sample x 1 1 x 2 x 3 x 1 2 x 2 x 3 x 1 3 x 2 x 3 1 0.28 1.31 -6.2 0.011 1.03 -0.
ID: 3853822 • Letter: S
Question
sample
x1
1
x2
x3
x1
2
x2
x3
x1
3
x2
x3
1
0.28
1.31
-6.2
0.011
1.03
-0.21
1.36
2.17
0.14
2
0.07
0.58
-0.78
1.27
1.28
0.08
1.41
1.45
-0.38
3
1.54
2.01
-1.63
0.13
3.12
0.16
1.22
0.99
0.69
4
-0.44
1.18
-4.32
-0.21
1.23
-0.11
2.46
2.19
1.31
5
-0.81
0.21
5.73
-2.18
1.39
-0.19
0.68
0.79
0.87
6
1.52
3.16
2.77
0.34
1.96
-0.16
2.51
3.22
1.35
7
2.20
2.42
-0.19
-1.38
0.94
0.45
0.60
2.44
0.92
8
0.91
1.94
6.21
-0.12
0.82
0.17
0.64
0.13
0.97
9
0.65
1.93
4.38
-1.44
2.31
0.14
0.85
0.58
0.99
10
-0.26
0.82
-0.96
0.26
1.94
0.08
0.66
0.51
0.88
Section 6.3
2. Create a 3-1-1 sigmoidal network with bias to be trained to classifypatterns from 1 and 2 in the table above. Use stochastic backpropagation to (Algorithm 1) with learning rate = 0.1 and sigmoid as described in Eq. 33 in Sect. 6.8.2.
(a) Initialize all weights randomlyin the range 1 w +1. Plot a learning curve — the training error as a function of epoch.
(b) Now repeat (a) but with weights initialized to be the same throughout each level. In particular, let all input-to-hidden weights be initialized with wji = 0.5 and all hidden-to-output weights with wkj = 0.5.
(c) Explain the source of the differences between your learning curves (cf. Problem 12).
Attachments:
sample
x1
1
x2
x3
x1
2
x2
x3
x1
3
x2
x3
1
0.28
1.31
-6.2
0.011
1.03
-0.21
1.36
2.17
0.14
2
0.07
0.58
-0.78
1.27
1.28
0.08
1.41
1.45
-0.38
3
1.54
2.01
-1.63
0.13
3.12
0.16
1.22
0.99
0.69
4
-0.44
1.18
-4.32
-0.21
1.23
-0.11
2.46
2.19
1.31
5
-0.81
0.21
5.73
-2.18
1.39
-0.19
0.68
0.79
0.87
6
1.52
3.16
2.77
0.34
1.96
-0.16
2.51
3.22
1.35
7
2.20
2.42
-0.19
-1.38
0.94
0.45
0.60
2.44
0.92
8
0.91
1.94
6.21
-0.12
0.82
0.17
0.64
0.13
0.97
9
0.65
1.93
4.38
-1.44
2.31
0.14
0.85
0.58
0.99
10
-0.26
0.82
-0.96
0.26
1.94
0.08
0.66
0.51
0.88
f(net) = a tanh(b net) = a [1+eb net1+ 1- eb net - e net 2 e-b net (33)Explanation / Answer
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