Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Using Optuna for hyperparameter search

Optuna is one of the hyperparameter search tools that supports PyTorch. You can read in detail about the search strategies used by the tool, such as Tree-Structured Parzen Estimation (TPE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), in the Optuna paper [11]. Besides the advanced hyperparameter search methodologies, the tool also provides a sleek API, which we will explore in a moment.

In this section, we will once again build and train the MNIST model, but this time, using Optuna to figure out the optimal hyperparameter setting. We will discuss important parts of the code step by step in the form of an exercise. The full code can be found on our GitHub [12].

Defining the model architecture and loading the dataset

First, we will define an Optuna-compliant model object. By Optuna-compliant, we mean adding APIs within the model definition code that are provided by Optuna to enable the parameterization of the model hyperparameters...