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  1. Initialized
  2. ('Loss :', 9.2027139663696289)
  3. ('Batch_input :', array([9971, 9972, 9974, 9975, 9976, 9980, 9981, 9982, 9983, 9984, 9986,
  4. 9987, 9988, 9989, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998,
  5. 9999, 2, 9256, 1, 3, 72, 393, 33, 2133, 0, 146,
  6. 19, 6, 9207, 276, 407, 3, 2, 23, 1, 13, 141,
  7. 4, 1, 5465, 0, 3081, 1596, 96, 2, 7682, 1, 3,
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  9. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
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  11. [ 0., 0., 0., ..., 0., 0., 0.],
  12. ...,
  13. [ 0., 0., 0., ..., 0., 0., 0.],
  14. [ 0., 0., 0., ..., 0., 0., 0.],
  15. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  16. Average loss at step 0: 0.092027 learning rate: 1.000000
  17. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  18. [ 0., 0., 0., ..., 0., 0., 0.],
  19. [ 0., 0., 0., ..., 0., 0., 0.],
  20. ...,
  21. [ 0., 0., 0., ..., 0., 0., 0.],
  22. [ 0., 0., 0., ..., 0., 0., 0.],
  23. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  24. ('Predicted:', array([[-0.36508381, -0.25612 , -0.26035795, ..., -0.42688274,
  25. -0.4078168 , -0.36345699],
  26. [-0.46035308, -0.27282876, -0.34078932, ..., -0.50623679,
  27. -0.47014061, -0.43237451],
  28. [-0.14694197, -0.07506246, -0.10392818, ..., -0.1128526 ,
  29. -0.12404554, -0.13495158],
  30. ...,
  31. [-0.07286638, -0.04560997, -0.05932444, ..., -0.08352474,
  32. -0.07679331, -0.07829094],
  33. [-0.13576414, -0.07057529, -0.1017022 , ..., -0.11192483,
  34. -0.14713599, -0.11757012],
  35. [-0.05446544, -0.02738103, -0.03401792, ..., -0.05073205,
  36. -0.03746928, -0.05750648]], dtype=float32))
  37. ================================================================================
  38. [[ 0. 0. 0. ..., 0. 0. 0.]]
  39. 8605
  40. ('f', u'altman')
  41. ('as', u'altman')
  42. ('feed', array([8605]))
  43. ('Sentence :', u'altman rake years regatta memotec pierre <unk> nonexecutive as will <eos> ssangyong director nahb group the cluett rubens snack-food fromstein calloway and memotec a board years regatta publishing fields rake group group rake cluett ssangyong pierre calloway memotec gitano gold rubens as as director sim is publishing gitano punts join <unk> and a old punts years memotec a rake is guterman cluett ssangyong will berlitz nahb <eos> of group join <unk> board join and pierre consolidated board cluett dutch gold as ipo ssangyong guterman a kia will dutch and director centrust consolidated rudolph guterman guterman cluett years n.v. old board rubens ')
  44. ================================================================================
  45. ('Loss :', 496.78199882507323)
  46. ('Batch_input :', array([4115, 5, 14, 45, 55, 3, 72, 195, 1244, 220, 2,
  47. 0, 3150, 7426, 1, 13, 4052, 1, 496, 14, 6885, 0,
  48. 1, 22, 113, 2652, 8068, 5, 14, 2474, 5250, 10, 464,
  49. 52, 3004, 466, 1244, 15, 2, 1, 80, 0, 167, 4,
  50. 35, 2645, 1, 65, 10, 558, 6092, 3574, 1898, 666, 1,
  51. 7, 27, 1, 4241, 6036, 7, 3, 2, 366], dtype=int32))
  52. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  53. [ 0., 0., 0., ..., 0., 0., 0.],
  54. [ 0., 0., 0., ..., 0., 0., 0.],
  55. ...,
  56. [ 0., 0., 1., ..., 0., 0., 0.],
  57. [ 0., 0., 0., ..., 0., 0., 0.],
  58. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  59. Average loss at step 100: 4.967820 learning rate: 1.000000
  60. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  61. [ 0., 0., 0., ..., 0., 0., 0.],
  62. [ 0., 0., 0., ..., 0., 0., 0.],
  63. ...,
  64. [ 0., 0., 1., ..., 0., 0., 0.],
  65. [ 0., 0., 0., ..., 0., 0., 0.],
  66. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  67. ('Predicted:', array([[ 4.41551352e+00, 9.98007679e+00, 1.75690575e+01, ...,
  68. 6.83443546e+00, -2.30797195e+00, 1.73750782e+00],
  69. [ 1.26826172e+01, 5.96618652e-03, 1.18247871e+01, ...,
  70. -3.70885038e+00, -8.55356884e+00, -9.16959190e+00],
  71. [ 1.44652233e+01, 5.12977028e+00, 9.42045784e+00, ...,
  72. 1.39444172e+00, 1.95213389e+00, -4.00810099e+00],
  73. ...,
  74. [ 2.93052626e+00, 9.41266441e+00, 1.79130135e+01, ...,
  75. 4.24245834e+00, -1.46551771e+01, -3.35697136e+01],
  76. [ 2.48945675e+01, 2.32091904e+01, 2.47276134e+01, ...,
  77. -6.39845896e+00, -2.66628218e+00, -4.59843445e+00],
  78. [ 1.34414902e+01, 4.80197811e+00, 1.89214745e+01, ...,
  79. -5.91268682e+00, -8.80736637e+00, -6.49542713e+00]], dtype=float32))
  80. ================================================================================
  81. [[ 0. 0. 0. ..., 0. 0. 0.]]
  82. 3619
  83. ('f', u'officially')
  84. ('as', u'officially')
  85. ('feed', array([3619]))
  86. ('Sentence :', u'officially <unk> to <eos> filters ago cigarettes is that cigarette stopped to <eos> researchers <unk> to <eos> filters ago cigarettes asbestos the filters ago cigarettes asbestos the filters ago cigarettes is that cigarette up the <eos> researchers to <eos> researchers <unk> to <eos> filters ago cigarettes asbestos the filters ago cigarettes asbestos <eos> filters ago cigarettes asbestos the filters ago cigarettes is that cigarette up the <eos> researchers <unk> to <eos> researchers <unk> to <eos> filters ago cigarettes asbestos of percentage years the the the <eos> researchers <unk> to <eos> filters ago cigarettes asbestos the filters ago cigarettes asbestos the filters ')
  87. ================================================================================
  88. ('Loss :', 581.9651535148621)
  89. ('Batch_input :', array([ 39, 13, 31, 393, 1366, 2, 64, 275, 1921, 43, 72,
  90. 195, 157, 1442, 2395, 4, 3150, 718, 106, 5791, 1304, 2,
  91. 83, 13, 102, 3150, 7, 228, 189, 99, 2, 1400, 1,
  92. 1415, 0, 1244, 56, 4375, 0, 431, 46, 2647, 4, 106,
  93. 319, 16, 8421, 4, 0, 6092, 3574, 2, 64, 34, 102,
  94. 4213, 529, 16, 369, 2365, 26, 18, 645, 15], dtype=int32))
  95. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  96. [ 0., 0., 0., ..., 0., 0., 0.],
  97. [ 0., 0., 0., ..., 0., 0., 0.],
  98. ...,
  99. [ 0., 0., 0., ..., 0., 0., 0.],
  100. [ 0., 0., 0., ..., 0., 0., 0.],
  101. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  102. Average loss at step 200: 5.819652 learning rate: 1.000000
  103. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  104. [ 0., 0., 0., ..., 0., 0., 0.],
  105. [ 0., 0., 0., ..., 0., 0., 0.],
  106. ...,
  107. [ 0., 0., 0., ..., 0., 0., 0.],
  108. [ 0., 0., 0., ..., 0., 0., 0.],
  109. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  110. ('Predicted:', array([[ 1.11496143e+01, 2.52034187e+00, 1.96437721e+01, ...,
  111. -1.50473619e+00, -8.62816811e+00, 1.24034882e-02],
  112. [ 7.78047562e+00, 1.48387527e+00, 1.96783009e+01, ...,
  113. -7.75834751e+00, -2.06198444e+01, -2.16614780e+01],
  114. [ 2.49285393e+01, 9.25475502e+00, 1.58422909e+01, ...,
  115. -6.17387116e-01, -7.49549580e+00, -3.64058924e+00],
  116. ...,
  117. [ 1.52376080e+01, 2.91777039e+00, 5.62801323e+01, ...,
  118. 1.27006912e+01, -6.71812582e+00, -1.61099453e+01],
  119. [ 1.31439075e+01, 2.54877434e+01, 2.91178341e+01, ...,
  120. 4.45230913e+00, -4.46597481e+00, -7.17428303e+00],
  121. [ 2.69684029e+00, 1.76576977e+01, 2.29938049e+01, ...,
  122. -7.32700920e+00, -1.84341221e+01, 1.32137108e+00]], dtype=float32))
  123. ================================================================================
  124. [[ 0. 0. 0. ..., 0. 0. 0.]]
  125. 1146
  126. ('f', u'improve')
  127. ('as', u'improve')
  128. ('feed', array([1146]))
  129. ('Sentence :', u'improve were smokers about <eos> the to <unk> questionable researchers any to of smokers about <eos> the questionable <eos> the to <unk> questionable researchers now any any any asbestos on smokers to questionable to <unk> questionable researchers now any any any nor asbestos on the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> the questionable <eos> ')
  130. ================================================================================
  131. ('Loss :', 657.95355551540376)
  132. ('Batch_input :', array([ 15, 3150, 24, 263, 7, 253, 1451, 1351, 7, 423, 398,
  133. 11, 0, 6036, 7, 0, 266, 5278, 8, 3122, 22, 6,
  134. 769, 2155, 4, 1, 7, 3, 2, 20, 3, 5, 3,
  135. 3, 48, 6092, 3574, 22, 0, 6036, 46, 238, 0, 37,
  136. 15, 2, 211, 3, 945, 56, 1389, 1101, 22, 0, 5545,
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  138. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  139. [ 0., 0., 0., ..., 0., 0., 0.],
  140. [ 0., 0., 0., ..., 0., 0., 0.],
  141. ...,
  142. [ 1., 0., 0., ..., 0., 0., 0.],
  143. [ 0., 0., 0., ..., 0., 0., 0.],
  144. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  145. Average loss at step 300: 6.579536 learning rate: 1.000000
  146. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  147. [ 0., 0., 0., ..., 0., 0., 0.],
  148. [ 0., 0., 0., ..., 0., 0., 0.],
  149. ...,
  150. [ 1., 0., 0., ..., 0., 0., 0.],
  151. [ 0., 0., 0., ..., 0., 0., 0.],
  152. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  153. ('Predicted:', array([[ 27.79744148, 23.37840462, 38.46372986, ..., -1.67567897,
  154. 1.32131767, -5.94825935],
  155. [ 18.3799057 , 16.12502098, 22.62628365, ..., -1.73421931,
  156. -7.86558867, -4.07993984],
  157. [ 21.18688202, 19.58936119, 37.37857056, ..., 2.72022462,
  158. 4.95587444, -3.86315012],
  159. ...,
  160. [ 57.44161606, 5.75437927, -3.88923836, ..., 9.1703558 ,
  161. -3.17029953, -12.2054348 ],
  162. [ 25.20575714, 21.54052734, 17.81223869, ..., -0.76101571,
  163. -12.47051144, -25.800457 ],
  164. [ 20.22606277, 29.38560104, 28.21241188, ..., -3.76599765,
  165. -12.07288361, -8.28661823]], dtype=float32))
  166. ================================================================================
  167. [[ 0. 0. 0. ..., 0. 0. 0.]]
  168. 1663
  169. ('f', u'recovery')
  170. ('as', u'recovery')
  171. ('feed', array([1663]))
  172. ('Sentence :', u'recovery replaced N N N modest the from and 1950s <eos> schools billion paper from from and 1950s <eos> schools billion paper from from and 1950s <eos> whether said N N N national in used a researchers risk the from and 1950s <eos> whether said N N N national in used <eos> schools billion paper from from and 1950s <eos> whether said N N N national led said N N N modest with type and N harvard N N N harvard <eos> schools billion paper from from and 1950s <eos> whether said N N N national in used a researchers early ')
  173. ================================================================================
  174. ('Loss :', 715.17567856243249)
  175. ('Batch_input :', array([1244, 15, 2, 0, 1, 158, 13, 6, 4196, 1883, 211,
  176. 150, 4, 505, 56, 938, 1, 4464, 15, 978, 1, 2,
  177. 0, 760, 4, 5172, 1015, 2786, 211, 0, 431, 18, 0,
  178. 379, 1, 1713, 398, 2025, 1310, 5, 25, 0, 1590, 11,
  179. 106, 3150, 431, 4375, 7, 650, 6080, 672, 28, 15, 2,
  180. 0, 402, 41, 13, 1023, 17, 1, 82, 1], dtype=int32))
  181. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  182. [ 0., 0., 1., ..., 0., 0., 0.],
  183. [ 1., 0., 0., ..., 0., 0., 0.],
  184. ...,
  185. [ 0., 0., 0., ..., 0., 0., 0.],
  186. [ 0., 1., 0., ..., 0., 0., 0.],
  187. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  188. Average loss at step 400: 7.151757 learning rate: 1.000000
  189. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  190. [ 0., 0., 1., ..., 0., 0., 0.],
  191. [ 1., 0., 0., ..., 0., 0., 0.],
  192. ...,
  193. [ 0., 0., 0., ..., 0., 0., 0.],
  194. [ 0., 1., 0., ..., 0., 0., 0.],
  195. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  196. ('Predicted:', array([[ 21.06395531, 41.48188782, 14.76991463, ..., 0.90518486,
  197. -9.15206528, -18.00244331],
  198. [ 27.82922745, 31.63118172, 58.02881622, ..., 7.33755016,
  199. 12.11525059, -2.95855141],
  200. [ 66.02002716, 48.96756744, -5.2470417 , ..., -11.50299168,
  201. -2.58890057, -9.010849 ],
  202. ...,
  203. [ -3.29854012, 25.49062538, 47.43249512, ..., -2.6510427 ,
  204. -16.02886581, -19.52365685],
  205. [ 42.7742424 , 38.42037201, 23.18925476, ..., 5.25420237,
  206. -9.42901707, -7.64269066],
  207. [ 0.7444973 , 25.17520905, 47.63552094, ..., -2.6350472 ,
  208. -14.94020557, -17.693964 ]], dtype=float32))
  209. ================================================================================
  210. [[ 0. 0. 0. ..., 0. 0. 0.]]
  211. 7237
  212. ('f', u'abbie')
  213. ('as', u'abbie')
  214. ('feed', array([7237]))
  215. ('Sentence :', u'abbie among industrialized study deaths cancer said <unk> workers workers N of lung at including among rate <unk> the for said lung N of lung mass. paper said than <unk> workers deaths cancer N cancer said <unk> workers workers N of lung at including <unk> researchers among cancer recently workers he lung <unk> workers workers of lung N among at <unk> research factory any western whether sold deaths cancer said <unk> workers workers of cancer said N he workers he lung <unk> workers <unk> deaths cancer said <unk> workers workers <unk> workers workers <unk> workers workers <unk> workers workers N of ')
  216. ================================================================================
  217. ('Loss :', 839.59790327381404)
  218. ('Batch_input :', array([ 944, 4, 3150, 1, 551, 7, 90, 1647, 8, 61, 1636,
  219. 978, 1, 15, 2, 0, 53, 13, 54, 4, 0, 261,
  220. 6080, 1618, 10, 175, 32, 34, 6, 206, 739, 4, 3119,
  221. 11, 0, 5533, 1, 5394, 88, 19, 1, 10, 26, 6349,
  222. 19, 1, 214, 5, 1, 2239, 1, 6, 2035, 4, 1,
  223. 18, 0, 596, 4, 5042, 1488, 4, 3199, 2], dtype=int32))
  224. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  225. [ 0., 0., 0., ..., 0., 0., 0.],
  226. [ 0., 1., 0., ..., 0., 0., 0.],
  227. ...,
  228. [ 0., 0., 0., ..., 0., 0., 0.],
  229. [ 0., 0., 1., ..., 0., 0., 0.],
  230. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  231. Average loss at step 500: 8.395979 learning rate: 1.000000
  232. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  233. [ 0., 0., 0., ..., 0., 0., 0.],
  234. [ 0., 1., 0., ..., 0., 0., 0.],
  235. ...,
  236. [ 0., 0., 0., ..., 0., 0., 0.],
  237. [ 0., 0., 1., ..., 0., 0., 0.],
  238. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  239. ('Predicted:', array([[ 24.61105537, 40.75873184, 14.10590649, ..., -4.77243757,
  240. -8.44593239, -7.23684168],
  241. [ 50.75180054, 54.97891617, 18.49082565, ..., 2.8976028 ,
  242. -6.811059 , -4.75290489],
  243. [ 50.92717743, 73.37365723, -29.40761757, ..., 7.97057295,
  244. -21.74515915, -15.22813034],
  245. ...,
  246. [ 81.55072021, 60.01564026, 11.3656292 , ..., -3.01502323,
  247. -15.37229347, -6.71199465],
  248. [ 56.06694794, 64.74343872, -11.93397331, ..., -16.31550217,
  249. -31.67933273, -9.8679533 ],
  250. [ 70.32337952, 55.06775665, -3.79033089, ..., -5.36194277,
  251. -1.45014143, -4.41785622]], dtype=float32))
  252. ================================================================================
  253. [[ 0. 0. 0. ..., 0. 0. 0.]]
  254. 825
  255. ('f', u'wants')
  256. ('as', u'wants')
  257. ('feed', array([825]))
  258. ('Sentence :', u'wants is smooth the among have among have among a among among have among have among have among have among have among have among have among have among have among have among have among a among among have among have among have among a among among have among a among among a among among have among a among among have among have among have among have among have among a among among have among have among have among a among among have among have among have among a among among a among among a among among have among a among among ')
  259. ================================================================================
  260. ('Loss :', 933.77549325011478)
  261. ('Batch_input :', array([ 3, 511, 73, 1435, 1870, 4, 1, 3150, 33, 25, 9231,
  262. 2, 43, 3, 431, 18, 6, 2025, 10, 159, 398, 11,
  263. 0, 6092, 6036, 46, 4115, 5, 3150, 7, 0, 5278, 2,
  264. 874, 4, 0, 2025, 46, 782, 9673, 251, 0, 1, 24,
  265. 263, 2, 431, 3820, 380, 1, 1, 4, 0, 3021, 1681,
  266. 91, 6, 828, 1, 7070, 7, 3260, 8, 1], dtype=int32))
  267. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  268. [ 0., 0., 0., ..., 0., 0., 0.],
  269. [ 0., 0., 0., ..., 0., 0., 0.],
  270. ...,
  271. [ 0., 0., 0., ..., 0., 0., 0.],
  272. [ 0., 1., 0., ..., 0., 0., 0.],
  273. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  274. Average loss at step 600: 9.337755 learning rate: 1.000000
  275. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  276. [ 0., 0., 0., ..., 0., 0., 0.],
  277. [ 0., 0., 0., ..., 0., 0., 0.],
  278. ...,
  279. [ 0., 0., 0., ..., 0., 0., 0.],
  280. [ 0., 1., 0., ..., 0., 0., 0.],
  281. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  282. ('Predicted:', array([[ 10.18803215, 47.19699097, 19.30631447, ..., -4.61812544,
  283. -9.48687172, -8.65189266],
  284. [ 34.51325989, 52.13234329, 38.19073486, ..., 2.01813602,
  285. -3.91805673, -1.44879436],
  286. [ 56.49404144, 67.29128265, 46.57570267, ..., 4.87337399,
  287. -0.90795827, -0.1669569 ],
  288. ...,
  289. [ 34.7301178 , 46.05511093, 28.7182045 , ..., 0.60070062,
  290. -10.29673481, -5.18640995],
  291. [ 49.51641083, 63.66670227, 25.37597084, ..., -1.07206571,
  292. 8.09779453, -15.13547134],
  293. [ 43.36688232, 36.55143356, 39.08946228, ..., -2.59661937,
  294. -12.32478523, -13.29827309]], dtype=float32))
  295. ================================================================================
  296. [[ 0. 0. 0. ..., 0. 0. 0.]]
  297. 8326
  298. ('f', u'prevention')
  299. ('as', u'prevention')
  300. ('feed', array([8326]))
  301. ('Sentence :', u'prevention that by workers used the <unk> all the on a a a a a used the <unk> all at <unk> kent <eos> made imposed <eos> made imposed in were <unk> all the on a a a a a used the <unk> all at <unk> kent filters <eos> made imposed <eos> made imposed in were of all be <unk> kent <eos> asbestos <unk> all the on a a a a a used the of ban into <unk> of all N were of all N were of all N were of all N were of all N were of all N were ')
  302. ================================================================================
  303. ('Loss :', 1062.2104308281921)
  304. ('Batch_input :', array([ 83, 9, 102, 798, 10, 57, 4, 150, 431, 8, 586,
  305. 5351, 1, 4464, 15, 1, 1890, 232, 70, 4, 1222, 1063,
  306. 11, 1, 82, 1, 2, 29, 110, 34, 5, 4173, 10,
  307. 144, 1466, 453, 665, 3, 72, 195, 2, 14, 30, 102,
  308. 5596, 16, 228, 221, 534, 325, 2, 927, 16, 3730, 1359,
  309. 193, 608, 5, 2276, 1633, 1692, 10, 683, 586], dtype=int32))
  310. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  311. [ 0., 0., 0., ..., 0., 0., 0.],
  312. [ 0., 0., 0., ..., 0., 0., 0.],
  313. ...,
  314. [ 0., 0., 0., ..., 0., 0., 0.],
  315. [ 0., 0., 0., ..., 0., 0., 0.],
  316. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  317. Average loss at step 700: 10.622104 learning rate: 1.000000
  318. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  319. [ 0., 0., 0., ..., 0., 0., 0.],
  320. [ 0., 0., 0., ..., 0., 0., 0.],
  321. ...,
  322. [ 0., 0., 0., ..., 0., 0., 0.],
  323. [ 0., 0., 0., ..., 0., 0., 0.],
  324. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  325. ('Predicted:', array([[ 26.62189865, 74.89472961, 77.30410767, ..., 1.25362027,
  326. 7.7031641 , -21.29500771],
  327. [ 29.20170784, 60.07447052, 38.95781708, ..., 8.34551048,
  328. -7.05633163, -11.66267395],
  329. [ 26.43227196, 66.50967407, 12.55805016, ..., 7.04926825,
  330. 0.87124491, -20.38945961],
  331. ...,
  332. [ 48.20450974, 55.11436462, 64.52549744, ..., -0.12630704,
  333. 7.41949749, -15.82486343],
  334. [ 27.50131607, 48.1931572 , 39.01482391, ..., 2.25321031,
  335. -5.42439842, -5.85474253],
  336. [ 37.04358673, 42.9875946 , 27.22852898, ..., -0.11088732,
  337. -6.61701012, -5.53644276]], dtype=float32))
  338. ================================================================================
  339. [[ 0. 0. 0. ..., 0. 0. 0.]]
  340. 94
  341. ('f', u'over')
  342. ('as', u'over')
  343. ('feed', array([94]))
  344. ('Sentence :', u'over our you work in no <eos> ago N all N all it events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events events ')
  345. ================================================================================
  346. ('Loss :', 1048.0584556075678)
  347. ('Batch_input :', array([ 3, 11, 0, 123, 327, 475, 2, 4096, 927, 2288, 6191,
  348. 4, 1422, 8, 10, 0, 264, 236, 1049, 11, 6, 40,
  349. 2, 196, 1531, 4, 0, 193, 134, 744, 1, 17, 6,
  350. 272, 5, 3, 171, 0, 8210, 155, 266, 397, 214, 5,
  351. 8024, 9, 2, 917, 4320, 26, 948, 5, 2176, 1607, 131,
  352. 172, 76, 38, 3304, 683, 586, 5, 2457, 1308], dtype=int32))
  353. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  354. [ 1., 0., 0., ..., 0., 0., 0.],
  355. [ 0., 0., 0., ..., 0., 0., 0.],
  356. ...,
  357. [ 0., 0., 0., ..., 0., 0., 0.],
  358. [ 0., 0., 0., ..., 0., 0., 0.],
  359. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  360. Average loss at step 800: 10.480585 learning rate: 1.000000
  361. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  362. [ 1., 0., 0., ..., 0., 0., 0.],
  363. [ 0., 0., 0., ..., 0., 0., 0.],
  364. ...,
  365. [ 0., 0., 0., ..., 0., 0., 0.],
  366. [ 0., 0., 0., ..., 0., 0., 0.],
  367. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  368. ('Predicted:', array([[ 51.68177795, 49.3968544 , 44.19010162, ..., -0.96025121,
  369. -2.75785208, -4.37986326],
  370. [ 74.0589447 , 51.08234024, 46.29760361, ..., 1.14275515,
  371. -3.16398978, -1.2056998 ],
  372. [ 73.70262146, 32.66469193, 39.23232269, ..., 3.43524694,
  373. -1.98378968, 0.50856781],
  374. ...,
  375. [ 58.23159027, 42.74452972, 25.42888641, ..., -1.53912532,
  376. 7.33349323, -9.04616547],
  377. [ 8.14913368, 36.12153625, 48.75240326, ..., 10.54703522,
  378. 3.37933898, -17.40168953],
  379. [ 31.39354706, 23.83557129, 57.65667725, ..., -10.08233261,
  380. -11.59241199, -10.97598267]], dtype=float32))
  381. ================================================================================
  382. [[ 0. 0. 0. ..., 0. 0. 0.]]
  383. 3604
  384. ('f', u'inventory')
  385. ('as', u'inventory')
  386. ('feed', array([3604]))
  387. ('Sentence :', u'inventory funds funds funds our <unk> reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment reinvestment ')
  388. ================================================================================
  389. ('Loss :', 1159.6224331202236)
  390. ('Batch_input :', array([ 57, 5, 25, 6, 1947, 4922, 76, 150, 586, 2245, 0,
  391. 47, 1101, 697, 6, 241, 374, 11, 0, 40, 3, 171,
  392. 2, 2937, 15, 1, 1, 1, 1704, 4, 161, 293, 237,
  393. 927, 93, 1, 52, 554, 157, 38, 1, 118, 76, 4,
  394. 181, 4783, 7, 966, 131, 172, 2, 0, 236, 16, 2015,
  395. 281, 613, 238, 18, 381, 9, 960, 11, 471], dtype=int32))
  396. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  397. [ 0., 0., 0., ..., 0., 0., 0.],
  398. [ 0., 0., 0., ..., 0., 0., 0.],
  399. ...,
  400. [ 0., 0., 0., ..., 0., 0., 0.],
  401. [ 0., 0., 0., ..., 0., 0., 0.],
  402. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  403. Average loss at step 900: 11.596224 learning rate: 1.000000
  404. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  405. [ 0., 0., 0., ..., 0., 0., 0.],
  406. [ 0., 0., 0., ..., 0., 0., 0.],
  407. ...,
  408. [ 0., 0., 0., ..., 0., 0., 0.],
  409. [ 0., 0., 0., ..., 0., 0., 0.],
  410. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  411. ('Predicted:', array([[ 7.78842468e+01, 6.70051498e+01, 5.68985748e+01, ...,
  412. 9.97701585e-02, 1.53537297e+00, -2.83592224e+01],
  413. [ 5.26693001e+01, 8.03840942e+01, 5.94459763e+01, ...,
  414. -9.41697407e+00, -5.38884020e+00, -2.27148266e+01],
  415. [ 7.19977875e+01, 7.00528336e+01, 4.83365097e+01, ...,
  416. 3.95029211e+00, -1.83616962e+01, -1.87646961e+01],
  417. ...,
  418. [ 6.56173630e+01, 3.64674530e+01, 1.02826248e+02, ...,
  419. -3.28653312e+00, -3.12544656e+00, -1.22953815e+01],
  420. [ 6.99354324e+01, 3.96749344e+01, 1.00339859e+02, ...,
  421. -4.53111172e+00, -1.20459199e+00, -8.92706871e+00],
  422. [ 4.16675301e+01, 2.65603065e+01, 5.89054337e+01, ...,
  423. -7.48283148e-01, -4.27502155e+00, -5.28357267e+00]], dtype=float32))
  424. ================================================================================
  425. [[ 0. 0. 0. ..., 0. 0. 0.]]
  426. 9980
  427. ('f', u'fromstein')
  428. ('as', u'fromstein')
  429. ('feed', array([9980]))
  430. ('Sentence :', u'fromstein short-term interest fund reinvestment money <eos> they they before rates <eos> they they they they before rates <eos> they they before rates <eos> they they before rates <eos> they they before rates <eos> they they before to in on because interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest interest ')
  431. ================================================================================
  432. ('Loss :', 1257.1257801287791)
  433. ('Batch_input :', array([ 123, 5, 12, 3, 48, 2, 1461, 9175, 927, 2770, 1991,
  434. 966, 744, 76, 683, 586, 89, 3238, 4320, 8, 386, 78,
  435. 0, 1590, 172, 2, 0, 524, 161, 193, 26, 488, 4406,
  436. 200, 94, 3, 3, 2, 6010, 1408, 332, 0, 1, 293,
  437. 50, 6, 5227, 4096, 236, 4, 3, 3, 198, 0, 467,
  438. 123, 118, 20, 3, 3, 6, 123, 133, 2], dtype=int32))
  439. ('Batch_labels :', array([[ 0., 0., 0., ..., 0., 0., 0.],
  440. [ 0., 0., 0., ..., 0., 0., 0.],
  441. [ 0., 0., 0., ..., 0., 0., 0.],
  442. ...,
  443. [ 0., 0., 0., ..., 0., 0., 0.],
  444. [ 0., 0., 1., ..., 0., 0., 0.],
  445. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  446. Average loss at step 1000: 12.571258 learning rate: 1.000000
  447. ('Label: ', array([[ 0., 0., 0., ..., 0., 0., 0.],
  448. [ 0., 0., 0., ..., 0., 0., 0.],
  449. [ 0., 0., 0., ..., 0., 0., 0.],
  450. ...,
  451. [ 0., 0., 0., ..., 0., 0., 0.],
  452. [ 0., 0., 1., ..., 0., 0., 0.],
  453. [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
  454. ('Predicted:', array([[ 81.48632812, 72.03902435, 46.43121719, ..., 3.6133337 ,
  455. -2.32356 , -15.21035957],
  456. [ 83.48338318, 42.52036285, 58.53778839, ..., -10.0191946 ,
  457. -5.18699551, -22.0647583 ],
  458. [ 119.05833435, 67.42713928, 74.83621216, ..., 2.84793997,
  459. 3.17879272, -26.87680054],
  460. ...,
  461. [ 77.65213776, 44.25524139, 71.1973114 , ..., 1.99433267,
  462. -5.10602999, -17.80796051],
  463. [ 56.73459244, 6.60482407, 109.00973511, ..., -4.26551771,
  464. -10.72382736, -7.26380682],
  465. [ 30.19046211, 30.17685699, 32.82802582, ..., 0.76188219,
  466. -9.53625393, -5.97190285]], dtype=float32))
  467. ================================================================================
  468. [[ 0. 0. 0. ..., 0. 0. 0.]]
  469. 2151
  470. ('f', u'shipping')
  471. ('as', u'shipping')
  472. ('feed', array([2151]))
  473. ('Sentence :', u'shipping N managers over <eos> short-term <eos> a the a a short-term <eos> a funds over investments N managers over on comparable rises funds rates yield example latest support currently point over the funds fund recent dollar for typically fund recent dollar for typically dollar for typically dollar for typically fund recent fund recent fund recent fund recent fund recent dollar for typically fund recent dollar for typically dollar for typically dollar for typically dollar for typically fund recent fund recent dollar for typically dollar for typically dollar for typically dollar for typically fund recent fund recent fund recent dollar for ')
  474. ================================================================================
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