simple_neuralnet_math.py

import numpy as np

def nonlin(x,deriv=False):
	if(deriv==True):
	    return x*(1-x)

	return 1/(1+np.exp(-x))
    
X = np.array([[0,0,1],
            [0,1,1],
            [1,0,1],
            [1,1,1]])
                
y = np.array([[0],
			[1],
			[1],
			[0]])

np.random.seed(1)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1

for j in xrange(60000):

	# Feed forward through layers 0, 1, and 2
    k0 = X
    k1 = nonlin(np.dot(k0,syn0))
    k2 = nonlin(np.dot(k1,syn1))

    # how much did we miss the target value?
    k2_error = y - k2

    if (j% 10000) == 0:
        print "Error:" + str(np.mean(np.abs(k2_error)))
        
    # in what direction is the target value?
    # were we really sure? if so, don't change too much.
    k2_delta = k2_error*nonlin(k2,deriv=True)

    # how much did each k1 value contribute to the k2 error (according to the weights)?
    k1_error = k2_delta.dot(syn1.T)
    
    # in what direction is the target k1?
    # were we really sure? if so, don't change too much.
    k1_delta = k1_error * nonlin(k1,deriv=True)

    syn1 += k1.T.dot(k2_delta)
    syn0 += k0.T.dot(k1_delta)