neurons 0.1.0

Modular neural network in Rust.
Documentation

Modular neural network in Rust.

Create modular neural networks in Rust with ease! For educational purposes; vector operations are not throughly optimized.

Quickstart

use neurons::network::Network;
use neurons::activation::Activation;
use neurons::optimizer::Optimizer;
use neurons::objective::Objective;

fn main() {
    let mut network = Network::new();

    network.add_layer(4, 50, activation::Activation::Linear, true);
    network.add_layer(50, 50, activation::Activation::Linear, true);
    network.add_layer(50, 1, activation::Activation::Linear, false);
    
    network.set_optimizer(
        optimizer::Optimizer::AdamW(
            optimizer::AdamW {
                learning_rate: 0.001,
                beta1: 0.9,
                beta2: 0.999,
                epsilon: 1e-8,
                decay: 0.01,

                momentum: vec![],           // To be filled by the network
                velocity: vec![],           // To be filled by the network
            }
        )
    );
    network.set_objective(
        objective::Objective::MSE,          // Objective function
        Some((-1f32, 1f32))                 // Gradient clipping
    );
  
    println!("{}", network);
  
    let (x, y) = ...;                       // Load data
    let epochs = 1000;
    let loss = network.learn(x, y, epochs); // Train the network
}

Examples can be found in the examples directory.

Progress

  • Layer types

    • Dense
    • Feedback
    • Convolutional

  • Activation functions

    • Linear
    • Sigmoid
    • Tanh
    • ReLU
    • LeakyReLU
    • Softmax

  • Objective functions

    • AE
    • MAE
    • MSE
    • RMSE
    • CrossEntropy
    • BinaryCrossEntropy
    • KLDivergence
    • Huber

  • Optimization techniques

    • SGD
    • SGDM
    • Adam
    • AdamW
    • RMSprop
    • Minibatch

  • Regularization

    • Dropout
    • Batch normalization
    • Early stopping

  • Parallelization

    • Multi-threading

  • Testing

    • Unit tests
    • Integration tests

  • Other

    • Documentation
    • Type conversion (e.g. f32, f64)
    • Network type specification (e.g. f32, f64)
    • Saving and loading
    • Logging
    • Data from file
    • Custom tensor/matrix types
    • Custom random weight initialization
    • Plotting

Inspiration

Sources