Project DNN From Scratch

This project implements a deep neural network (DNN) from scratch in Rust, focusing on two key experiments: image classification with the MNIST dataset and signal strength-based predictions using an RSSI dataset. The project demonstrates building and training a neural network without relying on external machine learning libraries.

🚀 Features

• Custom Neural Network Implementation: Build and train DNNs using only Rust libraries and custom modules.
• Examples for Two Experiments:
• MNIST Dataset: Handwritten digit classification.
• RSSI Dataset: Analysis and predictions based on signal strength data.
• Modular Codebase: Cleanly separated concerns such as activation functions, loss computation, and neural network architecture.
• Visualization: Generate reports and plots to visualize experiment results.

📂 Directory Structure

dnn-from-scratch/
├── README.md                # Project overview and instructions
├── Cargo.toml               # Project dependencies and configuration
├── LICENSE                  # License information
├── assets/                  # Datasets and auxiliary data
│   ├── mnist/
│   │   ├── x_test.npy       # MNIST test images
│   │   ├── x_train.npy      # MNIST training images
│   │   ├── y_test.npy       # MNIST test labels
│   │   └── y_train.npy      # MNIST training labels
│   └── rssi/
│       └── rssi-dataset.csv # RSSI dataset
├── dnn_from_scratch/        # Core library for the neural network
│   ├── Cargo.toml           # Library-specific dependencies
│   └── src/
│       ├── activation.rs    # Activation functions
│       ├── fully_connected.rs # Fully connected layer module
│       ├── lib.rs           # Entry point for the library
│       ├── loss.rs          # Loss functions
│       ├── neural_network.rs # Neural network definition
│       ├── optimizer.rs     # Optimizer implementations (e.g., Adam)
│       ├── report.rs        # Reporting and result output
│       ├── utils.rs         # Utility functions for regression/classification
│       └── weights_initializer.rs # Weight initialization strategies
└── src/                     # Main application for experiments
    ├── main.rs              # Entry point for the executable
    ├── mnist_experiment/    # MNIST Experiment-related modules
    │   ├── dataset_setup.rs # MNIST dataset preprocessing
    │   ├── mod.rs           # MNIST Experiment module entry point
    │   └── plot.rs          # Plotting results for MNIST Experiment
    └── rssi_experiment/     # RSSI Experiment-related modules
        ├── dataset_setup.rs # RSSI dataset preprocessing
        ├── mod.rs           # RSSI Experiment module entry point
        └── plot.rs          # Plotting results for RSSI Experiment

🛠️ Getting Started

To build and run the experiments, follow these steps:

Prerequisites

• Install Rust: rust-lang.org

Clone the Repository

git clone https://github.com/akaTsunemori/dnn-from-scratch.git
    cd dnn-from-scratch

Build the Project

cargo build --release

Run Experiments

cargo run --release

📚 Documentation

View detailed API documentation at this page, or generate it locally:

cargo doc --release --workspace --no-deps --target-dir=docs

🧪 Datasets

1. MNIST Dataset:
   • Stored in assets/mnist/.
   • Preprocessed as .npy files for seamless integration.
2. RSSI Dataset:
   • Found in assets/rssi/rssi-dataset.csv.
   • Contains signal strength data and coordinates (X, Y) for analysis.

📈 Results & Reporting

Each experiment generates reports and plots showcasing:

• Training history.
• Model performance metrics (e.g., accuracy for MNIST, CDF of RMSE for RSSI).

Plots and reports are saved in the output/ folder. You can check a preview of the expected results below by clicking to reveal the contents.

MNIST Experiment

Preview of training history:

Epoch 1/100 | Train: Loss 2.0665, Accuracy 0.1013 | Test: Loss 1.9560, Accuracy 0.3710
(...)
Epoch 100/100 | Train: Loss 0.1369, Accuracy 0.9609 | Test: Loss 0.1517, Accuracy 0.9519

Output plot:

RSSI Experiment

Preview of training history:

Epoch 1/2500 | Train: Loss 18046.3999, Error 134.3368 | Test: Loss 21310.2716, Error 145.9803
(...)
Epoch 2500/2500 | Train: Loss 5.5695, Error 2.3599 | Test: Loss 5.8578, Error 2.4202

Output plot:

📜 License

Licensed under the MIT License.

🤝 Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

1. Fork the repo.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes (git commit -m "Add feature").
4. Push to the branch (git push origin feature-name).
5. Open a pull request.

📧 Contact

Create an issue for any inquiries or support.

Enjoy building your neural networks from scratch! 🎉