Tensorflow.js tf.logSumExp() Function

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.logSumExp() function is used to calculate log sum exp of elements of a Tensor across its dimension. It reduces the given input elements along the dimensions of axes. If the parameter "keepDims" is true, the reduced dimensions are retained with length 1 else the rank of Tensor is reduced by 1. If the axes parameter has no entries, it returns a Tensor with a single element with all reduced dimensions.

Syntax:

tf.logSumExp (x, axis, keepDims)

Parameters: This function accepts three parameters which are illustrated below:

• x: The input tensor.
• axis: The specified dimension(s) to reduce. By default it reduces all dimensions. It is optional parameter.
• keepDims: If this parameter value is true, it retains reduced dimensions with length 1 else the rank of Tensor is reduced by 1. It is also optional parameter.

Return Value: It returns a Tensor of calculated value of log sum exp operation.

Example 1:

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Initializing a some tensors 
const a = tf.tensor1d([0, 1]);
const b = tf.tensor1d([3, 5]);
const c = tf.tensor1d([2, 4, 7]);

// Calling the .logSumExp() function over 
// the above tensors
a.logSumExp().print();
b.logSumExp().print();
c.logSumExp().print();

Output:

Tensor
   1.31326162815094
Tensor
   5.126927852630615
Tensor
   7.054985046386719

Example 2:

// Importing the tensorflow.js library
import * as tf from "@tensorflow/tfjs"

// Initializing a some tensors 
const a = tf.tensor1d([0, 1]);
const b = tf.tensor2d([3, 5, 2, 8], [2, 2]);
const c = tf.tensor1d([2, 4, 7]);

// Initializing a axis parameters
const axis1 = -1;
const axis2 = -2;
const axis3 = 0;

// Calling the .logSumExp() function over 
// the above tensors
a.logSumExp(axis1).print();
b.logSumExp(axis2, true).print();
c.logSumExp(axis1, false).print();
b.logSumExp(axis3, false).print();

Output:

Tensor
   1.31326162815094
Tensor
    [[3.3132617, 8.0485878],]
Tensor
   7.054985046386719
Tensor
   [3.3132617, 8.0485878]