├── .gitignore ├── Cargo.lock ├── Cargo.toml ├── LICENSE ├── README.md ├── rustfmt.toml └── src ├── activations.rs ├── main.rs ├── matrix.rs └── network.rs /.gitignore: -------------------------------------------------------------------------------- 1 | target/ 2 | -------------------------------------------------------------------------------- /Cargo.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Cargo. 2 | # It is not intended for manual editing. 3 | version = 3 4 | 5 | [[package]] 6 | name = "bitflags" 7 | version = "1.3.2" 8 | source = "registry+https://github.com/rust-lang/crates.io-index" 9 | checksum = "bef38d45163c2f1dde094a7dfd33ccf595c92905c8f8f4fdc18d06fb1037718a" 10 | 11 | [[package]] 12 | name = "cloudabi" 13 | version = "0.0.3" 14 | source = "registry+https://github.com/rust-lang/crates.io-index" 15 | checksum = "ddfc5b9aa5d4507acaf872de71051dfd0e309860e88966e1051e462a077aac4f" 16 | 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Citations 2 | 3 | https://medium.com/coinmonks/representing-neural-network-with-vectors-and-matrices-c6b0e64db9fb 4 | https://datascience.stackexchange.com/questions/75855/what-types-of-matrix-multiplication-are-used-in-machine-learning-when-are-they 5 | -------------------------------------------------------------------------------- /rustfmt.toml: -------------------------------------------------------------------------------- 1 | hard_tabs = true 2 | -------------------------------------------------------------------------------- /src/activations.rs: -------------------------------------------------------------------------------- 1 | use std::f64::consts::E; 2 | 3 | #[derive(Clone)] 4 | pub struct Activation<'a> { 5 | pub function: &'a dyn Fn(f64) -> f64, 6 | pub derivative: &'a dyn Fn(f64) -> f64, 7 | } 8 | 9 | pub const IDENTITY: Activation = Activation { 10 | function: &|x| x, 11 | derivative: &|_| 1.0, 12 | }; 13 | 14 | pub const SIGMOID: Activation = Activation { 15 | function: &|x| 1.0 / (1.0 + E.powf(-x)), 16 | derivative: &|x| x * (1.0 - x), 17 | }; 18 | 19 | pub const TANH: Activation = Activation { 20 | function: &|x| x.tanh(), 21 | derivative: &|x| 1.0 - (x.powi(2)), 22 | }; 23 | 24 | pub const RELU: Activation = Activation { 25 | function: &|x| x.max(0.0), 26 | derivative: &|x| if x > 0.0 { 1.0 } else { 0.0 }, 27 | }; 28 | -------------------------------------------------------------------------------- /src/main.rs: -------------------------------------------------------------------------------- 1 | use activations::SIGMOID; 2 | use network::Network; 3 | 4 | pub mod activations; 5 | pub mod matrix; 6 | pub mod network; 7 | 8 | fn main() { 9 | let inputs = vec![ 10 | vec![0.0, 0.0], 11 | vec![0.0, 1.0], 12 | vec![1.0, 0.0], 13 | vec![1.0, 1.0], 14 | ]; 15 | let targets = vec![vec![0.0], vec![1.0], vec![1.0], vec![0.0]]; 16 | 17 | let mut network = Network::new(vec![2, 3, 1], 0.5, SIGMOID); 18 | 19 | network.train(inputs, targets, 1000); 20 | 21 | println!("{:?}", network.feed_forward(vec![0.0, 0.0])); 22 | println!("{:?}", network.feed_forward(vec![0.0, 1.0])); 23 | println!("{:?}", network.feed_forward(vec![1.0, 0.0])); 24 | println!("{:?}", network.feed_forward(vec![1.0, 1.0])); 25 | } 26 | -------------------------------------------------------------------------------- /src/matrix.rs: -------------------------------------------------------------------------------- 1 | use rand::{thread_rng, Rng}; 2 | use std::fmt::{Debug, Formatter, Result}; 3 | 4 | #[derive(Clone)] 5 | pub struct Matrix { 6 | pub rows: usize, 7 | pub cols: usize, 8 | pub data: Vec>, 9 | } 10 | 11 | impl Matrix { 12 | pub fn zeros(rows: usize, cols: usize) -> Matrix { 13 | Matrix { 14 | rows, 15 | cols, 16 | data: vec![vec![0.0; cols]; rows], 17 | } 18 | } 19 | 20 | pub fn random(rows: usize, cols: usize) -> Matrix { 21 | let mut rng = thread_rng(); 22 | 23 | let mut res = Matrix::zeros(rows, cols); 24 | for i in 0..rows { 25 | for j in 0..cols { 26 | res.data[i][j] = rng.gen::() * 2.0 - 1.0; 27 | } 28 | } 29 | 30 | res 31 | } 32 | 33 | pub fn from(data: Vec>) -> Matrix { 34 | Matrix { 35 | rows: data.len(), 36 | cols: data[0].len(), 37 | data, 38 | } 39 | } 40 | 41 | pub fn multiply(&self, other: &Matrix) -> Matrix { 42 | if self.cols != other.rows { 43 | panic!("Attempted to multiply by matrix of incorrect dimensions"); 44 | } 45 | 46 | let mut res = Matrix::zeros(self.rows, other.cols); 47 | 48 | for i in 0..self.rows { 49 | for j in 0..other.cols { 50 | let mut sum = 0.0; 51 | for k in 0..self.cols { 52 | sum += self.data[i][k] * other.data[k][j]; 53 | } 54 | 55 | res.data[i][j] = sum; 56 | } 57 | } 58 | 59 | res 60 | } 61 | 62 | pub fn add(&self, other: &Matrix) -> Matrix { 63 | if self.rows != other.rows || self.cols != other.cols { 64 | panic!("Attempted to add matrix of incorrect dimensions"); 65 | } 66 | 67 | let mut res = Matrix::zeros(self.rows, self.cols); 68 | 69 | for i in 0..self.rows { 70 | for j in 0..self.cols { 71 | res.data[i][j] = self.data[i][j] + other.data[i][j]; 72 | } 73 | } 74 | 75 | res 76 | } 77 | 78 | pub fn dot_multiply(&self, other: &Matrix) -> Matrix { 79 | if self.rows != other.rows || self.cols != other.cols { 80 | panic!("Attempted to dot multiply by matrix of incorrect dimensions"); 81 | } 82 | 83 | let mut res = Matrix::zeros(self.rows, self.cols); 84 | 85 | for i in 0..self.rows { 86 | for j in 0..self.cols { 87 | res.data[i][j] = self.data[i][j] * other.data[i][j]; 88 | } 89 | } 90 | 91 | res 92 | } 93 | 94 | pub fn subtract(&self, other: &Matrix) -> Matrix { 95 | if self.rows != other.rows || self.cols != other.cols { 96 | panic!("Attempted to subtract matrix of incorrect dimensions"); 97 | } 98 | 99 | let mut res = Matrix::zeros(self.rows, self.cols); 100 | 101 | for i in 0..self.rows { 102 | for j in 0..self.cols { 103 | res.data[i][j] = self.data[i][j] - other.data[i][j]; 104 | } 105 | } 106 | 107 | res 108 | } 109 | 110 | pub fn map(&self, function: &dyn Fn(f64) -> f64) -> Matrix { 111 | Matrix::from( 112 | (self.data) 113 | .clone() 114 | .into_iter() 115 | .map(|row| row.into_iter().map(|value| function(value)).collect()) 116 | .collect(), 117 | ) 118 | } 119 | 120 | pub fn transpose(&self) -> Matrix { 121 | let mut res = Matrix::zeros(self.cols, self.rows); 122 | 123 | for i in 0..self.rows { 124 | for j in 0..self.cols { 125 | res.data[j][i] = self.data[i][j]; 126 | } 127 | } 128 | 129 | res 130 | } 131 | } 132 | 133 | impl Debug for Matrix { 134 | fn fmt(&self, f: &mut Formatter) -> Result { 135 | write!( 136 | f, 137 | "Matrix {{\n{}\n}}", 138 | (&self.data) 139 | .into_iter() 140 | .map(|row| " ".to_string() 141 | + &row 142 | .into_iter() 143 | .map(|value| value.to_string()) 144 | .collect::>() 145 | .join(" ")) 146 | .collect::>() 147 | .join("\n") 148 | ) 149 | } 150 | } 151 | -------------------------------------------------------------------------------- /src/network.rs: -------------------------------------------------------------------------------- 1 | use std::{ 2 | fs::File, 3 | io::{Read, Write}, 4 | }; 5 | 6 | use serde::{Deserialize, Serialize}; 7 | use serde_json::{from_str, json}; 8 | 9 | use super::{activations::Activation, matrix::Matrix}; 10 | 11 | pub struct Network<'a> { 12 | layers: Vec, 13 | weights: Vec, 14 | biases: Vec, 15 | data: Vec, 16 | learning_rate: f64, 17 | activation: Activation<'a>, 18 | } 19 | 20 | #[derive(Serialize, Deserialize)] 21 | struct SaveData { 22 | weights: Vec>>, 23 | biases: Vec>>, 24 | } 25 | 26 | impl Network<'_> { 27 | pub fn new<'a>( 28 | layers: Vec, 29 | learning_rate: f64, 30 | activation: Activation<'a>, 31 | ) -> Network<'a> { 32 | let mut weights = vec![]; 33 | let mut biases = vec![]; 34 | 35 | for i in 0..layers.len() - 1 { 36 | weights.push(Matrix::random(layers[i + 1], layers[i])); 37 | biases.push(Matrix::random(layers[i + 1], 1)); 38 | } 39 | 40 | Network { 41 | layers, 42 | weights, 43 | biases, 44 | data: vec![], 45 | learning_rate, 46 | activation, 47 | } 48 | } 49 | 50 | pub fn feed_forward(&mut self, inputs: Vec) -> Vec { 51 | if inputs.len() != self.layers[0] { 52 | panic!("Invalid inputs length"); 53 | } 54 | 55 | let mut current = Matrix::from(vec![inputs]).transpose(); 56 | self.data = vec![current.clone()]; 57 | 58 | for i in 0..self.layers.len() - 1 { 59 | current = self.weights[i] 60 | .multiply(¤t) 61 | .add(&self.biases[i]) 62 | .map(self.activation.function); 63 | self.data.push(current.clone()); 64 | } 65 | 66 | current.transpose().data[0].to_owned() 67 | } 68 | 69 | pub fn back_propogate(&mut self, outputs: Vec, targets: Vec) { 70 | if targets.len() != self.layers[self.layers.len() - 1] { 71 | panic!("Invalid targets length"); 72 | } 73 | 74 | let parsed = Matrix::from(vec![outputs]).transpose(); 75 | let mut errors = Matrix::from(vec![targets]).transpose().subtract(&parsed); 76 | let mut gradients = parsed.map(self.activation.derivative); 77 | 78 | for i in (0..self.layers.len() - 1).rev() { 79 | gradients = gradients 80 | .dot_multiply(&errors) 81 | .map(&|x| x * self.learning_rate); 82 | 83 | self.weights[i] = self.weights[i].add(&gradients.multiply(&self.data[i].transpose())); 84 | self.biases[i] = self.biases[i].add(&gradients); 85 | 86 | errors = self.weights[i].transpose().multiply(&errors); 87 | gradients = self.data[i].map(self.activation.derivative); 88 | } 89 | } 90 | 91 | pub fn train(&mut self, inputs: Vec>, targets: Vec>, epochs: u16) { 92 | for i in 1..=epochs { 93 | if epochs < 100 || i % (epochs / 100) == 0 { 94 | println!("Epoch {} of {}", i, epochs); 95 | } 96 | for j in 0..inputs.len() { 97 | let outputs = self.feed_forward(inputs[j].clone()); 98 | self.back_propogate(outputs, targets[j].clone()); 99 | } 100 | } 101 | } 102 | 103 | pub fn save(&self, file: String) { 104 | let mut file = File::create(file).expect("Unable to touch save file"); 105 | 106 | file.write_all( 107 | json!({ 108 | "weights": self.weights.clone().into_iter().map(|matrix| matrix.data).collect::>>>(), 109 | "biases": self.biases.clone().into_iter().map(|matrix| matrix.data).collect::>>>() 110 | }).to_string().as_bytes(), 111 | ).expect("Unable to write to save file"); 112 | } 113 | 114 | pub fn load(&mut self, file: String) { 115 | let mut file = File::open(file).expect("Unable to open save file"); 116 | let mut buffer = String::new(); 117 | 118 | file.read_to_string(&mut buffer) 119 | .expect("Unable to read save file"); 120 | 121 | let save_data: SaveData = from_str(&buffer).expect("Unable to serialize save data"); 122 | 123 | let mut weights = vec![]; 124 | let mut biases = vec![]; 125 | 126 | for i in 0..self.layers.len() - 1 { 127 | weights.push(Matrix::from(save_data.weights[i].clone())); 128 | biases.push(Matrix::from(save_data.biases[i].clone())); 129 | } 130 | 131 | self.weights = weights; 132 | self.biases = biases; 133 | } 134 | } 135 | --------------------------------------------------------------------------------