Tensorflow.js tf.data.Dataset class .prefetch() Method

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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.

The tf.data.Dataset class .prefetch() function is used to produce a dataset that prefetches the specified elements from this given dataset.

Syntax:

prefetch (bufferSize)

Parameters: This function accepts a parameter which is illustrated below:

bufferSize: It is an integer value that specifies the number of elements to be prefetched.

Return Value: It returns a dataset of elements.

Example 1:

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Calling the .prefetch() function over 
// the specified dataset of some elements 
const a = tf.data.array([5, 10, 15, 20]).prefetch(4); 
  
// Getting the dataset of prefetched elements 
await a.forEachAsync(a => console.log(a));

Output:

Example 2:

Javascript

// Importing the tensorflow.js library 
import * as tf from "@tensorflow/tfjs"
  
// Specifying a dataset of some elements 
const a = tf.data.array(["a", "b", "c", "d", "e"]); 
  
// Calling the .prefetch() function over 
// the above dataset along with the  
// batch of size 2 
const b = a.batch(2) 
const c = b.prefetch(2) 
  
// Getting the dataset of prefetched elements 
await c.forEachAsync(c => console.log(c));

Output:

Tensor
    ['a', 'b']
Tensor
    ['c', 'd']
Tensor
    ['e']

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

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