Introduction: Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.
The .save() function is used to save the structure and/or the weights of the stated GraphModel.
Note:
- An IOHandler is an object which possesses a save method concerning the accurate signature specified.
- The save method controls the accumulation or else transmission of sequential data i.e. artifacts which describes the model’s topology as well as weights upon or by means of a particular medium, like local storagefile, file downloads, IndexedDB in the web browser as well as HTTP requests to a server.
- TensorFlow.js enables IOHandler implementations in favor of a number of repeatedly utilized saving mediums, like tf.io.browserDownloads() and tf.io.browserLocalStorage.
- Moreover, this method also permits us to apply specific kinds of IOHandlers such as URL-like string techniques, like ‘localstorage://’ and ‘indexeddb://’.
Syntax:
save(handlerOrURL, config?)
Parameters:
- handlerOrURL: The stated instance of IOHandler or else a URL resembling, design based string techniques in favor of IOHandler. It is of type io.IOHandler|string.
- config: The stated options in order to save the stated model. It is optional and is of type object. It has two arguments under it, as given below:
- trainableOnly: It states if only the trainable weights of the stated model is saved, overlooking the non-trainable weights. It is of type Boolean and defaults to false.
- includeOptimizer: It states if the stated optimizer will be stored or not. It is of type Boolean and defaults to false.
Return Value: It returns promise of io.SaveResult.
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Defining model url const model_Url = // Calling the loadGraphModel() method const mymodel = await tf.loadGraphModel(model_Url); // Calling save() method // Printing output console.log(output) |
Output:
{
"modelArtifactsInfo": {
"dateSaved": "2021-08-19T12:00:15.603Z",
"modelTopologyType": "JSON",
"modelTopologyBytes": 90375,
"weightSpecsBytes": 15791,
"weightDataBytes": 13984940
}
}
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Calling the loadGraphModel() method const mymodel = await tf.loadGraphModel( // Calling save() method with all its // parameters // Printing output console.log(JSON.stringify(output)) |
Output:
{"modelArtifactsInfo":{"dateSaved":"2021-08-19T12:05:35.906Z",
"modelTopologyType":"JSON","modelTopologyBytes":90375,
"weightSpecsBytes":15791,"weightDataBytes":13984940}}
Reference: https://js.tensorflow.org/api/latest/#tf.GraphModel.save
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