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r_u_a_bird_training_100_epochs

lamiastella
Feb 15th, 2017
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  1. mona@pascal:~/computer_vision/python_playground$ python bird_classifier.py
  2. I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
  3. I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
  4. I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
  5. I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
  6. I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
  7. I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
  8. name: Tesla K40c
  9. major: 3 minor: 5 memoryClockRate (GHz) 0.8755
  10. pciBusID 0000:03:00.0
  11. Total memory: 11.92GiB
  12. Free memory: 11.85GiB
  13. W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x3e6fb80
  14. I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 1 with properties:
  15. name: Tesla K40c
  16. major: 3 minor: 5 memoryClockRate (GHz) 0.8755
  17. pciBusID 0000:83:00.0
  18. Total memory: 11.92GiB
  19. Free memory: 11.85GiB
  20. I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 0 and 1
  21. I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 1 and 0
  22. I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 1
  23. I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y N
  24. I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 1: N Y
  25. I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus id: 0000:03:00.0)
  26. I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K40c, pci bus id: 0000:83:00.0)
  27. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  28. Instructions for updating:
  29. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  30. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  31. Instructions for updating:
  32. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  33. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/trainer.py:766 in create_summaries.: merge_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  34. Instructions for updating:
  35. Please switch to tf.summary.merge.
  36. WARNING:tensorflow:VARIABLES collection name is deprecated, please use GLOBAL_VARIABLES instead; VARIABLES will be removed after 2017-03-02.
  37. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/trainer.py:130 in __init__.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
  38. Instructions for updating:
  39. Use `tf.global_variables_initializer` instead.
  40. I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus id: 0000:03:00.0)
  41. I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K40c, pci bus id: 0000:83:00.0)
  42. ---------------------------------
  43. Run id: bird-classifier
  44. Log directory: /tmp/tflearn_logs/
  45. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/trainer.py:210 in fit.: __init__ (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
  46. Instructions for updating:
  47. Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
  48. WARNING:tensorflow:Error encountered when serializing data_augmentation.
  49. Type is unsupported, or the types of the items don't match field type in CollectionDef.
  50. 'ImageAugmentation' object has no attribute 'name'
  51. WARNING:tensorflow:Error encountered when serializing data_preprocessing.
  52. Type is unsupported, or the types of the items don't match field type in CollectionDef.
  53. 'ImagePreprocessing' object has no attribute 'name'
  54. WARNING:tensorflow:Error encountered when serializing summary_tags.
  55. Type is unsupported, or the types of the items don't match field type in CollectionDef.
  56. 'dict' object has no attribute 'name'
  57. ---------------------------------
  58. Preprocessing... Calculating mean over all dataset (this may take long)...
  59. Mean: 0.472978413436 (To avoid repetitive computation, add it to argument 'mean' of `add_featurewise_zero_center`)
  60. ---------------------------------
  61. Preprocessing... Calculating std over all dataset (this may take long)...
  62. STD: 0.248771212446 (To avoid repetitive computation, add it to argument 'std' of `add_featurewise_stdnorm`)
  63. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  64. Instructions for updating:
  65. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  66. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/summarizer.py:89 in summarize.: merge_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  67. Instructions for updating:
  68. Please switch to tf.summary.merge.
  69. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  70. Instructions for updating:
  71. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  72. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/summarizer.py:89 in summarize.: merge_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  73. Instructions for updating:
  74. Please switch to tf.summary.merge.
  75. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  76. Instructions for updating:
  77. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  78. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/summarizer.py:89 in summarize.: merge_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  79. Instructions for updating:
  80. Please switch to tf.summary.merge.
  81. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/summaries.py:46 in get_summary.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  82. Instructions for updating:
  83. Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
  84. WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tflearn/helpers/summarizer.py:89 in summarize.: merge_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
  85. Instructions for updating:
  86. Please switch to tf.summary.merge.
  87. ---------------------------------
  88. Training samples: 56780
  89. Validation samples: 15000
  90. --
  91. Training Step: 592 | total loss: 0.38131
  92. | Adam | epoch: 001 | loss: 0.38131 - acc: 0.8262 | val_loss: 0.47022 - val_acc: 0.7920 -- iter: 56780/56780
  93. --
  94. Training Step: 1184 | total loss: 0.34871
  95. | Adam | epoch: 002 | loss: 0.34871 - acc: 0.8402 | val_loss: 0.30224 - val_acc: 0.8785 -- iter: 56780/56780
  96. --
  97. Training Step: 1776 | total loss: 0.32281
  98. | Adam | epoch: 003 | loss: 0.32281 - acc: 0.8668 | val_loss: 0.29615 - val_acc: 0.8728 -- iter: 56780/56780
  99. --
  100. Training Step: 2368 | total loss: 0.33464
  101. | Adam | epoch: 004 | loss: 0.33464 - acc: 0.8581 | val_loss: 0.27041 - val_acc: 0.8909 -- iter: 56780/56780
  102. --
  103. Training Step: 2960 | total loss: 0.30529
  104. | Adam | epoch: 005 | loss: 0.30529 - acc: 0.8640 | val_loss: 0.22851 - val_acc: 0.9099 -- iter: 56780/56780
  105. --
  106. Training Step: 3552 | total loss: 0.29919
  107. | Adam | epoch: 006 | loss: 0.29919 - acc: 0.8816 | val_loss: 0.21689 - val_acc: 0.9170 -- iter: 56780/56780
  108. --
  109. Training Step: 4144 | total loss: 0.30198
  110. | Adam | epoch: 007 | loss: 0.30198 - acc: 0.8722 | val_loss: 0.21361 - val_acc: 0.9179 -- iter: 56780/56780
  111. --
  112. Training Step: 4736 | total loss: 0.27709
  113. | Adam | epoch: 008 | loss: 0.27709 - acc: 0.8845 | val_loss: 0.21748 - val_acc: 0.9141 -- iter: 56780/56780
  114. --
  115. Training Step: 5328 | total loss: 0.29337
  116. | Adam | epoch: 009 | loss: 0.29337 - acc: 0.8686 | val_loss: 0.21173 - val_acc: 0.9213 -- iter: 56780/56780
  117. --
  118. Training Step: 5920 | total loss: 0.25989
  119. | Adam | epoch: 010 | loss: 0.25989 - acc: 0.9004 | val_loss: 0.19842 - val_acc: 0.9254 -- iter: 56780/56780
  120. --
  121. Training Step: 6512 | total loss: 0.27454
  122. | Adam | epoch: 011 | loss: 0.27454 - acc: 0.8932 | val_loss: 0.25719 - val_acc: 0.8979 -- iter: 56780/56780
  123. --
  124. Training Step: 7104 | total loss: 0.27403
  125. | Adam | epoch: 012 | loss: 0.27403 - acc: 0.8870 | val_loss: 0.20879 - val_acc: 0.9158 -- iter: 56780/56780
  126. --
  127. Training Step: 7696 | total loss: 0.26534
  128. | Adam | epoch: 013 | loss: 0.26534 - acc: 0.8909 | val_loss: 0.19298 - val_acc: 0.9231 -- iter: 56780/56780
  129. --
  130. Training Step: 8288 | total loss: 0.25412
  131. | Adam | epoch: 014 | loss: 0.25412 - acc: 0.8880 | val_loss: 0.21687 - val_acc: 0.9179 -- iter: 56780/56780
  132. --
  133. Training Step: 8880 | total loss: 0.25288
  134. | Adam | epoch: 015 | loss: 0.25288 - acc: 0.8942 | val_loss: 0.18956 - val_acc: 0.9253 -- iter: 56780/56780
  135. --
  136. Training Step: 9472 | total loss: 0.25877
  137. | Adam | epoch: 016 | loss: 0.25877 - acc: 0.8983 | val_loss: 0.18822 - val_acc: 0.9264 -- iter: 56780/56780
  138. --
  139. Training Step: 10064 | total loss: 0.26512
  140. | Adam | epoch: 017 | loss: 0.26512 - acc: 0.8877 | val_loss: 0.20234 - val_acc: 0.9181 -- iter: 56780/56780
  141. --
  142. Training Step: 10656 | total loss: 0.24863
  143. | Adam | epoch: 018 | loss: 0.24863 - acc: 0.8979 | val_loss: 0.18545 - val_acc: 0.9293 -- iter: 56780/56780
  144. --
  145. Training Step: 11248 | total loss: 0.27568
  146. | Adam | epoch: 019 | loss: 0.27568 - acc: 0.8886 | val_loss: 0.23251 - val_acc: 0.9011 -- iter: 56780/56780
  147. --
  148. Training Step: 11840 | total loss: 0.26081
  149. | Adam | epoch: 020 | loss: 0.26081 - acc: 0.9007 | val_loss: 0.18766 - val_acc: 0.9263 -- iter: 56780/56780
  150. --
  151. Training Step: 12432 | total loss: 0.25978
  152. | Adam | epoch: 021 | loss: 0.25978 - acc: 0.8941 | val_loss: 0.21067 - val_acc: 0.9142 -- iter: 56780/56780
  153. --
  154. Training Step: 13024 | total loss: 0.23778
  155. | Adam | epoch: 022 | loss: 0.23778 - acc: 0.9063 | val_loss: 0.18145 - val_acc: 0.9295 -- iter: 56780/56780
  156. --
  157. Training Step: 13616 | total loss: 0.26194
  158. | Adam | epoch: 023 | loss: 0.26194 - acc: 0.8917 | val_loss: 0.16927 - val_acc: 0.9357 -- iter: 56780/56780
  159. --
  160. Training Step: 14208 | total loss: 0.23156
  161. | Adam | epoch: 024 | loss: 0.23156 - acc: 0.9076 | val_loss: 0.16948 - val_acc: 0.9381 -- iter: 56780/56780
  162. --
  163. Training Step: 14800 | total loss: 0.27990
  164. | Adam | epoch: 025 | loss: 0.27990 - acc: 0.8949 | val_loss: 0.19736 - val_acc: 0.9219 -- iter: 56780/56780
  165. --
  166. Training Step: 15392 | total loss: 0.23838
  167. | Adam | epoch: 026 | loss: 0.23838 - acc: 0.9051 | val_loss: 0.23477 - val_acc: 0.9077 -- iter: 56780/56780
  168. --
  169. Training Step: 15984 | total loss: 0.23056
  170. | Adam | epoch: 027 | loss: 0.23056 - acc: 0.9145 | val_loss: 0.18203 - val_acc: 0.9302 -- iter: 56780/56780
  171. --
  172. Training Step: 16576 | total loss: 0.26332
  173. | Adam | epoch: 028 | loss: 0.26332 - acc: 0.9054 | val_loss: 0.19195 - val_acc: 0.9237 -- iter: 56780/56780
  174. --
  175. Training Step: 17168 | total loss: 0.24706
  176. | Adam | epoch: 029 | loss: 0.24706 - acc: 0.8994 | val_loss: 0.17842 - val_acc: 0.9343 -- iter: 56780/56780
  177. --
  178. Training Step: 17760 | total loss: 0.29040
  179. | Adam | epoch: 030 | loss: 0.29040 - acc: 0.9044 | val_loss: 0.16743 - val_acc: 0.9367 -- iter: 56780/56780
  180. --
  181. Training Step: 18352 | total loss: 0.26592
  182. | Adam | epoch: 031 | loss: 0.26592 - acc: 0.9083 | val_loss: 0.16748 - val_acc: 0.9383 -- iter: 56780/56780
  183. --
  184. Training Step: 18944 | total loss: 0.25706
  185. | Adam | epoch: 032 | loss: 0.25706 - acc: 0.9178 | val_loss: 0.17325 - val_acc: 0.9379 -- iter: 56780/56780
  186. --
  187. Training Step: 19536 | total loss: 0.20159
  188. | Adam | epoch: 033 | loss: 0.20159 - acc: 0.9176 | val_loss: 0.18873 - val_acc: 0.9325 -- iter: 56780/56780
  189. --
  190. Training Step: 20128 | total loss: 0.21798
  191. | Adam | epoch: 034 | loss: 0.21798 - acc: 0.9127 | val_loss: 0.16639 - val_acc: 0.9417 -- iter: 56780/56780
  192. --
  193. Training Step: 20720 | total loss: 0.22079
  194. | Adam | epoch: 035 | loss: 0.22079 - acc: 0.9137 | val_loss: 0.17444 - val_acc: 0.9390 -- iter: 56780/56780
  195. --
  196. Training Step: 21312 | total loss: 0.20561
  197. | Adam | epoch: 036 | loss: 0.20561 - acc: 0.9121 | val_loss: 0.16768 - val_acc: 0.9417 -- iter: 56780/56780
  198. --
  199. Training Step: 21904 | total loss: 0.22152
  200. | Adam | epoch: 037 | loss: 0.22152 - acc: 0.9060 | val_loss: 0.21069 - val_acc: 0.9301 -- iter: 56780/56780
  201. --
  202. Training Step: 22496 | total loss: 0.19496
  203. | Adam | epoch: 038 | loss: 0.19496 - acc: 0.9216 | val_loss: 0.16395 - val_acc: 0.9419 -- iter: 56780/56780
  204. --
  205. Training Step: 23088 | total loss: 0.19447
  206. | Adam | epoch: 039 | loss: 0.19447 - acc: 0.9306 | val_loss: 0.21015 - val_acc: 0.9297 -- iter: 56780/56780
  207. --
  208. Training Step: 23680 | total loss: 0.21090
  209. | Adam | epoch: 040 | loss: 0.21090 - acc: 0.9053 | val_loss: 0.16882 - val_acc: 0.9423 -- iter: 56780/56780
  210. --
  211. Training Step: 24272 | total loss: 0.20789
  212. | Adam | epoch: 041 | loss: 0.20789 - acc: 0.9099 | val_loss: 0.18452 - val_acc: 0.9356 -- iter: 56780/56780
  213. --
  214. Training Step: 24864 | total loss: 0.20169
  215. | Adam | epoch: 042 | loss: 0.20169 - acc: 0.9170 | val_loss: 0.17708 - val_acc: 0.9425 -- iter: 56780/56780
  216. --
  217. Training Step: 25456 | total loss: 0.19569
  218. | Adam | epoch: 043 | loss: 0.19569 - acc: 0.9170 | val_loss: 0.23312 - val_acc: 0.9248 -- iter: 56780/56780
  219. --
  220. Training Step: 26048 | total loss: 0.18697
  221. | Adam | epoch: 044 | loss: 0.18697 - acc: 0.9252 | val_loss: 0.19613 - val_acc: 0.9381 -- iter: 56780/56780
  222. --
  223. Training Step: 26640 | total loss: 0.22684
  224. | Adam | epoch: 045 | loss: 0.22684 - acc: 0.8999 | val_loss: 0.16066 - val_acc: 0.9429 -- iter: 56780/56780
  225. --
  226. Training Step: 27232 | total loss: 0.18028
  227. | Adam | epoch: 046 | loss: 0.18028 - acc: 0.9241 | val_loss: 0.19381 - val_acc: 0.9357 -- iter: 56780/56780
  228. --
  229. Training Step: 27824 | total loss: 0.20539
  230. | Adam | epoch: 047 | loss: 0.20539 - acc: 0.9217 | val_loss: 0.16741 - val_acc: 0.9448 -- iter: 56780/56780
  231. --
  232. Training Step: 28416 | total loss: 0.19080
  233. | Adam | epoch: 048 | loss: 0.19080 - acc: 0.9180 | val_loss: 0.18338 - val_acc: 0.9393 -- iter: 56780/56780
  234. --
  235. Training Step: 29008 | total loss: 0.20501
  236. | Adam | epoch: 049 | loss: 0.20501 - acc: 0.9198 | val_loss: 0.18229 - val_acc: 0.9373 -- iter: 56780/56780
  237. --
  238. Training Step: 29600 | total loss: 0.20727
  239. | Adam | epoch: 050 | loss: 0.20727 - acc: 0.9088 | val_loss: 0.16408 - val_acc: 0.9451 -- iter: 56780/56780
  240. --
  241. Training Step: 30192 | total loss: 0.20723
  242. | Adam | epoch: 051 | loss: 0.20723 - acc: 0.9228 | val_loss: 0.17829 - val_acc: 0.9422 -- iter: 56780/56780
  243. --
  244. Training Step: 30784 | total loss: 0.17597
  245. | Adam | epoch: 052 | loss: 0.17597 - acc: 0.9331 | val_loss: 0.19344 - val_acc: 0.9394 -- iter: 56780/56780
  246. --
  247. Training Step: 31376 | total loss: 0.18833
  248. | Adam | epoch: 053 | loss: 0.18833 - acc: 0.9186 | val_loss: 0.18768 - val_acc: 0.9393 -- iter: 56780/56780
  249. --
  250. Training Step: 31968 | total loss: 0.20505
  251. | Adam | epoch: 054 | loss: 0.20505 - acc: 0.9138 | val_loss: 0.16644 - val_acc: 0.9405 -- iter: 56780/56780
  252. --
  253. Training Step: 32560 | total loss: 0.16588
  254. | Adam | epoch: 055 | loss: 0.16588 - acc: 0.9330 | val_loss: 0.17241 - val_acc: 0.9440 -- iter: 56780/56780
  255. --
  256. Training Step: 33152 | total loss: 0.18150
  257. | Adam | epoch: 056 | loss: 0.18150 - acc: 0.9282 | val_loss: 0.22217 - val_acc: 0.9264 -- iter: 56780/56780
  258. --
  259. Training Step: 33744 | total loss: 0.18623
  260. | Adam | epoch: 057 | loss: 0.18623 - acc: 0.9258 | val_loss: 0.18693 - val_acc: 0.9409 -- iter: 56780/56780
  261. --
  262. Training Step: 34336 | total loss: 0.16764
  263. | Adam | epoch: 058 | loss: 0.16764 - acc: 0.9349 | val_loss: 0.20045 - val_acc: 0.9391 -- iter: 56780/56780
  264. --
  265. Training Step: 34928 | total loss: 0.17780
  266. | Adam | epoch: 059 | loss: 0.17780 - acc: 0.9274 | val_loss: 0.19269 - val_acc: 0.9401 -- iter: 56780/56780
  267. --
  268. Training Step: 35520 | total loss: 0.20136
  269. | Adam | epoch: 060 | loss: 0.20136 - acc: 0.9126 | val_loss: 0.16862 - val_acc: 0.9421 -- iter: 56780/56780
  270. --
  271. Training Step: 36112 | total loss: 0.18579
  272. | Adam | epoch: 061 | loss: 0.18579 - acc: 0.9153 | val_loss: 0.19218 - val_acc: 0.9413 -- iter: 56780/56780
  273. --
  274. Training Step: 36704 | total loss: 0.19734
  275. | Adam | epoch: 062 | loss: 0.19734 - acc: 0.9227 | val_loss: 0.19395 - val_acc: 0.9411 -- iter: 56780/56780
  276. --
  277. Training Step: 37296 | total loss: 0.19524
  278. | Adam | epoch: 063 | loss: 0.19524 - acc: 0.9232 | val_loss: 0.18694 - val_acc: 0.9445 -- iter: 56780/56780
  279. --
  280. Training Step: 37888 | total loss: 0.17370
  281. | Adam | epoch: 064 | loss: 0.17370 - acc: 0.9286 | val_loss: 0.21610 - val_acc: 0.9374 -- iter: 56780/56780
  282. --
  283. Training Step: 38480 | total loss: 0.16810
  284. | Adam | epoch: 065 | loss: 0.16810 - acc: 0.9339 | val_loss: 0.20480 - val_acc: 0.9430 -- iter: 56780/56780
  285. --
  286. Training Step: 39072 | total loss: 0.17130
  287. | Adam | epoch: 066 | loss: 0.17130 - acc: 0.9311 | val_loss: 0.21283 - val_acc: 0.9328 -- iter: 56780/56780
  288. --
  289. Training Step: 39664 | total loss: 0.19402
  290. | Adam | epoch: 067 | loss: 0.19402 - acc: 0.9262 | val_loss: 0.17575 - val_acc: 0.9453 -- iter: 56780/56780
  291. --
  292. Training Step: 40256 | total loss: 0.17647
  293. | Adam | epoch: 068 | loss: 0.17647 - acc: 0.9210 | val_loss: 0.17410 - val_acc: 0.9437 -- iter: 56780/56780
  294. --
  295. Training Step: 40848 | total loss: 0.19589
  296. | Adam | epoch: 069 | loss: 0.19589 - acc: 0.9151 | val_loss: 0.18524 - val_acc: 0.9431 -- iter: 56780/56780
  297. --
  298. Training Step: 41440 | total loss: 0.19701
  299. | Adam | epoch: 070 | loss: 0.19701 - acc: 0.9166 | val_loss: 0.16600 - val_acc: 0.9467 -- iter: 56780/56780
  300. --
  301. Training Step: 42032 | total loss: 0.18202
  302. | Adam | epoch: 071 | loss: 0.18202 - acc: 0.9271 | val_loss: 0.22758 - val_acc: 0.9319 -- iter: 56780/56780
  303. --
  304. Training Step: 42624 | total loss: 0.16501
  305. | Adam | epoch: 072 | loss: 0.16501 - acc: 0.9318 | val_loss: 0.18459 - val_acc: 0.9459 -- iter: 56780/56780
  306. --
  307. Training Step: 43216 | total loss: 0.17883
  308. | Adam | epoch: 073 | loss: 0.17883 - acc: 0.9339 | val_loss: 0.17911 - val_acc: 0.9423 -- iter: 56780/56780
  309. --
  310. Training Step: 43808 | total loss: 0.17440
  311. | Adam | epoch: 074 | loss: 0.17440 - acc: 0.9314 | val_loss: 0.22812 - val_acc: 0.9371 -- iter: 56780/56780
  312. --
  313. Training Step: 44400 | total loss: 0.18914
  314. | Adam | epoch: 075 | loss: 0.18914 - acc: 0.9198 | val_loss: 0.20514 - val_acc: 0.9408 -- iter: 56780/56780
  315. --
  316. Training Step: 44992 | total loss: 0.15914
  317. | Adam | epoch: 076 | loss: 0.15914 - acc: 0.9265 | val_loss: 0.19328 - val_acc: 0.9437 -- iter: 56780/56780
  318. --
  319. Training Step: 45584 | total loss: 0.19725
  320. | Adam | epoch: 077 | loss: 0.19725 - acc: 0.9181 | val_loss: 0.20995 - val_acc: 0.9381 -- iter: 56780/56780
  321. --
  322. Training Step: 46176 | total loss: 0.19954
  323. | Adam | epoch: 078 | loss: 0.19954 - acc: 0.9219 | val_loss: 0.18029 - val_acc: 0.9436 -- iter: 56780/56780
  324. --
  325. Training Step: 46768 | total loss: 0.18051
  326. | Adam | epoch: 079 | loss: 0.18051 - acc: 0.9191 | val_loss: 0.19211 - val_acc: 0.9393 -- iter: 56780/56780
  327. --
  328. Training Step: 47360 | total loss: 0.16871
  329. | Adam | epoch: 080 | loss: 0.16871 - acc: 0.9331 | val_loss: 0.21379 - val_acc: 0.9420 -- iter: 56780/56780
  330. --
  331. Training Step: 47952 | total loss: 0.18850
  332. | Adam | epoch: 081 | loss: 0.18850 - acc: 0.9240 | val_loss: 0.18426 - val_acc: 0.9435 -- iter: 56780/56780
  333. --
  334. Training Step: 48544 | total loss: 0.17831
  335. | Adam | epoch: 082 | loss: 0.17831 - acc: 0.9250 | val_loss: 0.18285 - val_acc: 0.9437 -- iter: 56780/56780
  336. --
  337. Training Step: 49136 | total loss: 0.16153
  338. | Adam | epoch: 083 | loss: 0.16153 - acc: 0.9348 | val_loss: 0.20661 - val_acc: 0.9445 -- iter: 56780/56780
  339. --
  340. Training Step: 49728 | total loss: 0.15815
  341. | Adam | epoch: 084 | loss: 0.15815 - acc: 0.9361 | val_loss: 0.21485 - val_acc: 0.9393 -- iter: 56780/56780
  342. --
  343. Training Step: 50320 | total loss: 0.16564
  344. | Adam | epoch: 085 | loss: 0.16564 - acc: 0.9349 | val_loss: 0.21804 - val_acc: 0.9434 -- iter: 56780/56780
  345. --
  346. Training Step: 50912 | total loss: 0.14148
  347. | Adam | epoch: 086 | loss: 0.14148 - acc: 0.9408 | val_loss: 0.20338 - val_acc: 0.9443 -- iter: 56780/56780
  348. --
  349. Training Step: 51504 | total loss: 0.17472
  350. | Adam | epoch: 087 | loss: 0.17472 - acc: 0.9273 | val_loss: 0.17587 - val_acc: 0.9470 -- iter: 56780/56780
  351. --
  352. Training Step: 52096 | total loss: 0.14303
  353. | Adam | epoch: 088 | loss: 0.14303 - acc: 0.9406 | val_loss: 0.20464 - val_acc: 0.9436 -- iter: 56780/56780
  354. --
  355. Training Step: 52688 | total loss: 0.15808
  356. | Adam | epoch: 089 | loss: 0.15808 - acc: 0.9363 | val_loss: 0.20220 - val_acc: 0.9448 -- iter: 56780/56780
  357. --
  358. Training Step: 53280 | total loss: 0.17106
  359. | Adam | epoch: 090 | loss: 0.17106 - acc: 0.9327 | val_loss: 0.19828 - val_acc: 0.9459 -- iter: 56780/56780
  360. --
  361. Training Step: 53872 | total loss: 0.15785
  362. | Adam | epoch: 091 | loss: 0.15785 - acc: 0.9341 | val_loss: 0.20204 - val_acc: 0.9469 -- iter: 56780/56780
  363. --
  364. Training Step: 54464 | total loss: 0.18560
  365. | Adam | epoch: 092 | loss: 0.18560 - acc: 0.9213 | val_loss: 0.20947 - val_acc: 0.9412 -- iter: 56780/56780
  366. --
  367. Training Step: 55056 | total loss: 0.16409
  368. | Adam | epoch: 093 | loss: 0.16409 - acc: 0.9263 | val_loss: 0.20096 - val_acc: 0.9488 -- iter: 56780/56780
  369. --
  370. Training Step: 55648 | total loss: 0.16949
  371. | Adam | epoch: 094 | loss: 0.16949 - acc: 0.9328 | val_loss: 0.19215 - val_acc: 0.9455 -- iter: 56780/56780
  372. --
  373. Training Step: 56240 | total loss: 0.16486
  374. | Adam | epoch: 095 | loss: 0.16486 - acc: 0.9317 | val_loss: 0.20186 - val_acc: 0.9447 -- iter: 56780/56780
  375. --
  376. Training Step: 56832 | total loss: 0.18117
  377. | Adam | epoch: 096 | loss: 0.18117 - acc: 0.9338 | val_loss: 0.20586 - val_acc: 0.9405 -- iter: 56780/56780
  378. --
  379. Training Step: 57424 | total loss: 0.16308
  380. | Adam | epoch: 097 | loss: 0.16308 - acc: 0.9352 | val_loss: 0.20993 - val_acc: 0.9441 -- iter: 56780/56780
  381. --
  382. Training Step: 58016 | total loss: 0.15278
  383. | Adam | epoch: 098 | loss: 0.15278 - acc: 0.9409 | val_loss: 0.21945 - val_acc: 0.9405 -- iter: 56780/56780
  384. --
  385. Training Step: 58608 | total loss: 0.15651
  386. | Adam | epoch: 099 | loss: 0.15651 - acc: 0.9359 | val_loss: 0.25318 - val_acc: 0.9359 -- iter: 56780/56780
  387. --
  388. Training Step: 59200 | total loss: 0.17185
  389. | Adam | epoch: 100 | loss: 0.17185 - acc: 0.9235 | val_loss: 0.21731 - val_acc: 0.9457 -- iter: 56780/56780
  390. --
  391. Network trained and saved as bird-classifier.tfl!
  392. mona@pascal:~/computer_vision/python_playground$ ls -ltr
  393. total 8370824
  394. -rw-rw-r-- 1 mona mona 153361 Sep 13 2015 cat.png
  395. -rw-rw-r-- 1 mona mona 48077 Jun 2 2016 nice.jpg
  396. -rw-rw-r-- 1 mona mona 5483602303 Jun 12 2016 full_dataset.pkl
  397. -rw-rw-r-- 1 mona mona 450189480 Jun 14 2016 data.zip
  398. -rw-rw-r-- 1 mona mona 316 Feb 10 17:54 psutil_example.py
  399. -rw-rw-r-- 1 mona mona 686 Feb 13 12:04 image_pyramid.py
  400. -rw-rw-r-- 1 mona mona 1191 Feb 13 12:50 pyramid.py
  401. drwxr-xr-x 2 mona mona 4096 Feb 14 22:30 MNIST_data
  402. -rw-rw-r-- 1 mona mona 11794 Feb 14 22:40 Untitled.ipynb
  403. -rw-rw-r-- 1 mona mona 2086 Feb 14 22:42 r_u_a_bird.py
  404. -rw-rw-r-- 1 mona mona 2724 Feb 14 22:57 bird_classifier.py
  405. -rw-rw-r-- 1 mona mona 2109 Feb 15 15:08 infer.py
  406. drwxrwxr-x 2 mona mona 4096 Feb 15 15:08 test_images
  407. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:18 bird-classifier.tfl.ckpt-592.data-00000-of-00001
  408. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:18 bird-classifier.tfl.ckpt-592.index
  409. -rw-rw-r-- 1 mona mona 223405 Feb 15 19:18 bird-classifier.tfl.ckpt-592.meta
  410. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:19 bird-classifier.tfl.ckpt-1184.data-00000-of-00001
  411. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:19 bird-classifier.tfl.ckpt-1184.index
  412. -rw-rw-r-- 1 mona mona 223873 Feb 15 19:19 bird-classifier.tfl.ckpt-1184.meta
  413. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:19 bird-classifier.tfl.ckpt-1776.data-00000-of-00001
  414. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:19 bird-classifier.tfl.ckpt-1776.index
  415. -rw-rw-r-- 1 mona mona 224344 Feb 15 19:19 bird-classifier.tfl.ckpt-1776.meta
  416. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:20 bird-classifier.tfl.ckpt-2368.data-00000-of-00001
  417. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:20 bird-classifier.tfl.ckpt-2368.index
  418. -rw-rw-r-- 1 mona mona 224818 Feb 15 19:20 bird-classifier.tfl.ckpt-2368.meta
  419. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:21 bird-classifier.tfl.ckpt-2960.data-00000-of-00001
  420. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:21 bird-classifier.tfl.ckpt-2960.index
  421. -rw-rw-r-- 1 mona mona 225292 Feb 15 19:21 bird-classifier.tfl.ckpt-2960.meta
  422. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:22 bird-classifier.tfl.ckpt-3552.data-00000-of-00001
  423. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:22 bird-classifier.tfl.ckpt-3552.index
  424. -rw-rw-r-- 1 mona mona 225766 Feb 15 19:22 bird-classifier.tfl.ckpt-3552.meta
  425. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:22 bird-classifier.tfl.ckpt-4144.data-00000-of-00001
  426. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:22 bird-classifier.tfl.ckpt-4144.index
  427. -rw-rw-r-- 1 mona mona 226240 Feb 15 19:22 bird-classifier.tfl.ckpt-4144.meta
  428. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:23 bird-classifier.tfl.ckpt-4736.data-00000-of-00001
  429. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:23 bird-classifier.tfl.ckpt-4736.index
  430. -rw-rw-r-- 1 mona mona 226714 Feb 15 19:23 bird-classifier.tfl.ckpt-4736.meta
  431. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:24 bird-classifier.tfl.ckpt-5328.data-00000-of-00001
  432. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:24 bird-classifier.tfl.ckpt-5328.index
  433. -rw-rw-r-- 1 mona mona 227188 Feb 15 19:24 bird-classifier.tfl.ckpt-5328.meta
  434. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:25 bird-classifier.tfl.ckpt-5920.data-00000-of-00001
  435. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:25 bird-classifier.tfl.ckpt-5920.index
  436. -rw-rw-r-- 1 mona mona 227662 Feb 15 19:25 bird-classifier.tfl.ckpt-5920.meta
  437. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:25 bird-classifier.tfl.ckpt-6512.index
  438. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:25 bird-classifier.tfl.ckpt-6512.data-00000-of-00001
  439. -rw-rw-r-- 1 mona mona 228136 Feb 15 19:26 bird-classifier.tfl.ckpt-6512.meta
  440. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:26 bird-classifier.tfl.ckpt-7104.data-00000-of-00001
  441. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:26 bird-classifier.tfl.ckpt-7104.index
  442. -rw-rw-r-- 1 mona mona 228610 Feb 15 19:26 bird-classifier.tfl.ckpt-7104.meta
  443. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:27 bird-classifier.tfl.ckpt-7696.data-00000-of-00001
  444. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:27 bird-classifier.tfl.ckpt-7696.index
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  446. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:28 bird-classifier.tfl.ckpt-8288.data-00000-of-00001
  447. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:28 bird-classifier.tfl.ckpt-8288.index
  448. -rw-rw-r-- 1 mona mona 229558 Feb 15 19:28 bird-classifier.tfl.ckpt-8288.meta
  449. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:28 bird-classifier.tfl.ckpt-8880.data-00000-of-00001
  450. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:28 bird-classifier.tfl.ckpt-8880.index
  451. -rw-rw-r-- 1 mona mona 230032 Feb 15 19:28 bird-classifier.tfl.ckpt-8880.meta
  452. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:29 bird-classifier.tfl.ckpt-9472.data-00000-of-00001
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  455. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:30 bird-classifier.tfl.ckpt-10064.data-00000-of-00001
  456. -rw-rw-r-- 1 mona mona 1667 Feb 15 19:30 bird-classifier.tfl.ckpt-10064.index
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  459. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:31 bird-classifier.tfl.ckpt-10656.data-00000-of-00001
  460. -rw-rw-r-- 1 mona mona 231454 Feb 15 19:31 bird-classifier.tfl.ckpt-10656.meta
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  464. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:32 bird-classifier.tfl.ckpt-11840.data-00000-of-00001
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  467. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:33 bird-classifier.tfl.ckpt-12432.data-00000-of-00001
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  473. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:34 bird-classifier.tfl.ckpt-13616.data-00000-of-00001
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  485. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:37 bird-classifier.tfl.ckpt-15984.data-00000-of-00001
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  512. -rw-rw-r-- 1 mona mona 25860184 Feb 15 19:44 bird-classifier.tfl.ckpt-21312.data-00000-of-00001
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