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.initializers.randomNormal() function produces random values that are initialized to a normal distribution.
tf.initializers.randomNormal(arguments)
Parameters:
- arguments: It is an object that contains 3 key-values listed below:
- mean: It is the mean of the random values to be generated.
- stddev: It is the standard deviation of the random values to be generated.
- seed: It is the random number generator seed.
Returns value: It returns tf.initializers.Initializer
Example 1:
Javascript
// Importing the tensorflow.js libraryimport * as tf from "@tensorflow/tfjs"Â
// Initializing the .initializers.randomNormal() functionlet geek = tf.initializers.randomNormal(3)Â
// Printing gain valueconsole.log(geek);Â
// Printing individual gain value.console.log('\nIndividual values:\n');console.log(geek.DEFAULT_MEAN);console.log(geek.DEFAULT_STDDEV);console.log(geek.mean);console.log(geek.stddev); |
Output:
{
"DEFAULT_MEAN": 0,
"DEFAULT_STDDEV": 0.05,
"mean": 0,
"stddev": 0.05
}
Individual values:
0
0.05
0
0.05
Example 2:Â
Javascript
// Importing the tensorflow.Js libraryimport * as tf from "@tensorflow/tfjsÂ
Â
// Defining the input valueconst inputValue = tf.input({shape:[4]});Â
// Initializing tf.initializers.randomNormal() function.const funcValue = tf.initializers.randomNormal(3)Â
// Creating dense layer 1const dense_layer_1 = tf.layers.dense({    units: 5,     activation: 'relu',    kernelInitialize: funcValue});Â
// Creating dense layer 2const dense_layer_2 = tf.layers.dense({Â Â Â Â units: 7, Â Â Â Â activation: 'softmax'});Â
// Output const outputValue = dense_layer_2.apply(Â Â Â Â dense_layer_1.apply(inputValue));Â
// Creation the model.const model = tf.model({    inputs: inputValue,    outputs: outputValue});Â
// Predicting the output.model.predict(tf.ones([2, 4])).print(); |
Output:
Tensor
[[0.1165049, 0.2952721, 0.1367277, 0.1689866,
0.0897676, 0.0964029, 0.0963382],
[0.1165049, 0.2952721, 0.1367277, 0.1689866,
0.0897676, 0.0964029, 0.0963382]]
Reference: https://js.tensorflow.org/api/3.6.0/#initializers.randomNormal
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