Hands-On Generative Adversarial Networks with PyTorch 1.x

Description

Downloaded from https://turbobit.net/download/started/7p7wa2vn90gi, it contains .pdf, .epub, and .mobi format. The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Generative-Adversarial-Networks-with-PyTorch-1.x. In case there's an update to the code, it will be updated on the existing GitHub repository. We also have other code bundles from our rich catalog of books and videos available at https://github.com/​PacktPublishing/​. Check them out! We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789530513_ColorImages.pdf . Cover Title Page Copyright and Credits About Packt Contributors Table of Contents Preface Section 1: Introduction to GANs and PyTorch Chapter 1: Generative Adversarial Networks Fundamentals Fundamentals of machine learning Machine learning – classification and generation Introducing adversarial learning Generator and discriminator networks Mathematical background of GANs Using NumPy to train a sine signal generator Designing the network architectures Defining activation functions and the loss function Working on forward pass and backpropagation Training our GAN model What GAN we do? Image processing Image synthesis Image translation Video synthesis and translation NLP 3D modeling Summary References and useful reading list Chapter 2: Getting Started with PyTorch 1.3 What's new in PyTorch 1.3? Easy switching from eager mode to graph mode The C++ frontend The redesigned distributed library Better research reproducibility Miscellaneous The PyTorch ecosystem Cloud support Migrating your previous code to 1.x CUDA – GPU acceleration for fast training and evaluation Installing NVIDIA driver Installing CUDA Installing cuDNN Evaluating your CUDA installation Installing PyTorch on Windows and Linux Setting up the Python environment Installing Python Installing Anaconda Python Prerequisites before we move on Installing PyTorch Installing official binaries Building Pytorch from source Evaluating your PyTorch installation Bonus: setting up VS Code for Python coding Configuring VS Code for Python development Recommended VS Code extensions References and useful reading list Summary Chapter 3: Best Practices for Model Design and Training Model design cheat sheet Overall model architecture design Choosing a convolution operation method Choosing a downsampling operation method More on model design Model training cheat sheet Parameter initialization Adjusting the loss function Choosing an optimization method Adjusting the learning rate Gradient clipping, weight clipping, and more Efficient coding in Python Reinventing the wheel wisely Advice for beginners in deep learning Summary  Section 2: Typical GAN Models for Image Synthesis Chapter 4: Building Your First GAN with PyTorch Introduction to Deep Convolutional GANs The architecture of generator The architecture of a discriminator Creating a DCGAN with PyTorch Generator network Discriminator network Model training and evaluation Training iteration Visualizing generated samples Checking GPU usage information Moving to larger datasets Generating human faces from the CelebA dataset Generating bedroom photos from the LSUN dataset Having fun with the generator network Image interpolation Semantic vector arithmetic Summary References and useful reading list Chapter 5: Generating Images Based on Label Information CGANs – how are labels used? Combining labels with the generator Integrating labels into the discriminator Generating images from labels with the CGAN One-stop model training API Argument parsing and model training Working with Fashion-MNIST InfoGAN – unsupervised attribute extraction Network definitions of InfoGAN Training and evaluation of InfoGAN References and useful reading list Summary Chapter 6: Image-to-Image Translation and Its Applications Using pixel-wise labels to translate images with pix2pix Generator architecture Discriminator architecture Training and evaluation of pix2pix Pix2pixHD – high-resolution image translation Model architecture Model training CycleGAN – image-to-image translation from unpaired collections Cycle consistency-based model design Model training and evaluation Summary Furthering reading Chapter 7: Image Restoration with GANs Image super-resolution with SRGAN Creating a generator Creating the discriminator Defining training loss Training SRGAN to generate high-resolution images Generative image inpainting Efficient convolution – from im2col to nn.Unfold WGAN – understanding the Wasserstein distance Analyzing the problems with vanilla GAN loss The advantages of Wasserstein distance Training GAN for image inpainting Model design for image inpainting Implementation of Wasserstein loss Summary Useful reading list and references Chapter 8: Training Your GANs to Break Different Models Adversarial examples – attacking deep learning models What are adversarial examples and how are they created? Adversarial attacking with PyTorch Generative adversarial examples Preparing an ensemble classifier for Kaggle's Cats vs. Dogs Breaking the classifier with advGAN Summary References and further reading list Chapter 9: Image Generation from Description Text Text-to-image synthesis with GANs Quick introduction to word embedding Translating text to image with zero-shot transfer learning Zero-shot learning GAN architecture and training Generating photo-realistic images with StackGAN++ High-resolution text-to-image synthesis with StackGAN From StackGAN to StackGAN++ Training StackGAN++ to generate images with better quality Summary  Further reading Chapter 10: Sequence Synthesis with GANs Text generation via SeqGAN – teaching GANs how to tell jokes Design of SeqGAN – GAN, LSTM, and RL A quick introduction to RNN and LSTM Reinforcement learning versus supervised learning Architecture of SeqGAN Creating your own vocabulary for training Speech quality enhancement with SEGAN SEGAN architecture Training SEGAN to enhance speech quality Summary Further reading Chapter 11: Reconstructing 3D models with GANs Fundamental concepts in computer graphics Representation of 3D objects Attributes of a 3D object Camera and projection Designing GANs for 3D data synthesis Generators and discriminators in 3D-GAN Training 3D-GAN Summary Further reading Other Books You May Enjoy Index