MultilayerPerceptronJS

!MultiLayerPerceptron

Refer to my blog post.

This is a small neural network "library" that's intended for educational purposes. I wanted to develop something that is easily understood and very readable, so this library is far from optimized or efficient.

Installing

You can install this via npm

`` bash
npm install multilayer-perceptron-js
`

Creating a neural network

` javascript
const { MultiLayerPerceptron, ActivationFunction } = require('multilayer-perceptron-js');

let sigmoid = new ActivationFunction(
x => 1 / (1 + Math.exp(-x)), // sigmoid
y => y * (1 - y) // derivative of sigmoid
);

let mlp = new MultiLayerPerceptron({inputDimension: 2})
.addLayer({nodes: 2, activation: sigmoid})
.addLayer({nodes: 2, activation: sigmoid})
.addLayer({nodes: 1, activation: sigmoid})
.randomizeWeights();
`

Training a neural network

` javascript

mlp.train({
trainData: dataset.inputs,
trainLabels: dataset.targets,
validationData: validationDataset.inputs,
validationLabels: validationDataset.targets,
numEpochs: numberOfEpochs,
learningRate: learningRate,
verbose: true
});
`

Where dataset.inputs, dataset.targets, validationDataset.inputs, and validationDataset.targets are arrays. If you were solving the XOR problem, dataset might look like this (such that the indexes of each array line up):

` javascript
let dataset = {
inputs: [
[0, 0],
[0, 1],
[1, 0],
[1, 1]
],
targets: [
[0],
[1],
[1],
[0]
]
};
`

You'll see something like this while training if verbose is true:

` bash
Epoch 10; Error 1.9860974914165456
...
Epoch 100; Error 0.76609707552872275
...
Epoch 1000; Error 0.166096867598113085
...
Epoch 5000; Error 0.036096035894226
...
Epoch 10000; Error 0.03609582796927307
...
`

Making predictions

To make a prediction, just call predict on the MultiLayerPerceptron object. You'll receive the predicted guess and the state of the neural network. If you have a model that solved the XOR problem, making predictions would look like this:

` javascript
console.log(mlp.predict([0, 0]).prediction); // [0.02039202706589195]
console.log(mlp.predict([0, 1]).prediction); // [0.9848467111547554]
console.log(mlp.predict([1, 0]).prediction); // [0.9850631024542238]
console.log(mlp.predict([1, 1]).prediction); // [0.013544196415469074]
``

Acknowledgements

Much of the implementation is inspired from The Coding Train's videos on neural networks, and the 3Blue1Brown videos on neural networks helped me understand what I was doing a lot better.