 | Name and Gender |  |
Given a dataset of names and their associated gender (male/female) try and have the neural network see if their is any correlating patterns.
Few things happening - the letters of the name are converted to a binary value (ascii to number scale). The names for males and females are loaded in and put in a large array.
| Complete Code | |
// Name to Gender
await xinitialize( {layers:[7,6,2], build:'cpu', learningrate:0.2} );
var INPUT_LENGTH = 7;
async function dynamicscript(url) {
let fp = await fetch( url );
let ft = await fp.text();
var script = document.createElement('script');
//script.src = 'https://cdn.plot.ly/plotly-2.1.0.min.js';
script.innerHTML = ft;
document.head.appendChild(script);
}
await dynamicscript( 'https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.9.1/underscore-min.js' );
await dynamicscript( 'https://webgpulab.xbdev.net/var/resources/female.json' );
await dynamicscript( 'https://webgpulab.xbdev.net/var/resources/male.json' );
var females = JSON.parse(female);
var males = JSON.parse(male);
/*
display the console.log output to the window
*/
let divLog = document.createElement('div');
divLog.id = 'debug';
divLog.style.left = "0px";
divLog.style.top = "0px";
divLog.style.width = "500";
divLog.style.height = "500";
divLog.style['box-sizing'] = 'border-box';
divLog.style['padding'] = "10px";
divLog.style["pointer-events"] = "none";
console.oldlog = console.log;
console.log7 = function(txt){
console.oldlog( txt );
// extract code to get the line number the log was called from
var err = new Error();
var line = err.stack.split("\n")[2];
var index = line.indexOf("at ");
var clean = line.slice(index+2, line.length);
clean = clean.split('/');
clean = clean[ clean.length-1 ];
// write the text to the debug div window
divLog.innerHTML += 'debug: ['+clean+'] ' + txt + '<br>';
//divLog += txt + '<br>';
}
function convertNameToInput(name) {
name = name.toLowerCase();
if(name.length > INPUT_LENGTH)
name = name.substring(INPUT_LENGTH);
while(name.length < INPUT_LENGTH)
name = " " + name;
var characters = name.split("");
return characters.map(
(c) => c == " " ? 0 : c.charCodeAt(0)/1000
);
}
var trainingData = [];
for(var i = 0; i < females.length; i++) {
trainingData.push({
inputs: convertNameToInput(females[i]),
outputs: [0, 1] // Male = false, Female = true
});
}
for(var i = 0; i < males.length; i++) {
for(var j = 0; j < 2; j++) {
trainingData.push({
inputs: convertNameToInput(males[i]),
outputs: [1, 0] // Male = true, Female = false
});
}
}
for(var i=0;i<10;i++)
trainingData = _.shuffle(trainingData);
console.log( trainingData[0] );
for (let epoch = 0; epoch <= 10; epoch++) {
let indexes = Array.from( Array( trainingData.length-100 ).keys() );
indexes.sort(() => Math.random() - 0.5);
for (let j of indexes) {
await xactivate( trainingData[0].inputs );
await xpropagate( trainingData[0].outputs);
}
if (epoch % 2 === 0) {
let cost = 0;
for (let j = 0; j < trainingData.length; j++) {
let o = await xactivate( trainingData[j].inputs );
for (let b=0; b<trainingData[j].outputs.length; b++)
{
cost += Math.pow( trainingData[j].outputs[b] - o[b], 2);
}
}
cost /= 4;
console.log(`epoch ${epoch} mean squared error: ${cost}`);
}
}
async function getGender(name) {
var result = await xactivate( convertNameToInput(name) );
if(result[0] > result[1])
return "Male (" + (result[0]*100).toFixed() + "% sure)";
return "Female (" + (result[1]*100).toFixed() + "% sure)";
}
console.log('ready to process names');
console.log( 'Bob:' + await getGender('Bob') );
console.log( 'John:' + await getGender('John') );
console.log( 'Alice:' + await getGender('Alice') );
| Things to Try | |
• Try linking other information into the dataset (e.g., country, city or age)
• Predict other information other than just if the name is linked to gender (e.g., hair color, spicy food, )
• Look around for datasets with names and associated characteristics online (free open source dataset) - see what information can be associated with a name (even if it seems random - can the neural network identify any characteristics/probabilities)
| Resources and Links | |
• WebGPU Lab Example [LINK]
|
