Function autograd::ops::grad

pub fn grad(ys: &[&Tensor], xs: &[&Tensor]) -> Vec<Tensor>

Returns gradient tensors wrt input tensors.

Arguments

• ys - Targets of differentiation.
• xs - tensors with which differentiate ys. So the length must be same as ys's.

NOTE: Each objective must be a scalar (0-ranked tensor). For multi dimensional objectives, use grad_with_default.

Returns

Symbolic gradient tensors corresponding to xs in the same order as xs

Example

Partial derivatives of z = 2x^2 + 3y + 1.

extern crate ndarray;
extern crate autograd as ag;

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

// dz/dy
let ref gy = ag::grad(&[z], &[y])[0];
// dz/dx
let ref gx = ag::grad(&[z], &[x])[0];

// ddz/dx (differentiates `z` again)
let ref ggx = ag::grad(&[gx], &[x])[0];

// evaluation of symbolic gradients
let mut ctx = ag::Context::new();
assert_eq!(3., gy.eval(&mut ctx)[ndarray::IxDyn(&[])]);
assert_eq!(4., ggx.eval(&mut ctx)[ndarray::IxDyn(&[])]);

// dz/dx requires to fill the placeholder `x`
ctx.feed_input(x, ndarray::arr0(2.));
assert_eq!(8., gx.eval(&mut ctx)[ndarray::IxDyn(&[])]);