CODEWITHSUNDEEP
pythonperceptron
JackAW
JackAW

Reputation: 173

Perceptron single layered

I'm struggling to implement a single layered perceptron: http://en.wikipedia.org/wiki/Perceptron. My program, depending on the weights, either is lost in the learning loop or find wrong weights. As a test case I use logical AND. Could you please give a hind why my perceptron does not converge? This is for my own learning. Thanks.

# learning rate
rate = 0.1

# Test data
# logical AND
# vector = (bias, coordinate1, coordinate2, targetedresult)

testdata = [[1, 0, 0, 0], [1, 0, 1, 0], [1, 1, 0, 0], [1, 1, 1, 1]]

# initial weigths
import random
w = [random.random(), random.random(), random.random()]
print 'initial weigths = ', w

def test(w, vector):
    if diff(w, vector) <= 0.1:
        return True
    else:
        return False

def diff(w, vector):
    from copy import deepcopy
    we = deepcopy(w)
    return dirac(sum(we[i]*vector[i] for i in range(3))) - vector[3]

def improve(w, vector):
    for i in range(3):
        w[i] += rate*diff(w, vector)*vector[i]
    return w

def dirac(z):
    if z > 0:
        return 1
    else:
        return 0

error = True

while error == True:
    discrepancy = 0
    for x in testdata:
        if not test(w, x):
            w = improve(w, x)
            discrepancy += 1
    if discrepancy == 0:
        print 'improved weigths = ', w
        error = False

Upvotes: 0

Views: 940

Answers (2)

JunkMechanic
JunkMechanic

Reputation: 438

  1. The only glitch I can see is in the activation function. Increase the cut off value, (z > 0.5).
  2. Also, since there are only 4 input cases in each epoch, it very difficult to work with 0 and 1 as the only output. Try removing the dirac function and increasing the threshold to 0.2. It might take longer to learn but will be much more precise. Of course in case of NAND you dont really need to be. But it helps in understanding.

Upvotes: 1

Drew Hall
Drew Hall

Reputation: 29047

It looks like you need an extra loop surrounding your for loop to iterate the improvement until your solutions converge (step 3 in the Wikipedia page you linked).

As it stands now, you give each training case exactly one chance to update the weights, so it has no chance to converge.

Upvotes: 1

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