我在使用一个简单的@tensorflow/tfjs模型，它仅显示准确率。相同的代码在代码沙箱中运行时没有错误，但在Visual Studio Code中运行时会出现一个不变类型错误。我的代码附在下面。另外，请指导关于代码中使用的输入形状和单位术语，以及如何在React Native中实现此代码。

import '@tensorflow/tfjs-react-native'import * as tf from "@tensorflow/tfjs";import * as ft from '@tensorflow/tfjs-backend-webgpu';//import { writeFileSync, readFileSync } from 'fs';(async() => {  await ft.ready  // then do all operations on the backend})()const model = tf.sequential({  layers: [    tf.layers.dense({ inputShape: [784], units: 32, activation: "relu" }),    tf.layers.dense({ units: 10, activation: "softmax" })  ]});model.weights.forEach(w => {  console.log(w.name, w.shape);});model.weights.forEach(w => {  const newVals = tf.randomNormal(w.shape);  // w.val is an instance of tf.Variable  w.val.assign(newVals);});model.compile({  optimizer: "sgd",  loss: "categoricalCrossentropy",  metrics: ["accuracy"]});const data = tf.randomNormal([100, 784]);const labels = tf.randomUniform([100, 10]);function onBatchEnd(batch, logs) {  console.log("Accuracy", logs.acc);}// Train for 5 epochs with batch size of 32.model  .fit(data, labels, {    epochs: 5,    batchSize: 32,    callbacks: { onBatchEnd }  })  .then(info => {    console.log("Final accuracy", info.history.acc);  });

以及错误

回答：

你需要导入@tensorflow/tfjs-react-native包。此外，如果后端是异步的，应该使用tf.ready()。这里是一个你的React应用应该如何编写的示例

import * as tf from '@tensorflow/tfjs';import '@tensorflow/tfjs-react-native';export class App extends React.Component {  constructor(props) {    super(props);    this.state = {      isTfReady: false,    };  }  init() {    const model = tf.sequential({        layers: [            tf.layers.dense({                inputShape: [784],                units: 32,                activation: "relu"            }),            tf.layers.dense({                units: 10,                activation: "softmax"            })        ]    });    model.weights.forEach(w => {        console.log(w.name, w.shape);    });    model.weights.forEach(w => {        const newVals = tf.randomNormal(w.shape);        // w.val is an instance of tf.Variable        w.val.assign(newVals);    });    model.compile({        optimizer: "sgd",        loss: "categoricalCrossentropy",        metrics: ["accuracy"]    });    const data = tf.randomNormal([100, 784]);    const labels = tf.randomUniform([100, 10]);    function onBatchEnd(batch, logs) {        console.log("Accuracy", logs.acc);    }    // Train for 5 epochs with batch size of 32.    model        .fit(data, labels, {            epochs: 5,            batchSize: 32,            callbacks: {                onBatchEnd            }        })        .then(info => {            console.log("Final accuracy", info.history.acc);        });  }  async componentDidMount() {    // Wait for tf to be ready.    await tf.ready();    // Signal to the app that tensorflow.js can now be used.    this.setState({      isTfReady: true,    });  }  render() {    init()    //  }}