import numpy as np

# sigmoid function

def sigmoid(x, derivative=False):

if derivative:

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

l1 = []

for iteration in range(1000000):

# forward propagation

l0 = X

l1 = sigmoid(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 * sigmoid(l1, True)

# update weights

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

print("Output After Training:")

print(l1)