1.0.1 • Published 8 years ago
neur v1.0.1
neur
Simple neural network implementation in JS
Installation
Install via npm:
npm install neur
Usage
Step 1: Initialize a network
var Neural = require('neur');
var neural = Neural();
or
var neural = require('neur')();
You can config the network by passing the options to the constructor:
var neural = Neural({
learningRate: 0.7,
iterations: 10000,
hiddenUnits: 3
});
By default, we have a network with 1
hidden layer which has 3
neurons in total. And it will train the input data 10,000
times.
Step 2: Train a network
neural.learn([
{ input: [ <input-values> ], output: [ <output-values> ] },
...
]);
Step 3: Predict
var result = neural.predict([ <predict-input-values> ]);
Examples
Example 1: Basic using
var Neural = require('neur');
var neural = Neural()
.learn([
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 1], output: [1] },
{ input: [1, 0], output: [0] }
]);
console.log(neural.predict([1, 0])); // ~0
console.log(neural.predict([1, 1])); // ~1
Example 2: Use Model mapping to train complex data
var Neural = require('neur');
var color = Neural().model({ r: 0, g: 0, b: 0 });
var guess = Neural().model({ black: 0, white: 0 });
var result = Neural()
.learn([
{
input: color.in({ r: 0.03, g: 0.7, b: 0.5 }),
output: guess.in({ black: 1, white: 0 })
},
{
input: color.in({ r: 0.16, g: 0.09, b: 0.2 }),
output: guess.in({ black: 0, white: 1 })
},
{
input: color.in({ r: 0.5, g: 0.5, b: 1.0 }),
output: guess.in({ black: 0, white: 1 })
}
])
.predict(color.in({ r: 1, g: 0.4, b: 0 }));
console.log(guess.out(result)); // { black: ~0, white: ~1 }