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...