Generative Deep Learning

Experiments based on O'Reilly's "Generative Deep Learning" books (1st & 2nd Editions).

Project Standards

All code and notebooks in this repository adhere to the following standards:

PEP 8 compliant code formatting
Comprehensive documentation and comments
Dynamic batch size and epoch scaling
W&B integration for experiment tracking
LRFinder for optimal learning rate
Step decay LR scheduler
Enhanced training visualizations
Kernel restart cell for GPU memory release

Project Structure

Generative_Deep_Learning/
├── utils/                  # Shared root utilities
│   ├── __init__.py
│   ├── callbacks.py        # LRFinder, LRLogger, get_lr_scheduler, get_early_stopping
│   ├── gpu_utils.py        # Dynamic batch size finder (binary search + OOM detection)
│   ├── wandb_utils.py      # W&B integration helpers (init_wandb, define_wgan_charts)
│   └── gan/                # GAN-specific utilities
│       ├── __init__.py
│       ├── metrics.py      # Per-epoch training metrics collection
│       ├── quality_metrics.py  # FID, IS, pixel variance
│       ├── stability_analysis.py  # Training stability indicators
│       ├── report_generator.py    # WGAN analysis reports
│       ├── cyclegan_report_generation.py  # CycleGAN reports
│       └── cyclegan_history_extraction.py # CycleGAN history extraction
│
├── scripts/                # Notebook standardization scripts (10 files)
│   ├── standardize_*.py    # Notebook standardization scripts
│   ├── update_*.py         # Cell update scripts
│   └── fix_*.py            # Bug fix scripts
│
├── v1/                     # 1st Edition (2019) - 22 notebooks
│   ├── notebooks/          # Jupyter notebooks (.ipynb)
│   │   ├── 02_*            # Deep Learning basics (MLP, CNN)
│   │   ├── 03_*            # Autoencoders & VAEs
│   │   ├── 04_*            # GANs (GAN, WGAN, WGANGP)
│   │   ├── 05_*            # CycleGAN
│   │   ├── 06_*            # Text generation (LSTM, Q&A)
│   │   ├── 07_*            # Music generation (MuseGAN)
│   │   └── 09_*            # Positional encoding
│   ├── data_download_scripts/  # Data download scripts
│   │   ├── download_camel_data.sh
│   │   ├── download_celeba_kaggle.sh
│   │   ├── download_cyclegan_data.sh
│   │   └── download_gutenburg_data.sh
│   ├── src/
│   │   ├── models/         # Model implementations
│   │   │   ├── AE.py       # Autoencoder
│   │   │   ├── VAE.py      # Variational Autoencoder
│   │   │   ├── GAN.py      # Vanilla GAN
│   │   │   ├── WGAN.py     # Wasserstein GAN (with per-epoch metrics)
│   │   │   ├── WGANGP.py   # WGAN with Gradient Penalty
│   │   │   ├── cycleGAN.py # Image-to-image translation
│   │   │   ├── MuseGAN.py  # Music generation
│   │   │   ├── RNNAttention.py  # Attention for sequences
│   │   │   └── layers/     # Custom layers (InstanceNorm, ReflectionPadding)
│   │   └── utils/          # V1-specific loaders, preprocessing
│   ├── data/               # Downloaded datasets (gitignored)
│   ├── run/                # Model outputs (gitignored)
│   └── AGENTS.md           # V1-specific AI agent context
│
├── v2/                     # 2nd Edition (2023) - Organized by chapter
│   ├── 02_deeplearning/    # MLP, CNN basics
│   ├── 03_vae/             # Variational Autoencoders
│   ├── 04_gan/             # GANs
│   ├── 05_autoregressive/  # LSTM, Transformers
│   ├── 06_normflow/        # Normalizing Flows
│   ├── 07_ebm/             # Energy-Based Models
│   ├── 08_diffusion/       # Diffusion Models
│   ├── 09_transformer/     # Attention Mechanisms
│   ├── 11_music/           # Music Generation
│   ├── src/                # V2 models & utilities
│   ├── utils.py            # Shared V2 utilities
│   └── AGENTS.md           # V2-specific AI agent context
│
├── docker/                 # Docker configuration
│   ├── Dockerfile.cpu      # CPU-only image
│   ├── Dockerfile.gpu      # GPU image (nvidia-docker)
│   ├── launch-docker-cpu.sh
│   ├── launch-docker-gpu.sh
│   └── README.md           # Docker usage instructions
│
├── documentation/          # Project documentation (5 consolidated guides)
│   ├── QUICKSTART.md       # Installation, UV, and GPU setup
│   ├── TRAINING_GUIDE.md   # Callbacks, batch sizing, W&B
│   ├── GAN_GUIDE.md        # GAN metrics, stability, triage
│   ├── NOTEBOOK_STANDARDIZATION.md  # Complete workflow
│   └── TRAINING_STABILITY_ANALYSIS_TEMPLATE.md  # Analysis template
│
├── tests/                  # Test files
├── AGENTS.md               # Root AI agent context
├── README.md               # Project overview
├── pyproject.toml          # UV/uv dependencies
├── uv.lock                 # Locked dependencies
├── sample.env              # Environment template
└── LICENSE                 # GPL-3.0 license

Quick Start

1. Install UV Package Manager

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Setup Environment

uv sync

3. Run Jupyter Lab

uv run jupyter lab

Navigate to v1/notebooks/ or v2/<chapter>/ and open a notebook.

Environment Setup (.env)

This project requires a .env file for dataset downloads and experiment tracking. Create it by copying the template:

cp sample.env .env

Then edit .env with your credentials:

JUPYTER_PORT=8888
TENSORBOARD_PORT=6006
KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_api_key
WANDB_API_KEY=your_wandb_api_key
WANDB_PROJECT=generative-deep-learning

Getting Kaggle Credentials

Kaggle credentials are required to download datasets like CelebA, CIFAR-10, etc.

Create a Kaggle account at kaggle.com
Go to Account Settings → API → Create New Token
This downloads kaggle.json containing your credentials:
```
{"username":"your_username","key":"your_api_key"}
```
Copy these values to your .env file:
- KAGGLE_USERNAME = username from kaggle.json
- KAGGLE_KEY = key from kaggle.json

The dataset download scripts in v1/data_download_scripts/ will automatically read these credentials.

Getting W&B (Weights & Biases) Credentials

W&B is used for experiment tracking, loss visualization, and model comparison.

Create a W&B account at wandb.ai
Go to Settings → API Keys → Copy your API key
Set in .env:
- WANDB_API_KEY = your API key
- WANDB_PROJECT = project name (default: generative-deep-learning)

Alternatively, log in via terminal:

wandb login

Environment Variables Reference

Variable	Description	Required For
`JUPYTER_PORT`	Local port for Jupyter Lab (default: 8888)	Docker
`TENSORBOARD_PORT`	Local port for TensorBoard (default: 6006)	Docker
`KAGGLE_USERNAME`	Your Kaggle username	Dataset downloads
`KAGGLE_KEY`	Your Kaggle API key	Dataset downloads
`WANDB_API_KEY`	Your W&B API key	Experiment tracking
`WANDB_PROJECT`	W&B project name	Experiment tracking

Verifying Setup

# Test Kaggle credentials
source .env
kaggle datasets list

# Test W&B login
wandb login --verify

Requirements

Component	Requirement
Python	3.13+
TensorFlow	2.20+ (with bundled CUDA 12.x)
GPU (Recommended)	NVIDIA GTX 1060+ (8GB VRAM recommended)

See QUICKSTART.md for detailed GPU configuration.

Key Features

W&B Integration

All notebooks support Weights & Biases for experiment tracking:

import wandb
from wandb.integration.keras import WandbMetricsLogger

wandb.init(project="generative-deep-learning", config={...})
model.fit(x, y, callbacks=[WandbMetricsLogger()])
wandb.finish()

W&B Output Locations:

Data	Method	W&B Location
Images	`wandb.Table`	Tables > `image_gallery`
Viz	`wandb.Table`	Tables > `model_architecture`
Report	`wandb.save()`	Files tab

See TRAINING_GUIDE.md.

Learning Rate Finder

Find optimal learning rates automatically:

from utils.callbacks import LRFinder

lr_finder = LRFinder(min_lr=1e-6, max_lr=1e-1, steps=100)
model.fit(x, y, epochs=2, callbacks=[lr_finder])
lr_finder.plot_loss()
optimal_lr = lr_finder.get_optimal_lr()

Selection Methods:

Color	Method	Description
🔴	`'steepest'`	Aggressive, fast training
🟠	`'recommended'` ★	DEFAULT - Steepest / 3
🟣	`'valley'`	Robust, data-driven (80% decline)
🟢	`'min_loss_10'`	Conservative, stable

See TRAINING_GUIDE.md.

Notebook Standardization

Standardized workflow for all notebooks:

Global configuration block (BATCH_SIZE, EPOCHS, etc.)
W&B initialization with learning_rate="auto"
LRFinder on cloned model
Training with WandbMetricsLogger, LRLogger, get_lr_scheduler, get_early_stopping
Post-training visualization with log-scale LR plot
wandb.finish() cleanup

See NOTEBOOK_STANDARDIZATION.md.

Dynamic Batch Size

Automatically find optimal batch size using binary search with OOM detection:

from utils.gpu_utils import find_optimal_batch_size, calculate_adjusted_epochs

# After building model
BATCH_SIZE = find_optimal_batch_size(
    model=my_model,
    input_shape=(28, 28, 1),
)
EPOCHS = calculate_adjusted_epochs(200, 32, BATCH_SIZE)

Output:

DYNAMIC BATCH SIZE FINDER
Model Parameters: 1,234,567
Estimated Model Memory: 19.8 MB
  batch_size=   64 ✓
  batch_size=  512 ✓
  batch_size= 1024 ✗ OOM
✓ Optimal batch size: 460

See TRAINING_GUIDE.md.

V1 Models

Model	File	Description	W&B Logging
Autoencoder	`v1/src/models/AE.py`	Standard autoencoder	—
VAE	`v1/src/models/VAE.py`	Variational Autoencoder	—
GAN	`v1/src/models/GAN.py`	Vanilla GAN	—
WGAN	`v1/src/models/WGAN.py`	Wasserstein GAN	✅ Per-epoch
WGANGP	`v1/src/models/WGANGP.py`	WGAN with Gradient Penalty	✅ Per-epoch
CycleGAN	`v1/src/models/cycleGAN.py`	Image-to-image translation	—
MuseGAN	`v1/src/models/MuseGAN.py`	Music generation	—
RNNAttention	`v1/src/models/RNNAttention.py`	Attention for sequences	—

V1 Data Download Scripts

Script	Dataset	Notebook
`download_camel_data.sh`	Quick Draw Camel	`04_01_gan_camel_train.ipynb`
`download_celeba_kaggle.sh`	CelebA Faces	`03_05_vae_faces_train.ipynb`
`download_cyclegan_data.sh`	Apple2Orange	`05_01_cyclegan_train.ipynb`
`download_gutenburg_data.sh`	Project Gutenberg	`06_01_lstm_text_train.ipynb`

Documentation

Guide	Description
QUICKSTART.md	Installation, UV, and GPU setup
TRAINING_GUIDE.md	Callbacks, batch sizing, W&B integration
GAN_GUIDE.md	GAN metrics, stability, and triage
NOTEBOOK_STANDARDIZATION.md	Complete notebook workflow
TRAINING_STABILITY_ANALYSIS_TEMPLATE.md	Training analysis report template

Versions

Version	Book Edition	Content
`v1/`	1st Edition (2019)	22 notebooks: AE, VAE, GAN, WGAN, WGANGP, CycleGAN, MuseGAN
`v2/`	2nd Edition (2023)	40+ notebooks: Diffusion, Transformers, NormFlows, EBMs

For AI Agents

This repository includes AGENTS.md files for AI coding assistants:

AGENTS.md - Root-level project context & conventions
v1/AGENTS.md - V1-specific conventions
v2/AGENTS.md - V2-specific conventions

Custom workflows are available in .agent/workflows/.

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Repository files navigation

Generative Deep Learning

Project Standards

Project Structure

Quick Start

1. Install UV Package Manager

2. Setup Environment

3. Run Jupyter Lab

Environment Setup (.env)

Getting Kaggle Credentials

Getting W&B (Weights & Biases) Credentials

Environment Variables Reference

Verifying Setup

Requirements

Key Features

W&B Integration

Learning Rate Finder

Notebook Standardization

Dynamic Batch Size

V1 Models

V1 Data Download Scripts

Documentation

Versions

For AI Agents

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Name		Name	Last commit message	Last commit date
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.gemini		.gemini
docker		docker
documentation		documentation
scripts		scripts
utils		utils
v1		v1
v2		v2
.gitignore		.gitignore
AGENTS.md		AGENTS.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
sample.env		sample.env

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Generative Deep Learning

Project Standards

Project Structure

Quick Start

1. Install UV Package Manager

2. Setup Environment

3. Run Jupyter Lab

Environment Setup (.env)

Getting Kaggle Credentials

Getting W&B (Weights & Biases) Credentials

Environment Variables Reference

Verifying Setup

Requirements

Key Features

W&B Integration

Learning Rate Finder

Notebook Standardization

Dynamic Batch Size

V1 Models

V1 Data Download Scripts

Documentation

Versions

For AI Agents

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