项目作者: lonski

项目描述 :
Neural network backpropagation alghoritm implementation.
高级语言: Java
项目地址: git://github.com/lonski/neuronomator.git
创建时间: 2018-03-13T22:32:03Z
项目社区:https://github.com/lonski/neuronomator

开源协议:

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Neuronomator

Neural network backpropagation alghoritm implementation.

Building

Enter source root and build jar

  1. ./gradlew jar

Then run

  1. java -jar build/libs/neuronomator-v*.jar

Where ‘*’ is current version number.

Example output

  1. :: Neuronomator 6000 ::
  2. -----[Menu]--------------------------------------------------------------------------
  3. 1. Enter network parameters
  4. 2. Use example parameter set
  5. 0. Exit
  6. >1
  7. Enter neuron values of input layer (space separated numbers)
  8. >0.1 0.5 -1.1
  9. Enter expected neuron values of output layer
  10. >1 0.5
  11. Enter number of hidden layers
  12. >3
  13. Enter amount of neurons in hidden layers
  14. >5
  15. |o| |o| |o|
  16. | 0.100| |o| |o| |o|
  17. | 0.500| |o| |o| |o| | 1.000|
  18. |-1.100| |o| |o| |o| | 0.500|
  19. |o| |o| |o|
  20. Ok? [y/n]>y
  21. Enter maximum number of iterations
  22. >10000
  23. Enter tolerated error value
  24. >0.001
  25. -----[Learning]----------------------------------------------------------------------
  26. Iteration 120/10000 : error = 0.000997
  27. Total error within limit: 0.000997 < 0.001000
  28. -----[Calculated weights]------------------------------------------------------------
  29. L1-L2
  30. N1: 0.900301527199445 0.338078030425155 0.894659920727425 0.808234182152787 0.509193591540590
  31. N2: 0.435294619497994 0.164606087790771 0.053671849125107 0.658242733174273 0.951942727415133
  32. N3: 0.209925662362869 0.592757088887489 0.153404466804703 0.685278864578829 0.001779232116602
  33. L2-L3
  34. N1: 0.697843709393024 0.443407312865462 0.386151950005018 0.037194289531313 0.559474308554924
  35. N2: 0.459147565346800 0.825132033730962 0.969622855532442 0.203889209859832 0.657990549792086
  36. N3: 0.925243051574435 0.174001367618224 0.055413026985691 0.128605962952163 0.694110619626155
  37. N4: 0.143744302016388 0.860644999496252 0.203079853817374 0.747446793810407 0.732982561784035
  38. N5: 0.884614386880747 0.721942872043656 0.074790634223207 0.848910016017671 0.585737527958521
  39. L3-L4
  40. N1: 0.384611369631635 0.542452644288881 0.912731374405039 0.157770701677564 0.163577706751285
  41. N2: 0.555455487684515 0.440466530418831 0.792419379920480 0.289957765502128 0.975146744655604
  42. N3: 0.504084019085070 0.632725412149788 0.434352748240295 0.511020974542947 0.446470797469131
  43. N4: 0.636095304544909 0.945417373300297 0.653745192801613 0.106415080842864 0.630196828410803
  44. N5: 0.017637606925763 0.761403063029708 0.026501561707617 0.699380182677549 0.580809787153521
  45. L4-L5
  46. N1: 0.873277463215451 0.105045263889206
  47. N2: 0.240665176531862 0.280888830309884
  48. N3: 1.007541148420476 0.263975751836411
  49. N4: 0.943221339467878 -0.099218291084971
  50. N5: 0.510657990692959 -0.564422705777302