kerasty

Keras for Candle (Rust ML framework) with support for Web Assembly.

Roadmap of Supported Layers

Examples

Solution to the classic XOR problem

use kerasty::{Dense, Device, Loss, Metric, Model, Optimizer, Sequential, Tensor};

// Define the XOR input and output data
let x_data = vec![0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0];
let x = Tensor::from_slice(&x_data, (4, 2), &Device::Cpu)?;
let y_data = vec![0.0, 1.0, 1.0, 0.0];
let y = Tensor::from_slice(&y_data, (4, 1), &Device::Cpu)?;

// Build the neural network model
let mut model = Sequential::new();
model.add(Dense::new(2, 2, "relu"));
model.add(Dense::new(1, 2, "sigmoid"));

// Compile the model
model.compile(
    Optimizer::Adam(0.001, 0.9, 0.999, 1e-8, 0.0),
    Loss::BinaryCrossEntropyWithLogit,
    vec![Metric::Accuracy],
)?;

// Train the model
model.fit(x.clone(), y.clone(), 10000)?;

// Make predictions
let predictions = model.predict(&x);
let predictions = predictions.reshape(4)?.to_vec1::<f64>()?;
let predictions: Vec<i32> = predictions
    .iter()
    .map(|&p| if p >= 0.5 { 1 } else { 0 })
    .collect();

println!("Predictions:");
for i in 0..4 {
    println!(
        "Input: {:?} => Predicted Output: {}, Actual Output: {}",
        &x_data[i * 2..i * 2 + 2],
        predictions[i],
        y_data[i]
    );
}

The expected output is as follows:

Predictions:
Input: [0.0, 0.0] => Predicted Output: 0, Actual Output: 0
Input: [0.0, 1.0] => Predicted Output: 1, Actual Output: 1
Input: [1.0, 0.0] => Predicted Output: 1, Actual Output: 1
Input: [1.0, 1.0] => Predicted Output: 0, Actual Output: 0

License

MIT

Copyright © 2025-2035 Homero Roman Roman
Copyright © 2025-2035 Frederick Roman

Contributing

Contributions are welcome.
Please open an issue or a pull request to report a bug or request a feature.