WordPress butchered the tabs and python loves tabs, would need some love before this snipit would run.

import numpy as np def nonlin(x,deriv=False): if(deriv==True): return x*(1-x) return 1/(1+np.exp(-x)) #small dataset X = np.array([[0,0,1], [0,1,1], [1,0,1], [1,1,1], [0,1,0], [0,0,0]]) #small result set y = np.array([[0], [1], [1], [0], [0], [0]]) np.random.seed(1) # randomly initialize our weights with mean 0 syn0 = 2*np.random.random((3,2)) - 1 syn1 = 2*np.random.random((2,1)) - 1 for j in xrange(90000): if (j == 89999): print j X = np.array([[1,0,0]]) # Feed forward through layers 0, 1, and 2 l0 = X l1 = nonlin(np.dot(l0,syn0)) l2 = nonlin(np.dot(l1,syn1)) if (j == 89999): print l2 # how much did we miss the target value? l2_error = y - l2 if (j% 10000) == 0: print "Error:" + str(np.mean(np.abs(l2_error))) # in what direction is the target value? # were we really sure? if so, don't change too much. l2_delta = l2_error*nonlin(l2,deriv=True) # how much did each l1 value contribute to the l2 error (according to the weights)? l1_error = l2_delta.dot(syn1.T) # in what direction is the target l1? # were we really sure? if so, don't change too much. l1_delta = l1_error * nonlin(l1,deriv=True) syn1 += l1.T.dot(l2_delta) syn0 += l0.T.dot(l1_delta)