5.7 Implementing PBP

Because PBP is quite complex, we’ll implement it as a class. Doing so will keep our example code tidy and allow us to easily compartmentalize our various blocks of code. It will also make it easier to experiment with, for example, if you want to explore changing the number of units or layers in your network.

Step 1: Importing libraries

We begin by importing various libraries. In this example, we will use scikit-learn’s California Housing dataset to predict house prices:

 
from typing import List, Union, Iterable  
import math  
from sklearn import datasets  
from sklearn.model_selection import train_test_split  
import tensorflow as tf  
import numpy as np  
from tensorflow.python.framework import tensor_shape  
import tensorflow_probability as tfp

To make sure we produce the same output every time, we initialize our seeds:

 
RANDOM_SEED...