XOR gate solution to my post @ https://rahulbhalley.github.io/posts/2017-02/neural-networks-explained.html

feedforward_2-snippet.py

# 2nd feedforward propagation
a_XW_c_w = tf.matmul(a_XW_c, w)
a_XW_c_w_b = tf.add(a_XW_c_w, b)

init = tf.global_variables_initializer()

# Launch the TensorFlow graph session
with tf.Session() as sess:
	# initialize all the variables
	sess.run(init)
	outputs = feedforward()
	
	print('XOR Inputs:')
	for i in range(4):
		print(sess.run(X[i]))
	print('XOR Outputs:')
	for i in range(4):
		print(sess.run(outputs[i]))