Brain.js sample train

const config = {
  hiddenLayers: [3],
  iterations: 2000,
  log: true,
  learningRate: 0.001,
  momentum: 0.1,
  errorThresh: 0.001,
  longPeriod: 50
}

const fs = require("fs")
const brain = require("brain.js")
const vex = new brain.recurrent.LSTM({
  activation: "leaky-relu"
})
const vex_neural = fs.readFileSync("vex_neural.json","utf8")
const trainData = JSON.parse(fs.readFileSync("data.json","utf8")).slice(151)

const log = l => console.log(l)


vex.fromJSON(JSON.parse(vex_neural))


let encode = (word) => {
  
let arr = word
    .normalize('NFD')
    .replace(/\p{Diacritic}/gu, "")
    .replace(/[\u0300-\u036f]/g, '')
    .toLowerCase()
    .split(' ')
    .filter(e => String(e).trim())
    
return arr.map(letter => (letter.charCodeAt(0) / 255))
};

const saveVex = ()=> {
	fs.writeFileSync("vex_neural.json",JSON.stringify(vex.toJSON()));
};

async function load(){
	for(let pos in trainData){
	let obj = trainData[pos];
	
	vex.train([{
	input: `Oque é ${obj["name"]}?`,
	output: obj.meanings
}], config)

vex.train([{
input: obj.name,
output: obj.synonyms
}], config)

log(`${pos} de ${trainData.length}`)
saveVex()
}


	}
load()
vex.maxPredictionLength = 100

log({gg:vex.run("oq é tempo")})