Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.
The .softplus() function is used to find softplus of the stated input tensor i.e. log(exp(x) + 1) and is done element wise.
Syntax :
tf.softplus(x)
Parameters:
- x: It is the stated tensor input, and it can be of type tf.Tensor, TypedArray, or Array.
Return Value: It returns the tf.Tensor object.
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Defining tensor input elements const y = tf.tensor1d([11, 17, 0, NaN, -41]); // Calling softplus() method and // Printing output y.softplus().print(); |
Output:
Tensor
[11.0000162, 17, 0.6931472, NaN, 0]
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs" // Defining tensor input var val = [1.5, .4, .23, null, 'a']; // Calling tensor1d method const y = tf.tensor1d(val); // Calling softplus() method var res = tf.softplus(y) // Printing output res.print(); |
Output:
Tensor
[1.7014132, 0.9130152, 0.8147451, 0.6931472, NaN]
Reference: https://js.tensorflow.org/api/latest/#softplus
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