Generative Machine Learning with GANs

Introduction to Generative Machine Learning

• Focus on Generative Adversarial Networks (GANs).
• Objective: Build a generator model that creates synthetic images, specifically for a fashion line.

Key Concepts

• Generator: Neural network that generates new images or objects from random input values (latent space).
• Discriminator: Neural network that evaluates images, determining whether they are real or fake; acts like an art critic.

GAN Workflow

1. Installation: Set up environment and install dependencies (TensorFlow, Matplotlib, etc.).
2. Data Acquisition: Use TensorFlow Datasets (TFDS) to acquire the Fashion MNIST dataset.
3. Data Visualization: Understand data characteristics through visualizations with Matplotlib.
4. Model Construction:
   • Build a Generator to create synthetic images.
   • Build a Discriminator to evaluate the images.
5. Custom Training Loop: Implement a training loop to train both generator and discriminator simultaneously.
6. Evaluation and Results Visualization: Check the output of the generator to analyze performance.

Steps to Build a GAN

1. Environment Setup

• Install core dependencies:

  pip install tensorflow tensorflow-gpu matplotlib tensorflow-datasets ipywidgets
  

• Limit GPU memory growth to prevent out of memory errors.

2. Dataset Preparation

• Load the Fashion MNIST dataset using TFDS:

  ds = tfds.load('fashion_mnist', split='train')
  

• Visualize the dataset before processing.

3. Data Normalization

• Scale images to between 0 and 1 to improve deep learning model performance.
• Create functions to manage the data pipeline (map, cache, shuffle, batch, prefetch).

4. Model Construction

Generator Model:

• Input random noise and reshape to form latent dimensions.
• Use Conv2D, LeakyReLU, and Upsampling layers to create a generator that outputs images of shape 28x28x1.

Discriminator Model:

• Similar architecture but in reverse: take an image as input and output whether it's real or fake (binary classification).
• Utilize convolutional layers and dropouts for regularization.

5. Training Loop

• Create a custom training loop to train both generator and discriminator:
  • Use Binary Cross Entropy as the loss function for both.
  • Adjust learning rates: generator typically faster than discriminator.
• Use TensorFlow Gradient Tape for backpropagation.

6. Training

• Monitor the losses of both models to ensure stability.
• Save generated images at the end of each epoch to visually assess the generator's performance over time.

Conclusion

• After sufficient training, the generator should produce meaningful synthetic clothing images.
• Pre-trained model weights can be provided for testing and further exploration.

Future Considerations

• Experiment with conditional GANs for more controlled image generation.
• Explore applications beyond fashion, such as synthetic data generation for various fields.