https://github.com/mjahmadee/autoencoders_for_classification

Auto-Encoders for Classification
https://github.com/mjahmadee/autoencoders_for_classification

autoencoder classification encoder-decoder

Last synced: 8 months ago
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Auto-Encoders for Classification

• Host: GitHub
• URL: https://github.com/mjahmadee/autoencoders_for_classification
• Owner: MJAHMADEE
• License: mit
• Created: 2023-07-10T09:23:28.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-16T12:50:31.000Z (about 2 years ago)
• Last Synced: 2025-10-12T04:32:15.869Z (8 months ago)
• Topics: autoencoder, classification, encoder-decoder
• Language: Jupyter Notebook
• Homepage:
• Size: 1.71 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# AutoEncoders for Classification 🤖

![Python](https://img.shields.io/badge/Python-3.x-blue.svg)

![PyTorch](https://img.shields.io/badge/PyTorch-1.x-orange.svg)

![Machine Learning](https://img.shields.io/badge/Machine%20Learning-AutoEncoders-green.svg)

This project leverages AutoEncoders in PyTorch for feature extraction and classification on the MNIST dataset, demonstrating how unsupervised learning can enhance supervised tasks.

## Features 🌟

- Implements an AutoEncoder for dimensionality reduction and feature extraction.

- Uses a neural network classifier to categorize images based on learned representations.

- Trains and evaluates on the MNIST dataset, providing insights into model performance.

- Includes data visualization of the training process and prediction results.

## Setup and Installation 🛠️

1. Clone the GitHub repository.

2. Ensure Python 3.x and PyTorch are installed.

3. Install additional dependencies as listed in `requirements.txt`.

## Data 📁

The project uses the MNIST dataset, a collection of handwritten digits, to train and test the model's performance.

## Usage 🚀

- Run the training script to build and train the AutoEncoder and classifier models.

- Evaluate the model using the test script, which outputs accuracy, precision, recall, and F1 score metrics.

- Visualize the training process through loss and accuracy plots, and understand the model decisions with a confusion matrix.

## Results 📊

The README includes a section on the results obtained from training, highlighting key performance metrics and visualizations like loss curves and confusion matrices.

## Contributing 🤝

Contributions to the project are welcome. Follow the standard fork, branch, and pull request workflow to propose changes.

## License 📜

This project is released under the MIT License. See the LICENSE file for more details.

## Acknowledgements 🙌

- The PyTorch team for an excellent deep learning framework.

- The MNIST dataset maintainers for providing a reliable dataset used widely in machine learning research.

For more details, please refer to the [project repository](https://github.com/MJAHMADEE/AutoEncoders_for_Classification).