Crate nn [] [src]

An easy to use neural network library written in Rust.


nn is a feedforward neural network library. The library generates fully connected multi-layer artificial neural networks that are trained via backpropagation. Networks are trained using an incremental training mode.

XOR example

This example creates a neural network with 2 nodes in the input layer, a single hidden layer containing 3 nodes, and 1 node in the output layer. The network is then trained on examples of the XOR function. All of the methods called after train(&examples) are optional and are just used to specify various options that dictate how the network should be trained. When the go() method is called the network will begin training on the given examples. See the documentation for the NN and Trainer structs for more details.

use nn::{NN, HaltCondition};

// create examples of the XOR function
// the network is trained on tuples of vectors where the first vector
// is the inputs and the second vector is the expected outputs
let examples = [
    (vec![0f64, 0f64], vec![0f64]),
    (vec![0f64, 1f64], vec![1f64]),
    (vec![1f64, 0f64], vec![1f64]),
    (vec![1f64, 1f64], vec![0f64]),

// create a new neural network by passing a pointer to an array
// that specifies the number of layers and the number of nodes in each layer
// in this case we have an input layer with 2 nodes, one hidden layer
// with 3 nodes and the output layer has 1 node
let mut net = NN::new(&[2, 3, 1]);

// train the network on the examples of the XOR function
// all methods seen here are optional except go() which must be called to begin training
// see the documentation for the Trainer struct for more info on what each method does
    .halt_condition( HaltCondition::Epochs(10000) )
    .log_interval( Some(100) )
    .momentum( 0.1 )
    .rate( 0.3 )

// evaluate the network to see if it learned the XOR function
for &(ref inputs, ref outputs) in examples.iter() {
    let results =;
    let (result, key) = (results[0].round(), outputs[0]);
    assert!(result == key);



Neural network


Used to specify options that dictate how a network will be trained



Specifies when to stop training the network


Specifies which learning mode to use when training the network