Rust-LSTM

A simple LSTM (Long Short-Term Memory) neural network library implemented in Rust. This library provides basic functionalities to create and train LSTM networks.

Features

• LSTM cell implementation
• Multi-layer LSTM network
• Random initialization of weights and biases
• Forward pass through the network

Getting Started

Prerequisites

Ensure you have Rust installed on your machine. If Rust is not already installed, you can install it by following the instructions on the official Rust website: https://www.rust-lang.org/tools/install.

Installing

To use Rust-LSTM in your project, add the following to your Cargo.toml:

[dependencies]
rust-lstm = "0.1.0"

Then, run the following command to build your project and download the Rust-LSTM crate:

cargo build

Usage

Here's a simple example demonstrating how to use the LSTM library:

use ndarray::Array2;
use rust_lstm::models::lstm_network::LSTMNetwork;

fn main() {
    let input_size = 3;
    let hidden_size = 2;
    let num_layers = 2;

    // Create an LSTM network
    let network = LSTMNetwork::new(input_size, hidden_size, num_layers);

    // Create some example input data
    let input = Array2::from_shape_vec((input_size, 1), vec![0.5, 0.1, -0.3]).unwrap();

    // Perform a forward pass
    let output = network.forward(&input);

    // Print the output
    println!("Output: {:?}", output);
}

To run this example, save it as main.rs, and run:

cargo run

Running the Tests

To run the tests included with Rust-LSTM, execute:

cargo test

This will run all the unit and integration tests defined in the library.

Contributing

Contributions to Rust-LSTM are welcome! Here are a few ways you can help:

• Report bugs and issues
• Suggest new features or improvements
• Open a pull request with improvements to code or documentation
• Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests to us.

License

This project is licensed under the MIT License - see the LICENSE file for details.