gistfile1.txt

import numpy as np

# input data
X = np.array([  [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ])
    
# output labels            
y = np.array([[0,0,1,1]]).T

# initialize weights randomly with mean 0
weights = 2*np.random.random((3,1)) - 1

#update weights (training/backpropagation)
for iter in xrange(10000):

    # forward propagation
    l0 = X
    l1 = nonlin(np.dot(l0,weights))

    # how much did we miss?
    l1_error = y - l1

    # multiply how much we missed by the 
    # slope of the sigmoid at the values in l1
    l1_delta = l1_error * nonlin(l1,True)

    # update weights
    weights += np.dot(l0.T,l1_delta)