ES6 hyperparameters optimization
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
lowandhighi.e. any value betweenlowandhighhas an equal probability of being selected
hpjs.quniform(low, high, q)
- returns a quantized value of
hp.uniformcalculated asround(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" />
- 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
- 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