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

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

# The project implements Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) on the MNIST dataset, showcasing advanced machine learning techniques. It serves as a practical demonstration of generative models, contributing to the understanding of deep learning applications.

git@github.com:shashankcm95/GAN-and-VAE-networks-on-MNIST-dataset

./check-credibility.sh

Personal project
1 contributor

cat stack.json

viewRecruiterEngineer

cat architecture.md

The system is built using a monolithic architecture that ensures all components are integrated within a single codebase. This design choice simplifies deployment and maintenance while supporting the scalability of the GAN and VAE implementations.

cat narrative.md

Utilizing Python as the primary programming language, the project leverages its rich ecosystem for machine learning. The choice of Python enables rapid development and integration of advanced algorithms, making it suitable for educational and research purposes.

cat deep-dive.md

The project tackles the complexities of training GANs and VAEs by providing structured training scripts. It emphasizes a systematic approach to model training, ensuring that both models are effectively optimized for the MNIST dataset.

cat architecture.md

The architecture is structured as a monolith with a layered pattern, facilitating separation of concerns within the codebase. This design allows for clear organization of GAN and VAE implementations, along with their respective training scripts, enhancing maintainability and readability.

cat narrative.md

The project is implemented entirely in Python, which is well-suited for machine learning tasks. The GAN and VAE models are organized in separate directories, with training scripts that facilitate the experimentation process. This organization supports modular development and testing.

cat deep-dive.md

In this project, the implementation of GAN and VAE networks involves careful consideration of architecture and training methodologies. The layered pattern allows for distinct separation of model components, facilitating easier debugging and enhancement. The choice of Python supports extensive libraries for numerical computations, which are crucial for training these models effectively.

cat tour.md

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
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
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
```
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
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
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.

grep -h '^Fact:' notes/

The repository contains implementations of Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE).from code
Evidence
Simulation of GAN and VAE networks and applied them on the MNIST dataset for Machine-Learning-Fall-2020-COP-6610
Source: README
The architecture of the system is monolithic.from code
Evidence
type: monolith
Source: architecture
The architecture pattern used is layered.from code
Evidence
pattern: layered
Source: architecture
The repository contains 31 files.from code
Evidence
fileCount: 31
Source: complexity
The repository is implemented in Python.from code
Evidence
languages: [ 'Python' ]
Source: techStack
The GAN and VAE implementations are applied on the MNIST dataset.from code
Evidence
Applied on the MNIST dataset
Source: keyFeatures

cd ..