// 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] }
];
// 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)
});
// 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 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 = []; // 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])); // rawData = [{ open: number, high: number, low: number, close: number }]
function scaleDown(step) { // normalize
return {
open: step.open / 138,
high: step.high / 138,
low: step.low / 138,
close: step.close / 138
};
}
console.log(scaleDown(rawData[0]));
function scaleUp(step) { // denormalize <html>
<head>
<script>
const rawData = [{"date":"2018-11-02","open":141.0716,"high":141.101
</script>
<script src="https://unpkg.com/brain.js@1.6.0/browser.js"></script>
<script src="index.pack.js"></script>
</head>
<body>
</body>
</html> // rawData = [{ open: number, high: number, low: number, close: number }]
function scaleDown(step) { // normalize
return {
open: step.open / 138,
high: step.high / 138,
low: step.low / 138,
close: step.close / 138
};
}
function scaleUp(step) { // denormalize
return {
open: step.open * 138, const trainingData = [
'0+0=0',
'0+1=1',
'0+2=2',
'0+3=3',
'0+4=4',
'0+5=5',
'1+0=1',
'1+1=2',
'1+2=3',
'1+3=4',
'1+4=5',
'1+5=6', // rawData = ' # ';
function toArray(string) { // normalize
if (string.length !== 7 * 7) throw new Error('string in wrong size');
return string.split('').map(toNumber);
}
function toNumber(character) {
return character === '#' ? 1 : 0;
}
const zero = toArray(
'#######' +
'# #' +
'# #' + const trainingData = [
'Jane saw Doug.',
'Doug saw Jane.',
'Spot saw Doug and Jane looking at each other.',
'It was love at first sight, and Spot had a frontrow seat. It was a very special moment for all.'
];
const net = new brain.recurrent.LSTM();
net.train(trainingData, {
iterations: 1500,
errorThresh: 0.011
});
console.log(net.run('Jane')); const trainingData = [
{ input: 'I am super happy!', output: 'happy' },
{ input: 'What a pill!', output: 'sarcastic' },
{ input: 'I am super unhappy!', output: 'sad' },
{ input: 'Are we there yet?', output: 'excited' }
];
const net = new brain.recurrent.LSTM();
net.train(trainingData, {
iterations: 100,
erroThresh: 0.011
});
console.log(net.run('I am unhappy!')); const trainingData = [
{ input: '1', output: '2' }
];
const net = new brain.recurrent.LSTM();
const inputMap = ['1', 'NEW IDEA', '2'];
[1,0,0]
[0,1,0]
[0,0,1]
// bonus: training data look like, if we start with input of '2', an const trainingData = [
{ 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] });
net.train(trainingData);
console.log('before reinforcement');
console.log(Array.from(net.run([0, 0])));
console.log(Array.from(net.run([1, 0]))); // color preference
const trainingData = [
{ input: { blue: 1 }, output: [1] },
{ input: { red: 1 }, output: [1] },
{ input: { black: 1 }, output: [0] },
{ input: { green: 1 }, output: [0] },
{ input: { brown: 1 }, output: [0] },
];
const net = new brain.NeuralNetwork();
net.train(trainingData);
console.log('before preference change');
console.log(Array.from(net.run({ blue: 1 })));
