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CS621 : Artificial Intelligence. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 22 Perceptron Training Algorithm and its Proof of Convergence. θ , ≤. θ , <. w 1. w 2. w n. w 1. w 2. w n. w 3. x 1. x 2. x 3. x n. x 1. x 2. x 3. x n. Perceptron Training Algorithm (PTA).

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cs621 artificial intelligence

CS621 : Artificial Intelligence

Pushpak BhattacharyyaCSE Dept., IIT Bombay

Lecture 22

Perceptron Training Algorithm and its Proof of Convergence

perceptron training algorithm pta

θ, ≤

θ, <

w1

w2

wn

w1

w2

wn

. . .

w3

. . .

x1

x2

x3

xn

x1

x2

x3

xn

Perceptron Training Algorithm (PTA)

Preprocessing:

  • The computation law is modified to

y = 1 if ∑wixi > θ

y = o if ∑wixi < θ

w3

pta preprocessing cont

θ

θ

wn

w0=θ

w1

w2

w3

. . .

x0= -1

x1

x2

x3

xn

w1

w2

w3

wn

. . .

x2

x3

xn

PTA – preprocessing cont…

2. Absorb θ as a weight

3. Negate all the zero-class examples

x1

example to demonstrate preprocessing
Example to demonstrate preprocessing
  • OR perceptron

1-class <1,1> , <1,0> , <0,1>

0-class <0,0>

Augmented x vectors:-

1-class <-1,1,1> , <-1,1,0> , <-1,0,1>

0-class <-1,0,0>

Negate 0-class:- <1,0,0>

example to demonstrate preprocessing cont
Example to demonstrate preprocessing cont..

Now the vectors are

x0 x1 x2

X1 -1 0 1

X2 -1 1 0

X3 -1 1 1

X4 1 0 0

perceptron training algorithm
Perceptron Training Algorithm
  • Start with a random value of w

ex: <0,0,0…>

  • Test for wxi > 0

If the test succeeds for i=1,2,…n

then return w

3. Modify w, wnext = wprev + xfail

tracing pta on or example
Tracing PTA on OR-example

w=<0,0,0> wx1 fails

w=<-1,0,1> wx4 fails

w=<0,0 ,1> wx2 fails

w=<-1,1,1> wx1 fails

w=<0,1,2> wx4 fails

w=<1,1,2> wx2 fails

w=<0,2,2> wx4 fails

w=<1,2,2> success

proof of convergence of pta
Proof of Convergence of PTA
  • Perceptron Training Algorithm (PTA)
  • Statement:

Whatever be the initial choice of weights and whatever be the vector chosen for testing, PTA converges if the vectors are from a linearly separable function.

proof of convergence of pta1
Proof of Convergence of PTA
  • Suppose wn is the weight vector at the nth step of the algorithm.
  • At the beginning, the weight vector is w0
  • Go from wi to wi+1 when a vector Xj fails the test wiXj > 0 and update wi as

wi+1 = wi + Xj

  • Since Xjs form a linearly separable function,

 w* s.t. w*Xj > 0 j

proof of convergence of pta2
Proof of Convergence of PTA
  • Consider the expression

G(wn) = wn . w*

| wn|

where wn = weight at nth iteration

  • G(wn) = |wn| . |w*| . cos 

|wn|

where  = angle between wn and w*

  • G(wn) = |w*| . cos 
  • G(wn) ≤ |w*| ( as -1 ≤ cos  ≤ 1)
behavior of numerator of g
Behavior of Numerator of G

wn . w* = (wn-1 + Xn-1fail ) . w*

  • wn-1 . w* + Xn-1fail . w*
  • (wn-2 + Xn-2fail ) . w* + Xn-1fail . w* …..
  • w0 . w*+ ( X0fail + X1fail +.... + Xn-1fail ). w*

w*.Xifail is always positive: note carefully

  • Suppose |Xj| ≥  , where  is the minimum magnitude.
  • Num of G ≥ |w0 . w*| + n  . |w*|
  • So, numerator of G grows with n.
behavior of denominator of g
Behavior of Denominator of G
  • |wn| =  wn . wn
  •  (wn-1 + Xn-1fail )2
  •  (wn-1)2+ 2. wn-1. Xn-1fail + (Xn-1fail )2
  •  (wn-1)2+ (Xn-1fail )2 (as wn-1. Xn-1fail ≤ 0 )
  •  (w0)2+ (X0fail )2 + (X1fail )2 +…. + (Xn-1fail )2
  • |Xj| ≤  (max magnitude)
  • So, Denom ≤ (w0)2+ n2
some observations
Some Observations
  • Numerator of G grows as n
  • Denominator of G grows as  n

=> Numerator grows faster than denominator

  • If PTA does not terminate, G(wn) values will become unbounded.
some observations contd
Some Observations contd.
  • But, as |G(wn)| ≤ |w*| which is finite, this is impossible!
  • Hence, PTA has to converge.
  • Proof is due to Marvin Minsky.
convergence of pta
Convergence of PTA
  • Whatever be the initial choice of weights and whatever be the vector chosen for testing, PTA converges if the vectors are from a linearly separable function.
assignment
Assignment
  • Implement the perceptron training algorithm
  • Run it on the 16 Boolean Functions of 2 inputs
  • Observe the behaviour
    • Take different initial values of the parameters
    • Note the number of iterations before convergence
    • Plot graphs for the functions which converge
ad