2layerNN.py

import numpy as np

# sigmoid function
def nonlin(x, deriv=False):
    if(deriv==True):
        return x*(1-x)
    return 1/(1+np.exp(-x))

# input dataset
X = np.array([ [0,0,1],
               [0,1,1],
               [1,0,1],
               [1,1,1]])

# output dataset
y = np.array([[0,0,1,1]]).T

# seed random numbers to make calculation
# deterministic (just a good practice)
np.random.seed(1)

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

for iter in xrange(10000):

    #forward propagation
    l0 = X
    l1 = nonlin(np.dot(l0,syn0))

    # 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
    syn0 += np.dot(l0.T, l1_delta)

print "Output After Training:"
print l1