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.forEachAsync() function is used after applying a function to each and every element of the dataset. We can use this method to print an array, modifying the values of the array etc.
Syntax:
forEachAsync(f)
Parameters:
- f: It accepts a function which is applied to each and every element of the dataset
Return Value: It returns a promise
Example 1:
Javascript
// Creating an array const arr = tf.data.array([10, 20, 30, 40, 50]); Â Â // Applying the function on the array await arr.forEachAsync(element => console.log(element)); |
Output:
10 20 30 40 50
Example 2: In this example we will pass a function that calculates the square of an element.
Javascript
// Creating an array const arr = tf.data.array([1, 2, 3, 4, 5]); Â Â // Applying the function on the array await arr.forEachAsync(element => console.log(element*element)); |
Output:
1 4 9 16 25
Reference: https://js.tensorflow.org/api/latest/#tf.data.Dataset.forEachAsync
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