Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/rafinskipg/neural-network-js
A neural network in JS
https://github.com/rafinskipg/neural-network-js
Last synced: 4 days ago
JSON representation
A neural network in JS
- Host: GitHub
- URL: https://github.com/rafinskipg/neural-network-js
- Owner: rafinskipg
- Created: 2019-05-08T17:49:29.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-07T05:19:45.000Z (almost 2 years ago)
- Last Synced: 2024-08-03T23:02:58.913Z (3 months ago)
- Language: JavaScript
- Size: 1.39 MB
- Stars: 8
- Watchers: 2
- Forks: 3
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Neural Network in JavaScript
Implementantion of a Perceptron neural network in JavaScript. It is a simple implementation that can serve as an example for learning, not for production use. It does not use GPU and the only activation function implemented is a `sigmoid` function.
For a ready to use implementation please refer to [BrainJS](https://github.com/BrainJS/brain.js)
## Installation
```
npm install --save vt-neural-network
```
## Usage
```javascript
import { Network } from 'vt-neural-network'
// Define the layer structure
const layers = [
2, // This is the input layer
10, // Hidden layer 1
10, // Hidden layer 2
1 // Output
]
const network = new Network(layers)
// Start training
const numberOfIterations = 20000
// Training data for a "XOR" logic gate
const trainingData = [{
input : [0,0],
output: [0]
}, {
input : [0,1],
output: [1]
}, {
input : [1,0],
output: [1]
}, {
input : [1,1],
output: [0]
}]
for(var i = 0; i < numberOfIterations; i ++) {
// Get a random training sample
const trainingItem = trainingData[Math.floor((Math.random()*trainingData.length))]
network.train(trainingItem.input, trainingItem.output);
}
// After training we can see if it works
// we call activate to set a input in the first layer
network.activate(trainingData[0].input)
const resultA = network.run()
network.activate(trainingData[1].input)
const resultB = network.run()
network.activate(trainingData[2].input)
const resultC = network.run()
network.activate(trainingData[3].input)
const resultD = network.run()
console.log('Expected 0 got', resultA[0])
console.log('Expected 1 got', resultB[0])
console.log('Expected 1 got', resultC[0])
console.log('Expected 0 got', resultD[0])
```
If you want to see other logic gates implementations, check the test folder.
## API
- `network.setLearningRate(0.3)`: Adjust the learning rate of the network,
- `network.toJSON()`: returns the structure of the network
- `network.layers`: contains the different layers of the network
- `layer.neurons`: contains the different neurons on each layer
## How to develop the application?
```bash
npm install
npm run watch
# Open public/ directory in browser
```
## Remove generated directory
If you would like to remove `public/dist` directory (created by Webpack):
```bash
npm run clear
```
If you would like to remove `node_modules/` and remove `public/dist/`
```bash
npm run clear:all
```
## Count LOC (Lines of Code)
If you would like to know how many lines of code you write:
```bash
npm run count
```
## Analysis of bundle file weight
If you would like to check how much a bundle file weight:
```bash
npm run audit
```
## Information of interest
### Backpropagation
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
### Neural networks
https://scrimba.com/g/gneuralnetworks
https://franpapers.com/en/machine-learning-ai-en/2017-neural-network-implementation-in-javascript-by-an-example/
http://karpathy.github.io/neuralnets/