This document describes the training of a neural network using gradient descent. It shows the weights and outputs over 15 epochs of training on a single data point. The squared error and mean squared error are calculated after each epoch, with the weights being adjusted based on the learning rate of 0.2. The network consists of 2 input nodes, 2 hidden nodes, and 1 output node.

1. Neural Network
Kecerdasan Buatan
Ferry Ariyanto
1122800020

2. I1
I2
H1
H2
C
1 1
W1 0.1
W2 0.2
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 Data 1
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 Data 1
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 Data 1
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 Data 1
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 Data 1
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 4 6 8 10 12 14 16
Gradient Descent
SSE MSE