NNMM

Neural Network Mind Map

Here is a comprehensive README.md file for NNMM repository:

# Neural Network Mind Map (NNMM)

This repository contains a neural network implementation using TensorFlow and the MNIST dataset.

## Table of Contents

- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Model Architecture](#model-architecture)
- [Training and Evaluation](#training-and-evaluation)
- [Contributing](#contributing)
- [License](#license)

## Introduction

This project demonstrates how to build, train, and evaluate a neural network model using TensorFlow. The model is trained on the MNIST dataset, which consists of handwritten digits, and can be used for digit classification.

## Installation

To get started, clone the repository and install the required dependencies.

```bash
git clone https://github.com/spiralgang/nnmm.git
cd nnmm
pip install -r requirements.txt

Ensure you have Python and pip installed on your system. You might also need to install TensorFlow if it's not included in the requirements file.

pip install tensorflow

Usage

To train and evaluate the model, run the main.py script.

python main.py

The script will:

1. Load the MNIST dataset.
2. Preprocess the data.
3. Define the neural network model.
4. Train the model.
5. Evaluate the model on the test set.

Model Architecture

The neural network model consists of the following layers:

• Input layer with 784 neurons (one for each pixel in the 28x28 images)
• Hidden layer with 16 neurons and ReLU activation
• Hidden layer with 8 neurons and ReLU activation
• Output layer with 10 neurons (one for each digit) and softmax activation

Training and Evaluation

The model is trained using the Adam optimizer and sparse categorical cross-entropy loss function. The training process includes:

• 5 epochs
• Batch size of 32
• 20% of the training data used for validation

After training, the model's performance is evaluated on the test set and the accuracy is printed.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.