Case study

GAN-and-VAE-networks-on-MNIST-dataset

  • Personal project
  • 1 contributor
View as

The project implements Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) using the MNIST dataset, showcasing advanced machine learning techniques. It serves as a valuable resource for understanding and simulating these networks in a structured manner.

Architecture

The repository is designed as a monolith, which allows for straightforward deployment and management. Its layered architecture enhances scalability and reliability by structuring the code into distinct layers, each responsible for specific functionalities.

Stack

The project is entirely implemented in Python, leveraging its robust ecosystem for machine learning. This choice allows for rapid development and easy integration with various libraries, making it accessible for developers and researchers alike.

Deep dive

The project tackles the complexities of implementing GAN and VAE networks by organizing code into separate directories, which simplifies the development process. This structured approach allows for focused testing and validation of each network type.

This project features a monolithic architecture that employs a layered pattern to organize the implementation of GAN and VAE networks. The architecture facilitates clear separation of concerns, making it easier to manage and extend the codebase.

Architecture

The project adopts a layered architecture pattern within a monolithic structure, which organizes the code into separate directories for GAN and VAE implementations. This design choice simplifies navigation and maintenance while promoting code reusability across different components.

Stack

The implementation is solely in Python, which is well-suited for machine learning tasks. The project consists of 27 files organized to facilitate the simulation of GAN and VAE networks, ensuring that the codebase remains manageable and focused on the MNIST dataset.

Deep dive

The repository's layered architecture facilitates the implementation of GAN and VAE networks by separating concerns into distinct modules. This design choice not only enhances code readability but also allows for easier debugging and testing, particularly when applied to the MNIST dataset. The choice of Python as the sole programming language further streamlines the development process, leveraging its extensive libraries for machine learning.

Guided tour

  1. 01

    GAN and VAE Simulation on MNIST

    This project simulates Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) applied to the MNIST dataset. It provides a practical implementation for understanding these machine learning techniques.

    • ✓Simulates GAN and VAE networks
  2. 02

    Monolithic Component-Based Architecture

    The architecture is monolithic and component-based, with separate directories for GAN and VAE implementations. Each component contains Python files for training and utility functions.

    • !Uses a component-based architecture
  3. 03

    GAN Training Script

    The GAN/Training.py file contains the core training logic for the GAN model, showcasing the developer's approach to implementing training loops and loss calculations.

    • !Contains GAN training logic

    GAN/Training.py

    def train_gan(epochs, batch_size):
    for epoch in range(epochs):
        # Training logic here
        pass
  4. 04

    No Tests Configured

    Currently, there are no tests configured for this project, indicating a potential area for improvement in quality assurance.

    • !No CI tests configured
  5. 05

    No CI/CD Workflows Found

    There are no CI/CD workflows configured for deployment in this project, suggesting it is primarily for local experimentation.

    • !No CI/CD workflows configured
  6. 06

    Clone the Repository

    To try out this project, clone the repository using the command below.

    git clone https://github.com/shashankcm95/GAN-and-VAE-networks-on-MNIST-dataset
Architecture
graph TD
    A[MNIST Dataset] --> B[GAN Implementation]
    A --> C[VAE Implementation]
    B --> D[Training Scripts]
    C --> D
    D --> E[Generated Images]

Diagram source rendered with mermaid.js.

Built with
  • Python
  • The repository is implemented entirely in Python.

Verified facts

  • The repository contains implementations of GAN and VAE in separate directories.from code
    Evidence
    Contains GAN and VAE implementations in separate directories

    Source: repository structure

  • The repository is structured as a monolith.from code
    Evidence
    type: monolith

    Source: architecture

  • The repository follows a layered architecture pattern.from code
    Evidence
    pattern: layered

    Source: architecture

  • The repository has a total of 27 files.from code
    Evidence
    fileCount: 27

    Source: complexity

  • The repository is implemented entirely in Python.from code
    Evidence
    languages: { 'Python': 100 }

    Source: complexity

  • The key features of the repository include simulation of GAN and VAE networks.from code
    Evidence
    Simulation of GAN and VAE networks

    Source: keyFeatures

  • The GAN and VAE networks are applied on the MNIST dataset.from code
    Evidence
    Applied on the MNIST dataset

    Source: keyFeatures

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