Import a TensorFlow model into TensorFlow.js

TensorFlow GraphDef-based models (typically created via the Python API) can be saved in one of following formats:

  1. TensorFlow SavedModel
  2. Frozen Model
  3. Tensorflow Hub module

All of the above formats can be converted by the TensorFlow.js converter into a format that can be loaded directly into TensorFlow.js for inference.

(Note: TensorFlow has deprecated the session bundle format. Please migrate your models to the SavedModel format.)


The conversion procedure requires a Python environment; you may want to keep an isolated one using pipenv or virtualenv.

To install the converter, run the following command:

 pip install tensorflowjs

Importing a TensorFlow model into TensorFlow.js is a two-step process. First, convert an existing model to the TensorFlow.js web format, and then load it into TensorFlow.js.

Step 1. Convert an existing TensorFlow model to the TensorFlow.js web format

Run the converter script provided by the pip package:

SavedModel example:

tensorflowjs_converter \
    --input_format=tf_saved_model \
    --output_node_names='MobilenetV1/Predictions/Reshape_1' \
    --saved_model_tags=serve \
    /mobilenet/saved_model \

Frozen model example:

tensorflowjs_converter \
    --input_format=tf_frozen_model \
    --output_node_names='MobilenetV1/Predictions/Reshape_1' \
    /mobilenet/frozen_model.pb \

Tensorflow Hub module example:

tensorflowjs_converter \
    --input_format=tf_hub \
    '' \
Positional Arguments Description
input_path Full path of the saved model directory, session bundle directory, frozen model file or TensorFlow Hub module handle or path.
output_path Path for all output artifacts.
Options Description
--input_format The format of input model. Use tf_saved_model for SavedModel, tf_frozen_model for frozen model, tf_session_bundle for session bundle, tf_hub for TensorFlow Hub module and keras for Keras HDF5.
--output_node_names The names of the output nodes, separated by commas.
--saved_model_tags Only applicable to SavedModel conversion. Tags of the MetaGraphDef to load, in comma separated format. Defaults to serve.
--signature_name Only applicable to TensorFlow Hub module conversion, signature to load. Defaults to default. See

Use following command to get a detailed help message:

tensorflowjs_converter --help

Converter generated files

The conversion script above produces two types of files:

  • model.json: The dataflow graph and weight manifest
  • group1-shard\*of\*: A collection of binary weight files

For example, here is the output from converting MobileNet v2:


Step 2: Loading and running in the browser

  1. Install the tfjs-converter npm package:

yarn add @tensorflow/tfjs or npm install @tensorflow/tfjs

  1. Instantiate the FrozenModel class and run inference.
import * as tf from '@tensorflow/tfjs';
import {loadGraphModel} from '@tensorflow/tfjs-converter';

const MODEL_URL = 'model_directory/model.json';

const model = await loadGraphModel(MODEL_URL);
const cat = document.getElementById('cat');

Check out the MobileNet demo.

The loadGraphModel API accepts an additional LoadOptions parameter, which can be used to send credentials or custom headers along with the request. For details, see the loadGraphModel() documentation.

Supported operations

Currently TensorFlow.js supports a limited set of TensorFlow ops. If your model uses an unsupported op, the tensorflowjs_converter script will fail and print out a list of the unsupported ops in your model. Please file an issue for each op to let us know which ops you need support for.

Loading the weights only

If you prefer to load the weights only, you can use the following code snippet:

import * as tf from '@tensorflow/tfjs';

const weightManifestUrl = "";

const manifest = await fetch(weightManifestUrl);
this.weightManifest = await manifest.json();
const weightMap = await
        this.weightManifest, "");
// Use `weightMap` ...