TensorFlow.js is a powerful JavaScript library for training and deploying machine learning models. It can be used with other ML frameworks, such as TensorFlow Lite, to run inference on Node.js.
In this post, we'll show you how to use TensorFlow.js with other ML frameworks in Node.js. We'll also provide some tips on how to get the most out of TensorFlow.js.
TensorFlow.js can be used with TensorFlow Lite to run inference on Node.js. TensorFlow Lite is a lightweight framework for running machine learning models on mobile devices.
To use TensorFlow.js with TensorFlow Lite, you'll need to install the TensorFlow Lite Node.js bindings. You can do this with npm:
npm install @tensorflow/tfjs-node
Once the bindings are installed, you can use the tensorflow.js
module to load a TensorFlow Lite model and run inference on it.
Here's an example of how to use TensorFlow.js with TensorFlow Lite:
const tf = require('@tensorflow/tfjs-node');
// Load a TensorFlow Lite model.
const model = await tf.loadLiteModel('https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_1.0_224/model.json');
// Run inference on a TensorFlow Lite model.
const output = model.predict(tf.zeros([1, 224, 224, 3]));
// Print the output of the model.
console.log(output);
TensorFlow.js can also be used with other ML frameworks, such as Keras, to run inference on Node.js.
To use TensorFlow.js with Keras, you'll need to install the TensorFlow.js Node.js bindings. You can do this with npm:
npm install @tensorflow/tfjs
Once the bindings are installed, you can use the tf
module to load a Keras model and run inference on it.
Here's an example of how to use TensorFlow.js with Keras:
const tf = require('@tensorflow/tfjs');
// Load a Keras model.
const model = await tf.loadModel('https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_1.0_224/model.json');
// Run inference on a Keras model.
const output = model.predict(tf.zeros([1, 224, 224, 3]));
// Print the output of the model.
console.log(output);
Here are some tips for using TensorFlow.js: