[TensorFlowJS只如初见]实战四·使用TensorFlowJS拟合曲线（类似TensorFlow原生实现方法）
-
问题描述
拟合y= x*x -2x +3 + 0.1(-1到1的随机值) 曲线
给定x范围（0，3） -
问题分析
在直线拟合博客中，我们使用最简单的y=wx+b的模型成功拟合了一条直线，现在我们在进一步进行曲线的拟合。简单的y=wx+b模型已经无法满足我们的需求，需要利用更多的神经元来解决问题了。 -
代码
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs">
</script>
</head>
<body>
<button class="btn btn-primary" onclick="fnRun0();">开始0</button>
<div id="p0Id">out0</div>
<button class="btn btn-primary" onclick="fnRun1();">开始1</button>
<div id="p1Id">out1</div>
<button class="btn btn-primary" onclick="learnLinear();">开始2</button>
<div id="p2Id">out2</div>
</body>
<script>
//document.getElementById("p0Id").innerHTML = 0;
function get_ys(xs) {
var ys = new Array();
for (var i = 0; i < xs.length; i++) {
ys[i] = xs[i] * xs[i] - 2 * xs[i] + 3 + (0.1 * (2 * Math.random() - 1));
}
return (ys);
}
var xs = new Array();
for (var i = 0; i < 100; i++) {
xs[i] = 0.02 * i;
}
var ys = get_ys(xs);
const xst = tf.tensor(xs, [xs.length, 1]);
const yst = tf.tensor(ys, [ys.length, 1]);
function get_weights(shape) {
return (tf.variable(tf.randomNormal(shape)));
}
function get_bais(shape) {
return (tf.variable(tf.fill(shape, 0.1)));
}
const w1 = get_weights([1, 10]);
const w2 = get_weights([10, 1]);
const b1 = get_bais([1, 10]);
const b2 = get_bais([1, 1]);
function predict(x) {
return tf.tidy(() => {
const l1 = tf.elu(tf.add(tf.matMul(x, w1), b1)); //不支持运算符重载 tf.add() 不能写成 +
const y = tf.add(tf.matMul(l1, w2), b2);
return y;
});
}
function loss(predictions, labels) {
// 将labels（实际的值）进行抽象
// 然后获取平均数.
return tf.tidy(() => {
const meanSquareError = tf.mean(tf.square(tf.sub(predictions, labels)));
return meanSquareError;
});
}
const learningRate = 1;
const optimizer = tf.train.adagrad(learningRate);
const numIterations = 1001;
function training() {
for (var iter = 0; iter < numIterations; iter++) {
optimizer.minimize(() => {
const loss_var = loss(predict(xst), yst);
if(iter % 100 ==0)
loss_var.print();
return loss_var;
})
}
}
training();
const text_xs = tf.tensor([0,1,3], [3, 1]);
predict(text_xs).print();
</script>
</html>
- 输出结果
进行1000轮训练以后，我们输入[0,1,3]进行预测，得到结果为
[[2.9647527],
[1.9793538],
[3.9484348]]
较好地拟合了曲线。
log
"Tensor
4.915620803833008"
"Tensor
0.018092654645442963"
"Tensor
0.01064694207161665"
"Tensor
0.008950537070631981"
"Tensor
0.008051211014389992"
"Tensor
0.007439687382429838"
"Tensor
0.006973605137318373"
"Tensor
0.006602670066058636"
"Tensor
0.006299363449215889"
"Tensor
0.0060538649559021"
"Tensor
0.005853266455233097"
"Tensor
[[2.9647527],
[1.9793538],
[3.9484348]]"