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

The project implements Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) on the MNIST dataset using Python. It showcases the ability to simulate complex neural network architectures, providing valuable insights into generative modeling.

Python

View on GitHub →

  • Personal project
  • 1 contributor

Stack

Python · The repository is implemented in Python.

View as

Architecture

The system is designed as a monolith, ensuring all components are integrated within a single codebase. This architecture supports scalability and reliability, as it allows for straightforward updates and maintenance of the GAN and VAE implementations.

Technical narrative

Utilizing Python as the sole programming language, the project leverages its extensive libraries and community support for machine learning. The choice of GAN and VAE networks reflects a mature understanding of generative models, making it a relevant tool for data scientists and machine learning practitioners.

Why it matters

This project demonstrates a strong grasp of generative modeling through the implementation of GAN and VAE networks. It highlights the ability to work with complex architectures and provides a practical application of machine learning techniques.

Deep dive

The project tackles the complexities of generative modeling by simulating GAN and VAE networks. The structured approach, with separate directories for each network, allows for focused development and testing, making it easier to iterate on models and improve performance.

Architecture

The project adopts a monolithic architecture with a component-based pattern, which allows for modular development of GAN and VAE networks. Each implementation resides in its own directory, promoting organization and separation of concerns. The architecture includes Python files dedicated to training and utility functions, which streamline the process of model training and evaluation.

Technical narrative

The project is implemented entirely in Python, which is well-suited for machine learning tasks due to its rich ecosystem. The GAN and VAE networks are specifically designed to work with the MNIST dataset, allowing for effective training and evaluation of generative models. The integration of training scripts and utility functions enhances the overall functionality and usability of the codebase.

Why it matters

The project showcases technical depth in implementing GAN and VAE networks, addressing challenges in generative modeling. It reflects problem-solving skills and innovation in utilizing Python for machine learning applications.

Deep dive

In this project, the implementation of GAN and VAE networks involves careful consideration of architecture and modularity. The component-based pattern facilitates the separation of functionalities, allowing for independent testing and enhancement of each network. The use of Python for all code ensures consistency and leverages the language's strengths in data manipulation and model training, while the inclusion of utility functions streamlines the workflow for training and evaluating the models on the MNIST dataset.

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 aims to provide insights into generative models in machine learning.

    • ✓Simulates GAN and VAE networks
  2. 02

    Monolithic Architecture Overview

    The project is structured as a monolith with separate directories for GAN and VAE implementations, containing Python files for training and utility functions. This organization facilitates modular development and testing.

    • !Uses component-based architecture
  3. 03

    Training Script for GAN

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

    GAN/Training.py

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

    No CI Testing Configured

    Currently, there are no configured CI workflows or testing frameworks in this project. This may limit automated testing capabilities.

    • !No CI workflows found
  5. 05

    No CI/CD Workflows Configured

    There are no CI/CD workflows or deployment targets configured for this project, indicating a focus on local execution and experimentation.

    • !No CI/CD workflows found
  6. 06

    Clone the Repository

    To explore the project, you can clone the repository from GitHub and run the simulations locally.

    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]
    C --> D

Diagram source rendered with mermaid.js.

Verified facts

  • The repository is implemented in Python.from code
    Evidence
    languages: [ 'Python' ]

    Source: context pack

  • The architecture type is monolith.from code
    Evidence
    type: 'monolith'

    Source: context pack

  • The architecture pattern is component-based.from code
    Evidence
    pattern: 'component-based'

    Source: context pack

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

    Source: context pack

  • The repository contains Python files for training and utility functions.from code
    Evidence
    Python files for training and utility functions

    Source: context pack

  • The repository simulates GAN and VAE networks.from code
    Evidence
    Simulation of GAN and VAE networks

    Source: context pack

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

    Source: context pack

  • The repository contains 27 files.from code
    Evidence
    fileCount: 27

    Source: context pack

← All work