🧠 Distributed MNIST Recognition (MapReduce)

A distributed machine learning engine built from scratch to recognize handwritten digits (MNIST dataset). It uses a MapReduce architecture and Python Multiprocessing to parallelize training across CPU cores.

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✨ Features

• Custom Neural Network: Built entirely with NumPy — no TensorFlow or PyTorch.
• Distributed Training: Master–Worker architecture using multiprocessing (MapReduce pattern).
• Smart GUI: Tkinter-based application for real-time digit testing.
  • Adaptive preprocessing based on ink density.
  • Center of Mass alignment (MNIST-style).
  • Real-time debug view.

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🛠️ Installation

1. Clone the repository

git clone https://github.com/ramt11n/Distributed-MNIST.git
cd Distributed-MNIST

2. Create a virtual environment & install dependencies

python3 -m venv venv
source venv/bin/activate     # Windows: venv\Scripts\activate
pip install numpy pillow

3. Download the MNIST dataset

Download the following files from Yann LeCun’s website and put them in the data/ folder:

• train-images-idx3-ubyte.gz
• train-labels-idx1-ubyte.gz
• t10k-images-idx3-ubyte.gz
• t10k-labels-idx1-ubyte.gz

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🚀 Usage

1. Train the model (Backend)

python3 main.py

2. Run the application (Frontend)

python3 app.py

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🏗️ Project Architecture

File	Description
src/neural_net.py	Matrix ops, forward/backward propagation.
src/worker.py	Worker process logic (Map phase).
src/mnist_loader.py	IDX file parsers.
main.py	Master process (Reduce phase).
app.py	GUI for drawing, preprocessing, prediction.

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Created by Ramtin Neshat