# Lecture21 - Zianet

Τεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 4 χρόνια και 10 μήνες)

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1

Pattern Recognition:

Statistical and Neural

Lonnie C. Ludeman

Lecture 21

Oct 28, 2005

Nanjing University of Science & Technology

2

Lecture 21 Topics

1.
Example

Analysis of simple Neural Network

2.
Example
-

Synthesis of special forms of
Artificial Neural Networks

3
.
General concepts of Training an Artificial Neural
Network
-

Supervised and unsupervised,training
sets

4. Neural Networks Nomenclature and Notation

5. Derivation and Description of the
Backpropagation Algorithm for Feedforward
Neural Networks

3

Example:

Analyze

the following Neural Network

-
1

1

-
1

1

1

0

0

0

1

4

Solution: Outputs of layer 1 ANEs

5

Output of layer 2 ANE is

Thus from layer 1 we have

-

2
≥ 0

<

0

6

7

Final
Solution:
Output
Function for
Given Neural
Network

8

Example:

Synthesize

a Neural Network

Given the following decision regions
build a
neural network to perform the classification
process

Solution:

Use
Hyperplane
-
AND
-
OR

structure

9

Each

g
k
(
x
)

specifies a
hyperplane boundary

10

Hyperplane Layer

AND

Layer

OR
Layer

all
f(

) =

μ
(

)

Solution:

11

Training a Neural Network

“With a teacher”

“Without a teacher”

12

13

Training Set

x
j

are the training samples

d
j

is the class assigned to training sample
x
j

14

Example of a training set
:

(

x
1

= [ 0, 1 ,2 ]
T

, d
1

= C
1

)

,

(

x
2

= [ 0, 1 ,0 ]
T

, d
2

= C
1

)

,

(

x
3

= [ 0, 1 ,1 ]
T

, d
3

= C
1
) ,

(

x
4

= [ 1, 0 ,2 ]
T

, d
4

= C
2
)

,

(

x
5

= [ 1, 0 ,3 ]
T

, d
5

= C
2

)
,

(

x
6

= [ 0, 0 ,1 ]
T

, d
6

= C
3
) ,

(

x
7

= [ 0, 0 ,2 ]
T

, d
7

= C
3
)

(

x
8

= [ 0, 0 ,3 ]
T

d
8

= C
3

)

(

x
9

= [ 0, 0 ,3 ]
T

d
9

= C
3

)

(

x
10

= [ 1, 1 ,0 ]
T

d
10

= C
4

)

(
x
11

= [ 2, 2 ,0 ]
T

d
11

= C
4

)

(
x
12

= [ 2, 2 ,2 ]
T

d
12

= C
5

)

(

x
13

= [ 3, 2, 2 ]
T

d
13

= C
6

)

{

}

15

General Weight Update Algorithm

x
(
k
)

is the training sample for the
k

th

iteration
d
(
k
)

is the class assigned to training sample
x
(
k
)
y
(
k
)

is the output vector for the
k

th

training sample

16

Training with a Teacher( Supervised)

1. Given a set of N ordered samples with their
known class assignments.

2. Randomly select all weights in the neural
network.

3. For each successive sample in the total set
of samples, evaluate the output.

4. Use these outputs and the input sample to
update the weights

5. Stop at some predetermined number of
iterations or if given performance measure is
satisfied. If not stopped go to step 3

17

Training without a Teacher( Unsupervised)

1. Given a set of N ordered samples with unknown
class assignments.

2. Randomly select all weights in the neural network.

3. For each successive sample in the total set of
samples, evaluate the outputs.

4. Using these outputs and the inputs update the
weights

5. If weights do not change significantly stop with that

18

Supervised Training of a
Feedforward Neural Network

Nomenclature

19

Output vector
of layer m

Output vector
of layer L

Node Number
Layer m

Node Number
Layer L

1

20

Weight Matrix for layer m

Node 1

Node 2

Node N
m

N

N
m

21

fix

Layers, Nets, Outputs, Nonlinearities

22

Define the performance

E
p

for sample

x
(
p
)

as

We wish to select weights so that
E
p

is
Minimized

23

weights

p

w
(
p
)

p

x
(
p
)

24

Derivation of weight update equation for Last
Layer (Rule #1) Backpropagation Algorihm

The partial of
y
m
(L)

with respect to
w
kj
(L)
is

25

General Rule #1 for Weight Update

Therefore

26

Derivation of weight update equation for Next to
Last Layer (L
-
1) Backpropagation Algorithm

27

28

General Rule #2 for Weight Update
-

Layer L
-
1
Backpropagation Algorithm

Therefore

and the weight correction is as follows

29

where weight correction (general Rule #2) is

w

(
L
-
1)

30

Backpropagation Training Algorithm
for Feedforward Neural networks

31

Input pattern
sample
x
k

32

Calculate Outputs
First Layer

33

Calculate Outputs
Second Layer

34

Calculate Outputs
Last Layer

35

Check Performance

E
TOTAL
(
p
)

½

(
d
[
x
(
p
-
i
)]

f(
w
T
(
p
-
i
)

x
(
p
-
i
) )
2

i
= 0

N
s

-

1

E
TOTAL
(
p+1
) =
E
TOTAL
(
p
) +
E
p+
1

(
p+
1)

E
p
-
Ns

(
p
-
N
s
)

Single Sample
Error

Over all
Samples Error

Can be computed recursively

36

Change Weights Last
Layer using Rule #1

37

Change Weights previous
Layer using Rule #2

38

Change Weights previous
Layer using Modified Rule #2

39

Input pattern
sample
x
k+1

Continue Iterations Until

40

Repeat process

until performance is
satisfied or maximum number of iterations
are reached.

If performance
not

satisfied

at
maximum number of iterations the algorithm
stops and
NO

design is obtained.

If performance
is satisfied

then the
current weights and structure provide the
required design
.

41

Freeze Weights to get Acceptable
Neural Net Design

42

Backpropagation
Algorithm for
Training
Feedforward
Artificial Neural
Networks

43

Summary Lecture 21

1.
Example

Analysis of simple Neural Network

2.
Example
-

Synthesis of special forms of
Artificial Neural Networks

3
.
General concepts of Training an Artificial
Neural Network
-

Supervised and
unsupervised,and description of training sets

4. Neural Networks Nomenclature and Notation

5. Derivation and Description of the
Backpropagation Algorithm for Feedforward
Neural Networks

44

End of Lecture 21