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