前端AI机器学习在浏览器中训练模型

目录

• 识别鸢尾花
• 测试集: testing.json
• 训练集: training.json
• 完整代码
• index.html
• index.js
• styles.css
• package.json

识别鸢尾花

本文将在浏览器中定义、训练和运行模型。 为了实现这一功能，我将构建一个识别鸢尾花的案例。

接下来，我们将创建一个神经网络。同时，根据开源数据集我们将鸢尾花分为三类：Setosa、Virginica 和 Versicolor。

每个机器学习项目的核心都是数据集。 我们需要采取的第一步是将这个数据集拆分为训练集和测试集。

这样做的原因是我们将使用我们的训练集来训练我们的算法和我们的测试集来检查我们的预测的准确性，以验证我们的模型是否可以使用或需要调整。

为了方便起见，我已经将训练集和测试集拆分为两个 JSON 文件:

测试集: testing.json

[{"sepal_length":6,"sepal_width":2.9,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":5.2,"sepal_width":3.4,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":6.5,"sepal_width":3,"petal_length":5.8,"petal_width":2.2,"species":"virginica"},
{"sepal_length":5.9,"sepal_width":3.2,"petal_length":4.8,"petal_width":1.8,"species":"versicolor"},
{"sepal_length":5.1,"sepal_width":3.8,"petal_length":1.9,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.4,"sepal_width":3,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":7,"sepal_width":3.2,"petal_length":4.7,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":5.7,"sepal_width":2.8,"petal_length":4.5,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.1,"sepal_width":2.5,"petal_length":3,"petal_width":1.1,"species":"versicolor"},
{"sepal_length":4.9,"sepal_width":2.4,"petal_length":3.3,"petal_width":1,"species":"versicolor"},
{"sepal_length":5.1,"sepal_width":3.7,"petal_length":1.5,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.7,"sepal_width":2.8,"petal_length":4.1,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.6,"sepal_width":3,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":6.1,"sepal_width":3,"petal_length":4.6,"petal_width":1.4,"species":"versicolor"}]

训练集: training.json

[{"sepal_length":5.1,"sepal_width":3.5,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.9,"sepal_width":3,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.7,"sepal_width":3.2,"petal_length":1.3,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.6,"sepal_width":3.1,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.6,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.6,"sepal_width":3.4,"petal_length":1.4,"petal_width":0.3,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.4,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.4,"sepal_width":2.9,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.9,"sepal_width":3.1,"petal_length":1.5,"petal_width":0.1,"species":"setosa"},
{"sepal_length":5.4,"sepal_width":3.7,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.8,"sepal_width":3.4,"petal_length":1.6,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.8,"sepal_width":3,"petal_length":1.4,"petal_width":0.1,"species":"setosa"},
{"sepal_length":4.3,"sepal_width":3,"petal_length":1.1,"petal_width":0.1,"species":"setosa"},
{"sepal_length":5.8,"sepal_width":4,"petal_length":1.2,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.7,"sepal_width":4.4,"petal_length":1.5,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.4,"sepal_width":3.9,"petal_length":1.3,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.1,"sepal_width":3.5,"petal_length":1.4,"petal_width":0.3,"species":"setosa"},
{"sepal_length":5.7,"sepal_width":3.8,"petal_length":1.7,"petal_width":0.3,"species":"setosa"},
{"sepal_length":5.1,"sepal_width":3.8,"petal_length":1.5,"petal_width":0.3,"species":"setosa"},
{"sepal_length":5.4,"sepal_width":3.4,"petal_length":1.7,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.6,"sepal_width":3.6,"petal_length":1,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.1,"sepal_width":3.3,"petal_length":1.7,"petal_width":0.5,"species":"setosa"},
{"sepal_length":4.8,"sepal_width":3.4,"petal_length":1.9,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3,"petal_length":1.6,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.4,"petal_length":1.6,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.2,"sepal_width":3.5,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.7,"sepal_width":3.2,"petal_length":1.6,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.8,"sepal_width":3.1,"petal_length":1.6,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.4,"sepal_width":3.4,"petal_length":1.5,"petal_width":0.4,"species":"setosa"},
{"sepal_length":5.2,"sepal_width":4.1,"petal_length":1.5,"petal_width":0.1,"species":"setosa"},
{"sepal_length":5.5,"sepal_width":4.2,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.9,"sepal_width":3.1,"petal_length":1.5,"petal_width":0.1,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.2,"petal_length":1.2,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.5,"sepal_width":3.5,"petal_length":1.3,"petal_width":0.2,"species":"setosa"},
{"sepal_length":4.9,"sepal_width":3.1,"petal_length":1.5,"petal_width":0.1,"species":"setosa"},
{"sepal_length":4.4,"sepal_width":3,"petal_length":1.3,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.1,"sepal_width":3.4,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.5,"petal_length":1.3,"petal_width":0.3,"species":"setosa"},
{"sepal_length":4.5,"sepal_width":2.3,"petal_length":1.3,"petal_width":0.3,"species":"setosa"},
{"sepal_length":4.4,"sepal_width":3.2,"petal_length":1.3,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.5,"petal_length":1.6,"petal_width":0.6,"species":"setosa"},
{"sepal_length":4.8,"sepal_width":3,"petal_length":1.4,"petal_width":0.3,"species":"setosa"},
{"sepal_length":5.1,"sepal_width":3.8,"petal_length":1.6,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5.3,"sepal_width":3.7,"petal_length":1.5,"petal_width":0.2,"species":"setosa"},
{"sepal_length":5,"sepal_width":3.3,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
{"sepal_length":6.4,"sepal_width":3.2,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":5.5,"sepal_width":2.3,"petal_length":4,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":6.5,"sepal_width":2.8,"petal_length":4.6,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":6.3,"sepal_width":3.3,"petal_length":4.7,"petal_width":1.6,"species":"versicolor"},
{"sepal_length":6.6,"sepal_width":2.9,"petal_length":4.6,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.2,"sepal_width":2.7,"petal_length":3.9,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":5,"sepal_width":2,"petal_length":3.5,"petal_width":1,"species":"versicolor"},
{"sepal_length":5.9,"sepal_width":3,"petal_length":4.2,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":6,"sepal_width":2.2,"petal_length":4,"petal_width":1,"species":"versicolor"},
{"sepal_length":6.1,"sepal_width":2.9,"petal_length":4.7,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":5.6,"sepal_width":2.9,"petal_length":3.6,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":6.7,"sepal_width":3.1,"petal_length":4.4,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":5.8,"sepal_width":2.7,"petal_length":4.1,"petal_width":1,"species":"versicolor"},
{"sepal_length":6.2,"sepal_width":2.2,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":5.6,"sepal_width":2.5,"petal_length":3.9,"petal_width":1.1,"species":"versicolor"},
{"sepal_length":6.1,"sepal_width":2.8,"petal_length":4,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":6.3,"sepal_width":2.5,"petal_length":4.9,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":6.1,"sepal_width":2.8,"petal_length":4.7,"petal_width":1.2,"species":"versicolor"},
{"sepal_length":6.4,"sepal_width":2.9,"petal_length":4.3,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":6.6,"sepal_width":3,"petal_length":4.4,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":6.8,"sepal_width":2.8,"petal_length":4.8,"petal_width":1.4,"species":"versicolor"},
{"sepal_length":6.7,"sepal_width":3,"petal_length":5,"petal_width":1.7,"species":"versicolor"},
{"sepal_length":5.7,"sepal_width":2.6,"petal_length":3.5,"petal_width":1,"species":"versicolor"},
{"sepal_length":5.5,"sepal_width":2.4,"petal_length":3.8,"petal_width":1.1,"species":"versicolor"},
{"sepal_length":5.5,"sepal_width":2.4,"petal_length":3.7,"petal_width":1,"species":"versicolor"},
{"sepal_length":5.8,"sepal_width":2.7,"petal_length":3.9,"petal_width":1.2,"species":"versicolor"},
{"sepal_length":6,"sepal_width":2.7,"petal_length":5.1,"petal_width":1.6,"species":"versicolor"},
{"sepal_length":6,"sepal_width":3.4,"petal_length":4.5,"petal_width":1.6,"species":"versicolor"},
{"sepal_length":6.7,"sepal_width":3.1,"petal_length":4.7,"petal_width":1.5,"species":"versicolor"},
{"sepal_length":6.3,"sepal_width":2.3,"petal_length":4.4,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.6,"sepal_width":3,"petal_length":4.1,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.5,"sepal_width":2.5,"petal_length":4,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.5,"sepal_width":2.6,"petal_length":4.4,"petal_width":1.2,"species":"versicolor"},
{"sepal_length":5.8,"sepal_width":2.6,"petal_length":4,"petal_width":1.2,"species":"versicolor"},
{"sepal_length":5,"sepal_width":2.3,"petal_length":3.3,"petal_width":1,"species":"versicolor"},
{"sepal_length":5.6,"sepal_width":2.7,"petal_length":4.2,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":5.7,"sepal_width":3,"petal_length":4.2,"petal_width":1.2,"species":"versicolor"},
{"sepal_length":6.2,"sepal_width":2.9,"petal_length":4.3,"petal_width":1.3,"species":"versicolor"},
{"sepal_length":6.3,"sepal_width":3.3,"petal_length":6,"petal_width":2.5,"species":"virginica"},
{"sepal_length":5.8,"sepal_width":2.7,"petal_length":5.1,"petal_width":1.9,"species":"virginica"},
{"sepal_length":7.1,"sepal_width":3,"petal_length":5.9,"petal_width":2.1,"species":"virginica"},
{"sepal_length":6.3,"sepal_width":2.9,"petal_length":5.6,"petal_width":1.8,"species":"virginica"},
{"sepal_length":7.6,"sepal_width":3,"petal_length":6.6,"petal_width":2.1,"species":"virginica"},
{"sepal_length":4.9,"sepal_width":2.5,"petal_length":4.5,"petal_width":1.7,"species":"virginica"},
{"sepal_length":7.3,"sepal_width":2.9,"petal_length":6.3,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.7,"sepal_width":2.5,"petal_length":5.8,"petal_width":1.8,"species":"virginica"},
{"sepal_length":7.2,"sepal_width":3.6,"petal_length":6.1,"petal_width":2.5,"species":"virginica"},
{"sepal_length":6.5,"sepal_width":3.2,"petal_length":5.1,"petal_width":2,"species":"virginica"},
{"sepal_length":6.4,"sepal_width":2.7,"petal_length":5.3,"petal_width":1.9,"species":"virginica"},
{"sepal_length":6.8,"sepal_width":3,"petal_length":5.5,"petal_width":2.1,"species":"virginica"},
{"sepal_length":5.7,"sepal_width":2.5,"petal_length":5,"petal_width":2,"species":"virginica"},
{"sepal_length":5.8,"sepal_width":2.8,"petal_length":5.1,"petal_width":2.4,"species":"virginica"},
{"sepal_length":6.4,"sepal_width":3.2,"petal_length":5.3,"petal_width":2.3,"species":"virginica"},
{"sepal_length":6.5,"sepal_width":3,"petal_length":5.5,"petal_width":1.8,"species":"virginica"},
{"sepal_length":7.7,"sepal_width":3.8,"petal_length":6.7,"petal_width":2.2,"species":"virginica"},
{"sepal_length":7.7,"sepal_width":2.6,"petal_length":6.9,"petal_width":2.3,"species":"virginica"},
{"sepal_length":6,"sepal_width":2.2,"petal_length":5,"petal_width":1.5,"species":"virginica"},
{"sepal_length":6.9,"sepal_width":3.2,"petal_length":5.7,"petal_width":2.3,"species":"virginica"},
{"sepal_length":5.6,"sepal_width":2.8,"petal_length":4.9,"petal_width":2,"species":"virginica"},
{"sepal_length":7.7,"sepal_width":2.8,"petal_length":6.7,"petal_width":2,"species":"virginica"},
{"sepal_length":6.3,"sepal_width":2.7,"petal_length":4.9,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.7,"sepal_width":3.3,"petal_length":5.7,"petal_width":2.1,"species":"virginica"},
{"sepal_length":7.2,"sepal_width":3.2,"petal_length":6,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.2,"sepal_width":2.8,"petal_length":4.8,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.1,"sepal_width":3,"petal_length":4.9,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.4,"sepal_width":2.8,"petal_length":5.6,"petal_width":2.1,"species":"virginica"},
{"sepal_length":7.2,"sepal_width":3,"petal_length":5.8,"petal_width":1.6,"species":"virginica"},
{"sepal_length":7.9,"sepal_width":3.8,"petal_length":6.4,"petal_width":2,"species":"virginica"},
{"sepal_length":6.4,"sepal_width":2.8,"petal_length":5.6,"petal_width":2.2,"species":"virginica"},
{"sepal_length":6.3,"sepal_width":2.8,"petal_length":5.1,"petal_width":1.5,"species":"virginica"},
{"sepal_length":6.1,"sepal_width":2.6,"petal_length":5.6,"petal_width":1.4,"species":"virginica"},
{"sepal_length":7.7,"sepal_width":3,"petal_length":6.1,"petal_width":2.3,"species":"virginica"},
{"sepal_length":6.3,"sepal_width":3.4,"petal_length":5.6,"petal_width":2.4,"species":"virginica"},
{"sepal_length":6.4,"sepal_width":3.1,"petal_length":5.5,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6,"sepal_width":3,"petal_length":4.8,"petal_width":1.8,"species":"virginica"},
{"sepal_length":6.9,"sepal_width":3.1,"petal_length":5.4,"petal_width":2.1,"species":"virginica"},
{"sepal_length":6.7,"sepal_width":3.1,"petal_length":5.6,"petal_width":2.4,"species":"virginica"},
{"sepal_length":6.9,"sepal_width":3.1,"petal_length":5.1,"petal_width":2.3,"species":"virginica"},
{"sepal_length":5.8,"sepal_width":2.7,"petal_length":5.1,"petal_width":1.9,"species":"virginica"},
{"sepal_length":6.8,"sepal_width":3.2,"petal_length":5.9,"petal_width":2.3,"species":"virginica"},
{"sepal_length":6.7,"sepal_width":3.3,"petal_length":5.7,"petal_width":2.5,"species":"virginica"},
{"sepal_length":6.7,"sepal_width":3,"petal_length":5.2,"petal_width":2.3,"species":"virginica"},
{"sepal_length":6.3,"sepal_width":2.5,"petal_length":5,"petal_width":1.9,"species":"virginica"},
{"sepal_length":6.5,"sepal_width":3,"petal_length":5.2,"petal_width":2,"species":"virginica"},
{"sepal_length":6.2,"sepal_width":3.4,"petal_length":5.4,"petal_width":2.3,"species":"virginica"}]

其中，训练集包含 130 个项目，测试集包含 14 个。如果你看看这些数据是什么样子，你会看到

如下内容：

{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2,
  "species": "setosa"
}

我们可以看到萼片和花瓣的长度和宽度的四个不同特征，以及物种的标签。

为了能够将它与 Tensorflow.js 一起使用，我们需要将这些数据塑造成框架能够理解的格式，在这种情况下，对于训练数据，它将是 [130, 4] 的 130 个样本，每个样本有四个特征。

import * as trainingSet from "training.json";
import * as testSet from "testing.json";
const trainingData = tf.tensor2d(
  trainingSet.map(item => [
    item.sepal_length,
    item.sepal_width,
    item.petal_length,
    item.petal_width
  ]),
  [130, 4]
);
const testData = tf.tensor2d(
  testSet.map(item => [
    item.sepal_length,
    item.sepal_width,
    item.petal_length,
    item.petal_width
  ]),
  [14, 4]
);

接下来，我们还需要对输出数据进行整形：

const output = tf.tensor2d(trainingSet.map(item => [
    item.species === 'setosa' ? 1 : 0,
    item.species === 'virginica' ? 1 : 0,
    item.species === 'versicolor' ? 1 : 0
]), [130,3])

然后，一旦我们的数据准备就绪，我们就可以继续创建模型：

const model = tf.sequential();
model.add(tf.layers.dense(
    {
        inputShape: 4,
        activation: 'sigmoid',
        units: 10
    }
));
model.add(tf.layers.dense(
    {
        inputShape: 10,
        units: 3,
        activation: 'softmax'
    }
));

在上面的代码示例中，我们首先实例化一个顺序模型，添加一个输入和输出层。

你可以看到内部使用的参数（inputShape, activation, and units）超出了本文的范围，因为它们可能会根据你创建的模型、使用的数据类型等而有所不同。

一旦我们的模型准备就绪，我们就可以使用我们的数据对其进行训练：

async function train_data(){
    for(let i=0;i<15;i++){
      const res = await model.fit(trainingData, outputData,{epochs: 40});
    }
}
async function main() {
  await train_data();
  model.predict(testSet).print();
}

如果这运作良好，你可以开始用自定义用户输入替换测试数据。

一旦我们调用我们的 main 函数，预测的输出将看起来像以下三个选项之一：

[1,0,0] // Setosa[0,1,0] // Virginica[0,0,1] // Versicolor

预测返回一个由三个数字组成的数组，表示数据属于三个类别之一的概率。 最接近 1 的数字是最高预测值。

例如，如果分类的输出为 [0.0002, 0.9494, 0.0503]，则数组的第二个元素最高，因此模型预测新的输入很可能是 Virginica。

这就是 Tensorflow.js 中的简单神经网络！

我们只讨论了 Irises 的一个小数据集，但如果您想继续使用更大的数据集或处理图像，步骤将是相同的：

• 收集数据；
• 在训练集和测试集之间拆分；
• 重新格式化数据以便 Tensorflow.js 可以理解它；
• 选择你的算法；
• 拟合数据；
• 预测。

如果你想保存创建的模型以便能够在另一个应用程序中加载它并预测新数据，你可以使用以下行来执行此操作：

await model.save('file:///path/to/my-model'); // in Node.js

完整代码

index.html

<!DOCTYPE html>
<html lang="en">
<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>Tensorflow.js</title>
	<link rel="stylesheet" href="src/styles.css" rel="external nofollow" >
</head>
<body>
	<h1>使用 Tensorflow.js 在 JavaScript 中定义、训练和运行机器学习模型</h1>
	<section class="data-inputs">
		<h3>鸢尾花分类</h3>
		<p>正在训练中...</p>
		<p class="training-steps"></p>
		<div class="input-block">
			<label for="sepal-length">Sepal lenth:</label>
			<input name="sepal-length" type="number" min="0" max="100" placeholder="1.5">
		</div>
		<div class="input-block">
			<label for="sepal-width">Sepal width:</label>
			<input name="sepal-width" type="number" min="0" max="100" placeholder="0.4">
		</div>
		<div class="input-block">
			<label for="petal-length">Petal length:</label>
			<input name="petal-length" type="number" min="0" max="100" placeholder="1.0">
		</div>
		<div class="input-block">
			<label for="petal-width">Petal width:</label>
			<input name="petal-width" type="number" min="0" max="100" placeholder="0.7">
		</div>
		<button class="predict" disabled>预测</button>
	</section>
	<section class="prediction-block">
		<p>鸢尾花 预测:</p>
		<p class="prediction"></p>
	</section>
	<script src="src/index.js"></script>
</body>
</html>

index.js

import * as tf from "@tensorflow/tfjs";
import trainingSet from "./training.json";
import testSet from "./testing.json";
let trainingData, testingData, outputData, model;
let training = true;
let predictButton = document.getElementsByClassName("predict")[0];
const init = async () => {
  splitData();
  createModel();
  await trainData();
  if (!training) {
    predictButton.disabled = false;
    predictButton.onclick = () => {
      const inputData = getInputData();
      predict(inputData);
    };
  }
};
const splitData = () => {
  trainingData = tf.tensor2d(
    trainingSet.map(item => [
      item.sepal_length,
      item.sepal_width,
      item.petal_length,
      item.petal_width
    ]),
    [130, 4]
  );
  testingData = tf.tensor2d(
    testSet.map(item => [
      item.sepal_length,
      item.sepal_width,
      item.petal_length,
      item.petal_width
    ]),
    [14, 4]
  );
  outputData = tf.tensor2d(
    trainingSet.map(item => [
      item.species === "setosa" ? 1 : 0,
      item.species === "virginica" ? 1 : 0,
      item.species === "versicolor" ? 1 : 0
    ]),
    [130, 3]
  );
};
const createModel = () => {
  model = tf.sequential();
  model.add(
    tf.layers.dense({ inputShape: 4, activation: "sigmoid", units: 10 })
  );
  model.add(
    tf.layers.dense({
      inputShape: 10,
      units: 3,
      activation: "softmax"
    })
  );
  model.compile({
    loss: "categoricalCrossentropy",
    optimizer: tf.train.adam()
  });
};
const trainData = async () => {
  let numSteps = 15;
  let trainingStepsDiv = document.getElementsByClassName("training-steps")[0];
  for (let i = 0; i < numSteps; i++) {
    let res = await model.fit(trainingData, outputData, { epochs: 40 });
    trainingStepsDiv.innerHTML = `Training step: ${i}/${numSteps - 1}, loss: ${
      res.history.loss[0]
    }`;
    if (i === numSteps - 1) {
      training = false;
    }
  }
};
const predict = async inputData => {
  for (let [key, value] of Object.entries(inputData)) {
    inputData[key] = parseFloat(value);
  }
  inputData = [inputData];
  let newDataTensor = tf.tensor2d(
    inputData.map(item => [
      item.sepal_length,
      item.sepal_width,
      item.petal_length,
      item.petal_width
    ]),
    [1, 4]
  );
  let prediction = model.predict(newDataTensor);
  displayPrediction(prediction);
};
const getInputData = () => {
  let sepalLength = document.getElementsByName("sepal-length")[0].value;
  let sepalWidth = document.getElementsByName("sepal-width")[0].value;
  let petalLength = document.getElementsByName("petal-length")[0].value;
  let petalWidth = document.getElementsByName("petal-width")[0].value;
  return {
    sepal_length: sepalLength,
    sepal_width: sepalWidth,
    petal_length: petalLength,
    petal_width: petalWidth
  };
};
const displayPrediction = prediction => {
  let predictionDiv = document.getElementsByClassName("prediction")[0];
  let predictionSection = document.getElementsByClassName(
    "prediction-block"
  )[0];
  let maxProbability = Math.max(...prediction.dataSync());
  let predictionIndex = prediction.dataSync().indexOf(maxProbability);
  let irisPrediction;
  switch (predictionIndex) {
    case 0:
      irisPrediction = "Setosa";
      break;
    case 1:
      irisPrediction = "Virginica";
      break;
    case 2:
      irisPrediction = "Versicolor";
      break;
    default:
      irisPrediction = "";
      break;
  }
  predictionDiv.innerHTML = irisPrediction;
  predictionSection.style.display = "block";
};
init();

styles.css

body {
  font-family: "Avenir";
}
h1 {
  text-align: center;
  width: 80%;
  margin: 0 auto;
}
.data-inputs {
  display: block;
  width: 80%;
  margin: 0 auto;
}
.input-block {
  display: inline-block;
  width: fit-content;
  margin: 1em 0.5em 2em 0.5em;
}
.input-block:first-of-type {
  margin-left: 0;
}
.input-block input {
  width: 7em;
  height: 2em;
}
.input-block input::placeholder {
  color: rgba(0, 0, 0, 0.3);
}
button {
  display: block;
  padding: 0.5em 1em;
  border-radius: 5px;
  font-size: 14px;
}
.prediction-block {
  display: none;
  width: 80%;
  margin: 0 auto;
}

package.json

{
  "name": "Irises Classficaton",
  "version": "1.0.0",
  "description": "",
  "main": "index.html",
  "scripts": {
    "start": "parcel index.html --open",
    "build": "parcel build index.html"
  },
  "dependencies": {
    "@tensorflow/tfjs": "1.1.2"
  },
  "devDependencies": {
    "@babel/core": "7.2.0",
    "parcel-bundler": "^1.6.1"
  },
  "keywords": []
}

效果如下：

以上就是前端AI机器学习在浏览器中训练模型的详细内容，更多关于前端AI浏览器训练模型的资料请关注我们其它相关文章！