I have converted a keras model to tensorflow json format and saved it locally in my computer. I am trying to load that json model in a javascript code using the below comman
LoadModel uses fetch under the hood. And fetch cannot access the local files directly. It is meant to be used to get files served by a server. More on this here.
To load a local file with the browser, there is two approaches, asking the user to upload the file with
Or serving the file by a server.
In these two scenarios, tf.js provides way to load the model.
html
js
const uploadJSONInput = document.getElementById('upload-json');
const uploadWeightsInput = document.getElementById('upload-weights');
const model = await tfl.loadModel(tf.io.browserFiles(
[uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
To do so, one can use the following npm module http-server to serve the directory containing both the weight and the model. It can be installed with the following command:
npm install http-server -g
Inside the directory, one can run the following command to launch the server:
http-server -c1 --cors .
Now the model can be loaded:
// load model in js script
(async () => {
...
const model = await tf.loadFrozenModel('http://localhost:8080/model.pb', 'http://localhost:8080/weights.json')
})()