perceptron.py

import numpy as np

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 dastaset
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 item in range(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 )