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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →To use TensorFlow in your browser, use TensorFlow.js—TensorFlow’s JavaScript library for machine learning. It lets a web page build and train a small model or run a compatible model in the browser; it does not install Python TensorFlow or turn the browser into a Python environment.
For a quick experiment, add TensorFlow.js with a script tag. For an existing JavaScript application, install it with npm and use your project’s build tool. The example below trains a tiny model on made-up numbers, so you can try the full workflow without a camera, dataset, or pretrained model.
Choose how to add TensorFlow.js to a web page
TensorFlow’s project setup guide recommends two common approaches. Pick based on how you are building the page, not on an assumed speed or accuracy difference.
| Approach | Setup effort | Best fit | Dependency and bundling workflow |
|---|---|---|---|
| Script tag | Shortest route for a one-page experiment: include the browser script and use the global tf namespace. |
A standalone demonstration or first test. | Loads the library in the page rather than importing it through your application’s build workflow. The official setup guide provides an example and explains serving the file locally. |
| npm and a build tool | Requires a JavaScript project and package installation. | An application that already uses a JavaScript build workflow or an example that is growing into one. | Install @tensorflow/tfjs and import it in your JavaScript. TensorFlow names Parcel, webpack, and Rollup as example build tools. |
The official setup page includes a CDN URL using a latest alias. That alias can change over time, so check the current TensorFlow.js setup instructions for the appropriate script and version rather than copying a version-sensitive URL from an old example.
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Train and run a tiny model in the browser
TensorFlow’s getting-started tutorial demonstrates regression: the model learns a relationship between input and output values. Its synthetic examples follow y = 2x - 1; after training, predicting for x = 20 should give a result close to 39. This is a teaching example, not a browser performance test.
The following compact version shows the core model steps. It assumes TensorFlow.js is already loaded, for example using the script-tag approach, and that the page contains an element with id="result".
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const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
const xs = tf.tensor1d([-1, 0, 1, 2, 3, 4]);
const ys = tf.tensor1d([-3, -1, 1, 3, 5, 7]);
await model.fit(xs, ys, { epochs: 250 });
const prediction = model.predict(tf.tensor1d([20]));
const value = (await prediction.data())[0];
document.getElementById('result').textContent = value;What each part does
- Create the model and layer:
tf.sequential()makes a model with a straightforward layer-by-layer structure. The dense layer accepts one input value and produces one output. - Compile it: Mean squared error is the loss used to measure prediction error; stochastic gradient descent (
sgd) is the optimizer used during training. - Prepare examples: The input tensor
xsand target tensoryscontain paired synthetic values from the ruley = 2x - 1. - Train:
model.fitadjusts the model using those examples. In browser code, wait for its promise to finish before requesting a prediction. - Predict and display:
model.predictruns inference for the unseen input20. Reading the tensor’s data is asynchronous; the displayed result should be approximately39, not necessarily an exact integer.
This example needs only JavaScript and synthetic numeric input. It does not require a webcam or a separately trained model. TensorFlow’s tutorial also shows how to display a prediction in a web page. Its repository workflow uses Node.js and Yarn to run a local example project; those are development tools for that workflow, not prerequisites for every browser experiment.
Load a model trained elsewhere
If you already have a TensorFlow model, you may be able to convert it to TensorFlow.js format and load it in the browser. The browser loads a model description and its associated weight files; a model JSON file is not necessarily the complete model on its own. See TensorFlow’s guides to importing a TensorFlow model and saving and loading models.
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| Consideration | Build and train a small model in JavaScript | Convert and load an existing model |
|---|---|---|
| Training from scratch | Train in the browser with example or application data. | Not needed to use the already-trained model for inference. |
| Compatibility | Uses TensorFlow.js operations and layers in the code you write. | Conversion can fail if the model uses TensorFlow operations that TensorFlow.js does not support; check compatibility before choosing this route. |
| Files to load | No converted model files are needed for the tutorial’s in-memory example. | Provide the model description and corresponding weight files where the browser application can load them. |
Plan for browser inputs and long-running work
Camera input is optional
TensorFlow.js demos include camera-based experiences, such as a webcam controller, but camera access is a feature of those particular projects—not a requirement for TensorFlow.js. The regression example above uses numbers and needs no camera. Browse TensorFlow’s TensorFlow.js demos to see examples with different inputs.
Keep expensive training from blocking the interface
Long-running JavaScript can make a page feel unresponsive. TensorFlow’s web-worker tutorial demonstrates moving training work off the main UI thread. A worker is a way to keep the interface responsive while work runs; it does not guarantee that every model or training job will finish quickly in a browser.
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Which browser approach should you use?
- Use a script tag when you want the shortest path to a single-page experiment.
- Use npm and a build tool when TensorFlow.js belongs in an existing JavaScript application.
- Build a small model in JavaScript to learn the model, training, and prediction flow with simple data.
- Convert an existing model when you need its trained behavior in the browser and its operations are supported by TensorFlow.js.
- Use a web worker when lengthy training work would otherwise interfere with the page’s interface.
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