In this post, we'll learn how to make predictions with a TensorFlow.js model in a Node.js environment. We'll use a pre-trained model to make predictions on images, and then we'll train a model to make predictions on new data.
Before we get started, there are a few things you'll need to have in order to follow along:
If you need help installing Node.js, check out this tutorial.
We'll start by loading a pre-trained model. For this example, we'll use a model that's been trained to classify images of handwritten digits.
First, we'll need to install the @tensorflow/tfjs-node
package. We can do this from the command line by running:
npm install @tensorflow/tfjs-node
Once the package is installed, we can import it in our code:
const tf = require('@tensorflow/tfjs-node');
Now we're ready to load the model. We'll use the tf.loadLayersModel()
function to load the model from a URL. We can get the model URL from the TensorFlow.js model zoo.
const modelUrl = 'https://storage.googleapis.com/tfjs-models/tfjs/mnist_model/model.json';
const model = tf.loadLayersModel(modelUrl);
The model we've loaded can classify images of handwritten digits with high accuracy. But before we can use the model to make predictions, we need to preprocess the images.
First, we'll convert the images to a format that the model can understand. We'll use the tf.tensor3d()
function to convert the images to a 3D tensor.
const imageData = [
// The first image is a zero.
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