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  1. C:\ProgramData\Anaconda2\envs\tensorflow\python.exe "C:/Users/Francesco Benintende/Desktop/mercedes_benz-master/models/model_1/model_1.py"
  2. Training:
  3. (4209, 378)
  4. --------------------------------------------------
  5. Test:
  6. (4209, 377)
  7.  
  8.  
  9. Training Data
  10.    ID       y  X0 X1  X2 X3 X4 X5 X6 X8  ...   X375  X376  X377  X378  X379  \
  11. 0   0  130.81   k  v  at  a  d  u  j  o  ...      0     0     1     0     0  
  12. 1   6   88.53   k  t  av  e  d  y  l  o  ...      1     0     0     0     0  
  13. 2   7   76.26  az  w   n  c  d  x  j  x  ...      0     0     0     0     0  
  14. 3   9   80.62  az  t   n  f  d  x  l  e  ...      0     0     0     0     0  
  15. 4  13   78.02  az  v   n  f  d  h  d  n  ...      0     0     0     0     0  
  16.  
  17.    X380  X382  X383  X384  X385  
  18. 0     0     0     0     0     0  
  19. 1     0     0     0     0     0  
  20. 2     0     1     0     0     0  
  21. 3     0     0     0     0     0  
  22. 4     0     0     0     0     0  
  23.  
  24. [5 rows x 378 columns]
  25. --------------------------------------------------
  26. Test Data
  27.    ID  X0 X1  X2 X3 X4 X5 X6 X8  X10  ...   X375  X376  X377  X378  X379  \
  28. 0   1  az  v   n  f  d  t  a  w    0  ...      0     0     0     1     0  
  29. 1   2   t  b  ai  a  d  b  g  y    0  ...      0     0     1     0     0  
  30. 2   3  az  v  as  f  d  a  j  j    0  ...      0     0     0     1     0  
  31. 3   4  az  l   n  f  d  z  l  n    0  ...      0     0     0     1     0  
  32. 4   5   w  s  as  c  d  y  i  m    0  ...      1     0     0     0     0  
  33.  
  34.    X380  X382  X383  X384  X385  
  35. 0     0     0     0     0     0  
  36. 1     0     0     0     0     0  
  37. 2     0     0     0     0     0  
  38. 3     0     0     0     0     0  
  39. 4     0     0     0     0     0  
  40.  
  41. [5 rows x 377 columns]
  42. --------------------------------------------------
  43.  
  44.  
  45. Training Data Types:
  46. ID        int64
  47. y       float64
  48. X0       object
  49. X1       object
  50. X2       object
  51. X3       object
  52. X4       object
  53. X5       object
  54. X6       object
  55. X8       object
  56. X10       int64
  57. X11       int64
  58. X12       int64
  59. X13       int64
  60. X14       int64
  61. X15       int64
  62. X16       int64
  63. X17       int64
  64. X18       int64
  65. X19       int64
  66. X20       int64
  67. X21       int64
  68. X22       int64
  69. X23       int64
  70. X24       int64
  71. X26       int64
  72. X27       int64
  73. X28       int64
  74. X29       int64
  75. X30       int64
  76.          ...  
  77. X355      int64
  78. X356      int64
  79. X357      int64
  80. X358      int64
  81. X359      int64
  82. X360      int64
  83. X361      int64
  84. X362      int64
  85. X363      int64
  86. X364      int64
  87. X365      int64
  88. X366      int64
  89. X367      int64
  90. X368      int64
  91. X369      int64
  92. X370      int64
  93. X371      int64
  94. X372      int64
  95. X373      int64
  96. X374      int64
  97. X375      int64
  98. X376      int64
  99. X377      int64
  100. X378      int64
  101. X379      int64
  102. X380      int64
  103. X382      int64
  104. X383      int64
  105. X384      int64
  106. X385      int64
  107. Length: 378, dtype: object
  108. --------------------------------------------------
  109. Test Data Types:
  110. ID        int64
  111. y       float64
  112. X0       object
  113. X1       object
  114. X2       object
  115. X3       object
  116. X4       object
  117. X5       object
  118. X6       object
  119. X8       object
  120. X10       int64
  121. X11       int64
  122. X12       int64
  123. X13       int64
  124. X14       int64
  125. X15       int64
  126. X16       int64
  127. X17       int64
  128. X18       int64
  129. X19       int64
  130. X20       int64
  131. X21       int64
  132. X22       int64
  133. X23       int64
  134. X24       int64
  135. X26       int64
  136. X27       int64
  137. X28       int64
  138. X29       int64
  139. X30       int64
  140.          ...  
  141. X355      int64
  142. X356      int64
  143. X357      int64
  144. X358      int64
  145. X359      int64
  146. X360      int64
  147. X361      int64
  148. X362      int64
  149. X363      int64
  150. X364      int64
  151. X365      int64
  152. X366      int64
  153. X367      int64
  154. X368      int64
  155. X369      int64
  156. X370      int64
  157. X371      int64
  158. X372      int64
  159. X373      int64
  160. X374      int64
  161. X375      int64
  162. X376      int64
  163. X377      int64
  164. X378      int64
  165. X379      int64
  166. X380      int64
  167. X382      int64
  168. X383      int64
  169. X384      int64
  170. X385      int64
  171. Length: 378, dtype: object
  172. --------------------------------------------------
  173.  
  174.  
  175. Training Data:
  176. Columns containing the unique values :  [0]
  177. ['X11', 'X93', 'X107', 'X233', 'X235', 'X268', 'X289', 'X290', 'X293', 'X297', 'X330', 'X347']
  178. --------------------------------------------------
  179. Columns containing the unique values :  [0, 1]
  180. ['X10', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18', 'X19', 'X20', 'X21', 'X22', 'X23', 'X24', 'X26', 'X27', 'X28', 'X29', 'X30', 'X31', 'X32', 'X33', 'X34', 'X35', 'X36', 'X37', 'X38', 'X39', 'X40', 'X41', 'X42', 'X43', 'X44', 'X45', 'X46', 'X47', 'X48', 'X49', 'X50', 'X51', 'X52', 'X53', 'X54', 'X55', 'X56', 'X57', 'X58', 'X59', 'X60', 'X61', 'X62', 'X63', 'X64', 'X65', 'X66', 'X67', 'X68', 'X69', 'X70', 'X71', 'X73', 'X74', 'X75', 'X76', 'X77', 'X78', 'X79', 'X80', 'X81', 'X82', 'X83', 'X84', 'X85', 'X86', 'X87', 'X88', 'X89', 'X90', 'X91', 'X92', 'X94', 'X95', 'X96', 'X97', 'X98', 'X99', 'X100', 'X101', 'X102', 'X103', 'X104', 'X105', 'X106', 'X108', 'X109', 'X110', 'X111', 'X112', 'X113', 'X114', 'X115', 'X116', 'X117', 'X118', 'X119', 'X120', 'X122', 'X123', 'X124', 'X125', 'X126', 'X127', 'X128', 'X129', 'X130', 'X131', 'X132', 'X133', 'X134', 'X135', 'X136', 'X137', 'X138', 'X139', 'X140', 'X141', 'X142', 'X143', 'X144', 'X145', 'X146', 'X147', 'X148', 'X150', 'X151', 'X152', 'X153', 'X154', 'X155', 'X156', 'X157', 'X158', 'X159', 'X160', 'X161', 'X162', 'X163', 'X164', 'X165', 'X166', 'X167', 'X168', 'X169', 'X170', 'X171', 'X172', 'X173', 'X174', 'X175', 'X176', 'X177', 'X178', 'X179', 'X180', 'X181', 'X182', 'X183', 'X184', 'X185', 'X186', 'X187', 'X189', 'X190', 'X191', 'X192', 'X194', 'X195', 'X196', 'X197', 'X198', 'X199', 'X200', 'X201', 'X202', 'X203', 'X204', 'X205', 'X206', 'X207', 'X208', 'X209', 'X210', 'X211', 'X212', 'X213', 'X214', 'X215', 'X216', 'X217', 'X218', 'X219', 'X220', 'X221', 'X222', 'X223', 'X224', 'X225', 'X226', 'X227', 'X228', 'X229', 'X230', 'X231', 'X232', 'X234', 'X236', 'X237', 'X238', 'X239', 'X240', 'X241', 'X242', 'X243', 'X244', 'X245', 'X246', 'X247', 'X248', 'X249', 'X250', 'X251', 'X252', 'X253', 'X254', 'X255', 'X256', 'X257', 'X258', 'X259', 'X260', 'X261', 'X262', 'X263', 'X264', 'X265', 'X266', 'X267', 'X269', 'X270', 'X271', 'X272', 'X273', 'X274', 'X275', 'X276', 'X277', 'X278', 'X279', 'X280', 'X281', 'X282', 'X283', 'X284', 'X285', 'X286', 'X287', 'X288', 'X291', 'X292', 'X294', 'X295', 'X296', 'X298', 'X299', 'X300', 'X301', 'X302', 'X304', 'X305', 'X306', 'X307', 'X308', 'X309', 'X310', 'X311', 'X312', 'X313', 'X314', 'X315', 'X316', 'X317', 'X318', 'X319', 'X320', 'X321', 'X322', 'X323', 'X324', 'X325', 'X326', 'X327', 'X328', 'X329', 'X331', 'X332', 'X333', 'X334', 'X335', 'X336', 'X337', 'X338', 'X339', 'X340', 'X341', 'X342', 'X343', 'X344', 'X345', 'X346', 'X348', 'X349', 'X350', 'X351', 'X352', 'X353', 'X354', 'X355', 'X356', 'X357', 'X358', 'X359', 'X360', 'X361', 'X362', 'X363', 'X364', 'X365', 'X366', 'X367', 'X368', 'X369', 'X370', 'X371', 'X372', 'X373', 'X374', 'X375', 'X376', 'X377', 'X378', 'X379', 'X380', 'X382', 'X383', 'X384', 'X385']
  181. --------------------------------------------------
  182.  
  183.  
  184. Test Data:
  185. Columns containing the unique values :  [0]
  186. ['X257', 'X258', 'X295', 'X296', 'X369']
  187. --------------------------------------------------
  188. Columns containing the unique values :  [0, 1]
  189. ['X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18', 'X19', 'X20', 'X21', 'X22', 'X23', 'X24', 'X26', 'X27', 'X28', 'X29', 'X30', 'X31', 'X32', 'X33', 'X34', 'X35', 'X36', 'X37', 'X38', 'X39', 'X40', 'X41', 'X42', 'X43', 'X44', 'X45', 'X46', 'X47', 'X48', 'X49', 'X50', 'X51', 'X52', 'X53', 'X54', 'X55', 'X56', 'X57', 'X58', 'X59', 'X60', 'X61', 'X62', 'X63', 'X64', 'X65', 'X66', 'X67', 'X68', 'X69', 'X70', 'X71', 'X73', 'X74', 'X75', 'X76', 'X77', 'X78', 'X79', 'X80', 'X81', 'X82', 'X83', 'X84', 'X85', 'X86', 'X87', 'X88', 'X89', 'X90', 'X91', 'X92', 'X93', 'X94', 'X95', 'X96', 'X97', 'X98', 'X99', 'X100', 'X101', 'X102', 'X103', 'X104', 'X105', 'X106', 'X107', 'X108', 'X109', 'X110', 'X111', 'X112', 'X113', 'X114', 'X115', 'X116', 'X117', 'X118', 'X119', 'X120', 'X122', 'X123', 'X124', 'X125', 'X126', 'X127', 'X128', 'X129', 'X130', 'X131', 'X132', 'X133', 'X134', 'X135', 'X136', 'X137', 'X138', 'X139', 'X140', 'X141', 'X142', 'X143', 'X144', 'X145', 'X146', 'X147', 'X148', 'X150', 'X151', 'X152', 'X153', 'X154', 'X155', 'X156', 'X157', 'X158', 'X159', 'X160', 'X161', 'X162', 'X163', 'X164', 'X165', 'X166', 'X167', 'X168', 'X169', 'X170', 'X171', 'X172', 'X173', 'X174', 'X175', 'X176', 'X177', 'X178', 'X179', 'X180', 'X181', 'X182', 'X183', 'X184', 'X185', 'X186', 'X187', 'X189', 'X190', 'X191', 'X192', 'X194', 'X195', 'X196', 'X197', 'X198', 'X199', 'X200', 'X201', 'X202', 'X203', 'X204', 'X205', 'X206', 'X207', 'X208', 'X209', 'X210', 'X211', 'X212', 'X213', 'X214', 'X215', 'X216', 'X217', 'X218', 'X219', 'X220', 'X221', 'X222', 'X223', 'X224', 'X225', 'X226', 'X227', 'X228', 'X229', 'X230', 'X231', 'X232', 'X233', 'X234', 'X235', 'X236', 'X237', 'X238', 'X239', 'X240', 'X241', 'X242', 'X243', 'X244', 'X245', 'X246', 'X247', 'X248', 'X249', 'X250', 'X251', 'X252', 'X253', 'X254', 'X255', 'X256', 'X259', 'X260', 'X261', 'X262', 'X263', 'X264', 'X265', 'X266', 'X267', 'X268', 'X269', 'X270', 'X271', 'X272', 'X273', 'X274', 'X275', 'X276', 'X277', 'X278', 'X279', 'X280', 'X281', 'X282', 'X283', 'X284', 'X285', 'X286', 'X287', 'X288', 'X289', 'X290', 'X291', 'X292', 'X293', 'X294', 'X297', 'X298', 'X299', 'X300', 'X301', 'X302', 'X304', 'X305', 'X306', 'X307', 'X308', 'X309', 'X310', 'X311', 'X312', 'X313', 'X314', 'X315', 'X316', 'X317', 'X318', 'X319', 'X320', 'X321', 'X322', 'X323', 'X324', 'X325', 'X326', 'X327', 'X328', 'X329', 'X330', 'X331', 'X332', 'X333', 'X334', 'X335', 'X336', 'X337', 'X338', 'X339', 'X340', 'X341', 'X342', 'X343', 'X344', 'X345', 'X346', 'X347', 'X348', 'X349', 'X350', 'X351', 'X352', 'X353', 'X354', 'X355', 'X356', 'X357', 'X358', 'X359', 'X360', 'X361', 'X362', 'X363', 'X364', 'X365', 'X366', 'X367', 'X368', 'X370', 'X371', 'X372', 'X373', 'X374', 'X375', 'X376', 'X377', 'X378', 'X379', 'X380', 'X382', 'X383', 'X384', 'X385']
  190. --------------------------------------------------
  191.  
  192.  
  193.   Column Type  Count
  194. 0       int64    368
  195. 1      object      8
  196.  
  197.  
  198. Dataframes are verified, everything should be fine
  199. Dataframes are verified, everything should be fine
  200. Dataframes are verified, everything should be fine
  201.    X10  X11  X12  X13  X14  X15  X16  X17  X18  X19  ...   X8_q  X8_w  X8_g  \
  202. 0    0    0    0    1    0    0    0    0    1    0  ...      0     0     0  
  203. 1    0    0    0    0    0    0    0    0    1    0  ...      0     0     0  
  204. 2    0    0    0    0    0    0    0    1    0    0  ...      0     0     0  
  205. 3    0    0    0    0    0    0    0    0    0    0  ...      0     0     0  
  206. 4    0    0    0    0    0    0    0    0    0    0  ...      0     0     0  
  207.  
  208.    X8_y  X8_l  X8_f  X8_u  X8_r  X8_t  X8_c  
  209. 0     0     0     0     0     0     0     0  
  210. 1     0     0     0     0     0     0     0  
  211. 2     0     0     0     0     0     0     0  
  212. 3     0     0     0     0     0     0     0  
  213. 4     0     0     0     0     0     0     0  
  214.  
  215. [5 rows x 579 columns]
  216.         y
  217. 0  130.81
  218. 1   88.53
  219. 2   76.26
  220. 3   80.62
  221. 4   78.02
  222.    X10  X11  X12  X13  X14  X15  X16  X17  X18  X19  ...   X8_q  X8_w  X8_g  \
  223. 0    0    0    0    0    0    0    0    0    0    0  ...      0     1     0  
  224. 1    0    0    0    0    0    0    0    0    0    1  ...      0     0     0  
  225. 2    0    0    0    0    1    0    0    0    0    0  ...      0     0     0  
  226. 3    0    0    0    0    0    0    0    0    0    0  ...      0     0     0  
  227. 4    0    0    0    0    1    0    0    0    0    0  ...      0     0     0  
  228.  
  229.    X8_y  X8_l  X8_f  X8_u  X8_r  X8_t  X8_c  
  230. 0     0     0     0     0     0     0     0  
  231. 1     1     0     0     0     0     0     0  
  232. 2     0     0     0     0     0     0     0  
  233. 3     0     0     0     0     0     0     0  
  234. 4     0     0     0     0     0     0     0  
  235.  
  236. [5 rows x 579 columns]
  237. (3500, 579)
  238. (3500,)
  239. (708, 579)
  240. (708,)
  241. 2017-06-02 20:41:26.570978: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
  242. 2017-06-02 20:41:26.571214: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
  243. 2017-06-02 20:41:26.571425: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
  244. 2017-06-02 20:41:26.571733: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
  245. 2017-06-02 20:41:26.571960: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
  246. 2017-06-02 20:41:26.572196: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
  247. 2017-06-02 20:41:26.572424: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
  248. 2017-06-02 20:41:26.572693: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
  249. 2017-06-02 20:41:27.003091: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:887] Found device 0 with properties:
  250. name: GeForce GTX 1060
  251. major: 6 minor: 1 memoryClockRate (GHz) 1.6705
  252. pciBusID 0000:01:00.0
  253. Total memory: 6.00GiB
  254. Free memory: 5.01GiB
  255. 2017-06-02 20:41:27.003329: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA: 0
  256. 2017-06-02 20:41:27.003447: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0:   Y
  257. 2017-06-02 20:41:27.003777: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0)
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  1996. Average test loss in past 100 iterations 97711.7
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  1999. Average test loss in past 100 iterations 97387.9
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  2011. Average test loss in past 100 iterations 93888.0
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  2014. Average test loss in past 100 iterations 96326.3
  2015. --------------------------------------------------
  2016. Average train loss in past 100 iterations 639.586
  2017. Average test loss in past 100 iterations 96753.6
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  2019. Average train loss in past 100 iterations 545.081
  2020. Average test loss in past 100 iterations 93828.3
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  2047. Average test loss in past 100 iterations 93880.8
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  2050. Average test loss in past 100 iterations 96940.1
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  2056. Average test loss in past 100 iterations 92961.1
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  2062. Average test loss in past 100 iterations 92691.8
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  2083. Average test loss in past 100 iterations 93019.3
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  2086. Average test loss in past 100 iterations 91201.7
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  2089. Average test loss in past 100 iterations 91657.2
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  2104. Average test loss in past 100 iterations 91288.6
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  2116. Average test loss in past 100 iterations 94519.2
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  2119. Average test loss in past 100 iterations 92783.6
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  2122. Average test loss in past 100 iterations 90985.5
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  2125. Average test loss in past 100 iterations 92676.3
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  2128. Average test loss in past 100 iterations 91921.3
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  2131. Average test loss in past 100 iterations 93948.2
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  2137. Average test loss in past 100 iterations 92745.3
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  2488. Average test loss in past 100 iterations 79154.1
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  2575. Average test loss in past 100 iterations 78089.3
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  2968. Average test loss in past 100 iterations 72213.2
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  2991. Average train loss in past 100 iterations 443.996
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  3001. Average test loss in past 100 iterations 72563.2
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  3010. Average test loss in past 100 iterations 72021.8
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  3013. Average test loss in past 100 iterations 72119.1
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  3019. Average test loss in past 100 iterations 71255.2
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  3022. Average test loss in past 100 iterations 71723.6
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  3028. Average test loss in past 100 iterations 72299.8
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  3034. Average test loss in past 100 iterations 71393.4
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  3040. Average test loss in past 100 iterations 71112.9
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  3046. Average test loss in past 100 iterations 70878.0
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  3052. Average test loss in past 100 iterations 71028.5
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  3055. Average test loss in past 100 iterations 71367.6
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  3058. Average test loss in past 100 iterations 70878.1
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  3061. Average test loss in past 100 iterations 71163.1
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  3064. Average test loss in past 100 iterations 71445.0
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  3067. Average test loss in past 100 iterations 71112.7
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  3070. Average test loss in past 100 iterations 70847.2
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  3073. Average test loss in past 100 iterations 71502.1
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  3076. Average test loss in past 100 iterations 71378.1
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  3079. Average test loss in past 100 iterations 72019.4
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  3082. Average test loss in past 100 iterations 71156.3
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  3085. Average test loss in past 100 iterations 71251.7
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  3088. Average test loss in past 100 iterations 71476.2
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  3091. Average test loss in past 100 iterations 71667.7
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  3094. Average test loss in past 100 iterations 71668.5
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  3097. Average test loss in past 100 iterations 71293.7
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  3100. Average test loss in past 100 iterations 71333.8
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  3103. Average test loss in past 100 iterations 71280.2
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  3106. Average test loss in past 100 iterations 71090.4
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  3109. Average test loss in past 100 iterations 70870.1
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  3112. Average test loss in past 100 iterations 71484.2
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  3115. Average test loss in past 100 iterations 71496.1
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  3118. Average test loss in past 100 iterations 71681.4
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  3121. Average test loss in past 100 iterations 71216.2
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  3124. Average test loss in past 100 iterations 71634.3
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  3127. Average test loss in past 100 iterations 70362.1
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  3130. Average test loss in past 100 iterations 71388.9
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  3133. Average test loss in past 100 iterations 71418.1
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  3136. Average test loss in past 100 iterations 71142.5
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  3139. Average test loss in past 100 iterations 71270.1
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  3142. Average test loss in past 100 iterations 71138.8
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  3145. Average test loss in past 100 iterations 71212.3
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  3148. Average test loss in past 100 iterations 71469.4
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  3151. Average test loss in past 100 iterations 70692.7
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  3154. Average test loss in past 100 iterations 70844.6
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  3157. Average test loss in past 100 iterations 71176.1
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  3160. Average test loss in past 100 iterations 70559.7
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  3163. Average test loss in past 100 iterations 70217.3
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  3166. Average test loss in past 100 iterations 70655.9
  3167. --------------------------------------------------
  3168. Average train loss in past 100 iterations 404.216
  3169. Average test loss in past 100 iterations 70222.2
  3170. --------------------------------------------------
  3171. Average train loss in past 100 iterations 508.36
  3172. Average test loss in past 100 iterations 70458.9
  3173. --------------------------------------------------
  3174. Average train loss in past 100 iterations 502.617
  3175. Average test loss in past 100 iterations 70601.7
  3176. --------------------------------------------------
  3177. Average train loss in past 100 iterations 527.64
  3178. Average test loss in past 100 iterations 70335.2
  3179. --------------------------------------------------
  3180. Average train loss in past 100 iterations 442.897
  3181. Average test loss in past 100 iterations 70363.6
  3182. --------------------------------------------------
  3183. Average train loss in past 100 iterations 511.392
  3184. Average test loss in past 100 iterations 69937.3
  3185. --------------------------------------------------
  3186. Average train loss in past 100 iterations 521.261
  3187. Average test loss in past 100 iterations 70527.7
  3188. --------------------------------------------------
  3189. Average train loss in past 100 iterations 406.742
  3190. Average test loss in past 100 iterations 69894.3
  3191. --------------------------------------------------
  3192. Average train loss in past 100 iterations 492.719
  3193. Average test loss in past 100 iterations 70067.6
  3194. --------------------------------------------------
  3195. Average train loss in past 100 iterations 501.543
  3196. Average test loss in past 100 iterations 70348.8
  3197. --------------------------------------------------
  3198. Average train loss in past 100 iterations 486.628
  3199. Average test loss in past 100 iterations 70399.0
  3200. --------------------------------------------------
  3201. Average train loss in past 100 iterations 452.403
  3202. Average test loss in past 100 iterations 70388.4
  3203. --------------------------------------------------
  3204. Average train loss in past 100 iterations 500.375
  3205. Average test loss in past 100 iterations 70216.3
  3206. --------------------------------------------------
  3207. Average train loss in past 100 iterations 519.27
  3208. Average test loss in past 100 iterations 70552.3
  3209. --------------------------------------------------
  3210. Average train loss in past 100 iterations 426.468
  3211. Average test loss in past 100 iterations 70009.0
  3212. --------------------------------------------------
  3213. Average train loss in past 100 iterations 503.448
  3214. Average test loss in past 100 iterations 70579.6
  3215. --------------------------------------------------
  3216. Average train loss in past 100 iterations 514.195
  3217. Average test loss in past 100 iterations 70264.7
  3218. --------------------------------------------------
  3219. Average train loss in past 100 iterations 493.735
  3220. Average test loss in past 100 iterations 70344.7
  3221. --------------------------------------------------
  3222. Average train loss in past 100 iterations 411.405
  3223. Average test loss in past 100 iterations 70379.0
  3224. --------------------------------------------------
  3225. Average train loss in past 100 iterations 517.399
  3226. Average test loss in past 100 iterations 70744.2
  3227. --------------------------------------------------
  3228. Average train loss in past 100 iterations 485.938
  3229. Average test loss in past 100 iterations 70164.5
  3230. --------------------------------------------------
  3231. Average train loss in past 100 iterations 418.656
  3232. Average test loss in past 100 iterations 70585.6
  3233. --------------------------------------------------
  3234. Average train loss in past 100 iterations 443.658
  3235. Average test loss in past 100 iterations 70440.4
  3236. --------------------------------------------------
  3237. Average train loss in past 100 iterations 492.063
  3238. Average test loss in past 100 iterations 70643.3
  3239. --------------------------------------------------
  3240. Average train loss in past 100 iterations 508.556
  3241. Average test loss in past 100 iterations 70205.0
  3242. --------------------------------------------------
  3243. Average train loss in past 100 iterations 446.202
  3244. Average test loss in past 100 iterations 69612.6
  3245. --------------------------------------------------
  3246. Average train loss in past 100 iterations 487.03
  3247. Average test loss in past 100 iterations 70000.8
  3248. --------------------------------------------------
  3249. Average train loss in past 100 iterations 471.416
  3250. Average test loss in past 100 iterations 70001.1
  3251. --------------------------------------------------
  3252. Average train loss in past 100 iterations 413.446
  3253. Average test loss in past 100 iterations 70502.5
  3254. --------------------------------------------------
  3255. Average train loss in past 100 iterations 496.718
  3256. Average test loss in past 100 iterations 70235.9
  3257. --------------------------------------------------
  3258. Traceback (most recent call last):
  3259. Optimization finished
  3260.   File "C:/Users/Francesco Benintende/Desktop/mercedes_benz-master/models/model_1/model_1.py", line 38, in <module>
  3261.     starter_learning_rate=0.001)
  3262.   File "C:\Users\Francesco Benintende\Desktop\mercedes_benz-master\models\abstractions.py", line 160, in train_and_eval
  3263.     r_sqr = r2_score(y_eval, self.infer(x_eval))
  3264. NameError: name 'r2_score' is not defined
  3265.  
  3266. Process finished with exit code 1
RAW Paste Data
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