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NN Backprop Calculation.pdf
- 1. Neural Network Kecerdasan Buatan FerryAriyanto 1122800020
- 2. I1 I2 H1 H2 C 1 1 W1 0.1 W20.2 W3 0.3 W4 0.4 W5 0.5 W6 0.6 W7 0.7 W8 0.8 W9 0.9 Learning Rate = 0.2
- 3. Epoch 1 Data1 Forward Net H1 0.5 Net H2 0.2 Out H1 0.622459331 Out H2 0.549833997 Net C 1.692818063 Out C 0.844594403 Squared Error 0.71334 New Weight W1 0.095903619 W2 0.19512789 W3 0.3 W4 0.4 W5 0.5 W6 0.6 W7 0.677828603 W8 0.786199207 W9 0.887809412
- 4. Epoch 5 Data1 Forward Net H1 0.489880679 Net H2 0.17413205 Out H1 0.62008323 Out H2 0.543423345 Net C 1.433796638 Out C 0.807492187 Squared Error 0.65204 New Weight W1 0.072358788 W2 0.169185798 W3 0.29035259 W4 0.390929085 W5 0.48988068 W6 0.590404503 W7 0.532198544 W8 0.690074757 W9 0.794089312
- 5. Epoch 9 Data1 Forward Net H1 0.48025404 Net H2 0.14988401 Out H1 0.617807861 Out H2 0.53740101 Net C 1.433796638 Out C 0.807492187 Squared Error 0.65204 New Weight W1 0.050872608 W2 0.145099991 W3 0.28129087 W4 0.382331105 W5 0.48025404 W6 0.581188886 W7 0.417462434 W8 0.614764176 W9 0.720745821
- 6. Epoch 13 Data1 Forward Net H1 0.471986873 Net H2 0.12922536 Out H1 0.615853915 Out H2 0.532261457 Net C 0.900947915 Out C 0.711144261 Squared Error 0.50573 New Weight W1 0.033211725 W2 0.124851542 W3 0.27364825 W4 0.375042047 W5 0.47198687 W6 0.57323053 W7 0.229322268 W8 0.491985951 W9 0.601320363
- 7. Epoch 15 Data1 Forward Net H1 0.46856638 Net H2 0.12070407 Out H1 0.615044382 Out H2 0.530139432 Net C 0.779320643 Out C 0.685533678 Squared Error 0.46996 New Weight W1 0.026162448 W2 0.116599631 W3 0.27054852 W4 0.372093604 W5 0.46856638 W6 0.569944296 W7 0.15988765 W8 0.446868301 W9 0.557484016
- 8. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 2 46 8 10 12 14 16 Gradient Descent SSE MSE