TensorFlow.js is a powerful tool for machine learning in JavaScript. In this post, we'll show you how to use TensorFlow.js with databases in Node.js.
Before we get started, we need to set up our environment. We'll need to install Node.js and the TensorFlow.js Node.js API.
You can install Node.js from the Node.js website.
Once you have Node.js installed, you can install the TensorFlow.js Node.js API with the following command:
npm install @tensorflow/tfjs-node
Now that we have our environment set up, we can start working with databases.
We'll use the sqlite3 Node.js module to connect to an SQLite database. SQLite is a lightweight database that doesn't require a separate server.
First, we need to install the sqlite3 module:
npm install sqlite3
Next, we'll create a file called database.js
with the following contents:
const sqlite3 = require('sqlite3').verbose();
const db = new sqlite3.Database('./database.sqlite3');
db.serialize(() => {
// create a table
db.run('CREATE TABLE IF NOT EXISTS items (name, value)');
// insert some data
const stmt = db.prepare('INSERT INTO items VALUES (?, ?)');
for (let i = 0; i < 10; i++) {
stmt.run(`Item ${i}`, i);
}
stmt.finalize();
// query the data
db.each('SELECT * FROM items', (err, row) => {
console.log(row.name + ': ' + row.value);
});
});
db.close();
In this file, we're connecting to an SQLite database called database.sqlite3
. We're then creating a table called items
and inserting some data into it.
Finally, we're querying the data from the database and printing it to the console.
To run this file, you can use the following command:
node database.js
Now that we've seen how to connect to a database, let's take a look at how to use TensorFlow.js with databases.
We'll use the same database.js
file from the previous section. We'll just add a few lines of code to load the data from the database into a TensorFlow.js tf.data.Dataset
.
const sqlite3 = require('sqlite3').verbose();
const tf = require('@tensorflow/tfjs-node');
const db = new sqlite3.Database('./database.sqlite3');
db.serialize(() => {
// create a table
db.run('CREATE TABLE IF NOT EXISTS items (name, value)');
// insert some data
const stmt = db.prepare('INSERT INTO items VALUES (?, ?)');
for (let i = 0; i < 10; i++) {
stmt.run(`Item ${i}`, i);
}
stmt.finalize();
// query the data
const dataset = tf.data.Dataset.fromSqlQuery('SELECT * FROM items', db, (err, row) => {
return {
name: row.name,
value: row.value
};
});
// print the data
dataset.forEachAsync(item => {
console.log(item.name + ': ' + item.value);
});
});
db.close();
In this file, we're using the tf.data.Dataset.fromSqlQuery()
function to load the data from the database into a tf.data.Dataset
. We're then printing the data to the console.
To run this file, you can use the following command:
node database.js
In this post, we've seen how to use TensorFlow.js with databases in Node.js. We've seen how to connect to a database and how to use TensorFlow.js to load data from a database.