Intricate

A GPU accelerated library that creates/trains/runs neural networks in pure safe Rust code.

Architechture overview

Intricate has a layout very similar to popular libraries out there such as Keras.

Models

As said before, similar to Tensorflow, Intricate defines Models as basically a list of Layers that are explained down bellow.

Layers

Every layer receives inputs and returns outputs, they must also implement a back_propagate method that will mutate the layer if needed and then return the derivatives of the loss function with respected to the inputs, written with I as the inputs of the layer, E as the loss and O as the outputs of the layer:

dE/dI <- Model <- dE/dO

These layers can be anything you want and just propagates the previous inputs to the next inputs for the next layer or for the outputs of the whole Model.

There are a few activations already implemented, but still many to be implemented.

XoR using Intricate

If you look at the examples/ in the repository you will find XoR implemented using Intricate. The code goes like this:

// Defining the training data
let training_inputs = Vec::from([
    Vec::from([0.0, 0.0]),
    Vec::from([0.0, 1.0]),
    Vec::from([1.0, 0.0]),
    Vec::from([1.0, 1.0]),
]);
let expected_outputs = Vec::from([
    Vec::from([0.0]),
    Vec::from([1.0]),
    Vec::from([1.0]),
    Vec::from([0.0]),
]);

// Defining the layers for our XoR Model
let mut layers: Vec<Box<dyn Layer<f64>>> = Vec::new();

layers.push(Box::new(DenseF64::new(2, 3)));
// The tanh activation function
layers.push(Box::new(TanHF64::new()));
layers.push(Box::new(DenseF64::new(3, 1)));
layers.push(Box::new(TanHF64::new()));

// Instantiate our model using the layers
let mut xor_model = ModelF64::new(layers);
// mutable because the 'fit' method tweaks the layers

// Fit the model however many times we want
xor_model.fit(
    &training_inputs, 
    &expected_outputs, 
    TrainingOptionsF64 {
        learning_rate: 0.1,
        loss_algorithm: Box::new(MeanSquared), // The Mean Squared loss function
        should_print_information: true, // Should be verbose
        instantiate_gpu: false, // Should initialize WGPU Device and Queue for GPU layers
        epochs: 10000,
    },
).await;
// we await here because for a GPU computation type of layer
// the responses from the GPU must be awaited on the CPU

As you can see it is extremely easy creating these models, and blazingly fast as well.

Things to be done still