NeuroNet

NeuroNet is a neural network implementation in C++ leveraging the Eigen library for matrix operations. This project is structured to support custom neural network layers and optimized backpropagation, with an example classifier for the MNIST dataset.

Features

• Layered Architecture: Utilizes a DenseLayer structure, supporting forward and backward propagation.
• Optimization and Loss Functions:
  • Optimizer: Stochastic Gradient Descent (SGD) with weight updates.
  • Loss Function: Cross-Entropy loss, ideal for classification tasks.
• Debug Logging: Debug statements log matrix dimensions and provide insights into forward and backward passes for troubleshooting.
• Eigen Matrix Broadcasting: Customized solution for bias broacasting issues with Eigen, using manual replication for compatibility.

Installation

Prerequisites

• Eigen Library: Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. Ensure Eigen is available in the third_party directory or download Eigen from the official website or install via your package manager.
• CMake and Make: Required to build the project.

Building NeuroNet

1. Clone the repository:

git clone https://github.com/chizy7/NeuroNet.git
cd NeuroNet

2. Build with CMake:

mkdir build && cd build
cmake ..
make

3. Run the executable:

./NeuroNet

Dataset

To run the example MNIST classifier, download the MNIST dataset in CSV format from this Kaggle dataset.

1. Download both mnist_train.csv and mnist_test.csv.
2. Place them in the data/ directory of the project.

Usage

1. Train the Model: NeuroNet loads MNIST data, trains for specified epochs, and prints accuracy.
2. Debugging Mode: Enable debug in logger.h to get detailed layer-wise output of matrix sizes during the forward and backward passes.

Code Structure

• src/core/Layer.cpp: Contains layer definitions and forward/backward functions.
• src/core/NeuralNet.cpp: Core neural network functionality, including training and evaluation.
• include/: Headers for neural network components and utilities.
• data/: MNIST CSV files.

Known Issues and Improvements

• Additional Layers: Currently, only DenseLayer is implemented. Other layer types (e.g., convolutional) could be added.
• Evaluation Metrics: Extend beyond accuracy for multi-class tasks.
• Memory Optimization: Explore more efficient memory handling for large datasets.

License

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