একটি প্রশ্ন আছে? টেনসরফ্লো ফোরাম সম্প্রদায়ের সাথে সংযুক্ত হন

# 设置

## 浏览器设置

### 使用脚本代码

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script>


model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training. const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data. model.fit(xs, ys, {epochs: 10}).then(() => { // Use the model to do inference on a data point the model hasn't seen before: model.predict(tf.tensor2d([5], [1, 1])).print(); // Open the browser devtools to see the output });

### 从 NPM 安装

yarn add @tensorflow/tfjs


npm install @tensorflow/tfjs


// Define a model for linear regression. const model = tf.sequential(); model.add(tf.layers.dense({units: 1, inputShape: [1]}));

model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training. const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]); const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data. model.fit(xs, ys, {epochs: 10}).then(() => { // Use the model to do inference on a data point the model hasn't seen before: model.predict(tf.tensor2d([5], [1, 1])).print(); // Open the browser devtools to see the output });

## Node.js 设置

yarn add @tensorflow/tfjs-node


npm install @tensorflow/tfjs-node


yarn add @tensorflow/tfjs-node-gpu


npm install @tensorflow/tfjs-node-gpu


yarn add @tensorflow/tfjs


npm install @tensorflow/tfjs


// Optional Load the binding: // Use '@tensorflow/tfjs-node-gpu' if running with GPU. require('@tensorflow/tfjs-node');

// Train a simple model: const model = tf.sequential(); model.add(tf.layers.dense({units: 100, activation: 'relu', inputShape: [10]})); model.add(tf.layers.dense({units: 1, activation: 'linear'})); model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

const xs = tf.randomNormal([100, 10]); const ys = tf.randomNormal([100, 1]);

model.fit(xs, ys, { epochs: 100, callbacks: { onEpochEnd: (epoch, log) => console.log(Epoch ${epoch}: loss =${log.loss}) } });