Generative Adversarial Networks Projects :: Build Next-Generation Generative Models Using TensorFlow and Keras.
In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleG...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases. |
| Beschreibung: | Training the DCGAN |
| Beschreibung: | 1 online resource (310 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781789134193 1789134196 |
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| 245 | 1 | 0 | |a Generative Adversarial Networks Projects : |b Build Next-Generation Generative Models Using TensorFlow and Keras. |
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score | |
| 505 | 8 | |a The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN | |
| 505 | 8 | |a The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary | |
| 505 | 8 | |a Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN | |
| 505 | 8 | |a Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator | |
| 500 | |a Training the DCGAN | ||
| 520 | |a In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases. | ||
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Neural Networks, Computer |0 https://id.nlm.nih.gov/mesh/D016571 | |
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| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
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| 776 | 0 | 8 | |i Print version: |a Ahirwar, Kailash. |t Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789136678 |
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Ahirwar, Kailash. Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. Birmingham : Packt Publishing Ltd, 2019. 1 online resource (310 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator Training the DCGAN In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases. Includes bibliographical references. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Machine learning. bicssc Mathematical theory of computation. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Print version: Ahirwar, Kailash. Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. Birmingham : Packt Publishing Ltd, ©2019 9781789136678 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2018972 Volltext |
| spellingShingle | Ahirwar, Kailash Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Machine learning. bicssc Mathematical theory of computation. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_auth | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_exact_search | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_full | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_fullStr | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_full_unstemmed | Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |
| title_short | Generative Adversarial Networks Projects : |
| title_sort | generative adversarial networks projects build next generation generative models using tensorflow and keras |
| title_sub | Build Next-Generation Generative Models Using TensorFlow and Keras. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Machine learning. bicssc Mathematical theory of computation. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| topic_facet | Machine learning. Neural networks (Computer science) Artificial intelligence. Neural Networks, Computer Artificial Intelligence Machine Learning Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. Mathematical theory of computation. COMPUTERS General. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2018972 |
| work_keys_str_mv | AT ahirwarkailash generativeadversarialnetworksprojectsbuildnextgenerationgenerativemodelsusingtensorflowandkeras |