Getting Started with Delta

Adding Delta to Your Project

To add the Delta library to your Rust project, you need to include it in your Cargo.toml file. Follow these steps:

  1. Open your project’s Cargo.toml file.
  2. Add the following line under [dependencies]:
Cargo.toml
[dependencies]
deltaml = "0.1.0"

Currently we have published Delta to deltaml, but not that this is still experimental in alpha stage so things might break in the upcoming iterations.

Delta Usage Example

src/main.rs
use deltaml::activations::{ReluActivation, SoftmaxActivation};
use deltaml::common::ndarray::{IxDyn, Shape};
use deltaml::dataset::Cifar10Dataset;
use deltaml::dataset::base::ImageDatasetOps;
use deltaml::losses::MeanSquaredLoss;
use deltaml::neuralnet::{Dense, Flatten, Sequential};
use deltaml::optimizers::Adam;


#[tokio::main]
async fn main() {
    // Create a neural network
    let mut model = Sequential::new()
        .add(Flatten::new(Shape::from(IxDyn(&[32, 32, 3])))) // CIFAR-10: 32x32x3 -> 3072
        .add(Dense::new(128, Some(ReluActivation::new()), true)) // Input: 3072, Output: 128
        .add(Dense::new(10, Some(SoftmaxActivation::new()), false)); // Output: 10 classes


    // Display the model summary
    model.summary();


    // Define an optimizer
    let optimizer = Adam::new(0.001);


    // Compile the model
    model.compile(optimizer, MeanSquaredLoss::new());


    // Loading the train and test dataset
    let mut train_data = Cifar10Dataset::load_train().await;
    let test_data = Cifar10Dataset::load_test().await;
    let val_data = Cifar10Dataset::load_val().await;


    println!("Training the model...");
    println!("Train dataset size: {}", train_data.len());


    let epoch = 10;
    let batch_size = 32;


    match model.fit(&mut train_data, epoch, batch_size) {
        Ok(_) => println!("Model trained successfully"),
        Err(e) => println!("Failed to train model: {}", e),
    }


    // Validate the model
    match model.validate(&val_data, batch_size) {
        Ok(validation_loss) => println!("Validation Loss: {:.6}", validation_loss),
        Err(e) => println!("Failed to validate model: {}", e),
    }


    // Evaluate the model
    let accuracy = model.evaluate(&test_data, batch_size).expect("Failed to evaluate the model");
    println!("Test Accuracy: {:.2}%", accuracy * 100.0);


    // Save the model
    model.save("model_path").unwrap();
}