Tensorflow.js tf.loadGraphModel() Function

0 0 2 minutes read

Tensorflow.js is an open-source library developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.

The .loadGraphModel() function is used to Load a graph model given a URL to the model definition.

Syntax:

tf.loadGraphModel (modelUrl, options)

Parameters:

modelUrl: The first tensor input that can be of type string or io.IOHandler. This parameter is the URL or an io.IOHandler that helps to load the models.
options: The second tensor input that is optional. Options are for the HTTP request, which allows sending credentials and custom headers. The types of options are –
- requestInit: RequestInit are for HTTP requests.
- onProgress: OnProgress is for progress callback.
- fetchFunc: It is a function used to override the window.fetch function.
- strict: Strict is a loading model: whether it is an extraneous weight or missing weights should trigger an Error.
- weightPathPrefix: Path prefix is for weight files which is by default that is calculated from the path of the model JSON file.
- fromTFHub: It is a boolean which is whether the module or model is to be loaded from TF Hub.

Return value: It returns the Promise <tf.GraphModel>.

Example 1: In this example, we are loading MobileNetV2 from a URL and making a prediction with a zeros input.

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Defining tensor input elements  
const modelUrl = 
'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; 
  
// Calling the loadGraphModel () method 
const model = await tf.loadGraphModel(modelUrl); 
  
// Printing the zeroes 
const zeros = tf.zeros([1, 224, 224, 3]); 
model.predict(zeros).print(); 

Output:

Tensor
     [[-0.1412081, -0.5656458, 0.7578365, ..., 
     -1.0148169, -0.81284, 1.1898142],]

Example 2: In this example, we are loading MobileNetV2 from a TF Hub URL and making a prediction with a zeros input.

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Defining tensor input elements  
const modelUrl = 
'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2'; 
  
// Calling the loadGraphModel () method 
const model = await tf.loadGraphModel( 
        modelUrl, {fromTFHub: true}); 
  
// Printing the zeores 
const zeros = tf.zeros([1, 224, 224, 3]); 
model.predict(zeros).print(); 

Output:

Tensor
     [[-1.0764486, 0.0097444, 1.1630495, ..., 
     -0.345558, 0.035432, 0.9112286],]

Reference: https://js.tensorflow.org/api/1.0.0/#loadGraphModel

Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, zambiatek Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we’ve already empowered, and we’re here to do the same for you. Don’t miss out – check it out now!