I am trying to implement a simple neural net. I want to print the initial pattern, weights, activation. I then want it to print the learning process (i.e. every pattern it goes through as it learns). I am as yet unable to do this - it returns the initial and final pattern (whn I put print p in appropriate places), but nothing else. Hints and tips appreciated - I'm a complete newbie to Python!
#!/usr/bin/python
import random
p = [ [1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1] ] # pattern I want the net to learn
n = 5
alpha = 0.01
activation = [] # unit activations
weights = [] # weights
output = [] # output
def initWeights(n): # set weights to zero, n is the number of units
global weights
weights = [[[0]*n]*n] # initialised to zero
def initNetwork(p): # initialises units to activation
global activation
activation = p
def updateNetwork(k): # pick unit at random and update k times
for l in range(k):
unit = random.randint(0,n-1)
activation[unit] = 0
for i in range(n):
activation[unit] += output[i] * weights[unit][i]
output[unit] = 1 if activation[unit] > 0 else -1
def learn(p):
for i in range(n):
for j in range(n):
weights += alpha * p[i] * p[j]