Yes, you can serve a TensorFlow model in the browser using TensorFlow.js. TensorFlow.js is a JavaScript library that allows you to define, train, and run machine learning models directly in the browser or on Node.js. Here are the general steps to achieve this:
Convert the TensorFlow Model to TensorFlow.js format:
- Use the TensorFlow.js Converter tool to convert your model from the .h5 format to the TensorFlow.js format. You can use the
tensorflowjs_converter
script provided by TensorFlow.js.
tensorflowjs_converter --input_format=keras my_model.h5 ./tfjs_model
This command will create a directory called tfjs_model
with the converted TensorFlow.js model files.
Serve the Model Files:
- You need to serve the converted model files using a web server. You can use a simple HTTP server like
http-server
or any other web server of your choice.
http-server
npm install -g http-server
cd tfjs_model
http-server
This will start a web server, and you can access the model files through a URL like http://localhost:8080
.
Load and Run the Model in the Browser:
- In your web application, you can use the TensorFlow.js library to load and run the model. You can include TensorFlow.js in your HTML file using a script tag or by installing it through npm.
Example HTML file:
<!DOCTYPE html>
<html>
<head>
<title>TensorFlow.js Model in the Browser</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
</head>
<body>
<script>
async function runModel() {
const model = await tf.loadLayersModel('http://localhost:8080/model.json');
// Make predictions or perform other tasks with the loaded model
// ...
}
runModel();
</script>
</body>
</html>
Replace the URL in tf.loadLayersModel
with the correct path to your model.json file.
Remember to adjust the paths and URLs based on your setup. This is a high-level overview, and the details might vary based on your specific use case and web application structure.