Book Image

Hyperparameter Tuning with Python

By : Louis Owen
Book Image

Hyperparameter Tuning with Python

By: Louis Owen

Overview of this book

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.
Table of Contents (19 chapters)
1
Section 1:The Methods
8
Section 2:The Implementation
13
Section 3:Putting Things into Practice

Implementing Population-Based Training

Population-Based Training (PBT) is one of the variants of the Heuristic Search hyperparameter tuning group (see Chapter 5) that the NNI package can implement. To show you how to implement PBT with NNI using pure Python code, let’s use the same example provided by the NNI package. Here, the MNIST dataset and a convolutional neural network model are utilized. We’ll use PyTorch to implement the neural network model. For details of the code example provided by NNI, please refer to the NNI GitHub repository (https://github.com/microsoft/nni/tree/1546962f83397710fe095538d052dc74bd981707/examples/trials/mnist-pbt-tuner-pytorch).

MNIST Dataset

MNIST is a dataset of handwritten digits that have been size-normalized and centered in a fixed-size image. Here, we’ll use the MNIST dataset provided directly by the PyTorch package (https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST...