Crate fast_neural_network

source ·
Expand description

Fast Neural Network Library

This library is a simple neural network library written in Rust. It is designed to be fast and easy to use. It supports saving and loading networks to and from JSON files. All of the heavy operations are parallelized.

Example

use fast_neural_network::neural_network::*;
use fast_neural_network::activation::*;
use fast_neural_network::matrix::*;
 
fn main() {
    let mut network = Network::empty_network(3, 1, ActivationType::Relu, 0.005);
    network.add_hidden_layer_with_size(4);
    network.add_hidden_layer_with_size(4);
    network.compile();  // Compile the network to prepare it for training
                        // (will be done automatically during training)
                        // The API is exposed so that the user can compile 
                        // the network on a different thread before training if they want to
 
    // setting up the weights and biases of the network manually
    let layer_1_weights = Matrix::from_vec(
        vec![
            0.03, 0.62, 0.85,
            0.60, 0.62, 0.64,
            0.75, 0.73, 0.34,
            0.46, 0.14, 0.06,
        ],
        4,
        3,
    );
    let layer_1_biases = Matrix::from_vec(vec![0.14, 0.90, 0.65, 0.32], 4, 1);
    let layer_2_weights = Matrix::from_vec(
        vec![
            0.90, 0.95, 0.26, 0.70,
            0.12, 0.84, 0.58, 0.78,
            0.92, 0.16, 0.49, 0.90,
            0.64, 0.60, 0.64, 0.85,
        ],
        4,
        4,
    );
    let layer_2_biases = Matrix::from_vec(vec![0.41, 0.09, 0.28, 0.70], 4, 1);
    let layer_3_weights = Matrix::from_vec(vec![0.23, 0.34, 0.24, 0.67], 1, 4);
    let layer_3_biases = Matrix::from_vec(vec![0.23], 1, 1);
 
    network.set_layer_weights(0, layer_1_weights);
    network.set_layer_biases(0, layer_1_biases);
    network.set_layer_weights(1, layer_2_weights);
    network.set_layer_biases(1, layer_2_biases);
    network.set_layer_weights(2, layer_3_weights);
    network.set_layer_biases(2, layer_3_biases);
 
    // defining the input for the itteration
    let input: Vec<f64> = vec![2., 1., -1.];
 
    let prediction = network.forward_propagate(&input); // Predict the output of the network
    let error = network.back_propagate(&input, &vec![9.0]); // Backpropagate the input with a target output of 9.0
    let new_prediction = network.forward_propagate(&input); // Predict the output of the network again
 
    println!("{:?}", prediction);
    println!("{:?}", new_prediction);
 
    network.save("network.json"); // Save the model as a json to a file
 
    let mut network = Network::load("network.json"); // Load the model from a json file
 
    println!("{:?}", network.forward_propagate(&input));
}

Modules