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different_kernels_epochs50

lamiastella
Oct 22nd, 2018
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  1. [jalal@scc-c13 pset3_CNN]$ python different_kernels.py
  2. 2018-10-22 22:32:30.574332: 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 22:32:30.775170: 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: 0000:83:00.0
  6. totalMemory: 11.91GiB freeMemory: 11.63GiB
  7. 2018-10-22 22:32:30.775208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  8. 2018-10-22 22:32:31.109403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  9. 2018-10-22 22:32:31.109441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  10. 2018-10-22 22:32:31.109447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  11. 2018-10-22 22:32:31.109701: 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: 0000:83:00.0, compute capability: 6.0)
  12. 2018-10-22 22:32:31.351121: 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 kernel size is: 3
  14. 2018-10-22 22:32:35.172076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  15. 2018-10-22 22:32:35.172115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  16. 2018-10-22 22:32:35.172122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  17. 2018-10-22 22:32:35.172127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  18. 2018-10-22 22:32:35.172253: 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: 0000:83:00.0, compute capability: 6.0)
  19. epoch 0 iter 0, loss: 86.982, accuracy: 0.137
  20. epoch 0 iter 10, loss: 8.251, accuracy: 0.098
  21. epoch 0 iter 20, loss: 2.277, accuracy: 0.207
  22. epoch 0 iter 30, loss: 2.233, accuracy: 0.210
  23. epoch 0 iter 40, loss: 2.053, accuracy: 0.332
  24. epoch 0 iter 50, loss: 1.884, accuracy: 0.390
  25. epoch 0 iter 60, loss: 1.738, accuracy: 0.449
  26. epoch 0 iter 70, loss: 1.645, accuracy: 0.482
  27. epoch 0 iter 80, loss: 1.550, accuracy: 0.521
  28. epoch 0 iter 90, loss: 1.493, accuracy: 0.535
  29. epoch 0 iter 100, loss: 1.402, accuracy: 0.573
  30. epoch 0 iter 110, loss: 1.385, accuracy: 0.578
  31. epoch 0 iter 120, loss: 1.328, accuracy: 0.597
  32. epoch 0 iter 130, loss: 1.257, accuracy: 0.629
  33. epoch 0 iter 140, loss: 1.180, accuracy: 0.650
  34. epoch 0 iter 150, loss: 1.172, accuracy: 0.651
  35. epoch 0 iter 160, loss: 1.142, accuracy: 0.661
  36. epoch 0 iter 170, loss: 1.103, accuracy: 0.670
  37. epoch 0 iter 180, loss: 1.081, accuracy: 0.678
  38. epoch 0 iter 190, loss: 1.045, accuracy: 0.693
  39. epoch 0 iter 200, loss: 1.061, accuracy: 0.688
  40. epoch 0 iter 210, loss: 1.065, accuracy: 0.689
  41. epoch 0 iter 220, loss: 1.050, accuracy: 0.691
  42. epoch 0 iter 230, loss: 1.018, accuracy: 0.703
  43. epoch 0 iter 240, loss: 1.003, accuracy: 0.706
  44. epoch 0 iter 250, loss: 0.998, accuracy: 0.708
  45. epoch 0 iter 260, loss: 0.958, accuracy: 0.720
  46. epoch 0 iter 270, loss: 0.945, accuracy: 0.724
  47. epoch 0 iter 280, loss: 0.929, accuracy: 0.729
  48. epoch 1 iter 0, loss: 0.909, accuracy: 0.737
  49. epoch 1 iter 10, loss: 0.911, accuracy: 0.736
  50. epoch 1 iter 20, loss: 0.888, accuracy: 0.742
  51. epoch 1 iter 30, loss: 0.898, accuracy: 0.742
  52. epoch 1 iter 40, loss: 0.900, accuracy: 0.741
  53. epoch 1 iter 50, loss: 0.873, accuracy: 0.749
  54. epoch 1 iter 60, loss: 0.889, accuracy: 0.744
  55. epoch 1 iter 70, loss: 0.862, accuracy: 0.751
  56. epoch 1 iter 80, loss: 0.872, accuracy: 0.746
  57. epoch 1 iter 90, loss: 0.858, accuracy: 0.751
  58. epoch 1 iter 100, loss: 0.856, accuracy: 0.751
  59. epoch 1 iter 110, loss: 0.855, accuracy: 0.752
  60. epoch 1 iter 120, loss: 0.833, accuracy: 0.763
  61. epoch 1 iter 130, loss: 0.858, accuracy: 0.753
  62. epoch 1 iter 140, loss: 0.845, accuracy: 0.759
  63. epoch 1 iter 150, loss: 0.825, accuracy: 0.765
  64. epoch 1 iter 160, loss: 0.884, accuracy: 0.746
  65. epoch 1 iter 170, loss: 0.857, accuracy: 0.756
  66. epoch 1 iter 180, loss: 0.854, accuracy: 0.753
  67. epoch 1 iter 190, loss: 0.814, accuracy: 0.768
  68. epoch 1 iter 200, loss: 0.869, accuracy: 0.750
  69. epoch 1 iter 210, loss: 0.825, accuracy: 0.764
  70. epoch 1 iter 220, loss: 0.827, accuracy: 0.765
  71. epoch 1 iter 230, loss: 0.819, accuracy: 0.764
  72. epoch 1 iter 240, loss: 0.839, accuracy: 0.758
  73. epoch 1 iter 250, loss: 0.814, accuracy: 0.770
  74. epoch 1 iter 260, loss: 0.811, accuracy: 0.768
  75. epoch 1 iter 270, loss: 0.803, accuracy: 0.773
  76. epoch 1 iter 280, loss: 0.811, accuracy: 0.772
  77. epoch 2 iter 0, loss: 0.821, accuracy: 0.768
  78. epoch 2 iter 10, loss: 0.782, accuracy: 0.779
  79. epoch 2 iter 20, loss: 0.780, accuracy: 0.779
  80. epoch 2 iter 30, loss: 0.818, accuracy: 0.769
  81. epoch 2 iter 40, loss: 0.819, accuracy: 0.768
  82. epoch 2 iter 50, loss: 0.799, accuracy: 0.774
  83. epoch 2 iter 60, loss: 0.807, accuracy: 0.772
  84. epoch 2 iter 70, loss: 0.772, accuracy: 0.783
  85. epoch 2 iter 80, loss: 0.795, accuracy: 0.776
  86. epoch 2 iter 90, loss: 0.786, accuracy: 0.780
  87. epoch 2 iter 100, loss: 0.794, accuracy: 0.775
  88. epoch 2 iter 110, loss: 0.786, accuracy: 0.777
  89. epoch 2 iter 120, loss: 0.768, accuracy: 0.785
  90. epoch 2 iter 130, loss: 0.819, accuracy: 0.767
  91. epoch 2 iter 140, loss: 0.803, accuracy: 0.775
  92. epoch 2 iter 150, loss: 0.785, accuracy: 0.780
  93. epoch 2 iter 160, loss: 0.819, accuracy: 0.769
  94. epoch 2 iter 170, loss: 0.792, accuracy: 0.780
  95. epoch 2 iter 180, loss: 0.795, accuracy: 0.776
  96. epoch 2 iter 190, loss: 0.769, accuracy: 0.783
  97. epoch 2 iter 200, loss: 0.804, accuracy: 0.772
  98. epoch 2 iter 210, loss: 0.769, accuracy: 0.785
  99. epoch 2 iter 220, loss: 0.782, accuracy: 0.778
  100. epoch 2 iter 230, loss: 0.797, accuracy: 0.775
  101. epoch 2 iter 240, loss: 0.795, accuracy: 0.775
  102. epoch 2 iter 250, loss: 0.780, accuracy: 0.781
  103. epoch 2 iter 260, loss: 0.863, accuracy: 0.765
  104. epoch 2 iter 270, loss: 0.768, accuracy: 0.788
  105. epoch 2 iter 280, loss: 0.782, accuracy: 0.783
  106. epoch 3 iter 0, loss: 0.826, accuracy: 0.772
  107. epoch 3 iter 10, loss: 0.756, accuracy: 0.792
  108. epoch 3 iter 20, loss: 0.777, accuracy: 0.784
  109. epoch 3 iter 30, loss: 0.799, accuracy: 0.779
  110. epoch 3 iter 40, loss: 0.779, accuracy: 0.786
  111. epoch 3 iter 50, loss: 0.753, accuracy: 0.790
  112. epoch 3 iter 60, loss: 0.759, accuracy: 0.790
  113. epoch 3 iter 70, loss: 0.784, accuracy: 0.779
  114. epoch 3 iter 80, loss: 0.790, accuracy: 0.781
  115. epoch 3 iter 90, loss: 0.785, accuracy: 0.788
  116. epoch 3 iter 100, loss: 0.825, accuracy: 0.771
  117. epoch 3 iter 110, loss: 0.772, accuracy: 0.785
  118. epoch 3 iter 120, loss: 0.745, accuracy: 0.795
  119. epoch 3 iter 130, loss: 0.788, accuracy: 0.783
  120. epoch 3 iter 140, loss: 0.752, accuracy: 0.793
  121. epoch 3 iter 150, loss: 0.779, accuracy: 0.784
  122. epoch 3 iter 160, loss: 0.768, accuracy: 0.789
  123. epoch 3 iter 170, loss: 0.760, accuracy: 0.792
  124. epoch 3 iter 180, loss: 0.775, accuracy: 0.785
  125. epoch 3 iter 190, loss: 0.774, accuracy: 0.785
  126. epoch 3 iter 200, loss: 0.735, accuracy: 0.800
  127. epoch 3 iter 210, loss: 0.785, accuracy: 0.782
  128. epoch 3 iter 220, loss: 0.721, accuracy: 0.803
  129. epoch 3 iter 230, loss: 0.772, accuracy: 0.786
  130. epoch 3 iter 240, loss: 0.765, accuracy: 0.792
  131. epoch 3 iter 250, loss: 0.735, accuracy: 0.799
  132. epoch 3 iter 260, loss: 0.752, accuracy: 0.792
  133. epoch 3 iter 270, loss: 0.739, accuracy: 0.799
  134. epoch 3 iter 280, loss: 0.759, accuracy: 0.794
  135. epoch 4 iter 0, loss: 0.777, accuracy: 0.790
  136. epoch 4 iter 10, loss: 0.747, accuracy: 0.795
  137. epoch 4 iter 20, loss: 0.752, accuracy: 0.797
  138. epoch 4 iter 30, loss: 0.745, accuracy: 0.800
  139. epoch 4 iter 40, loss: 0.732, accuracy: 0.801
  140. epoch 4 iter 50, loss: 0.775, accuracy: 0.787
  141. epoch 4 iter 60, loss: 0.804, accuracy: 0.777
  142. epoch 4 iter 70, loss: 0.754, accuracy: 0.796
  143. epoch 4 iter 80, loss: 0.800, accuracy: 0.777
  144. epoch 4 iter 90, loss: 0.742, accuracy: 0.798
  145. epoch 4 iter 100, loss: 0.752, accuracy: 0.793
  146. epoch 4 iter 110, loss: 0.735, accuracy: 0.800
  147. epoch 4 iter 120, loss: 0.749, accuracy: 0.798
  148. epoch 4 iter 130, loss: 0.782, accuracy: 0.790
  149. epoch 4 iter 140, loss: 0.737, accuracy: 0.803
  150. epoch 4 iter 150, loss: 0.751, accuracy: 0.796
  151. epoch 4 iter 160, loss: 0.751, accuracy: 0.797
  152. epoch 4 iter 170, loss: 0.733, accuracy: 0.802
  153. epoch 4 iter 180, loss: 0.746, accuracy: 0.800
  154. epoch 4 iter 190, loss: 0.749, accuracy: 0.796
  155. epoch 4 iter 200, loss: 0.710, accuracy: 0.813
  156. epoch 4 iter 210, loss: 0.737, accuracy: 0.804
  157. epoch 4 iter 220, loss: 0.738, accuracy: 0.799
  158. epoch 4 iter 230, loss: 0.759, accuracy: 0.796
  159. epoch 4 iter 240, loss: 0.715, accuracy: 0.810
  160. epoch 4 iter 250, loss: 0.708, accuracy: 0.812
  161. epoch 4 iter 260, loss: 0.711, accuracy: 0.811
  162. epoch 4 iter 270, loss: 0.709, accuracy: 0.813
  163. epoch 4 iter 280, loss: 0.716, accuracy: 0.812
  164. epoch 5 iter 0, loss: 0.750, accuracy: 0.804
  165. epoch 5 iter 10, loss: 0.758, accuracy: 0.800
  166. epoch 5 iter 20, loss: 0.752, accuracy: 0.803
  167. epoch 5 iter 30, loss: 0.745, accuracy: 0.807
  168. epoch 5 iter 40, loss: 0.754, accuracy: 0.800
  169. epoch 5 iter 50, loss: 0.753, accuracy: 0.799
  170. epoch 5 iter 60, loss: 0.826, accuracy: 0.778
  171. epoch 5 iter 70, loss: 0.750, accuracy: 0.803
  172. epoch 5 iter 80, loss: 0.754, accuracy: 0.799
  173. epoch 5 iter 90, loss: 0.751, accuracy: 0.801
  174. epoch 5 iter 100, loss: 0.760, accuracy: 0.795
  175. epoch 5 iter 110, loss: 0.721, accuracy: 0.805
  176. epoch 5 iter 120, loss: 0.715, accuracy: 0.812
  177. epoch 5 iter 130, loss: 0.762, accuracy: 0.800
  178. epoch 5 iter 140, loss: 0.742, accuracy: 0.806
  179. epoch 5 iter 150, loss: 0.731, accuracy: 0.811
  180. epoch 5 iter 160, loss: 0.749, accuracy: 0.805
  181. epoch 5 iter 170, loss: 0.744, accuracy: 0.807
  182. epoch 5 iter 180, loss: 0.746, accuracy: 0.809
  183. epoch 5 iter 190, loss: 0.777, accuracy: 0.797
  184. epoch 5 iter 200, loss: 0.761, accuracy: 0.806
  185. epoch 5 iter 210, loss: 0.789, accuracy: 0.794
  186. epoch 5 iter 220, loss: 0.793, accuracy: 0.786
  187. epoch 5 iter 230, loss: 0.757, accuracy: 0.805
  188. epoch 5 iter 240, loss: 0.770, accuracy: 0.798
  189. epoch 5 iter 250, loss: 0.729, accuracy: 0.811
  190. epoch 5 iter 260, loss: 0.732, accuracy: 0.812
  191. epoch 5 iter 270, loss: 0.761, accuracy: 0.805
  192. epoch 5 iter 280, loss: 0.749, accuracy: 0.813
  193. epoch 6 iter 0, loss: 0.759, accuracy: 0.808
  194. epoch 6 iter 10, loss: 0.745, accuracy: 0.812
  195. epoch 6 iter 20, loss: 0.768, accuracy: 0.805
  196. epoch 6 iter 30, loss: 0.754, accuracy: 0.806
  197. epoch 6 iter 40, loss: 0.807, accuracy: 0.791
  198. epoch 6 iter 50, loss: 0.783, accuracy: 0.800
  199. epoch 6 iter 60, loss: 0.855, accuracy: 0.776
  200. epoch 6 iter 70, loss: 0.759, accuracy: 0.801
  201. epoch 6 iter 80, loss: 0.792, accuracy: 0.790
  202. epoch 6 iter 90, loss: 0.741, accuracy: 0.811
  203. epoch 6 iter 100, loss: 0.763, accuracy: 0.805
  204. epoch 6 iter 110, loss: 0.791, accuracy: 0.790
  205. epoch 6 iter 120, loss: 0.733, accuracy: 0.813
  206. epoch 6 iter 130, loss: 0.788, accuracy: 0.806
  207. epoch 6 iter 140, loss: 0.763, accuracy: 0.809
  208. epoch 6 iter 150, loss: 0.759, accuracy: 0.809
  209. epoch 6 iter 160, loss: 0.780, accuracy: 0.804
  210. epoch 6 iter 170, loss: 0.784, accuracy: 0.802
  211. epoch 6 iter 180, loss: 0.754, accuracy: 0.810
  212. epoch 6 iter 190, loss: 0.791, accuracy: 0.799
  213. epoch 6 iter 200, loss: 0.774, accuracy: 0.811
  214. epoch 6 iter 210, loss: 0.797, accuracy: 0.801
  215. epoch 6 iter 220, loss: 0.756, accuracy: 0.811
  216. epoch 6 iter 230, loss: 0.741, accuracy: 0.811
  217. epoch 6 iter 240, loss: 0.754, accuracy: 0.813
  218. epoch 6 iter 250, loss: 0.787, accuracy: 0.801
  219. epoch 6 iter 260, loss: 0.760, accuracy: 0.807
  220. epoch 6 iter 270, loss: 0.804, accuracy: 0.801
  221. epoch 6 iter 280, loss: 0.778, accuracy: 0.809
  222. epoch 7 iter 0, loss: 0.803, accuracy: 0.804
  223. epoch 7 iter 10, loss: 0.793, accuracy: 0.803
  224. epoch 7 iter 20, loss: 0.775, accuracy: 0.810
  225. epoch 7 iter 30, loss: 0.812, accuracy: 0.801
  226. epoch 7 iter 40, loss: 0.777, accuracy: 0.806
  227. epoch 7 iter 50, loss: 0.753, accuracy: 0.816
  228. epoch 7 iter 60, loss: 0.762, accuracy: 0.806
  229. epoch 7 iter 70, loss: 0.768, accuracy: 0.800
  230. epoch 7 iter 80, loss: 0.784, accuracy: 0.799
  231. epoch 7 iter 90, loss: 0.773, accuracy: 0.808
  232. epoch 7 iter 100, loss: 0.792, accuracy: 0.806
  233. epoch 7 iter 110, loss: 0.748, accuracy: 0.813
  234. epoch 7 iter 120, loss: 0.767, accuracy: 0.811
  235. epoch 7 iter 130, loss: 0.803, accuracy: 0.808
  236. epoch 7 iter 140, loss: 0.773, accuracy: 0.816
  237. epoch 7 iter 150, loss: 0.793, accuracy: 0.812
  238. epoch 7 iter 160, loss: 0.795, accuracy: 0.810
  239. epoch 7 iter 170, loss: 0.757, accuracy: 0.820
  240. epoch 7 iter 180, loss: 0.769, accuracy: 0.814
  241. epoch 7 iter 190, loss: 0.795, accuracy: 0.805
  242. epoch 7 iter 200, loss: 0.871, accuracy: 0.796
  243. epoch 7 iter 210, loss: 0.828, accuracy: 0.798
  244. epoch 7 iter 220, loss: 0.781, accuracy: 0.813
  245. epoch 7 iter 230, loss: 0.752, accuracy: 0.820
  246. epoch 7 iter 240, loss: 0.801, accuracy: 0.806
  247. epoch 7 iter 250, loss: 0.771, accuracy: 0.813
  248. epoch 7 iter 260, loss: 0.839, accuracy: 0.797
  249. epoch 7 iter 270, loss: 0.787, accuracy: 0.816
  250. epoch 7 iter 280, loss: 0.805, accuracy: 0.811
  251. epoch 8 iter 0, loss: 0.816, accuracy: 0.811
  252. epoch 8 iter 10, loss: 0.876, accuracy: 0.798
  253. epoch 8 iter 20, loss: 0.801, accuracy: 0.809
  254. epoch 8 iter 30, loss: 0.831, accuracy: 0.808
  255. epoch 8 iter 40, loss: 0.821, accuracy: 0.810
  256. epoch 8 iter 50, loss: 0.758, accuracy: 0.819
  257. epoch 8 iter 60, loss: 0.763, accuracy: 0.815
  258. epoch 8 iter 70, loss: 0.792, accuracy: 0.807
  259. epoch 8 iter 80, loss: 0.804, accuracy: 0.808
  260. epoch 8 iter 90, loss: 0.805, accuracy: 0.805
  261. epoch 8 iter 100, loss: 0.809, accuracy: 0.811
  262. epoch 8 iter 110, loss: 0.793, accuracy: 0.810
  263. epoch 8 iter 120, loss: 0.791, accuracy: 0.817
  264. epoch 8 iter 130, loss: 0.781, accuracy: 0.821
  265. epoch 8 iter 140, loss: 0.795, accuracy: 0.818
  266. epoch 8 iter 150, loss: 0.844, accuracy: 0.813
  267. epoch 8 iter 160, loss: 0.848, accuracy: 0.801
  268. epoch 8 iter 170, loss: 0.817, accuracy: 0.811
  269. epoch 8 iter 180, loss: 0.790, accuracy: 0.810
  270. epoch 8 iter 190, loss: 0.825, accuracy: 0.807
  271. epoch 8 iter 200, loss: 0.897, accuracy: 0.795
  272. epoch 8 iter 210, loss: 0.806, accuracy: 0.806
  273. epoch 8 iter 220, loss: 0.790, accuracy: 0.811
  274. epoch 8 iter 230, loss: 0.794, accuracy: 0.820
  275. epoch 8 iter 240, loss: 0.826, accuracy: 0.813
  276. epoch 8 iter 250, loss: 0.760, accuracy: 0.822
  277. epoch 8 iter 260, loss: 0.831, accuracy: 0.805
  278. epoch 8 iter 270, loss: 0.845, accuracy: 0.807
  279. epoch 8 iter 280, loss: 0.795, accuracy: 0.821
  280. epoch 9 iter 0, loss: 0.842, accuracy: 0.807
  281. epoch 9 iter 10, loss: 0.867, accuracy: 0.805
  282. epoch 9 iter 20, loss: 0.848, accuracy: 0.814
  283. epoch 9 iter 30, loss: 0.872, accuracy: 0.808
  284. epoch 9 iter 40, loss: 0.845, accuracy: 0.815
  285. epoch 9 iter 50, loss: 0.794, accuracy: 0.819
  286. epoch 9 iter 60, loss: 0.826, accuracy: 0.811
  287. epoch 9 iter 70, loss: 0.817, accuracy: 0.815
  288. epoch 9 iter 80, loss: 0.855, accuracy: 0.808
  289. epoch 9 iter 90, loss: 0.836, accuracy: 0.805
  290. epoch 9 iter 100, loss: 0.862, accuracy: 0.798
  291. epoch 9 iter 110, loss: 0.876, accuracy: 0.796
  292. epoch 9 iter 120, loss: 0.861, accuracy: 0.815
  293. epoch 9 iter 130, loss: 0.806, accuracy: 0.818
  294. epoch 9 iter 140, loss: 0.818, accuracy: 0.821
  295. epoch 9 iter 150, loss: 0.872, accuracy: 0.810
  296. epoch 9 iter 160, loss: 0.836, accuracy: 0.809
  297. epoch 9 iter 170, loss: 0.861, accuracy: 0.806
  298. epoch 9 iter 180, loss: 0.780, accuracy: 0.822
  299. epoch 9 iter 190, loss: 0.903, accuracy: 0.795
  300. epoch 9 iter 200, loss: 0.879, accuracy: 0.802
  301. epoch 9 iter 210, loss: 0.824, accuracy: 0.806
  302. epoch 9 iter 220, loss: 0.837, accuracy: 0.810
  303. epoch 9 iter 230, loss: 0.821, accuracy: 0.816
  304. epoch 9 iter 240, loss: 0.808, accuracy: 0.820
  305. epoch 9 iter 250, loss: 0.819, accuracy: 0.814
  306. epoch 9 iter 260, loss: 0.801, accuracy: 0.821
  307. epoch 9 iter 270, loss: 0.838, accuracy: 0.812
  308. epoch 9 iter 280, loss: 0.895, accuracy: 0.814
  309. epoch 10 iter 0, loss: 0.896, accuracy: 0.800
  310. epoch 10 iter 10, loss: 0.883, accuracy: 0.808
  311. epoch 10 iter 20, loss: 0.847, accuracy: 0.819
  312. epoch 10 iter 30, loss: 0.868, accuracy: 0.807
  313. epoch 10 iter 40, loss: 0.870, accuracy: 0.815
  314. epoch 10 iter 50, loss: 0.821, accuracy: 0.819
  315. epoch 10 iter 60, loss: 0.817, accuracy: 0.819
  316. epoch 10 iter 70, loss: 0.815, accuracy: 0.818
  317. epoch 10 iter 80, loss: 0.877, accuracy: 0.817
  318. epoch 10 iter 90, loss: 0.833, accuracy: 0.819
  319. epoch 10 iter 100, loss: 0.833, accuracy: 0.810
  320. epoch 10 iter 110, loss: 0.849, accuracy: 0.813
  321. epoch 10 iter 120, loss: 0.881, accuracy: 0.811
  322. epoch 10 iter 130, loss: 0.852, accuracy: 0.812
  323. epoch 10 iter 140, loss: 0.892, accuracy: 0.814
  324. epoch 10 iter 150, loss: 0.846, accuracy: 0.819
  325. epoch 10 iter 160, loss: 0.862, accuracy: 0.817
  326. epoch 10 iter 170, loss: 0.843, accuracy: 0.818
  327. epoch 10 iter 180, loss: 0.804, accuracy: 0.817
  328. epoch 10 iter 190, loss: 0.858, accuracy: 0.809
  329. epoch 10 iter 200, loss: 0.900, accuracy: 0.796
  330. epoch 10 iter 210, loss: 0.836, accuracy: 0.811
  331. epoch 10 iter 220, loss: 0.883, accuracy: 0.811
  332. epoch 10 iter 230, loss: 0.841, accuracy: 0.820
  333. epoch 10 iter 240, loss: 0.847, accuracy: 0.814
  334. epoch 10 iter 250, loss: 0.855, accuracy: 0.817
  335. epoch 10 iter 260, loss: 0.859, accuracy: 0.821
  336. epoch 10 iter 270, loss: 0.903, accuracy: 0.810
  337. epoch 10 iter 280, loss: 0.899, accuracy: 0.823
  338. epoch 11 iter 0, loss: 0.871, accuracy: 0.822
  339. epoch 11 iter 10, loss: 0.905, accuracy: 0.813
  340. epoch 11 iter 20, loss: 0.970, accuracy: 0.810
  341. epoch 11 iter 30, loss: 0.908, accuracy: 0.809
  342. epoch 11 iter 40, loss: 0.924, accuracy: 0.815
  343. epoch 11 iter 50, loss: 0.857, accuracy: 0.824
  344. epoch 11 iter 60, loss: 0.845, accuracy: 0.823
  345. epoch 11 iter 70, loss: 0.848, accuracy: 0.822
  346. epoch 11 iter 80, loss: 0.886, accuracy: 0.818
  347. epoch 11 iter 90, loss: 0.897, accuracy: 0.821
  348. epoch 11 iter 100, loss: 0.841, accuracy: 0.819
  349. epoch 11 iter 110, loss: 0.861, accuracy: 0.819
  350. epoch 11 iter 120, loss: 0.922, accuracy: 0.813
  351. epoch 11 iter 130, loss: 0.898, accuracy: 0.822
  352. epoch 11 iter 140, loss: 0.939, accuracy: 0.814
  353. epoch 11 iter 150, loss: 0.917, accuracy: 0.814
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  1457. epoch 49 iter 170, loss: 2.663, accuracy: 0.814
  1458. epoch 49 iter 180, loss: 2.621, accuracy: 0.830
  1459. epoch 49 iter 190, loss: 2.617, accuracy: 0.832
  1460. epoch 49 iter 200, loss: 2.594, accuracy: 0.826
  1461. epoch 49 iter 210, loss: 2.704, accuracy: 0.823
  1462. epoch 49 iter 220, loss: 2.607, accuracy: 0.817
  1463. epoch 49 iter 230, loss: 2.649, accuracy: 0.830
  1464. epoch 49 iter 240, loss: 2.604, accuracy: 0.829
  1465. epoch 49 iter 250, loss: 2.580, accuracy: 0.824
  1466. epoch 49 iter 260, loss: 2.626, accuracy: 0.834
  1467. epoch 49 iter 270, loss: 2.613, accuracy: 0.835
  1468. epoch 49 iter 280, loss: 2.678, accuracy: 0.834
  1469. current kernel size is: 5
  1470. 2018-10-22 23:02:44.172341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  1471. 2018-10-22 23:02:44.172380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  1472. 2018-10-22 23:02:44.172387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  1473. 2018-10-22 23:02:44.172392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  1474. 2018-10-22 23:02:44.172557: 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: 0000:83:00.0, compute capability: 6.0)
  1475. epoch 0 iter 0, loss: 108.486, accuracy: 0.109
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  1562. epoch 3 iter 0, loss: 1.058, accuracy: 0.688
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  1811. epoch 11 iter 170, loss: 0.912, accuracy: 0.768
  1812. epoch 11 iter 180, loss: 0.898, accuracy: 0.771
  1813. epoch 11 iter 190, loss: 0.890, accuracy: 0.769
  1814. epoch 11 iter 200, loss: 0.890, accuracy: 0.773
  1815. epoch 11 iter 210, loss: 0.924, accuracy: 0.764
  1816. epoch 11 iter 220, loss: 0.914, accuracy: 0.764
  1817. epoch 11 iter 230, loss: 0.954, accuracy: 0.758
  1818. epoch 11 iter 240, loss: 0.898, accuracy: 0.774
  1819. epoch 11 iter 250, loss: 0.877, accuracy: 0.768
  1820. epoch 11 iter 260, loss: 0.881, accuracy: 0.771
  1821. epoch 11 iter 270, loss: 0.875, accuracy: 0.770
  1822. epoch 11 iter 280, loss: 0.866, accuracy: 0.775
  1823. epoch 12 iter 0, loss: 0.873, accuracy: 0.775
  1824. epoch 12 iter 10, loss: 0.939, accuracy: 0.759
  1825. epoch 12 iter 20, loss: 0.973, accuracy: 0.760
  1826. epoch 12 iter 30, loss: 0.918, accuracy: 0.766
  1827. epoch 12 iter 40, loss: 0.903, accuracy: 0.770
  1828. epoch 12 iter 50, loss: 0.932, accuracy: 0.760
  1829. epoch 12 iter 60, loss: 0.966, accuracy: 0.752
  1830. epoch 12 iter 70, loss: 0.849, accuracy: 0.777
  1831. epoch 12 iter 80, loss: 0.841, accuracy: 0.782
  1832. epoch 12 iter 90, loss: 0.854, accuracy: 0.779
  1833. epoch 12 iter 100, loss: 0.875, accuracy: 0.781
  1834. epoch 12 iter 110, loss: 0.973, accuracy: 0.749
  1835. epoch 12 iter 120, loss: 0.887, accuracy: 0.775
  1836. epoch 12 iter 130, loss: 0.874, accuracy: 0.776
  1837. epoch 12 iter 140, loss: 0.866, accuracy: 0.785
  1838. epoch 12 iter 150, loss: 0.931, accuracy: 0.773
  1839. epoch 12 iter 160, loss: 0.877, accuracy: 0.770
  1840. epoch 12 iter 170, loss: 0.919, accuracy: 0.770
  1841. epoch 12 iter 180, loss: 0.897, accuracy: 0.780
  1842. epoch 12 iter 190, loss: 0.892, accuracy: 0.773
  1843. epoch 12 iter 200, loss: 0.892, accuracy: 0.784
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  1845. epoch 12 iter 220, loss: 0.897, accuracy: 0.772
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  1848. epoch 12 iter 250, loss: 0.881, accuracy: 0.777
  1849. epoch 12 iter 260, loss: 0.838, accuracy: 0.794
  1850. epoch 12 iter 270, loss: 0.900, accuracy: 0.777
  1851. epoch 12 iter 280, loss: 0.866, accuracy: 0.783
  1852. epoch 13 iter 0, loss: 0.868, accuracy: 0.784
  1853. epoch 13 iter 10, loss: 0.894, accuracy: 0.777
  1854. epoch 13 iter 20, loss: 0.900, accuracy: 0.779
  1855. epoch 13 iter 30, loss: 0.887, accuracy: 0.777
  1856. epoch 13 iter 40, loss: 0.840, accuracy: 0.788
  1857. epoch 13 iter 50, loss: 0.921, accuracy: 0.775
  1858. epoch 13 iter 60, loss: 0.985, accuracy: 0.757
  1859. epoch 13 iter 70, loss: 0.910, accuracy: 0.772
  1860. epoch 13 iter 80, loss: 0.852, accuracy: 0.790
  1861. epoch 13 iter 90, loss: 0.847, accuracy: 0.792
  1862. epoch 13 iter 100, loss: 0.869, accuracy: 0.788
  1863. epoch 13 iter 110, loss: 0.928, accuracy: 0.767
  1864. epoch 13 iter 120, loss: 0.853, accuracy: 0.792
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  1866. epoch 13 iter 140, loss: 0.894, accuracy: 0.778
  1867. epoch 13 iter 150, loss: 0.914, accuracy: 0.790
  1868. epoch 13 iter 160, loss: 0.882, accuracy: 0.775
  1869. epoch 13 iter 170, loss: 0.951, accuracy: 0.782
  1870. epoch 13 iter 180, loss: 0.931, accuracy: 0.770
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  1872. epoch 13 iter 200, loss: 0.874, accuracy: 0.795
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  1881. epoch 14 iter 0, loss: 0.961, accuracy: 0.780
  1882. epoch 14 iter 10, loss: 0.894, accuracy: 0.779
  1883. epoch 14 iter 20, loss: 0.905, accuracy: 0.775
  1884. epoch 14 iter 30, loss: 0.845, accuracy: 0.790
  1885. epoch 14 iter 40, loss: 0.871, accuracy: 0.799
  1886. epoch 14 iter 50, loss: 0.949, accuracy: 0.790
  1887. epoch 14 iter 60, loss: 0.870, accuracy: 0.798
  1888. epoch 14 iter 70, loss: 0.876, accuracy: 0.782
  1889. epoch 14 iter 80, loss: 0.902, accuracy: 0.786
  1890. epoch 14 iter 90, loss: 0.876, accuracy: 0.789
  1891. epoch 14 iter 100, loss: 0.874, accuracy: 0.798
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  1893. epoch 14 iter 120, loss: 0.846, accuracy: 0.798
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  1895. epoch 14 iter 140, loss: 0.891, accuracy: 0.789
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  1900. epoch 14 iter 190, loss: 0.881, accuracy: 0.801
  1901. epoch 14 iter 200, loss: 0.892, accuracy: 0.793
  1902. epoch 14 iter 210, loss: 0.974, accuracy: 0.784
  1903. epoch 14 iter 220, loss: 0.931, accuracy: 0.799
  1904. epoch 14 iter 230, loss: 0.943, accuracy: 0.786
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  1906. epoch 14 iter 250, loss: 0.921, accuracy: 0.804
  1907. epoch 14 iter 260, loss: 0.930, accuracy: 0.797
  1908. epoch 14 iter 270, loss: 0.983, accuracy: 0.793
  1909. epoch 14 iter 280, loss: 0.918, accuracy: 0.798
  1910. epoch 15 iter 0, loss: 0.876, accuracy: 0.801
  1911. epoch 15 iter 10, loss: 0.919, accuracy: 0.791
  1912. epoch 15 iter 20, loss: 0.941, accuracy: 0.784
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  1914. epoch 15 iter 40, loss: 0.848, accuracy: 0.808
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  1916. epoch 15 iter 60, loss: 0.928, accuracy: 0.785
  1917. epoch 15 iter 70, loss: 0.926, accuracy: 0.791
  1918. epoch 15 iter 80, loss: 0.870, accuracy: 0.796
  1919. epoch 15 iter 90, loss: 0.897, accuracy: 0.782
  1920. epoch 15 iter 100, loss: 0.904, accuracy: 0.801
  1921. epoch 15 iter 110, loss: 0.954, accuracy: 0.784
  1922. epoch 15 iter 120, loss: 0.917, accuracy: 0.795
  1923. epoch 15 iter 130, loss: 0.908, accuracy: 0.796
  1924. epoch 15 iter 140, loss: 0.960, accuracy: 0.785
  1925. epoch 15 iter 150, loss: 0.948, accuracy: 0.804
  1926. epoch 15 iter 160, loss: 0.891, accuracy: 0.793
  1927. epoch 15 iter 170, loss: 0.964, accuracy: 0.795
  1928. epoch 15 iter 180, loss: 0.924, accuracy: 0.800
  1929. epoch 15 iter 190, loss: 0.905, accuracy: 0.802
  1930. epoch 15 iter 200, loss: 0.874, accuracy: 0.803
  1931. epoch 15 iter 210, loss: 0.926, accuracy: 0.804
  1932. epoch 15 iter 220, loss: 0.972, accuracy: 0.803
  1933. epoch 15 iter 230, loss: 0.941, accuracy: 0.801
  1934. epoch 15 iter 240, loss: 0.861, accuracy: 0.807
  1935. epoch 15 iter 250, loss: 0.938, accuracy: 0.811
  1936. epoch 15 iter 260, loss: 1.050, accuracy: 0.784
  1937. epoch 15 iter 270, loss: 1.138, accuracy: 0.786
  1938. epoch 15 iter 280, loss: 0.958, accuracy: 0.795
  1939. epoch 16 iter 0, loss: 0.906, accuracy: 0.807
  1940. epoch 16 iter 10, loss: 0.958, accuracy: 0.801
  1941. epoch 16 iter 20, loss: 0.945, accuracy: 0.786
  1942. epoch 16 iter 30, loss: 0.922, accuracy: 0.783
  1943. epoch 16 iter 40, loss: 0.971, accuracy: 0.798
  1944. epoch 16 iter 50, loss: 0.945, accuracy: 0.796
  1945. epoch 16 iter 60, loss: 0.920, accuracy: 0.806
  1946. epoch 16 iter 70, loss: 0.949, accuracy: 0.804
  1947. epoch 16 iter 80, loss: 0.870, accuracy: 0.801
  1948. epoch 16 iter 90, loss: 0.860, accuracy: 0.806
  1949. epoch 16 iter 100, loss: 0.942, accuracy: 0.797
  1950. epoch 16 iter 110, loss: 0.890, accuracy: 0.803
  1951. epoch 16 iter 120, loss: 0.895, accuracy: 0.809
  1952. epoch 16 iter 130, loss: 0.916, accuracy: 0.811
  1953. epoch 16 iter 140, loss: 0.994, accuracy: 0.777
  1954. epoch 16 iter 150, loss: 0.934, accuracy: 0.805
  1955. epoch 16 iter 160, loss: 0.896, accuracy: 0.797
  1956. epoch 16 iter 170, loss: 1.076, accuracy: 0.784
  1957. epoch 16 iter 180, loss: 0.980, accuracy: 0.799
  1958. epoch 16 iter 190, loss: 0.928, accuracy: 0.807
  1959. epoch 16 iter 200, loss: 0.967, accuracy: 0.794
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  1961. epoch 16 iter 220, loss: 1.145, accuracy: 0.793
  1962. epoch 16 iter 230, loss: 1.057, accuracy: 0.785
  1963. epoch 16 iter 240, loss: 0.931, accuracy: 0.806
  1964. epoch 16 iter 250, loss: 0.930, accuracy: 0.817
  1965. epoch 16 iter 260, loss: 1.098, accuracy: 0.795
  1966. epoch 16 iter 270, loss: 1.166, accuracy: 0.774
  1967. epoch 16 iter 280, loss: 0.979, accuracy: 0.807
  1968. epoch 17 iter 0, loss: 1.042, accuracy: 0.801
  1969. epoch 17 iter 10, loss: 0.984, accuracy: 0.809
  1970. epoch 17 iter 20, loss: 0.961, accuracy: 0.807
  1971. epoch 17 iter 30, loss: 1.014, accuracy: 0.797
  1972. epoch 17 iter 40, loss: 1.011, accuracy: 0.798
  1973. epoch 17 iter 50, loss: 0.917, accuracy: 0.802
  1974. epoch 17 iter 60, loss: 0.923, accuracy: 0.811
  1975. epoch 17 iter 70, loss: 0.891, accuracy: 0.814
  1976. epoch 17 iter 80, loss: 0.924, accuracy: 0.806
  1977. epoch 17 iter 90, loss: 0.880, accuracy: 0.808
  1978. epoch 17 iter 100, loss: 0.986, accuracy: 0.803
  1979. epoch 17 iter 110, loss: 0.968, accuracy: 0.809
  1980. epoch 17 iter 120, loss: 0.934, accuracy: 0.814
  1981. epoch 17 iter 130, loss: 0.927, accuracy: 0.815
  1982. epoch 17 iter 140, loss: 0.917, accuracy: 0.809
  1983. epoch 17 iter 150, loss: 0.970, accuracy: 0.816
  1984. epoch 17 iter 160, loss: 0.919, accuracy: 0.811
  1985. epoch 17 iter 170, loss: 1.119, accuracy: 0.769
  1986. epoch 17 iter 180, loss: 1.037, accuracy: 0.804
  1987. epoch 17 iter 190, loss: 0.970, accuracy: 0.817
  1988. epoch 17 iter 200, loss: 1.064, accuracy: 0.792
  1989. epoch 17 iter 210, loss: 1.014, accuracy: 0.807
  1990. epoch 17 iter 220, loss: 0.965, accuracy: 0.811
  1991. epoch 17 iter 230, loss: 0.983, accuracy: 0.814
  1992. epoch 17 iter 240, loss: 0.947, accuracy: 0.811
  1993. epoch 17 iter 250, loss: 0.983, accuracy: 0.820
  1994. epoch 17 iter 260, loss: 1.131, accuracy: 0.803
  1995. epoch 17 iter 270, loss: 1.277, accuracy: 0.764
  1996. epoch 17 iter 280, loss: 0.989, accuracy: 0.810
  1997. epoch 18 iter 0, loss: 1.146, accuracy: 0.785
  1998. epoch 18 iter 10, loss: 1.022, accuracy: 0.816
  1999. epoch 18 iter 20, loss: 0.938, accuracy: 0.813
  2000. epoch 18 iter 30, loss: 0.985, accuracy: 0.798
  2001. epoch 18 iter 40, loss: 0.973, accuracy: 0.814
  2002. epoch 18 iter 50, loss: 0.942, accuracy: 0.806
  2003. epoch 18 iter 60, loss: 0.937, accuracy: 0.817
  2004. epoch 18 iter 70, loss: 0.943, accuracy: 0.815
  2005. epoch 18 iter 80, loss: 1.033, accuracy: 0.801
  2006. epoch 18 iter 90, loss: 0.940, accuracy: 0.814
  2007. epoch 18 iter 100, loss: 1.200, accuracy: 0.770
  2008. epoch 18 iter 110, loss: 1.137, accuracy: 0.790
  2009. epoch 18 iter 120, loss: 0.917, accuracy: 0.815
  2010. epoch 18 iter 130, loss: 0.939, accuracy: 0.820
  2011. epoch 18 iter 140, loss: 0.988, accuracy: 0.795
  2012. epoch 18 iter 150, loss: 0.994, accuracy: 0.812
  2013. epoch 18 iter 160, loss: 0.956, accuracy: 0.812
  2014. epoch 18 iter 170, loss: 0.982, accuracy: 0.812
  2015. epoch 18 iter 180, loss: 1.011, accuracy: 0.805
  2016. epoch 18 iter 190, loss: 0.974, accuracy: 0.818
  2017. epoch 18 iter 200, loss: 0.979, accuracy: 0.817
  2018. epoch 18 iter 210, loss: 1.028, accuracy: 0.800
  2019. epoch 18 iter 220, loss: 1.080, accuracy: 0.799
  2020. epoch 18 iter 230, loss: 1.058, accuracy: 0.813
  2021. epoch 18 iter 240, loss: 0.952, accuracy: 0.801
  2022. epoch 18 iter 250, loss: 1.089, accuracy: 0.788
  2023. epoch 18 iter 260, loss: 0.961, accuracy: 0.810
  2024. epoch 18 iter 270, loss: 1.112, accuracy: 0.791
  2025. epoch 18 iter 280, loss: 1.009, accuracy: 0.806
  2026. epoch 19 iter 0, loss: 1.113, accuracy: 0.788
  2027. epoch 19 iter 10, loss: 1.000, accuracy: 0.816
  2028. epoch 19 iter 20, loss: 0.977, accuracy: 0.805
  2029. epoch 19 iter 30, loss: 0.975, accuracy: 0.811
  2030. epoch 19 iter 40, loss: 0.996, accuracy: 0.816
  2031. epoch 19 iter 50, loss: 0.918, accuracy: 0.819
  2032. epoch 19 iter 60, loss: 0.989, accuracy: 0.815
  2033. epoch 19 iter 70, loss: 0.973, accuracy: 0.809
  2034. epoch 19 iter 80, loss: 0.984, accuracy: 0.802
  2035. epoch 19 iter 90, loss: 0.963, accuracy: 0.809
  2036. epoch 19 iter 100, loss: 0.944, accuracy: 0.812
  2037. epoch 19 iter 110, loss: 1.026, accuracy: 0.796
  2038. epoch 19 iter 120, loss: 0.963, accuracy: 0.822
  2039. epoch 19 iter 130, loss: 1.015, accuracy: 0.797
  2040. epoch 19 iter 140, loss: 0.989, accuracy: 0.801
  2041. epoch 19 iter 150, loss: 0.935, accuracy: 0.818
  2042. epoch 19 iter 160, loss: 0.932, accuracy: 0.819
  2043. epoch 19 iter 170, loss: 0.986, accuracy: 0.811
  2044. epoch 19 iter 180, loss: 0.958, accuracy: 0.805
  2045. epoch 19 iter 190, loss: 1.009, accuracy: 0.816
  2046. epoch 19 iter 200, loss: 0.980, accuracy: 0.807
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  2050. epoch 19 iter 240, loss: 0.973, accuracy: 0.815
  2051. epoch 19 iter 250, loss: 1.202, accuracy: 0.792
  2052. epoch 19 iter 260, loss: 0.991, accuracy: 0.815
  2053. epoch 19 iter 270, loss: 1.070, accuracy: 0.796
  2054. epoch 19 iter 280, loss: 0.994, accuracy: 0.814
  2055. epoch 20 iter 0, loss: 0.997, accuracy: 0.802
  2056. epoch 20 iter 10, loss: 0.995, accuracy: 0.815
  2057. epoch 20 iter 20, loss: 1.055, accuracy: 0.811
  2058. epoch 20 iter 30, loss: 1.035, accuracy: 0.812
  2059. epoch 20 iter 40, loss: 0.944, accuracy: 0.819
  2060. epoch 20 iter 50, loss: 1.005, accuracy: 0.822
  2061. epoch 20 iter 60, loss: 1.001, accuracy: 0.816
  2062. epoch 20 iter 70, loss: 0.995, accuracy: 0.816
  2063. epoch 20 iter 80, loss: 1.039, accuracy: 0.806
  2064. epoch 20 iter 90, loss: 0.971, accuracy: 0.821
  2065. epoch 20 iter 100, loss: 0.955, accuracy: 0.818
  2066. epoch 20 iter 110, loss: 0.950, accuracy: 0.821
  2067. epoch 20 iter 120, loss: 0.984, accuracy: 0.823
  2068. epoch 20 iter 130, loss: 1.059, accuracy: 0.811
  2069. epoch 20 iter 140, loss: 0.976, accuracy: 0.820
  2070. epoch 20 iter 150, loss: 1.046, accuracy: 0.823
  2071. epoch 20 iter 160, loss: 1.063, accuracy: 0.816
  2072. epoch 20 iter 170, loss: 1.001, accuracy: 0.825
  2073. epoch 20 iter 180, loss: 1.056, accuracy: 0.815
  2074. epoch 20 iter 190, loss: 1.166, accuracy: 0.812
  2075. epoch 20 iter 200, loss: 1.018, accuracy: 0.812
  2076. epoch 20 iter 210, loss: 1.037, accuracy: 0.815
  2077. epoch 20 iter 220, loss: 1.285, accuracy: 0.790
  2078. epoch 20 iter 230, loss: 1.035, accuracy: 0.816
  2079. epoch 20 iter 240, loss: 0.998, accuracy: 0.824
  2080. epoch 20 iter 250, loss: 1.164, accuracy: 0.811
  2081. epoch 20 iter 260, loss: 1.021, accuracy: 0.808
  2082. epoch 20 iter 270, loss: 1.351, accuracy: 0.776
  2083. epoch 20 iter 280, loss: 1.155, accuracy: 0.803
  2084. epoch 21 iter 0, loss: 1.040, accuracy: 0.818
  2085. epoch 21 iter 10, loss: 1.098, accuracy: 0.819
  2086. epoch 21 iter 20, loss: 1.077, accuracy: 0.819
  2087. epoch 21 iter 30, loss: 1.024, accuracy: 0.821
  2088. epoch 21 iter 40, loss: 1.029, accuracy: 0.816
  2089. epoch 21 iter 50, loss: 1.110, accuracy: 0.818
  2090. epoch 21 iter 60, loss: 0.985, accuracy: 0.822
  2091. epoch 21 iter 70, loss: 1.038, accuracy: 0.817
  2092. epoch 21 iter 80, loss: 1.047, accuracy: 0.810
  2093. epoch 21 iter 90, loss: 1.015, accuracy: 0.820
  2094. epoch 21 iter 100, loss: 1.074, accuracy: 0.818
  2095. epoch 21 iter 110, loss: 1.015, accuracy: 0.822
  2096. epoch 21 iter 120, loss: 0.995, accuracy: 0.828
  2097. epoch 21 iter 130, loss: 1.015, accuracy: 0.828
  2098. epoch 21 iter 140, loss: 0.980, accuracy: 0.817
  2099. epoch 21 iter 150, loss: 1.078, accuracy: 0.815
  2100. epoch 21 iter 160, loss: 1.064, accuracy: 0.818
  2101. epoch 21 iter 170, loss: 1.129, accuracy: 0.821
  2102. epoch 21 iter 180, loss: 1.092, accuracy: 0.818
  2103. epoch 21 iter 190, loss: 1.232, accuracy: 0.812
  2104. epoch 21 iter 200, loss: 1.053, accuracy: 0.819
  2105. epoch 21 iter 210, loss: 1.134, accuracy: 0.806
  2106. epoch 21 iter 220, loss: 1.240, accuracy: 0.810
  2107. epoch 21 iter 230, loss: 1.118, accuracy: 0.807
  2108. epoch 21 iter 240, loss: 1.157, accuracy: 0.807
  2109. epoch 21 iter 250, loss: 1.072, accuracy: 0.819
  2110. epoch 21 iter 260, loss: 1.043, accuracy: 0.814
  2111. epoch 21 iter 270, loss: 1.358, accuracy: 0.783
  2112. epoch 21 iter 280, loss: 1.077, accuracy: 0.810
  2113. epoch 22 iter 0, loss: 1.179, accuracy: 0.805
  2114. epoch 22 iter 10, loss: 1.112, accuracy: 0.816
  2115. epoch 22 iter 20, loss: 1.151, accuracy: 0.821
  2116. epoch 22 iter 30, loss: 1.055, accuracy: 0.822
  2117. epoch 22 iter 40, loss: 1.078, accuracy: 0.813
  2118. epoch 22 iter 50, loss: 1.166, accuracy: 0.814
  2119. epoch 22 iter 60, loss: 1.077, accuracy: 0.818
  2120. epoch 22 iter 70, loss: 1.191, accuracy: 0.807
  2121. epoch 22 iter 80, loss: 0.949, accuracy: 0.825
  2122. epoch 22 iter 90, loss: 1.053, accuracy: 0.820
  2123. epoch 22 iter 100, loss: 1.051, accuracy: 0.824
  2124. epoch 22 iter 110, loss: 1.083, accuracy: 0.824
  2125. epoch 22 iter 120, loss: 1.144, accuracy: 0.818
  2126. epoch 22 iter 130, loss: 1.217, accuracy: 0.824
  2127. epoch 22 iter 140, loss: 1.026, accuracy: 0.819
  2128. epoch 22 iter 150, loss: 1.145, accuracy: 0.825
  2129. epoch 22 iter 160, loss: 1.120, accuracy: 0.813
  2130. epoch 22 iter 170, loss: 1.167, accuracy: 0.821
  2131. epoch 22 iter 180, loss: 1.171, accuracy: 0.810
  2132. epoch 22 iter 190, loss: 1.256, accuracy: 0.813
  2133. epoch 22 iter 200, loss: 1.090, accuracy: 0.817
  2134. epoch 22 iter 210, loss: 1.061, accuracy: 0.821
  2135. epoch 22 iter 220, loss: 1.138, accuracy: 0.825
  2136. epoch 22 iter 230, loss: 1.179, accuracy: 0.793
  2137. epoch 22 iter 240, loss: 1.237, accuracy: 0.804
  2138. epoch 22 iter 250, loss: 1.077, accuracy: 0.824
  2139. epoch 22 iter 260, loss: 1.149, accuracy: 0.815
  2140. epoch 22 iter 270, loss: 1.218, accuracy: 0.807
  2141. epoch 22 iter 280, loss: 1.092, accuracy: 0.811
  2142. epoch 23 iter 0, loss: 1.203, accuracy: 0.793
  2143. epoch 23 iter 10, loss: 1.147, accuracy: 0.822
  2144. epoch 23 iter 20, loss: 1.142, accuracy: 0.825
  2145. epoch 23 iter 30, loss: 1.120, accuracy: 0.826
  2146. epoch 23 iter 40, loss: 1.108, accuracy: 0.815
  2147. epoch 23 iter 50, loss: 1.157, accuracy: 0.819
  2148. epoch 23 iter 60, loss: 1.099, accuracy: 0.817
  2149. epoch 23 iter 70, loss: 1.331, accuracy: 0.804
  2150. epoch 23 iter 80, loss: 1.110, accuracy: 0.804
  2151. epoch 23 iter 90, loss: 1.101, accuracy: 0.820
  2152. epoch 23 iter 100, loss: 1.137, accuracy: 0.813
  2153. epoch 23 iter 110, loss: 1.093, accuracy: 0.820
  2154. epoch 23 iter 120, loss: 1.107, accuracy: 0.824
  2155. epoch 23 iter 130, loss: 1.286, accuracy: 0.814
  2156. epoch 23 iter 140, loss: 1.082, accuracy: 0.813
  2157. epoch 23 iter 150, loss: 1.163, accuracy: 0.821
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  2159. epoch 23 iter 170, loss: 1.230, accuracy: 0.809
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  2161. epoch 23 iter 190, loss: 1.240, accuracy: 0.810
  2162. epoch 23 iter 200, loss: 1.178, accuracy: 0.818
  2163. epoch 23 iter 210, loss: 1.143, accuracy: 0.810
  2164. epoch 23 iter 220, loss: 1.322, accuracy: 0.815
  2165. epoch 23 iter 230, loss: 1.287, accuracy: 0.811
  2166. epoch 23 iter 240, loss: 1.191, accuracy: 0.820
  2167. epoch 23 iter 250, loss: 1.161, accuracy: 0.828
  2168. epoch 23 iter 260, loss: 1.279, accuracy: 0.812
  2169. epoch 23 iter 270, loss: 1.374, accuracy: 0.804
  2170. epoch 23 iter 280, loss: 1.276, accuracy: 0.807
  2171. epoch 24 iter 0, loss: 1.146, accuracy: 0.816
  2172. epoch 24 iter 10, loss: 1.131, accuracy: 0.822
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  2924. epoch 49 iter 280, loss: 3.386, accuracy: 0.812
  2925. current kernel size is: 7
  2926. 2018-10-22 23:33:48.253002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  2927. 2018-10-22 23:33:48.253048: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  2928. 2018-10-22 23:33:48.253055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  2929. 2018-10-22 23:33:48.253060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  2930. 2018-10-22 23:33:48.253197: 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: 0000:83:00.0, compute capability: 6.0)
  2931. epoch 0 iter 0, loss: 143.763, accuracy: 0.196
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  4317. epoch 47 iter 230, loss: 3.126, accuracy: 0.834
  4318. epoch 47 iter 240, loss: 3.090, accuracy: 0.832
  4319. epoch 47 iter 250, loss: 3.327, accuracy: 0.821
  4320. epoch 47 iter 260, loss: 3.316, accuracy: 0.842
  4321. epoch 47 iter 270, loss: 3.240, accuracy: 0.842
  4322. epoch 47 iter 280, loss: 2.961, accuracy: 0.841
  4323. epoch 48 iter 0, loss: 2.957, accuracy: 0.834
  4324. epoch 48 iter 10, loss: 2.956, accuracy: 0.841
  4325. epoch 48 iter 20, loss: 3.094, accuracy: 0.847
  4326. epoch 48 iter 30, loss: 3.264, accuracy: 0.848
  4327. epoch 48 iter 40, loss: 3.211, accuracy: 0.847
  4328. epoch 48 iter 50, loss: 3.100, accuracy: 0.839
  4329. epoch 48 iter 60, loss: 3.131, accuracy: 0.836
  4330. epoch 48 iter 70, loss: 3.314, accuracy: 0.825
  4331. epoch 48 iter 80, loss: 3.152, accuracy: 0.827
  4332. epoch 48 iter 90, loss: 3.617, accuracy: 0.802
  4333. epoch 48 iter 100, loss: 3.083, accuracy: 0.831
  4334. epoch 48 iter 110, loss: 3.164, accuracy: 0.836
  4335. epoch 48 iter 120, loss: 3.225, accuracy: 0.830
  4336. epoch 48 iter 130, loss: 3.124, accuracy: 0.832
  4337. epoch 48 iter 140, loss: 3.339, accuracy: 0.828
  4338. epoch 48 iter 150, loss: 3.364, accuracy: 0.832
  4339. epoch 48 iter 160, loss: 2.936, accuracy: 0.834
  4340. epoch 48 iter 170, loss: 2.926, accuracy: 0.805
  4341. epoch 48 iter 180, loss: 3.187, accuracy: 0.831
  4342. epoch 48 iter 190, loss: 3.236, accuracy: 0.838
  4343. epoch 48 iter 200, loss: 3.231, accuracy: 0.826
  4344. epoch 48 iter 210, loss: 3.246, accuracy: 0.836
  4345. epoch 48 iter 220, loss: 3.086, accuracy: 0.832
  4346. epoch 48 iter 230, loss: 3.087, accuracy: 0.839
  4347. epoch 48 iter 240, loss: 3.405, accuracy: 0.837
  4348. epoch 48 iter 250, loss: 3.396, accuracy: 0.825
  4349. epoch 48 iter 260, loss: 3.069, accuracy: 0.840
  4350. epoch 48 iter 270, loss: 3.504, accuracy: 0.839
  4351. epoch 48 iter 280, loss: 3.481, accuracy: 0.839
  4352. epoch 49 iter 0, loss: 3.231, accuracy: 0.833
  4353. epoch 49 iter 10, loss: 2.997, accuracy: 0.841
  4354. epoch 49 iter 20, loss: 3.064, accuracy: 0.844
  4355. epoch 49 iter 30, loss: 3.395, accuracy: 0.836
  4356. epoch 49 iter 40, loss: 3.369, accuracy: 0.839
  4357. epoch 49 iter 50, loss: 3.187, accuracy: 0.840
  4358. epoch 49 iter 60, loss: 3.097, accuracy: 0.833
  4359. epoch 49 iter 70, loss: 3.644, accuracy: 0.818
  4360. epoch 49 iter 80, loss: 3.143, accuracy: 0.826
  4361. epoch 49 iter 90, loss: 3.842, accuracy: 0.795
  4362. epoch 49 iter 100, loss: 3.130, accuracy: 0.830
  4363. epoch 49 iter 110, loss: 3.722, accuracy: 0.835
  4364. epoch 49 iter 120, loss: 3.682, accuracy: 0.823
  4365. epoch 49 iter 130, loss: 3.047, accuracy: 0.829
  4366. epoch 49 iter 140, loss: 3.295, accuracy: 0.827
  4367. epoch 49 iter 150, loss: 3.215, accuracy: 0.831
  4368. epoch 49 iter 160, loss: 3.306, accuracy: 0.824
  4369. epoch 49 iter 170, loss: 3.534, accuracy: 0.796
  4370. epoch 49 iter 180, loss: 3.036, accuracy: 0.824
  4371. epoch 49 iter 190, loss: 3.551, accuracy: 0.830
  4372. epoch 49 iter 200, loss: 3.671, accuracy: 0.818
  4373. epoch 49 iter 210, loss: 3.395, accuracy: 0.816
  4374. epoch 49 iter 220, loss: 3.254, accuracy: 0.823
  4375. epoch 49 iter 230, loss: 3.552, accuracy: 0.835
  4376. epoch 49 iter 240, loss: 3.486, accuracy: 0.831
  4377. epoch 49 iter 250, loss: 3.669, accuracy: 0.821
  4378. epoch 49 iter 260, loss: 3.089, accuracy: 0.832
  4379. epoch 49 iter 270, loss: 3.405, accuracy: 0.828
  4380. epoch 49 iter 280, loss: 3.354, accuracy: 0.831
  4381. [jalal@scc-c13 pset3_CNN]$
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