Tensorflow.js tf.io.removeModel() Function

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 .removeModel() function is used to remove a stated model by means of a URL provided from a recorded repository medium.

Syntax:

tf.io.removeModel(url)

Parameters:  

• url: It is the stated URL within a recorded model, along with a pattern prefix i.e. ‘localstorage://my-mode-2’, ‘indexeddb://my/mode/3’. It is of type string.

Return Value: It returns Promise of ModelArtifactsInfo.

Example 1: Using “logSigmoid” as activation, “Local Storage” as the storage medium.

Javascript

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Creating model
const mymodel = tf.sequential();

// Calling add() method
mymodel.add(tf.layers.dense(
     {units: 3, inputShape: [20], stimulation: 'logSigmoid'}));

// Calling save() method with a storage medium
await mymodel.save('localstorage://display/command/mymodel1');

// Calling removeModel() method
await tf.io.removeModel('localstorage://display/command/mymodel1');

// Calling listModels() method and
// Printing output
console.log(await tf.io.listModels());

Output:

{
  "localstorage://demo/manage/model1": {
    "dateSaved": "2021-06-24T11:53:05.626Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model1": {
    "dateSaved": "2021-06-24T11:52:29.368Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 611,
    "weightSpecsBytes": 124,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model2": {
    "dateSaved": "2021-06-24T11:53:33.384Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model": {
    "dateSaved": "2021-06-24T11:53:26.006Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://display/command/mymodel2": {
    "dateSaved": "2021-06-24T19:02:03.367Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 612,
    "weightSpecsBytes": 125,
    "weightDataBytes": 32
  },
  "indexeddb://demo/management/model1": {
    "dateSaved": "2021-06-24T13:02:20.265Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 614,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "indexeddb://display/command/mymodel": {
    "dateSaved": "2021-06-24T18:50:50.602Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 252
  },
  "indexeddb://display/command/mymodel1": {
    "dateSaved": "2021-06-24T18:59:17.435Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 612,
    "weightSpecsBytes": 125,
    "weightDataBytes": 32
  },
  "indexeddb://example/command/mymodel": {
    "dateSaved": "2021-06-24T12:33:06.208Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 1428
  }
}

Example 2: Using “prelu” as activation, “IndexedDB” as a storage medium, and “JSON.stringify” in order to return the output in string format.

Javascript

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Creating model
const mymodel = tf.sequential();

// Calling add() method
mymodel.add(tf.layers.dense(
     {units: 11, inputShape: [6], stimulation: 'prelu'}));

// Calling save() method with a storage medium
await mymodel.save('indexeddb://display/command/mymodel1');

// Calling removeModel() method
await tf.io.removeModel('indexeddb://display/command/mymodel1');

// Calling listModels() method and
// Printing output
console.log(JSON.stringify(await tf.io.listModels()));

Output:

{
  "localstorage://demo/manage/model1": {
    "dateSaved": "2021-06-24T11:53:05.626Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model1": {
    "dateSaved": "2021-06-24T11:52:29.368Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 611,
    "weightSpecsBytes": 124,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model2": {
    "dateSaved": "2021-06-24T11:53:33.384Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://demo/management/model": {
    "dateSaved": "2021-06-24T11:53:26.006Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "localstorage://display/command/mymodel2": {
    "dateSaved": "2021-06-24T19:02:03.367Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 612,
    "weightSpecsBytes": 125,
    "weightDataBytes": 32
  },
  "indexeddb://demo/management/model1": {
    "dateSaved": "2021-06-24T13:02:20.265Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 614,
    "weightSpecsBytes": 126,
    "weightDataBytes": 44
  },
  "indexeddb://display/command/mymodel": {
    "dateSaved": "2021-06-24T18:50:50.602Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 252
  },
  "indexeddb://example/command/mymodel": {
    "dateSaved": "2021-06-24T12:33:06.208Z",
    "modelTopologyType": "JSON",
    "modelTopologyBytes": 613,
    "weightSpecsBytes": 126,
    "weightDataBytes": 1428
  }
}

Reference: https://js.tensorflow.org/api/latest/#io.removeModel