Published
Edited
Apr 6, 2022
Brain.js Issue #712
{
// provide optional config object (or undefined). Defaults shown.
const config = {
binaryThresh: 0.5,
hiddenLayers: [3], // array of ints for the sizes of the hidden layers in the network
activation: 'sigmoid', // supported activation types: ['sigmoid', 'relu', 'leaky-relu', 'tanh'],
leakyReluAlpha: 0.01, // supported for activation type 'leaky-relu'
};
// create a simple feed forward neural network with backpropagation
const net = new brain.NeuralNetwork(config);
const red = [255, 0, 0];
const green = [0, 255, 0];
const blue = [0, 0, 255];
const yellow = [255, 255, 0];
const orange = [255, 128, 0];
function normiliseData(colour) {
return colour.map((c) => c / 255);
}
console.log(normiliseData(orange));
net.train([
{ input: normiliseData(red), output: normiliseData(blue) },
{ input: normiliseData(blue), output: normiliseData(red) },
{ input: normiliseData(green), output: normiliseData(yellow) },
{ input: normiliseData(yellow), output: normiliseData(orange) },
], { log: true });
return [
net.run(normiliseData(red)),
net.run(normiliseData(blue)),
net.run(normiliseData(green)),
net.run(normiliseData(yellow)),
];
}
brain = require('brain.js')
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