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/*
* @Author: victorsun
* @Date: 2019-12-04 20:15:29
* @LastEditors: victorsun - csxiaoyao
* @LastEditTime: 2020-03-21 12:54:08
* @Description: [email protected]
*/
import * as tf from '@tensorflow/tfjs';
import * as tfvis from '@tensorflow/tfjs-vis';
window.onload = async () => {
/**
* 归一化
* 数据调整为 0 - 1 之间，避免某些数据影响过大，同时方便 tensorflow 处理
*/
const heights = [150, 160, 170]; // 身高 cm
const weights = [40, 50, 60]; // 体重 kg
// 散点图
/*
tfvis.render.scatterplot(
{ name: '身高体重训练数据' },
{ values: heights.map((x, i) => ({ x, y: weights[i] })) },
{
xAxisDomain: [140, 180],
yAxisDomain: [30, 70]
}
); */
// 1. 训练数据归一化 (减去最小值，除以最大最小值差值)
const inputs = tf.tensor(heights).sub(150).div(20);
const labels = tf.tensor(weights).sub(40).div(20);
inputs.print();
labels.print();
// 2. 初始化神经网络模型，参考03线性回归
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({ loss: tf.losses.meanSquaredError, optimizer: tf.train.sgd(0.1) });
// 3. 训练，参考03线性回归
await model.fit(inputs, labels, {
batchSize: 3,
epochs: 200,
callbacks: tfvis.show.fitCallbacks(
{ name: '训练过程' },
['loss']
)
});
// 4. 预测，输入数据归一化
const output = model.predict(tf.tensor([180]).sub(150).div(20));
// 5. 对结果数据反归一化
alert(`如果身高为 180cm，那么预测体重为 ${output.mul(20).add(40).dataSync()[0]}kg`);
};