changed to f32: ~30s -> ~25s
This commit is contained in:
Shautvast
parent
f0876967d2
commit
0bd18ba314
4 changed files with 189 additions and 71 deletions
171
Cargo.lock
generated
171
Cargo.lock
generated
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@ -1,6 +1,6 @@
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# This file is automatically @generated by Cargo.
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# It is not intended for manual editing.
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version = 3
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version = 4
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[[package]]
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@ -17,6 +17,12 @@ version = "1.1.0"
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||||
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||||
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@ -31,13 +37,14 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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"cfg-if",
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"libc",
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"wasi",
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"windows-targets",
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]
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[[package]]
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@ -48,9 +55,9 @@ checksum = "fad582f4b9e86b6caa621cabeb0963332d92eea04729ab12892c2533951e6440"
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[[package]]
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[[package]]
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@ -80,9 +87,9 @@ dependencies = [
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[[package]]
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dependencies = [
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"approx",
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"matrixmultiply",
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@ -96,13 +103,13 @@ dependencies = [
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[[package]]
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name = "nalgebra-macros"
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dependencies = [
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"proc-macro2",
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"quote",
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"syn",
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"syn 2.0.98",
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]
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[[package]]
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@ -159,38 +166,38 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de"
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[[package]]
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]
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[[package]]
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dependencies = [
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"libc",
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"rand_chacha",
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"rand_core",
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"zerocopy",
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]
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[[package]]
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name = "rand_chacha"
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dependencies = [
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"ppv-lite86",
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"rand_core",
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@ -198,18 +205,19 @@ dependencies = [
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[[package]]
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name = "rand_core"
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version = "0.6.4"
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dependencies = [
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"getrandom",
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"zerocopy",
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]
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[[package]]
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name = "rand_distr"
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version = "0.4.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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dependencies = [
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"num-traits",
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"rand",
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@ -253,7 +261,7 @@ checksum = "af487d118eecd09402d70a5d72551860e788df87b464af30e5ea6a38c75c541e"
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dependencies = [
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"proc-macro2",
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"quote",
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"syn 1.0.107",
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]
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[[package]]
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@ -269,9 +277,9 @@ dependencies = [
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[[package]]
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version = "0.8.0"
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dependencies = [
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"approx",
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"num-complex",
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@ -291,6 +299,17 @@ dependencies = [
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"unicode-ident",
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]
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[[package]]
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name = "syn"
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"unicode-ident",
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]
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@ -305,9 +324,12 @@ checksum = "84a22b9f218b40614adcb3f4ff08b703773ad44fa9423e4e0d346d5db86e4ebc"
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name = "wide"
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@ -318,3 +340,96 @@ dependencies = [
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"bytemuck",
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"safe_arch",
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]
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[[package]]
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"windows_aarch64_msvc",
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"windows_i686_gnu",
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"windows_i686_gnullvm",
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"windows_i686_msvc",
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"windows_x86_64_gnu",
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"windows_x86_64_gnullvm",
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"windows_x86_64_msvc",
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]
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[[package]]
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name = "windows_aarch64_gnullvm"
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name = "windows_i686_gnullvm"
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name = "windows_x86_64_msvc"
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dependencies = [
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"bitflags",
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]
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[[package]]
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name = "zerocopy"
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version = "0.8.15"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a1e101d4bc320b6f9abb68846837b70e25e380ca2f467ab494bf29fcc435fcc3"
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dependencies = [
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"zerocopy-derive",
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]
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[[package]]
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name = "zerocopy-derive"
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version = "0.8.15"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "03a73df1008145cd135b3c780d275c57c3e6ba8324a41bd5e0008fe167c3bc7c"
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dependencies = [
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"proc-macro2",
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"quote",
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"syn 2.0.98",
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]
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@ -4,8 +4,8 @@ version = "1.0.0"
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edition = "2021"
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[dependencies]
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rand = "0.8"
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rand_distr = "0.4"
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nalgebra = "0.32"
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rand = "0.9"
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rand_distr = "0.5"
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nalgebra = "0.33"
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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@ -4,15 +4,18 @@ use nalgebra::DMatrix;
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use rand::prelude::*;
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use serde::Deserialize;
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pub fn load_data() -> (Data<f64, OneHotVector>, Data<f64, OneHotVector>) {
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pub fn load_data() -> (Data<f32, OneHotVector>, Data<f32, OneHotVector>)
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{
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// the mnist data is structured as
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// x: [[[pixels]],[[pixels]], etc],
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// y: [label1, label2, etc]
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// this is transformed to:
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// Data : Vec<DataLine>
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// DataLine {inputs: Vec<pixels as f64>, label: f64}
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let raw_training_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/training_data.json")).unwrap();
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let raw_test_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/test_data.json")).unwrap();
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let raw_training_data: Vec<RawData> =
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serde_json::from_slice(include_bytes!("data/training_data.json")).unwrap();
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let raw_test_data: Vec<RawData> =
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serde_json::from_slice(include_bytes!("data/test_data.json")).unwrap();
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let train = vectorize(raw_training_data);
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let test = vectorize(raw_test_data);
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@ -20,17 +23,19 @@ pub fn load_data() -> (Data<f64, OneHotVector>, Data<f64, OneHotVector>) {
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(Data(train), Data(test))
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}
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fn vectorize(raw_training_data: Vec<RawData>) -> Vec<DataLine<f64, OneHotVector>> {
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fn vectorize(raw_data: Vec<RawData>) -> Vec<DataLine<f32, OneHotVector>>
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{
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let mut result = Vec::new();
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for line in raw_training_data {
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for line in raw_data {
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result.push(DataLine { inputs: DMatrix::from_vec(line.x.len(), 1, line.x), label: onehot(line.y) });
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}
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result
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}
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#[derive(Deserialize)]
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struct RawData {
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x: Vec<f64>,
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struct RawData
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{
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x: Vec<f32>,
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y: u8,
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}
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@ -43,19 +48,17 @@ pub struct DataLine<X, Y> where X: Clone, Y: Clone {
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}
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/// simple way to encode a onehot vector. An object that returns 1.0 if you get the 'right' index, or 0.0 otherwise
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#[derive(Debug, Clone)]
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#[derive(Debug, Clone, PartialEq)]
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pub struct OneHotVector {
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pub val: usize,
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}
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impl OneHotVector {
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impl OneHotVector{
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pub fn new(val: usize) -> Self {
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Self {
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val
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}
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Self { val }
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}
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pub fn get(&self, index: usize) -> f64 {
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pub fn get(&self, index: usize) -> f32 {
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if self.val == index {
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1.0
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} else {
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|
|
@ -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 {
|
||||
pub fn shuffle(&mut self) {
|
||||
let mut rng = thread_rng();
|
||||
let mut rng = rand::rng();
|
||||
self.0.shuffle(&mut rng);
|
||||
}
|
||||
|
||||
|
|
|
|||
52
src/net.rs
52
src/net.rs
|
|
@ -11,11 +11,11 @@ use crate::dataloader::{Data, DataLine, OneHotVector};
|
|||
pub struct Network {
|
||||
_sizes: Vec<usize>,
|
||||
num_layers: usize,
|
||||
pub biases: Vec<DMatrix<f64>>,
|
||||
pub weights: Vec<DMatrix<f64>>,
|
||||
pub biases: Vec<DMatrix<f32>>,
|
||||
pub weights: Vec<DMatrix<f32>>,
|
||||
}
|
||||
|
||||
impl Network {
|
||||
impl Network{
|
||||
/// The list `sizes` contains the number of neurons in the
|
||||
/// respective layers of the network. For example, if the list
|
||||
/// 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)
|
||||
}
|
||||
|
||||
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();
|
||||
for (b, w) in zip(&self.biases, &self.weights) {
|
||||
a = b + w * a;
|
||||
|
|
@ -61,7 +61,7 @@ impl Network {
|
|||
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 {
|
||||
training_data.shuffle();
|
||||
let mini_batches = training_data.as_batches(minibatch_size);
|
||||
|
|
@ -81,7 +81,7 @@ impl Network {
|
|||
/// gradient descent using backpropagation to a single mini batch.
|
||||
/// The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
|
||||
/// 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();
|
||||
for line in mini_batch.iter() {
|
||||
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)
|
||||
.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)
|
||||
.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
|
||||
/// network outputs the correct result. Note that the neural
|
||||
/// network's output is assumed to be the index of whichever
|
||||
/// 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()
|
||||
.map(|line| (argmax(self.feed_forward(&line.inputs)), line.label.val))
|
||||
.collect();
|
||||
|
|
@ -113,7 +113,7 @@ impl Network {
|
|||
/// gradient for the cost function C_x. `nabla_b` and
|
||||
/// `nabla_w` are layer-by-layer lists of matrices, similar
|
||||
/// 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();
|
||||
|
||||
// feedforward
|
||||
|
|
@ -128,7 +128,7 @@ impl Network {
|
|||
activations.push(activation.clone());
|
||||
}
|
||||
// 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;
|
||||
nabla_b[index] = delta.clone();
|
||||
|
||||
|
|
@ -148,12 +148,12 @@ impl Network {
|
|||
(nabla_b, nabla_w)
|
||||
}
|
||||
|
||||
fn zero_gradient(&self) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) {
|
||||
let nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
|
||||
fn zero_gradient(&self) -> (Vec<DMatrix<f32>>, Vec<DMatrix<f32>>) {
|
||||
let nabla_b: Vec<DMatrix<f32>> = self.biases.iter()
|
||||
.map(|b| b.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
let nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
|
||||
let nabla_w: Vec<DMatrix<f32>> = self.weights.iter()
|
||||
.map(|w| w.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.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();
|
||||
DMatrix::from_iterator(shape.0, shape.1, output_activations.iter().enumerate()
|
||||
.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
|
||||
/// 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 index = 0;
|
||||
for (i, x) in val.iter().enumerate() {
|
||||
|
|
@ -181,30 +181,30 @@ fn argmax(val: DMatrix<f64>) -> usize {
|
|||
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()
|
||||
}
|
||||
|
||||
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()
|
||||
}
|
||||
|
||||
fn random_matrix(rows: usize, cols: usize) -> DMatrix<f64> {
|
||||
let normal: Normal<f64> = Normal::new(0.0, 1.0).unwrap();
|
||||
fn random_matrix(rows: usize, cols: usize) -> DMatrix<f32> {
|
||||
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);
|
||||
}
|
||||
|
||||
fn sigmoid(val: f64) -> f64 {
|
||||
fn sigmoid(val: f32) -> f32 {
|
||||
1.0 / (1.0 + (-val).exp())
|
||||
}
|
||||
|
||||
/// Derivative of the sigmoid function.
|
||||
fn sigmoid_prime(val: f64) -> f64 {
|
||||
fn sigmoid_prime(val: f32) -> f32 {
|
||||
sigmoid(val) * (1.0 - sigmoid(val))
|
||||
}
|
||||
|
||||
|
|
@ -216,7 +216,7 @@ mod test {
|
|||
|
||||
#[test]
|
||||
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);
|
||||
assert_eq!(mat.get(0), Some(&0.5));
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue