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@tensorflow/tfjs
Advanced tools
@tensorflow/tfjs is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. It allows you to develop machine learning models in JavaScript and use them in the browser or on Node.js.
Creating and Training Models
This code demonstrates how to create a simple neural network model, compile it, and train it using synthetic data. The model is then used for inference.
const tf = require('@tensorflow/tfjs');
// Define a simple model
const model = tf.sequential();
model.add(tf.layers.dense({units: 100, activation: 'relu', inputShape: [10]}));
model.add(tf.layers.dense({units: 1, activation: 'linear'}));
// Compile the model
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
// Generate some synthetic data for training
const xs = tf.randomNormal([100, 10]);
const ys = tf.randomNormal([100, 1]);
// Train the model
model.fit(xs, ys, {epochs: 10}).then(() => {
// Use the model for inference
model.predict(tf.randomNormal([1, 10])).print();
});
Loading Pre-trained Models
This code demonstrates how to load a pre-trained model from a URL and use it for inference on an image. The image is preprocessed to match the input requirements of the model.
const tf = require('@tensorflow/tfjs');
// Load a pre-trained model from a URL
const modelUrl = 'https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_0.25_224/model.json';
tf.loadGraphModel(modelUrl).then(model => {
// Use the model for inference
const img = tf.browser.fromPixels(document.getElementById('image'));
const resizedImg = tf.image.resizeBilinear(img, [224, 224]);
const input = resizedImg.expandDims(0).toFloat().div(tf.scalar(127)).sub(tf.scalar(1));
model.predict(input).print();
});
Tensor Operations
This code demonstrates basic tensor operations such as addition. Tensors are the core data structure in TensorFlow.js, and you can perform various mathematical operations on them.
const tf = require('@tensorflow/tfjs');
// Create tensors
const a = tf.tensor([1, 2, 3, 4]);
const b = tf.tensor([5, 6, 7, 8]);
// Perform tensor operations
const c = a.add(b);
c.print(); // Output: [6, 8, 10, 12]
Brain.js is a JavaScript library for neural networks. It is simpler and more beginner-friendly compared to TensorFlow.js, but it is less powerful and flexible. Brain.js is suitable for smaller projects and simpler neural network tasks.
Synaptic is a JavaScript neural network library for node.js and the browser. It provides a wide range of neural network architectures and is relatively easy to use. However, it does not offer the same level of performance and hardware acceleration as TensorFlow.js.
ml5.js is a high-level library built on top of TensorFlow.js, designed to make machine learning accessible to artists, creative coders, and students. It provides a simplified API for common machine learning tasks, making it easier to use but less flexible for advanced users.
TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models.
Develop ML in the Browser
Use flexible and intuitive APIs to build models from scratch using the low-level
JavaScript linear algebra library or the high-level layers API.
Run Existing models
Use TensorFlow.js model converters to run pre-existing TensorFlow models right
in the browser.
Retrain Existing models
Retrain pre-existing ML models using sensor data connected to the browser, or
other client-side data.
You can import TensorFlow.js directly via yarn or npm:
yarn add @tensorflow/tfjs
or npm install @tensorflow/tfjs
.
Alternatively you can use a script tag. The library will be available as
a global variable named tf
:
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
<!-- or -->
<script src="https://unpkg.com/@tensorflow/tfjs@latest"></script>
You can also specify which version to load replacing @latest
with a specific version string (e.g. 0.6.0
).
This repository contains the logic and scripts that combine two packages:
If you care about bundle size, you can import those packages individually.
Check out our examples repository and our tutorials.
See these release notes for how to migrate from deeplearn.js to TensorFlow.js.
Let's add a scalar value to a vector. TensorFlow.js supports broadcasting the value of scalar over all the elements in the tensor.
import * as tf from '@tensorflow/tfjs'; // If not loading the script as a global
const a = tf.tensor1d([1, 2, 3]);
const b = tf.scalar(2);
const result = a.add(b); // a is not modified, result is a new tensor
result.data().then(data => console.log(data)); // Float32Array([3, 4, 5]
// Alternatively you can use a blocking call to get the data.
// However this might slow your program down if called repeatedly.
console.log(result.dataSync()); // Float32Array([3, 4, 5]
See the core-concepts tutorial for more.
Now, let's build a toy model to perform linear regression.
import * as tf from '@tensorflow/tfjs';
// A sequential model is a container which you can add layers to.
const model = tf.sequential();
// Add a dense layer with 1 output unit.
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Specify the loss type and optimizer for training.
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.
await model.fit(xs, ys, {epochs: 500});
// After the training, perform inference.
const output = model.predict(tf.tensor2d([[5]], [1, 1]));
output.print();
For a deeper dive into building models, see the MNIST tutorial
We support porting pre-trained models from:
TensorFlow.js is a part of the TensorFlow ecosystem. For more info:
FAQs
An open-source machine learning framework.
The npm package @tensorflow/tfjs receives a total of 121,183 weekly downloads. As such, @tensorflow/tfjs popularity was classified as popular.
We found that @tensorflow/tfjs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 10 open source maintainers collaborating on the project.
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