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.leCunUniform() function takes samples from a uniform distribution in the interval [-cap, cap] with cap = sqrt(3 / fanIn). Note that the fanIn is the number of inputs in the tensor weight.
Syntax:
tf.initializers.leCunUniform(arguments).
Parameters:
- arguments: It is an object that contains seed (a number) which is the random number generator seed/number.
Returns value: It returns tf.initializers.Initializer.
Example 1:
Javascript
// Importing the tensorflow.Js libraryimport * as tf from "@tensorflow/tfjs"// Initialising the .initializers.leCunUniform() functionconsole.log(tf.initializers.leCunUniform(4));// Printing individual values from the gainconsole.log("\nIndividual Values\n");console.log(tf.initializers.leCunUniform(4).scale);console.log(tf.initializers.leCunUniform(4).mode);console.log(tf.initializers.leCunUniform(4).distribution); |
Output:
{
"scale": 1,
"mode": "fanIn",
"distribution": "uniform"
}
Individual Values
1
fanIn
uniform
Example 2:
Javascript
// Importing the tensorflow.Js libraryimport * as tf from "@tensorflow/tfjs"// Defining the input valuelet inputValue = tf.input({ shape: [4] });// Initializing tf.initializers.leCunUniform()// functionlet funcValue = tf.initializers.leCunUniform(6)// Creating dense layer 1let dense_layer_1 = tf.layers.dense({ units: 3, activation: 'relu', kernelInitialize: funcValue});// Creating dense layer 2let dense_layer_2 = tf.layers.dense({ units: 6, activation: 'softmax'});// Output Valuelet outputValue = dense_layer_2.apply( dense_layer_1.apply(inputValue));// Creation the modellet model = tf.model({ inputs: inputValue, outputs: outputValue});// Predicting the outputlet finalOutput = model.predict(tf.ones([2, 4]));finalOutput.print(); |
Output:
Tensor
[[0.1853671, 0.1406064, 0.1505066, 0.1183221, 0.2430924, 0.1621054],
[0.1853671, 0.1406064, 0.1505066, 0.1183221, 0.2430924, 0.1621054]]
Reference: https://js.tensorflow.org/api/latest/#initializers.leCunUniform
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