Can ReLU replace a Sigmoid Activation Function in Neural Network

Question

I'm new into this and I'm trying to replace the sigmoid activation function in the following simple NN with ReLU. Can I do that? I've tried replacing the sigmoid function, but it's not working. The output should be the AND gate(if input (0,0)-> output 0).

import numpy as np

 # sigmoid function
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],
          [0, 1],
          [1, 0],
          [1, 1]])

# output dataset            
y = np.array([[0, 0, 0, 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((2, 1)) - 1

for iter in xrange(10000):

    # forward propagation
    l0 = X
    l1 = nonlin(np.dot(l0,syn0))

    # how much did we miss?
    l1_error = y - l1

    l1_delta = l1_error * nonlin(l1, True)

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