API tools faq
paste
Advertisement
Guest User

Untitled

a guest
Mar 20th, 2019
79
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 13.24 KB | None | 0 0
raw download clone embed print report
  1. >> EXAMPLE
  2. dropout:0
  3. 16384 4000 2000 1000 200 100 20 2
  4.  
  5. epoch 1/5. Took 26.1095 seconds. Mini-batch mean squared error on training set is 0.35599; Full-batch train mse = 0.329173, val mse = 0.326525
  6. epoch 2/5. Took 25.9808 seconds. Mini-batch mean squared error on training set is 0.35719; Full-batch train mse = 0.328199, val mse = 0.325627
  7. epoch 3/5. Took 25.57 seconds. Mini-batch mean squared error on training set is 0.35136; Full-batch train mse = 0.327184, val mse = 0.324667
  8. epoch 4/5. Took 27.2809 seconds. Mini-batch mean squared error on training set is 0.34995; Full-batch train mse = 0.325889, val mse = 0.323465
  9. epoch 5/5. Took 26.1321 seconds. Mini-batch mean squared error on training set is 0.3571; Full-batch train mse = 0.324425, val mse = 0.322081
  10. dropout:0.2
  11. 16384 4000 2000 1000 200 100 20 2
  12.  
  13. epoch 1/5. Took 25.609 seconds. Mini-batch mean squared error on training set is 0.35599; Full-batch train mse = 0.329173, val mse = 0.326525
  14. epoch 2/5. Took 26.1486 seconds. Mini-batch mean squared error on training set is 0.35719; Full-batch train mse = 0.328199, val mse = 0.325627
  15. epoch 3/5. Took 26.1618 seconds. Mini-batch mean squared error on training set is 0.35136; Full-batch train mse = 0.327184, val mse = 0.324667
  16. epoch 4/5. Took 25.8879 seconds. Mini-batch mean squared error on training set is 0.34995; Full-batch train mse = 0.325889, val mse = 0.323465
  17. epoch 5/5. Took 26.4325 seconds. Mini-batch mean squared error on training set is 0.3571; Full-batch train mse = 0.324425, val mse = 0.322081
  18. dropout:0.4
  19. 16384 4000 2000 1000 200 100 20 2
  20.  
  21. epoch 1/5. Took 26.6334 seconds. Mini-batch mean squared error on training set is 0.35599; Full-batch train mse = 0.329173, val mse = 0.326525
  22. epoch 2/5. Took 26.5553 seconds. Mini-batch mean squared error on training set is 0.35719; Full-batch train mse = 0.328199, val mse = 0.325627
  23. epoch 3/5. Took 27.0048 seconds. Mini-batch mean squared error on training set is 0.35136; Full-batch train mse = 0.327184, val mse = 0.324667
  24. epoch 4/5. Took 26.9622 seconds. Mini-batch mean squared error on training set is 0.34995; Full-batch train mse = 0.325889, val mse = 0.323465
  25. epoch 5/5. Took 26.1383 seconds. Mini-batch mean squared error on training set is 0.3571; Full-batch train mse = 0.324425, val mse = 0.322081
  26. dropout:0.6
  27. 16384 4000 2000 1000 200 100 20 2
  28.  
  29. epoch 1/5. Took 25.8527 seconds. Mini-batch mean squared error on training set is 0.35599; Full-batch train mse = 0.329173, val mse = 0.326525
  30. epoch 2/5. Took 25.8787 seconds. Mini-batch mean squared error on training set is 0.35719; Full-batch train mse = 0.328199, val mse = 0.325627
  31. epoch 3/5. Took 26.0757 seconds. Mini-batch mean squared error on training set is 0.35136; Full-batch train mse = 0.327184, val mse = 0.324667
  32. epoch 4/5. Took 26.2697 seconds. Mini-batch mean squared error on training set is 0.34995; Full-batch train mse = 0.325889, val mse = 0.323465
  33. epoch 5/5. Took 26.1289 seconds. Mini-batch mean squared error on training set is 0.3571; Full-batch train mse = 0.324425, val mse = 0.322081
  34. dropout:0
  35. 16384 4000 2000 1000 200 100 40 2
  36.  
  37. epoch 1/5. Took 26.0774 seconds. Mini-batch mean squared error on training set is 0.3094; Full-batch train mse = 0.246732, val mse = 0.246656
  38. epoch 2/5. Took 26.2593 seconds. Mini-batch mean squared error on training set is 0.30684; Full-batch train mse = 0.249667, val mse = 0.249055
  39. epoch 3/5. Took 26.2061 seconds. Mini-batch mean squared error on training set is 0.29512; Full-batch train mse = 0.254167, val mse = 0.253239
  40. epoch 4/5. Took 26.1055 seconds. Mini-batch mean squared error on training set is 0.30253; Full-batch train mse = 0.256653, val mse = 0.255600
  41. epoch 5/5. Took 26.426 seconds. Mini-batch mean squared error on training set is 0.29885; Full-batch train mse = 0.259356, val mse = 0.258193
  42. dropout:0.2
  43. 16384 4000 2000 1000 200 100 40 2
  44.  
  45. epoch 1/5. Took 27.2476 seconds. Mini-batch mean squared error on training set is 0.3094; Full-batch train mse = 0.246732, val mse = 0.246656
  46. epoch 2/5. Took 26.5624 seconds. Mini-batch mean squared error on training set is 0.30684; Full-batch train mse = 0.249667, val mse = 0.249055
  47. epoch 3/5. Took 26.6439 seconds. Mini-batch mean squared error on training set is 0.29512; Full-batch train mse = 0.254167, val mse = 0.253239
  48. epoch 4/5. Took 26.5441 seconds. Mini-batch mean squared error on training set is 0.30253; Full-batch train mse = 0.256653, val mse = 0.255600
  49. epoch 5/5. Took 26.335 seconds. Mini-batch mean squared error on training set is 0.29885; Full-batch train mse = 0.259356, val mse = 0.258193
  50. dropout:0.4
  51. 16384 4000 2000 1000 200 100 40 2
  52.  
  53. epoch 1/5. Took 25.8353 seconds. Mini-batch mean squared error on training set is 0.3094; Full-batch train mse = 0.246732, val mse = 0.246656
  54. epoch 2/5. Took 25.8681 seconds. Mini-batch mean squared error on training set is 0.30684; Full-batch train mse = 0.249667, val mse = 0.249055
  55. epoch 3/5. Took 26.6513 seconds. Mini-batch mean squared error on training set is 0.29512; Full-batch train mse = 0.254167, val mse = 0.253239
  56. epoch 4/5. Took 26.7138 seconds. Mini-batch mean squared error on training set is 0.30253; Full-batch train mse = 0.256653, val mse = 0.255600
  57. epoch 5/5. Took 26.7044 seconds. Mini-batch mean squared error on training set is 0.29885; Full-batch train mse = 0.259356, val mse = 0.258193
  58. dropout:0.6
  59. 16384 4000 2000 1000 200 100 40 2
  60.  
  61. epoch 1/5. Took 25.3131 seconds. Mini-batch mean squared error on training set is 0.3094; Full-batch train mse = 0.246732, val mse = 0.246656
  62. epoch 2/5. Took 25.8635 seconds. Mini-batch mean squared error on training set is 0.30684; Full-batch train mse = 0.249667, val mse = 0.249055
  63. epoch 3/5. Took 25.7485 seconds. Mini-batch mean squared error on training set is 0.29512; Full-batch train mse = 0.254167, val mse = 0.253239
  64. epoch 4/5. Took 25.4723 seconds. Mini-batch mean squared error on training set is 0.30253; Full-batch train mse = 0.256653, val mse = 0.255600
  65. epoch 5/5. Took 25.1365 seconds. Mini-batch mean squared error on training set is 0.29885; Full-batch train mse = 0.259356, val mse = 0.258193
  66. dropout:0
  67. 16384 4000 2000 1000 200 100 60 2
  68.  
  69. epoch 1/5. Took 25.4246 seconds. Mini-batch mean squared error on training set is 0.30601; Full-batch train mse = 0.272219, val mse = 0.268657
  70. epoch 2/5. Took 25.84 seconds. Mini-batch mean squared error on training set is 0.30909; Full-batch train mse = 0.261392, val mse = 0.258238
  71. epoch 3/5. Took 26.2294 seconds. Mini-batch mean squared error on training set is 0.29911; Full-batch train mse = 0.253702, val mse = 0.250924
  72. epoch 4/5. Took 26.7621 seconds. Mini-batch mean squared error on training set is 0.30012; Full-batch train mse = 0.250048, val mse = 0.247505
  73. epoch 5/5. Took 26.8733 seconds. Mini-batch mean squared error on training set is 0.30016; Full-batch train mse = 0.248720, val mse = 0.246312
  74. dropout:0.2
  75. 16384 4000 2000 1000 200 100 60 2
  76.  
  77. epoch 1/5. Took 26.014 seconds. Mini-batch mean squared error on training set is 0.30601; Full-batch train mse = 0.272219, val mse = 0.268657
  78. epoch 2/5. Took 25.8996 seconds. Mini-batch mean squared error on training set is 0.30909; Full-batch train mse = 0.261392, val mse = 0.258238
  79. epoch 3/5. Took 26.9852 seconds. Mini-batch mean squared error on training set is 0.29911; Full-batch train mse = 0.253702, val mse = 0.250924
  80. epoch 4/5. Took 25.28 seconds. Mini-batch mean squared error on training set is 0.30012; Full-batch train mse = 0.250048, val mse = 0.247505
  81. epoch 5/5. Took 25.0148 seconds. Mini-batch mean squared error on training set is 0.30016; Full-batch train mse = 0.248720, val mse = 0.246312
  82. dropout:0.4
  83. 16384 4000 2000 1000 200 100 60 2
  84.  
  85. epoch 1/5. Took 25.1973 seconds. Mini-batch mean squared error on training set is 0.30601; Full-batch train mse = 0.272219, val mse = 0.268657
  86. epoch 2/5. Took 25.8016 seconds. Mini-batch mean squared error on training set is 0.30909; Full-batch train mse = 0.261392, val mse = 0.258238
  87. epoch 3/5. Took 25.8401 seconds. Mini-batch mean squared error on training set is 0.29911; Full-batch train mse = 0.253702, val mse = 0.250924
  88. epoch 4/5. Took 25.9306 seconds. Mini-batch mean squared error on training set is 0.30012; Full-batch train mse = 0.250048, val mse = 0.247505
  89. epoch 5/5. Took 25.772 seconds. Mini-batch mean squared error on training set is 0.30016; Full-batch train mse = 0.248720, val mse = 0.246312
  90. dropout:0.6
  91. 16384 4000 2000 1000 200 100 60 2
  92.  
  93. epoch 1/5. Took 25.8234 seconds. Mini-batch mean squared error on training set is 0.30601; Full-batch train mse = 0.272219, val mse = 0.268657
  94. epoch 2/5. Took 26.3756 seconds. Mini-batch mean squared error on training set is 0.30909; Full-batch train mse = 0.261392, val mse = 0.258238
  95. epoch 3/5. Took 25.9542 seconds. Mini-batch mean squared error on training set is 0.29911; Full-batch train mse = 0.253702, val mse = 0.250924
  96. epoch 4/5. Took 26.0673 seconds. Mini-batch mean squared error on training set is 0.30012; Full-batch train mse = 0.250048, val mse = 0.247505
  97. epoch 5/5. Took 26.39 seconds. Mini-batch mean squared error on training set is 0.30016; Full-batch train mse = 0.248720, val mse = 0.246312
  98. dropout:0
  99. 16384 4000 2000 1000 200 100 80 2
  100.  
  101. epoch 1/5. Took 24.7526 seconds. Mini-batch mean squared error on training set is 0.43499; Full-batch train mse = 0.411375, val mse = 0.412397
  102. epoch 2/5. Took 25.3426 seconds. Mini-batch mean squared error on training set is 0.43227; Full-batch train mse = 0.411257, val mse = 0.412217
  103. epoch 3/5. Took 25.9111 seconds. Mini-batch mean squared error on training set is 0.42211; Full-batch train mse = 0.409692, val mse = 0.410609
  104. epoch 4/5. Took 25.7289 seconds. Mini-batch mean squared error on training set is 0.42182; Full-batch train mse = 0.407772, val mse = 0.408581
  105. epoch 5/5. Took 27.3961 seconds. Mini-batch mean squared error on training set is 0.41327; Full-batch train mse = 0.401532, val mse = 0.402282
  106. dropout:0.2
  107. 16384 4000 2000 1000 200 100 80 2
  108.  
  109. epoch 1/5. Took 26.2975 seconds. Mini-batch mean squared error on training set is 0.43499; Full-batch train mse = 0.411375, val mse = 0.412397
  110. epoch 2/5. Took 28.1238 seconds. Mini-batch mean squared error on training set is 0.43227; Full-batch train mse = 0.411257, val mse = 0.412217
  111. epoch 3/5. Took 27.4354 seconds. Mini-batch mean squared error on training set is 0.42211; Full-batch train mse = 0.409692, val mse = 0.410609
  112. epoch 4/5. Took 26.063 seconds. Mini-batch mean squared error on training set is 0.42182; Full-batch train mse = 0.407772, val mse = 0.408581
  113. epoch 5/5. Took 27.8037 seconds. Mini-batch mean squared error on training set is 0.41327; Full-batch train mse = 0.401532, val mse = 0.402282
  114. dropout:0.4
  115. 16384 4000 2000 1000 200 100 80 2
  116.  
  117. epoch 1/5. Took 26.9512 seconds. Mini-batch mean squared error on training set is 0.43499; Full-batch train mse = 0.411375, val mse = 0.412397
  118. epoch 2/5. Took 26.9537 seconds. Mini-batch mean squared error on training set is 0.43227; Full-batch train mse = 0.411257, val mse = 0.412217
  119. epoch 3/5. Took 26.8516 seconds. Mini-batch mean squared error on training set is 0.42211; Full-batch train mse = 0.409692, val mse = 0.410609
  120. epoch 4/5. Took 26.7601 seconds. Mini-batch mean squared error on training set is 0.42182; Full-batch train mse = 0.407772, val mse = 0.408581
  121. epoch 5/5. Took 25.7531 seconds. Mini-batch mean squared error on training set is 0.41327; Full-batch train mse = 0.401532, val mse = 0.402282
  122. dropout:0.6
  123. 16384 4000 2000 1000 200 100 80 2
  124.  
  125. epoch 1/5. Took 26.4361 seconds. Mini-batch mean squared error on training set is 0.43499; Full-batch train mse = 0.411375, val mse = 0.412397
  126. epoch 2/5. Took 25.7577 seconds. Mini-batch mean squared error on training set is 0.43227; Full-batch train mse = 0.411257, val mse = 0.412217
  127. epoch 3/5. Took 25.978 seconds. Mini-batch mean squared error on training set is 0.42211; Full-batch train mse = 0.409692, val mse = 0.410609
  128. epoch 4/5. Took 25.798 seconds. Mini-batch mean squared error on training set is 0.42182; Full-batch train mse = 0.407772, val mse = 0.408581
  129. epoch 5/5. Took 25.3369 seconds. Mini-batch mean squared error on training set is 0.41327; Full-batch train mse = 0.401532, val mse = 0.402282
  130. dropout:0
  131. 16384 4000 2000 1000 200 100 100 2
  132.  
  133. epoch 1/5. Took 25.7267 seconds. Mini-batch mean squared error on training set is 0.4101; Full-batch train mse = 0.388687, val mse = 0.390565
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement
Public Pastes
Advertisement
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!