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

  • Last Updated : 21 Jun, 2021

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

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

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Syntax:

skip(count)

Parameters: This method has as single parameter as mentioned above and described below:



  • count: It is a tensor input where the number of element of this dataset that should be skipped to form the new dataset. When the count is greater than the size of this dataset, the new dataset will contain no elements. When the count is undefined or negative, it skips the entire dataset.

Return Value: It returns the tf.data.Dataset.

The below examples demonstrate the tf.data.Dataset.skip() method:

Example 1:

Javascript




// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
// Defining input elements
const a = 
  tf.data.array([4, 5, 6, 7, 8, 9]).skip(3);
await a.forEachAsync(e => console.log(e));

Output:

7
8
9

Example 2:

Javascript




// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"
  
// Defining input elements
const a = 
  tf.data.array([4, 5, 6, 7, 8, 9]).skip(4);
await a.forEachAsync(e => console.log(e));

Output:

8
9

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

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