Hands-On Machine Learning with TensorFlow.js

Hands-On Machine Learning with TensorFlow.js

TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser. This hands-on tutorial shows you how to build a simple machine learning model in JavaScript, using the TensorFlow.js library.

Check out our video for more information:

Introduction to TensorFlow.js

TensorFlow.js is an open-source JavaScript library for training and deploying machine learning models in the browser and on Node.js.

TensorFlow.js offers a community-supported way of creating machine learning models that can be used in the browser or in Node.js applications, without the need to install any other dependencies.

In this guide, we will take a hands-on approach to learning how to use TensorFlow.js by working through a simple, end-to-end example. By the end of this guide, you will have created a machine learning model that can be trained and deployed in the browser or on Node.js, without any other dependencies required.

Setting up your development environment

In this section, you will set up your development environment so that you can follow along with the examples in this book. You will need to install Node.js, which is a JavaScript runtime that allows you to run JavaScript code on your computer. You will also need to install a text editor so that you can edit your code.

Once you have Node.js and a text editor installed, you will need to install the TensorFlow.js library. The TensorFlow.js library is a JavaScript library that allows you to build and train machine learning models in the browser.

To install Node.js, go to https://nodejs.org/en/ and download the latest LTS (Long Term Support) version of Node.js for your operating system. Once you have installed Node.js, open a terminal window and type node -v . This will print the version of Node.js that you have installed.

To install a text editor, we recommend Visual Studio Code, which is a free and open source code editor from Microsoft (https://code.visualstudio.com). Once you have installed Visual Studio Code, open it and go to File > Open Folder . Create a new folder called tensorflow-js-book and open it in Visual Studio Code.

To install the TensorFlow.js library, open a terminal window and navigate to the tensorflow-js-book folder that you created in the previous step. Type npm init -y into the terminal to initialize a package.json file for your project (the -y flag will automatically fill in the defaults for the package.json file). Next, type npm install @tensorflow/tfjs – save into the terminal to install TensorFlow.js and add it as a dependency in your package

Hello TensorFlow.js!

In this section, we will take a look at the basic concepts of machine learning and deep learning, review the main types of neural networks, and build our first very simple neural network from scratch using TensorFlow.js.

We will cover the following topics in this section:

-What is machine learning?
-Types of neural networks
-How to build a simple neural network in TensorFlow.js

Building your first machine learning model

In this section, you will learn the fundamental steps required to build your first machine learning model with TensorFlow.js. You will begin by loading and preparing your data. Next, you will define your model. Finally, you will train your model and use it to make predictions.

Training and evaluating your model

After you have defined your model, the next step is to train it. training a model means making it better at doing whatever task it is that you want it to do. In the case of a classification model, this means showing it lots of examples of different types of things and letting it learn to classify them.

There are two ways to train a TensorFlow.js model: online and offline training. Online training is where you use the actual data that you want the model to learn from, feeding it into the model one example at a time. Offline training is where you first feed the model a large dataset, often called a “training set”, and then let it learn from that.

Once your model is trained, you will want to evaluate it to see how well it performs. Evaluation is usually done by feeding the model examples of things that it has never seen before and seeing how well it can classify them. This process is sometimes called “testing”.

Using your trained model in the browser

Once you have trained and saved your model, you can use it in your web app by loading it into the browser. To do this, you will need to use the tf.loadLayersModel() function:

const model = await tf.loadLayersModel(‘/my_model.json’);

This function returns a promise that resolves to an instance of tf.LayersModel, which you can then use to run inference on your inputs. For example, if you have a form with two input fields (x and y), you can use the following code to predict the output for a given input:

const x = tf.tensor2d([[1, 2], [3, 4]]); // two rows, two columns (i.e., two examples)
const y = model.predict(x); // runs inference with the loaded model on x and outputs the predicted values of y
y.print(); // [[0.1, 0.9], [0.2, 0.8]] (e.g., predictions for first and second example respectively)

Saving and loading models

TensorFlow.js provides various ways to save your models—to stored model artifacts, your local filesystem, or to a central hosted storage service that provides APIs to access your models from a web browser, mobile app, or back-end service. You can also take a snapshot of the state of your model during training and use that snapshot to start training from that point later on.

### Saving and loading models
The simplest way to save TensorFlow.js model is by serializing the entire model object:
const model = tf.sequential({…});
tf.models.save(“/model-1”, model); // Saves the model’s topology and weights under “/model-1” directory

TensorFlow.js operations and types

TensorFlow.js is a powerful library for machine learning that allows you to define, train, and deploy models directly in the browser. In this article, we’ll go over some of the basic operations and types that you’ll need to know in order to get started with TensorFlow.js.

TensorFlow.js operations are used to manipulate data in Tensors. There are a variety of operations available, including math operations like addition and multiplication, as well as more complex operations like convolution and transpose. In addition to operations, TensorFlow.js also has a number of different data types that you can use, including tf.Tensor1D , tf.Tensor2D , and tf.Tensor3D .

Once you’ve defined your Tensors and operations, you can then use them to train models using the various TensorFlow.js APIs. After training, you can then deploy your models directly in the browser or in a Node.js server environment.

Custom layers and models

In this section, we’ll learn how to create custom layers and models with TensorFlow.js. We’ll start by creating a simple custom layer that performs a linear transformation on its input. We’ll then create a more complex model that includes our custom layer, and we’ll see how to train this model using the fit API.

Advanced topics

In this section, we will explore some advanced topics in machine learning with TensorFlow.js. We will cover topics such as:

– Reinforcement learning
– Transfer learning
– Neural architecture search
– Generative models

Keyword: Hands-On Machine Learning with TensorFlow.js

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