Dendritic Autodifferentiation Crate

This crate allows for autodifferentiation to be performed during backpropogation of some ML algorithms. The autodiff library currently supports operations for weight regularization, dot product and elementwise operations. This crate serves as the base depedencies for most of the algorithms in the regression package

Features

- **Node**: Node structure for holding shared methods across all values in a computation graph.
- **Ops**: Operations with forward and backward pass implemented
- **Regularizers**: Operations specific to weight regualarization to prevent overfitting

Disclaimer

The dendritic project is a toy machine learning library built for learning and research purposes. It is not advised by the maintainer to use this library as a production ready machine learning library. This is a project that is still very much a work in progress.

use dendritic_ndarray::ndarray::NDArray;
use dendritic_ndarray::ops::*;
use dendritic_autodiff::node::*; 
use dendritic_autodiff::ops::*;
use dendritic_autodiff::regularizers::*; 

fn main() {

    //Load saved ndarrays (model parameters)
    let x_path = "data/linear_modeling_data/inputs"; 
    let w_path = "data/linear_modeling_data/weights";
    let b_path = "data/linear_modeling_data/bias";

    let x: NDArray<f64> = NDArray::load(x_path).unwrap();
    let w: NDArray<f64> = NDArray::load(w_path).unwrap();
    let b: NDArray<f64> = NDArray::load(b_path).unwrap();

    // Convert ndarrays to value nodes
    let inputs = Value::new(&x);
    let weights = Value::new(&w);
    let bias = Value::new(&b);

    // Create computation graph for linear layer
    let mut linear= ScaleAdd::new(
        Dot::new(inputs.clone(), weights.clone()),
        bias
    );

    linear.forward(); // perform forward pass
}