Brain.js
Brain.js is a JavaScript library that makes it easy to understand Neural Networks because it hides the complexity of the mathematics.
Building a Neural Network
Building a neural network with Brain.js:
Example:
// Create a Neural Network
const network = new brain.NeuralNetwork();
// Train the Network with 4 input objects
network.train([
{input:[0,0], output:{zero:1}},
{input:[0,1], output:{one:1}},
{input:[1,0], output:{one:1},
{input:[1,1], output:{zero:1},
]);
// What is the expected output of [1,0]?
result = network.run([1,0]);
// Display the probability for "zero" and "one"
... result["one"] + " " + result["zero"];
Try it Yourself »
Example Explained:
A Neural Network is created with: new brain.NeuralNetwork()
The network is trained with network.train([examples])
The examples represent 4 input values with a corresponding output value.
With network.run([1,0]), you ask "What is the likely output of [1,0]?"
The answer from the network is:
- one: 93% (close to 1)
- zero: 6% (close to 0)
How to Predict a Contrast
With CSS, colors can be set by RGB:
Example
| Color | RGB |
|---|---|
| Black | RGB(0,0,0) |
| Yellow | RGB(255,255,0) |
| Red | RGB(255,0,0) |
| White | RGB(255,255,255) |
| Light Gray | RGB(192,192,192) |
| Dark Gray | RGB(65,65,65) |
The example below demonstrates how to predict the darkness of a color:
Example:
// Create a Neural Network
const net = new brain.NeuralNetwork();
// Train the Network with 4 input objects
net.train([
// White RGB(255, 255, 255)
{input:[255/255, 255/255, 255/255], output:{light:1}},
// Light grey (192,192,192)
{input:[192/255, 192/255, 192/255], output:{light:1}},
// Darkgrey (64, 64, 64)
{ input:[65/255, 65/255, 65/255], output:{dark:1}},
// Black (0, 0, 0)
{ input:[0, 0, 0], output:{dark:1}},
]);
// What is the expected output of Dark Blue (0, 0, 128)?
let result = net.run([0, 0, 128/255]);
// Display the probability of "dark" and "light"
... result["dark"] + " " + result["light"];
Try it Yourself »
Example Explained:
A Neural Network is created with: new brain.NeuralNetwork()
The network is trained with network.train([examples])
The examples represent 4 input values a corresponding output value.
With network.run([0,0,128/255]), you ask "What is the likely output of dark blue?"
The answer from the network is:
- Dark: 95%
- Light: 4%
Why not edit the example to test the likely output of yellow or red?
