Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
README.md
index.js
loss.js
methods.js
optimize.js
tensorStruct.js

README.md

The opt module

This module provides methods for optimizing functions which take multiple parameters and produce a single output loss (i.e. negative objective) value. It implements several stochastic gradient optimization methods which can use either automatically-computed or user-provided gradients. In addition, the module exposes convenience utilities to make common neural net training tasks easier.

Training neural nets

opt.nnTrain is a convenience function for training a single neural net from data:

var ad = require('adnn/ad');
var nn = require('adnn/nn');
var opt = require('adnn/opt');

// Train a classifier
// e.g. 20 features -> 5 classes
// data is an array of {input: Tensor([20]), output: Number},
//   where the output number is a class label
var classifier = nn.sequence([
  nn.linear(20, 10),
  nn.tanh,
  nn.linear(10, 5),
  nn.softmax
]);
var data = ...; // Load training data here
opt.nnTrain(classifier, data, opt.classificationLoss, {
  batchSize: 10,
  iterations: 1000,
  method: opt.adagrad(),
  verbose: true   // prints iteration count
});

// Train a regressor
// e.g. linear function R^20 -> R^5
// data is an array of {input: Tensor([20]), output: Tensor([5])}
var regressor = nn.linear(20, 5);
var data = ...; // Load training data here
opt.nnTrain(regressor, data, opt.regressionLoss, {
  batchSize: 10,
  iterations: 1000,
  method: opt.adagrad()
});

// Can use your own loss functions instead of 'classificationLoss' or 'regressionLoss'
// argument 0: the output of the neural net
// argument 1: the 'output' field from a training data point
// Returns an AD node containing the (scalar) loss value
function customLoss(nnOutput, trainingDataOutput) { ... }

// There are several optimization methods available
var optMethods = [
  // stepSizeDecay: multiplicative decay factor on stepSize after each update
  opt.sgd({stepSize: 0.1, stepSizeDecay: 0.99}),
  opt.adagrad({stepSize: 0.1}),
  opt.rmsprop({stepSize: 0.1, decayRate: 0.9}),
  // decayRate1: decay rate for first moment estimate
  // decayRate2: decay rate for second moment estimate
  opt.adam({stepSize: 0.1, decayRate1: 0.9, decayRate2: 0.99})
];

Training AD functions

opt.adTrain provides a nearly identical interface to opt.nnTrain, but for training general AD functions that are not encapsulated in a neural net. In this case, in addition to its output, the function must also return its parameters so that the optimizer knows what free parameters to optimize. opt.nnTrain is actually implemented in terms of opt.adTrain.

var ad = require('adnn/ad');
var opt = require('adnn/opt');

// Learn the parameters of a simple linear function
// (i.e. 'params' dot 'input')
var params = ad.params([10]);
function dot(input) {
  var output = ad.tensor.sumreduce(ad.scalar.mul(input, params));
  return {
    output: output,
    parameters: params
  };
}
var data = ...;   // Load training data here
opt.adTrain(dot, data, opt.regressionLoss, {
  batchSize: 1,
  iterations: 1000,
  method: opt.sgd({stepSize: 0.1})
});

// In the above example, 'parameters' was a single lifted Tensor
// In general, 'parameters' can be an arbitrary structure of the following type:
//    ParamStruct = Tensor | array(ParamStruct) | object(ParamStruct})
// So the following are all valid parameter structures:
var parameters1 = ad.params([20]);
var parameters2 = [ ad.params([5]), ad.params([10]) ];
var parameters3 = { p1: ad.params([5]), p2: ad.params([10]);
var parameters4 = {
  p1: [ ad.params([5]), ad.params([10]) ],
  p2: [ ad.params([6]), ad.params([14]) ]
};

Optimizing AD functions

If you want to optimize an objective that isn't based on training data, then you want opt.adOptimize. The function to be optimized must return a loss value (a scalar AD node) as well as its free parameters:

var ad = require('adnn/ad');
var opt = require('adnn/opt');
var tensor = require('adnn/tensor');

// Find the minimum of some arbitrary function
var params = ad.params([10]);
function foo() {
  var x = ad.lift(new tensor([10]).fromFlatArray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]));
  var diff = ad.tensor.sub(params, x);
  var sqdiff = ad.tensor.mul(diff, diff);
  var output = ad.tensor.sumreduce(sqdiff);
  return {
    loss: output,
    parameters: params
  };
}
opt.adOptimize(foo, {
  iterations: 1000,
  method: opt.sgd({stepSize: 0.1})
});

Optimizing with user-provided gradients

It is also possible to use opt's optimization facilities without using AD (i.e. by calculating gradients yourself). opt.optimize does this: it expects a function that returns both its free parameters as well as the gradient of the loss with respect to those parameters. All of the other optimization/training methods in opt are implemented in terms of this method.

var Tensor = require('adnn/tensor');
var opt = require('adnn/opt');

// Find the minimum of some arbitrary function
var params = new Tensor([10]).fillRandom();
function foo(input) {
  var output = input.add(params).sqrt().exp().sumreduce();
  var gradients = ...;  // Compute gradients here (I'm too lazy... :P)
  return {
    gradients: gradients,
    parameters: params
  };
}
opt.optimize(foo, {
  iterations: 1000,
  method: opt.sgd({stepSize: 0.1})
});