我正在学习使用神经网络，并且遇到了问题。
我不知道如何转换神经网络的数据。
据我了解，我需要对数据进行标准化，在标准化和学习之后，答案总是平均的。
https://jsfiddle.net/eoy7krzj/ https://jsfiddle.net/eoy7krzj/
<html>
<head>
<script src="https://cdn.rawgit.com/BrainJS/brain.js/5797b875/browser.js"></script>
</head>
<body>
<div>
<button onclick="train()">train</button><button onclick="Generate.next(); Generate.draw();">generate</button><button onclick="calculate()">calculate</button>
</div>
<canvas id="generate" style="border: 1px solid #000"></canvas>
</body>
<script type="text/javascript">
var trainData = [];
function randomInteger(min, max) {
var rand = min - 0.5 + Math.random() * (max - min + 1)
//rand = Math.round(rand);
return rand;
}
function getRandomColor() {
var letters = '0123456789ABCDEF';
var color = '#';
for (var i = 0; i < 6; i++) {
color += letters[Math.floor(Math.random() * 16)];
}
return color;
}
var Generate = new function(){
var canvas = document.getElementById('generate');
var ctx = canvas.getContext('2d');
var elem = {
input: [],
output: []
}
var size = {
width: 240,
height: 140
}
canvas.width = 500;
canvas.height = 250;
this.next = function(){
this.build();
trainData.push({
input: elem.input,
output: elem.output
});
}
this.clear = function(){
ctx.clearRect(0, 0, canvas.width, canvas.height);
}
this.draw = function(){
this.clear();
this.item(elem.input, function(item){
ctx.strokeStyle = "green";
ctx.strokeRect(item[0], item[1], item[2], item[3]);
})
this.item(elem.output, function(item){
ctx.strokeStyle = "blue";
ctx.strokeRect(item[0], item[1], item[2], item[3]);
})
}
this.item = function(where, call){
for (var i = 0; i < where.length; i+=4) {
var input = [
where[i],
where[i+1],
where[i+2],
where[i+3],
];
this.denormalize(input);
call(input)
}
}
this.normalize = function(input){
input[0] = input[0] / 500;
input[1] = input[1] / 250;
input[2] = input[2] / 500;
input[3] = input[3] / 250;
}
this.denormalize = function(input){
input[0] = input[0] * 500;
input[1] = input[1] * 250;
input[2] = input[2] * 500;
input[3] = input[3] * 250;
}
this.empty = function(add){
var data = [];
for (var i = 0; i < add; i++) {
data = data.concat([0,0,0,0]);
}
return data;
}
this.build = function(){
var output = [];
var input = [];
size.width = randomInteger(100,500);
size.height = randomInteger(50,250);
var lines = 1;//Math.round(size.height / 100);
var line_size = 0;
var line_offset = 0;
for(var i = 0; i < lines; i++){
line_size = randomInteger(30,Math.round(size.height / lines));
var columns = Math.round(randomInteger(1,3));
var columns_width = 0;
var columns_offset = 0;
for(var c = 0; c < columns; c++){
columns_width = randomInteger(30,Math.round(size.width / columns));
var item = [
columns_offset + 10,
line_offset + 10,
columns_width - 20,
line_size - 20
];
this.normalize(item);
input = input.concat(item);
columns_offset += columns_width;
}
var box = [
0,
line_offset,
columns_offset,
line_size
]
this.normalize(box);
output = output.concat(box);
line_offset += line_size + 10;
}
elem.input = input.concat(this.empty(5 - Math.round(input.length / 4)));
elem.output = output.concat(this.empty(2 - Math.round(output.length / 4)));
}
this.get = function(){
return elem.input;
}
this.calculate = function(result, stat){
console.log('brain:',result);
this.item(result, function(item){
ctx.strokeStyle = "red";
ctx.strokeRect(item[0], item[1], item[2], item[3]);
})
}
this.train = function(){
for(var i = 0; i < 40; i++){
this.next();
}
}
}
Generate.train();
Generate.log = true;
var net,stat;
function train(){
net = new brain.NeuralNetwork({ hiddenLayers: [8,4]});
stat = net.train(trainData,{log: true, iterations: 250,learningRate: 0.01,errorThresh:0.05});
console.log('stat:',stat)
}
function calculate(){
Generate.calculate(net.run(Generate.get()))
}
</script>
</html>
我的目标是训练网络找到元素并显示它们的大小。
程序：
点击训练
点击生成
点击计算
结果，网络显示平均结果，红色表示网络已找到。
也许不是这样我传输数据，可以在另一个有必要吗？
