book

Generative Deep Learning

by David Foster

June 2019

Intermediate to advanced

327 pages

7h 36m

English

O'Reilly Media, Inc.

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Objective and ApproachPrerequisitesOther ResourcesConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
What Is Generative Modeling?Generative Versus Discriminative ModelingAdvances in Machine LearningThe Rise of Generative ModelingThe Generative Modeling FrameworkProbabilistic Generative ModelsHello Wrodl!Your First Probabilistic Generative ModelNaive BayesHello Wrodl! ContinuedThe Challenges of Generative ModelingRepresentation LearningSetting Up Your EnvironmentSummary
Structured and Unstructured DataDeep Neural NetworksKeras and TensorFlowYour First Deep Neural NetworkLoading the DataBuilding the ModelCompiling the ModelTraining the ModelEvaluating the ModelImproving the ModelConvolutional LayersBatch NormalizationDropout LayersPutting It All TogetherSummary
The Art ExhibitionAutoencodersYour First AutoencoderThe EncoderThe DecoderJoining the Encoder to the DecoderAnalysis of the AutoencoderThe Variational Art ExhibitionBuilding a Variational AutoencoderThe EncoderThe Loss FunctionAnalysis of the Variational AutoencoderUsing VAEs to Generate FacesTraining the VAEAnalysis of the VAEGenerating New FacesLatent Space ArithmeticMorphing Between FacesSummary
GanimalsIntroduction to GANsYour First GANThe DiscriminatorThe GeneratorTraining the GANGAN ChallengesOscillating LossMode CollapseUninformative LossHyperparametersTackling the GAN ChallengesWasserstein GANWasserstein LossThe Lipschitz ConstraintWeight ClippingTraining the WGANAnalysis of the WGANWGAN-GPThe Gradient Penalty LossAnalysis of WGAN-GPSummary
Apples and OrgangesCycleGANYour First CycleGANOverviewThe Generators (U-Net)The DiscriminatorsCompiling the CycleGANTraining the CycleGANAnalysis of the CycleGANCreating a CycleGAN to Paint Like MonetThe Generators (ResNet)Analysis of the CycleGANNeural Style TransferContent LossStyle LossTotal Variance LossRunning the Neural Style TransferAnalysis of the Neural Style Transfer ModelSummary
The Literary Society for Troublesome MiscreantsLong Short-Term Memory NetworksYour First LSTM NetworkTokenizationBuilding the DatasetThe LSTM ArchitectureThe Embedding LayerThe LSTM LayerThe LSTM CellGenerating New TextRNN ExtensionsStacked Recurrent NetworksGated Recurrent UnitsBidirectional CellsEncoder–Decoder ModelsA Question and Answer GeneratorA Question-Answer DatasetModel ArchitectureInferenceModel ResultsSummary
PreliminariesMusical NotationYour First Music-Generating RNNAttentionBuilding an Attention Mechanism in KerasAnalysis of the RNN with AttentionAttention in Encoder–Decoder NetworksGenerating Polyphonic MusicThe Musical OrganYour First MuseGANThe MuseGAN GeneratorChords, Style, Melody, and GrooveThe Bar GeneratorPutting It All TogetherThe CriticAnalysis of the MuseGANSummary

Reinforcement LearningOpenAI GymWorld Model ArchitectureThe Variational AutoencoderThe MDN-RNNThe ControllerSetupTraining Process OverviewCollecting Random Rollout DataTraining the VAEThe VAE ArchitectureExploring the VAECollecting Data to Train the RNNTraining the MDN-RNNThe MDN-RNN ArchitectureSampling the Next z and Reward from the MDN-RNNThe MDN-RNN Loss FunctionTraining the ControllerThe Controller ArchitectureCMA-ESParallelizing CMA-ESOutput from the Controller TrainingIn-Dream TrainingIn-Dream Training the ControllerChallenges of In-Dream TrainingSummary
Five Years of ProgressThe TransformerPositional EncodingMultihead AttentionThe DecoderAnalysis of the TransformerBERTGPT-2MuseNetAdvances in Image GenerationProGANSelf-Attention GAN (SAGAN)BigGANStyleGANApplications of Generative ModelingAI ArtAI Music

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Preface

What I cannot create, I do not understand.

Richard Feynman

An undeniable part of the human condition is our ability to create. Since our earliest days as cave people, we have sought opportunities to generate original and beautiful creations. For early man, this took the form of cave paintings depicting wild animals and abstract patterns, created with pigments placed carefully and methodically onto rock. The Romantic Era gave us the mastery of Tchaikovsky symphonies, with their ability to inspire feelings of triumph and tragedy through sound waves, woven together to form beautiful melodies and harmonies. And in recent times, we have found ourselves rushing to bookshops at midnight to buy stories about a fictional wizard, because the combination of letters creates a narrative that wills us to turn the page and find out what happens to our hero.

It is therefore not surprising that humanity has started to ask the ultimate question of creativity: can we create something that is in itself creative?

This is the question that generative modeling aims to answer. With recent advances in methodology and technology, we are now able to build machines that can paint original artwork in a given style, write coherent paragraphs with long-term structure, compose music that is pleasant to listen to, and develop winning strategies for complex games by generating imaginary future scenarios. This is just the start of a generative revolution that will leave us with no choice but to find answers ...