neuralnet.py

import numpy as np

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

#input data
X = np.array([  [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ])
#output
y = np.array([[0],
              [1],
              [1],
              [0]])

np.random.seed(1)

#synapses
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1

#training
for j in xrange(60000):
  l0 = X
  l1 = nonlin(np.dot(l0,syn0))
  l2 = nonlin(np.dot(l1,syn1))
  
  l2_error = y - l2
  
  if(j%10000) == 0:
    print "Error rate: " + str(np.mean(np.abs(l2_error)))
  
  l2_delta = l2_error*nonlin(l2, deriv=True)
  l1_error = l2_delta.dot(syn1.T)
  l1_delta = l1_error*nonlin(l1, deriv=True)
  
  syn1 += l1.T.dot(l2_delta)
  syn0 += l0.T.dot(l1_delta)

print "Output after training:"
print l2