import numpy as np

sample = np.array([[0, 0, 1],
              [0, 1, 1],
              [1, 0, 1],
              [1, 1, 1]])

label = np.array([[0], [0], [1], [1]])

def activate(x):
    return 1/(1+np.exp(-x))

learning_rate = 1.0
w0 = 2 * np.random.random((3, 1)) - 1  # le it cast in -1 to 1

for iter in range(1000):
    l0 = sample
    l1 = activate(np.dot(l0,w0))
    l1_err = label - l1
    w0_delta = l1_err * l1 * (1-l1)
    w0 += learning_rate*np.dot(l0.T,w0_delta)
print w0_delta
print l1