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changed to f32: ~30s -> ~25s

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Shautvast 2025-02-04 08:09:00 +01:00
parent f0876967d2
commit 0bd18ba314
4 changed files with 189 additions and 71 deletions
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171
Cargo.lock generated
View file

@ -1,6 +1,6 @@
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# It is not intended for manual editing. # It is not intended for manual editing.
version = 3 version = 4
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@ -17,6 +17,12 @@ version = "1.1.0"
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"libc", "libc",
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"windows-targets",
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[[package]] [[package]]
@ -48,9 +55,9 @@ checksum = "fad582f4b9e86b6caa621cabeb0963332d92eea04729ab12892c2533951e6440"
[[package]] [[package]]
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[[package]] [[package]]
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@ -80,9 +87,9 @@ dependencies = [
[[package]] [[package]]
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dependencies = [ dependencies = [
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"matrixmultiply", "matrixmultiply",
@ -96,13 +103,13 @@ dependencies = [
[[package]] [[package]]
name = "nalgebra-macros" name = "nalgebra-macros"
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dependencies = [ dependencies = [
"proc-macro2", "proc-macro2",
"quote", "quote",
"syn", "syn 2.0.98",
] ]
[[package]] [[package]]
@ -159,38 +166,38 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de"
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"libc",
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"rand_core", "rand_core",
"zerocopy",
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@ -198,18 +205,19 @@ dependencies = [
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dependencies = [ dependencies = [
"getrandom", "getrandom",
"zerocopy",
] ]
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dependencies = [ dependencies = [
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"rand", "rand",
@ -253,7 +261,7 @@ checksum = "af487d118eecd09402d70a5d72551860e788df87b464af30e5ea6a38c75c541e"
dependencies = [ dependencies = [
"proc-macro2", "proc-macro2",
"quote", "quote",
"syn", "syn 1.0.107",
] ]
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@ -269,9 +277,9 @@ dependencies = [
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dependencies = [ dependencies = [
"approx", "approx",
"num-complex", "num-complex",
@ -291,6 +299,17 @@ dependencies = [
"unicode-ident", "unicode-ident",
] ]
[[package]]
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"unicode-ident",
]
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@ -305,9 +324,12 @@ checksum = "84a22b9f218b40614adcb3f4ff08b703773ad44fa9423e4e0d346d5db86e4ebc"
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]
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@ -318,3 +340,96 @@ dependencies = [
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"windows_i686_gnu",
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"windows_x86_64_gnu",
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6
Cargo.toml
View file

@ -4,8 +4,8 @@ version = "1.0.0"
edition = "2021" edition = "2021"
[dependencies] [dependencies]
rand = "0.8" rand = "0.9"
rand_distr = "0.4" rand_distr = "0.5"
nalgebra = "0.32" nalgebra = "0.33"
serde = { version = "1.0", features = ["derive"] } serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0" serde_json = "1.0"

31
src/dataloader.rs
View file

@ -4,15 +4,18 @@ use nalgebra::DMatrix;
use rand::prelude::*; use rand::prelude::*;
use serde::Deserialize; use serde::Deserialize;
pub fn load_data() -> (Data<f64, OneHotVector>, Data<f64, OneHotVector>) { pub fn load_data() -> (Data<f32, OneHotVector>, Data<f32, OneHotVector>)
{
// the mnist data is structured as // the mnist data is structured as
// x: [[[pixels]],[[pixels]], etc], // x: [[[pixels]],[[pixels]], etc],
// y: [label1, label2, etc] // y: [label1, label2, etc]
// this is transformed to: // this is transformed to:
// Data : Vec<DataLine> // Data : Vec<DataLine>
// DataLine {inputs: Vec<pixels as f64>, label: f64} // DataLine {inputs: Vec<pixels as f64>, label: f64}
let raw_training_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/training_data.json")).unwrap(); let raw_training_data: Vec<RawData> =
let raw_test_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/test_data.json")).unwrap(); serde_json::from_slice(include_bytes!("data/training_data.json")).unwrap();
let raw_test_data: Vec<RawData> =
serde_json::from_slice(include_bytes!("data/test_data.json")).unwrap();
let train = vectorize(raw_training_data); let train = vectorize(raw_training_data);
let test = vectorize(raw_test_data); let test = vectorize(raw_test_data);
@ -20,17 +23,19 @@ pub fn load_data() -> (Data<f64, OneHotVector>, Data<f64, OneHotVector>) {
(Data(train), Data(test)) (Data(train), Data(test))
} }
fn vectorize(raw_training_data: Vec<RawData>) -> Vec<DataLine<f64, OneHotVector>> { fn vectorize(raw_data: Vec<RawData>) -> Vec<DataLine<f32, OneHotVector>>
{
let mut result = Vec::new(); let mut result = Vec::new();
for line in raw_training_data { for line in raw_data {
result.push(DataLine { inputs: DMatrix::from_vec(line.x.len(), 1, line.x), label: onehot(line.y) }); result.push(DataLine { inputs: DMatrix::from_vec(line.x.len(), 1, line.x), label: onehot(line.y) });
} }
result result
} }
#[derive(Deserialize)] #[derive(Deserialize)]
struct RawData { struct RawData
x: Vec<f64>, {
x: Vec<f32>,
y: u8, y: u8,
} }
@ -43,19 +48,17 @@ pub struct DataLine<X, Y> where X: Clone, Y: Clone {
} }
/// simple way to encode a onehot vector. An object that returns 1.0 if you get the 'right' index, or 0.0 otherwise /// simple way to encode a onehot vector. An object that returns 1.0 if you get the 'right' index, or 0.0 otherwise
#[derive(Debug, Clone)] #[derive(Debug, Clone, PartialEq)]
pub struct OneHotVector { pub struct OneHotVector {
pub val: usize, pub val: usize,
} }
impl OneHotVector { impl OneHotVector{
pub fn new(val: usize) -> Self { pub fn new(val: usize) -> Self {
Self { Self { val }
val
}
} }
pub fn get(&self, index: usize) -> f64 { pub fn get(&self, index: usize) -> f32 {
if self.val == index { if self.val == index {
1.0 1.0
} else { } else {
@ -69,7 +72,7 @@ pub struct Data<X, Y>(pub Vec<DataLine<X, Y>>) where X: Clone, Y: Clone;
impl<X, Y> Data<X, Y> where X: Clone, Y: Clone { impl<X, Y> Data<X, Y> where X: Clone, Y: Clone {
pub fn shuffle(&mut self) { pub fn shuffle(&mut self) {
let mut rng = thread_rng(); let mut rng = rand::rng();
self.0.shuffle(&mut rng); self.0.shuffle(&mut rng);
} }

52
src/net.rs
View file

@ -11,11 +11,11 @@ use crate::dataloader::{Data, DataLine, OneHotVector};
pub struct Network { pub struct Network {
_sizes: Vec<usize>, _sizes: Vec<usize>,
num_layers: usize, num_layers: usize,
pub biases: Vec<DMatrix<f64>>, pub biases: Vec<DMatrix<f32>>,
pub weights: Vec<DMatrix<f64>>, pub weights: Vec<DMatrix<f32>>,
} }
impl Network { impl Network{
/// The list `sizes` contains the number of neurons in the /// The list `sizes` contains the number of neurons in the
/// respective layers of the network. For example, if the list /// respective layers of the network. For example, if the list
/// was [2, 3, 1] then it would be a three-layer network, with the /// was [2, 3, 1] then it would be a three-layer network, with the
@ -48,11 +48,11 @@ impl Network {
} }
} }
fn feed_forward(&self, input: &DMatrix<f64>) -> DMatrix<f64> { fn feed_forward(&self, input: &DMatrix<f32>) -> DMatrix<f32> {
self.feed_forward_activation(input, sigmoid_inplace) self.feed_forward_activation(input, sigmoid_inplace)
} }
fn feed_forward_activation(&self, input: &DMatrix<f64>, activation: fn(&mut f64)) -> DMatrix<f64> { fn feed_forward_activation(&self, input: &DMatrix<f32>, activation: fn(&mut f32)) -> DMatrix<f32> {
let mut a = input.clone(); let mut a = input.clone();
for (b, w) in zip(&self.biases, &self.weights) { for (b, w) in zip(&self.biases, &self.weights) {
a = b + w * a; a = b + w * a;
@ -61,7 +61,7 @@ impl Network {
a a
} }
pub fn sgd(&mut self, mut training_data: Data<f64, OneHotVector>, epochs: usize, minibatch_size: usize, eta: f64, test_data: Option<Data<f64, OneHotVector>>) { pub fn sgd(&mut self, mut training_data: Data<f32, OneHotVector>, epochs: usize, minibatch_size: usize, eta: f32, test_data: Option<Data<f32, OneHotVector>>) {
for j in 0..epochs { for j in 0..epochs {
training_data.shuffle(); training_data.shuffle();
let mini_batches = training_data.as_batches(minibatch_size); let mini_batches = training_data.as_batches(minibatch_size);
@ -81,7 +81,7 @@ impl Network {
/// gradient descent using backpropagation to a single mini batch. /// gradient descent using backpropagation to a single mini batch.
/// The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta`` /// The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
/// is the learning rate. /// is the learning rate.
fn update_mini_batch(&mut self, mini_batch: &[DataLine<f64, OneHotVector>], eta: f64) { fn update_mini_batch(&mut self, mini_batch: &[DataLine<f32, OneHotVector>], eta: f32) {
let (mut nabla_b, mut nabla_w) = self.zero_gradient(); let (mut nabla_b, mut nabla_w) = self.zero_gradient();
for line in mini_batch.iter() { for line in mini_batch.iter() {
let (delta_nabla_b, delta_nabla_w) = self.backprop(&line.inputs, &line.label); let (delta_nabla_b, delta_nabla_w) = self.backprop(&line.inputs, &line.label);
@ -91,17 +91,17 @@ impl Network {
} }
self.weights = zip(&self.weights, &nabla_w) self.weights = zip(&self.weights, &nabla_w)
.map(|(w, nw)| w.sub(nw.scale(eta / mini_batch.len() as f64))).collect(); .map(|(w, nw)| w.sub(nw.scale(eta / mini_batch.len() as f32))).collect();
self.biases = zip(&self.biases, &nabla_b) self.biases = zip(&self.biases, &nabla_b)
.map(|(b, nb)| b.sub(nb.scale(eta / mini_batch.len() as f64))).collect(); .map(|(b, nb)| b.sub(nb.scale(eta / mini_batch.len() as f32))).collect();
} }
/// Return the number of test inputs for which the neural /// Return the number of test inputs for which the neural
/// network outputs the correct result. Note that the neural /// network outputs the correct result. Note that the neural
/// network's output is assumed to be the index of whichever /// network's output is assumed to be the index of whichever
/// neuron in the final layer has the highest activation. /// neuron in the final layer has the highest activation.
fn evaluate(&self, test_data: &Data<f64, OneHotVector>) -> usize { fn evaluate(&self, test_data: &Data<f32, OneHotVector>) -> usize {
let test_results: Vec<(usize, usize)> = test_data.0.iter() let test_results: Vec<(usize, usize)> = test_data.0.iter()
.map(|line| (argmax(self.feed_forward(&line.inputs)), line.label.val)) .map(|line| (argmax(self.feed_forward(&line.inputs)), line.label.val))
.collect(); .collect();
@ -113,7 +113,7 @@ impl Network {
/// gradient for the cost function C_x. `nabla_b` and /// gradient for the cost function C_x. `nabla_b` and
/// `nabla_w` are layer-by-layer lists of matrices, similar /// `nabla_w` are layer-by-layer lists of matrices, similar
/// to `self.biases` and `self.weights`. /// to `self.biases` and `self.weights`.
fn backprop(&self, x: &DMatrix<f64>, y: &OneHotVector) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) { fn backprop(&self, x: &DMatrix<f32>, y: &OneHotVector) -> (Vec<DMatrix<f32>>, Vec<DMatrix<f32>>) {
let (mut nabla_b, mut nabla_w) = self.zero_gradient(); let (mut nabla_b, mut nabla_w) = self.zero_gradient();
// feedforward // feedforward
@ -128,7 +128,7 @@ impl Network {
activations.push(activation.clone()); activations.push(activation.clone());
} }
// backward pass // backward pass
let delta: DMatrix<f64> = cost_derivative(&activations[activations.len() - 1], y).component_mul(&zs[zs.len() - 1].map(sigmoid_prime)); let delta: DMatrix<f32> = cost_derivative(&activations[activations.len() - 1], y).component_mul(&zs[zs.len() - 1].map(sigmoid_prime));
let index = nabla_b.len() - 1; let index = nabla_b.len() - 1;
nabla_b[index] = delta.clone(); nabla_b[index] = delta.clone();
@ -148,12 +148,12 @@ impl Network {
(nabla_b, nabla_w) (nabla_b, nabla_w)
} }
fn zero_gradient(&self) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) { fn zero_gradient(&self) -> (Vec<DMatrix<f32>>, Vec<DMatrix<f32>>) {
let nabla_b: Vec<DMatrix<f64>> = self.biases.iter() let nabla_b: Vec<DMatrix<f32>> = self.biases.iter()
.map(|b| b.shape()) .map(|b| b.shape())
.map(|s| DMatrix::zeros(s.0, s.1)) .map(|s| DMatrix::zeros(s.0, s.1))
.collect(); .collect();
let nabla_w: Vec<DMatrix<f64>> = self.weights.iter() let nabla_w: Vec<DMatrix<f32>> = self.weights.iter()
.map(|w| w.shape()) .map(|w| w.shape())
.map(|s| DMatrix::zeros(s.0, s.1)) .map(|s| DMatrix::zeros(s.0, s.1))
.collect(); .collect();
@ -161,7 +161,7 @@ impl Network {
} }
} }
fn cost_derivative(output_activations: &DMatrix<f64>, y: &OneHotVector) -> DMatrix<f64> { fn cost_derivative(output_activations: &DMatrix<f32>, y: &OneHotVector) -> DMatrix<f32> {
let shape = output_activations.shape(); let shape = output_activations.shape();
DMatrix::from_iterator(shape.0, shape.1, output_activations.iter().enumerate() DMatrix::from_iterator(shape.0, shape.1, output_activations.iter().enumerate()
.map(|(index, a)| a - y.get(index))) .map(|(index, a)| a - y.get(index)))
@ -169,7 +169,7 @@ fn cost_derivative(output_activations: &DMatrix<f64>, y: &OneHotVector) -> DMatr
/// index of max value /// index of max value
/// only meaningful for single row or column matrix /// only meaningful for single row or column matrix
fn argmax(val: DMatrix<f64>) -> usize { fn argmax(val: DMatrix<f32>) -> usize {
let mut max = 0.0; let mut max = 0.0;
let mut index = 0; let mut index = 0;
for (i, x) in val.iter().enumerate() { for (i, x) in val.iter().enumerate() {
@ -181,30 +181,30 @@ fn argmax(val: DMatrix<f64>) -> usize {
index index
} }
fn biases(sizes: Vec<usize>, init: fn(&usize) -> DMatrix<f64>) -> Vec<DMatrix<f64>> { fn biases(sizes: Vec<usize>, init: fn(&usize) -> DMatrix<f32>) -> Vec<DMatrix<f32>> {
sizes.iter().map(init).collect() sizes.iter().map(init).collect()
} }
fn weights(sizes: Vec<(usize, usize)>, init: fn(&(usize, usize)) -> DMatrix<f64>) -> Vec<DMatrix<f64>> { fn weights(sizes: Vec<(usize, usize)>, init: fn(&(usize, usize)) -> DMatrix<f32>) -> Vec<DMatrix<f32>> {
sizes.iter().map(init).collect() sizes.iter().map(init).collect()
} }
fn random_matrix(rows: usize, cols: usize) -> DMatrix<f64> { fn random_matrix(rows: usize, cols: usize) -> DMatrix<f32> {
let normal: Normal<f64> = Normal::new(0.0, 1.0).unwrap(); let normal: Normal<f32> = Normal::new(0.0, 1.0).unwrap();
DMatrix::from_fn(rows, cols, |_, _| normal.sample(&mut thread_rng())) DMatrix::from_fn(rows, cols, |_, _| normal.sample(&mut rand::rng()))
} }
fn sigmoid_inplace(val: &mut f64) { fn sigmoid_inplace(val: &mut f32) {
*val = sigmoid(*val); *val = sigmoid(*val);
} }
fn sigmoid(val: f64) -> f64 { fn sigmoid(val: f32) -> f32 {
1.0 / (1.0 + (-val).exp()) 1.0 / (1.0 + (-val).exp())
} }
/// Derivative of the sigmoid function. /// Derivative of the sigmoid function.
fn sigmoid_prime(val: f64) -> f64 { fn sigmoid_prime(val: f32) -> f32 {
sigmoid(val) * (1.0 - sigmoid(val)) sigmoid(val) * (1.0 - sigmoid(val))
} }
@ -216,7 +216,7 @@ mod test {
#[test] #[test]
fn test_sigmoid() { fn test_sigmoid() {
let mut mat: DMatrix<f64> = DMatrix::from_vec(1, 1, vec![0.0]); let mut mat: DMatrix<f32> = DMatrix::from_vec(1, 1, vec![0.0]);
mat.apply(sigmoid_inplace); mat.apply(sigmoid_inplace);
assert_eq!(mat.get(0), Some(&0.5)); assert_eq!(mat.get(0), Some(&0.5));
} }