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编程网其它相关文章！
