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

hyperparameters_1-snippet.py

import tensorflow as tf

# 1st layer hyperparameters of our network
X = tf.Variable([[0, 0], [0, 1], [1, 0], [1, 1]])   # inputs where the rows correspond to a single input.
W = tf.Variable([[1, 1], [1, 1]])                   # weights between hidden layer and input layer.
c = tf.Variable([[0], [-1]])                        # biases for hidden units.