.274, accuracy: 0.833
  2649. epoch 40 iter 140, loss: 2.249, accuracy: 0.838
  2650. epoch 40 iter 150, loss: 2.612, accuracy: 0.829
  2651. epoch 40 iter 160, loss: 2.316, accuracy: 0.839
  2652. epoch 40 iter 170, loss: 2.149, accuracy: 0.831
  2653. epoch 40 iter 180, loss: 2.498, accuracy: 0.833
  2654. epoch 40 iter 190, loss: 2.335, accuracy: 0.824
  2655. epoch 40 iter 200, loss: 2.380, accuracy: 0.826
  2656. epoch 40 iter 210, loss: 2.768, accuracy: 0.823
  2657. epoch 40 iter 220, loss: 2.495, accuracy: 0.828
  2658. epoch 40 iter 230, loss: 2.593, accuracy: 0.829
  2659. epoch 40 iter 240, loss: 2.526, accuracy: 0.815
  2660. epoch 40 iter 250, loss: 2.419, accuracy: 0.826
  2661. epoch 40 iter 260, loss: 2.396, accuracy: 0.825
  2662. epoch 40 iter 270, loss: 2.677, accuracy: 0.820
  2663. epoch 40 iter 280, loss: 2.648, accuracy: 0.831
  2664. epoch 41 iter 0, loss: 2.491, accuracy: 0.826
  2665. epoch 41 iter 10, loss: 2.581, accuracy: 0.831
  2666. epoch 41 iter 20, loss: 2.329, accuracy: 0.837
  2667. epoch 41 iter 30, loss: 2.249, accuracy: 0.832
  2668. epoch 41 iter 40, loss: 2.481, accuracy: 0.824
  2669. epoch 41 iter 50, loss: 2.454, accuracy: 0.832
  2670. epoch 41 iter 60, loss: 2.374, accuracy: 0.831
  2671. epoch 41 iter 70, loss: 2.459, accuracy: 0.833
  2672. epoch 41 iter 80, loss: 2.451, accuracy: 0.827
  2673. epoch 41 iter 90, loss: 2.298, accuracy: 0.819
  2674. epoch 41 iter 100, loss: 2.671, accuracy: 0.832
  2675. epoch 41 iter 110, loss: 2.446, accuracy: 0.825
  2676. epoch 41 iter 120, loss: 2.569, accuracy: 0.831
  2677. epoch 41 iter 130, loss: 2.341, accuracy: 0.835
  2678. epoch 41 iter 140, loss: 2.329, accuracy: 0.836
  2679. epoch 41 iter 150, loss: 2.742, accuracy: 0.828
  2680. epoch 41 iter 160, loss: 2.293, accuracy: 0.832
  2681. epoch 41 iter 170, loss: 2.198, accuracy: 0.828
  2682. epoch 41 iter 180, loss: 2.430, accuracy: 0.831
  2683. epoch 41 iter 190, loss: 2.314, accuracy: 0.829
  2684. epoch 41 iter 200, loss: 2.379, accuracy: 0.835
  2685. epoch 41 iter 210, loss: 2.677, accuracy: 0.828
  2686. epoch 41 iter 220, loss: 2.572, accuracy: 0.831
  2687. epoch 41 iter 230, loss: 2.383, accuracy: 0.826
  2688. epoch 41 iter 240, loss: 2.583, accuracy: 0.820
  2689. epoch 41 iter 250, loss: 2.366, accuracy: 0.832
  2690. epoch 41 iter 260, loss: 2.572, accuracy: 0.838
  2691. epoch 41 iter 270, loss: 2.524, accuracy: 0.829
  2692. epoch 41 iter 280, loss: 2.558, accuracy: 0.844
  2693. epoch 42 iter 0, loss: 2.729, accuracy: 0.830
  2694. epoch 42 iter 10, loss: 2.429, accuracy: 0.829
  2695. epoch 42 iter 20, loss: 2.529, accuracy: 0.838
  2696. epoch 42 iter 30, loss: 2.401, accuracy: 0.835
  2697. epoch 42 iter 40, loss: 2.525, accuracy: 0.831
  2698. epoch 42 iter 50, loss: 2.515, accuracy: 0.832
  2699. epoch 42 iter 60, loss: 2.467, accuracy: 0.829
  2700. epoch 42 iter 70, loss: 2.504, accuracy: 0.833
  2701. epoch 42 iter 80, loss: 2.517, accuracy: 0.834
  2702. epoch 42 iter 90, loss: 2.330, accuracy: 0.829
  2703. epoch 42 iter 100, loss: 2.693, accuracy: 0.829
  2704. epoch 42 iter 110, loss: 2.604, accuracy: 0.825
  2705. epoch 42 iter 120, loss: 2.681, accuracy: 0.830
  2706. epoch 42 iter 130, loss: 2.374, accuracy: 0.828
  2707. epoch 42 iter 140, loss: 2.435, accuracy: 0.830
  2708. epoch 42 iter 150, loss: 2.692, accuracy: 0.828
  2709. epoch 42 iter 160, loss: 2.538, accuracy: 0.837
  2710. epoch 42 iter 170, loss: 2.392, accuracy: 0.831
  2711. epoch 42 iter 180, loss: 2.543, accuracy: 0.837
  2712. epoch 42 iter 190, loss: 2.675, accuracy: 0.827
  2713. epoch 42 iter 200, loss: 2.454, accuracy: 0.834
  2714. epoch 42 iter 210, loss: 2.830, accuracy: 0.823
  2715. epoch 42 iter 220, loss: 2.698, accuracy: 0.830
  2716. epoch 42 iter 230, loss: 2.467, accuracy: 0.832
  2717. epoch 42 iter 240, loss: 2.559, accuracy: 0.818
  2718. epoch 42 iter 250, loss: 2.417, accuracy: 0.830
  2719. epoch 42 iter 260, loss: 2.534, accuracy: 0.838
  2720. epoch 42 iter 270, loss: 2.690, accuracy: 0.827
  2721. epoch 42 iter 280, loss: 2.649, accuracy: 0.839
  2722. epoch 43 iter 0, loss: 2.602, accuracy: 0.832
  2723. epoch 43 iter 10, loss: 2.580, accuracy: 0.837
  2724. epoch 43 iter 20, loss: 2.425, accuracy: 0.836
  2725. epoch 43 iter 30, loss: 2.626, accuracy: 0.841
  2726. epoch 43 iter 40, loss: 2.576, accuracy: 0.835
  2727. epoch 43 iter 50, loss: 2.671, accuracy: 0.837
  2728. epoch 43 iter 60, loss: 2.517, accuracy: 0.834
  2729. epoch 43 iter 70, loss: 2.648, accuracy: 0.838
  2730. epoch 43 iter 80, loss: 2.561, accuracy: 0.831
  2731. epoch 43 iter 90, loss: 2.464, accuracy: 0.830
  2732. epoch 43 iter 100, loss: 2.817, accuracy: 0.827
  2733. epoch 43 iter 110, loss: 2.695, accuracy: 0.807
  2734. epoch 43 iter 120, loss: 2.663, accuracy: 0.822
  2735. epoch 43 iter 130, loss: 2.389, accuracy: 0.822
  2736. epoch 43 iter 140, loss: 2.577, accuracy: 0.825
  2737. epoch 43 iter 150, loss: 2.821, accuracy: 0.821
  2738. epoch 43 iter 160, loss: 2.548, accuracy: 0.828
  2739. epoch 43 iter 170, loss: 2.428, accuracy: 0.823
  2740. epoch 43 iter 180, loss: 2.696, accuracy: 0.834
  2741. epoch 43 iter 190, loss: 2.557, accuracy: 0.830
  2742. epoch 43 iter 200, loss: 2.669, accuracy: 0.828
  2743. epoch 43 iter 210, loss: 2.859, accuracy: 0.828
  2744. epoch 43 iter 220, loss: 2.890, accuracy: 0.827
  2745. epoch 43 iter 230, loss: 2.735, accuracy: 0.830
  2746. epoch 43 iter 240, loss: 2.692, accuracy: 0.829
  2747. epoch 43 iter 250, loss: 2.545, accuracy: 0.831
  2748. epoch 43 iter 260, loss: 2.596, accuracy: 0.840
  2749. epoch 43 iter 270, loss: 2.793, accuracy: 0.828
  2750. epoch 43 iter 280, loss: 2.844, accuracy: 0.832
  2751. epoch 44 iter 0, loss: 2.697, accuracy: 0.835
  2752. epoch 44 iter 10, loss: 2.641, accuracy: 0.832
  2753. epoch 44 iter 20, loss: 2.486, accuracy: 0.838
  2754. epoch 44 iter 30, loss: 2.457, accuracy: 0.840
  2755. epoch 44 iter 40, loss: 2.573, accuracy: 0.840
  2756. epoch 44 iter 50, loss: 2.566, accuracy: 0.835
  2757. epoch 44 iter 60, loss: 2.469, accuracy: 0.838
  2758. epoch 44 iter 70, loss: 2.617, accuracy: 0.832
  2759. epoch 44 iter 80, loss: 2.596, accuracy: 0.827
  2760. epoch 44 iter 90, loss: 2.639, accuracy: 0.836
  2761. epoch 44 iter 100, loss: 2.718, accuracy: 0.831
  2762. epoch 44 iter 110, loss: 2.697, accuracy: 0.823
  2763. epoch 44 iter 120, loss: 2.535, accuracy: 0.830
  2764. epoch 44 iter 130, loss: 2.584, accuracy: 0.830
  2765. epoch 44 iter 140, loss: 2.603, accuracy: 0.830
  2766. epoch 44 iter 150, loss: 3.027, accuracy: 0.822
  2767. epoch 44 iter 160, loss: 2.659, accuracy: 0.826
  2768. epoch 44 iter 170, loss: 2.487, accuracy: 0.820
  2769. epoch 44 iter 180, loss: 2.600, accuracy: 0.838
  2770. epoch 44 iter 190, loss: 2.564, accuracy: 0.829
  2771. epoch 44 iter 200, loss: 2.588, accuracy: 0.835
  2772. epoch 44 iter 210, loss: 2.717, accuracy: 0.833
  2773. epoch 44 iter 220, loss: 2.895, accuracy: 0.833
  2774. epoch 44 iter 230, loss: 2.781, accuracy: 0.829
  2775. epoch 44 iter 240, loss: 2.948, accuracy: 0.821
  2776. epoch 44 iter 250, loss: 2.658, accuracy: 0.826
  2777. epoch 44 iter 260, loss: 2.889, accuracy: 0.838
  2778. epoch 44 iter 270, loss: 2.934, accuracy: 0.818
  2779. epoch 44 iter 280, loss: 2.884, accuracy: 0.833
  2780. epoch 45 iter 0, loss: 2.951, accuracy: 0.830
  2781. epoch 45 iter 10, loss: 3.041, accuracy: 0.829
  2782. epoch 45 iter 20, loss: 2.579, accuracy: 0.830
  2783. epoch 45 iter 30, loss: 2.563, accuracy: 0.833
  2784. epoch 45 iter 40, loss: 2.885, accuracy: 0.830
  2785. epoch 45 iter 50, loss: 2.585, accuracy: 0.821
  2786. epoch 45 iter 60, loss: 2.657, accuracy: 0.835
  2787. epoch 45 iter 70, loss: 2.702, accuracy: 0.833
  2788. epoch 45 iter 80, loss: 2.704, accuracy: 0.829
  2789. epoch 45 iter 90, loss: 2.812, accuracy: 0.830
  2790. epoch 45 iter 100, loss: 2.911, accuracy: 0.825
  2791. epoch 45 iter 110, loss: 2.693, accuracy: 0.825
  2792. epoch 45 iter 120, loss: 2.632, accuracy: 0.831
  2793. epoch 45 iter 130, loss: 2.538, accuracy: 0.833
  2794. epoch 45 iter 140, loss: 2.664, accuracy: 0.826
  2795. epoch 45 iter 150, loss: 3.083, accuracy: 0.823
  2796. epoch 45 iter 160, loss: 2.650, accuracy: 0.834
  2797. epoch 45 iter 170, loss: 2.472, accuracy: 0.821
  2798. epoch 45 iter 180, loss: 2.909, accuracy: 0.833
  2799. epoch 45 iter 190, loss: 2.726, accuracy: 0.834
  2800. epoch 45 iter 200, loss: 2.568, accuracy: 0.833
  2801. epoch 45 iter 210, loss: 2.788, accuracy: 0.831
  2802. epoch 45 iter 220, loss: 2.810, accuracy: 0.834
  2803. epoch 45 iter 230, loss: 2.694, accuracy: 0.831
  2804. epoch 45 iter 240, loss: 2.715, accuracy: 0.817
  2805. epoch 45 iter 250, loss: 2.667, accuracy: 0.830
  2806. epoch 45 iter 260, loss: 2.978, accuracy: 0.839
  2807. epoch 45 iter 270, loss: 3.134, accuracy: 0.823
  2808. epoch 45 iter 280, loss: 2.797, accuracy: 0.833
  2809. epoch 46 iter 0, loss: 2.808, accuracy: 0.838
  2810. epoch 46 iter 10, loss: 2.930, accuracy: 0.834
  2811. epoch 46 iter 20, loss: 2.768, accuracy: 0.838
  2812. epoch 46 iter 30, loss: 2.543, accuracy: 0.836
  2813. epoch 46 iter 40, loss: 2.843, accuracy: 0.835
  2814. epoch 46 iter 50, loss: 2.583, accuracy: 0.826
  2815. epoch 46 iter 60, loss: 2.647, accuracy: 0.833
  2816. epoch 46 iter 70, loss: 2.684, accuracy: 0.835
  2817. epoch 46 iter 80, loss: 2.673, accuracy: 0.835
  2818. epoch 46 iter 90, loss: 2.666, accuracy: 0.826
  2819. epoch 46 iter 100, loss: 2.888, accuracy: 0.831
  2820. epoch 46 iter 110, loss: 2.788, accuracy: 0.832
  2821. epoch 46 iter 120, loss: 2.726, accuracy: 0.831
  2822. epoch 46 iter 130, loss: 2.912, accuracy: 0.829
  2823. epoch 46 iter 140, loss: 2.580, accuracy: 0.819
  2824. epoch 46 iter 150, loss: 3.166, accuracy: 0.817
  2825. epoch 46 iter 160, loss: 2.828, accuracy: 0.818
  2826. epoch 46 iter 170, loss: 2.610, accuracy: 0.821
  2827. epoch 46 iter 180, loss: 2.943, accuracy: 0.831
  2828. epoch 46 iter 190, loss: 2.906, accuracy: 0.831
  2829. epoch 46 iter 200, loss: 2.638, accuracy: 0.833
  2830. epoch 46 iter 210, loss: 2.861, accuracy: 0.824
  2831. epoch 46 iter 220, loss: 2.818, accuracy: 0.831
  2832. epoch 46 iter 230, loss: 2.880, accuracy: 0.831
  2833. epoch 46 iter 240, loss: 2.931, accuracy: 0.818
  2834. epoch 46 iter 250, loss: 2.757, accuracy: 0.828
  2835. epoch 46 iter 260, loss: 2.804, accuracy: 0.844
  2836. epoch 46 iter 270, loss: 3.141, accuracy: 0.828
  2837. epoch 46 iter 280, loss: 2.798, accuracy: 0.825
  2838. epoch 47 iter 0, loss: 2.758, accuracy: 0.838
  2839. epoch 47 iter 10, loss: 2.998, accuracy: 0.832
  2840. epoch 47 iter 20, loss: 2.564, accuracy: 0.831
  2841. epoch 47 iter 30, loss: 2.757, accuracy: 0.839
  2842. epoch 47 iter 40, loss: 2.719, accuracy: 0.840
  2843. epoch 47 iter 50, loss: 2.735, accuracy: 0.836
  2844. epoch 47 iter 60, loss: 2.564, accuracy: 0.840
  2845. epoch 47 iter 70, loss: 2.859, accuracy: 0.826
  2846. epoch 47 iter 80, loss: 2.669, accuracy: 0.836
  2847. epoch 47 iter 90, loss: 2.562, accuracy: 0.832
  2848. epoch 47 iter 100, loss: 3.135, accuracy: 0.826
  2849. epoch 47 iter 110, loss: 2.983, accuracy: 0.826
  2850. epoch 47 iter 120, loss: 2.870, accuracy: 0.831
  2851. epoch 47 iter 130, loss: 2.629, accuracy: 0.833
  2852. epoch 47 iter 140, loss: 2.586, accuracy: 0.831
  2853. epoch 47 iter 150, loss: 2.959, accuracy: 0.830
  2854. epoch 47 iter 160, loss: 2.745, accuracy: 0.832
  2855. epoch 47 iter 170, loss: 2.523, accuracy: 0.829
  2856. epoch 47 iter 180, loss: 2.712, accuracy: 0.838
  2857. epoch 47 iter 190, loss: 2.859, accuracy: 0.831
  2858. epoch 47 iter 200, loss: 2.732, accuracy: 0.833
  2859. epoch 47 iter 210, loss: 2.877, accuracy: 0.829
  2860. epoch 47 iter 220, loss: 3.051, accuracy: 0.831
  2861. epoch 47 iter 230, loss: 2.716, accuracy: 0.825
  2862. epoch 47 iter 240, loss: 2.774, accuracy: 0.827
  2863. epoch 47 iter 250, loss: 2.923, accuracy: 0.820
  2864. epoch 47 iter 260, loss: 2.840, accuracy: 0.843
  2865. epoch 47 iter 270, loss: 3.154, accuracy: 0.822
  2866. epoch 47 iter 280, loss: 3.161, accuracy: 0.825
  2867. epoch 48 iter 0, loss: 3.052, accuracy: 0.842
  2868. epoch 48 iter 10, loss: 2.892, accuracy: 0.831
  2869. epoch 48 iter 20, loss: 2.884, accuracy: 0.840
  2870. epoch 48 iter 30, loss: 2.770, accuracy: 0.840
  2871. epoch 48 iter 40, loss: 2.978, accuracy: 0.838
  2872. epoch 48 iter 50, loss: 2.794, accuracy: 0.839
  2873. epoch 48 iter 60, loss: 2.623, accuracy: 0.832
  2874. epoch 48 iter 70, loss: 2.884, accuracy: 0.829
  2875. epoch 48 iter 80, loss: 2.826, accuracy: 0.838
  2876. epoch 48 iter 90, loss: 2.664, accuracy: 0.829
  2877. epoch 48 iter 100, loss: 2.907, accuracy: 0.828
  2878. epoch 48 iter 110, loss: 2.847, accuracy: 0.839
  2879. epoch 48 iter 120, loss: 2.887, accuracy: 0.819
  2880. epoch 48 iter 130, loss: 2.995, accuracy: 0.834
  2881. epoch 48 iter 140, loss: 2.578, accuracy: 0.832
  2882. epoch 48 iter 150, loss: 3.308, accuracy: 0.815
  2883. epoch 48 iter 160, loss: 3.066, accuracy: 0.828
  2884. epoch 48 iter 170, loss: 2.681, accuracy: 0.834
  2885. epoch 48 iter 180, loss: 2.818, accuracy: 0.834
  2886. epoch 48 iter 190, loss: 3.102, accuracy: 0.826
  2887. epoch 48 iter 200, loss: 2.993, accuracy: 0.835
  2888. epoch 48 iter 210, loss: 3.177, accuracy: 0.824
  2889. epoch 48 iter 220, loss: 3.187, accuracy: 0.835
  2890. epoch 48 iter 230, loss: 3.221, accuracy: 0.822
  2891. epoch 48 iter 240, loss: 2.956, accuracy: 0.818
  2892. epoch 48 iter 250, loss: 3.175, accuracy: 0.817
  2893. epoch 48 iter 260, loss: 2.914, accuracy: 0.842
  2894. epoch 48 iter 270, loss: 2.911, accuracy: 0.842
  2895. epoch 48 iter 280, loss: 3.169, accuracy: 0.837
  2896. epoch 49 iter 0, loss: 3.014, accuracy: 0.839
  2897. epoch 49 iter 10, loss: 2.963, accuracy: 0.834
  2898. epoch 49 iter 20, loss: 3.000, accuracy: 0.841
  2899. epoch 49 iter 30, loss: 2.867, accuracy: 0.838
  2900. epoch 49 iter 40, loss: 3.060, accuracy: 0.837
  2901. epoch 49 iter 50, loss: 2.927, accuracy: 0.834
  2902. epoch 49 iter 60, loss: 2.716, accuracy: 0.839
  2903. epoch 49 iter 70, loss: 3.017, accuracy: 0.834
  2904. epoch 49 iter 80, loss: 2.832, accuracy: 0.831
  2905. epoch 49 iter 90, loss: 2.865, accuracy: 0.832
  2906. epoch 49 iter 100, loss: 2.927, accuracy: 0.836
  2907. epoch 49 iter 110, loss: 2.912, accuracy: 0.835
  2908. epoch 49 iter 120, loss: 2.761, accuracy: 0.826
  2909. epoch 49 iter 130, loss: 2.920, accuracy: 0.830
  2910. epoch 49 iter 140, loss: 2.696, accuracy: 0.837
  2911. epoch 49 iter 150, loss: 2.974, accuracy: 0.833
  2912. epoch 49 iter 160, loss: 3.438, accuracy: 0.820
  2913. epoch 49 iter 170, loss: 2.886, accuracy: 0.828
  2914. epoch 49 iter 180, loss: 2.949, accuracy: 0.833
  2915. epoch 49 iter 190, loss: 3.247, accuracy: 0.828
  2916. epoch 49 iter 200, loss: 2.999, accuracy: 0.830
  2917. epoch 49 iter 210, loss: 3.207, accuracy: 0.834
  2918. epoch 49 iter 220, loss: 3.207, accuracy: 0.836
  2919. epoch 49 iter 230, loss: 3.037, accuracy: 0.835
  2920. epoch 49 iter 240, loss: 2.793, accuracy: 0.823
  2921. epoch 49 iter 250, loss: 3.012, accuracy: 0.823
  2922. epoch 49 iter 260, loss: 3.242, accuracy: 0.839
  2923. epoch 49 iter 270, loss: 2.998, accuracy: 0.830
  2924. epoch 49 iter 280, loss: 3.102, accuracy: 0.831
  2925. current filter size is: 64
  2926. 2018-10-22 23:34:19.142023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  2927. 2018-10-22 23:34:19.142070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  2928. 2018-10-22 23:34:19.142077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  2929. 2018-10-22 23:34:19.142082: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  2930. 2018-10-22 23:34:19.142224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11251 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-12GB, pci bus id: 0000:02:00.0, compute capability: 6.0)
  2931. epoch 0 iter 0, loss: 118.245, accuracy: 0.189
  2932. epoch 0 iter 10, loss: 2.288, accuracy: 0.150
  2933. epoch 0 iter 20, loss: 2.244, accuracy: 0.174
  2934. epoch 0 iter 30, loss: 2.245, accuracy: 0.192
  2935. epoch 0 iter 40, loss: 2.239, accuracy: 0.195
  2936. epoch 0 iter 50, loss: 2.214, accuracy: 0.213
  2937. epoch 0 iter 60, loss: 2.189, accuracy: 0.221
  2938. epoch 0 iter 70, loss: 2.218, accuracy: 0.200
  2939. epoch 0 iter 80, loss: 2.172, accuracy: 0.222
  2940. epoch 0 iter 90, loss: 2.188, accuracy: 0.206
  2941. epoch 0 iter 100, loss: 2.150, accuracy: 0.231
  2942. epoch 0 iter 110, loss: 2.134, accuracy: 0.226
  2943. epoch 0 iter 120, loss: 2.104, accuracy: 0.257
  2944. epoch 0 iter 130, loss: 2.119, accuracy: 0.263
  2945. epoch 0 iter 140, loss: 2.102, accuracy: 0.254
  2946. epoch 0 iter 150, loss: 2.082, accuracy: 0.268
  2947. epoch 0 iter 160, loss: 2.074, accuracy: 0.265
  2948. epoch 0 iter 170, loss: 2.075, accuracy: 0.265
  2949. epoch 0 iter 180, loss: 2.077, accuracy: 0.258
  2950. epoch 0 iter 190, loss: 2.060, accuracy: 0.272
  2951. epoch 0 iter 200, loss: 2.078, accuracy: 0.263
  2952. epoch 0 iter 210, loss: 2.053, accuracy: 0.278
  2953. epoch 0 iter 220, loss: 2.050, accuracy: 0.272
  2954. epoch 0 iter 230, loss: 2.128, accuracy: 0.237
  2955. epoch 0 iter 240, loss: 2.071, accuracy: 0.262
  2956. epoch 0 iter 250, loss: 2.046, accuracy: 0.270
  2957. epoch 0 iter 260, loss: 2.028, accuracy: 0.284
  2958. epoch 0 iter 270, loss: 2.023, accuracy: 0.286
  2959. epoch 0 iter 280, loss: 2.045, accuracy: 0.279
  2960. epoch 1 iter 0, loss: 2.078, accuracy: 0.260
  2961. epoch 1 iter 10, loss: 2.034, accuracy: 0.278
  2962. epoch 1 iter 20, loss: 2.023, accuracy: 0.285
  2963. epoch 1 iter 30, loss: 2.064, accuracy: 0.260
  2964. epoch 1 iter 40, loss: 2.058, accuracy: 0.280
  2965. epoch 1 iter 50, loss: 2.022, accuracy: 0.283
  2966. epoch 1 iter 60, loss: 2.038, accuracy: 0.271
  2967. epoch 1 iter 70, loss: 2.017, accuracy: 0.287
  2968. epoch 1 iter 80, loss: 1.984, accuracy: 0.303
  2969. epoch 1 iter 90, loss: 2.233, accuracy: 0.171
  2970. epoch 1 iter 100, loss: 2.225, accuracy: 0.200
  2971. epoch 1 iter 110, loss: 2.218, accuracy: 0.206
  2972. epoch 1 iter 120, loss: 2.229, accuracy: 0.201
  2973. epoch 1 iter 130, loss: 2.215, accuracy: 0.207
  2974. epoch 1 iter 140, loss: 2.229, accuracy: 0.196
  2975. epoch 1 iter 150, loss: 2.227, accuracy: 0.196
  2976. epoch 1 iter 160, loss: 2.221, accuracy: 0.198
  2977. epoch 1 iter 170, loss: 2.218, accuracy: 0.207
  2978. epoch 1 iter 180, loss: 2.215, accuracy: 0.210
  2979. epoch 1 iter 190, loss: 2.209, accuracy: 0.216
  2980. epoch 1 iter 200, loss: 2.205, accuracy: 0.221
  2981. epoch 1 iter 210, loss: 2.246, accuracy: 0.203
  2982. epoch 1 iter 220, loss: 2.222, accuracy: 0.199
  2983. epoch 1 iter 230, loss: 2.221, accuracy: 0.200
  2984. epoch 1 iter 240, loss: 2.214, accuracy: 0.203
  2985. epoch 1 iter 250, loss: 2.193, accuracy: 0.225
  2986. epoch 1 iter 260, loss: 2.220, accuracy: 0.198
  2987. epoch 1 iter 270, loss: 2.221, accuracy: 0.198
  2988. epoch 1 iter 280, loss: 2.217, accuracy: 0.204
  2989. epoch 2 iter 0, loss: 2.205, accuracy: 0.218
  2990. epoch 2 iter 10, loss: 2.181, accuracy: 0.230
  2991. epoch 2 iter 20, loss: 2.166, accuracy: 0.243
  2992. epoch 2 iter 30, loss: 2.222, accuracy: 0.198
  2993. epoch 2 iter 40, loss: 2.224, accuracy: 0.197
  2994. epoch 2 iter 50, loss: 2.224, accuracy: 0.197
  2995. epoch 2 iter 60, loss: 2.223, accuracy: 0.197
  2996. epoch 2 iter 70, loss: 2.222, accuracy: 0.197
  2997. epoch 2 iter 80, loss: 2.222, accuracy: 0.197
  2998. epoch 2 iter 90, loss: 2.221, accuracy: 0.200
  2999. epoch 2 iter 100, loss: 2.203, accuracy: 0.222
  3000. epoch 2 iter 110, loss: 2.218, accuracy: 0.203
  3001. epoch 2 iter 120, loss: 2.166, accuracy: 0.240
  3002. epoch 2 iter 130, loss: 2.122, accuracy: 0.261
  3003. epoch 2 iter 140, loss: 2.141, accuracy: 0.266
  3004. epoch 2 iter 150, loss: 2.032, accuracy: 0.322
  3005. epoch 2 iter 160, loss: 2.059, accuracy: 0.303
  3006. epoch 2 iter 170, loss: 2.019, accuracy: 0.319
  3007. epoch 2 iter 180, loss: 1.836, accuracy: 0.385
  3008. epoch 2 iter 190, loss: 1.562, accuracy: 0.487
  3009. epoch 2 iter 200, loss: 1.511, accuracy: 0.514
  3010. epoch 2 iter 210, loss: 1.448, accuracy: 0.527
  3011. epoch 2 iter 220, loss: 1.375, accuracy: 0.561
  3012. epoch 2 iter 230, loss: 1.309, accuracy: 0.587
  3013. epoch 2 iter 240, loss: 1.222, accuracy: 0.619
  3014. epoch 2 iter 250, loss: 1.222, accuracy: 0.616
  3015. epoch 2 iter 260, loss: 1.228, accuracy: 0.624
  3016. epoch 2 iter 270, loss: 1.176, accuracy: 0.636
  3017. epoch 2 iter 280, loss: 1.214, accuracy: 0.622
  3018. epoch 3 iter 0, loss: 1.242, accuracy: 0.604
  3019. epoch 3 iter 10, loss: 1.116, accuracy: 0.656
  3020. epoch 3 iter 20, loss: 1.102, accuracy: 0.659
  3021. epoch 3 iter 30, loss: 1.089, accuracy: 0.668
  3022. epoch 3 iter 40, loss: 1.119, accuracy: 0.657
  3023. epoch 3 iter 50, loss: 1.086, accuracy: 0.671
  3024. epoch 3 iter 60, loss: 1.144, accuracy: 0.647
  3025. epoch 3 iter 70, loss: 1.070, accuracy: 0.671
  3026. epoch 3 iter 80, loss: 1.045, accuracy: 0.675
  3027. epoch 3 iter 90, loss: 1.028, accuracy: 0.681
  3028. epoch 3 iter 100, loss: 1.052, accuracy: 0.677
  3029. epoch 3 iter 110, loss: 1.040, accuracy: 0.682
  3030. epoch 3 iter 120, loss: 1.032, accuracy: 0.683
  3031. epoch 3 iter 130, loss: 1.009, accuracy: 0.695
  3032. epoch 3 iter 140, loss: 1.003, accuracy: 0.692
  3033. epoch 3 iter 150, loss: 0.999, accuracy: 0.697
  3034. epoch 3 iter 160, loss: 1.005, accuracy: 0.690
  3035. epoch 3 iter 170, loss: 0.999, accuracy: 0.694
  3036. epoch 3 iter 180, loss: 1.023, accuracy: 0.694
  3037. epoch 3 iter 190, loss: 1.026, accuracy: 0.684
  3038. epoch 3 iter 200, loss: 1.054, accuracy: 0.684
  3039. epoch 3 iter 210, loss: 0.988, accuracy: 0.702
  3040. epoch 3 iter 220, loss: 0.960, accuracy: 0.712
  3041. epoch 3 iter 230, loss: 1.017, accuracy: 0.697
  3042. epoch 3 iter 240, loss: 0.965, accuracy: 0.711
  3043. epoch 3 iter 250, loss: 0.963, accuracy: 0.713
  3044. epoch 3 iter 260, loss: 0.952, accuracy: 0.714
  3045. epoch 3 iter 270, loss: 0.963, accuracy: 0.710
  3046. epoch 3 iter 280, loss: 0.952, accuracy: 0.712
  3047. epoch 4 iter 0, loss: 0.921, accuracy: 0.723
  3048. epoch 4 iter 10, loss: 0.911, accuracy: 0.729
  3049. epoch 4 iter 20, loss: 0.913, accuracy: 0.727
  3050. epoch 4 iter 30, loss: 0.923, accuracy: 0.726
  3051. epoch 4 iter 40, loss: 0.922, accuracy: 0.723
  3052. epoch 4 iter 50, loss: 0.946, accuracy: 0.720
  3053. epoch 4 iter 60, loss: 0.916, accuracy: 0.726
  3054. epoch 4 iter 70, loss: 0.902, accuracy: 0.729
  3055. epoch 4 iter 80, loss: 0.894, accuracy: 0.730
  3056. epoch 4 iter 90, loss: 0.880, accuracy: 0.733
  3057. epoch 4 iter 100, loss: 0.906, accuracy: 0.728
  3058. epoch 4 iter 110, loss: 0.915, accuracy: 0.730
  3059. epoch 4 iter 120, loss: 0.900, accuracy: 0.733
  3060. epoch 4 iter 130, loss: 0.870, accuracy: 0.741
  3061. epoch 4 iter 140, loss: 0.892, accuracy: 0.737
  3062. epoch 4 iter 150, loss: 0.883, accuracy: 0.739
  3063. epoch 4 iter 160, loss: 0.880, accuracy: 0.740
  3064. epoch 4 iter 170, loss: 0.896, accuracy: 0.731
  3065. epoch 4 iter 180, loss: 0.925, accuracy: 0.732
  3066. epoch 4 iter 190, loss: 0.924, accuracy: 0.727
  3067. epoch 4 iter 200, loss: 0.895, accuracy: 0.734
  3068. epoch 4 iter 210, loss: 0.879, accuracy: 0.739
  3069. epoch 4 iter 220, loss: 0.864, accuracy: 0.749
  3070. epoch 4 iter 230, loss: 0.910, accuracy: 0.734
  3071. epoch 4 iter 240, loss: 0.882, accuracy: 0.740
  3072. epoch 4 iter 250, loss: 0.877, accuracy: 0.741
  3073. epoch 4 iter 260, loss: 0.853, accuracy: 0.748
  3074. epoch 4 iter 270, loss: 0.913, accuracy: 0.730
  3075. epoch 4 iter 280, loss: 0.898, accuracy: 0.737
  3076. epoch 5 iter 0, loss: 0.861, accuracy: 0.746
  3077. epoch 5 iter 10, loss: 0.846, accuracy: 0.752
  3078. epoch 5 iter 20, loss: 0.853, accuracy: 0.752
  3079. epoch 5 iter 30, loss: 0.904, accuracy: 0.739
  3080. epoch 5 iter 40, loss: 0.846, accuracy: 0.749
  3081. epoch 5 iter 50, loss: 0.866, accuracy: 0.747
  3082. epoch 5 iter 60, loss: 0.861, accuracy: 0.748
  3083. epoch 5 iter 70, loss: 0.832, accuracy: 0.755
  3084. epoch 5 iter 80, loss: 0.851, accuracy: 0.744
  3085. epoch 5 iter 90, loss: 0.828, accuracy: 0.754
  3086. epoch 5 iter 100, loss: 0.841, accuracy: 0.758
  3087. epoch 5 iter 110, loss: 0.842, accuracy: 0.753
  3088. epoch 5 iter 120, loss: 0.831, accuracy: 0.757
  3089. epoch 5 iter 130, loss: 0.835, accuracy: 0.757
  3090. epoch 5 iter 140, loss: 0.844, accuracy: 0.751
  3091. epoch 5 iter 150, loss: 0.818, accuracy: 0.764
  3092. epoch 5 iter 160, loss: 0.838, accuracy: 0.757
  3093. epoch 5 iter 170, loss: 0.842, accuracy: 0.751
  3094. epoch 5 iter 180, loss: 0.863, accuracy: 0.750
  3095. epoch 5 iter 190, loss: 0.842, accuracy: 0.755
  3096. epoch 5 iter 200, loss: 0.820, accuracy: 0.759
  3097. epoch 5 iter 210, loss: 0.834, accuracy: 0.756
  3098. epoch 5 iter 220, loss: 0.834, accuracy: 0.759
  3099. epoch 5 iter 230, loss: 0.885, accuracy: 0.744
  3100. epoch 5 iter 240, loss: 0.848, accuracy: 0.756
  3101. epoch 5 iter 250, loss: 0.822, accuracy: 0.762
  3102. epoch 5 iter 260, loss: 0.813, accuracy: 0.764
  3103. epoch 5 iter 270, loss: 0.890, accuracy: 0.741
  3104. epoch 5 iter 280, loss: 0.844, accuracy: 0.756
  3105. epoch 6 iter 0, loss: 0.822, accuracy: 0.763
  3106. epoch 6 iter 10, loss: 0.812, accuracy: 0.766
  3107. epoch 6 iter 20, loss: 0.858, accuracy: 0.752
  3108. epoch 6 iter 30, loss: 0.846, accuracy: 0.757
  3109. epoch 6 iter 40, loss: 0.810, accuracy: 0.765
  3110. epoch 6 iter 50, loss: 0.838, accuracy: 0.760
  3111. epoch 6 iter 60, loss: 0.859, accuracy: 0.757
  3112. epoch 6 iter 70, loss: 0.822, accuracy: 0.766
  3113. epoch 6 iter 80, loss: 0.819, accuracy: 0.758
  3114. epoch 6 iter 90, loss: 0.810, accuracy: 0.763
  3115. epoch 6 iter 100, loss: 0.866, accuracy: 0.756
  3116. epoch 6 iter 110, loss: 0.827, accuracy: 0.763
  3117. epoch 6 iter 120, loss: 0.820, accuracy: 0.763
  3118. epoch 6 iter 130, loss: 0.796, accuracy: 0.770
  3119. epoch 6 iter 140, loss: 0.815, accuracy: 0.763
  3120. epoch 6 iter 150, loss: 0.789, accuracy: 0.771
  3121. epoch 6 iter 160, loss: 0.827, accuracy: 0.766
  3122. epoch 6 iter 170, loss: 0.832, accuracy: 0.757
  3123. epoch 6 iter 180, loss: 0.865, accuracy: 0.750
  3124. epoch 6 iter 190, loss: 0.840, accuracy: 0.758
  3125. epoch 6 iter 200, loss: 0.808, accuracy: 0.766
  3126. epoch 6 iter 210, loss: 0.823, accuracy: 0.760
  3127. epoch 6 iter 220, loss: 0.799, accuracy: 0.771
  3128. epoch 6 iter 230, loss: 0.849, accuracy: 0.757
  3129. epoch 6 iter 240, loss: 0.822, accuracy: 0.764
  3130. epoch 6 iter 250, loss: 0.817, accuracy: 0.767
  3131. epoch 6 iter 260, loss: 0.830, accuracy: 0.763
  3132. epoch 6 iter 270, loss: 0.839, accuracy: 0.758
  3133. epoch 6 iter 280, loss: 0.841, accuracy: 0.757
  3134. epoch 7 iter 0, loss: 0.827, accuracy: 0.763
  3135. epoch 7 iter 10, loss: 0.844, accuracy: 0.761
  3136. epoch 7 iter 20, loss: 0.812, accuracy: 0.769
  3137. epoch 7 iter 30, loss: 0.855, accuracy: 0.757
  3138. epoch 7 iter 40, loss: 0.854, accuracy: 0.757
  3139. epoch 7 iter 50, loss: 0.831, accuracy: 0.758
  3140. epoch 7 iter 60, loss: 0.884, accuracy: 0.752
  3141. epoch 7 iter 70, loss: 0.852, accuracy: 0.763
  3142. epoch 7 iter 80, loss: 0.796, accuracy: 0.770
  3143. epoch 7 iter 90, loss: 0.802, accuracy: 0.770
  3144. epoch 7 iter 100, loss: 0.846, accuracy: 0.761
  3145. epoch 7 iter 110, loss: 0.820, accuracy: 0.766
  3146. epoch 7 iter 120, loss: 0.859, accuracy: 0.755
  3147. epoch 7 iter 130, loss: 0.824, accuracy: 0.765
  3148. epoch 7 iter 140, loss: 0.819, accuracy: 0.766
  3149. epoch 7 iter 150, loss: 0.781, accuracy: 0.776
  3150. epoch 7 iter 160, loss: 0.848, accuracy: 0.763
  3151. epoch 7 iter 170, loss: 0.833, accuracy: 0.761
  3152. epoch 7 iter 180, loss: 0.851, accuracy: 0.759
  3153. epoch 7 iter 190, loss: 0.825, accuracy: 0.770
  3154. epoch 7 iter 200, loss: 0.793, accuracy: 0.776
  3155. epoch 7 iter 210, loss: 0.810, accuracy: 0.768
  3156. epoch 7 iter 220, loss: 0.794, accuracy: 0.771
  3157. epoch 7 iter 230, loss: 0.843, accuracy: 0.761
  3158. epoch 7 iter 240, loss: 0.824, accuracy: 0.764
  3159. epoch 7 iter 250, loss: 0.849, accuracy: 0.759
  3160. epoch 7 iter 260, loss: 0.837, accuracy: 0.766
  3161. epoch 7 iter 270, loss: 0.844, accuracy: 0.760
  3162. epoch 7 iter 280, loss: 0.849, accuracy: 0.763
  3163. epoch 8 iter 0, loss: 0.879, accuracy: 0.756
  3164. epoch 8 iter 10, loss: 0.826, accuracy: 0.768
  3165. epoch 8 iter 20, loss: 0.795, accuracy: 0.774
  3166. epoch 8 iter 30, loss: 0.804, accuracy: 0.775
  3167. epoch 8 iter 40, loss: 0.789, accuracy: 0.777
  3168. epoch 8 iter 50, loss: 0.791, accuracy: 0.776
  3169. epoch 8 iter 60, loss: 0.855, accuracy: 0.758
  3170. epoch 8 iter 70, loss: 0.831, accuracy: 0.766
  3171. epoch 8 iter 80, loss: 0.813, accuracy: 0.770
  3172. epoch 8 iter 90, loss: 0.789, accuracy: 0.773
  3173. epoch 8 iter 100, loss: 0.820, accuracy: 0.774
  3174. epoch 8 iter 110, loss: 0.835, accuracy: 0.763
  3175. epoch 8 iter 120, loss: 0.938, accuracy: 0.746
  3176. epoch 8 iter 130, loss: 0.891, accuracy: 0.759
  3177. epoch 8 iter 140, loss: 0.846, accuracy: 0.766
  3178. epoch 8 iter 150, loss: 0.802, accuracy: 0.775
  3179. epoch 8 iter 160, loss: 0.852, accuracy: 0.765
  3180. epoch 8 iter 170, loss: 0.850, accuracy: 0.761
  3181. epoch 8 iter 180, loss: 0.842, accuracy: 0.762
  3182. epoch 8 iter 190, loss: 0.848, accuracy: 0.766
  3183. epoch 8 iter 200, loss: 0.832, accuracy: 0.773
  3184. epoch 8 iter 210, loss: 0.830, accuracy: 0.771
  3185. epoch 8 iter 220, loss: 0.792, accuracy: 0.777
  3186. epoch 8 iter 230, loss: 0.838, accuracy: 0.763
  3187. epoch 8 iter 240, loss: 0.857, accuracy: 0.761
  3188. epoch 8 iter 250, loss: 0.843, accuracy: 0.764
  3189. epoch 8 iter 260, loss: 0.866, accuracy: 0.759
  3190. epoch 8 iter 270, loss: 0.870, accuracy: 0.755
  3191. epoch 8 iter 280, loss: 0.866, accuracy: 0.764
  3192. epoch 9 iter 0, loss: 0.868, accuracy: 0.763
  3193. epoch 9 iter 10, loss: 0.841, accuracy: 0.769
  3194. epoch 9 iter 20, loss: 0.805, accuracy: 0.772
  3195. epoch 9 iter 30, loss: 0.808, accuracy: 0.775
  3196. epoch 9 iter 40, loss: 0.809, accuracy: 0.775
  3197. epoch 9 iter 50, loss: 0.791, accuracy: 0.776
  3198. epoch 9 iter 60, loss: 0.842, accuracy: 0.767
  3199. epoch 9 iter 70, loss: 0.806, accuracy: 0.774
  3200. epoch 9 iter 80, loss: 0.818, accuracy: 0.764
  3201. epoch 9 iter 90, loss: 0.786, accuracy: 0.778
  3202. epoch 9 iter 100, loss: 0.830, accuracy: 0.775
  3203. epoch 9 iter 110, loss: 0.829, accuracy: 0.770
  3204. epoch 9 iter 120, loss: 0.881, accuracy: 0.762
  3205. epoch 9 iter 130, loss: 1.014, accuracy: 0.744
  3206. epoch 9 iter 140, loss: 0.867, accuracy: 0.769
  3207. epoch 9 iter 150, loss: 0.829, accuracy: 0.776
  3208. epoch 9 iter 160, loss: 0.860, accuracy: 0.767
  3209. epoch 9 iter 170, loss: 0.851, accuracy: 0.764
  3210. epoch 9 iter 180, loss: 0.838, accuracy: 0.764
  3211. epoch 9 iter 190, loss: 0.826, accuracy: 0.773
  3212. epoch 9 iter 200, loss: 0.849, accuracy: 0.768
  3213. epoch 9 iter 210, loss: 0.873, accuracy: 0.768
  3214. epoch 9 iter 220, loss: 0.815, accuracy: 0.772
  3215. epoch 9 iter 230, loss: 0.836, accuracy: 0.772
  3216. epoch 9 iter 240, loss: 0.849, accuracy: 0.763
  3217. epoch 9 iter 250, loss: 0.842, accuracy: 0.766
  3218. epoch 9 iter 260, loss: 0.873, accuracy: 0.761
  3219. epoch 9 iter 270, loss: 0.871, accuracy: 0.767
  3220. epoch 9 iter 280, loss: 0.863, accuracy: 0.767
  3221. epoch 10 iter 0, loss: 0.850, accuracy: 0.772
  3222. epoch 10 iter 10, loss: 0.902, accuracy: 0.759
  3223. epoch 10 iter 20, loss: 0.821, accuracy: 0.770
  3224. epoch 10 iter 30, loss: 0.804, accuracy: 0.779
  3225. epoch 10 iter 40, loss: 0.829, accuracy: 0.773
  3226. epoch 10 iter 50, loss: 0.805, accuracy: 0.774
  3227. epoch 10 iter 60, loss: 0.850, accuracy: 0.767
  3228. epoch 10 iter 70, loss: 0.801, accuracy: 0.774
  3229. epoch 10 iter 80, loss: 0.821, accuracy: 0.769
  3230. epoch 10 iter 90, loss: 0.795, accuracy: 0.778
  3231. epoch 10 iter 100, loss: 0.827, accuracy: 0.778
  3232. epoch 10 iter 110, loss: 0.821, accuracy: 0.771
  3233. epoch 10 iter 120, loss: 0.939, accuracy: 0.748
  3234. epoch 10 iter 130, loss: 1.035, accuracy: 0.741
  3235. epoch 10 iter 140, loss: 0.923, accuracy: 0.765
  3236. epoch 10 iter 150, loss: 0.909, accuracy: 0.769
  3237. epoch 10 iter 160, loss: 0.884, accuracy: 0.768
  3238. epoch 10 iter 170, loss: 0.847, accuracy: 0.768
  3239. epoch 10 iter 180, loss: 0.863, accuracy: 0.766
  3240. epoch 10 iter 190, loss: 0.857, accuracy: 0.763
  3241. epoch 10 iter 200, loss: 0.827, accuracy: 0.774
  3242. epoch 10 iter 210, loss: 0.905, accuracy: 0.763
  3243. epoch 10 iter 220, loss: 0.850, accuracy: 0.767
  3244. epoch 10 iter 230, loss: 0.874, accuracy: 0.766
  3245. epoch 10 iter 240, loss: 0.884, accuracy: 0.766
  3246. epoch 10 iter 250, loss: 0.847, accuracy: 0.772
  3247. epoch 10 iter 260, loss: 0.873, accuracy: 0.767
  3248. epoch 10 iter 270, loss: 0.871, accuracy: 0.772
  3249. epoch 10 iter 280, loss: 0.864, accuracy: 0.769
  3250. epoch 11 iter 0, loss: 0.890, accuracy: 0.759
  3251. epoch 11 iter 10, loss: 0.848, accuracy: 0.771
  3252. epoch 11 iter 20, loss: 0.829, accuracy: 0.771
  3253. epoch 11 iter 30, loss: 0.822, accuracy: 0.776
  3254. epoch 11 iter 40, loss: 0.843, accuracy: 0.772
  3255. epoch 11 iter 50, loss: 0.807, accuracy: 0.780
  3256. epoch 11 iter 60, loss: 0.891, accuracy: 0.770
  3257. epoch 11 iter 70, loss: 0.806, accuracy: 0.780
  3258. epoch 11 iter 80, loss: 0.795, accuracy: 0.781
  3259. epoch 11 iter 90, loss: 0.773, accuracy: 0.788
  3260. epoch 11 iter 100, loss: 0.835, accuracy: 0.781
  3261. epoch 11 iter 110, loss: 0.803, accuracy: 0.784
  3262. epoch 11 iter 120, loss: 0.825, accuracy: 0.783
  3263. epoch 11 iter 130, loss: 0.965, accuracy: 0.764
  3264. epoch 11 iter 140, loss: 0.873, accuracy: 0.775
  3265. epoch 11 iter 150, loss: 0.861, accuracy: 0.779
  3266. epoch 11 iter 160, loss: 0.809, accuracy: 0.790
  3267. epoch 11 iter 170, loss: 0.815, accuracy: 0.783
  3268. epoch 11 iter 180, loss: 0.891, accuracy: 0.768
  3269. epoch 11 iter 190, loss: 0.831, accuracy: 0.773
  3270. epoch 11 iter 200, loss: 0.818, accuracy: 0.787
  3271. epoch 11 iter 210, loss: 0.793, accuracy: 0.791
  3272. epoch 11 iter 220, loss: 0.822, accuracy: 0.783
  3273. epoch 11 iter 230, loss: 0.832, accuracy: 0.790
  3274. epoch 11 iter 240, loss: 0.829, accuracy: 0.789
  3275. epoch 11 iter 250, loss: 0.807, accuracy: 0.782
  3276. epoch 11 iter 260, loss: 0.816, accuracy: 0.793
  3277. epoch 11 iter 270, loss: 0.940, accuracy: 0.764
  3278. epoch 11 iter 280, loss: 0.835, accuracy: 0.783
  3279. epoch 12 iter 0, loss: 0.850, accuracy: 0.783
  3280. epoch 12 iter 10, loss: 0.793, accuracy: 0.792
  3281. epoch 12 iter 20, loss: 0.778, accuracy: 0.793
  3282. epoch 12 iter 30, loss: 0.792, accuracy: 0.793
  3283. epoch 12 iter 40, loss: 0.796, accuracy: 0.787
  3284. epoch 12 iter 50, loss: 0.805, accuracy: 0.786
  3285. epoch 12 iter 60, loss: 0.884, accuracy: 0.782
  3286. epoch 12 iter 70, loss: 0.778, accuracy: 0.788
  3287. epoch 12 iter 80, loss: 0.750, accuracy: 0.798
  3288. epoch 12 iter 90, loss: 0.771, accuracy: 0.795
  3289. epoch 12 iter 100, loss: 0.780, accuracy: 0.799
  3290. epoch 12 iter 110, loss: 0.750, accuracy: 0.801
  3291. epoch 12 iter 120, loss: 0.819, accuracy: 0.791
  3292. epoch 12 iter 130, loss: 1.015, accuracy: 0.765
  3293. epoch 12 iter 140, loss: 0.858, accuracy: 0.783
  3294. epoch 12 iter 150, loss: 0.799, accuracy: 0.799
  3295. epoch 12 iter 160, loss: 0.785, accuracy: 0.796
  3296. epoch 12 iter 170, loss: 0.792, accuracy: 0.790
  3297. epoch 12 iter 180, loss: 0.829, accuracy: 0.791
  3298. epoch 12 iter 190, loss: 0.790, accuracy: 0.793
  3299. epoch 12 iter 200, loss: 0.796, accuracy: 0.796
  3300. epoch 12 iter 210, loss: 0.768, accuracy: 0.798
  3301. epoch 12 iter 220, loss: 0.758, accuracy: 0.805
  3302. epoch 12 iter 230, loss: 0.765, accuracy: 0.804
  3303. epoch 12 iter 240, loss: 0.806, accuracy: 0.800
  3304. epoch 12 iter 250, loss: 0.782, accuracy: 0.800
  3305. epoch 12 iter 260, loss: 0.773, accuracy: 0.808
  3306. epoch 12 iter 270, loss: 0.941, accuracy: 0.778
  3307. epoch 12 iter 280, loss: 0.786, accuracy: 0.800
  3308. epoch 13 iter 0, loss: 0.807, accuracy: 0.799
  3309. epoch 13 iter 10, loss: 0.777, accuracy: 0.807
  3310. epoch 13 iter 20, loss: 0.753, accuracy: 0.808
  3311. epoch 13 iter 30, loss: 0.825, accuracy: 0.790
  3312. epoch 13 iter 40, loss: 0.774, accuracy: 0.799
  3313. epoch 13 iter 50, loss: 0.799, accuracy: 0.797
  3314. epoch 13 iter 60, loss: 0.824, accuracy: 0.791
  3315. epoch 13 iter 70, loss: 0.754, accuracy: 0.810
  3316. epoch 13 iter 80, loss: 0.769, accuracy: 0.797
  3317. epoch 13 iter 90, loss: 0.787, accuracy: 0.799
  3318. epoch 13 iter 100, loss: 0.802, accuracy: 0.804
  3319. epoch 13 iter 110, loss: 0.820, accuracy: 0.798
  3320. epoch 13 iter 120, loss: 0.847, accuracy: 0.788
  3321. epoch 13 iter 130, loss: 1.039, accuracy: 0.771
  3322. epoch 13 iter 140, loss: 0.868, accuracy: 0.798
  3323. epoch 13 iter 150, loss: 0.865, accuracy: 0.796
  3324. epoch 13 iter 160, loss: 0.885, accuracy: 0.786
  3325. epoch 13 iter 170, loss: 0.765, accuracy: 0.800
  3326. epoch 13 iter 180, loss: 0.834, accuracy: 0.804
  3327. epoch 13 iter 190, loss: 0.773, accuracy: 0.809
  3328. epoch 13 iter 200, loss: 0.782, accuracy: 0.809
  3329. epoch 13 iter 210, loss: 0.796, accuracy: 0.800
  3330. epoch 13 iter 220, loss: 0.763, accuracy: 0.810
  3331. epoch 13 iter 230, loss: 0.739, accuracy: 0.814
  3332. epoch 13 iter 240, loss: 0.796, accuracy: 0.805
  3333. epoch 13 iter 250, loss: 0.793, accuracy: 0.802
  3334. epoch 13 iter 260, loss: 0.825, accuracy: 0.808
  3335. epoch 13 iter 270, loss: 0.932, accuracy: 0.781
  3336. epoch 13 iter 280, loss: 0.826, accuracy: 0.800
  3337. epoch 14 iter 0, loss: 0.866, accuracy: 0.804
  3338. epoch 14 iter 10, loss: 0.819, accuracy: 0.805
  3339. epoch 14 iter 20, loss: 0.807, accuracy: 0.808
  3340. epoch 14 iter 30, loss: 0.836, accuracy: 0.794
  3341. epoch 14 iter 40, loss: 0.810, accuracy: 0.794
  3342. epoch 14 iter 50, loss: 0.831, accuracy: 0.798
  3343. epoch 14 iter 60, loss: 0.797, accuracy: 0.805
  3344. epoch 14 iter 70, loss: 0.817, accuracy: 0.804
  3345. epoch 14 iter 80, loss: 0.810, accuracy: 0.796
  3346. epoch 14 iter 90, loss: 0.804, accuracy: 0.802
  3347. epoch 14 iter 100, loss: 0.818, accuracy: 0.806
  3348. epoch 14 iter 110, loss: 0.898, accuracy: 0.785
  3349. epoch 14 iter 120, loss: 0.889, accuracy: 0.786
  3350. epoch 14 iter 130, loss: 1.066, accuracy: 0.763
  3351. epoch 14 iter 140, loss: 0.917, accuracy: 0.798
  3352. epoch 14 iter 150, loss: 0.802, accuracy: 0.811
  3353. epoch 14 iter 160, loss: 0.844, accuracy: 0.791
  3354. epoch 14 iter 170, loss: 0.785, accuracy: 0.809
  3355. epoch 14 iter 180, loss: 0.859, accuracy: 0.804
  3356. epoch 14 iter 190, loss: 0.790, accuracy: 0.810
  3357. epoch 14 iter 200, loss: 0.801, accuracy: 0.811
  3358. epoch 14 iter 210, loss: 0.735, accuracy: 0.812
  3359. epoch 14 iter 220, loss: 0.770, accuracy: 0.807
  3360. epoch 14 iter 230, loss: 0.765, accuracy: 0.812
  3361. epoch 14 iter 240, loss: 0.814, accuracy: 0.808
  3362. epoch 14 iter 250, loss: 0.794, accuracy: 0.809
  3363. epoch 14 iter 260, loss: 0.806, accuracy: 0.813
  3364. epoch 14 iter 270, loss: 1.028, accuracy: 0.777
  3365. epoch 14 iter 280, loss: 0.937, accuracy: 0.794
  3366. epoch 15 iter 0, loss: 0.856, accuracy: 0.810
  3367. epoch 15 iter 10, loss: 0.845, accuracy: 0.807
  3368. epoch 15 iter 20, loss: 0.830, accuracy: 0.806
  3369. epoch 15 iter 30, loss: 0.779, accuracy: 0.813
  3370. epoch 15 iter 40, loss: 0.803, accuracy: 0.806
  3371. epoch 15 iter 50, loss: 0.843, accuracy: 0.807
  3372. epoch 15 iter 60, loss: 0.804, accuracy: 0.797
  3373. epoch 15 iter 70, loss: 0.855, accuracy: 0.803
  3374. epoch 15 iter 80, loss: 0.819, accuracy: 0.789
  3375. epoch 15 iter 90, loss: 0.805, accuracy: 0.809
  3376. epoch 15 iter 100, loss: 0.831, accuracy: 0.804
  3377. epoch 15 iter 110, loss: 0.866, accuracy: 0.802
  3378. epoch 15 iter 120, loss: 0.847, accuracy: 0.801
  3379. epoch 15 iter 130, loss: 1.044, accuracy: 0.775
  3380. epoch 15 iter 140, loss: 0.864, accuracy: 0.800
  3381. epoch 15 iter 150, loss: 0.821, accuracy: 0.813
  3382. epoch 15 iter 160, loss: 0.826, accuracy: 0.812
  3383. epoch 15 iter 170, loss: 0.830, accuracy: 0.817
  3384. epoch 15 iter 180, loss: 0.891, accuracy: 0.814
  3385. epoch 15 iter 190, loss: 0.821, accuracy: 0.814
  3386. epoch 15 iter 200, loss: 0.812, accuracy: 0.816
  3387. epoch 15 iter 210, loss: 0.788, accuracy: 0.811
  3388. epoch 15 iter 220, loss: 0.769, accuracy: 0.819
  3389. epoch 15 iter 230, loss: 0.826, accuracy: 0.805
  3390. epoch 15 iter 240, loss: 0.851, accuracy: 0.816
  3391. epoch 15 iter 250, loss: 0.813, accuracy: 0.810
  3392. epoch 15 iter 260, loss: 0.917, accuracy: 0.801
  3393. epoch 15 iter 270, loss: 0.929, accuracy: 0.796
  3394. epoch 15 iter 280, loss: 0.948, accuracy: 0.791
  3395. epoch 16 iter 0, loss: 0.968, accuracy: 0.804
  3396. epoch 16 iter 10, loss: 0.917, accuracy: 0.807
  3397. epoch 16 iter 20, loss: 0.830, accuracy: 0.814
  3398. epoch 16 iter 30, loss: 0.813, accuracy: 0.811
  3399. epoch 16 iter 40, loss: 0.853, accuracy: 0.813
  3400. epoch 16 iter 50, loss: 0.849, accuracy: 0.807
  3401. epoch 16 iter 60, loss: 0.846, accuracy: 0.798
  3402. epoch 16 iter 70, loss: 0.828, accuracy: 0.812
  3403. epoch 16 iter 80, loss: 0.830, accuracy: 0.798
  3404. epoch 16 iter 90, loss: 0.835, accuracy: 0.806
  3405. epoch 16 iter 100, loss: 0.835, accuracy: 0.814
  3406. epoch 16 iter 110, loss: 0.872, accuracy: 0.800
  3407. epoch 16 iter 120, loss: 0.829, accuracy: 0.821
  3408. epoch 16 iter 130, loss: 0.991, accuracy: 0.785
  3409. epoch 16 iter 140, loss: 0.951, accuracy: 0.805
  3410. epoch 16 iter 150, loss: 0.917, accuracy: 0.803
  3411. epoch 16 iter 160, loss: 0.896, accuracy: 0.807
  3412. epoch 16 iter 170, loss: 0.905, accuracy: 0.814
  3413. epoch 16 iter 180, loss: 0.878, accuracy: 0.807
  3414. epoch 16 iter 190, loss: 0.861, accuracy: 0.814
  3415. epoch 16 iter 200, loss: 0.853, accuracy: 0.814
  3416. epoch 16 iter 210, loss: 0.829, accuracy: 0.814
  3417. epoch 16 iter 220, loss: 0.826, accuracy: 0.814
  3418. epoch 16 iter 230, loss: 0.805, accuracy: 0.820
  3419. epoch 16 iter 240, loss: 0.896, accuracy: 0.804
  3420. epoch 16 iter 250, loss: 0.880, accuracy: 0.808
  3421. epoch 16 iter 260, loss: 0.880, accuracy: 0.810
  3422. epoch 16 iter 270, loss: 0.886, accuracy: 0.808
  3423. epoch 16 iter 280, loss: 1.003, accuracy: 0.797
  3424. epoch 17 iter 0, loss: 0.985, accuracy: 0.787
  3425. epoch 17 iter 10, loss: 0.940, accuracy: 0.794
  3426. epoch 17 iter 20, loss: 0.861, accuracy: 0.806
  3427. epoch 17 iter 30, loss: 0.842, accuracy: 0.805
  3428. epoch 17 iter 40, loss: 0.832, accuracy: 0.812
  3429. epoch 17 iter 50, loss: 0.850, accuracy: 0.811
  3430. epoch 17 iter 60, loss: 0.827, accuracy: 0.812
  3431. epoch 17 iter 70, loss: 0.848, accuracy: 0.811
  3432. epoch 17 iter 80, loss: 0.816, accuracy: 0.810
  3433. epoch 17 iter 90, loss: 0.841, accuracy: 0.808
  3434. epoch 17 iter 100, loss: 0.885, accuracy: 0.813
  3435. epoch 17 iter 110, loss: 0.896, accuracy: 0.814
  3436. epoch 17 iter 120, loss: 0.830, accuracy: 0.819
  3437. epoch 17 iter 130, loss: 1.020, accuracy: 0.796
  3438. epoch 17 iter 140, loss: 1.010, accuracy: 0.802
  3439. epoch 17 iter 150, loss: 0.915, accuracy: 0.808
  3440. epoch 17 iter 160, loss: 0.975, accuracy: 0.802
  3441. epoch 17 iter 170, loss: 1.036, accuracy: 0.806
  3442. epoch 17 iter 180, loss: 0.852, accuracy: 0.815
  3443. epoch 17 iter 190, loss: 0.926, accuracy: 0.809
  3444. epoch 17 iter 200, loss: 0.888, accuracy: 0.812
  3445. epoch 17 iter 210, loss: 0.830, accuracy: 0.815
  3446. epoch 17 iter 220, loss: 0.851, accuracy: 0.816
  3447. epoch 17 iter 230, loss: 0.842, accuracy: 0.817
  3448. epoch 17 iter 240, loss: 0.935, accuracy: 0.805
  3449. epoch 17 iter 250, loss: 0.898, accuracy: 0.807
  3450. epoch 17 iter 260, loss: 0.915, accuracy: 0.806
  3451. epoch 17 iter 270, loss: 0.993, accuracy: 0.808
  3452. epoch 17 iter 280, loss: 1.120, accuracy: 0.795
  3453. epoch 18 iter 0, loss: 0.989, accuracy: 0.809
  3454. epoch 18 iter 10, loss: 0.997, accuracy: 0.809
  3455. epoch 18 iter 20, loss: 0.982, accuracy: 0.794
  3456. epoch 18 iter 30, loss: 0.954, accuracy: 0.805
  3457. epoch 18 iter 40, loss: 0.934, accuracy: 0.806
  3458. epoch 18 iter 50, loss: 0.933, accuracy: 0.811
  3459. epoch 18 iter 60, loss: 0.905, accuracy: 0.801
  3460. epoch 18 iter 70, loss: 0.888, accuracy: 0.813
  3461. epoch 18 iter 80, loss: 0.873, accuracy: 0.813
  3462. epoch 18 iter 90, loss: 0.873, accuracy: 0.803
  3463. epoch 18 iter 100, loss: 0.886, accuracy: 0.817
  3464. epoch 18 iter 110, loss: 1.040, accuracy: 0.810
  3465. epoch 18 iter 120, loss: 0.881, accuracy: 0.815
  3466. epoch 18 iter 130, loss: 1.006, accuracy: 0.802
  3467. epoch 18 iter 140, loss: 1.110, accuracy: 0.796
  3468. epoch 18 iter 150, loss: 1.018, accuracy: 0.806
  3469. epoch 18 iter 160, loss: 0.960, accuracy: 0.807
  3470. epoch 18 iter 170, loss: 1.027, accuracy: 0.790
  3471. epoch 18 iter 180, loss: 0.958, accuracy: 0.798
  3472. epoch 18 iter 190, loss: 1.057, accuracy: 0.807
  3473. epoch 18 iter 200, loss: 0.883, accuracy: 0.815
  3474. epoch 18 iter 210, loss: 0.906, accuracy: 0.815
  3475. epoch 18 iter 220, loss: 0.892, accuracy: 0.813
  3476. epoch 18 iter 230, loss: 0.929, accuracy: 0.809
  3477. epoch 18 iter 240, loss: 0.934, accuracy: 0.815
  3478. epoch 18 iter 250, loss: 0.970, accuracy: 0.807
  3479. epoch 18 iter 260, loss: 0.911, accuracy: 0.817
  3480. epoch 18 iter 270, loss: 1.010, accuracy: 0.810
  3481. epoch 18 iter 280, loss: 1.116, accuracy: 0.800
  3482. epoch 19 iter 0, loss: 1.036, accuracy: 0.809
  3483. epoch 19 iter 10, loss: 1.087, accuracy: 0.812
  3484. epoch 19 iter 20, loss: 0.962, accuracy: 0.811
  3485. epoch 19 iter 30, loss: 0.974, accuracy: 0.816
  3486. epoch 19 iter 40, loss: 0.951, accuracy: 0.817
  3487. epoch 19 iter 50, loss: 0.948, accuracy: 0.814
  3488. epoch 19 iter 60, loss: 0.956, accuracy: 0.811
  3489. epoch 19 iter 70, loss: 0.987, accuracy: 0.806
  3490. epoch 19 iter 80, loss: 0.907, accuracy: 0.816
  3491. epoch 19 iter 90, loss: 0.945, accuracy: 0.808
  3492. epoch 19 iter 100, loss: 0.936, accuracy: 0.821
  3493. epoch 19 iter 110, loss: 1.070, accuracy: 0.810
  3494. epoch 19 iter 120, loss: 0.999, accuracy: 0.818
  3495. epoch 19 iter 130, loss: 1.123, accuracy: 0.802
  3496. epoch 19 iter 140, loss: 1.165, accuracy: 0.803
  3497. epoch 19 iter 150, loss: 1.048, accuracy: 0.806
  3498. epoch 19 iter 160, loss: 1.054, accuracy: 0.802
  3499. epoch 19 iter 170, loss: 1.084, accuracy: 0.806
  3500. epoch 19 iter 180, loss: 1.008, accuracy: 0.818
  3501. epoch 19 iter 190, loss: 1.045, accuracy: 0.815
  3502. epoch 19 iter 200, loss: 0.971, accuracy: 0.812
  3503. epoch 19 iter 210, loss: 0.980, accuracy: 0.816
  3504. epoch 19 iter 220, loss: 0.907, accuracy: 0.818
  3505. epoch 19 iter 230, loss: 0.947, accuracy: 0.808
  3506. epoch 19 iter 240, loss: 0.980, accuracy: 0.818
  3507. epoch 19 iter 250, loss: 1.076, accuracy: 0.806
  3508. epoch 19 iter 260, loss: 0.901, accuracy: 0.821
  3509. epoch 19 iter 270, loss: 1.038, accuracy: 0.813
  3510. epoch 19 iter 280, loss: 1.169, accuracy: 0.810
  3511. epoch 20 iter 0, loss: 1.115, accuracy: 0.801
  3512. epoch 20 iter 10, loss: 1.233, accuracy: 0.810
  3513. epoch 20 iter 20, loss: 0.973, accuracy: 0.814
  3514. epoch 20 iter 30, loss: 1.094, accuracy: 0.808
  3515. epoch 20 iter 40, loss: 1.039, accuracy: 0.814
  3516. epoch 20 iter 50, loss: 1.006, accuracy: 0.817
  3517. epoch 20 iter 60, loss: 1.038, accuracy: 0.817
  3518. epoch 20 iter 70, loss: 1.001, accuracy: 0.815
  3519. epoch 20 iter 80, loss: 0.918, accuracy: 0.814
  3520. epoch 20 iter 90, loss: 1.019, accuracy: 0.803
  3521. epoch 20 iter 100, loss: 0.948, accuracy: 0.825
  3522. epoch 20 iter 110, loss: 1.064, accuracy: 0.811
  3523. epoch 20 iter 120, loss: 1.028, accuracy: 0.820
  3524. epoch 20 iter 130, loss: 1.083, accuracy: 0.811
  3525. epoch 20 iter 140, loss: 1.118, accuracy: 0.805
  3526. epoch 20 iter 150, loss: 1.153, accuracy: 0.812
  3527. epoch 20 iter 160, loss: 1.000, accuracy: 0.811
  3528. epoch 20 iter 170, loss: 1.160, accuracy: 0.800
  3529. epoch 20 iter 180, loss: 1.167, accuracy: 0.806
  3530. epoch 20 iter 190, loss: 1.120, accuracy: 0.822
  3531. epoch 20 iter 200, loss: 1.101, accuracy: 0.808
  3532. epoch 20 iter 210, loss: 1.021, accuracy: 0.812
  3533. epoch 20 iter 220, loss: 1.003, accuracy: 0.820
  3534. epoch 20 iter 230, loss: 0.939, accuracy: 0.826
  3535. epoch 20 iter 240, loss: 1.101, accuracy: 0.814
  3536. epoch 20 iter 250, loss: 1.006, accuracy: 0.809
  3537. epoch 20 iter 260, loss: 1.103, accuracy: 0.812
  3538. epoch 20 iter 270, loss: 1.079, accuracy: 0.817
  3539. epoch 20 iter 280, loss: 1.265, accuracy: 0.808
  3540. epoch 21 iter 0, loss: 1.173, accuracy: 0.816
  3541. epoch 21 iter 10, loss: 1.230, accuracy: 0.812
  3542. epoch 21 iter 20, loss: 1.073, accuracy: 0.817
  3543. epoch 21 iter 30, loss: 1.161, accuracy: 0.801
  3544. epoch 21 iter 40, loss: 1.100, accuracy: 0.809
  3545. epoch 21 iter 50, loss: 1.111, accuracy: 0.809
  3546. epoch 21 iter 60, loss: 1.045, accuracy: 0.806
  3547. epoch 21 iter 70, loss: 1.034, accuracy: 0.812
  3548. epoch 21 iter 80, loss: 1.039, accuracy: 0.806
  3549. epoch 21 iter 90, loss: 1.048, accuracy: 0.804
  3550. epoch 21 iter 100, loss: 1.008, accuracy: 0.816
  3551. epoch 21 iter 110, loss: 1.131, accuracy: 0.815
  3552. epoch 21 iter 120, loss: 1.242, accuracy: 0.804
  3553. epoch 21 iter 130, loss: 1.132, accuracy: 0.810
  3554. epoch 21 iter 140, loss: 1.109, accuracy: 0.802
  3555. epoch 21 iter 150, loss: 1.335, accuracy: 0.803
  3556. epoch 21 iter 160, loss: 0.982, accuracy: 0.820
  3557. epoch 21 iter 170, loss: 1.147, accuracy: 0.821
  3558. epoch 21 iter 180, loss: 1.241, accuracy: 0.818
  3559. epoch 21 iter 190, loss: 1.094, accuracy: 0.826
  3560. epoch 21 iter 200, loss: 1.144, accuracy: 0.815
  3561. epoch 21 iter 210, loss: 1.154, accuracy: 0.799
  3562. epoch 21 iter 220, loss: 1.080, accuracy: 0.817
  3563. epoch 21 iter 230, loss: 0.997, accuracy: 0.814
  3564. epoch 21 iter 240, loss: 1.160, accuracy: 0.817
  3565. epoch 21 iter 250, loss: 1.101, accuracy: 0.809
  3566. epoch 21 iter 260, loss: 1.075, accuracy: 0.818
  3567. epoch 21 iter 270, loss: 1.232, accuracy: 0.808
  3568. epoch 21 iter 280, loss: 1.269, accuracy: 0.815
  3569. epoch 22 iter 0, loss: 1.307, accuracy: 0.814
  3570. epoch 22 iter 10, loss: 1.257, accuracy: 0.803
  3571. epoch 22 iter 20, loss: 1.182, accuracy: 0.823
  3572. epoch 22 iter 30, loss: 1.195, accuracy: 0.803
  3573. epoch 22 iter 40, loss: 1.114, accuracy: 0.809
  3574. epoch 22 iter 50, loss: 1.278, accuracy: 0.795
  3575. epoch 22 iter 60, loss: 1.093, accuracy: 0.810
  3576. epoch 22 iter 70, loss: 1.129, accuracy: 0.811
  3577. epoch 22 iter 80, loss: 1.114, accuracy: 0.804
  3578. epoch 22 iter 90, loss: 1.174, accuracy: 0.798
  3579. epoch 22 iter 100, loss: 1.026, accuracy: 0.818
  3580. epoch 22 iter 110, loss: 1.125, accuracy: 0.821
  3581. epoch 22 iter 120, loss: 1.196, accuracy: 0.812
  3582. epoch 22 iter 130, loss: 1.177, accuracy: 0.811
  3583. epoch 22 iter 140, loss: 1.152, accuracy: 0.806
  3584. epoch 22 iter 150, loss: 1.372, accuracy: 0.800
  3585. epoch 22 iter 160, loss: 1.066, accuracy: 0.791
  3586. epoch 22 iter 170, loss: 1.238, accuracy: 0.797
  3587. epoch 22 iter 180, loss: 1.277, accuracy: 0.815
  3588. epoch 22 iter 190, loss: 1.181, accuracy: 0.818
  3589. epoch 22 iter 200, loss: 1.152, accuracy: 0.816
  3590. epoch 22 iter 210, loss: 1.108, accuracy: 0.814
  3591. epoch 22 iter 220, loss: 1.135, accuracy: 0.820
  3592. epoch 22 iter 230, loss: 1.016, accuracy: 0.819
  3593. epoch 22 iter 240, loss: 1.116, accuracy: 0.825
  3594. epoch 22 iter 250, loss: 1.113, accuracy: 0.821
  3595. epoch 22 iter 260, loss: 1.118, accuracy: 0.816
  3596. epoch 22 iter 270, loss: 1.203, accuracy: 0.816
  3597. epoch 22 iter 280, loss: 1.226, accuracy: 0.816
  3598. epoch 23 iter 0, loss: 1.378, accuracy: 0.806
  3599. epoch 23 iter 10, loss: 1.305, accuracy: 0.810
  3600. epoch 23 iter 20, loss: 1.204, accuracy: 0.824
  3601. epoch 23 iter 30, loss: 1.225, accuracy: 0.804
  3602. epoch 23 iter 40, loss: 1.067, accuracy: 0.810
  3603. epoch 23 iter 50, loss: 1.173, accuracy: 0.820
  3604. epoch 23 iter 60, loss: 1.196, accuracy: 0.808
  3605. epoch 23 iter 70, loss: 1.091, accuracy: 0.820
  3606. epoch 23 iter 80, loss: 1.083, accuracy: 0.818
  3607. epoch 23 iter 90, loss: 1.113, accuracy: 0.814
  3608. epoch 23 iter 100, loss: 1.063, accuracy: 0.815
  3609. epoch 23 iter 110, loss: 1.162, accuracy: 0.816
  3610. epoch 23 iter 120, loss: 1.289, accuracy: 0.801
  3611. epoch 23 iter 130, loss: 1.136, accuracy: 0.816
  3612. epoch 23 iter 140, loss: 1.230, accuracy: 0.797
  3613. epoch 23 iter 150, loss: 1.281, accuracy: 0.800
  3614. epoch 23 iter 160, loss: 1.162, accuracy: 0.800
  3615. epoch 23 iter 170, loss: 1.239, accuracy: 0.809
  3616. epoch 23 iter 180, loss: 1.163, accuracy: 0.815
  3617. epoch 23 iter 190, loss: 1.245, accuracy: 0.817
  3618. epoch 23 iter 200, loss: 1.220, accuracy: 0.814
  3619. epoch 23 iter 210, loss: 1.092, accuracy: 0.808
  3620. epoch 23 iter 220, loss: 1.279, accuracy: 0.801
  3621. epoch 23 iter 230, loss: 1.131, accuracy: 0.824
  3622. epoch 23 iter 240, loss: 1.154, accuracy: 0.812
  3623. epoch 23 iter 250, loss: 1.152, accuracy: 0.820
  3624. epoch 23 iter 260, loss: 1.219, accuracy: 0.807
  3625. epoch 23 iter 270, loss: 1.379, accuracy: 0.811
  3626. epoch 23 iter 280, loss: 1.296, accuracy: 0.816
  3627. epoch 24 iter 0, loss: 1.468, accuracy: 0.802
  3628. epoch 24 iter 10, loss: 1.321, accuracy: 0.815
  3629. epoch 24 iter 20, loss: 1.214, accuracy: 0.827
  3630. epoch 24 iter 30, loss: 1.214, accuracy: 0.800
  3631. epoch 24 iter 40, loss: 1.169, accuracy: 0.809
  3632. epoch 24 iter 50, loss: 1.201, accuracy: 0.813
  3633. epoch 24 iter 60, loss: 1.284, accuracy: 0.808
  3634. epoch 24 iter 70, loss: 1.112, accuracy: 0.816
  3635. epoch 24 iter 80, loss: 1.096, accuracy: 0.821
  3636. epoch 24 iter 90, loss: 1.111, accuracy: 0.814
  3637. epoch 24 iter 100, loss: 1.134, accuracy: 0.820
  3638. epoch 24 iter 110, loss: 1.145, accuracy: 0.828
  3639. epoch 24 iter 120, loss: 1.375, accuracy: 0.804
  3640. epoch 24 iter 130, loss: 1.230, accuracy: 0.814
  3641. epoch 24 iter 140, loss: 1.364, accuracy: 0.790
  3642. epoch 24 iter 150, loss: 1.333, accuracy: 0.798
  3643. epoch 24 iter 160, loss: 1.421, accuracy: 0.802
  3644. epoch 24 iter 170, loss: 1.189, accuracy: 0.814
  3645. epoch 24 iter 180, loss: 1.240, accuracy: 0.817
  3646. epoch 24 iter 190, loss: 1.256, accuracy: 0.817
  3647. epoch 24 iter 200, loss: 1.227, accuracy: 0.821
  3648. epoch 24 iter 210, loss: 1.150, accuracy: 0.822
  3649. epoch 24 iter 220, loss: 1.209, accuracy: 0.820
  3650. epoch 24 iter 230, loss: 1.232, accuracy: 0.821
  3651. epoch 24 iter 240, loss: 1.222, accuracy: 0.825
  3652. epoch 24 iter 250, loss: 1.145, accuracy: 0.816
  3653. epoch 24 iter 260, loss: 1.144, accuracy: 0.811
  3654. epoch 24 iter 270, loss: 1.521, accuracy: 0.797
  3655. epoch 24 iter 280, loss: 1.301, accuracy: 0.816
  3656. epoch 25 iter 0, loss: 1.375, accuracy: 0.814
  3657. epoch 25 iter 10, loss: 1.322, accuracy: 0.812
  3658. epoch 25 iter 20, loss: 1.251, accuracy: 0.822
  3659. epoch 25 iter 30, loss: 1.212, accuracy: 0.811
  3660. epoch 25 iter 40, loss: 1.136, accuracy: 0.822
  3661. epoch 25 iter 50, loss: 1.224, accuracy: 0.815
  3662. epoch 25 iter 60, loss: 1.325, accuracy: 0.814
  3663. epoch 25 iter 70, loss: 1.213, accuracy: 0.820
  3664. epoch 25 iter 80, loss: 1.218, accuracy: 0.820
  3665. epoch 25 iter 90, loss: 1.209, accuracy: 0.823
  3666. epoch 25 iter 100, loss: 1.208, accuracy: 0.816
  3667. epoch 25 iter 110, loss: 1.255, accuracy: 0.822
  3668. epoch 25 iter 120, loss: 1.402, accuracy: 0.815
  3669. epoch 25 iter 130, loss: 1.283, accuracy: 0.824
  3670. epoch 25 iter 140, loss: 1.407, accuracy: 0.794
  3671. epoch 25 iter 150, loss: 1.439, accuracy: 0.780
  3672. epoch 25 iter 160, loss: 1.435, accuracy: 0.800
  3673. epoch 25 iter 170, loss: 1.354, accuracy: 0.814
  3674. epoch 25 iter 180, loss: 1.312, accuracy: 0.826
  3675. epoch 25 iter 190, loss: 1.262, accuracy: 0.832
  3676. epoch 25 iter 200, loss: 1.353, accuracy: 0.825
  3677. epoch 25 iter 210, loss: 1.166, accuracy: 0.826
  3678. epoch 25 iter 220, loss: 1.213, accuracy: 0.823
  3679. epoch 25 iter 230, loss: 1.238, accuracy: 0.829
  3680. epoch 25 iter 240, loss: 1.280, accuracy: 0.829
  3681. epoch 25 iter 250, loss: 1.206, accuracy: 0.824
  3682. epoch 25 iter 260, loss: 1.160, accuracy: 0.825
  3683. epoch 25 iter 270, loss: 1.435, accuracy: 0.814
  3684. epoch 25 iter 280, loss: 1.360, accuracy: 0.814
  3685. epoch 26 iter 0, loss: 1.353, accuracy: 0.821
  3686. epoch 26 iter 10, loss: 1.477, accuracy: 0.813
  3687. epoch 26 iter 20, loss: 1.306, accuracy: 0.823
  3688. epoch 26 iter 30, loss: 1.295, accuracy: 0.824
  3689. epoch 26 iter 40, loss: 1.236, accuracy: 0.809
  3690. epoch 26 iter 50, loss: 1.233, accuracy: 0.812
  3691. epoch 26 iter 60, loss: 1.325, accuracy: 0.815
  3692. epoch 26 iter 70, loss: 1.249, accuracy: 0.818
  3693. epoch 26 iter 80, loss: 1.258, accuracy: 0.827
  3694. epoch 26 iter 90, loss: 1.224, accuracy: 0.820
  3695. epoch 26 iter 100, loss: 1.333, accuracy: 0.813
  3696. epoch 26 iter 110, loss: 1.272, accuracy: 0.823
  3697. epoch 26 iter 120, loss: 1.437, accuracy: 0.819
  3698. epoch 26 iter 130, loss: 1.357, accuracy: 0.820
  3699. epoch 26 iter 140, loss: 1.626, accuracy: 0.800
  3700. epoch 26 iter 150, loss: 1.413, accuracy: 0.781
  3701. epoch 26 iter 160, loss: 1.682, accuracy: 0.800
  3702. epoch 26 iter 170, loss: 1.504, accuracy: 0.812
  3703. epoch 26 iter 180, loss: 1.324, accuracy: 0.824
  3704. epoch 26 iter 190, loss: 1.353, accuracy: 0.828
  3705. epoch 26 iter 200, loss: 1.462, accuracy: 0.825
  3706. epoch 26 iter 210, loss: 1.300, accuracy: 0.820
  3707. epoch 26 iter 220, loss: 1.298, accuracy: 0.825
  3708. epoch 26 iter 230, loss: 1.437, accuracy: 0.822
  3709. epoch 26 iter 240, loss: 1.397, accuracy: 0.822
  3710. epoch 26 iter 250, loss: 1.387, accuracy: 0.817
  3711. epoch 26 iter 260, loss: 1.298, accuracy: 0.820
  3712. epoch 26 iter 270, loss: 1.406, accuracy: 0.828
  3713. epoch 26 iter 280, loss: 1.528, accuracy: 0.814
  3714. epoch 27 iter 0, loss: 1.386, accuracy: 0.814
  3715. epoch 27 iter 10, loss: 1.612, accuracy: 0.814
  3716. epoch 27 iter 20, loss: 1.409, accuracy: 0.817
  3717. epoch 27 iter 30, loss: 1.413, accuracy: 0.818
  3718. epoch 27 iter 40, loss: 1.335, accuracy: 0.808
  3719. epoch 27 iter 50, loss: 1.382, accuracy: 0.813
  3720. epoch 27 iter 60, loss: 1.357, accuracy: 0.816
  3721. epoch 27 iter 70, loss: 1.318, accuracy: 0.821
  3722. epoch 27 iter 80, loss: 1.429, accuracy: 0.812
  3723. epoch 27 iter 90, loss: 1.294, accuracy: 0.822
  3724. epoch 27 iter 100, loss: 1.363, accuracy: 0.817
  3725. epoch 27 iter 110, loss: 1.443, accuracy: 0.810
  3726. epoch 27 iter 120, loss: 1.491, accuracy: 0.821
  3727. epoch 27 iter 130, loss: 1.420, accuracy: 0.817
  3728. epoch 27 iter 140, loss: 1.923, accuracy: 0.787
  3729. epoch 27 iter 150, loss: 1.393, accuracy: 0.780
  3730. epoch 27 iter 160, loss: 1.641, accuracy: 0.809
  3731. epoch 27 iter 170, loss: 1.624, accuracy: 0.813
  3732. epoch 27 iter 180, loss: 1.462, accuracy: 0.817
  3733. epoch 27 iter 190, loss: 1.410, accuracy: 0.825
  3734. epoch 27 iter 200, loss: 1.498, accuracy: 0.826
  3735. epoch 27 iter 210, loss: 1.403, accuracy: 0.831
  3736. epoch 27 iter 220, loss: 1.368, accuracy: 0.824
  3737. epoch 27 iter 230, loss: 1.581, accuracy: 0.822
  3738. epoch 27 iter 240, loss: 1.588, accuracy: 0.814
  3739. epoch 27 iter 250, loss: 1.557, accuracy: 0.815
  3740. epoch 27 iter 260, loss: 1.331, accuracy: 0.813
  3741. epoch 27 iter 270, loss: 1.583, accuracy: 0.822
  3742. epoch 27 iter 280, loss: 1.869, accuracy: 0.802
  3743. epoch 28 iter 0, loss: 1.509, accuracy: 0.814
  3744. epoch 28 iter 10, loss: 1.594, accuracy: 0.811
  3745. epoch 28 iter 20, loss: 1.666, accuracy: 0.814
  3746. epoch 28 iter 30, loss: 1.455, accuracy: 0.828
  3747. epoch 28 iter 40, loss: 1.301, accuracy: 0.812
  3748. epoch 28 iter 50, loss: 1.571, accuracy: 0.794
  3749. epoch 28 iter 60, loss: 1.604, accuracy: 0.800
  3750. epoch 28 iter 70, loss: 1.507, accuracy: 0.807
  3751. epoch 28 iter 80, loss: 1.458, accuracy: 0.807
  3752. epoch 28 iter 90, loss: 1.347, accuracy: 0.814
  3753. epoch 28 iter 100, loss: 1.516, accuracy: 0.814
  3754. epoch 28 iter 110, loss: 1.434, accuracy: 0.818
  3755. epoch 28 iter 120, loss: 1.547, accuracy: 0.821
  3756. epoch 28 iter 130, loss: 1.690, accuracy: 0.801
  3757. epoch 28 iter 140, loss: 1.617, accuracy: 0.811
  3758. epoch 28 iter 150, loss: 1.489, accuracy: 0.787
  3759. epoch 28 iter 160, loss: 1.450, accuracy: 0.816
  3760. epoch 28 iter 170, loss: 1.723, accuracy: 0.816
  3761. epoch 28 iter 180, loss: 1.564, accuracy: 0.806
  3762. epoch 28 iter 190, loss: 1.433, accuracy: 0.830
  3763. epoch 28 iter 200, loss: 1.703, accuracy: 0.819
  3764. epoch 28 iter 210, loss: 1.487, accuracy: 0.819
  3765. epoch 28 iter 220, loss: 1.400, accuracy: 0.825
  3766. epoch 28 iter 230, loss: 1.577, accuracy: 0.818
  3767. epoch 28 iter 240, loss: 1.542, accuracy: 0.807
  3768. epoch 28 iter 250, loss: 1.917, accuracy: 0.788
  3769. epoch 28 iter 260, loss: 1.461, accuracy: 0.805
  3770. epoch 28 iter 270, loss: 1.708, accuracy: 0.819
  3771. epoch 28 iter 280, loss: 2.138, accuracy: 0.792
  3772. epoch 29 iter 0, loss: 1.785, accuracy: 0.811
  3773. epoch 29 iter 10, loss: 1.562, accuracy: 0.819
  3774. epoch 29 iter 20, loss: 1.667, accuracy: 0.820
  3775. epoch 29 iter 30, loss: 1.581, accuracy: 0.819
  3776. epoch 29 iter 40, loss: 1.370, accuracy: 0.820
  3777. epoch 29 iter 50, loss: 1.453, accuracy: 0.813
  3778. epoch 29 iter 60, loss: 1.584, accuracy: 0.803
  3779. epoch 29 iter 70, loss: 1.397, accuracy: 0.826
  3780. epoch 29 iter 80, loss: 1.472, accuracy: 0.809
  3781. epoch 29 iter 90, loss: 1.353, accuracy: 0.817
  3782. epoch 29 iter 100, loss: 1.547, accuracy: 0.817
  3783. epoch 29 iter 110, loss: 1.501, accuracy: 0.811
  3784. epoch 29 iter 120, loss: 1.744, accuracy: 0.813
  3785. epoch 29 iter 130, loss: 1.530, accuracy: 0.826
  3786. epoch 29 iter 140, loss: 1.747, accuracy: 0.811
  3787. epoch 29 iter 150, loss: 1.547, accuracy: 0.810
  3788. epoch 29 iter 160, loss: 1.395, accuracy: 0.817
  3789. epoch 29 iter 170, loss: 1.731, accuracy: 0.812
  3790. epoch 29 iter 180, loss: 1.743, accuracy: 0.806
  3791. epoch 29 iter 190, loss: 1.555, accuracy: 0.829
  3792. epoch 29 iter 200, loss: 1.482, accuracy: 0.818
  3793. epoch 29 iter 210, loss: 1.628, accuracy: 0.820
  3794. epoch 29 iter 220, loss: 1.531, accuracy: 0.808
  3795. epoch 29 iter 230, loss: 1.513, accuracy: 0.818
  3796. epoch 29 iter 240, loss: 1.531, accuracy: 0.815
  3797. epoch 29 iter 250, loss: 1.759, accuracy: 0.802
  3798. epoch 29 iter 260, loss: 1.447, accuracy: 0.804
  3799. epoch 29 iter 270, loss: 1.518, accuracy: 0.821
  3800. epoch 29 iter 280, loss: 1.826, accuracy: 0.791
  3801. epoch 30 iter 0, loss: 1.650, accuracy: 0.807
  3802. epoch 30 iter 10, loss: 1.723, accuracy: 0.805
  3803. epoch 30 iter 20, loss: 1.683, accuracy: 0.816
  3804. epoch 30 iter 30, loss: 1.535, accuracy: 0.819
  3805. epoch 30 iter 40, loss: 1.501, accuracy: 0.812
  3806. epoch 30 iter 50, loss: 1.541, accuracy: 0.800
  3807. epoch 30 iter 60, loss: 1.559, accuracy: 0.802
  3808. epoch 30 iter 70, loss: 1.382, accuracy: 0.816
  3809. epoch 30 iter 80, loss: 1.451, accuracy: 0.810
  3810. epoch 30 iter 90, loss: 1.415, accuracy: 0.819
  3811. epoch 30 iter 100, loss: 1.516, accuracy: 0.819
  3812. epoch 30 iter 110, loss: 1.493, accuracy: 0.812
  3813. epoch 30 iter 120, loss: 1.677, accuracy: 0.821
  3814. epoch 30 iter 130, loss: 1.558, accuracy: 0.822
  3815. epoch 30 iter 140, loss: 1.690, accuracy: 0.817
  3816. epoch 30 iter 150, loss: 1.564, accuracy: 0.814
  3817. epoch 30 iter 160, loss: 1.492, accuracy: 0.803
  3818. epoch 30 iter 170, loss: 1.655, accuracy: 0.816
  3819. epoch 30 iter 180, loss: 1.816, accuracy: 0.812
  3820. epoch 30 iter 190, loss: 1.534, accuracy: 0.822
  3821. epoch 30 iter 200, loss: 1.632, accuracy: 0.822
  3822. epoch 30 iter 210, loss: 1.446, accuracy: 0.826
  3823. epoch 30 iter 220, loss: 1.647, accuracy: 0.820
  3824. epoch 30 iter 230, loss: 1.523, accuracy: 0.820
  3825. epoch 30 iter 240, loss: 1.490, accuracy: 0.831
  3826. epoch 30 iter 250, loss: 1.606, accuracy: 0.810
  3827. epoch 30 iter 260, loss: 1.549, accuracy: 0.813
  3828. epoch 30 iter 270, loss: 1.509, accuracy: 0.820
  3829. epoch 30 iter 280, loss: 2.183, accuracy: 0.797
  3830. epoch 31 iter 0, loss: 2.122, accuracy: 0.788
  3831. epoch 31 iter 10, loss: 1.673, accuracy: 0.813
  3832. epoch 31 iter 20, loss: 1.723, accuracy: 0.805
  3833. epoch 31 iter 30, loss: 1.818, accuracy: 0.804
  3834. epoch 31 iter 40, loss: 1.570, accuracy: 0.809
  3835. epoch 31 iter 50, loss: 1.564, accuracy: 0.810
  3836. epoch 31 iter 60, loss: 1.568, accuracy: 0.811
  3837. epoch 31 iter 70, loss: 1.466, accuracy: 0.818
  3838. epoch 31 iter 80, loss: 1.447, accuracy: 0.822
  3839. epoch 31 iter 90, loss: 1.422, accuracy: 0.819
  3840. epoch 31 iter 100, loss: 1.562, accuracy: 0.817
  3841. epoch 31 iter 110, loss: 1.533, accuracy: 0.812
  3842. epoch 31 iter 120, loss: 1.694, accuracy: 0.813
  3843. epoch 31 iter 130, loss: 1.569, accuracy: 0.812
  3844. epoch 31 iter 140, loss: 1.721, accuracy: 0.812
  3845. epoch 31 iter 150, loss: 1.670, accuracy: 0.816
  3846. epoch 31 iter 160, loss: 1.549, accuracy: 0.793
  3847. epoch 31 iter 170, loss: 1.789, accuracy: 0.815
  3848. epoch 31 iter 180, loss: 1.785, accuracy: 0.821
  3849. epoch 31 iter 190, loss: 1.573, accuracy: 0.811
  3850. epoch 31 iter 200, loss: 1.721, accuracy: 0.823
  3851. epoch 31 iter 210, loss: 1.602, accuracy: 0.826
  3852. epoch 31 iter 220, loss: 1.522, accuracy: 0.824
  3853. epoch 31 iter 230, loss: 1.615, accuracy: 0.826
  3854. epoch 31 iter 240, loss: 1.618, accuracy: 0.823
  3855. epoch 31 iter 250, loss: 1.617, accuracy: 0.818
  3856. epoch 31 iter 260, loss: 1.728, accuracy: 0.819
  3857. epoch 31 iter 270, loss: 1.520, accuracy: 0.818
  3858. epoch 31 iter 280, loss: 1.995, accuracy: 0.817
  3859. epoch 32 iter 0, loss: 2.172, accuracy: 0.799
  3860. epoch 32 iter 10, loss: 1.648, accuracy: 0.825
  3861. epoch 32 iter 20, loss: 1.743, accuracy: 0.815
  3862. epoch 32 iter 30, loss: 1.657, accuracy: 0.819
  3863. epoch 32 iter 40, loss: 1.647, accuracy: 0.819
  3864. epoch 32 iter 50, loss: 1.461, accuracy: 0.818
  3865. epoch 32 iter 60, loss: 1.657, accuracy: 0.812
  3866. epoch 32 iter 70, loss: 1.610, accuracy: 0.816
  3867. epoch 32 iter 80, loss: 1.468, accuracy: 0.821
  3868. epoch 32 iter 90, loss: 1.519, accuracy: 0.825
  3869. epoch 32 iter 100, loss: 1.484, accuracy: 0.818
  3870. epoch 32 iter 110, loss: 1.602, accuracy: 0.824
  3871. epoch 32 iter 120, loss: 1.622, accuracy: 0.822
  3872. epoch 32 iter 130, loss: 1.803, accuracy: 0.819
  3873. epoch 32 iter 140, loss: 1.621, accuracy: 0.826
  3874. epoch 32 iter 150, loss: 1.782, accuracy: 0.806
  3875. epoch 32 iter 160, loss: 1.498, accuracy: 0.818
  3876. epoch 32 iter 170, loss: 1.679, accuracy: 0.823
  3877. epoch 32 iter 180, loss: 1.943, accuracy: 0.818
  3878. epoch 32 iter 190, loss: 1.563, accuracy: 0.812
  3879. epoch 32 iter 200, loss: 1.758, accuracy: 0.826
  3880. epoch 32 iter 210, loss: 1.716, accuracy: 0.816
  3881. epoch 32 iter 220, loss: 1.610, accuracy: 0.826
  3882. epoch 32 iter 230, loss: 1.607, accuracy: 0.829
  3883. epoch 32 iter 240, loss: 1.658, accuracy: 0.824
  3884. epoch 32 iter 250, loss: 1.720, accuracy: 0.830
  3885. epoch 32 iter 260, loss: 1.730, accuracy: 0.815
  3886. epoch 32 iter 270, loss: 1.615, accuracy: 0.813
  3887. epoch 32 iter 280, loss: 1.744, accuracy: 0.828
  3888. epoch 33 iter 0, loss: 1.950, accuracy: 0.815
  3889. epoch 33 iter 10, loss: 1.883, accuracy: 0.823
  3890. epoch 33 iter 20, loss: 1.735, accuracy: 0.817
  3891. epoch 33 iter 30, loss: 1.740, accuracy: 0.827
  3892. epoch 33 iter 40, loss: 1.777, accuracy: 0.825
  3893. epoch 33 iter 50, loss: 1.487, accuracy: 0.832
  3894. epoch 33 iter 60, loss: 1.733, accuracy: 0.816
  3895. epoch 33 iter 70, loss: 1.793, accuracy: 0.811
  3896. epoch 33 iter 80, loss: 1.544, accuracy: 0.823
  3897. epoch 33 iter 90, loss: 1.632, accuracy: 0.826
  3898. epoch 33 iter 100, loss: 1.650, accuracy: 0.815
  3899. epoch 33 iter 110, loss: 1.632, accuracy: 0.828
  3900. epoch 33 iter 120, loss: 1.585, accuracy: 0.823
  3901. epoch 33 iter 130, loss: 1.733, accuracy: 0.825
  3902. epoch 33 iter 140, loss: 1.724, accuracy: 0.829
  3903. epoch 33 iter 150, loss: 1.884, accuracy: 0.808
  3904. epoch 33 iter 160, loss: 1.697, accuracy: 0.819
  3905. epoch 33 iter 170, loss: 1.736, accuracy: 0.816
  3906. epoch 33 iter 180, loss: 2.033, accuracy: 0.820
  3907. epoch 33 iter 190, loss: 1.813, accuracy: 0.813
  3908. epoch 33 iter 200, loss: 1.624, accuracy: 0.822
  3909. epoch 33 iter 210, loss: 1.809, accuracy: 0.828
  3910. epoch 33 iter 220, loss: 1.676, accuracy: 0.824
  3911. epoch 33 iter 230, loss: 1.609, accuracy: 0.829
  3912. epoch 33 iter 240, loss: 1.771, accuracy: 0.827
  3913. epoch 33 iter 250, loss: 1.769, accuracy: 0.832
  3914. epoch 33 iter 260, loss: 1.789, accuracy: 0.816
  3915. epoch 33 iter 270, loss: 1.591, accuracy: 0.828
  3916. epoch 33 iter 280, loss: 1.867, accuracy: 0.830
  3917. epoch 34 iter 0, loss: 1.996, accuracy: 0.822
  3918. epoch 34 iter 10, loss: 1.882, accuracy: 0.823
  3919. epoch 34 iter 20, loss: 1.659, accuracy: 0.824
  3920. epoch 34 iter 30, loss: 1.714, accuracy: 0.824
  3921. epoch 34 iter 40, loss: 1.740, accuracy: 0.823
  3922. epoch 34 iter 50, loss: 1.577, accuracy: 0.825
  3923. epoch 34 iter 60, loss: 1.855, accuracy: 0.809
  3924. epoch 34 iter 70, loss: 1.582, accuracy: 0.825
  3925. epoch 34 iter 80, loss: 1.549, accuracy: 0.829
  3926. epoch 34 iter 90, loss: 1.724, accuracy: 0.820
  3927. epoch 34 iter 100, loss: 1.645, accuracy: 0.814
  3928. epoch 34 iter 110, loss: 1.648, accuracy: 0.828
  3929. epoch 34 iter 120, loss: 1.667, accuracy: 0.819
  3930. epoch 34 iter 130, loss: 1.708, accuracy: 0.825
  3931. epoch 34 iter 140, loss: 1.769, accuracy: 0.833
  3932. epoch 34 iter 150, loss: 1.831, accuracy: 0.824
  3933. epoch 34 iter 160, loss: 1.764, accuracy: 0.820
  3934. epoch 34 iter 170, loss: 1.701, accuracy: 0.829
  3935. epoch 34 iter 180, loss: 1.992, accuracy: 0.828
  3936. epoch 34 iter 190, loss: 1.907, accuracy: 0.825
  3937. epoch 34 iter 200, loss: 1.702, accuracy: 0.821
  3938. epoch 34 iter 210, loss: 1.785, accuracy: 0.832
  3939. epoch 34 iter 220, loss: 1.920, accuracy: 0.822
  3940. epoch 34 iter 230, loss: 1.808, accuracy: 0.825
  3941. epoch 34 iter 240, loss: 1.955, accuracy: 0.828
  3942. epoch 34 iter 250, loss: 1.878, accuracy: 0.833
  3943. epoch 34 iter 260, loss: 1.880, accuracy: 0.820
  3944. epoch 34 iter 270, loss: 1.848, accuracy: 0.829
  3945. epoch 34 iter 280, loss: 1.779, accuracy: 0.839
  3946. epoch 35 iter 0, loss: 2.035, accuracy: 0.823
  3947. epoch 35 iter 10, loss: 1.966, accuracy: 0.826
  3948. epoch 35 iter 20, loss: 1.720, accuracy: 0.828
  3949. epoch 35 iter 30, loss: 1.736, accuracy: 0.825
  3950. epoch 35 iter 40, loss: 1.916, accuracy: 0.819
  3951. epoch 35 iter 50, loss: 1.829, accuracy: 0.816
  3952. epoch 35 iter 60, loss: 1.747, accuracy: 0.824
  3953. epoch 35 iter 70, loss: 1.691, accuracy: 0.816
  3954. epoch 35 iter 80, loss: 1.868, accuracy: 0.822
  3955. epoch 35 iter 90, loss: 1.743, accuracy: 0.819
  3956. epoch 35 iter 100, loss: 1.754, accuracy: 0.818
  3957. epoch 35 iter 110, loss: 1.873, accuracy: 0.828
  3958. epoch 35 iter 120, loss: 1.885, accuracy: 0.819
  3959. epoch 35 iter 130, loss: 1.914, accuracy: 0.827
  3960. epoch 35 iter 140, loss: 1.928, accuracy: 0.831
  3961. epoch 35 iter 150, loss: 1.957, accuracy: 0.816
  3962. epoch 35 iter 160, loss: 1.889, accuracy: 0.821
  3963. epoch 35 iter 170, loss: 1.820, accuracy: 0.825
  3964. epoch 35 iter 180, loss: 2.019, accuracy: 0.832
  3965. epoch 35 iter 190, loss: 2.011, accuracy: 0.831
  3966. epoch 35 iter 200, loss: 1.928, accuracy: 0.820
  3967. epoch 35 iter 210, loss: 1.863, accuracy: 0.828
  3968. epoch 35 iter 220, loss: 2.018, accuracy: 0.820
  3969. epoch 35 iter 230, loss: 1.797, accuracy: 0.830
  3970. epoch 35 iter 240, loss: 1.922, accuracy: 0.829
  3971. epoch 35 iter 250, loss: 1.894, accuracy: 0.835
  3972. epoch 35 iter 260, loss: 1.832, accuracy: 0.824
  3973. epoch 35 iter 270, loss: 1.721, accuracy: 0.831
  3974. epoch 35 iter 280, loss: 1.820, accuracy: 0.837
  3975. epoch 36 iter 0, loss: 2.061, accuracy: 0.826
  3976. epoch 36 iter 10, loss: 2.017, accuracy: 0.829
  3977. epoch 36 iter 20, loss: 1.861, accuracy: 0.831
  3978. epoch 36 iter 30, loss: 1.840, accuracy: 0.834
  3979. epoch 36 iter 40, loss: 1.711, accuracy: 0.834
  3980. epoch 36 iter 50, loss: 1.668, accuracy: 0.820
  3981. epoch 36 iter 60, loss: 1.714, accuracy: 0.823
  3982. epoch 36 iter 70, loss: 1.995, accuracy: 0.829
  3983. epoch 36 iter 80, loss: 1.790, accuracy: 0.831
  3984. epoch 36 iter 90, loss: 1.823, accuracy: 0.819
  3985. epoch 36 iter 100, loss: 1.950, accuracy: 0.820
  3986. epoch 36 iter 110, loss: 1.934, accuracy: 0.828
  3987. epoch 36 iter 120, loss: 1.890, accuracy: 0.815
  3988. epoch 36 iter 130, loss: 2.053, accuracy: 0.808
  3989. epoch 36 iter 140, loss: 2.190, accuracy: 0.812
  3990. epoch 36 iter 150, loss: 1.923, accuracy: 0.821
  3991. epoch 36 iter 160, loss: 1.915, accuracy: 0.818
  3992. epoch 36 iter 170, loss: 1.867, accuracy: 0.823
  3993. epoch 36 iter 180, loss: 2.150, accuracy: 0.831
  3994. epoch 36 iter 190, loss: 2.192, accuracy: 0.827
  3995. epoch 36 iter 200, loss: 1.927, accuracy: 0.823
  3996. epoch 36 iter 210, loss: 1.792, accuracy: 0.830
  3997. epoch 36 iter 220, loss: 2.043, accuracy: 0.807
  3998. epoch 36 iter 230, loss: 1.996, accuracy: 0.833
  3999. epoch 36 iter 240, loss: 1.910, accuracy: 0.828
  4000. epoch 36 iter 250, loss: 2.083, accuracy: 0.829
  4001. epoch 36 iter 260, loss: 1.918, accuracy: 0.822
  4002. epoch 36 iter 270, loss: 1.925, accuracy: 0.834
  4003. epoch 36 iter 280, loss: 1.910, accuracy: 0.836
  4004. epoch 37 iter 0, loss: 2.093, accuracy: 0.828
  4005. epoch 37 iter 10, loss: 2.148, accuracy: 0.832
  4006. epoch 37 iter 20, loss: 2.022, accuracy: 0.817
  4007. epoch 37 iter 30, loss: 1.968, accuracy: 0.824
  4008. epoch 37 iter 40, loss: 1.830, accuracy: 0.828
  4009. epoch 37 iter 50, loss: 1.873, accuracy: 0.824
  4010. epoch 37 iter 60, loss: 1.845, accuracy: 0.822
  4011. epoch 37 iter 70, loss: 2.034, accuracy: 0.821
  4012. epoch 37 iter 80, loss: 2.049, accuracy: 0.816
  4013. epoch 37 iter 90, loss: 1.842, accuracy: 0.810
  4014. epoch 37 iter 100, loss: 1.845, accuracy: 0.821
  4015. epoch 37 iter 110, loss: 1.960, accuracy: 0.828
  4016. epoch 37 iter 120, loss: 1.974, accuracy: 0.819
  4017. epoch 37 iter 130, loss: 1.892, accuracy: 0.817
  4018. epoch 37 iter 140, loss: 2.109, accuracy: 0.814
  4019. epoch 37 iter 150, loss: 1.967, accuracy: 0.817
  4020. epoch 37 iter 160, loss: 1.891, accuracy: 0.832
  4021. epoch 37 iter 170, loss: 1.825, accuracy: 0.832
  4022. epoch 37 iter 180, loss: 2.029, accuracy: 0.834
  4023. epoch 37 iter 190, loss: 2.219, accuracy: 0.834
  4024. epoch 37 iter 200, loss: 2.032, accuracy: 0.833
  4025. epoch 37 iter 210, loss: 1.788, accuracy: 0.830
  4026. epoch 37 iter 220, loss: 2.179, accuracy: 0.813
  4027. epoch 37 iter 230, loss: 2.023, accuracy: 0.828
  4028. epoch 37 iter 240, loss: 1.830, accuracy: 0.827
  4029. epoch 37 iter 250, loss: 1.968, accuracy: 0.837
  4030. epoch 37 iter 260, loss: 2.057, accuracy: 0.813
  4031. epoch 37 iter 270, loss: 1.931, accuracy: 0.832
  4032. epoch 37 iter 280, loss: 2.031, accuracy: 0.840
  4033. epoch 38 iter 0, loss: 2.139, accuracy: 0.835
  4034. epoch 38 iter 10, loss: 2.255, accuracy: 0.835
  4035. epoch 38 iter 20, loss: 2.198, accuracy: 0.825
  4036. epoch 38 iter 30, loss: 2.049, accuracy: 0.835
  4037. epoch 38 iter 40, loss: 1.901, accuracy: 0.839
  4038. epoch 38 iter 50, loss: 1.828, accuracy: 0.833
  4039. epoch 38 iter 60, loss: 1.758, accuracy: 0.831
  4040. epoch 38 iter 70, loss: 1.969, accuracy: 0.818
  4041. epoch 38 iter 80, loss: 2.192, accuracy: 0.827
  4042. epoch 38 iter 90, loss: 1.893, accuracy: 0.824
  4043. epoch 38 iter 100, loss: 1.922, accuracy: 0.825
  4044. epoch 38 iter 110, loss: 1.953, accuracy: 0.832
  4045. epoch 38 iter 120, loss: 1.915, accuracy: 0.827
  4046. epoch 38 iter 130, loss: 1.987, accuracy: 0.833
  4047. epoch 38 iter 140, loss: 2.437, accuracy: 0.806
  4048. epoch 38 iter 150, loss: 2.106, accuracy: 0.821
  4049. epoch 38 iter 160, loss: 1.900, accuracy: 0.824
  4050. epoch 38 iter 170, loss: 1.869, accuracy: 0.836
  4051. epoch 38 iter 180, loss: 2.241, accuracy: 0.835
  4052. epoch 38 iter 190, loss: 2.147, accuracy: 0.836
  4053. epoch 38 iter 200, loss: 2.287, accuracy: 0.832
  4054. epoch 38 iter 210, loss: 1.907, accuracy: 0.827
  4055. epoch 38 iter 220, loss: 2.091, accuracy: 0.826
  4056. epoch 38 iter 230, loss: 2.089, accuracy: 0.823
  4057. epoch 38 iter 240, loss: 1.921, accuracy: 0.827
  4058. epoch 38 iter 250, loss: 2.252, accuracy: 0.836
  4059. epoch 38 iter 260, loss: 2.036, accuracy: 0.818
  4060. epoch 38 iter 270, loss: 2.066, accuracy: 0.825
  4061. epoch 38 iter 280, loss: 2.057, accuracy: 0.836
  4062. epoch 39 iter 0, loss: 2.239, accuracy: 0.835
  4063. epoch 39 iter 10, loss: 2.302, accuracy: 0.833
  4064. epoch 39 iter 20, loss: 2.122, accuracy: 0.830
  4065. epoch 39 iter 30, loss: 2.079, accuracy: 0.832
  4066. epoch 39 iter 40, loss: 2.066, accuracy: 0.834
  4067. epoch 39 iter 50, loss: 2.068, accuracy: 0.821
  4068. epoch 39 iter 60, loss: 1.980, accuracy: 0.828
  4069. epoch 39 iter 70, loss: 2.116, accuracy: 0.830
  4070. epoch 39 iter 80, loss: 2.198, accuracy: 0.830
  4071. epoch 39 iter 90, loss: 1.974, accuracy: 0.826
  4072. epoch 39 iter 100, loss: 2.002, accuracy: 0.824
  4073. epoch 39 iter 110, loss: 2.142, accuracy: 0.824
  4074. epoch 39 iter 120, loss: 2.037, accuracy: 0.827
  4075. epoch 39 iter 130, loss: 2.203, accuracy: 0.824
  4076. epoch 39 iter 140, loss: 2.372, accuracy: 0.815
  4077. epoch 39 iter 150, loss: 2.120, accuracy: 0.820
  4078. epoch 39 iter 160, loss: 2.088, accuracy: 0.813
  4079. epoch 39 iter 170, loss: 2.045, accuracy: 0.827
  4080. epoch 39 iter 180, loss: 2.183, accuracy: 0.837
  4081. epoch 39 iter 190, loss: 2.431, accuracy: 0.839
  4082. epoch 39 iter 200, loss: 2.229, accuracy: 0.840
  4083. epoch 39 iter 210, loss: 2.087, accuracy: 0.827
  4084. epoch 39 iter 220, loss: 2.170, accuracy: 0.835
  4085. epoch 39 iter 230, loss: 2.175, accuracy: 0.825
  4086. epoch 39 iter 240, loss: 2.056, accuracy: 0.835
  4087. epoch 39 iter 250, loss: 2.079, accuracy: 0.833
  4088. epoch 39 iter 260, loss: 2.171, accuracy: 0.824
  4089. epoch 39 iter 270, loss: 2.225, accuracy: 0.824
  4090. epoch 39 iter 280, loss: 2.289, accuracy: 0.830
  4091. epoch 40 iter 0, loss: 2.224, accuracy: 0.833
  4092. epoch 40 iter 10, loss: 2.572, accuracy: 0.824
  4093. epoch 40 iter 20, loss: 2.208, accuracy: 0.823
  4094. epoch 40 iter 30, loss: 2.210, accuracy: 0.832
  4095. epoch 40 iter 40, loss: 2.184, accuracy: 0.831
  4096. epoch 40 iter 50, loss: 2.054, accuracy: 0.833
  4097. epoch 40 iter 60, loss: 1.993, accuracy: 0.822
  4098. epoch 40 iter 70, loss: 2.139, accuracy: 0.817
  4099. epoch 40 iter 80, loss: 2.282, accuracy: 0.824
  4100. epoch 40 iter 90, loss: 2.135, accuracy: 0.819
  4101. epoch 40 iter 100, loss: 2.037, accuracy: 0.820
  4102. epoch 40 iter 110, loss: 2.261, accuracy: 0.826
  4103. epoch 40 iter 120, loss: 2.013, accuracy: 0.830
  4104. epoch 40 iter 130, loss: 2.111, accuracy: 0.822
  4105. epoch 40 iter 140, loss: 2.326, accuracy: 0.821
  4106. epoch 40 iter 150, loss: 2.163, accuracy: 0.819
  4107. epoch 40 iter 160, loss: 2.132, accuracy: 0.814
  4108. epoch 40 iter 170, loss: 2.023, accuracy: 0.831
  4109. epoch 40 iter 180, loss: 2.308, accuracy: 0.829
  4110. epoch 40 iter 190, loss: 2.679, accuracy: 0.832
  4111. epoch 40 iter 200, loss: 2.347, accuracy: 0.833
  4112. epoch 40 iter 210, loss: 2.200, accuracy: 0.822
  4113. epoch 40 iter 220, loss: 2.162, accuracy: 0.834
  4114. epoch 40 iter 230, loss: 2.287, accuracy: 0.827
  4115. epoch 40 iter 240, loss: 2.122, accuracy: 0.836
  4116. epoch 40 iter 250, loss: 2.191, accuracy: 0.831
  4117. epoch 40 iter 260, loss: 2.171, accuracy: 0.826
  4118. epoch 40 iter 270, loss: 2.196, accuracy: 0.825
  4119. epoch 40 iter 280, loss: 2.508, accuracy: 0.830
  4120. epoch 41 iter 0, loss: 2.593, accuracy: 0.832
  4121. epoch 41 iter 10, loss: 2.616, accuracy: 0.829
  4122. epoch 41 iter 20, loss: 2.454, accuracy: 0.826
  4123. epoch 41 iter 30, loss: 2.350, accuracy: 0.820
  4124. epoch 41 iter 40, loss: 2.443, accuracy: 0.835
  4125. epoch 41 iter 50, loss: 2.225, accuracy: 0.829
  4126. epoch 41 iter 60, loss: 2.045, accuracy: 0.823
  4127. epoch 41 iter 70, loss: 2.286, accuracy: 0.822
  4128. epoch 41 iter 80, loss: 2.228, accuracy: 0.829
  4129. epoch 41 iter 90, loss: 2.303, accuracy: 0.826
  4130. epoch 41 iter 100, loss: 1.983, accuracy: 0.813
  4131. epoch 41 iter 110, loss: 2.436, accuracy: 0.823
  4132. epoch 41 iter 120, loss: 2.311, accuracy: 0.831
  4133. epoch 41 iter 130, loss: 2.304, accuracy: 0.819
  4134. epoch 41 iter 140, loss: 2.688, accuracy: 0.808
  4135. epoch 41 iter 150, loss: 2.169, accuracy: 0.827
  4136. epoch 41 iter 160, loss: 2.125, accuracy: 0.806
  4137. epoch 41 iter 170, loss: 2.123, accuracy: 0.833
  4138. epoch 41 iter 180, loss: 2.152, accuracy: 0.833
  4139. epoch 41 iter 190, loss: 2.468, accuracy: 0.833
  4140. epoch 41 iter 200, loss: 2.239, accuracy: 0.835
  4141. epoch 41 iter 210, loss: 2.222, accuracy: 0.828
  4142. epoch 41 iter 220, loss: 2.074, accuracy: 0.837
  4143. epoch 41 iter 230, loss: 2.408, accuracy: 0.816
  4144. epoch 41 iter 240, loss: 2.217, accuracy: 0.836
  4145. epoch 41 iter 250, loss: 2.273, accuracy: 0.831
  4146. epoch 41 iter 260, loss: 2.307, accuracy: 0.821
  4147. epoch 41 iter 270, loss: 2.344, accuracy: 0.820
  4148. epoch 41 iter 280, loss: 2.354, accuracy: 0.838
  4149. epoch 42 iter 0, loss: 2.432, accuracy: 0.834
  4150. epoch 42 iter 10, loss: 2.459, accuracy: 0.835
  4151. epoch 42 iter 20, loss: 2.408, accuracy: 0.834
  4152. epoch 42 iter 30, loss: 2.395, accuracy: 0.820
  4153. epoch 42 iter 40, loss: 2.329, accuracy: 0.838
  4154. epoch 42 iter 50, loss: 2.085, accuracy: 0.828
  4155. epoch 42 iter 60, loss: 2.158, accuracy: 0.827
  4156. epoch 42 iter 70, loss: 2.268, accuracy: 0.824
  4157. epoch 42 iter 80, loss: 2.272, accuracy: 0.833
  4158. epoch 42 iter 90, loss: 2.345, accuracy: 0.831
  4159. epoch 42 iter 100, loss: 2.203, accuracy: 0.826
  4160. epoch 42 iter 110, loss: 2.257, accuracy: 0.830
  4161. epoch 42 iter 120, loss: 2.312, accuracy: 0.831
  4162. epoch 42 iter 130, loss: 2.296, accuracy: 0.825
  4163. epoch 42 iter 140, loss: 2.475, accuracy: 0.826
  4164. epoch 42 iter 150, loss: 2.376, accuracy: 0.822
  4165. epoch 42 iter 160, loss: 2.236, accuracy: 0.807
  4166. epoch 42 iter 170, loss: 2.256, accuracy: 0.832
  4167. epoch 42 iter 180, loss: 2.406, accuracy: 0.827
  4168. epoch 42 iter 190, loss: 2.465, accuracy: 0.838
  4169. epoch 42 iter 200, loss: 2.339, accuracy: 0.830
  4170. epoch 42 iter 210, loss: 2.251, accuracy: 0.836
  4171. epoch 42 iter 220, loss: 2.238, accuracy: 0.836
  4172. epoch 42 iter 230, loss: 2.430, accuracy: 0.814
  4173. epoch 42 iter 240, loss: 2.329, accuracy: 0.839
  4174. epoch 42 iter 250, loss: 2.421, accuracy: 0.839
  4175. epoch 42 iter 260, loss: 2.408, accuracy: 0.823
  4176. epoch 42 iter 270, loss: 2.449, accuracy: 0.817
  4177. epoch 42 iter 280, loss: 2.406, accuracy: 0.831
  4178. epoch 43 iter 0, loss: 2.624, accuracy: 0.828
  4179. epoch 43 iter 10, loss: 2.853, accuracy: 0.822
  4180. epoch 43 iter 20, loss: 2.456, accuracy: 0.831
  4181. epoch 43 iter 30, loss: 2.438, accuracy: 0.820
  4182. epoch 43 iter 40, loss: 2.446, accuracy: 0.832
  4183. epoch 43 iter 50, loss: 2.460, accuracy: 0.830
  4184. epoch 43 iter 60, loss: 2.103, accuracy: 0.812
  4185. epoch 43 iter 70, loss: 2.489, accuracy: 0.830
  4186. epoch 43 iter 80, loss: 2.509, accuracy: 0.825
  4187. epoch 43 iter 90, loss: 2.355, accuracy: 0.827
  4188. epoch 43 iter 100, loss: 2.066, accuracy: 0.827
  4189. epoch 43 iter 110, loss: 2.331, accuracy: 0.827
  4190. epoch 43 iter 120, loss: 2.422, accuracy: 0.832
  4191. epoch 43 iter 130, loss: 2.370, accuracy: 0.816
  4192. epoch 43 iter 140, loss: 2.524, accuracy: 0.821
  4193. epoch 43 iter 150, loss: 2.613, accuracy: 0.819
  4194. epoch 43 iter 160, loss: 2.316, accuracy: 0.800
  4195. epoch 43 iter 170, loss: 2.170, accuracy: 0.833
  4196. epoch 43 iter 180, loss: 2.394, accuracy: 0.835
  4197. epoch 43 iter 190, loss: 2.509, accuracy: 0.838
  4198. epoch 43 iter 200, loss: 2.533, accuracy: 0.833
  4199. epoch 43 iter 210, loss: 2.355, accuracy: 0.832
  4200. epoch 43 iter 220, loss: 2.269, accuracy: 0.833
  4201. epoch 43 iter 230, loss: 2.339, accuracy: 0.830
  4202. epoch 43 iter 240, loss: 2.377, accuracy: 0.832
  4203. epoch 43 iter 250, loss: 2.350, accuracy: 0.825
  4204. epoch 43 iter 260, loss: 2.477, accuracy: 0.825
  4205. epoch 43 iter 270, loss: 2.379, accuracy: 0.811
  4206. epoch 43 iter 280, loss: 2.579, accuracy: 0.834
  4207. epoch 44 iter 0, loss: 2.709, accuracy: 0.832
  4208. epoch 44 iter 10, loss: 2.717, accuracy: 0.825
  4209. epoch 44 iter 20, loss: 2.467, accuracy: 0.833
  4210. epoch 44 iter 30, loss: 2.442, accuracy: 0.821
  4211. epoch 44 iter 40, loss: 2.499, accuracy: 0.830
  4212. epoch 44 iter 50, loss: 2.524, accuracy: 0.833
  4213. epoch 44 iter 60, loss: 2.319, accuracy: 0.817
  4214. epoch 44 iter 70, loss: 2.622, accuracy: 0.818
  4215. epoch 44 iter 80, loss: 2.484, accuracy: 0.834
  4216. epoch 44 iter 90, loss: 2.327, accuracy: 0.834
  4217. epoch 44 iter 100, loss: 2.190, accuracy: 0.834
  4218. epoch 44 iter 110, loss: 2.217, accuracy: 0.831
  4219. epoch 44 iter 120, loss: 2.427, accuracy: 0.839
  4220. epoch 44 iter 130, loss: 2.279, accuracy: 0.827
  4221. epoch 44 iter 140, loss: 2.448, accuracy: 0.831
  4222. epoch 44 iter 150, loss: 2.753, accuracy: 0.816
  4223. epoch 44 iter 160, loss: 2.354, accuracy: 0.799
  4224. epoch 44 iter 170, loss: 2.345, accuracy: 0.826
  4225. epoch 44 iter 180, loss: 2.264, accuracy: 0.832
  4226. epoch 44 iter 190, loss: 2.755, accuracy: 0.834
  4227. epoch 44 iter 200, loss: 2.825, accuracy: 0.826
  4228. epoch 44 iter 210, loss: 2.433, accuracy: 0.824
  4229. epoch 44 iter 220, loss: 2.345, accuracy: 0.835
  4230. epoch 44 iter 230, loss: 2.528, accuracy: 0.830
  4231. epoch 44 iter 240, loss: 2.363, accuracy: 0.839
  4232. epoch 44 iter 250, loss: 2.264, accuracy: 0.833
  4233. epoch 44 iter 260, loss: 2.327, accuracy: 0.834
  4234. epoch 44 iter 270, loss: 2.429, accuracy: 0.823
  4235. epoch 44 iter 280, loss: 2.414, accuracy: 0.835
  4236. epoch 45 iter 0, loss: 2.804, accuracy: 0.814
  4237. epoch 45 iter 10, loss: 2.924, accuracy: 0.825
  4238. epoch 45 iter 20, loss: 2.669, accuracy: 0.835
  4239. epoch 45 iter 30, loss: 2.421, accuracy: 0.832
  4240. epoch 45 iter 40, loss: 2.469, accuracy: 0.834
  4241. epoch 45 iter 50, loss: 2.342, accuracy: 0.836
  4242. epoch 45 iter 60, loss: 2.302, accuracy: 0.826
  4243. epoch 45 iter 70, loss: 2.538, accuracy: 0.812
  4244. epoch 45 iter 80, loss: 2.511, accuracy: 0.830
  4245. epoch 45 iter 90, loss: 2.594, accuracy: 0.832
  4246. epoch 45 iter 100, loss: 2.261, accuracy: 0.824
  4247. epoch 45 iter 110, loss: 2.373, accuracy: 0.834
  4248. epoch 45 iter 120, loss: 2.523, accuracy: 0.840
  4249. epoch 45 iter 130, loss: 2.450, accuracy: 0.833
  4250. epoch 45 iter 140, loss: 2.531, accuracy: 0.831
  4251. epoch 45 iter 150, loss: 2.836, accuracy: 0.807
  4252. epoch 45 iter 160, loss: 2.728, accuracy: 0.776
  4253. epoch 45 iter 170, loss: 2.377, accuracy: 0.829
  4254. epoch 45 iter 180, loss: 2.505, accuracy: 0.830
  4255. epoch 45 iter 190, loss: 2.681, accuracy: 0.831
  4256. epoch 45 iter 200, loss: 2.663, accuracy: 0.828
  4257. epoch 45 iter 210, loss: 2.573, accuracy: 0.816
  4258. epoch 45 iter 220, loss: 2.264, accuracy: 0.833
  4259. epoch 45 iter 230, loss: 2.382, accuracy: 0.830
  4260. epoch 45 iter 240, loss: 2.436, accuracy: 0.841
  4261. epoch 45 iter 250, loss: 2.521, accuracy: 0.836
  4262. epoch 45 iter 260, loss: 2.515, accuracy: 0.834
  4263. epoch 45 iter 270, loss: 2.522, accuracy: 0.828
  4264. epoch 45 iter 280, loss: 2.524, accuracy: 0.835
  4265. epoch 46 iter 0, loss: 2.729, accuracy: 0.832
  4266. epoch 46 iter 10, loss: 2.630, accuracy: 0.831
  4267. epoch 46 iter 20, loss: 2.722, accuracy: 0.835
  4268. epoch 46 iter 30, loss: 2.611, accuracy: 0.827
  4269. epoch 46 iter 40, loss: 2.545, accuracy: 0.822
  4270. epoch 46 iter 50, loss: 2.781, accuracy: 0.828
  4271. epoch 46 iter 60, loss: 2.595, accuracy: 0.808
  4272. epoch 46 iter 70, loss: 2.741, accuracy: 0.822
  4273. epoch 46 iter 80, loss: 2.722, accuracy: 0.830
  4274. epoch 46 iter 90, loss: 2.621, accuracy: 0.834
  4275. epoch 46 iter 100, loss: 2.480, accuracy: 0.830
  4276. epoch 46 iter 110, loss: 2.465, accuracy: 0.828
  4277. epoch 46 iter 120, loss: 2.669, accuracy: 0.835
  4278. epoch 46 iter 130, loss: 2.420, accuracy: 0.838
  4279. epoch 46 iter 140, loss: 2.490, accuracy: 0.833
  4280. epoch 46 iter 150, loss: 2.772, accuracy: 0.825
  4281. epoch 46 iter 160, loss: 2.772, accuracy: 0.808
  4282. epoch 46 iter 170, loss: 2.551, accuracy: 0.810
  4283. epoch 46 iter 180, loss: 2.630, accuracy: 0.821
  4284. epoch 46 iter 190, loss: 2.996, accuracy: 0.819
  4285. epoch 46 iter 200, loss: 2.836, accuracy: 0.828
  4286. epoch 46 iter 210, loss: 2.838, accuracy: 0.822
  4287. epoch 46 iter 220, loss: 2.594, accuracy: 0.828
  4288. epoch 46 iter 230, loss: 2.726, accuracy: 0.832
  4289. epoch 46 iter 240, loss: 2.753, accuracy: 0.828
  4290. epoch 46 iter 250, loss: 2.531, accuracy: 0.829
  4291. epoch 46 iter 260, loss: 2.386, accuracy: 0.828
  4292. epoch 46 iter 270, loss: 2.646, accuracy: 0.826
  4293. epoch 46 iter 280, loss: 2.684, accuracy: 0.834
  4294. epoch 47 iter 0, loss: 2.736, accuracy: 0.830
  4295. epoch 47 iter 10, loss: 2.731, accuracy: 0.826
  4296. epoch 47 iter 20, loss: 2.787, accuracy: 0.821
  4297. epoch 47 iter 30, loss: 2.638, accuracy: 0.832
  4298. epoch 47 iter 40, loss: 2.466, accuracy: 0.825
  4299. epoch 47 iter 50, loss: 2.597, accuracy: 0.828
  4300. epoch 47 iter 60, loss: 2.578, accuracy: 0.826
  4301. epoch 47 iter 70, loss: 2.337, accuracy: 0.828
  4302. epoch 47 iter 80, loss: 2.616, accuracy: 0.831
  4303. epoch 47 iter 90, loss: 2.765, accuracy: 0.830
  4304. epoch 47 iter 100, loss: 2.554, accuracy: 0.815
  4305. epoch 47 iter 110, loss: 2.361, accuracy: 0.822
  4306. epoch 47 iter 120, loss: 2.527, accuracy: 0.831
  4307. epoch 47 iter 130, loss: 2.538, accuracy: 0.835
  4308. epoch 47 iter 140, loss: 2.577, accuracy: 0.832
  4309. epoch 47 iter 150, loss: 2.887, accuracy: 0.825
  4310. epoch 47 iter 160, loss: 2.539, accuracy: 0.823
  4311. epoch 47 iter 170, loss: 2.415, accuracy: 0.818
  4312. epoch 47 iter 180, loss: 2.670, accuracy: 0.831
  4313. epoch 47 iter 190, loss: 2.943, accuracy: 0.829
  4314. epoch 47 iter 200, loss: 2.959, accuracy: 0.831
  4315. epoch 47 iter 210, loss: 2.559, accuracy: 0.822
  4316. epoch 47 iter 220, loss: 2.434, accuracy: 0.833
  4317. epoch 47 iter 230, loss: 2.697, accuracy: 0.834
  4318. epoch 47 iter 240, loss: 2.798, accuracy: 0.835
  4319. epoch 47 iter 250, loss: 2.525, accuracy: 0.839
  4320. epoch 47 iter 260, loss: 2.562, accuracy: 0.837
  4321. epoch 47 iter 270, loss: 2.423, accuracy: 0.827
  4322. epoch 47 iter 280, loss: 2.651, accuracy: 0.836
  4323. epoch 48 iter 0, loss: 2.859, accuracy: 0.836
  4324. epoch 48 iter 10, loss: 2.773, accuracy: 0.842
  4325. epoch 48 iter 20, loss: 2.816, accuracy: 0.826
  4326. epoch 48 iter 30, loss: 2.608, accuracy: 0.833
  4327. epoch 48 iter 40, loss: 2.465, accuracy: 0.834
  4328. epoch 48 iter 50, loss: 2.609, accuracy: 0.836
  4329. epoch 48 iter 60, loss: 2.532, accuracy: 0.835
  4330. epoch 48 iter 70, loss: 2.446, accuracy: 0.829
  4331. epoch 48 iter 80, loss: 2.760, accuracy: 0.830
  4332. epoch 48 iter 90, loss: 2.913, accuracy: 0.829
  4333. epoch 48 iter 100, loss: 2.548, accuracy: 0.816
  4334. epoch 48 iter 110, loss: 2.452, accuracy: 0.827
  4335. epoch 48 iter 120, loss: 2.538, accuracy: 0.832
  4336. epoch 48 iter 130, loss: 2.897, accuracy: 0.831
  4337. epoch 48 iter 140, loss: 2.690, accuracy: 0.830
  4338. epoch 48 iter 150, loss: 2.922, accuracy: 0.829
  4339. epoch 48 iter 160, loss: 2.864, accuracy: 0.829
  4340. epoch 48 iter 170, loss: 2.714, accuracy: 0.821
  4341. epoch 48 iter 180, loss: 2.756, accuracy: 0.828
  4342. epoch 48 iter 190, loss: 2.912, accuracy: 0.836
  4343. epoch 48 iter 200, loss: 3.049, accuracy: 0.842
  4344. epoch 48 iter 210, loss: 2.808, accuracy: 0.832
  4345. epoch 48 iter 220, loss: 2.556, accuracy: 0.831
  4346. epoch 48 iter 230, loss: 2.722, accuracy: 0.840
  4347. epoch 48 iter 240, loss: 2.770, accuracy: 0.832
  4348. epoch 48 iter 250, loss: 2.535, accuracy: 0.842
  4349. epoch 48 iter 260, loss: 2.560, accuracy: 0.838
  4350. epoch 48 iter 270, loss: 2.646, accuracy: 0.820
  4351. epoch 48 iter 280, loss: 2.656, accuracy: 0.840
  4352. epoch 49 iter 0, loss: 2.925, accuracy: 0.834
  4353. epoch 49 iter 10, loss: 2.940, accuracy: 0.839
  4354. epoch 49 iter 20, loss: 3.031, accuracy: 0.829
  4355. epoch 49 iter 30, loss: 2.806, accuracy: 0.826
  4356. epoch 49 iter 40, loss: 2.782, accuracy: 0.833
  4357. epoch 49 iter 50, loss: 2.688, accuracy: 0.839
  4358. epoch 49 iter 60, loss: 2.744, accuracy: 0.832
  4359. epoch 49 iter 70, loss: 2.597, accuracy: 0.831
  4360. epoch 49 iter 80, loss: 2.937, accuracy: 0.826
  4361. epoch 49 iter 90, loss: 2.981, accuracy: 0.829
  4362. epoch 49 iter 100, loss: 2.639, accuracy: 0.835
  4363. epoch 49 iter 110, loss: 2.467, accuracy: 0.828
  4364. epoch 49 iter 120, loss: 2.595, accuracy: 0.840
  4365. epoch 49 iter 130, loss: 2.657, accuracy: 0.842
  4366. epoch 49 iter 140, loss: 2.684, accuracy: 0.836
  4367. epoch 49 iter 150, loss: 2.857, accuracy: 0.832
  4368. epoch 49 iter 160, loss: 3.040, accuracy: 0.826
  4369. epoch 49 iter 170, loss: 3.011, accuracy: 0.808
  4370. epoch 49 iter 180, loss: 3.022, accuracy: 0.826
  4371. epoch 49 iter 190, loss: 2.857, accuracy: 0.840
  4372. epoch 49 iter 200, loss: 2.971, accuracy: 0.837
  4373. epoch 49 iter 210, loss: 2.916, accuracy: 0.834
  4374. epoch 49 iter 220, loss: 2.774, accuracy: 0.824
  4375. epoch 49 iter 230, loss: 2.867, accuracy: 0.837
  4376. epoch 49 iter 240, loss: 2.782, accuracy: 0.830
  4377. epoch 49 iter 250, loss: 2.779, accuracy: 0.836
  4378. epoch 49 iter 260, loss: 2.810, accuracy: 0.836
  4379. epoch 49 iter 270, loss: 3.029, accuracy: 0.822
  4380. epoch 49 iter 280, loss: 2.895, accuracy: 0.834
  4381. [jalal@scc-x04 pset3_CNN]$
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement
Public Pastes
Advertisement
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!