16. Consider the parameters learned for the neural network in Example 7.20. Give a logical formula

(or a decision tree) representing the Boolean function that is the value for the hidden units and the output units. This formula should not refer to any real numbers. [Suppose that, in the output of a neural network, any value greater than 0.5 is considered true and any less than 0.5 is false

(i.e., any positive value before the activation function is true, and a negative value is false). Also consider whether intermediate values of the hidden units matter.]
[Hint: One brute-force method is to go through the 16 combinations of values for the inputs to each hidden unit and determine the truth value of the output. A better method is to try to understand the functions themselves.]
Does the neural network learn the same function as a decision tree with classifications at the leaves? If not, what is the smallest decision tree that represents the same function?