Lecture: Applying Generative Adversarial Networks (GANs) in Python using Keras

Overview

• Implementation of GANs in Python using Keras
• Cover both training and testing parts
• Quick summary of GANs:
  • Generative: Generating new data/images
  • Adversarial: Generator and discriminator compete
  • Objective: Generator creates realistic fake data; Discriminator distinguishes real vs fake

Implementation Steps

1. Create Random Seeds - Generate random noise
2. Generator Network
   • Define the architecture
   • Use existing architectures (research, publications)
3. Discriminator Network
   • Receive real images and classify real and fake images
4. Training the Networks
   • Define loss for both networks
   • Retrain iteratively

Version Information & Setup

• Python: 3.5.5
• Keras: 2.0.8
• TensorFlow: 1.4
• GPU: NVIDIA Quadro K5000 (importance of compatible versions)

Importing Necessary Libraries

• Keras layers: Input, Dense, Reshape, Flatten
• TensorFlow as tf

Image and Data Preparation

• Dataset: MNIST
  • Images: 28x28x1 (grayscale)
  • Labels: Handwritten digits (0-9)
• Steps:
  • Download dataset (Keras.datasets or local download)
  • Visualize images

Building the GAN

Generator Network

• Input: Random noise (latent vector of size 100)
• Layers: Dense, BatchNormalization, LeakyReLU
• Output: Image (28x28x1)

Discriminator Network

• Input: Image
• Layers: Similar structure (Dense layers)
• Output: Real or fake probability (binary classifier)

Training Process

• Parameters:
  • Epochs, Batch Size, Save Interval
• Steps:
  • Load real images (MNIST)
  • Normalize and reshape data
• Discriminator Training:
  • Train on real and fake images
  • Calculate and track losses
• Generator Training:
  • Generate fake images and trick discriminator
  • Track generator loss
• Print and Save results every specified intervals

Main Code Structure

• Define Optimizer: Adam
• Compile Models: Discriminator and Generator
• Combine Models: Attach generator to discriminator
• Training Loop:
  • Epochs: Train discriminator and generator alternatively
  • Print loss and accuracy for each epoch
  • Save generated images at intervals

Post-training

• Generating Images:
  • Load saved generator model
  • Generate and visualize fake images
• Example Outputs: Showcases gradual improvement over epochs

Conclusion

• Code structure and step-by-step explanation

• Emphasis on practice by running code and experimenting with parameters

• Encouragement to refer to additional resources

• Future Topics: Advanced GAN techniques and applications

References and Resources

• Keras documentation
• TensorFlow guides
• Research papers on GANs