Struct Classifier

pub struct Classifier<C: Class> { /* private fields */ }

A classifier.

Implementations

impl<C: Class> Classifier<C>

pub fn new(dev: &Device, config: ClassifierConfig) -> Result<Self>

Create a new classifier.

Example

use kalosm_learning::{Class, Classifier, ClassifierConfig};

#[derive(Debug, Clone, Copy, Class)]
enum MyClass {
    Person,
    Thing,
}

let dev = candle_core::Device::Cpu;
let classifier = Classifier::<MyClass>::new(&dev, ClassifierConfig::new()).unwrap();

pub fn config(&self) -> ClassifierConfig

Get the config of the classifier.

pub fn train( &self, m: &ClassificationDataset, epochs: usize, learning_rate: f64, batch_size: usize, progress: impl FnMut(ClassifierProgress), ) -> Result<f32>

Train the model on the given dataset.

Example

use kalosm_learning::{Class, ClassificationDatasetBuilder, Classifier, ClassifierConfig};

#[derive(Debug, Clone, Copy, Class)]
enum MyClass {
    Person,
    Thing,
}

let dev = candle_core::Device::Cpu;
let classifier = Classifier::<MyClass>::new(&dev, ClassifierConfig::new()).unwrap();
let mut dataset = ClassificationDatasetBuilder::new();
dataset.add(vec![1.0, 2.0, 3.0, 4.0], MyClass::Person);
dataset.add(vec![4.0, 3.0, 2.0, 1.0], MyClass::Thing);

classifier
    .train(&dataset.build(&dev).unwrap(), 20, 0.05, 3, |_| {})
    .unwrap();

pub fn save(&self, path: impl AsRef<Path>) -> Result<()>

Save the model to a safetensors file at the given path.

Example

use kalosm_learning::{Class, Classifier, ClassifierConfig};

#[derive(Debug, Clone, Copy, Class)]
enum MyClass {
    Person,
    Thing,
}

let dev = candle_core::Device::Cpu;
let classifier = Classifier::<MyClass>::new(&dev, ClassifierConfig::new()).unwrap();
classifier.save("classifier.safetensors").unwrap();

pub fn load( path: impl AsRef<Path>, dev: &Device, config: ClassifierConfig, ) -> Result<Self>

Load the model from a safetensors file at the given path.

Example

use kalosm_learning::{Class, Classifier, ClassifierConfig};

#[derive(Debug, Clone, Copy, Class)]
enum MyClass {
    Person,
    Thing,
}

let dev = candle_core::Device::Cpu;
let classifier =
    Classifier::<MyClass>::load("classifier.safetensors", &dev, ClassifierConfig::new())
        .unwrap();

pub fn run(&self, input: &[f32]) -> Result<ClassifierOutput<C>>

Run the model on the given input.

Example

use kalosm_learning::{Class, Classifier, ClassifierConfig};

#[derive(Debug, Clone, Copy, Class)]
enum MyClass {
    Person,
    Thing,
}

let dev = candle_core::Device::Cpu;
let classifier = Classifier::<MyClass>::new(&dev, ClassifierConfig::new()).unwrap();
let result = classifier.run(&[1.0, 2.0, 3.0, 4.0]).unwrap();
println!("Result: {:?}", result);