TensorFlow.js is an open-source library developed by Google that allows you to build and deploy machine learning models right in your browser or in Node.js. In this tutorial, we’ll walk you through the quick start guide for TensorFlow.js and show you how to get started building your own machine learning models.
Step 1: Installation
To get started with TensorFlow.js, you can either include the library in your project using a script tag in your HTML file or install it via npm. To install TensorFlow.js via npm, you can run the following command in your terminal:
npm install @tensorflow/tfjsStep 2: Import TensorFlow.js
Once you have installed TensorFlow.js, you can import it into your project using the following code:
import * as tf from '@tensorflow/tfjs';Step 3: Creating a simple model
Now that you have imported TensorFlow.js into your project, you can start creating your own machine learning models. Let’s start by creating a simple model that predicts the output of a linear equation. Here’s how you can do it:
// Define the input data
const input = tf.tensor1d([1, 2, 3, 4]);
// Define the labels
const labels = tf.tensor1d([2, 4, 6, 8]);
// Define the model architecture
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Compile the model
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
// Train the model
model.fit(input, labels, {epochs: 1000}).then(() => {
// Make predictions
const output = model.predict(tf.tensor1d([5]));
output.print();
});In this example, we defined the input data and labels, created a simple model with one dense layer, compiled the model with a Stochastic Gradient Descent optimizer and Mean Squared Error loss function, trained the model on the input data and labels for 1000 epochs, and made a prediction for a new input value (5).
Step 4: Deploying your model
Once you have trained your model, you can deploy it to a webpage using TensorFlow.js’s browser support. Here’s how you can do it:
<!DOCTYPE html>
<html>
<head>
<title>TensorFlow.js Model Deployment</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
</head>
<body>
<h1>TensorFlow.js Model Deployment</h1>
<p id="output"></p>
<script>
// Define the input data
const input = tf.tensor1d([5]);
// Load the model
tf.loadLayersModel('model.json').then(model => {
// Make predictions
const output = model.predict(input);
document.getElementById('output').innerText = `Prediction: ${output.dataSync()[0]}`;
});
</script>
</body>
</html>In this example, we loaded the saved model from a JSON file using tf.loadLayersModel(), made a prediction for a new input value (5), and displayed the prediction on a webpage.
And that’s it! You have now completed the quick start guide for TensorFlow.js and have built and deployed your own machine learning model. TensorFlow.js allows you to build powerful machine learning applications right in your browser or in Node.js, making it a great tool for developers looking to experiment with and deploy machine learning models. You can explore more advanced features and models with TensorFlow.js to take your machine learning projects to the next level.
Welcoming TensorFlow to the web – tons of potential for web developers with this library. Let me know if you want to see more TensorFlow.js content in the comments.
This video is tensor tensor tensor flow tensor. Oh and tensor and tensor flow
To this day, are those resources still relevant to learn ML?
Video from the past. When there is no crypto, no a.i ,no layoffs bullshit was there. 😢😢
Can you please show us how you connect the drawn image to the typescript code
I just want to be honest. This quick start tutorial was too abstract. I need to be able to visualize the model and be able to know what each line does in the model.
ce set me rappel mes débuts sur le jeu avec l atwork de yasuo élu et les synergies bien meilleures que les actuelles, j'espère juste que toutes les compo auront du potentielles et pas juste une à deux, ça dégouterai vite
keep going bro your videos are awesome
.
I didnt realize it was this simple. FML.
I didn’t have a background.
THIS VIDEO IS SO DIFFERENT AND somewhat nice than the fast paced videos you make today
Ty
Can you make a TensorFlow in C video?
Where do I find the download to the Keras file? I am trying to follow along and I'm stuck around 7:44
This line does not work in Angular 14: this.linearModel.compile({loss: 'meanSquareError', optimizer: 'sgd'}); – I get: Error: Uncaught (in promise): Error: Unknown loss meanSquareError
Error: Unknown loss meanSquareError … Any ideas?
Dude, when did you learn all the stuff? Do you even sleep? 😅
this is the only useful tutorial on whole youtube
This video is still as good as gold even after 4 years.
"Because you're most likely a human being"