autograd v0.0.2

autograd

A collection of automatic differentiation tools for operations on ndarrays.

Usage

First, install the library using npm:

npm install autograd

Then you can import the library by doing:

const autograd = require("autograd");

Then you can use the functions as in the following example:

const autograd = require("autograd");
const ndarray = require("ndarray");
const ops = require("ndarray-ops");

// re-create multiplication as differentiable function.
const mul = autograd.op({
  name: '*',
  forward(x1, x2) {
    // clone x1 to create a new `autograd.variable` as a result of the operation `*`.
    // if x1 is not a `autograd.variable` but an `ndarray`, this is the same as
    // calling `require('ndarray-scratch').clone(x1)`.
    const y = autograd.clone(x1);
    ops.muleq(y, x2);
    return y;
  },
  backward(y, x1, x2) {
    // only differentiate if the input `x1` or `x2` is a `autograd.variable`.
    if (autograd.isvariable(x1)) {
      ops.mul(x1.grad, y.grad, x2);
    }
    if (autograd.isvariable(x2)) {
      ops.mul(x2.grad, y.grad, x1);
    }
  }
});

// call the op with constants
const x1 = ndarray(new Float32Array(128*128).fill(2));
const x2 = ndarray(new Float32Array(128*128).fill(4));

let y = mul(x1, x2);
// y = [8 8 8 ...]

// ...

// call the op with variables!
// create differentiable variables to track gradients.
const varx1 = autograd.variable(x1, "x1");
const varx2 = autograd.variable(x2, "x2");

y = mul(varx1, varx2);
// y = [8 8 8 ...]

// with variables, access to gradients via `<variable>.grad` is available.
// call backward to compute partial derivatives of `y` w.r.t each of it's inputs.
autograd.backward(y);

// backward also accepts input gradient information from external sources.
// this by default makes `y.grad` === the gradient input.
// Without a gradient input, `y.grad` === dy/dy === [1 1 1 ...].
autograd.backward(y, ndarray(new Float32Array(128*128).fill(1)));

// ** pulls out Calculus book **
// At a glance, `autograd.backward(y)` does the following:
//
// y = x1 * x2
// dy/dx1 = d(y) / dx1
// dy/dx1 = d(x1 * x2) / dx1  (x2 treated as constant)
// dy/dx1 = (dx1 * x2) / dx1  (take the derivative w.r.t x1)
// dy/dx1 = x2                (dx1/dx1 = 1)
//
// asserting the identity of our partial derivatives,
// (dy/dy) * (dy/dx1) = (dy/dx1)