MNIST Digit Recognition with Convolutional Neural Networks

This project is an implementation of a Convolutional Neural Network (CNN) for recognizing handwritten digits from the MNIST dataset. The CNN is trained to classify images of handwritten digits into the corresponding digit classes (0-9).

Table of Contents

• Prerequisites
• Getting Started
• Usage
• Dependencies
• License

Prerequisites

Before running the project, make sure you have the following prerequisites installed:

• Python (3.6 or higher)
• Conda (for managing dependencies)
• Git (for version control)

Getting Started

1. Clone the repository to your local machine:

   git clone https://github.com/heib6xinyu/Mnist_Model_with_High_Noise_Level.git

2. Change to the project directory:

   cd mnist-cnn

3. Create a Conda environment (optional but recommended):

   conda create --name mnist-cnn python=3.8

4. Activate the Conda environment:

   conda activate mnist-cnn

5. Install project dependencies:

   ./compile.sh

Usage

To train and evaluate the CNN model on the MNIST dataset, you can run the following command:

python mnist_cnn_model.py

This will train the model, evaluate its accuracy, and save the trained model as mnist_cnn_model.h5.

python test_model.py

This file creates 10 test data, one with 0 noise, other with 9 level of gaussian noise. Then test the model on the 10 test data.

Dependencies

The project depends on the following Python libraries and packages:

• Pandas
• Scikit-learn
• TensorFlow
• Joblib

All dependencies are listed in the compile.sh file and can be installed using Conda.