# autograd
[](https://travis-ci.org/perrier1034/rust-autograd)
[](https://crates.io/crates/autograd)
A library to run the computation graphs whose backend is
[rust-ndarray](https://github.com/bluss/rust-ndarray).
Documentation: https://docs.rs/autograd/
## Overview
* Automatic differentiation
* Pure Rust
* Neural net first APIs
* Dynamic/static graph construction with shared variables
## Example
Here we are computing partial derivatives of `z = 2x^2 + 3y + 1`.
```rust
extern crate ndarray;
extern crate autograd as ag;
let ref x = ag::placeholder();
let ref y = ag::variable(ndarray::arr1(&[0]));
let ref z = 2*x*x + 3*y + 1;
// dz/dy
let ref g1 = ag::gradients(z, &[y], None)[0];
// dz/dx
let ref g2 = ag::gradients(z, &[x], None)[0];
// ddz/dx (differentiates `z` again)
let ref gg = ag::gradients(g2, &[x], None)[0];
// evaluation of symbolic gradients
assert_eq!(3., g1.eval()[0]);
assert_eq!(4., gg.eval()[0]);
// dz/dx requires to fill the placeholder `x`
let feed = ag::Feed::new().add(x, ndarray::arr1(&[2.]));
assert_eq!(8., g2.eval_with_input(feed)[0]);
```
For more, see
[examples](https://github.com/perrier1034/rust-autograd/tree/master/examples) or
[tests](https://github.com/perrier1034/rust-autograd/tree/master/tests).
## License
MIT