Brief Introduction to Deep Learning + Solving XOR using ANNs
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This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers. Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning. Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer. أحمد فوزي جاد Ahmed Fawzy Gad قسم تكنولوجيا المعلومات Information Technology (IT) Department كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI) جامعة المنوفية, مصر Menoufia University, Egypt Teaching Assistant/Demonstrator ahmed.fawzy@ci.menofia.edu.eg : AFCIT http://www.afcit.xyz YouTube https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw Google Plus https://plus.google.com/u/0/+AhmedGadIT SlideShare https://www.slideshare.net/AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad/ ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12/ ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad/
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Brief Introduction to Deep Learning + Solving XOR using ANNs
- 1. Brief Introduction to Deep Learning + Solving XOR using ANN MENOUFIA UNIVERSITY FACULTY OF COMPUTERS AND INFORMATION المنوفية جامعة الحاسبات كليةوالمعلومات المنوفية جامعة Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg
- 3. Neural Networks Input Hidden Output BA 01 1 10 00 0 11
- 4. Neural Networks BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 5. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 6. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 7. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 8. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 9. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 10. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 11. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2
- 12. = +0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 13. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Network Architecture!!
- 14. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 = + 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 15. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 16. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 17. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 Input OutputHidden
- 18. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 Input OutputHidden
- 19. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 Input OutputHidden
- 20. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output
- 21. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output
- 22. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output
- 23. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output A B
- 24. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output A B
- 25. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output A B
- 26. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output A B
- 27. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 BA 01 1 10 00 0 11 Input Output A B 1/0
- 28. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 Input Output A B 1/0 𝑾 𝟏 𝑾 𝟐
- 29. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 = + 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 30. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 31. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 32. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 Input Output A B 1/0 𝑾 𝟑 𝑾 𝟒
- 33. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 A B 1/0 𝑾 𝟑 𝑾 𝟒 A B 1/0 𝑾 𝟏 𝑾 𝟐 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5
- 34. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 A B 𝑾 𝟑 𝑾 𝟒
- 35. A B 𝑾 𝟏 𝑾 𝟐 A B 𝑾 𝟑 𝑾 𝟒 𝑾 𝟓 𝑾 𝟔
- 36. 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 A B 𝑾 𝟑 𝑾 𝟒 𝑾 𝟓 𝑾 𝟔 A 𝑾 𝟏 𝑾 𝟐 B 𝑾 𝟑 𝑾 𝟒
- 37. 𝑾 𝟓 𝑾 𝟔 A 𝑾 𝟏 𝑾 𝟐 B 𝑾 𝟑 𝑾 𝟒
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- 52. Input Output BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Hidden Weights=𝑾𝒊 𝑾 𝟓 𝑾 𝟔 1/0 𝒀𝒋 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 53. Activation Function Output 𝒀𝒋 BA 01 1 10 00 0 11 Input Hidden 1/0 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 54. Activation Function Output 𝒀𝒋 BA 01 1 10 00 0 11 Input Hidden 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 55. Activation Function Output 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 56. Activation Function Components Output 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 57. Activation Function Inputs Output s 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 58. Activation Function Inputs Output s 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 s=SOP(𝑿𝒊, 𝑾𝒊)
- 59. Activation Function Inputs Output s 𝑿𝒊=Inputs 𝑾𝒊=Weights 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 s=SOP(𝑿𝒊, 𝑾𝒊)
- 60. Activation Function Inputs Output s 𝑿𝒊=Inputs 𝑾𝒊=Weights 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 s=SOP(𝑿𝒊, 𝑾𝒊)
- 61. 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 Activation Function Inputs Output s 𝑿𝒊=Inputs 𝑾𝒊=Weights 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden S= 𝟏 𝒎 𝑿𝒊 𝑾𝒊 s=SOP(𝑿𝒊, 𝑾𝒊) 1/0
- 62. Activation Function Inputs Output s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 S= 𝟏 𝒎 𝑿𝒊 𝑾𝒊 1/0 Each Hidden/Output Layer Neuron has its SOP.
- 63. Activation Function Inputs Output s 𝑿 𝟏 𝑿 𝟐 𝑺 𝟏=(𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 S= 𝟏 𝒎 𝑿𝒊 𝑾𝒊 1/0
- 64. Activation Function Inputs Output s 𝑿 𝟏 𝑿 𝟐 𝑺 𝟐=(𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 S= 𝟏 𝒎 𝑿𝒊 𝑾𝒊 1/0
- 65. Activation Function Inputs Output s 𝑿 𝟏 𝑿 𝟐 𝑺 𝟑=(𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 S= 𝟏 𝒎 𝑿𝒊 𝑾𝒊 1/0
- 66. Activation Function Outputs Output F(s)s 𝑿 𝟏 𝑿 𝟐 Class Label 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 67. Activation Function Outputs Output F(s)s 𝑿 𝟏 𝑿 𝟐 Class Label 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 Input Hidden 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 69. Activation Functions Which activation function to use? Outputs Class Labels Activation Function TWO Class Labels TWO Outputs One that gives two outputs. Which activation function to use? 𝑪𝒋𝒀𝒋 BA 01 1 10 00 0 11 BA 01 1 10 00 0 11
- 70. Activation Functions Piecewise Linear Sigmoid BinaryBinary
- 71. Activation Function Output F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 BA 01 1 10 00 0 11 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 1 1.2 1/0 Input Hidden 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 72. Bias Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 73. Bias Hidden Layer Neurons Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 +1 𝒃 𝟏 +1 𝒃 𝟐 1/0
- 74. Bias Output Layer Neurons Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟑 1/0 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 75. All Bias Values Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 1/0 +1 𝒃 𝟑 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 76. Bias Add Bias to SOP Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 𝑺 𝟏=(𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) 𝑺 𝟐=(𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) 𝑺 𝟑=(𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) 1/0 +1 𝒃 𝟑 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 77. Bias Add Bias to SOP Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 𝑺 𝟏=(𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) 1/0 +1 𝒃 𝟑 𝑺 𝟏=(+𝟏𝒃 𝟏+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 78. Bias Add Bias to SOP Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 𝑺 𝟐=(𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) 1/0 +1 𝒃 𝟑 𝑺 𝟐=(+𝟏𝒃 𝟐+𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 79. Bias Add Bias to SOP Input Output BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 𝑺 𝟑=(𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) 1/0 +1 𝒃 𝟑 𝑺 𝟑=(+𝟏𝒃 𝟑+𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 80. Learning Rate 𝟎 ≤ η ≤ 𝟏 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 1/0 +1 𝒃 𝟑 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒
- 81. Other Parameters Step n 𝒏 = 𝟎, 𝟏, 𝟐, … F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 1/0 +1 𝒃 𝟑 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 s=(𝑿 𝟎 𝑾 𝟎+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟐+…) 𝟎 ≤ η ≤ 𝟏 𝑿(𝒏)=(𝑿 𝟎, 𝑿 𝟏,𝑿 𝟐, …) W(𝒏)=(𝑾 𝟎, 𝑾 𝟏,𝑾 𝟐, …)
- 82. Other Parameters Desired Output 𝒅𝒋 𝒏 = 𝟎, 𝟏, 𝟐, … 𝒅 𝒏 = 𝟏, 𝒙 𝒏 𝒃𝒆𝒍𝒐𝒏𝒈𝒔 𝒕𝒐 𝑪𝟏 (𝟏) 𝟎, 𝒙 𝒏 𝒃𝒆𝒍𝒐𝒏𝒈𝒔 𝒕𝒐 𝑪𝟐 (𝟎) BA 01 1 10 00 0 11 F(s)s 𝑿 𝟏 𝑿 𝟐 bin 𝒀𝒋 +1 𝒃 𝟏 +1 𝒃 𝟐 1/0 +1 𝒃 𝟑 𝑾 𝟓 𝑾 𝟔 A B 𝑾 𝟏 𝑾 𝟐 𝑾 𝟑 𝑾 𝟒 s=(𝑿 𝟎 𝑾 𝟎+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟐+…) 𝟎 ≤ η ≤ 𝟏 𝑿(𝒏)=(𝑿 𝟎, 𝑿 𝟏,𝑿 𝟐, …) W(𝒏)=(𝑾 𝟎, 𝑾 𝟏,𝑾 𝟐, …)
- 83. Neural Networks Training Steps Weights Initialization Inputs Application Sum of Inputs-Weights Products Activation Function Response Calculation Weights Adaptation Back to Step 2 1 2 3 4 5 6
- 84. Regarding 5th Step: Weights Adaptation • If the predicted output Y is not the same as the desired output d, then weights are to be adapted according to the following equation: 𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + η 𝒅 𝒏 − 𝒀 𝒏 𝑿(𝒏) Where 𝑾 𝒏 = [𝒃 𝒏 , 𝑾 𝟏(𝒏), 𝑾 𝟐(𝒏), 𝑾 𝟑(𝒏), … , 𝑾 𝒎(𝒏)]
- 85. Neural Networks Training Example Step n=0 • In each step in the solution, the parameters of the neural network must be known. • Parameters of step n=0: η = .001 𝑋 𝑛 = 𝑋 0 = +1, +1, +1,1, 0 𝑊 𝑛 = 𝑊 0 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6 = −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1 𝑑 𝑛 = 𝑑 0 = 1 BA 01 1 => 1 10 00 0 => 0 11
- 86. Neural Networks Training Example Step n=0 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin BA 01 1 => 1 10 00 0 => 0 11
- 87. Neural Networks Training Example Step n=0 – SOP – 𝑺 𝟏 𝑺 𝟏=(+𝟏𝒃 𝟏+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) =+1*-1.5+1*1+0*1 =-.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 88. Neural Networks Training Example Step n=0 – Output – 𝑺 𝟏 𝒀 𝑺 𝟏 = = 𝑩𝑰𝑵 𝑺 𝟏 = 𝑩𝑰𝑵 −. 𝟓 = 𝟎 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 89. Neural Networks Training Example Step n=0 – SOP – 𝑺 𝟐 𝑺 𝟐=(+𝟏𝒃 𝟐+𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) =+1*-.5+1*1+0*1 =.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 90. Neural Networks Training Example Step n=0 – Output – 𝑺 𝟐 𝒀 𝑺2 = = 𝑩𝑰𝑵 𝑺2 = 𝑩𝑰𝑵 . 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 −𝟏, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 91. Neural Networks Training Example Step n=0 – SOP – 𝑺 𝟑 𝑺 𝟑=(+𝟏𝒃 𝟑+𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) =+1*-.5+0*-2+1*1 =.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 92. Neural Networks Training Example Step n=0 – Output – 𝑺 𝟑 𝒀 𝑺3 = = 𝑩𝑰𝑵 𝑺3 = 𝑩𝑰𝑵 . 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 93. Neural Networks Training Example Step n=0 - Output 𝒀 𝒏 = 𝒀 𝟎 = 𝒀 𝑺3 = 1 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 94. Neural Networks Training Example Step n=0 Predicted Vs. Desired 𝒀 𝒏 = 𝒀 𝟎 = 1 𝐝 𝒏 = 𝒅 𝟎 = 1 ∵ 𝒀 𝒏 = 𝒅 𝒏 ∴ Weights are Correct. No Adaptation BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 95. Neural Networks Training Example Step n=1 • In each step in the solution, the parameters of the neural network must be known. • Parameters of step n=1: η = .001 𝑋 𝑛 = 𝑋 1 = +1, +1, +1,0, 1 𝑊 𝑛 = 𝑊 1 = 𝑊 0 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6 = −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1 𝑑 𝑛 = 𝑑 1 = +1 BA 01 1 => 1 10 00 0 => 0 11
- 96. Neural Networks Training Example Step n=1 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin BA 01 1 => 1 10 00 0 => 0 11
- 97. Neural Networks Training Example Step n=1 – SOP – 𝑺 𝟏 𝑺 𝟏=(+𝟏𝒃 𝟏+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) =+1*-1.5+0*1+1*1 =-.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 98. Neural Networks Training Example Step n=1 – Output – 𝑺 𝟏 𝒀 𝑺 𝟏 = = 𝑩𝑰𝑵 𝑺 𝟏 = 𝑩𝑰𝑵 −. 𝟓 = 𝟎 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 99. Neural Networks Training Example Step n=1 – SOP – 𝑺 𝟐 𝑺 𝟐=(+𝟏𝒃 𝟐+𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) =+1*-.5+0*1+1*1 =.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 100. Neural Networks Training Example Step n=1 – Output – 𝑺 𝟐 𝒀 𝑺2 = = 𝑩𝑰𝑵 𝑺2 = 𝑩𝑰𝑵 . 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 −𝟏, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 101. Neural Networks Training Example Step n=1 – SOP – 𝑺 𝟑 𝑺 𝟑=(+𝟏𝒃 𝟑+𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) =+1*-.5+0*-2+1*1 =.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 102. Neural Networks Training Example Step n=1 – Output – 𝑺 𝟑 𝒀 𝑺3 = = 𝑩𝑰𝑵 𝑺3 = 𝑩𝑰𝑵 . 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 103. Neural Networks Training Example Step n=1 - Output 𝒀 𝒏 = 𝒀 𝟏 = 𝒀 𝑺3 = 1 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 104. Neural Networks Training Example Step n=1 Predicted Vs. Desired 𝒀 𝒏 = 𝒀 𝟏 = 1 𝐝 𝒏 = 𝒅 𝟏 = 1 ∵ 𝒀 𝒏 = 𝒅 𝒏 ∴ Weights are Correct. No Adaptation BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −2 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 105. Neural Networks Training Example Step n=2 • In each step in the solution, the parameters of the neural network must be known. • Parameters of step n=2: η = .001 𝑋 𝑛 = 𝑋 2 = +1, +1, +1,0, 0 𝑊 𝑛 = 𝑊 2 = 𝑊 1 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6 = −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1 𝑑 𝑛 = 𝑑 2 = 0 BA 01 1 => 1 10 00 0 => 0 11
- 106. Neural Networks Training Example Step n=2 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin BA 01 1 => 1 10 00 0 => 0 11
- 107. Neural Networks Training Example Step n=2 – SOP – 𝑺 𝟏 𝑺 𝟏=(+𝟏𝒃 𝟏+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) =+1*-1.5+0*1+0*1 =-1.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 108. Neural Networks Training Example Step n=2 – Output – 𝑺 𝟏 𝒀 𝑺 𝟏 = = 𝑩𝑰𝑵 𝑺 𝟏 = 𝑩𝑰𝑵 −𝟏. 𝟓 = 𝟎 𝒃𝒊n 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 109. Neural Networks Training Example Step n=2 – SOP – 𝑺 𝟐 𝑺 𝟐=(+𝟏𝒃 𝟐+𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) =+1*-.5+0*1+0*1 =-.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 110. Neural Networks Training Example Step n=2 – Output – 𝑺 𝟐 𝒀 𝑺2 = = 𝑺𝑮𝑵 𝑺2 = 𝑺𝑮𝑵 −. 𝟓 =0 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 −𝟏, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 111. Neural Networks Training Example Step n=2 – SOP – 𝑺 𝟑 𝑺 𝟑=(+𝟏𝒃 𝟑+𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) =+1*-.5+0*-2+0*1 =-.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 112. Neural Networks Training Example Step n=2 – Output – 𝑺 𝟑 𝒀 𝑺3 = = 𝑩𝑰𝑵 𝑺3 = 𝑩𝑰𝑵 −. 𝟓 = 𝟎 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 113. Neural Networks Training Example Step n=2 - Output 𝒀 𝒏 = 𝒀 𝟐 = 𝒀 𝑺3 = 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 114. Neural Networks Training Example Step n=2 Predicted Vs. Desired 𝒀 𝒏 = 𝒀 𝟐 = 𝟎 𝐝 𝒏 = 𝒅 𝟐 = 𝟎 ∵ 𝒀 𝒏 = 𝒅 𝒏 ∴ Weights are Correct. No Adaptation BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 115. Neural Networks Training Example Step n=3 • In each step in the solution, the parameters of the neural network must be known. • Parameters of step n=3: η = .001 𝑋 𝑛 = 𝑋 3 = +1, +1, +1,1, 1 𝑊 𝑛 = 𝑊 3 = 𝑊 2 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6 = −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1 𝑑 𝑛 = 𝑑 3 = 0 BA 01 1 => 1 10 00 0 => 0 11
- 116. Neural Networks Training Example Step n=3 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin BA 01 1 => 1 10 00 0 => 0 11
- 117. Neural Networks Training Example Step n=3 – SOP – 𝑺 𝟏 𝑺 𝟏=(+𝟏𝒃 𝟏+𝑿 𝟏 𝑾 𝟏+𝑿 𝟐 𝑾 𝟑) =+1*-1.5+1*1+1*1 =.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 118. Neural Networks Training Example Step n=3 – Output – 𝑺 𝟏 𝒀 𝑺 𝟏 = = 𝑩𝑰𝑵 𝑺 𝟏 = 𝑩𝑰𝑵 . 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 119. Neural Networks Training Example Step n=3 – SOP – 𝑺 𝟐 𝑺 𝟐=(+𝟏𝒃 𝟐+𝑿 𝟏 𝑾 𝟐+𝑿 𝟐 𝑾 𝟒) =+1*-.5+1*1+1*1 =1.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 120. Neural Networks Training Example Step n=3 – Output – 𝑺 𝟐 𝒀 𝑺2 = = 𝑩𝑰𝑵 𝑺2 = 𝑩𝑰𝑵 𝟏. 𝟓 = 1 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 −𝟏, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 121. Neural Networks Training Example Step n=3 – SOP – 𝑺 𝟑 𝑺 𝟑=(+𝟏𝒃 𝟑+𝑺 𝟏 𝑾 𝟓+𝑺 𝟐 𝑾 𝟔) =+1*-.5+1*-2+1*1 =-1.5 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 122. Neural Networks Training Example Step n=3 – Output – 𝑺 𝟑 𝒀 𝑺3 = = 𝑩𝑰𝑵 𝑺3 = 𝑩𝑰𝑵 −𝟏. 𝟓 = 𝟎 𝒃𝒊𝒏 𝒔 = +𝟏, 𝒔 ≥ 𝟎 𝟎, 𝒔 < 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 123. Neural Networks Training Example Step n=3 - Output 𝒀 𝒏 = 𝒀 𝟑 = 𝒀 𝑺3 = 𝟎 BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 124. Neural Networks Training Example Step n=3 Predicted Vs. Desired 𝒀 𝒏 = 𝒀 𝟑 = 𝟎 𝐝 𝒏 = 𝒅 𝟑 = 𝟎 ∵ 𝒀 𝒏 = 𝒅 𝒏 ∴ Weights are Correct. No Adaptation BA 01 1 => 1 10 00 0 => 0 11 s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin
- 125. Final Weights s 𝑿 𝟏 𝑿 𝟐 𝒀𝒋 +1 −𝟏. 𝟓 +1 −. 𝟓 1/0 +1 −. 𝟓 −𝟐 +𝟏 A B +𝟏 +𝟏 +𝟏 +𝟏 bin Current weights predicted the desired outputs.