machine-learning/second.html at master · generationzcode/machine-learning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
<html>
    <head>
        <title>should I give this guy money?</title>
    </head>
    <body>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.14.1/dist/tf.min.js"></script>
<script>
   //smart,good idea, is rich
    function func(){
                 model.predict(x).print();
        var iq =parseInt(prompt("is he/she smart??(1/0)"));
        var idea=parseInt(prompt("does he/she have a good idea?(1/0)"));
        var rich = parseInt(prompt("is he/she rich?(1/0)"));
        document.body.innerHTML="<p style='font-family: sans-serif'>the probability of this turning out to be a good investment is: </p>";
        console.log("training complete");
        document.body.innerHTML+=Math.ceil(model.predict(tf.tensor2d([[iq,idea,rich]])).dataSync()*100);
        document.body.innerHTML+="<button onclick='func()'>more prediction</button>"
    }
    var xs=[[1,0,1],[0,1,0],[1,0,1],[1,1,1],[0.56,1,1],[0.89,0,0],[0.50,0,0],[0.80,1,1],[0.29,0,1],[0.920,1,0],[0.810,0.5,1],[0.100,0.5,0.7],[0,0,0]];
    var ys=[[0.06],[1],[1],[0.789],[1],[1],[0.678],[0.5234],[0.28],[0.999],[0.234],[0.96],[0]];
    document.body.innerHTML="<p style='font-family: sans-serif'>pls wait the program is still training</p>";
    const x=tf.tensor2d(xs);
    const y=tf.tensor2d(ys);
    const model=tf.sequential();
    const hidden = tf.layers.dense({
        units:4,
        inputShape:[3],
        activation:'sigmoid'
    });
    const output = tf.layers.dense({
        units:1,
        activation:'sigmoid'
    });
    model.add(hidden);
    model.add(output);
    const config = {
        optimizer:tf.train.sgd(0.3),
        loss:tf.losses.absoluteDifference
    }
    model.compile(config);
    train().then(function (){
func();
        document.body.innerHTML+="<button onclick='func()'>more prediction</button>"
    });
    async function train(){
        for(let i=0;i<1000;i++){
        const h = await model.fit(x,y,{epochs:10});
        console.log(h.history.loss[0]);
        }
    }
</script>
<span id="span"></span>
    </body>
</html>