Lessons
Labs
Templates
Forks
// Count to 5
// 1-5, 5-1
const trainingData = [
[1,2,3,4,5],
[5,4,3,2,1]
];
const net = new brain.recurrent.LSTMTimeStep();
net.train(trainingData);
console.log(net.run([1,2,3,4]));
console.log(net.run([5,4,3,2])); const restaurants = {
"Brilliant Yellow Corral": "Monday",
"Penny’s": "Tuesday",
"Right Coast Wings": "Wednesday",
"The Delusion Last Railway Car": "Thursday",
"Fun Day Inn": "Friday",
"JHOP": "Saturday",
"Owls": "Sunday"
};
// input: { Monday, Tuesday, Wednesday, etc. }
// output: { Restaurant1, Restaurant2 }
const trainingData = []; // input: { red, green, blue }
// ouput: { light, neutral, dark }
const colors = [
{ green: 0.2, blue: 0.4 },
{ green: 0.4, blue: 0.6 },
{ red: 0.2, green: 0.8, blue: 0.8 },
{ green: 1, blue: 1 },
{ red: 0.8, green: 1, blue: 1 },
{ red: 1, green: 1, blue: 1 },
{ red: 1, green: 0.8, blue: 0.8 },
{ red: 1, green: 0.6, blue: 0.6 },
{ red: 1, green: 0.4, blue: 0.4 },
{ red: 1, green: 0.31, blue: 0.31 }, const net = new brain.NeuralNetwork({ hiddenLayers: [2, 2] });
const trainingData = [
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] }
];
net.train(trainingData, {
log: (error) => console.log(error)
});
Learn Neural Networks
// input 0 0, output 0
// input 0 1, output 1
// input 1 0, output 1
// input 1 1, output 0
const net = new brain.NeuralNetwork({ hiddenLayers: [3] });
const trainingData = [
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] }
];
// input 0 0, output 0
// input 0 1, output 1
// input 1 0, output 1
// input 1 1, output 0
const net = new brain.NeuralNetwork({ hiddenLayers: [3] });
const trainingData = [
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] }
];
// inputs & outputs
(inputs) => outputs;
// random values
Math.random();
// activation "relu"
function relu(value) {
return value < 0 ? 0 : value;
}
// bonus material
// https://en.wikipedia.org/wiki/Activation_function
/* const net = new brain.NeuralNetwork({ hiddenLayers: [2, 2] });
const trainingData = [
{ input: [0, 0], output: [0] },
{ input: [0, 1], output: [1] },
{ input: [1, 0], output: [1] },
{ input: [1, 1], output: [0] }
];
net.train(trainingData, {
log: (error) => console.log(error)
});