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lamiastella

different_FC_sizes_50 epochs

lamiastella
Oct 22nd, 2018
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  1. [jalal@scc-x04 pset3_CNN]$ python different_FC_sizes.py
  2. 2018-10-22 17:14:21.184061: 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 17:14:21.355329: 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 17:14:21.355362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  8. 2018-10-22 17:14:21.647479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  9. 2018-10-22 17:14:21.647510: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  10. 2018-10-22 17:14:21.647516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  11. 2018-10-22 17:14:21.647765: 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 17:14:21.861666: 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 FC size is: 200
  14. 2018-10-22 17:14:24.954712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  15. 2018-10-22 17:14:24.954752: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  16. 2018-10-22 17:14:24.954759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  17. 2018-10-22 17:14:24.954763: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  18. 2018-10-22 17:14:24.954883: 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: 91.935, accuracy: 0.097
  20. epoch 0 iter 10, loss: 4.485, accuracy: 0.076
  21. epoch 0 iter 20, loss: 2.337, accuracy: 0.153
  22. epoch 0 iter 30, loss: 2.259, accuracy: 0.178
  23. epoch 0 iter 40, loss: 2.231, accuracy: 0.203
  24. epoch 0 iter 50, loss: 2.226, accuracy: 0.203
  25. epoch 0 iter 60, loss: 2.210, accuracy: 0.205
  26. epoch 0 iter 70, loss: 2.145, accuracy: 0.261
  27. epoch 0 iter 80, loss: 2.014, accuracy: 0.318
  28. epoch 0 iter 90, loss: 1.863, accuracy: 0.375
  29. epoch 0 iter 100, loss: 1.675, accuracy: 0.444
  30. epoch 0 iter 110, loss: 1.532, accuracy: 0.511
  31. epoch 0 iter 120, loss: 1.349, accuracy: 0.584
  32. epoch 0 iter 130, loss: 1.267, accuracy: 0.609
  33. epoch 0 iter 140, loss: 1.239, accuracy: 0.622
  34. epoch 0 iter 150, loss: 1.147, accuracy: 0.653
  35. epoch 0 iter 160, loss: 1.059, accuracy: 0.689
  36. epoch 0 iter 170, loss: 1.048, accuracy: 0.692
  37. epoch 0 iter 180, loss: 0.930, accuracy: 0.731
  38. epoch 0 iter 190, loss: 0.948, accuracy: 0.727
  39. epoch 0 iter 200, loss: 0.907, accuracy: 0.740
  40. epoch 0 iter 210, loss: 0.868, accuracy: 0.756
  41. epoch 0 iter 220, loss: 0.847, accuracy: 0.758
  42. epoch 0 iter 230, loss: 0.846, accuracy: 0.763
  43. epoch 0 iter 240, loss: 0.799, accuracy: 0.777
  44. epoch 0 iter 250, loss: 0.818, accuracy: 0.770
  45. epoch 0 iter 260, loss: 0.785, accuracy: 0.780
  46. epoch 0 iter 270, loss: 0.780, accuracy: 0.781
  47. epoch 0 iter 280, loss: 0.851, accuracy: 0.760
  48. epoch 1 iter 0, loss: 0.793, accuracy: 0.778
  49. epoch 1 iter 10, loss: 0.757, accuracy: 0.790
  50. epoch 1 iter 20, loss: 0.766, accuracy: 0.789
  51. epoch 1 iter 30, loss: 0.781, accuracy: 0.783
  52. epoch 1 iter 40, loss: 0.770, accuracy: 0.784
  53. epoch 1 iter 50, loss: 0.767, accuracy: 0.786
  54. epoch 1 iter 60, loss: 0.747, accuracy: 0.795
  55. epoch 1 iter 70, loss: 0.764, accuracy: 0.789
  56. epoch 1 iter 80, loss: 0.748, accuracy: 0.792
  57. epoch 1 iter 90, loss: 0.731, accuracy: 0.797
  58. epoch 1 iter 100, loss: 0.725, accuracy: 0.801
  59. epoch 1 iter 110, loss: 0.724, accuracy: 0.800
  60. epoch 1 iter 120, loss: 0.698, accuracy: 0.804
  61. epoch 1 iter 130, loss: 0.728, accuracy: 0.799
  62. epoch 1 iter 140, loss: 0.703, accuracy: 0.806
  63. epoch 1 iter 150, loss: 0.703, accuracy: 0.807
  64. epoch 1 iter 160, loss: 0.688, accuracy: 0.810
  65. epoch 1 iter 170, loss: 0.725, accuracy: 0.798
  66. epoch 1 iter 180, loss: 0.707, accuracy: 0.801
  67. epoch 1 iter 190, loss: 0.684, accuracy: 0.811
  68. epoch 1 iter 200, loss: 0.675, accuracy: 0.815
  69. epoch 1 iter 210, loss: 0.684, accuracy: 0.810
  70. epoch 1 iter 220, loss: 0.670, accuracy: 0.813
  71. epoch 1 iter 230, loss: 0.697, accuracy: 0.807
  72. epoch 1 iter 240, loss: 0.687, accuracy: 0.814
  73. epoch 1 iter 250, loss: 0.668, accuracy: 0.816
  74. epoch 1 iter 260, loss: 0.670, accuracy: 0.815
  75. epoch 1 iter 270, loss: 0.686, accuracy: 0.811
  76. epoch 1 iter 280, loss: 0.720, accuracy: 0.803
  77. epoch 2 iter 0, loss: 0.682, accuracy: 0.815
  78. epoch 2 iter 10, loss: 0.663, accuracy: 0.817
  79. epoch 2 iter 20, loss: 0.656, accuracy: 0.823
  80. epoch 2 iter 30, loss: 0.678, accuracy: 0.812
  81. epoch 2 iter 40, loss: 0.650, accuracy: 0.821
  82. epoch 2 iter 50, loss: 0.658, accuracy: 0.816
  83. epoch 2 iter 60, loss: 0.677, accuracy: 0.812
  84. epoch 2 iter 70, loss: 0.642, accuracy: 0.821
  85. epoch 2 iter 80, loss: 0.656, accuracy: 0.817
  86. epoch 2 iter 90, loss: 0.638, accuracy: 0.827
  87. epoch 2 iter 100, loss: 0.658, accuracy: 0.820
  88. epoch 2 iter 110, loss: 0.658, accuracy: 0.819
  89. epoch 2 iter 120, loss: 0.657, accuracy: 0.818
  90. epoch 2 iter 130, loss: 0.647, accuracy: 0.825
  91. epoch 2 iter 140, loss: 0.631, accuracy: 0.827
  92. epoch 2 iter 150, loss: 0.652, accuracy: 0.820
  93. epoch 2 iter 160, loss: 0.635, accuracy: 0.827
  94. epoch 2 iter 170, loss: 0.654, accuracy: 0.821
  95. epoch 2 iter 180, loss: 0.647, accuracy: 0.820
  96. epoch 2 iter 190, loss: 0.630, accuracy: 0.826
  97. epoch 2 iter 200, loss: 0.630, accuracy: 0.828
  98. epoch 2 iter 210, loss: 0.661, accuracy: 0.817
  99. epoch 2 iter 220, loss: 0.616, accuracy: 0.830
  100. epoch 2 iter 230, loss: 0.623, accuracy: 0.829
  101. epoch 2 iter 240, loss: 0.645, accuracy: 0.825
  102. epoch 2 iter 250, loss: 0.629, accuracy: 0.827
  103. epoch 2 iter 260, loss: 0.618, accuracy: 0.831
  104. epoch 2 iter 270, loss: 0.622, accuracy: 0.830
  105. epoch 2 iter 280, loss: 0.659, accuracy: 0.818
  106. epoch 3 iter 0, loss: 0.621, accuracy: 0.831
  107. epoch 3 iter 10, loss: 0.627, accuracy: 0.829
  108. epoch 3 iter 20, loss: 0.634, accuracy: 0.832
  109. epoch 3 iter 30, loss: 0.638, accuracy: 0.825
  110. epoch 3 iter 40, loss: 0.625, accuracy: 0.832
  111. epoch 3 iter 50, loss: 0.604, accuracy: 0.834
  112. epoch 3 iter 60, loss: 0.644, accuracy: 0.824
  113. epoch 3 iter 70, loss: 0.612, accuracy: 0.830
  114. epoch 3 iter 80, loss: 0.636, accuracy: 0.821
  115. epoch 3 iter 90, loss: 0.616, accuracy: 0.833
  116. epoch 3 iter 100, loss: 0.620, accuracy: 0.829
  117. epoch 3 iter 110, loss: 0.621, accuracy: 0.830
  118. epoch 3 iter 120, loss: 0.619, accuracy: 0.832
  119. epoch 3 iter 130, loss: 0.614, accuracy: 0.835
  120. epoch 3 iter 140, loss: 0.595, accuracy: 0.839
  121. epoch 3 iter 150, loss: 0.614, accuracy: 0.832
  122. epoch 3 iter 160, loss: 0.601, accuracy: 0.836
  123. epoch 3 iter 170, loss: 0.644, accuracy: 0.825
  124. epoch 3 iter 180, loss: 0.607, accuracy: 0.836
  125. epoch 3 iter 190, loss: 0.597, accuracy: 0.835
  126. epoch 3 iter 200, loss: 0.612, accuracy: 0.838
  127. epoch 3 iter 210, loss: 0.600, accuracy: 0.836
  128. epoch 3 iter 220, loss: 0.635, accuracy: 0.833
  129. epoch 3 iter 230, loss: 0.623, accuracy: 0.830
  130. epoch 3 iter 240, loss: 0.613, accuracy: 0.836
  131. epoch 3 iter 250, loss: 0.625, accuracy: 0.832
  132. epoch 3 iter 260, loss: 0.611, accuracy: 0.834
  133. epoch 3 iter 270, loss: 0.599, accuracy: 0.838
  134. epoch 3 iter 280, loss: 0.692, accuracy: 0.818
  135. epoch 4 iter 0, loss: 0.607, accuracy: 0.840
  136. epoch 4 iter 10, loss: 0.640, accuracy: 0.836
  137. epoch 4 iter 20, loss: 0.635, accuracy: 0.831
  138. epoch 4 iter 30, loss: 0.621, accuracy: 0.833
  139. epoch 4 iter 40, loss: 0.614, accuracy: 0.839
  140. epoch 4 iter 50, loss: 0.619, accuracy: 0.836
  141. epoch 4 iter 60, loss: 0.636, accuracy: 0.827
  142. epoch 4 iter 70, loss: 0.590, accuracy: 0.841
  143. epoch 4 iter 80, loss: 0.628, accuracy: 0.824
  144. epoch 4 iter 90, loss: 0.642, accuracy: 0.827
  145. epoch 4 iter 100, loss: 0.630, accuracy: 0.832
  146. epoch 4 iter 110, loss: 0.605, accuracy: 0.837
  147. epoch 4 iter 120, loss: 0.620, accuracy: 0.835
  148. epoch 4 iter 130, loss: 0.642, accuracy: 0.834
  149. epoch 4 iter 140, loss: 0.600, accuracy: 0.836
  150. epoch 4 iter 150, loss: 0.604, accuracy: 0.839
  151. epoch 4 iter 160, loss: 0.619, accuracy: 0.835
  152. epoch 4 iter 170, loss: 0.699, accuracy: 0.819
  153. epoch 4 iter 180, loss: 0.603, accuracy: 0.836
  154. epoch 4 iter 190, loss: 0.611, accuracy: 0.833
  155. epoch 4 iter 200, loss: 0.606, accuracy: 0.843
  156. epoch 4 iter 210, loss: 0.610, accuracy: 0.838
  157. epoch 4 iter 220, loss: 0.620, accuracy: 0.838
  158. epoch 4 iter 230, loss: 0.628, accuracy: 0.834
  159. epoch 4 iter 240, loss: 0.629, accuracy: 0.836
  160. epoch 4 iter 250, loss: 0.639, accuracy: 0.840
  161. epoch 4 iter 260, loss: 0.611, accuracy: 0.836
  162. epoch 4 iter 270, loss: 0.626, accuracy: 0.839
  163. epoch 4 iter 280, loss: 0.624, accuracy: 0.830
  164. epoch 5 iter 0, loss: 0.643, accuracy: 0.837
  165. epoch 5 iter 10, loss: 0.633, accuracy: 0.840
  166. epoch 5 iter 20, loss: 0.682, accuracy: 0.824
  167. epoch 5 iter 30, loss: 0.667, accuracy: 0.828
  168. epoch 5 iter 40, loss: 0.652, accuracy: 0.829
  169. epoch 5 iter 50, loss: 0.633, accuracy: 0.839
  170. epoch 5 iter 60, loss: 0.652, accuracy: 0.828
  171. epoch 5 iter 70, loss: 0.628, accuracy: 0.833
  172. epoch 5 iter 80, loss: 0.633, accuracy: 0.826
  173. epoch 5 iter 90, loss: 0.613, accuracy: 0.840
  174. epoch 5 iter 100, loss: 0.622, accuracy: 0.840
  175. epoch 5 iter 110, loss: 0.667, accuracy: 0.824
  176. epoch 5 iter 120, loss: 0.605, accuracy: 0.840
  177. epoch 5 iter 130, loss: 0.626, accuracy: 0.843
  178. epoch 5 iter 140, loss: 0.624, accuracy: 0.840
  179. epoch 5 iter 150, loss: 0.601, accuracy: 0.841
  180. epoch 5 iter 160, loss: 0.622, accuracy: 0.836
  181. epoch 5 iter 170, loss: 0.658, accuracy: 0.835
  182. epoch 5 iter 180, loss: 0.631, accuracy: 0.834
  183. epoch 5 iter 190, loss: 0.597, accuracy: 0.842
  184. epoch 5 iter 200, loss: 0.606, accuracy: 0.846
  185. epoch 5 iter 210, loss: 0.611, accuracy: 0.839
  186. epoch 5 iter 220, loss: 0.599, accuracy: 0.847
  187. epoch 5 iter 230, loss: 0.621, accuracy: 0.836
  188. epoch 5 iter 240, loss: 0.628, accuracy: 0.840
  189. epoch 5 iter 250, loss: 0.651, accuracy: 0.841
  190. epoch 5 iter 260, loss: 0.624, accuracy: 0.838
  191. epoch 5 iter 270, loss: 0.637, accuracy: 0.840
  192. epoch 5 iter 280, loss: 0.661, accuracy: 0.830
  193. epoch 6 iter 0, loss: 0.713, accuracy: 0.826
  194. epoch 6 iter 10, loss: 0.696, accuracy: 0.825
  195. epoch 6 iter 20, loss: 0.695, accuracy: 0.828
  196. epoch 6 iter 30, loss: 0.726, accuracy: 0.823
  197. epoch 6 iter 40, loss: 0.677, accuracy: 0.825
  198. epoch 6 iter 50, loss: 0.665, accuracy: 0.835
  199. epoch 6 iter 60, loss: 0.687, accuracy: 0.825
  200. epoch 6 iter 70, loss: 0.671, accuracy: 0.828
  201. epoch 6 iter 80, loss: 0.638, accuracy: 0.832
  202. epoch 6 iter 90, loss: 0.603, accuracy: 0.843
  203. epoch 6 iter 100, loss: 0.751, accuracy: 0.813
  204. epoch 6 iter 110, loss: 0.688, accuracy: 0.825
  205. epoch 6 iter 120, loss: 0.612, accuracy: 0.844
  206. epoch 6 iter 130, loss: 0.653, accuracy: 0.839
  207. epoch 6 iter 140, loss: 0.647, accuracy: 0.838
  208. epoch 6 iter 150, loss: 0.621, accuracy: 0.843
  209. epoch 6 iter 160, loss: 0.643, accuracy: 0.835
  210. epoch 6 iter 170, loss: 0.680, accuracy: 0.836
  211. epoch 6 iter 180, loss: 0.682, accuracy: 0.831
  212. epoch 6 iter 190, loss: 0.629, accuracy: 0.843
  213. epoch 6 iter 200, loss: 0.663, accuracy: 0.840
  214. epoch 6 iter 210, loss: 0.654, accuracy: 0.836
  215. epoch 6 iter 220, loss: 0.651, accuracy: 0.843
  216. epoch 6 iter 230, loss: 0.628, accuracy: 0.838
  217. epoch 6 iter 240, loss: 0.677, accuracy: 0.835
  218. epoch 6 iter 250, loss: 0.682, accuracy: 0.832
  219. epoch 6 iter 260, loss: 0.636, accuracy: 0.840
  220. epoch 6 iter 270, loss: 0.652, accuracy: 0.837
  221. epoch 6 iter 280, loss: 0.665, accuracy: 0.839
  222. epoch 7 iter 0, loss: 0.759, accuracy: 0.820
  223. epoch 7 iter 10, loss: 0.695, accuracy: 0.837
  224. epoch 7 iter 20, loss: 0.660, accuracy: 0.839
  225. epoch 7 iter 30, loss: 0.769, accuracy: 0.817
  226. epoch 7 iter 40, loss: 0.703, accuracy: 0.828
  227. epoch 7 iter 50, loss: 0.687, accuracy: 0.828
  228. epoch 7 iter 60, loss: 0.707, accuracy: 0.821
  229. epoch 7 iter 70, loss: 0.665, accuracy: 0.835
  230. epoch 7 iter 80, loss: 0.637, accuracy: 0.835
  231. epoch 7 iter 90, loss: 0.633, accuracy: 0.841
  232. epoch 7 iter 100, loss: 0.770, accuracy: 0.813
  233. epoch 7 iter 110, loss: 0.738, accuracy: 0.828
  234. epoch 7 iter 120, loss: 0.661, accuracy: 0.838
  235. epoch 7 iter 130, loss: 0.724, accuracy: 0.824
  236. epoch 7 iter 140, loss: 0.679, accuracy: 0.837
  237. epoch 7 iter 150, loss: 0.676, accuracy: 0.840
  238. epoch 7 iter 160, loss: 0.655, accuracy: 0.841
  239. epoch 7 iter 170, loss: 0.687, accuracy: 0.843
  240. epoch 7 iter 180, loss: 0.691, accuracy: 0.834
  241. epoch 7 iter 190, loss: 0.659, accuracy: 0.847
  242. epoch 7 iter 200, loss: 0.689, accuracy: 0.835
  243. epoch 7 iter 210, loss: 0.700, accuracy: 0.830
  244. epoch 7 iter 220, loss: 0.687, accuracy: 0.838
  245. epoch 7 iter 230, loss: 0.649, accuracy: 0.840
  246. epoch 7 iter 240, loss: 0.707, accuracy: 0.827
  247. epoch 7 iter 250, loss: 0.733, accuracy: 0.822
  248. epoch 7 iter 260, loss: 0.680, accuracy: 0.830
  249. epoch 7 iter 270, loss: 0.692, accuracy: 0.836
  250. epoch 7 iter 280, loss: 0.671, accuracy: 0.841
  251. epoch 8 iter 0, loss: 0.842, accuracy: 0.819
  252. epoch 8 iter 10, loss: 0.738, accuracy: 0.836
  253. epoch 8 iter 20, loss: 0.718, accuracy: 0.830
  254. epoch 8 iter 30, loss: 0.770, accuracy: 0.826
  255. epoch 8 iter 40, loss: 0.754, accuracy: 0.821
  256. epoch 8 iter 50, loss: 0.718, accuracy: 0.836
  257. epoch 8 iter 60, loss: 0.688, accuracy: 0.829
  258. epoch 8 iter 70, loss: 0.687, accuracy: 0.840
  259. epoch 8 iter 80, loss: 0.668, accuracy: 0.837
  260. epoch 8 iter 90, loss: 0.669, accuracy: 0.838
  261. epoch 8 iter 100, loss: 0.741, accuracy: 0.820
  262. epoch 8 iter 110, loss: 0.836, accuracy: 0.806
  263. epoch 8 iter 120, loss: 0.710, accuracy: 0.828
  264. epoch 8 iter 130, loss: 0.711, accuracy: 0.827
  265. epoch 8 iter 140, loss: 0.703, accuracy: 0.834
  266. epoch 8 iter 150, loss: 0.691, accuracy: 0.842
  267. epoch 8 iter 160, loss: 0.692, accuracy: 0.831
  268. epoch 8 iter 170, loss: 0.749, accuracy: 0.838
  269. epoch 8 iter 180, loss: 0.718, accuracy: 0.834
  270. epoch 8 iter 190, loss: 0.683, accuracy: 0.844
  271. epoch 8 iter 200, loss: 0.695, accuracy: 0.843
  272. epoch 8 iter 210, loss: 0.758, accuracy: 0.824
  273. epoch 8 iter 220, loss: 0.755, accuracy: 0.831
  274. epoch 8 iter 230, loss: 0.683, accuracy: 0.840
  275. epoch 8 iter 240, loss: 0.759, accuracy: 0.822
  276. epoch 8 iter 250, loss: 0.754, accuracy: 0.830
  277. epoch 8 iter 260, loss: 0.722, accuracy: 0.834
  278. epoch 8 iter 270, loss: 0.743, accuracy: 0.833
  279. epoch 8 iter 280, loss: 0.687, accuracy: 0.846
  280. epoch 9 iter 0, loss: 1.019, accuracy: 0.800
  281. epoch 9 iter 10, loss: 0.746, accuracy: 0.836
  282. epoch 9 iter 20, loss: 0.744, accuracy: 0.837
  283. epoch 9 iter 30, loss: 0.796, accuracy: 0.819
  284. epoch 9 iter 40, loss: 0.807, accuracy: 0.824
  285. epoch 9 iter 50, loss: 0.777, accuracy: 0.823
  286. epoch 9 iter 60, loss: 0.736, accuracy: 0.821
  287. epoch 9 iter 70, loss: 0.688, accuracy: 0.835
  288. epoch 9 iter 80, loss: 0.701, accuracy: 0.833
  289. epoch 9 iter 90, loss: 0.703, accuracy: 0.833
  290. epoch 9 iter 100, loss: 0.770, accuracy: 0.824
  291. epoch 9 iter 110, loss: 0.799, accuracy: 0.826
  292. epoch 9 iter 120, loss: 0.738, accuracy: 0.828
  293. epoch 9 iter 130, loss: 0.726, accuracy: 0.841
  294. epoch 9 iter 140, loss: 0.759, accuracy: 0.829
  295. epoch 9 iter 150, loss: 0.726, accuracy: 0.846
  296. epoch 9 iter 160, loss: 0.756, accuracy: 0.823
  297. epoch 9 iter 170, loss: 0.766, accuracy: 0.841
  298. epoch 9 iter 180, loss: 0.779, accuracy: 0.833
  299. epoch 9 iter 190, loss: 0.760, accuracy: 0.835
  300. epoch 9 iter 200, loss: 0.725, accuracy: 0.842
  301. epoch 9 iter 210, loss: 0.714, accuracy: 0.836
  302. epoch 9 iter 220, loss: 0.734, accuracy: 0.838
  303. epoch 9 iter 230, loss: 0.718, accuracy: 0.840
  304. epoch 9 iter 240, loss: 0.776, accuracy: 0.825
  305. epoch 9 iter 250, loss: 0.824, accuracy: 0.821
  306. epoch 9 iter 260, loss: 0.767, accuracy: 0.836
  307. epoch 9 iter 270, loss: 0.777, accuracy: 0.832
  308. epoch 9 iter 280, loss: 0.769, accuracy: 0.833
  309. epoch 10 iter 0, loss: 0.825, accuracy: 0.834
  310. epoch 10 iter 10, loss: 0.769, accuracy: 0.846
  311. epoch 10 iter 20, loss: 0.780, accuracy: 0.839
  312. epoch 10 iter 30, loss: 0.834, accuracy: 0.827
  313. epoch 10 iter 40, loss: 0.819, accuracy: 0.834
  314. epoch 10 iter 50, loss: 0.781, accuracy: 0.836
  315. epoch 10 iter 60, loss: 0.737, accuracy: 0.836
  316. epoch 10 iter 70, loss: 0.719, accuracy: 0.842
  317. epoch 10 iter 80, loss: 0.716, accuracy: 0.839
  318. epoch 10 iter 90, loss: 0.740, accuracy: 0.836
  319. epoch 10 iter 100, loss: 0.762, accuracy: 0.835
  320. epoch 10 iter 110, loss: 0.828, accuracy: 0.832
  321. epoch 10 iter 120, loss: 0.806, accuracy: 0.831
  322. epoch 10 iter 130, loss: 0.767, accuracy: 0.837
  323. epoch 10 iter 140, loss: 0.729, accuracy: 0.831
  324. epoch 10 iter 150, loss: 0.772, accuracy: 0.836
  325. epoch 10 iter 160, loss: 0.748, accuracy: 0.833
  326. epoch 10 iter 170, loss: 0.739, accuracy: 0.847
  327. epoch 10 iter 180, loss: 0.816, accuracy: 0.838
  328. epoch 10 iter 190, loss: 0.767, accuracy: 0.835
  329. epoch 10 iter 200, loss: 0.835, accuracy: 0.832
  330. epoch 10 iter 210, loss: 0.758, accuracy: 0.839
  331. epoch 10 iter 220, loss: 0.783, accuracy: 0.827
  332. epoch 10 iter 230, loss: 0.720, accuracy: 0.837
  333. epoch 10 iter 240, loss: 0.777, accuracy: 0.830
  334. epoch 10 iter 250, loss: 0.776, accuracy: 0.828
  335. epoch 10 iter 260, loss: 0.755, accuracy: 0.837
  336. epoch 10 iter 270, loss: 0.773, accuracy: 0.834
  337. epoch 10 iter 280, loss: 0.786, accuracy: 0.837
  338. epoch 11 iter 0, loss: 0.868, accuracy: 0.836
  339. epoch 11 iter 10, loss: 0.834, accuracy: 0.835
  340. epoch 11 iter 20, loss: 0.817, accuracy: 0.841
  341. epoch 11 iter 30, loss: 0.848, accuracy: 0.829
  342. epoch 11 iter 40, loss: 0.857, accuracy: 0.833
  343. epoch 11 iter 50, loss: 0.820, accuracy: 0.830
  344. epoch 11 iter 60, loss: 0.822, accuracy: 0.830
  345. epoch 11 iter 70, loss: 0.784, accuracy: 0.835
  346. epoch 11 iter 80, loss: 0.781, accuracy: 0.829
  347. epoch 11 iter 90, loss: 0.808, accuracy: 0.833
  348. epoch 11 iter 100, loss: 0.809, accuracy: 0.831
  349. epoch 11 iter 110, loss: 0.964, accuracy: 0.816
  350. epoch 11 iter 120, loss: 0.888, accuracy: 0.830
  351. epoch 11 iter 130, loss: 0.815, accuracy: 0.838
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  1454. epoch 49 iter 140, loss: 2.513, accuracy: 0.841
  1455. epoch 49 iter 150, loss: 2.322, accuracy: 0.842
  1456. epoch 49 iter 160, loss: 2.509, accuracy: 0.843
  1457. epoch 49 iter 170, loss: 2.432, accuracy: 0.852
  1458. epoch 49 iter 180, loss: 2.638, accuracy: 0.843
  1459. epoch 49 iter 190, loss: 2.429, accuracy: 0.851
  1460. epoch 49 iter 200, loss: 2.505, accuracy: 0.839
  1461. epoch 49 iter 210, loss: 2.633, accuracy: 0.851
  1462. epoch 49 iter 220, loss: 2.369, accuracy: 0.849
  1463. epoch 49 iter 230, loss: 2.476, accuracy: 0.852
  1464. epoch 49 iter 240, loss: 2.477, accuracy: 0.850
  1465. epoch 49 iter 250, loss: 2.568, accuracy: 0.849
  1466. epoch 49 iter 260, loss: 2.514, accuracy: 0.848
  1467. epoch 49 iter 270, loss: 2.386, accuracy: 0.837
  1468. epoch 49 iter 280, loss: 2.360, accuracy: 0.847
  1469. current FC size is: 500
  1470. 2018-10-22 17:44:47.817053: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  1471. 2018-10-22 17:44:47.817090: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  1472. 2018-10-22 17:44:47.817097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  1473. 2018-10-22 17:44:47.817101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  1474. 2018-10-22 17:44:47.817236: 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: 98.742, accuracy: 0.123
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  1511. epoch 1 iter 70, loss: 1.238, accuracy: 0.617
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  1562. epoch 3 iter 0, loss: 0.740, accuracy: 0.789
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  1591. epoch 4 iter 0, loss: 0.769, accuracy: 0.793
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  1600. epoch 4 iter 90, loss: 0.688, accuracy: 0.807
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  1814. epoch 11 iter 200, loss: 0.879, accuracy: 0.832
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  1816. epoch 11 iter 220, loss: 1.037, accuracy: 0.807
  1817. epoch 11 iter 230, loss: 0.919, accuracy: 0.826
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  1822. epoch 11 iter 280, loss: 0.937, accuracy: 0.826
  1823. epoch 12 iter 0, loss: 0.990, accuracy: 0.813
  1824. epoch 12 iter 10, loss: 0.950, accuracy: 0.817
  1825. epoch 12 iter 20, loss: 1.002, accuracy: 0.825
  1826. epoch 12 iter 30, loss: 0.961, accuracy: 0.821
  1827. epoch 12 iter 40, loss: 0.993, accuracy: 0.822
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  1830. epoch 12 iter 70, loss: 0.915, accuracy: 0.824
  1831. epoch 12 iter 80, loss: 0.982, accuracy: 0.818
  1832. epoch 12 iter 90, loss: 0.976, accuracy: 0.819
  1833. epoch 12 iter 100, loss: 1.075, accuracy: 0.813
  1834. epoch 12 iter 110, loss: 0.999, accuracy: 0.819
  1835. epoch 12 iter 120, loss: 0.906, accuracy: 0.834
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  1853. epoch 13 iter 10, loss: 1.086, accuracy: 0.811
  1854. epoch 13 iter 20, loss: 1.059, accuracy: 0.822
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  1856. epoch 13 iter 40, loss: 1.079, accuracy: 0.810
  1857. epoch 13 iter 50, loss: 1.029, accuracy: 0.821
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  1859. epoch 13 iter 70, loss: 0.955, accuracy: 0.822
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  1861. epoch 13 iter 90, loss: 1.097, accuracy: 0.808
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  1882. epoch 14 iter 10, loss: 1.165, accuracy: 0.811
  1883. epoch 14 iter 20, loss: 1.091, accuracy: 0.823
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  1886. epoch 14 iter 50, loss: 1.053, accuracy: 0.819
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  1890. epoch 14 iter 90, loss: 1.131, accuracy: 0.819
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  1911. epoch 15 iter 10, loss: 1.163, accuracy: 0.815
  1912. epoch 15 iter 20, loss: 1.109, accuracy: 0.826
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  1914. epoch 15 iter 40, loss: 1.191, accuracy: 0.816
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  1916. epoch 15 iter 60, loss: 1.100, accuracy: 0.820
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  1918. epoch 15 iter 80, loss: 1.062, accuracy: 0.833
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  1920. epoch 15 iter 100, loss: 1.242, accuracy: 0.812
  1921. epoch 15 iter 110, loss: 1.125, accuracy: 0.815
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  1927. epoch 15 iter 170, loss: 1.122, accuracy: 0.828
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  1930. epoch 15 iter 200, loss: 1.120, accuracy: 0.832
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  1939. epoch 16 iter 0, loss: 1.251, accuracy: 0.812
  1940. epoch 16 iter 10, loss: 1.278, accuracy: 0.813
  1941. epoch 16 iter 20, loss: 1.347, accuracy: 0.819
  1942. epoch 16 iter 30, loss: 1.199, accuracy: 0.814
  1943. epoch 16 iter 40, loss: 1.218, accuracy: 0.817
  1944. epoch 16 iter 50, loss: 1.275, accuracy: 0.816
  1945. epoch 16 iter 60, loss: 1.267, accuracy: 0.805
  1946. epoch 16 iter 70, loss: 1.114, accuracy: 0.820
  1947. epoch 16 iter 80, loss: 1.171, accuracy: 0.827
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  1968. epoch 17 iter 0, loss: 1.328, accuracy: 0.810
  1969. epoch 17 iter 10, loss: 1.288, accuracy: 0.820
  1970. epoch 17 iter 20, loss: 1.344, accuracy: 0.819
  1971. epoch 17 iter 30, loss: 1.331, accuracy: 0.813
  1972. epoch 17 iter 40, loss: 1.265, accuracy: 0.815
  1973. epoch 17 iter 50, loss: 1.199, accuracy: 0.816
  1974. epoch 17 iter 60, loss: 1.408, accuracy: 0.792
  1975. epoch 17 iter 70, loss: 1.210, accuracy: 0.809
  1976. epoch 17 iter 80, loss: 1.224, accuracy: 0.826
  1977. epoch 17 iter 90, loss: 1.283, accuracy: 0.819
  1978. epoch 17 iter 100, loss: 1.301, accuracy: 0.810
  1979. epoch 17 iter 110, loss: 1.242, accuracy: 0.823
  1980. epoch 17 iter 120, loss: 1.244, accuracy: 0.821
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  1987. epoch 17 iter 190, loss: 1.357, accuracy: 0.817
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  1990. epoch 17 iter 220, loss: 1.273, accuracy: 0.819
  1991. epoch 17 iter 230, loss: 1.284, accuracy: 0.817
  1992. epoch 17 iter 240, loss: 1.219, accuracy: 0.827
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  1997. epoch 18 iter 0, loss: 1.351, accuracy: 0.813
  1998. epoch 18 iter 10, loss: 1.335, accuracy: 0.819
  1999. epoch 18 iter 20, loss: 1.454, accuracy: 0.815
  2000. epoch 18 iter 30, loss: 1.335, accuracy: 0.818
  2001. epoch 18 iter 40, loss: 1.281, accuracy: 0.827
  2002. epoch 18 iter 50, loss: 1.312, accuracy: 0.813
  2003. epoch 18 iter 60, loss: 1.336, accuracy: 0.807
  2004. epoch 18 iter 70, loss: 1.276, accuracy: 0.804
  2005. epoch 18 iter 80, loss: 1.258, accuracy: 0.820
  2006. epoch 18 iter 90, loss: 1.330, accuracy: 0.818
  2007. epoch 18 iter 100, loss: 1.316, accuracy: 0.814
  2008. epoch 18 iter 110, loss: 1.357, accuracy: 0.820
  2009. epoch 18 iter 120, loss: 1.314, accuracy: 0.814
  2010. epoch 18 iter 130, loss: 1.319, accuracy: 0.822
  2011. epoch 18 iter 140, loss: 1.297, accuracy: 0.827
  2012. epoch 18 iter 150, loss: 1.355, accuracy: 0.819
  2013. epoch 18 iter 160, loss: 1.323, accuracy: 0.823
  2014. epoch 18 iter 170, loss: 1.309, accuracy: 0.817
  2015. epoch 18 iter 180, loss: 1.299, accuracy: 0.821
  2016. epoch 18 iter 190, loss: 1.451, accuracy: 0.815
  2017. epoch 18 iter 200, loss: 1.253, accuracy: 0.831
  2018. epoch 18 iter 210, loss: 1.233, accuracy: 0.824
  2019. epoch 18 iter 220, loss: 1.284, accuracy: 0.826
  2020. epoch 18 iter 230, loss: 1.371, accuracy: 0.825
  2021. epoch 18 iter 240, loss: 1.276, accuracy: 0.826
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  2024. epoch 18 iter 270, loss: 1.274, accuracy: 0.832
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  2026. epoch 19 iter 0, loss: 1.261, accuracy: 0.829
  2027. epoch 19 iter 10, loss: 1.359, accuracy: 0.817
  2028. epoch 19 iter 20, loss: 1.464, accuracy: 0.814
  2029. epoch 19 iter 30, loss: 1.422, accuracy: 0.820
  2030. epoch 19 iter 40, loss: 1.263, accuracy: 0.830
  2031. epoch 19 iter 50, loss: 1.417, accuracy: 0.817
  2032. epoch 19 iter 60, loss: 1.297, accuracy: 0.813
  2033. epoch 19 iter 70, loss: 1.433, accuracy: 0.792
  2034. epoch 19 iter 80, loss: 1.368, accuracy: 0.824
  2035. epoch 19 iter 90, loss: 1.318, accuracy: 0.831
  2036. epoch 19 iter 100, loss: 1.347, accuracy: 0.822
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  2038. epoch 19 iter 120, loss: 1.399, accuracy: 0.817
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  2055. epoch 20 iter 0, loss: 1.289, accuracy: 0.828
  2056. epoch 20 iter 10, loss: 1.442, accuracy: 0.814
  2057. epoch 20 iter 20, loss: 1.426, accuracy: 0.810
  2058. epoch 20 iter 30, loss: 1.551, accuracy: 0.812
  2059. epoch 20 iter 40, loss: 1.406, accuracy: 0.820
  2060. epoch 20 iter 50, loss: 1.424, accuracy: 0.826
  2061. epoch 20 iter 60, loss: 1.308, accuracy: 0.813
  2062. epoch 20 iter 70, loss: 1.363, accuracy: 0.818
  2063. epoch 20 iter 80, loss: 1.298, accuracy: 0.817
  2064. epoch 20 iter 90, loss: 1.385, accuracy: 0.827
  2065. epoch 20 iter 100, loss: 1.498, accuracy: 0.823
  2066. epoch 20 iter 110, loss: 1.449, accuracy: 0.824
  2067. epoch 20 iter 120, loss: 1.400, accuracy: 0.818
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  2084. epoch 21 iter 0, loss: 1.350, accuracy: 0.827
  2085. epoch 21 iter 10, loss: 1.529, accuracy: 0.818
  2086. epoch 21 iter 20, loss: 1.506, accuracy: 0.819
  2087. epoch 21 iter 30, loss: 1.593, accuracy: 0.820
  2088. epoch 21 iter 40, loss: 1.507, accuracy: 0.825
  2089. epoch 21 iter 50, loss: 1.482, accuracy: 0.831
  2090. epoch 21 iter 60, loss: 1.437, accuracy: 0.828
  2091. epoch 21 iter 70, loss: 1.440, accuracy: 0.824
  2092. epoch 21 iter 80, loss: 1.397, accuracy: 0.825
  2093. epoch 21 iter 90, loss: 1.507, accuracy: 0.821
  2094. epoch 21 iter 100, loss: 1.422, accuracy: 0.825
  2095. epoch 21 iter 110, loss: 1.553, accuracy: 0.820
  2096. epoch 21 iter 120, loss: 1.433, accuracy: 0.812
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  2112. epoch 21 iter 280, loss: 1.531, accuracy: 0.826
  2113. epoch 22 iter 0, loss: 1.453, accuracy: 0.827
  2114. epoch 22 iter 10, loss: 1.506, accuracy: 0.822
  2115. epoch 22 iter 20, loss: 1.568, accuracy: 0.820
  2116. epoch 22 iter 30, loss: 1.619, accuracy: 0.822
  2117. epoch 22 iter 40, loss: 1.517, accuracy: 0.829
  2118. epoch 22 iter 50, loss: 1.502, accuracy: 0.828
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  2122. epoch 22 iter 90, loss: 1.507, accuracy: 0.829
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  2887. epoch 48 iter 200, loss: 3.302, accuracy: 0.832
  2888. epoch 48 iter 210, loss: 3.027, accuracy: 0.832
  2889. epoch 48 iter 220, loss: 2.895, accuracy: 0.824
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  2893. epoch 48 iter 260, loss: 2.930, accuracy: 0.834
  2894. epoch 48 iter 270, loss: 2.813, accuracy: 0.831
  2895. epoch 48 iter 280, loss: 2.880, accuracy: 0.826
  2896. epoch 49 iter 0, loss: 2.941, accuracy: 0.842
  2897. epoch 49 iter 10, loss: 3.178, accuracy: 0.834
  2898. epoch 49 iter 20, loss: 2.845, accuracy: 0.831
  2899. epoch 49 iter 30, loss: 2.935, accuracy: 0.835
  2900. epoch 49 iter 40, loss: 2.968, accuracy: 0.833
  2901. epoch 49 iter 50, loss: 3.231, accuracy: 0.835
  2902. epoch 49 iter 60, loss: 3.260, accuracy: 0.834
  2903. epoch 49 iter 70, loss: 3.094, accuracy: 0.832
  2904. epoch 49 iter 80, loss: 3.208, accuracy: 0.836
  2905. epoch 49 iter 90, loss: 3.173, accuracy: 0.835
  2906. epoch 49 iter 100, loss: 3.058, accuracy: 0.838
  2907. epoch 49 iter 110, loss: 3.194, accuracy: 0.835
  2908. epoch 49 iter 120, loss: 3.236, accuracy: 0.834
  2909. epoch 49 iter 130, loss: 2.980, accuracy: 0.828
  2910. epoch 49 iter 140, loss: 3.127, accuracy: 0.822
  2911. epoch 49 iter 150, loss: 3.282, accuracy: 0.832
  2912. epoch 49 iter 160, loss: 3.068, accuracy: 0.823
  2913. epoch 49 iter 170, loss: 2.981, accuracy: 0.833
  2914. epoch 49 iter 180, loss: 3.546, accuracy: 0.837
  2915. epoch 49 iter 190, loss: 3.532, accuracy: 0.840
  2916. epoch 49 iter 200, loss: 3.659, accuracy: 0.824
  2917. epoch 49 iter 210, loss: 3.212, accuracy: 0.835
  2918. epoch 49 iter 220, loss: 3.284, accuracy: 0.828
  2919. epoch 49 iter 230, loss: 3.055, accuracy: 0.826
  2920. epoch 49 iter 240, loss: 3.251, accuracy: 0.829
  2921. epoch 49 iter 250, loss: 3.314, accuracy: 0.832
  2922. epoch 49 iter 260, loss: 3.108, accuracy: 0.829
  2923. epoch 49 iter 270, loss: 2.965, accuracy: 0.833
  2924. epoch 49 iter 280, loss: 2.853, accuracy: 0.838
  2925. current FC size is: 1000
  2926. 2018-10-22 18:15:00.623646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  2927. 2018-10-22 18:15:00.623691: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  2928. 2018-10-22 18:15:00.623699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  2929. 2018-10-22 18:15:00.623704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  2930. 2018-10-22 18:15:00.623829: 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: 42.076, accuracy: 0.110
  2932. epoch 0 iter 10, loss: 2.297, accuracy: 0.181
  2933. epoch 0 iter 20, loss: 2.240, accuracy: 0.199
  2934. epoch 0 iter 30, loss: 2.239, accuracy: 0.197
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  2936. epoch 0 iter 50, loss: 2.241, accuracy: 0.193
  2937. epoch 0 iter 60, loss: 2.234, accuracy: 0.191
  2938. epoch 0 iter 70, loss: 2.228, accuracy: 0.196
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  2940. epoch 0 iter 90, loss: 2.229, accuracy: 0.197
  2941. epoch 0 iter 100, loss: 2.225, accuracy: 0.197
  2942. epoch 0 iter 110, loss: 2.230, accuracy: 0.196
  2943. epoch 0 iter 120, loss: 2.243, accuracy: 0.193
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  2960. epoch 1 iter 0, loss: 2.231, accuracy: 0.196
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  2963. epoch 1 iter 30, loss: 2.230, accuracy: 0.196
  2964. epoch 1 iter 40, loss: 2.223, accuracy: 0.196
  2965. epoch 1 iter 50, loss: 2.229, accuracy: 0.187
  2966. epoch 1 iter 60, loss: 2.228, accuracy: 0.196
  2967. epoch 1 iter 70, loss: 2.219, accuracy: 0.196
  2968. epoch 1 iter 80, loss: 2.223, accuracy: 0.196
  2969. epoch 1 iter 90, loss: 2.225, accuracy: 0.196
  2970. epoch 1 iter 100, loss: 2.223, accuracy: 0.196
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  2978. epoch 1 iter 180, loss: 2.216, accuracy: 0.200
  2979. epoch 1 iter 190, loss: 2.217, accuracy: 0.197
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  2989. epoch 2 iter 0, loss: 2.219, accuracy: 0.197
  2990. epoch 2 iter 10, loss: 2.224, accuracy: 0.196
  2991. epoch 2 iter 20, loss: 2.212, accuracy: 0.197
  2992. epoch 2 iter 30, loss: 2.214, accuracy: 0.195
  2993. epoch 2 iter 40, loss: 2.219, accuracy: 0.197
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  2996. epoch 2 iter 70, loss: 2.205, accuracy: 0.198
  2997. epoch 2 iter 80, loss: 2.208, accuracy: 0.195
  2998. epoch 2 iter 90, loss: 2.204, accuracy: 0.198
  2999. epoch 2 iter 100, loss: 2.216, accuracy: 0.201
  3000. epoch 2 iter 110, loss: 2.205, accuracy: 0.201
  3001. epoch 2 iter 120, loss: 2.213, accuracy: 0.205
  3002. epoch 2 iter 130, loss: 2.214, accuracy: 0.204
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  3004. epoch 2 iter 150, loss: 2.194, accuracy: 0.216
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  3009. epoch 2 iter 200, loss: 2.176, accuracy: 0.233
  3010. epoch 2 iter 210, loss: 2.178, accuracy: 0.225
  3011. epoch 2 iter 220, loss: 2.152, accuracy: 0.243
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  3014. epoch 2 iter 250, loss: 2.099, accuracy: 0.254
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  3016. epoch 2 iter 270, loss: 2.055, accuracy: 0.276
  3017. epoch 2 iter 280, loss: 1.991, accuracy: 0.299
  3018. epoch 3 iter 0, loss: 2.010, accuracy: 0.283
  3019. epoch 3 iter 10, loss: 1.938, accuracy: 0.319
  3020. epoch 3 iter 20, loss: 1.906, accuracy: 0.334
  3021. epoch 3 iter 30, loss: 1.998, accuracy: 0.304
  3022. epoch 3 iter 40, loss: 2.024, accuracy: 0.297
  3023. epoch 3 iter 50, loss: 1.982, accuracy: 0.319
  3024. epoch 3 iter 60, loss: 1.858, accuracy: 0.359
  3025. epoch 3 iter 70, loss: 1.839, accuracy: 0.371
  3026. epoch 3 iter 80, loss: 1.886, accuracy: 0.350
  3027. epoch 3 iter 90, loss: 1.889, accuracy: 0.357
  3028. epoch 3 iter 100, loss: 1.853, accuracy: 0.369
  3029. epoch 3 iter 110, loss: 1.816, accuracy: 0.388
  3030. epoch 3 iter 120, loss: 1.787, accuracy: 0.387
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  3032. epoch 3 iter 140, loss: 1.722, accuracy: 0.441
  3033. epoch 3 iter 150, loss: 1.623, accuracy: 0.482
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  3035. epoch 3 iter 170, loss: 1.456, accuracy: 0.554
  3036. epoch 3 iter 180, loss: 1.300, accuracy: 0.614
  3037. epoch 3 iter 190, loss: 1.370, accuracy: 0.580
  3038. epoch 3 iter 200, loss: 1.265, accuracy: 0.608
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  3040. epoch 3 iter 220, loss: 1.112, accuracy: 0.665
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  3044. epoch 3 iter 260, loss: 1.022, accuracy: 0.688
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  3047. epoch 4 iter 0, loss: 0.963, accuracy: 0.711
  3048. epoch 4 iter 10, loss: 1.009, accuracy: 0.697
  3049. epoch 4 iter 20, loss: 0.987, accuracy: 0.708
  3050. epoch 4 iter 30, loss: 0.932, accuracy: 0.720
  3051. epoch 4 iter 40, loss: 0.859, accuracy: 0.748
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  3055. epoch 4 iter 80, loss: 0.833, accuracy: 0.752
  3056. epoch 4 iter 90, loss: 0.829, accuracy: 0.751
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  3059. epoch 4 iter 120, loss: 0.813, accuracy: 0.759
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  3076. epoch 5 iter 0, loss: 0.738, accuracy: 0.786
  3077. epoch 5 iter 10, loss: 0.723, accuracy: 0.794
  3078. epoch 5 iter 20, loss: 0.709, accuracy: 0.795
  3079. epoch 5 iter 30, loss: 0.721, accuracy: 0.793
  3080. epoch 5 iter 40, loss: 0.713, accuracy: 0.794
  3081. epoch 5 iter 50, loss: 0.697, accuracy: 0.799
  3082. epoch 5 iter 60, loss: 0.675, accuracy: 0.808
  3083. epoch 5 iter 70, loss: 0.665, accuracy: 0.810
  3084. epoch 5 iter 80, loss: 0.659, accuracy: 0.811
  3085. epoch 5 iter 90, loss: 0.686, accuracy: 0.806
  3086. epoch 5 iter 100, loss: 0.661, accuracy: 0.814
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  3115. epoch 6 iter 100, loss: 0.619, accuracy: 0.831
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  4327. epoch 48 iter 40, loss: 3.337, accuracy: 0.847
  4328. epoch 48 iter 50, loss: 3.128, accuracy: 0.854
  4329. epoch 48 iter 60, loss: 3.045, accuracy: 0.841
  4330. epoch 48 iter 70, loss: 3.409, accuracy: 0.849
  4331. epoch 48 iter 80, loss: 2.960, accuracy: 0.850
  4332. epoch 48 iter 90, loss: 2.731, accuracy: 0.856
  4333. epoch 48 iter 100, loss: 3.203, accuracy: 0.851
  4334. epoch 48 iter 110, loss: 3.315, accuracy: 0.853
  4335. epoch 48 iter 120, loss: 3.491, accuracy: 0.846
  4336. epoch 48 iter 130, loss: 3.078, accuracy: 0.843
  4337. epoch 48 iter 140, loss: 3.241, accuracy: 0.837
  4338. epoch 48 iter 150, loss: 2.988, accuracy: 0.849
  4339. epoch 48 iter 160, loss: 3.101, accuracy: 0.849
  4340. epoch 48 iter 170, loss: 3.182, accuracy: 0.841
  4341. epoch 48 iter 180, loss: 3.100, accuracy: 0.843
  4342. epoch 48 iter 190, loss: 2.901, accuracy: 0.851
  4343. epoch 48 iter 200, loss: 3.660, accuracy: 0.844
  4344. epoch 48 iter 210, loss: 3.291, accuracy: 0.843
  4345. epoch 48 iter 220, loss: 3.401, accuracy: 0.841
  4346. epoch 48 iter 230, loss: 3.132, accuracy: 0.852
  4347. epoch 48 iter 240, loss: 2.962, accuracy: 0.855
  4348. epoch 48 iter 250, loss: 3.210, accuracy: 0.851
  4349. epoch 48 iter 260, loss: 3.136, accuracy: 0.852
  4350. epoch 48 iter 270, loss: 3.026, accuracy: 0.852
  4351. epoch 48 iter 280, loss: 2.818, accuracy: 0.851
  4352. epoch 49 iter 0, loss: 2.907, accuracy: 0.851
  4353. epoch 49 iter 10, loss: 3.175, accuracy: 0.854
  4354. epoch 49 iter 20, loss: 3.360, accuracy: 0.842
  4355. epoch 49 iter 30, loss: 3.404, accuracy: 0.842
  4356. epoch 49 iter 40, loss: 3.491, accuracy: 0.849
  4357. epoch 49 iter 50, loss: 3.311, accuracy: 0.853
  4358. epoch 49 iter 60, loss: 2.892, accuracy: 0.843
  4359. epoch 49 iter 70, loss: 3.693, accuracy: 0.848
  4360. epoch 49 iter 80, loss: 3.368, accuracy: 0.852
  4361. epoch 49 iter 90, loss: 2.769, accuracy: 0.842
  4362. epoch 49 iter 100, loss: 3.232, accuracy: 0.839
  4363. epoch 49 iter 110, loss: 3.099, accuracy: 0.851
  4364. epoch 49 iter 120, loss: 3.347, accuracy: 0.843
  4365. epoch 49 iter 130, loss: 3.312, accuracy: 0.841
  4366. epoch 49 iter 140, loss: 3.537, accuracy: 0.834
  4367. epoch 49 iter 150, loss: 2.946, accuracy: 0.847
  4368. epoch 49 iter 160, loss: 3.051, accuracy: 0.847
  4369. epoch 49 iter 170, loss: 3.280, accuracy: 0.840
  4370. epoch 49 iter 180, loss: 3.231, accuracy: 0.841
  4371. epoch 49 iter 190, loss: 3.014, accuracy: 0.850
  4372. epoch 49 iter 200, loss: 3.385, accuracy: 0.854
  4373. epoch 49 iter 210, loss: 3.028, accuracy: 0.850
  4374. epoch 49 iter 220, loss: 3.355, accuracy: 0.849
  4375. epoch 49 iter 230, loss: 3.725, accuracy: 0.853
  4376. epoch 49 iter 240, loss: 2.852, accuracy: 0.850
  4377. epoch 49 iter 250, loss: 3.194, accuracy: 0.839
  4378. epoch 49 iter 260, loss: 3.337, accuracy: 0.847
  4379. epoch 49 iter 270, loss: 3.266, accuracy: 0.853
  4380. epoch 49 iter 280, loss: 3.328, accuracy: 0.855
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