Book Image

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
Book Image

Hands-On Machine Learning with C++

By: Kirill Kolodiazhnyi

Overview of this book

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.
Table of Contents (19 chapters)
Section 1: Overview of Machine Learning
Section 2: Machine Learning Algorithms
Section 3: Advanced Examples
Section 4: Production and Deployment Challenges

Exporting and Importing Models

In this chapter, we will discuss how to save and load model parameters during and after training. This is quite an important issue since real model training can take a very long time (from days to weeks), and we want to be able to save intermediate results and then load them for use in evaluation mode in production.

Such regular save operations can be beneficial in the case of a random application crash. Another substantial feature of any machine learning (ML) framework is its ability to export the model architecture, which allows us to share models between frameworks and makes model deployment easier. The main topic of this chapter is to show how to export and import model parameters such as weights and bias values with different C++ libraries. The second part of this chapter is all about the Open Neural Network Exchange (ONNX) format, which is...