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Tensorflow.js tf.data.Dataset.skip() Function

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  • Last Updated : 22 Apr, 2022
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Tensorflow.js is an open-source library developed by Google for running machine learning models and deep learning neural networks in the browser or node environment. It also helps the developers to develop ML models in JavaScript language and can use ML directly in the browser or in Node.js.

The tf.data.Dataset.skip() function is used to create a Dataset that skips count initial elements from this dataset

Syntax:

skip(count)

Parameters

  • count: The number of elements of this dataset that should be skipped to form the new dataset.

Return Value: It returns tf.data.Dataset.

Example 1:

Javascript




// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
const a = tf.data.array(
    [5, 10, 15, 20, 25, 30]).skip(2);
  
await a.forEachAsync(e => console.log(e));

Output:

15
20
25
30

Example 2:

Javascript




// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
const gfg = tf.data.array(
    ['geeksforgeeks', 'gfg', 'geeks'
    'for', 'geeks']).skip(2);
      
await gfg.forEachAsync(
    geeks => console.log(geeks));

Output:

geeks
for
geeks

Reference: https://js.tensorflow.org/api/latest/#tf.data.Dataset.skip

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