Crate autograd[−][src]

This library provides differentiable operations and tensors. The current backend is rust-ndarray.

Examples

Here we are computing partial derivatives of `z = 2x^2 + 3y + 1`.

```extern crate autograd as ag;
extern crate ndarray;

let ref x = ag::placeholder(&[]);
let ref y = ag::placeholder(&[]);
let ref z = 2*x*x + 3*y + 1;

// dz/dy
let gy = &ag::grad(&[z], &[y])[0];
println!("{:?}", gy.eval(&[]));   // => Some(3.)

// dz/dx (requires to fill the placeholder `x`)
let gx = &ag::grad(&[z], &[x])[0];
println!("{:?}", gx.eval(&[(x, &ndarray::arr0(2.).into_dyn())]));  // => Some(8.)

// ddz/dx (differentiates `z` again)
let ggx = &ag::grad(&[gx], &[x])[0];
println!("{:?}", ggx.eval(&[]));  // => Some(4.)```

Another example: softmax regression for MNIST digits classification.

```extern crate autograd as ag;
// -- graph def --
let ref x = ag::placeholder(&[-1, 28*28]);
let ref y = ag::placeholder(&[-1]);
let ref w = ag::variable(ag::ndarray_ext::glorot_uniform(&[28*28, 10]));
let ref b = ag::variable(ag::ndarray_ext::zeros(&[1, 10]));
let ref z = ag::matmul(x, w) + b;
let ref loss = ag::reduce_mean(&ag::sparse_softmax_cross_entropy(z, y), &[0, 1], false);
let ref params = [w, b];
let ref grads = ag::grad(&[loss], params);
let ref predictions = ag::argmax(z, -1, true);
let ref accuracy = ag::reduce_mean(&ag::equal(predictions, y), &[0], false);
let ref adam = ag::gradient_descent_ops::Adam::default();
let mut stateful_params = ag::gradient_descent_ops::Adam::vars_with_states(params);
let ref update_ops = adam.compute_updates(&stateful_params, grads);

// -- dataset --
// let ((x_train, y_train), (x_test, y_test)) = dataset::load();
//
// -- training loop --
// for epoch in 0..30 {
// ...
// ag::run(update_ops, &[(x, &x_batch), (y, &y_batch)]);
// }```

Re-exports

 `pub use ndarray_ext::array_gen;` `pub use tensor::Tensor;` `pub use ops::*;` `pub use ops::gradient_descent_ops;`

Modules

 ndarray_ext op ops tensor

Structs

 Eval Helper structure for batched evaluation.

Functions

 eval Evaluates given symbolic tensors. run Runs given symbolic tensors.