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9 changed files with 260 additions and 350 deletions
173
Cargo.lock
generated
173
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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name = "approx"
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@ -17,6 +17,12 @@ version = "1.1.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "d468802bab17cbc0cc575e9b053f41e72aa36bfa6b7f55e3529ffa43161b97fa"
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|
||||
[[package]]
|
||||
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||||
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|
||||
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||||
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[[package]]
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version = "1.13.0"
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@ -31,13 +37,14 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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[[package]]
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||||
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||||
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||||
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||||
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dependencies = [
|
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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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||||
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@ -48,9 +55,9 @@ checksum = "fad582f4b9e86b6caa621cabeb0963332d92eea04729ab12892c2533951e6440"
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||||
[[package]]
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||||
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||||
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||||
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||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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[[package]]
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name = "libm"
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@ -69,7 +76,7 @@ dependencies = [
|
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[[package]]
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name = "mnist-rs"
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version = "0.1.0"
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version = "1.0.0"
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dependencies = [
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"nalgebra",
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"rand",
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@ -80,9 +87,9 @@ dependencies = [
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[[package]]
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version = "0.32.1"
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||||
version = "0.33.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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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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version = "0.2.0"
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||||
version = "0.2.2"
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||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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|
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dependencies = [
|
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"proc-macro2",
|
||||
"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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||||
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@ -159,38 +166,38 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de"
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[[package]]
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|
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||||
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"libc",
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"rand_core",
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"zerocopy",
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]
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[[package]]
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source = "registry+https://github.com/rust-lang/crates.io-index"
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"rand_core",
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@ -198,18 +205,19 @@ dependencies = [
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|||
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[[package]]
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name = "rand_core"
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version = "0.6.4"
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version = "0.9.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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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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source = "registry+https://github.com/rust-lang/crates.io-index"
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"rand",
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@ -253,7 +261,7 @@ checksum = "af487d118eecd09402d70a5d72551860e788df87b464af30e5ea6a38c75c541e"
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dependencies = [
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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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name = "simba"
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version = "0.8.0"
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version = "0.9.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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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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||||
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||||
[[package]]
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||||
name = "syn"
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version = "2.0.98"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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"quote",
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"unicode-ident",
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]
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[[package]]
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name = "typenum"
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version = "1.16.0"
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@ -305,9 +324,12 @@ checksum = "84a22b9f218b40614adcb3f4ff08b703773ad44fa9423e4e0d346d5db86e4ebc"
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[[package]]
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name = "wasi"
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]
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[[package]]
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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",
|
||||
"windows_i686_gnullvm",
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||||
"windows_i686_msvc",
|
||||
"windows_x86_64_gnu",
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||||
"windows_x86_64_gnullvm",
|
||||
"windows_x86_64_msvc",
|
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]
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[[package]]
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[[package]]
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name = "zerocopy"
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checksum = "a1e101d4bc320b6f9abb68846837b70e25e380ca2f467ab494bf29fcc435fcc3"
|
||||
dependencies = [
|
||||
"zerocopy-derive",
|
||||
]
|
||||
|
||||
[[package]]
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||||
name = "zerocopy-derive"
|
||||
version = "0.8.15"
|
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "03a73df1008145cd135b3c780d275c57c3e6ba8324a41bd5e0008fe167c3bc7c"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.98",
|
||||
]
|
||||
|
|
|
|||
10
Cargo.toml
10
Cargo.toml
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@ -1,13 +1,11 @@
|
|||
[package]
|
||||
name = "mnist-rs"
|
||||
version = "0.1.0"
|
||||
version = "1.0.0"
|
||||
edition = "2021"
|
||||
|
||||
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
|
||||
|
||||
[dependencies]
|
||||
rand = "0.8"
|
||||
rand_distr = "0.4"
|
||||
nalgebra = "0.32"
|
||||
rand = "0.9"
|
||||
rand_distr = "0.5"
|
||||
nalgebra = "0.33"
|
||||
serde = { version = "1.0", features = ["derive"] }
|
||||
serde_json = "1.0"
|
||||
19
README.md
19
README.md
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|
@ -2,14 +2,15 @@ rust port of python in http://neuralnetworksanddeeplearning.com/chap1.html
|
|||
|
||||
main goal: me understanding what's going on
|
||||
|
||||
done:
|
||||
* implementation that 'works' without runtime errors
|
||||
results: ~95% accuracy in 30 epochs, learning rate = 3.0, batchsize = 10
|
||||
|
||||
to do:
|
||||
* verify correctness
|
||||
* add unit tests
|
||||
* train using actual training data
|
||||
* evaluate with test/validation data
|
||||
* make more efficient
|
||||
*training_data/test_data not included*<br/> too big for github
|
||||
|
||||
training_data/test_data not included
|
||||
Format: json: <br/>
|
||||
[{"x":[float;784], "y": u32}]<br/>
|
||||
=> x: 28x28 gray image as float<br/>
|
||||
=> y: label 0.. 9
|
||||
|
||||
the data can be found here:
|
||||
https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz
|
||||
albeit in a different format, convert it with `convert_pickle.py`
|
||||
22
convert_pickle.py
Normal file
22
convert_pickle.py
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
import pickle
|
||||
import gzip
|
||||
import json
|
||||
|
||||
# Load the data from the .pkl.gz file
|
||||
with gzip.open("mnist.pkl.gz", "rb") as f:
|
||||
training_data, validation_data, test_data = pickle.load(f, encoding="latin1")
|
||||
|
||||
# Define a helper function to convert the data into JSON serializable format
|
||||
def convert_data(data):
|
||||
features, labels = data
|
||||
return [{"x": features[i].tolist(), "y": int(labels[i])} for i in range(len(features))]
|
||||
|
||||
# Convert and save to JSON
|
||||
with open("training_data.json", "w") as train_json:
|
||||
json.dump(convert_data(training_data), train_json)
|
||||
|
||||
with open("validation_data.json", "w") as val_json:
|
||||
json.dump(convert_data(validation_data), val_json)
|
||||
|
||||
with open("test_data.json", "w") as test_json:
|
||||
json.dump(convert_data(test_data), test_json)
|
||||
|
|
@ -1,17 +1,21 @@
|
|||
use std::fmt::Debug;
|
||||
use nalgebra::DMatrix;
|
||||
|
||||
use rand::prelude::*;
|
||||
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
|
||||
// x: [[[pixels]],[[pixels]], etc],
|
||||
// y: [label1, label2, etc]
|
||||
// this is transformed to:
|
||||
// Data : Vec<DataLine>
|
||||
// DataLine {inputs: Vec<pixels as f64>, label: f64}
|
||||
let raw_training_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/training.json")).unwrap();
|
||||
let raw_test_data: Vec<RawData> = serde_json::from_slice(include_bytes!("data/test.json")).unwrap();
|
||||
let raw_training_data: Vec<RawData> =
|
||||
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 test = vectorize(raw_test_data);
|
||||
|
|
@ -19,17 +23,19 @@ pub fn load_data() -> (Data<f64, OneHotVector>, Data<f64, OneHotVector>) {
|
|||
(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();
|
||||
for line in raw_training_data {
|
||||
result.push(DataLine { inputs: line.x, label: onehot(line.y) });
|
||||
for line in raw_data {
|
||||
result.push(DataLine { inputs: DMatrix::from_vec(line.x.len(), 1, line.x), label: onehot(line.y) });
|
||||
}
|
||||
result
|
||||
}
|
||||
|
||||
#[derive(Deserialize)]
|
||||
struct RawData {
|
||||
x: Vec<f64>,
|
||||
struct RawData
|
||||
{
|
||||
x: Vec<f32>,
|
||||
y: u8,
|
||||
}
|
||||
|
||||
|
|
@ -37,24 +43,22 @@ struct RawData {
|
|||
/// Y is type of output
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct DataLine<X, Y> where X: Clone, Y: Clone {
|
||||
pub inputs: Vec<X>,
|
||||
pub inputs: DMatrix<X>,
|
||||
pub label: Y,
|
||||
}
|
||||
|
||||
/// 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 val: usize,
|
||||
}
|
||||
|
||||
impl OneHotVector{
|
||||
pub fn new(val: usize) -> Self {
|
||||
Self {
|
||||
val
|
||||
}
|
||||
Self { val }
|
||||
}
|
||||
|
||||
pub fn get(&self, index: usize) -> f64 {
|
||||
pub fn get(&self, index: usize) -> f32 {
|
||||
if self.val == index {
|
||||
1.0
|
||||
} else {
|
||||
|
|
@ -68,7 +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);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,2 @@
|
|||
pub mod net;
|
||||
pub mod dataloader;
|
||||
mod mat;
|
||||
12
src/main.rs
12
src/main.rs
|
|
@ -1,16 +1,12 @@
|
|||
use mnist_rs::dataloader::load_data;
|
||||
use std::time::Instant;
|
||||
|
||||
fn main() {
|
||||
let mut net = mnist_rs::net::Network::gaussian(vec![784, 30, 10]);
|
||||
let (training_data, test_data) = load_data();
|
||||
|
||||
net.sgd(training_data, 30, 1, 0.01, Some(test_data));
|
||||
|
||||
|
||||
// let sizes = vec![5,3,2];
|
||||
// let net = mnist_rs::net::Network::from(sizes);
|
||||
// println!("biases {:?}", net.biases.iter().map(|b|b.shape()).collect::<Vec<(usize,usize)>>());
|
||||
// println!("weights {:?}", net.weights.iter().map(|b|b.shape()).collect::<Vec<(usize,usize)>>());
|
||||
|
||||
let t0 = Instant::now();
|
||||
|
||||
net.sgd(training_data, 30, 10, 3.0, Some(test_data));
|
||||
println!("{}", t0.elapsed().as_millis());
|
||||
}
|
||||
209
src/mat.rs
209
src/mat.rs
|
|
@ -1,209 +0,0 @@
|
|||
use core::ops::Add;
|
||||
use std::fmt::Debug;
|
||||
use std::ops::AddAssign;
|
||||
use nalgebra::DMatrix;
|
||||
|
||||
/// matrix add with broadcasting
|
||||
/// like the numpy add operation
|
||||
/// not sure I even need it anymore, after fixing inconsistencies with the matrix shapes
|
||||
/// TODO see if it's still needed, or that standard matrix addition suffices
|
||||
pub fn add<T>(v1: DMatrix<T>, v2: DMatrix<T>) -> Result<DMatrix<T>, String>
|
||||
where T: PartialEq + Copy + Clone + Debug + Add + Add<Output=T> + AddAssign + 'static
|
||||
{
|
||||
let (r1, c1) = v1.shape();
|
||||
let (r2, c2) = v2.shape();
|
||||
|
||||
if r1 == r2 && c1 == c2 {
|
||||
// same size, no broadcasting needed
|
||||
Ok(v1 + v2)
|
||||
} else if r1 == 1 && c2 == 1 {
|
||||
Ok(DMatrix::from_fn(r2, c1, |r, c| *v1.get(c).unwrap() + *v2.get(r).unwrap()))
|
||||
} else if c1 == 1 && r2 == 1 {
|
||||
Ok(DMatrix::from_fn(r1, c2, |r, c| *v1.get(r).unwrap() + *v2.get(c).unwrap()))
|
||||
} else if r1 == 1 && c1 == c2 {
|
||||
Ok(DMatrix::from_fn(r2, c1, |r, c| *v1.get(c).unwrap() + *v2.get(c * r2 + r).unwrap()))
|
||||
} else if r2 == 1 && c1 == c2 {
|
||||
Ok(DMatrix::from_fn(r1, c2, |r, c| *v2.get(c).unwrap() + *v1.get(c * r1 + r).unwrap()))
|
||||
} else if c1 == 1 && r1 == r2 {
|
||||
Ok(DMatrix::from_fn(r1, c2, |r, c| *v1.get(r).unwrap() + *v2.get(c * r2 + r).unwrap()))
|
||||
} else if c2 == 1 && r1 == r2 {
|
||||
Ok(DMatrix::from_fn(r2, c1, |r, c| *v2.get(r).unwrap() + *v1.get(c * r2 + r).unwrap()))
|
||||
} else {
|
||||
Err(format!("ValueError: operands could not be broadcast together ({},{}), ({},{})", r1,c1, r2, c2))
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod test {
|
||||
use nalgebra::dmatrix;
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn stretch_row_column_to_square() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3];
|
||||
let v2: DMatrix<u32> = dmatrix![1;2;3];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (3, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_row_column_to_rect() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3];
|
||||
let v2: DMatrix<u32> = dmatrix![1;2];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;3,4,5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_column_row_to_square() {
|
||||
let v1: DMatrix<u32> = dmatrix![1;2;3];
|
||||
let v2: DMatrix<u32> = dmatrix![1,2,3];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (3, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_column_row_to_rect() {
|
||||
let v1: DMatrix<u32> = dmatrix![1;2;3];
|
||||
let v2: DMatrix<u32> = dmatrix![1,2];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (3, 2));
|
||||
assert_eq!(sum, dmatrix![2,3;3,4;4,5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_row() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3];
|
||||
let v2: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 3));
|
||||
assert_eq!(sum, dmatrix![2,4,6;5,7,9]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_row_commute() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
|
||||
let v2: DMatrix<u32> = dmatrix![1,2,3];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 3));
|
||||
assert_eq!(sum, dmatrix![2,4,6;5,7,9]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_column() {
|
||||
let v1: DMatrix<u32> = dmatrix![1;2];
|
||||
let v2: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;6,7,8]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stretch_column_commute() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
|
||||
let v2: DMatrix<u32> = dmatrix![1;2];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;6,7,8]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_broadcast_2dims() {
|
||||
let v1: DMatrix<u32> = dmatrix![1,2,3];
|
||||
let v2: DMatrix<u32> = dmatrix![1;2;3];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (3, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_commutative() {
|
||||
let v1 = dmatrix![1,2,3];
|
||||
let v2 = dmatrix![1;2;3];
|
||||
|
||||
let sum = add(v2, v1).unwrap();
|
||||
assert_eq!(sum.shape(), (3, 3));
|
||||
assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_same_size() {
|
||||
let v1 = dmatrix![1,2;3,4];
|
||||
let v2 = dmatrix![3,4;5,6];
|
||||
|
||||
let sum = add(v2, v1).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 2));
|
||||
assert_eq!(sum, dmatrix![4,6;8,10]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_row_broadcast() {//
|
||||
let v1 = dmatrix![1,2;3,4];
|
||||
let v2 = dmatrix![3,4];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum, dmatrix![4,6;6,8]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_add_row_broadcast2() {
|
||||
let v1 = dmatrix![1,1];
|
||||
let v2 = dmatrix![1,2;3,4];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 2));
|
||||
assert_eq!(sum, dmatrix![2,3;4,5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_broadcast() {
|
||||
let v1 = dmatrix![1;1];
|
||||
let v2 = dmatrix![1,2;3,4];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 2));
|
||||
assert_eq!(sum, dmatrix![2,3;4,5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_broadcast2() {
|
||||
let v1 = dmatrix![1,2;3,4];
|
||||
let v2 = dmatrix![1;1];
|
||||
|
||||
let sum = add(v1, v2).unwrap();
|
||||
assert_eq!(sum.shape(), (2, 2));
|
||||
assert_eq!(sum, dmatrix![2,3;4,5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn column_too_long() {
|
||||
let v1 = dmatrix![1;1;1];
|
||||
let v2 = dmatrix![1,2;3,4];
|
||||
|
||||
let result = add(v1, v2);
|
||||
assert_eq!(result, Err("ValueError: operands could not be broadcast together".to_owned()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn row_too_long() {
|
||||
let v1 = dmatrix![1,1,1];
|
||||
let v2 = dmatrix![1,2;3,4];
|
||||
|
||||
let result = add(v1, v2);
|
||||
assert_eq!(result, Err("ValueError: operands could not be broadcast together".to_owned()));
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
122
src/net.rs
122
src/net.rs
|
|
@ -6,14 +6,13 @@ use rand::prelude::*;
|
|||
use rand_distr::Normal;
|
||||
|
||||
use crate::dataloader::{Data, DataLine, OneHotVector};
|
||||
use crate::mat::add;
|
||||
|
||||
#[derive(Debug)]
|
||||
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{
|
||||
|
|
@ -49,20 +48,20 @@ impl Network {
|
|||
}
|
||||
}
|
||||
|
||||
fn feed_forward(&self, input: Vec<f64>) -> Vec<f64> {
|
||||
fn feed_forward(&self, input: &DMatrix<f32>) -> DMatrix<f32> {
|
||||
self.feed_forward_activation(input, sigmoid_inplace)
|
||||
}
|
||||
|
||||
fn feed_forward_activation(&self, input: Vec<f64>, activation: fn(&mut f64)) -> Vec<f64> {
|
||||
let mut a = DMatrix::from_vec(input.len(), 1, input);
|
||||
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 = add(b.clone(), w * a).unwrap();
|
||||
a = b + w * a;
|
||||
a.apply(activation);
|
||||
}
|
||||
a.column(0).iter().copied().collect()
|
||||
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);
|
||||
|
|
@ -82,38 +81,29 @@ 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) {
|
||||
let mut nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
|
||||
.map(|b| b.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
let mut nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
|
||||
.map(|w| w.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
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.to_vec(), &line.label);
|
||||
let (delta_nabla_b, delta_nabla_w) = self.backprop(&line.inputs, &line.label);
|
||||
|
||||
// nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
|
||||
// nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
|
||||
nabla_b = zip(&nabla_b, &delta_nabla_b).map(|(nb, dnb)| nb.add(dnb)).collect();
|
||||
nabla_w = zip(&nabla_w, &delta_nabla_w).map(|(nw, dnw)| nw.add(dnw)).collect();
|
||||
}
|
||||
|
||||
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.clone())), line.label.val))
|
||||
.map(|line| (argmax(self.feed_forward(&line.inputs)), line.label.val))
|
||||
.collect();
|
||||
|
||||
test_results.into_iter().filter(|(x, y)| *x == *y).count()
|
||||
|
|
@ -123,38 +113,27 @@ 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: Vec<f64>, y: &OneHotVector) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) {
|
||||
// zero_grad ie. set gradient to zero
|
||||
let mut nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
|
||||
.map(|b| b.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
let mut nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
|
||||
.map(|w| w.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
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
|
||||
let mut activation = DMatrix::from_vec(x.len(), 1, x);
|
||||
let mut activation = x.clone();
|
||||
let mut activations = vec![activation.clone()];
|
||||
let mut zs = vec![];
|
||||
|
||||
for (b, w) in zip(&self.biases, &self.weights) {
|
||||
let z = (w * &activation)+b.clone();
|
||||
let z = (w * activation) + b;
|
||||
zs.push(z.clone());
|
||||
activation = z.map(sigmoid);
|
||||
activations.push(activation.clone());
|
||||
}
|
||||
// backward pass
|
||||
// delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
|
||||
let delta: DMatrix<f64> = cost_derivative(&activations[activations.len() - 1], y).component_mul(&zs[zs.len() - 1].map(sigmoid_prime));
|
||||
// println!("delta {:?}", delta);
|
||||
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();
|
||||
|
||||
let index = nabla_w.len() - 1;
|
||||
let ac = &activations[activations.len() - 2].transpose();
|
||||
nabla_w[index] = &delta * ac;
|
||||
nabla_w[index] = &delta * (&activations[activations.len() - 2].transpose());
|
||||
let lens_zs = zs.len();
|
||||
for l in 2..self.num_layers {
|
||||
let z = &zs[lens_zs - l];
|
||||
|
|
@ -168,71 +147,76 @@ impl Network {
|
|||
|
||||
(nabla_b, nabla_w)
|
||||
}
|
||||
|
||||
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<f32>> = self.weights.iter()
|
||||
.map(|w| w.shape())
|
||||
.map(|s| DMatrix::zeros(s.0, s.1))
|
||||
.collect();
|
||||
(nabla_b, nabla_w)
|
||||
}
|
||||
}
|
||||
|
||||
fn cost_derivative(output_activations: &DMatrix<f64>, y: &OneHotVector) -> DMatrix<f64> {
|
||||
// output_activations - y
|
||||
// println!("output {:?}", output_activations);
|
||||
// println!("expected {:?}", y);
|
||||
|
||||
fn cost_derivative(output_activations: &DMatrix<f32>, y: &OneHotVector) -> DMatrix<f32> {
|
||||
let shape = output_activations.shape();
|
||||
let t = DMatrix::from_iterator(shape.0, shape.1, output_activations.iter().enumerate()
|
||||
.map(|(index, a)| a - y.get(index)));
|
||||
// println!("t {:?}",t);
|
||||
t
|
||||
DMatrix::from_iterator(shape.0, shape.1, output_activations.iter().enumerate()
|
||||
.map(|(index, a)| a - y.get(index)))
|
||||
}
|
||||
|
||||
fn argmax(val: Vec<f64>) -> usize {
|
||||
/// index of max value
|
||||
/// only meaningful for single row or column matrix
|
||||
fn argmax(val: DMatrix<f32>) -> usize {
|
||||
let mut max = 0.0;
|
||||
let mut index = 0;
|
||||
for (i, x) in val.iter().enumerate() {
|
||||
// print!("{},",x);
|
||||
if *x > max {
|
||||
index = i;
|
||||
max = *x;
|
||||
}
|
||||
}
|
||||
// println!();
|
||||
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))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod test {
|
||||
use std::convert::identity;
|
||||
use nalgebra::DMatrix;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[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));
|
||||
}
|
||||
|
|
@ -241,7 +225,7 @@ mod test {
|
|||
fn test_sigmoid_inplace() {
|
||||
let mut v = 10.0;
|
||||
sigmoid_inplace(&mut v);
|
||||
assert_eq!(0.9999546, v);
|
||||
assert_eq!(0.9999546021312976, v);
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
|
@ -251,7 +235,7 @@ mod test {
|
|||
|
||||
#[test]
|
||||
fn test_argmax() {
|
||||
assert_eq!(5, argmax(vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0]));
|
||||
assert_eq!(5, argmax(DMatrix::from_vec(10, 1, vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0])));
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
|
@ -266,15 +250,15 @@ mod test {
|
|||
// 2 layers of 2 units
|
||||
let mut net = Network::ones(vec![2, 2]);
|
||||
|
||||
let prediction = net.feed_forward_activation(vec![2.0, 2.0], |a| {});
|
||||
assert_eq!(prediction, vec![5.0, 5.0])
|
||||
let prediction = net.feed_forward_activation(&DMatrix::from_vec(2, 1, vec![2.0, 2.0]), |a| {});
|
||||
assert_eq!(prediction, DMatrix::from_vec(2, 1, vec![5.0, 5.0]))
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_sgd() {
|
||||
// 2 layers of 2 units
|
||||
let mut net = Network::ones(vec![2, 2]);
|
||||
let data = Data(vec![DataLine { inputs: vec![1.0, 1.0], label: OneHotVector::new(1) }]);
|
||||
let data = Data(vec![DataLine { inputs: DMatrix::from_vec(2, 1, vec![1.0, 1.0]), label: OneHotVector::new(1) }]);
|
||||
net.sgd(data, 1, 1, 0.001, None);
|
||||
println!("{:?}", net);
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue