Tutorial_11_Generative_Modeling_GAN

Generative Modeling with GANs

This repository offers two comprehensive tutorials on Generative Adversarial Networks (GANs) implemented using TensorFlow and PyTorch. Both tutorials guide you through building, training, and evaluating GANs for various generative modeling tasks in materials science, while also addressing key AI for materials topics.

Tutorials Overview

1. TensorFlow GAN Tutorial

• File: Tutorial#11 (tf) Generative Modeling GAN.ipynb
• Description: This notebook covers the implementation of GANs using TensorFlow. It introduces key concepts, the architectural design of GANs, and effective training strategies for generative modeling with applications in materials science.
• Requirements:
  • Python 3.7 or higher
  • TensorFlow 2.x
  • NumPy, Matplotlib
• AI for Materials Focus: Although the primary focus is on general generative modeling, this tutorial outlines how these techniques can be adapted for materials data analysis.

2. PyTorch GAN Tutorial

• File: Tutorial#11 (torch) Generative Modeling GAN.ipynb
• Description: This notebook provides a detailed implementation of GANs using PyTorch. It demonstrates similar GAN concepts as the TensorFlow version but leverages PyTorch’s dynamic computation graph and custom training loops.
• Special Note: The dataset used in this tutorial is based on material electron microscopy data, providing real-world images ideal for training and evaluating generative models in the context of AI for materials.
• Requirements:
  • Python 3.7 or higher
  • PyTorch 1.x or later
  • NumPy, Matplotlib

3. AI for Materials

• Generative Modeling in Materials Science: Techniques for simulating and analyzing material microstructures.
• Material Electron Microscopy Data: Utilizing real-world microscopy images to train and validate models.
• Integrating Deep Learning in Materials Research: Approaches for leveraging GANs to accelerate discovery and analysis in materials science.

Acknowledgments

• François Chollet
• TensorFlow
• PyTorch