TensorFlow.js is a powerful tool for using JavaScript for machine learning and deep learning. Here are some basic steps:
1. Installation: First, you need to include TensorFlow.js in your project. You can either include it as a script in your HTML file or install it via npm. Assuming you have Node.js installed, the installation via npm is as simple as running this command:
```
npm install @tensorflow/tfjs
```
1. Load or Train a Model: With TensorFlow.js you can either use a pre-trained model or train a model from scratch.
Loading a pre-trained model:
```
const model = await tf.loadLayersModel(‘https://……/model.json’);
```
Training a model from scratch:
```
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
model.compile({optimizer: ‘sgd’, loss: ‘meanSquaredError’});
```
1. Preprocess Data: Depending on your use case, you may need to preprocess data before feeding it into a model. TensorFlow.js provides utilities for this, including one-hot encoding, normalization, and reshaping.
1. Make Predictions or Train: With a pre-trained model, you can make predictions by calling `model.predict()`. If you want to train your model, TensorFlow.js provides a `model.fit()` function which will iteratively optimize your model parameters.
1. Evaluate: Finally, after training a model, you’ll want to evaluate how well it did. This can involve using a test dataset and computing metrics like accuracy or loss.
Here’s a small example of creating, training and using a model for prediction.
```
import * as tf from ‘@tensorflow/tfjs’;
// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Prepare the model for training: Specify the loss and the optimizer.
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).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();
});
```
In the example above, the model is trained to learn the relationship between X and Y in the equation Y=2×-1 and to predict Y when an unseen X is given as input.
Remember, machine learning involves a lot of trial and error! Don’t be discouraged if you need a few tries to get things right.
