ES6 hyperparameters optimization

Early version subject to changes.

Features

• written in javascript - Use with tensorflow.js as a replacement to your python hyperparameters library
• use from cdn or npm - Link hpjs in your html file from a cdn, or install in your project with npm
• versatile - Utilize multiple parameters and multiple search algorithms (grid search, random, bayesian)

Installation

  $ npm install hyperparameters

Parameter Expressions

  import * as hpjs from 'hyperparameters';

hpjs.choice(options)

• Randomly returns one of the options

hpjs.randint(upper)

• Return a random integer in the range [0, upper)

hpjs.uniform(low, high)

• Returns a single value uniformly between low and high i.e. any value between low and high has an equal probability of being selected

hpjs.quniform(low, high, q)

• returns a quantized value of hp.uniform calculated as round(uniform(low, high) / q) * q

hpjs.loguniform(low, high)

• Returns a value exp(uniform(low, high)) so the logarithm of the return value is uniformly distributed.

hpjs.qloguniform(low, high, q)

• Returns a value round(exp(uniform(low, high)) / q) * q

hpjs.normal(mu, sigma)

• Returns a real number that’s normally-distributed with mean mu and standard deviation sigma

hpjs.qnormal(mu, sigma, q)

• Returns a value round(normal(mu, sigma) / q) * q

hpjs.lognormal(mu, sigma)

• Returns a value exp(normal(mu, sigma))

hpjs.qlognormal(mu, sigma, q)

• Returns a value round(exp(normal(mu, sigma)) / q) * q

Random numbers generator

  import { RandomState } from 'hyperparameters';

example:

  const rng = new RandomState(12345);
  console.log(rng.randrange(0, 5, 0.5));

Spaces

  import { sample } from 'hyperparameters';

example:

  import * as hpjs from 'hyperparameters';
  const space = {
    x: hpjs.normal(0, 2),
    y: hpjs.uniform(0, 1),
    choice: hpjs.choice([
      undefined, hp.uniform('float', 0, 1),
    ]),
    array: [
      hpjs.normal(0, 2), hpjs.uniform(0, 3), hpjs.choice([false, true]),
    ],
    obj: {
      u: hpjs.uniform(0, 3),
      v: hpjs.uniform(0, 3),
      w: hpjs.uniform(-3, 0)
    }
  };
  console.log(hpjs.sample.randomSample(space));

fmin - find best value of a function over the arguments

  import * as hpjs from 'hyperparameters';
  const trials = hpjs.fmin(optimizationFunction, space, estimator, max_estimates, options);

example:

  import * as hpjs from 'hyperparameters';
  const fn = x => ((x ** 2) - (x + 1));
  const space = hpjs.uniform(-5, 5);
  fmin(fn, space, hpjs.search.randomSearch, 1000, { rng: new hpjs.RandomState(123456) })
    .then(trials => console.log(result.argmin));

Getting started with tensorflow.js

1. include javascript file

• include (latest) version from cdn

<script src="https://cdn.jsdelivr.net/npm/hyperparameters@latest/dist/hyperparameters.min.js" ></script>

• create search space
  ```
  const space = {
  optimizer: hpjs.choice([‘sgd’, ‘adam’, ‘adagrad’, ‘rmsprop’]),
  epochs: hpjs.quniform(50, 250, 50),
  };

* create tensorflow.js train function. Parameters are optimizer and epochs. input and output data passed as second argument

const trainModel = async ({ optimizer, epochs }, { xs, ys }) => {
// Create a simple model.
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({
loss: ‘meanSquaredError’,
optimizer
});
// Train the model using the data.
const h = await model.fit(xs, ys, { epochs });
return { model, loss: h.history.loss[h.history.loss.length - 1] };
};

* create optimization function

const modelOpt = async ({ optimizer, epochs }, { xs, ys }) => {
const { loss } = await trainModel({ optimizer, epochs }, { xs, ys });
return { loss, status: hpjs.STATUS_OK };
};

* find optimal hyperparameters

const trials = await hpjs.fmin(
modelOpt, space, hpjs.search.randomSearch, 10,
{ rng: new hpjs.RandomState(654321), xs, ys }
);
const opt = trials.argmin;
console.log(‘best optimizer’,opt.optimizer);
console.log(‘best no of epochs’, opt.epochs);

### 2. [install with npm](https://github.com/atanasster/hyperparameters/tree/master/examples/react-sample)
* install hyperparameters in your package.json

$ npm install hyperparameters

* import hyperparameters

import as tf from ‘@tensorflow/tfjs’;
import as hpjs from ‘hyperparameters’;

* create search space

const space = {
optimizer: hpjs.choice([‘sgd’, ‘adam’, ‘adagrad’, ‘rmsprop’]),
epochs: hpjs.quniform(50, 250, 50),
};

* create tensorflow.js train function. Parameters are optimizer and epochs. input and output data passed as second argument

const trainModel = async ({ optimizer, epochs }, { xs, ys }) => {
// Create a simple model.
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({
loss: ‘meanSquaredError’,
optimizer
});
// Train the model using the data.
const h = await model.fit(xs, ys, { epochs });
return { model, loss: h.history.loss[h.history.loss.length - 1] };
};

* create optimization function

const modelOpt = async ({ optimizer, epochs }, { xs, ys }) => {
const { loss } = await trainModel({ optimizer, epochs }, { xs, ys });
return { loss, status: hpjs.STATUS_OK };
};

* find optimal hyperparameters

const trials = await hpjs.fmin(
modelOpt, space, hpjs.search.randomSearch, 10,
{ rng: new hpjs.RandomState(654321), xs, ys }
);
const opt = trials.argmin;
console.log(‘best optimizer’,opt.optimizer);
console.log(‘best no of epochs’, opt.epochs);
```

License

MIT © Atanas Stoyanov & Martin Stoyanov