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lamiastella

different_strides_50epochs

lamiastella
Oct 22nd, 2018
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  1. [jalal@scc-c13 pset3_CNN]$ python different_strides.py
  2. 2018-10-22 17:13:32.323955: 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 17:13:32.513994: 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 17:13:32.514031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  8. 2018-10-22 17:13:32.845667: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  9. 2018-10-22 17:13:32.845707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  10. 2018-10-22 17:13:32.845714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  11. 2018-10-22 17:13:32.845968: 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 17:13:33.080275: 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 stride is: 2
  14. 2018-10-22 17:13:36.865573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  15. 2018-10-22 17:13:36.865620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  16. 2018-10-22 17:13:36.865627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  17. 2018-10-22 17:13:36.865632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  18. 2018-10-22 17:13:36.865774: 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: 90.102, accuracy: 0.159
  20. epoch 0 iter 10, loss: 2.417, accuracy: 0.137
  21. epoch 0 iter 20, loss: 2.274, accuracy: 0.188
  22. epoch 0 iter 30, loss: 2.259, accuracy: 0.195
  23. epoch 0 iter 40, loss: 2.249, accuracy: 0.196
  24. epoch 0 iter 50, loss: 2.244, accuracy: 0.197
  25. epoch 0 iter 60, loss: 2.240, accuracy: 0.194
  26. epoch 0 iter 70, loss: 2.238, accuracy: 0.196
  27. epoch 0 iter 80, loss: 2.237, accuracy: 0.196
  28. epoch 0 iter 90, loss: 2.236, accuracy: 0.196
  29. epoch 0 iter 100, loss: 2.232, accuracy: 0.196
  30. epoch 0 iter 110, loss: 2.234, accuracy: 0.196
  31. epoch 0 iter 120, loss: 2.229, accuracy: 0.196
  32. epoch 0 iter 130, loss: 2.228, accuracy: 0.196
  33. epoch 0 iter 140, loss: 2.229, accuracy: 0.196
  34. epoch 0 iter 150, loss: 2.225, accuracy: 0.196
  35. epoch 0 iter 160, loss: 2.222, accuracy: 0.196
  36. epoch 0 iter 170, loss: 2.221, accuracy: 0.196
  37. epoch 0 iter 180, loss: 2.220, accuracy: 0.196
  38. epoch 0 iter 190, loss: 2.217, accuracy: 0.196
  39. epoch 0 iter 200, loss: 2.222, accuracy: 0.196
  40. epoch 0 iter 210, loss: 2.222, accuracy: 0.196
  41. epoch 0 iter 220, loss: 2.213, accuracy: 0.201
  42. epoch 0 iter 230, loss: 2.203, accuracy: 0.213
  43. epoch 0 iter 240, loss: 2.208, accuracy: 0.201
  44. epoch 0 iter 250, loss: 2.213, accuracy: 0.205
  45. epoch 0 iter 260, loss: 2.200, accuracy: 0.215
  46. epoch 0 iter 270, loss: 2.221, accuracy: 0.215
  47. epoch 0 iter 280, loss: 2.174, accuracy: 0.239
  48. epoch 1 iter 0, loss: 2.207, accuracy: 0.204
  49. epoch 1 iter 10, loss: 2.172, accuracy: 0.234
  50. epoch 1 iter 20, loss: 2.179, accuracy: 0.235
  51. epoch 1 iter 30, loss: 2.206, accuracy: 0.200
  52. epoch 1 iter 40, loss: 2.225, accuracy: 0.197
  53. epoch 1 iter 50, loss: 2.225, accuracy: 0.196
  54. epoch 1 iter 60, loss: 2.231, accuracy: 0.196
  55. epoch 1 iter 70, loss: 2.222, accuracy: 0.196
  56. epoch 1 iter 80, loss: 2.216, accuracy: 0.196
  57. epoch 1 iter 90, loss: 2.212, accuracy: 0.196
  58. epoch 1 iter 100, loss: 2.206, accuracy: 0.197
  59. epoch 1 iter 110, loss: 2.214, accuracy: 0.198
  60. epoch 1 iter 120, loss: 2.204, accuracy: 0.197
  61. epoch 1 iter 130, loss: 2.207, accuracy: 0.201
  62. epoch 1 iter 140, loss: 2.209, accuracy: 0.201
  63. epoch 1 iter 150, loss: 2.214, accuracy: 0.225
  64. epoch 1 iter 160, loss: 2.186, accuracy: 0.223
  65. epoch 1 iter 170, loss: 2.176, accuracy: 0.235
  66. epoch 1 iter 180, loss: 2.149, accuracy: 0.251
  67. epoch 1 iter 190, loss: 2.135, accuracy: 0.260
  68. epoch 1 iter 200, loss: 2.129, accuracy: 0.254
  69. epoch 1 iter 210, loss: 2.151, accuracy: 0.259
  70. epoch 1 iter 220, loss: 2.108, accuracy: 0.262
  71. epoch 1 iter 230, loss: 2.114, accuracy: 0.267
  72. epoch 1 iter 240, loss: 2.090, accuracy: 0.273
  73. epoch 1 iter 250, loss: 2.085, accuracy: 0.271
  74. epoch 1 iter 260, loss: 2.083, accuracy: 0.272
  75. epoch 1 iter 270, loss: 2.050, accuracy: 0.280
  76. epoch 1 iter 280, loss: 2.044, accuracy: 0.281
  77. epoch 2 iter 0, loss: 2.101, accuracy: 0.260
  78. epoch 2 iter 10, loss: 2.074, accuracy: 0.278
  79. epoch 2 iter 20, loss: 2.091, accuracy: 0.273
  80. epoch 2 iter 30, loss: 2.045, accuracy: 0.277
  81. epoch 2 iter 40, loss: 2.050, accuracy: 0.284
  82. epoch 2 iter 50, loss: 2.040, accuracy: 0.284
  83. epoch 2 iter 60, loss: 2.064, accuracy: 0.276
  84. epoch 2 iter 70, loss: 1.996, accuracy: 0.296
  85. epoch 2 iter 80, loss: 1.984, accuracy: 0.304
  86. epoch 2 iter 90, loss: 2.004, accuracy: 0.297
  87. epoch 2 iter 100, loss: 1.970, accuracy: 0.306
  88. epoch 2 iter 110, loss: 1.988, accuracy: 0.302
  89. epoch 2 iter 120, loss: 2.006, accuracy: 0.302
  90. epoch 2 iter 130, loss: 1.972, accuracy: 0.302
  91. epoch 2 iter 140, loss: 1.986, accuracy: 0.300
  92. epoch 2 iter 150, loss: 1.950, accuracy: 0.312
  93. epoch 2 iter 160, loss: 2.015, accuracy: 0.302
  94. epoch 2 iter 170, loss: 1.981, accuracy: 0.303
  95. epoch 2 iter 180, loss: 1.947, accuracy: 0.314
  96. epoch 2 iter 190, loss: 1.971, accuracy: 0.311
  97. epoch 2 iter 200, loss: 1.956, accuracy: 0.315
  98. epoch 2 iter 210, loss: 1.943, accuracy: 0.321
  99. epoch 2 iter 220, loss: 1.944, accuracy: 0.324
  100. epoch 2 iter 230, loss: 1.909, accuracy: 0.329
  101. epoch 2 iter 240, loss: 1.910, accuracy: 0.329
  102. epoch 2 iter 250, loss: 1.912, accuracy: 0.333
  103. epoch 2 iter 260, loss: 1.921, accuracy: 0.332
  104. epoch 2 iter 270, loss: 1.910, accuracy: 0.332
  105. epoch 2 iter 280, loss: 1.907, accuracy: 0.329
  106. epoch 3 iter 0, loss: 1.918, accuracy: 0.331
  107. epoch 3 iter 10, loss: 1.897, accuracy: 0.337
  108. epoch 3 iter 20, loss: 1.897, accuracy: 0.333
  109. epoch 3 iter 30, loss: 1.913, accuracy: 0.332
  110. epoch 3 iter 40, loss: 1.884, accuracy: 0.344
  111. epoch 3 iter 50, loss: 1.887, accuracy: 0.342
  112. epoch 3 iter 60, loss: 1.927, accuracy: 0.334
  113. epoch 3 iter 70, loss: 1.906, accuracy: 0.335
  114. epoch 3 iter 80, loss: 1.885, accuracy: 0.342
  115. epoch 3 iter 90, loss: 1.862, accuracy: 0.349
  116. epoch 3 iter 100, loss: 1.854, accuracy: 0.351
  117. epoch 3 iter 110, loss: 1.844, accuracy: 0.364
  118. epoch 3 iter 120, loss: 1.872, accuracy: 0.353
  119. epoch 3 iter 130, loss: 1.894, accuracy: 0.343
  120. epoch 3 iter 140, loss: 1.833, accuracy: 0.359
  121. epoch 3 iter 150, loss: 1.784, accuracy: 0.391
  122. epoch 3 iter 160, loss: 1.691, accuracy: 0.431
  123. epoch 3 iter 170, loss: 1.700, accuracy: 0.435
  124. epoch 3 iter 180, loss: 1.558, accuracy: 0.486
  125. epoch 3 iter 190, loss: 1.416, accuracy: 0.543
  126. epoch 3 iter 200, loss: 1.353, accuracy: 0.569
  127. epoch 3 iter 210, loss: 1.271, accuracy: 0.603
  128. epoch 3 iter 220, loss: 1.243, accuracy: 0.615
  129. epoch 3 iter 230, loss: 1.176, accuracy: 0.638
  130. epoch 3 iter 240, loss: 1.260, accuracy: 0.610
  131. epoch 3 iter 250, loss: 1.182, accuracy: 0.630
  132. epoch 3 iter 260, loss: 1.057, accuracy: 0.682
  133. epoch 3 iter 270, loss: 1.048, accuracy: 0.688
  134. epoch 3 iter 280, loss: 1.027, accuracy: 0.699
  135. epoch 4 iter 0, loss: 0.984, accuracy: 0.706
  136. epoch 4 iter 10, loss: 0.969, accuracy: 0.715
  137. epoch 4 iter 20, loss: 0.952, accuracy: 0.724
  138. epoch 4 iter 30, loss: 0.915, accuracy: 0.733
  139. epoch 4 iter 40, loss: 0.897, accuracy: 0.741
  140. epoch 4 iter 50, loss: 0.907, accuracy: 0.739
  141. epoch 4 iter 60, loss: 0.885, accuracy: 0.743
  142. epoch 4 iter 70, loss: 0.847, accuracy: 0.750
  143. epoch 4 iter 80, loss: 0.857, accuracy: 0.743
  144. epoch 4 iter 90, loss: 0.826, accuracy: 0.757
  145. epoch 4 iter 100, loss: 0.815, accuracy: 0.760
  146. epoch 4 iter 110, loss: 0.811, accuracy: 0.764
  147. epoch 4 iter 120, loss: 0.776, accuracy: 0.776
  148. epoch 4 iter 130, loss: 0.769, accuracy: 0.781
  149. epoch 4 iter 140, loss: 0.763, accuracy: 0.780
  150. epoch 4 iter 150, loss: 0.764, accuracy: 0.782
  151. epoch 4 iter 160, loss: 0.733, accuracy: 0.791
  152. epoch 4 iter 170, loss: 0.729, accuracy: 0.789
  153. epoch 4 iter 180, loss: 0.747, accuracy: 0.783
  154. epoch 4 iter 190, loss: 0.735, accuracy: 0.788
  155. epoch 4 iter 200, loss: 0.714, accuracy: 0.794
  156. epoch 4 iter 210, loss: 0.685, accuracy: 0.801
  157. epoch 4 iter 220, loss: 0.715, accuracy: 0.794
  158. epoch 4 iter 230, loss: 0.701, accuracy: 0.800
  159. epoch 4 iter 240, loss: 0.696, accuracy: 0.799
  160. epoch 4 iter 250, loss: 0.715, accuracy: 0.792
  161. epoch 4 iter 260, loss: 0.680, accuracy: 0.803
  162. epoch 4 iter 270, loss: 0.701, accuracy: 0.795
  163. epoch 4 iter 280, loss: 0.699, accuracy: 0.800
  164. epoch 5 iter 0, loss: 0.739, accuracy: 0.792
  165. epoch 5 iter 10, loss: 0.695, accuracy: 0.806
  166. epoch 5 iter 20, loss: 0.682, accuracy: 0.804
  167. epoch 5 iter 30, loss: 0.674, accuracy: 0.808
  168. epoch 5 iter 40, loss: 0.697, accuracy: 0.802
  169. epoch 5 iter 50, loss: 0.713, accuracy: 0.797
  170. epoch 5 iter 60, loss: 0.677, accuracy: 0.805
  171. epoch 5 iter 70, loss: 0.666, accuracy: 0.811
  172. epoch 5 iter 80, loss: 0.722, accuracy: 0.792
  173. epoch 5 iter 90, loss: 0.652, accuracy: 0.814
  174. epoch 5 iter 100, loss: 0.666, accuracy: 0.809
  175. epoch 5 iter 110, loss: 0.648, accuracy: 0.817
  176. epoch 5 iter 120, loss: 0.659, accuracy: 0.813
  177. epoch 5 iter 130, loss: 0.652, accuracy: 0.816
  178. epoch 5 iter 140, loss: 0.656, accuracy: 0.818
  179. epoch 5 iter 150, loss: 0.649, accuracy: 0.820
  180. epoch 5 iter 160, loss: 0.645, accuracy: 0.820
  181. epoch 5 iter 170, loss: 0.642, accuracy: 0.819
  182. epoch 5 iter 180, loss: 0.650, accuracy: 0.816
  183. epoch 5 iter 190, loss: 0.654, accuracy: 0.817
  184. epoch 5 iter 200, loss: 0.650, accuracy: 0.818
  185. epoch 5 iter 210, loss: 0.648, accuracy: 0.819
  186. epoch 5 iter 220, loss: 0.625, accuracy: 0.825
  187. epoch 5 iter 230, loss: 0.630, accuracy: 0.823
  188. epoch 5 iter 240, loss: 0.636, accuracy: 0.822
  189. epoch 5 iter 250, loss: 0.646, accuracy: 0.819
  190. epoch 5 iter 260, loss: 0.648, accuracy: 0.815
  191. epoch 5 iter 270, loss: 0.640, accuracy: 0.820
  192. epoch 5 iter 280, loss: 0.632, accuracy: 0.827
  193. epoch 6 iter 0, loss: 0.622, accuracy: 0.830
  194. epoch 6 iter 10, loss: 0.651, accuracy: 0.820
  195. epoch 6 iter 20, loss: 0.635, accuracy: 0.824
  196. epoch 6 iter 30, loss: 0.669, accuracy: 0.822
  197. epoch 6 iter 40, loss: 0.663, accuracy: 0.819
  198. epoch 6 iter 50, loss: 0.657, accuracy: 0.822
  199. epoch 6 iter 60, loss: 0.656, accuracy: 0.817
  200. epoch 6 iter 70, loss: 0.649, accuracy: 0.817
  201. epoch 6 iter 80, loss: 0.667, accuracy: 0.819
  202. epoch 6 iter 90, loss: 0.695, accuracy: 0.815
  203. epoch 6 iter 100, loss: 0.637, accuracy: 0.825
  204. epoch 6 iter 110, loss: 0.631, accuracy: 0.829
  205. epoch 6 iter 120, loss: 0.640, accuracy: 0.827
  206. epoch 6 iter 130, loss: 0.637, accuracy: 0.821
  207. epoch 6 iter 140, loss: 0.662, accuracy: 0.823
  208. epoch 6 iter 150, loss: 0.650, accuracy: 0.828
  209. epoch 6 iter 160, loss: 0.646, accuracy: 0.823
  210. epoch 6 iter 170, loss: 0.649, accuracy: 0.820
  211. epoch 6 iter 180, loss: 0.652, accuracy: 0.825
  212. epoch 6 iter 190, loss: 0.640, accuracy: 0.829
  213. epoch 6 iter 200, loss: 0.623, accuracy: 0.834
  214. epoch 6 iter 210, loss: 0.617, accuracy: 0.836
  215. epoch 6 iter 220, loss: 0.626, accuracy: 0.831
  216. epoch 6 iter 230, loss: 0.624, accuracy: 0.833
  217. epoch 6 iter 240, loss: 0.617, accuracy: 0.831
  218. epoch 6 iter 250, loss: 0.641, accuracy: 0.827
  219. epoch 6 iter 260, loss: 0.631, accuracy: 0.834
  220. epoch 6 iter 270, loss: 0.624, accuracy: 0.836
  221. epoch 6 iter 280, loss: 0.641, accuracy: 0.829
  222. epoch 7 iter 0, loss: 0.621, accuracy: 0.837
  223. epoch 7 iter 10, loss: 0.644, accuracy: 0.832
  224. epoch 7 iter 20, loss: 0.627, accuracy: 0.836
  225. epoch 7 iter 30, loss: 0.674, accuracy: 0.822
  226. epoch 7 iter 40, loss: 0.708, accuracy: 0.816
  227. epoch 7 iter 50, loss: 0.860, accuracy: 0.790
  228. epoch 7 iter 60, loss: 0.692, accuracy: 0.816
  229. epoch 7 iter 70, loss: 0.669, accuracy: 0.822
  230. epoch 7 iter 80, loss: 0.664, accuracy: 0.830
  231. epoch 7 iter 90, loss: 0.767, accuracy: 0.811
  232. epoch 7 iter 100, loss: 0.671, accuracy: 0.818
  233. epoch 7 iter 110, loss: 0.645, accuracy: 0.825
  234. epoch 7 iter 120, loss: 0.669, accuracy: 0.826
  235. epoch 7 iter 130, loss: 0.717, accuracy: 0.818
  236. epoch 7 iter 140, loss: 0.699, accuracy: 0.823
  237. epoch 7 iter 150, loss: 0.665, accuracy: 0.829
  238. epoch 7 iter 160, loss: 0.645, accuracy: 0.826
  239. epoch 7 iter 170, loss: 0.661, accuracy: 0.819
  240. epoch 7 iter 180, loss: 0.730, accuracy: 0.813
  241. epoch 7 iter 190, loss: 0.650, accuracy: 0.828
  242. epoch 7 iter 200, loss: 0.674, accuracy: 0.823
  243. epoch 7 iter 210, loss: 0.671, accuracy: 0.830
  244. epoch 7 iter 220, loss: 0.667, accuracy: 0.827
  245. epoch 7 iter 230, loss: 0.688, accuracy: 0.826
  246. epoch 7 iter 240, loss: 0.636, accuracy: 0.833
  247. epoch 7 iter 250, loss: 0.681, accuracy: 0.826
  248. epoch 7 iter 260, loss: 0.662, accuracy: 0.829
  249. epoch 7 iter 270, loss: 0.657, accuracy: 0.834
  250. epoch 7 iter 280, loss: 0.635, accuracy: 0.838
  251. epoch 8 iter 0, loss: 0.693, accuracy: 0.831
  252. epoch 8 iter 10, loss: 0.700, accuracy: 0.828
  253. epoch 8 iter 20, loss: 0.738, accuracy: 0.825
  254. epoch 8 iter 30, loss: 0.764, accuracy: 0.806
  255. epoch 8 iter 40, loss: 0.741, accuracy: 0.813
  256. epoch 8 iter 50, loss: 0.731, accuracy: 0.826
  257. epoch 8 iter 60, loss: 0.738, accuracy: 0.812
  258. epoch 8 iter 70, loss: 0.711, accuracy: 0.824
  259. epoch 8 iter 80, loss: 0.728, accuracy: 0.819
  260. epoch 8 iter 90, loss: 0.710, accuracy: 0.825
  261. epoch 8 iter 100, loss: 0.695, accuracy: 0.817
  262. epoch 8 iter 110, loss: 0.728, accuracy: 0.810
  263. epoch 8 iter 120, loss: 0.699, accuracy: 0.827
  264. epoch 8 iter 130, loss: 0.693, accuracy: 0.830
  265. epoch 8 iter 140, loss: 0.722, accuracy: 0.827
  266. epoch 8 iter 150, loss: 0.691, accuracy: 0.833
  267. epoch 8 iter 160, loss: 0.656, accuracy: 0.835
  268. epoch 8 iter 170, loss: 0.681, accuracy: 0.830
  269. epoch 8 iter 180, loss: 0.681, accuracy: 0.836
  270. epoch 8 iter 190, loss: 0.677, accuracy: 0.833
  271. epoch 8 iter 200, loss: 0.694, accuracy: 0.832
  272. epoch 8 iter 210, loss: 0.694, accuracy: 0.836
  273. epoch 8 iter 220, loss: 0.695, accuracy: 0.829
  274. epoch 8 iter 230, loss: 0.726, accuracy: 0.828
  275. epoch 8 iter 240, loss: 0.664, accuracy: 0.836
  276. epoch 8 iter 250, loss: 0.655, accuracy: 0.841
  277. epoch 8 iter 260, loss: 0.676, accuracy: 0.834
  278. epoch 8 iter 270, loss: 0.697, accuracy: 0.832
  279. epoch 8 iter 280, loss: 0.693, accuracy: 0.842
  280. epoch 9 iter 0, loss: 0.704, accuracy: 0.837
  281. epoch 9 iter 10, loss: 0.725, accuracy: 0.833
  282. epoch 9 iter 20, loss: 0.802, accuracy: 0.825
  283. epoch 9 iter 30, loss: 0.769, accuracy: 0.819
  284. epoch 9 iter 40, loss: 0.781, accuracy: 0.811
  285. epoch 9 iter 50, loss: 0.758, accuracy: 0.827
  286. epoch 9 iter 60, loss: 0.767, accuracy: 0.818
  287. epoch 9 iter 70, loss: 0.745, accuracy: 0.828
  288. epoch 9 iter 80, loss: 0.799, accuracy: 0.814
  289. epoch 9 iter 90, loss: 0.749, accuracy: 0.824
  290. epoch 9 iter 100, loss: 0.686, accuracy: 0.834
  291. epoch 9 iter 110, loss: 0.736, accuracy: 0.828
  292. epoch 9 iter 120, loss: 0.721, accuracy: 0.831
  293. epoch 9 iter 130, loss: 0.726, accuracy: 0.826
  294. epoch 9 iter 140, loss: 0.782, accuracy: 0.823
  295. epoch 9 iter 150, loss: 0.822, accuracy: 0.823
  296. epoch 9 iter 160, loss: 0.736, accuracy: 0.832
  297. epoch 9 iter 170, loss: 0.718, accuracy: 0.832
  298. epoch 9 iter 180, loss: 0.728, accuracy: 0.835
  299. epoch 9 iter 190, loss: 0.716, accuracy: 0.832
  300. epoch 9 iter 200, loss: 0.759, accuracy: 0.829
  301. epoch 9 iter 210, loss: 0.770, accuracy: 0.830
  302. epoch 9 iter 220, loss: 0.778, accuracy: 0.821
  303. epoch 9 iter 230, loss: 0.793, accuracy: 0.826
  304. epoch 9 iter 240, loss: 0.764, accuracy: 0.819
  305. epoch 9 iter 250, loss: 0.721, accuracy: 0.834
  306. epoch 9 iter 260, loss: 0.728, accuracy: 0.833
  307. epoch 9 iter 270, loss: 0.812, accuracy: 0.826
  308. epoch 9 iter 280, loss: 0.787, accuracy: 0.835
  309. epoch 10 iter 0, loss: 0.737, accuracy: 0.832
  310. epoch 10 iter 10, loss: 0.778, accuracy: 0.833
  311. epoch 10 iter 20, loss: 0.845, accuracy: 0.826
  312. epoch 10 iter 30, loss: 0.861, accuracy: 0.818
  313. epoch 10 iter 40, loss: 0.848, accuracy: 0.809
  314. epoch 10 iter 50, loss: 0.955, accuracy: 0.809
  315. epoch 10 iter 60, loss: 0.845, accuracy: 0.814
  316. epoch 10 iter 70, loss: 0.761, accuracy: 0.830
  317. epoch 10 iter 80, loss: 0.904, accuracy: 0.803
  318. epoch 10 iter 90, loss: 0.987, accuracy: 0.798
  319. epoch 10 iter 100, loss: 0.872, accuracy: 0.818
  320. epoch 10 iter 110, loss: 0.785, accuracy: 0.827
  321. epoch 10 iter 120, loss: 0.769, accuracy: 0.831
  322. epoch 10 iter 130, loss: 0.813, accuracy: 0.824
  323. epoch 10 iter 140, loss: 0.784, accuracy: 0.831
  324. epoch 10 iter 150, loss: 0.881, accuracy: 0.823
  325. epoch 10 iter 160, loss: 0.809, accuracy: 0.815
  326. epoch 10 iter 170, loss: 0.841, accuracy: 0.818
  327. epoch 10 iter 180, loss: 0.798, accuracy: 0.827
  328. epoch 10 iter 190, loss: 0.795, accuracy: 0.828
  329. epoch 10 iter 200, loss: 0.833, accuracy: 0.824
  330. epoch 10 iter 210, loss: 0.782, accuracy: 0.829
  331. epoch 10 iter 220, loss: 0.886, accuracy: 0.808
  332. epoch 10 iter 230, loss: 0.818, accuracy: 0.828
  333. epoch 10 iter 240, loss: 0.770, accuracy: 0.829
  334. epoch 10 iter 250, loss: 0.815, accuracy: 0.828
  335. epoch 10 iter 260, loss: 0.782, accuracy: 0.828
  336. epoch 10 iter 270, loss: 0.836, accuracy: 0.828
  337. epoch 10 iter 280, loss: 0.789, accuracy: 0.835
  338. epoch 11 iter 0, loss: 0.792, accuracy: 0.841
  339. epoch 11 iter 10, loss: 0.823, accuracy: 0.836
  340. epoch 11 iter 20, loss: 0.839, accuracy: 0.833
  341. epoch 11 iter 30, loss: 0.970, accuracy: 0.814
  342. epoch 11 iter 40, loss: 0.958, accuracy: 0.813
  343. epoch 11 iter 50, loss: 0.841, accuracy: 0.830
  344. epoch 11 iter 60, loss: 0.881, accuracy: 0.820
  345. epoch 11 iter 70, loss: 0.801, accuracy: 0.824
  346. epoch 11 iter 80, loss: 0.880, accuracy: 0.823
  347. epoch 11 iter 90, loss: 0.917, accuracy: 0.819
  348. epoch 11 iter 100, loss: 0.829, accuracy: 0.819
  349. epoch 11 iter 110, loss: 0.817, accuracy: 0.827
  350. epoch 11 iter 120, loss: 0.795, accuracy: 0.834
  351. epoch 11 iter 130, loss: 0.854, accuracy: 0.823
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  1453. epoch 49 iter 130, loss: 3.093, accuracy: 0.825
  1454. epoch 49 iter 140, loss: 3.337, accuracy: 0.837
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  1456. epoch 49 iter 160, loss: 2.888, accuracy: 0.844
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  1459. epoch 49 iter 190, loss: 2.908, accuracy: 0.848
  1460. epoch 49 iter 200, loss: 2.818, accuracy: 0.848
  1461. epoch 49 iter 210, loss: 3.106, accuracy: 0.841
  1462. epoch 49 iter 220, loss: 3.237, accuracy: 0.843
  1463. epoch 49 iter 230, loss: 3.422, accuracy: 0.834
  1464. epoch 49 iter 240, loss: 3.168, accuracy: 0.845
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  1466. epoch 49 iter 260, loss: 3.061, accuracy: 0.845
  1467. epoch 49 iter 270, loss: 3.469, accuracy: 0.845
  1468. epoch 49 iter 280, loss: 3.242, accuracy: 0.850
  1469. current stride is: 4
  1470. 2018-10-22 17:43:38.540876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  1471. 2018-10-22 17:43:38.540920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  1472. 2018-10-22 17:43:38.540927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  1473. 2018-10-22 17:43:38.540932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  1474. 2018-10-22 17:43:38.541056: 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: 14.104, accuracy: 0.088
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  1822. epoch 11 iter 280, loss: 0.658, accuracy: 0.819
  1823. epoch 12 iter 0, loss: 0.683, accuracy: 0.818
  1824. epoch 12 iter 10, loss: 0.699, accuracy: 0.818
  1825. epoch 12 iter 20, loss: 0.742, accuracy: 0.794
  1826. epoch 12 iter 30, loss: 0.723, accuracy: 0.807
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  1832. epoch 12 iter 90, loss: 0.668, accuracy: 0.817
  1833. epoch 12 iter 100, loss: 0.681, accuracy: 0.812
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  1835. epoch 12 iter 120, loss: 0.713, accuracy: 0.799
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  1853. epoch 13 iter 10, loss: 0.707, accuracy: 0.818
  1854. epoch 13 iter 20, loss: 0.791, accuracy: 0.783
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  1911. epoch 15 iter 10, loss: 0.712, accuracy: 0.820
  1912. epoch 15 iter 20, loss: 0.721, accuracy: 0.814
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  1914. epoch 15 iter 40, loss: 0.727, accuracy: 0.812
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  1920. epoch 15 iter 100, loss: 0.685, accuracy: 0.821
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  1922. epoch 15 iter 120, loss: 0.702, accuracy: 0.822
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  1930. epoch 15 iter 200, loss: 0.743, accuracy: 0.811
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  1938. epoch 15 iter 280, loss: 0.723, accuracy: 0.813
  1939. epoch 16 iter 0, loss: 0.776, accuracy: 0.809
  1940. epoch 16 iter 10, loss: 0.768, accuracy: 0.801
  1941. epoch 16 iter 20, loss: 0.769, accuracy: 0.798
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  1968. epoch 17 iter 0, loss: 0.772, accuracy: 0.814
  1969. epoch 17 iter 10, loss: 0.741, accuracy: 0.816
  1970. epoch 17 iter 20, loss: 0.712, accuracy: 0.818
  1971. epoch 17 iter 30, loss: 0.733, accuracy: 0.818
  1972. epoch 17 iter 40, loss: 0.730, accuracy: 0.818
  1973. epoch 17 iter 50, loss: 0.743, accuracy: 0.810
  1974. epoch 17 iter 60, loss: 0.724, accuracy: 0.823
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  1977. epoch 17 iter 90, loss: 0.710, accuracy: 0.820
  1978. epoch 17 iter 100, loss: 0.696, accuracy: 0.822
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  1980. epoch 17 iter 120, loss: 0.742, accuracy: 0.819
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  1985. epoch 17 iter 170, loss: 0.733, accuracy: 0.819
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  1987. epoch 17 iter 190, loss: 0.773, accuracy: 0.805
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  1989. epoch 17 iter 210, loss: 0.746, accuracy: 0.812
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  1992. epoch 17 iter 240, loss: 0.734, accuracy: 0.816
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  1997. epoch 18 iter 0, loss: 0.746, accuracy: 0.822
  1998. epoch 18 iter 10, loss: 0.784, accuracy: 0.806
  1999. epoch 18 iter 20, loss: 0.754, accuracy: 0.808
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  2001. epoch 18 iter 40, loss: 0.757, accuracy: 0.825
  2002. epoch 18 iter 50, loss: 0.760, accuracy: 0.812
  2003. epoch 18 iter 60, loss: 0.716, accuracy: 0.827
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  2006. epoch 18 iter 90, loss: 0.759, accuracy: 0.817
  2007. epoch 18 iter 100, loss: 0.743, accuracy: 0.818
  2008. epoch 18 iter 110, loss: 0.738, accuracy: 0.819
  2009. epoch 18 iter 120, loss: 0.765, accuracy: 0.816
  2010. epoch 18 iter 130, loss: 0.740, accuracy: 0.820
  2011. epoch 18 iter 140, loss: 0.782, accuracy: 0.816
  2012. epoch 18 iter 150, loss: 0.753, accuracy: 0.820
  2013. epoch 18 iter 160, loss: 0.836, accuracy: 0.801
  2014. epoch 18 iter 170, loss: 0.801, accuracy: 0.806
  2015. epoch 18 iter 180, loss: 0.766, accuracy: 0.812
  2016. epoch 18 iter 190, loss: 0.781, accuracy: 0.814
  2017. epoch 18 iter 200, loss: 0.777, accuracy: 0.814
  2018. epoch 18 iter 210, loss: 0.836, accuracy: 0.800
  2019. epoch 18 iter 220, loss: 0.774, accuracy: 0.812
  2020. epoch 18 iter 230, loss: 0.804, accuracy: 0.801
  2021. epoch 18 iter 240, loss: 0.769, accuracy: 0.811
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  2027. epoch 19 iter 10, loss: 0.827, accuracy: 0.804
  2028. epoch 19 iter 20, loss: 0.773, accuracy: 0.811
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  2055. epoch 20 iter 0, loss: 0.780, accuracy: 0.821
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  2059. epoch 20 iter 40, loss: 0.807, accuracy: 0.822
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  2064. epoch 20 iter 90, loss: 0.815, accuracy: 0.813
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  2066. epoch 20 iter 110, loss: 0.897, accuracy: 0.789
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  2084. epoch 21 iter 0, loss: 0.812, accuracy: 0.818
  2085. epoch 21 iter 10, loss: 0.884, accuracy: 0.802
  2086. epoch 21 iter 20, loss: 0.781, accuracy: 0.818
  2087. epoch 21 iter 30, loss: 0.806, accuracy: 0.812
  2088. epoch 21 iter 40, loss: 0.817, accuracy: 0.819
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  2090. epoch 21 iter 60, loss: 0.781, accuracy: 0.822
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  2092. epoch 21 iter 80, loss: 0.834, accuracy: 0.813
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  2094. epoch 21 iter 100, loss: 0.814, accuracy: 0.818
  2095. epoch 21 iter 110, loss: 0.887, accuracy: 0.793
  2096. epoch 21 iter 120, loss: 0.826, accuracy: 0.818
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  2104. epoch 21 iter 200, loss: 0.831, accuracy: 0.814
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  2110. epoch 21 iter 260, loss: 0.773, accuracy: 0.827
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  2113. epoch 22 iter 0, loss: 0.882, accuracy: 0.802
  2114. epoch 22 iter 10, loss: 0.971, accuracy: 0.792
  2115. epoch 22 iter 20, loss: 0.828, accuracy: 0.815
  2116. epoch 22 iter 30, loss: 0.865, accuracy: 0.801
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  2119. epoch 22 iter 60, loss: 0.857, accuracy: 0.805
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  2902. epoch 49 iter 60, loss: 1.537, accuracy: 0.807
  2903. epoch 49 iter 70, loss: 1.478, accuracy: 0.806
  2904. epoch 49 iter 80, loss: 1.464, accuracy: 0.808
  2905. epoch 49 iter 90, loss: 1.472, accuracy: 0.817
  2906. epoch 49 iter 100, loss: 1.500, accuracy: 0.808
  2907. epoch 49 iter 110, loss: 1.554, accuracy: 0.806
  2908. epoch 49 iter 120, loss: 1.504, accuracy: 0.808
  2909. epoch 49 iter 130, loss: 1.486, accuracy: 0.815
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  2911. epoch 49 iter 150, loss: 1.455, accuracy: 0.821
  2912. epoch 49 iter 160, loss: 1.516, accuracy: 0.806
  2913. epoch 49 iter 170, loss: 1.634, accuracy: 0.803
  2914. epoch 49 iter 180, loss: 1.423, accuracy: 0.809
  2915. epoch 49 iter 190, loss: 1.561, accuracy: 0.807
  2916. epoch 49 iter 200, loss: 1.531, accuracy: 0.817
  2917. epoch 49 iter 210, loss: 1.588, accuracy: 0.804
  2918. epoch 49 iter 220, loss: 1.585, accuracy: 0.804
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  2922. epoch 49 iter 260, loss: 1.549, accuracy: 0.812
  2923. epoch 49 iter 270, loss: 1.504, accuracy: 0.818
  2924. epoch 49 iter 280, loss: 1.518, accuracy: 0.817
  2925. current stride is: 6
  2926. 2018-10-22 18:11:51.953810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
  2927. 2018-10-22 18:11:51.953862: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
  2928. 2018-10-22 18:11:51.953871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
  2929. 2018-10-22 18:11:51.953876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
  2930. 2018-10-22 18:11:51.954010: 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: 7.758, accuracy: 0.107
  2932. epoch 0 iter 10, loss: 2.651, accuracy: 0.076
  2933. epoch 0 iter 20, loss: 2.289, accuracy: 0.193
  2934. epoch 0 iter 30, loss: 2.254, accuracy: 0.182
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  2936. epoch 0 iter 50, loss: 2.177, accuracy: 0.226
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  2969. epoch 1 iter 90, loss: 1.335, accuracy: 0.575
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  2989. epoch 2 iter 0, loss: 1.131, accuracy: 0.657
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  2998. epoch 2 iter 90, loss: 1.078, accuracy: 0.670
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  3018. epoch 3 iter 0, loss: 1.027, accuracy: 0.694
  3019. epoch 3 iter 10, loss: 1.018, accuracy: 0.692
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  3022. epoch 3 iter 40, loss: 1.070, accuracy: 0.676
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  3047. epoch 4 iter 0, loss: 0.975, accuracy: 0.711
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  4337. epoch 48 iter 140, loss: 1.055, accuracy: 0.763
  4338. epoch 48 iter 150, loss: 1.095, accuracy: 0.750
  4339. epoch 48 iter 160, loss: 1.035, accuracy: 0.759
  4340. epoch 48 iter 170, loss: 1.029, accuracy: 0.761
  4341. epoch 48 iter 180, loss: 1.034, accuracy: 0.763
  4342. epoch 48 iter 190, loss: 1.039, accuracy: 0.764
  4343. epoch 48 iter 200, loss: 1.038, accuracy: 0.762
  4344. epoch 48 iter 210, loss: 1.029, accuracy: 0.756
  4345. epoch 48 iter 220, loss: 0.982, accuracy: 0.769
  4346. epoch 48 iter 230, loss: 1.041, accuracy: 0.759
  4347. epoch 48 iter 240, loss: 1.019, accuracy: 0.766
  4348. epoch 48 iter 250, loss: 1.001, accuracy: 0.769
  4349. epoch 48 iter 260, loss: 1.051, accuracy: 0.752
  4350. epoch 48 iter 270, loss: 1.086, accuracy: 0.752
  4351. epoch 48 iter 280, loss: 1.127, accuracy: 0.744
  4352. epoch 49 iter 0, loss: 1.086, accuracy: 0.750
  4353. epoch 49 iter 10, loss: 1.010, accuracy: 0.762
  4354. epoch 49 iter 20, loss: 1.029, accuracy: 0.757
  4355. epoch 49 iter 30, loss: 1.050, accuracy: 0.758
  4356. epoch 49 iter 40, loss: 1.037, accuracy: 0.764
  4357. epoch 49 iter 50, loss: 1.060, accuracy: 0.754
  4358. epoch 49 iter 60, loss: 1.023, accuracy: 0.766
  4359. epoch 49 iter 70, loss: 1.026, accuracy: 0.765
  4360. epoch 49 iter 80, loss: 1.013, accuracy: 0.764
  4361. epoch 49 iter 90, loss: 1.033, accuracy: 0.761
  4362. epoch 49 iter 100, loss: 1.094, accuracy: 0.763
  4363. epoch 49 iter 110, loss: 1.106, accuracy: 0.752
  4364. epoch 49 iter 120, loss: 1.027, accuracy: 0.765
  4365. epoch 49 iter 130, loss: 1.078, accuracy: 0.761
  4366. epoch 49 iter 140, loss: 1.061, accuracy: 0.764
  4367. epoch 49 iter 150, loss: 1.106, accuracy: 0.752
  4368. epoch 49 iter 160, loss: 1.038, accuracy: 0.759
  4369. epoch 49 iter 170, loss: 1.069, accuracy: 0.756
  4370. epoch 49 iter 180, loss: 1.068, accuracy: 0.757
  4371. epoch 49 iter 190, loss: 1.050, accuracy: 0.759
  4372. epoch 49 iter 200, loss: 1.075, accuracy: 0.753
  4373. epoch 49 iter 210, loss: 1.072, accuracy: 0.746
  4374. epoch 49 iter 220, loss: 0.997, accuracy: 0.768
  4375. epoch 49 iter 230, loss: 1.078, accuracy: 0.751
  4376. epoch 49 iter 240, loss: 1.058, accuracy: 0.762
  4377. epoch 49 iter 250, loss: 1.012, accuracy: 0.770
  4378. epoch 49 iter 260, loss: 1.055, accuracy: 0.758
  4379. epoch 49 iter 270, loss: 1.103, accuracy: 0.749
  4380. epoch 49 iter 280, loss: 1.133, accuracy: 0.749
  4381. [jalal@scc-c13 pset3_CNN]$
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