TensorFlow.js is a powerful tool for machine learning in JavaScript. It can be used in Node.js with the Express.js web framework to create powerful applications. In this post, we'll show you how to get started with TensorFlow.js and Express.js.
First, we need to install the dependencies for our project. We'll need Express.js and TensorFlow.js. We can install these with the following command:
npm install express @tensorflow/tfjs
Now that we have our dependencies installed, let's create a simple "Hello world" application. We'll create a file called index.js
in our project's root directory and add the following code:
const express = require('express');
const tf = require('@tensorflow/tfjs');
const app = express();
app.get('/', (req, res) => {
res.send('Hello, world!');
});
app.listen(3000, () => {
console.log('Example app listening on port 3000!');
});
This code creates an Express.js server that listens on port 3000 and responds with "Hello, world!" when the root URL is accessed.
Now that we have our simple Express.js server up and running, let's add some TensorFlow.js code. We'll start by adding a route that responds with a string generated by a TensorFlow.js model.
First, we need to create the model. We'll do this in a file called model.js
. We'll create a simple model that takes an input string and generates an output string. The input string will be fed into the model as a one-hot encoded vector. The output string will be generated by the model using a sequence-to-sequence architecture.
const tf = require('@tensorflow/tfjs');
// Create the model
const model = tf.sequential();
// Add an LSTM layer
model.add(tf.layers.lstm({
units: 8,
inputShape: [8],
returnSequences: true
}));
// Add a second LSTM layer
model.add(tf.layers.lstm({
units: 8,
returnSequences: true
}));
// Add a dense layer
model.add(tf.layers.dense({
units: 8,
activation: 'softmax'
}));
// Compile the model
model.compile({
loss: 'categoricalCrossentropy',
optimizer: 'rmsprop'
});
// Export the model
module.exports = model;
This code creates a simple LSTM-based model. The model takes an input string of 8 characters and outputs a string of 8 characters.
Now that we have our model, we can add a route that uses it. We'll add the following route to our index.js
file:
app.get('/generate', (req, res) => {
// Load the model
const model = require('./model');
// Generate a string
const string = model.predict(tf.oneHot('Hello, world!'.split(''), 8));
// Send the string to the client
res.send(string);
});
This route loads the model we created in model.js
and uses it to generate a string. The generated string is then sent to the client.
In this post, we've shown you how to get started with TensorFlow.js and Express.js. We've created a simple "Hello world" application and a route that uses a TensorFlow.js model to generate a string.