ANN | Python Fiddle

X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
l2_delta = (y - l2)*(l2*(1-l2))
l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
syn1 += l1.T.dot(l2_delta)
syn0 += X.T.dot(l1_delta)

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

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