Open In App

Tensorflow.js tf.initializers.constant() Method

The initializers in Tensorflow.js are used to initialize the starting values of kernel, weights, and biases. The tf.initializers.constant() is initializer function inherited from Initializer base class. This function is used to generate the values initialized to some constant. In this post, we are going to know about tf.initializers.constant() function in Tensorflow.js.

Syntax:



tf.initializers.constant(args)

Parameters: The args object contains the following props.

Return Value: It returns the tf.initializers.Initializer.

Example 1: In this example, we are going to see standalone use of tf.initializers.constant() function. 






// Importing the tensorflow.js library
const tf = require("@tensorflow/tfjs")
 
// Use  tf.initializers.constant() function
var initializer = tf.initializers.constant({ value: 7, })
 
// Print the value of constant
console.log(initializer);

Output:

Constant { value: 7 }

Example 2: In this example, we are going to use the constant() function in model creation to initialize the kernel.




// Importing the tensorflow.js library
const tf = require("@tensorflow/tfjs")
 
// Using tf.initializers.constant() function
var initializer = tf.initializers.constant({ value: 7, })
 
// Create model
const model = tf.sequential();
 
// Add layer and initialize the kernel
model.add(tf.layers.dense({
    units: 3,
    activation: 'softmax',
    kernelInitializer: initializer,
    inputShape: [2]
}));
 
// Print the summary
model.summary();

Output:

Layer (type)                 Output shape              Param #   
=================================================================
dense_Dense1 (Dense)         [null,3]                  9
=================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
_________________________________________________________________

Reference: https://js.tensorflow.org/api/latest/#initializers.constant


Article Tags :