The preparations

You will find most of the code for this example at https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter13/Masks_part1.ipynb, up to the code used in the Certifying robustness with randomized smoothing section. The code for that section alone is located at https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter13/Masks_part2.ipynb.

Loading the libraries

To run this example, you need to install the following libraries:

• mldatasets to load the dataset
• numpy and sklearn (scikit-learn) to manipulate it
• tensorflow to fit the models
• matplotlib and seaborn to visualize the interpretations

You should load all of them first, as follows:

import math
import os
import warnings
warnings.filterwarnings("ignore")
import mldatasets
import numpy as np
from sklearn import preprocessing
import tensorflow as tf
from tensorflow.keras.utils import get_file
import...