⭐️ 本文首发自 前端修罗场(点击加入)，是一个由资深开发者独立运行的专业技术社区，我专注 Web 技术、答疑解惑、面试辅导以及职业发展。现在加入，私聊我即可获取一次免费的模拟面试机会，帮你评估知识点的掌握程度，获得更全面的学习指导意见，交个朋友，不走弯路，少吃亏！

最近公司在研发分布式高性能的云计算平台，其中涉及到了 AI 方面的处理。所以我也在自学 Machine Learning。不过在 AI 方面的知识却是需要花功夫花时间学习的。在学习的过程中我发现了一个不错的学习教程(点击收藏)，推荐给大伙😋，我个人觉得这个教程讲解的通俗易懂，帮我省去了自己苦苦专研的时间，能够得到快速的进步。下一阶段，我也会在这里和大家分享我的学习笔记。

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：

  1. 收集数据；
  2. 在训练集和测试集之间拆分；
  3. 重新格式化数据以便 Tensorflow.js 可以理解它；
  4. 选择你的算法；
  5. 拟合数据；
  6. 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">
</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：

在这里插入图片描述

如果你觉得这篇文章还不错，请点击下方小红心 👍🏻 ❤️，鼓励一下！我会继续为你带来优质的内容~我是前端修罗场，一个独立运行的专业技术社区，感谢你关注我！

# 机器学习 # 人工智能 # python
Logo
开放原子开发者工作坊

开放原子开发者工作坊旨在鼓励更多人参与开源活动，与志同道合的开发者们相互交流开发经验、分享开发心得、获取前沿技术趋势。工作坊有多种形式的开发者活动，如meetup、训练营等，主打技术交流，干货满满，真诚地邀请各位开发者共同参与！

更多推荐

第二届openEuler生态大会（中国·湖南）成功举办

10月30日，第二届openEuler生态大会（中国·湖南）成功举办。

avatar 开放原子开发者工作坊

“小满”安全车控操作系统正式在AtomGit开源

10月24日，由中国汽车工业协会指导，普华基础软件股份有限公司主办的“小满”安全车控操作系统开源发布会暨共建计划说明会成功举行。普华基础软件宣布将安全车控操作系统“小满”（简称“小满”）V24.10源代码正式在开放原子开源基金会（简称“基金会”）旗下AtomGit开源协作平台开源，并在AtomGit平

avatar 开放原子开发者工作坊

开放原子大赛——CIKM 2024赛果揭晓，10强争霸，角逐第33届国际信息与知识管理大会

开放原子大赛——CIKM 2024赛果揭晓，10强争霸，角逐第33届国际信息与知识管理大会

avatar 开放原子开发者工作坊