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 data
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 step
for j in range(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:" + 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)

    #update weights
    syn1 += l1.T.dot(l2_delta)
    syn0 += l0.T.dot(l1_delta)

print("Output after training")
print(l2)