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Mastering PyTorch
Mastering PyTorch

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond , Second Edition

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Mastering PyTorch

Deep CNN Architectures

In this chapter, we will first briefly review the evolution of Convolutional Neural Network (CNN) architectures, and then we will study the different CNN architectures in detail. We will implement these CNN architectures using PyTorch, and in doing so, we aim to exhaustively explore the tools (modules and built-in functions) that PyTorch has to offer in the context of building Deep CNNs. Gaining strong CNN expertise in PyTorch will enable us to solve a number of deep learning problems involving CNNs. This will also help us in building more complex deep learning models or applications of which CNNs are a part.

This chapter will cover the following topics:

  • Why are CNNs so powerful?
  • Evolution of CNN architectures
  • Developing LeNet from scratch
  • Fine-tuning the AlexNet model
  • Running a pretrained VGG model
  • Exploring GoogLeNet and Inception v3
  • Discussing ResNet and DenseNet architectures
  • Understanding EfficientNets and the future of CNN architectures

All the code files for this chapter can be found at https://github.com/arj7192/MasteringPyTorchV2/tree/main/Chapter03.

Let us start by discussing the key features of CNNs.

Why are CNNs so powerful?

CNNs are among the most powerful machine learning models at solving challenging problems such as image classification, object detection, object segmentation, video processing, natural language processing, and speech recognition. Their success is attributed to various factors, such as the following:

  • Weight sharing: This makes CNNs parameter-efficient; that is, different features are extracted using the same set of weights or parameters. Features are the high-level representations of input data that the model generates with its parameters.
  • Automatic feature extraction: Multiple feature extraction stages help a CNN to automatically learn feature representations in a dataset.
  • Hierarchical learning: The multi-layered CNN structure helps CNNs to learn low-, mid-, and high-level features.
  • The ability to explore both spatial and temporal correlations in the data, such as in video-processing tasks.

Besides these pre-existing fundamental characteristics, CNNs have advanced over the years with the help of improvements in the following areas:

  • The use of better activation and loss functions, such as using ReLU to overcome the vanishing gradient problem.
  • Parameter optimization, such as using an optimizer based on Adaptive Momentum (Adam) instead of simple stochastic gradient descent.
  • Regularization: Applying dropouts and batch normalization besides L2 regularization.

FAQ – What is the vanishing gradient problem?

Backpropagation in neural networks works on the basis of the chain rule of differentiation. According to the chain rule, the gradient of the loss function with respect to the input layer parameters can be written as a product of gradients at each layer. If these gradients are all less than 1 – and worse still, tending toward 0 – then the product of these gradients will be a vanishingly small value. The vanishing gradient problem can cause serious trouble in the optimization process by preventing the network parameters from changing their values, which is equivalent to stunted learning.

But some of the most significant drivers of development in CNNs over the years have been the various architectural innovations:

  • Spatial exploration-based CNNs: The idea behind spatial exploration is using different kernel sizes in order to explore different levels of visual features in input data. The following diagram shows a sample architecture for a spatial exploration-based CNN model:
Figure 3.1 – Spatial exploration-based CNN

Figure 2.1: Spatial exploration-based CNN

  • Depth-based CNNs: The depth here refers to the depth of the neural network, that is, the number of layers. So, the idea here is to create a CNN model with multiple convolutional layers in order to extract highly complex visual features. The following diagram shows an example of such a model architecture:
Figure 3.2 – Depth-based CNN

Figure 2.2: Depth-based CNN

  • Width-based CNNs: Width refers to the number of channels or feature maps in the data or features extracted from the data. So, width-based CNNs are all about increasing the number of feature maps as we go from the input to the output layers, as demonstrated in the following diagram:
Figure 3.3 – Width-based CNN

Figure 2.3: Width-based CNN

  • Multi-path-based CNNs: So far, the preceding three types of architectures have had monotonicity in connections between layers; that is, direct connections exist only between consecutive layers. Multi-path CNNs brought the idea of making shortcut connections or skip connections between non-consecutive layers. The following diagram shows an example of a multi-path CNN model architecture:
Figure 3.4 – Multi-path CNN

Figure 2.4: Multi-path CNN

A key advantage of multi-path architectures is a better flow of information across several layers, thanks to the skip connections. This, in turn, also lets the gradient flow back to the input layers without too much dissipation.

Having looked at the different architectural setups found in CNN models, we will now look at how CNNs have evolved over the years ever since they were first used.

Evolution of CNN architectures

CNNs have been in existence since 1989, when the first multi-layered CNN was developed by Yann LeCun. This model could perform the visual cognition task of identifying handwritten digits. In 1998, LeCun developed an improved ConvNet model called LeNet. Due to its high accuracy in optical recognition tasks, LeNet was adopted for industrial use soon after its invention. Ever since, CNNs have been successful not only in academic research but also in practical industry use cases. The following diagram shows a brief timeline of architectural developments in the lifetime of CNNs, starting from 1989 all the way to 2020:

Figure 3.5 – CNN architecture evolution – a broad picture

Figure 2.5: CNN architecture evolution – a broad picture

As we can see, there is a significant gap between the years 1998 and 2012. This was for two reasons:

  1. There wasn’t a dataset big and suitable enough to demonstrate the capabilities of CNNs, especially deep CNNs.
  2. The available computing power was limited.

And to add to the first reason, on the existing small datasets of the time such as MNIST, classical machine learning models such as SVMs were starting to beat CNN’s performance.

The above two limitations were alleviated as we transitioned from 1998 to 2012 and beyond. Firstly, we had an exponential growth in digital data thanks to the advent of the internet and access to affordable devices such as digital cameras and smartphones. Secondly, we saw an enormous increase in our computational capabilities including the arrival of GPUs.

These changes led to a few CNN developments. The ReLU activation function was developed in order to deal with the gradient explosion and decay problem during backpropagation. Non-random initialization of network parameter values proved to be crucial. Max pooling was invented as an effective method for subsampling. GPUs were getting popular for training neural networks, especially CNNs, at scale.

Finally, and most importantly, a large-scale dedicated dataset of annotated images called ImageNet [1] was created by a research group at Stanford. This dataset is still one of the primary benchmarking datasets for CNN models to date.

With all of these developments compounding over the years, in 2012, a different architectural design brought about a massive improvement in CNN performance on the ImageNet dataset. This network was called AlexNet (named after the creator, Alex Krizhevsky). AlexNet, along with having various novel aspects such as random cropping and pretraining, established the trend of uniform and modular convolutional layer design. The uniform and modular layer structure was taken forward by repeatedly stacking such modules (of convolutional layers), resulting in very deep CNNs also known as VGGs.

Another approach of branching the blocks/modules of convolutional layers and stacking these branched blocks on top of each other proved extremely effective for tailored visual tasks. This network was called GoogLeNet (as it was developed at Google) or Inception v1 (inception being the term for those branched blocks). Several variants of the VGG and Inception networks followed, such as VGG16, VGG19, Inception v2, Inception v3, and so on.

The next phase of development began with skip connections. To tackle the problem of gradient decay while training CNNs, non-consecutive layers were connected via skip connections lest information dissipate between them due to small gradients. It should be noted that skip connections are essentially a special case of multi-path-based CNNs discussed earlier. A popular type of network that emerged with this trick, among other novel characteristics such as batch normalization, was ResNet.

A logical extension of ResNet was DenseNet, where layers were densely connected to each other; that is, each layer gets the input from all the previous layers’ output feature maps. Furthermore, hybrid architectures were then developed by mixing successful architectures from the past such as Inception-ResNet and ResNeXt, where the parallel branches within a block were increased in number.

Lately, the channel boosting technique has proven useful in improving CNN performance. The idea here is to learn novel features and exploit pre-learned features through transfer learning. Most recently, automatically designing new blocks and finding optimal CNN architectures has been a growing trend in CNN research. Examples of such CNNs are MnasNets and EfficientNets. The approach behind these models is to perform a neural architecture search to deduce an optimal CNN architecture with a uniform model scaling approach.

In the next section, we will go back to one of the earliest CNN models and take a closer look at the various CNN architectures developed since. We will build these architectures using PyTorch, training some of the models on real-world datasets. We will also explore PyTorch’s pretrained CNN models repository, popularly known as model-zoo. We will learn how to fine-tune these pretrained models as well as running predictions on them.

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Key benefits

  • Understand how to use PyTorch to build advanced neural network models
  • Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker
  • Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Description

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.

Who is this book for?

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

What you will learn

  • Implement text, vision, and music generation models using PyTorch
  • Build a deep Q-network (DQN) model in PyTorch
  • Deploy PyTorch models on mobile devices (Android and iOS)
  • Become well versed in rapid prototyping using PyTorch with fastai
  • Perform neural architecture search effectively using AutoML
  • Easily interpret machine learning models using Captum
  • Design ResNets, LSTMs, and graph neural networks (GNNs)
  • Create language and vision transformer models using Hugging Face
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Table of Contents

20 Chapters
Overview of Deep Learning Using PyTorch Chevron down icon Chevron up icon
Deep CNN Architectures Chevron down icon Chevron up icon
Combining CNNs and LSTMs Chevron down icon Chevron up icon
Deep Recurrent Model Architectures Chevron down icon Chevron up icon
Advanced Hybrid Models Chevron down icon Chevron up icon
Graph Neural Networks Chevron down icon Chevron up icon
Music and Text Generation with PyTorch Chevron down icon Chevron up icon
Neural Style Transfer Chevron down icon Chevron up icon
Deep Convolutional GANs Chevron down icon Chevron up icon
Image Generation Using Diffusion Chevron down icon Chevron up icon
Deep Reinforcement Learning Chevron down icon Chevron up icon
Model Training Optimizations Chevron down icon Chevron up icon
Operationalizing PyTorch Models into Production Chevron down icon Chevron up icon
PyTorch on Mobile Devices Chevron down icon Chevron up icon
Rapid Prototyping with PyTorch Chevron down icon Chevron up icon
PyTorch and AutoML Chevron down icon Chevron up icon
PyTorch and Explainable AI Chevron down icon Chevron up icon
Recommendation Systems with PyTorch Chevron down icon Chevron up icon
PyTorch and Hugging Face Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Franziska Kirschner Sep 02, 2024
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Absolutely love this book both as a reference and to learn new techniques.I'm a ML researcher converting from Tensorflow to Pytorch and wanted a reference guide as I made the transition. The hands-on code examples were super useful to get up and running, and much more clearly explained than just trying to Google what to do.The pieces on engineering included a bunch of optimisations I hadn't considered in in the past, so I ended up learning a lot more than I anticipated. This book is very well-rounded and considers both the practical application and the theory behind it.I would highly recommend to any ML researcher or engineer!
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Fabio Milano Sep 07, 2024
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If you're looking for a hands-on, comprehensive guide to modern neural network architectures and the PyTorch ecosystem, this book is a gem! The author's decade of deep learning experience shines through with practical, step-by-step instructions that truly guide you in building state-of-the-art neural networks. The balance between depth and breadth is perfect.Whether you're a data scientist or machine learning engineer wanting to upskill in the latest deep learning tools and frameworks, or a software engineer curious about modern machine learning, this book has got you covered. The clear explanations and practical examples make complex concepts easy to grasp and apply. Highly recommended!
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Arun May 31, 2024
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The book is an easy-read. It is quite resourceful for anyone looking to deepen their understanding of neural network models and its practical implementation using pytorch.The concepts are well-explained with examples. I like the way the book covered different type of data: images, text, sounds, etc. There are things for everyone.Other attraction is that it adapts to emerging concepts like diffusion models and integration with huggingface leveraging off-the-shelf pretrained models. Further, it provides practical guidance on deploying models to production, including on mobile devices.Overall, Mastering PyTorch is a must-have for a deep learning enthusiast who looks into recent learning techniques and its applications. Highly recommended!
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Kelsey S. Jul 27, 2024
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I've been working with PyTorch for a while now, but I really feel like this book took my skills to the next level. The explanations are clear and concise, and the code examples really helped me to understand the concepts. I especially appreciated the coverage of advanced techniques like generative models and graph neural networks, but most of the content is also completely suitable for beginners. I appreciate that the author also focuses on the modern landscape, including how to transition from TensorFlow to PyTorch, coverage of LLMs, neural networks in mobile settings (eg quantized models), autoML, integration with Hugging Face and beyond. I've already used some of the techniques from the book in my own work, and I'm really excited to see what else I can accomplish with PyTorch. Overall, I highly recommend this book to anyone who wants to learn more about PyTorch and deep learning.
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Saloni Shukla Jun 09, 2024
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I enjoyed reading "Mastering PyTorch" because it offered a hands-on, practical approach to deep learning with PyTorch. The clear explanations, real-world examples, and up-to-date content kept me engaged and informed. The expert insights and comprehensive coverage made complex concepts accessible and relevant, significantly enhancing my learning experience.
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