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.mod() function returns element-wise remainder of division.
Operation: floor(x / y) * y + mod(x, y) = x.
Note: It supports broadcasting.
Syntax:
tf.mod(x, y).
Parameters: This function accepts three parameters which are illustrated below:
- x: It is a Tensor.
- y: It is a Tensor. It must be of the same type as x.
Return Value: A Tensor. Has the same type as x.
Example 1: We also expose tf.mod() which has the same signature as this operation and asserts that a and b have the same shape (does not broadcast).
Javascript
// Importting the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Creating the tensor const x = tf.tensor([9, 12, 3, 20, 7]); const y = tf.tensor([2, 2, 9, 4, 2]); tf.mod(x,y).print(); |
Output:
Tensor
[1, 0, 3, 0, 1]
Example 2: The simplest broadcasting example when an array and a scalar value are combined in an operation:
Javascript
// Importing the tensorflow library import * as tf from "@tensorflow/tfjs" // Broadcast a mod b. const x = tf.tensor([2, 4, 5, 8]); const y = tf.scalar(5); x.mod(y).print(); |
Output:
Tensor
[2, 4, 0, 3]
Reference: https://js.tensorflow.org/api/latest/#mod
