Tensorflow.js tf.layers.dense() Function

0 0 2 minutes read

The tf.layers.dense() is an inbuilt function of Tensorflow.js library. This function is used to create fully connected layers, in which every output depends on every input.

Syntax:

tf.layers.dense(args)

Parameters: This function takes the args object as a parameter which can have the following properties:

units: It is a positive number that defines the dimensionality of the output space.
activation: Specifies which activation function to use.
useBias: specifies whether to apply a bias or not.
kernelInitializer: Specifies which initializer to use for the dense kernel weight matrix.
biasInitializer: Specifies the bias vector for this layer.
inputDim: Defines input shape as [inputDim].
kernelConstraint: Specifies the constraint for the kernel.
biasConstraint: Specifics constraint for the bias vector.
kernelRegularizer: Specifies the regularizer function applied to the dense kernel weights matrix.
biasRegularizer: Specifies the regularizer function applied to the bias vector.
activityRegularizer: Specifies the regularizer function applied to the activation.
inputShape: If this parameter is defined, it will create another input layer to insert before this layer.
batchInputShape: If this parameter is defined, it will create another input layer to insert before this layer.
batchSize : Used to construct batchInputShape, if not already specified.
dtype: Specifies the data type for this layer. The default value of this parameter is ‘float32’.
name: Specifies name for this layer.
trainable: Specifies whether the weights of this layer are updated by fit.
weights: Specifies the initial weight values of the layer.
inputDType : It is used to denote the inputDType and its value can be ‘float32’ or ‘int32’ or ‘bool’ or ‘complex64’ or ‘string’.

Return value: It returns a Dense object.

Example 1:

Javascript

import * as tf from "@tensorflow/tfjs"
  
// Create a new dense layer 
const denseLayer = tf.layers.dense({ 
   units: 2, 
   kernelInitializer: 'heNormal', 
   useBias: true
}); 
    
const input = tf.ones([2, 3]); 
const output = denseLayer.apply(input); 
    
// Print the output 
output.print()

Output:

Example 2:

Javascript

import * as tf from "@tensorflow/tfjs"
  
// Create a new dense layer 
const denseLayer = tf.layers.dense({ 
   units: 3, 
   kernelInitializer: 'heNormal', 
   useBias: false
}); 
    
const input = tf.ones([2, 3, 3]); 
const output = denseLayer.apply(input); 
    
// Print the output 
output.print()

Output:

Example 3:

Javascript

import * as tf from "@tensorflow/tfjs"
    
// Create a new dense layer 
const denseLayer = tf.layers.dense({ 
   units: 3, 
   kernelInitializer: 'ones', 
   useBias: false
}); 
    
const input = tf.ones([2, 3, 3]); 
const output = denseLayer.apply(input); 
    
// Print the output 
output.print()

Output:

Example 4:

Javascript

import * as tf from "@tensorflow/tfjs"
  
// Create a new dense layer 
const denseLayer = tf.layers.dense({ 
   units: 3, 
   kernelInitializer: 'randomUniform', 
   useBias: false
}); 
    
const input = tf.ones([2, 3, 3]); 
const output = denseLayer.apply(input); 
    
// Print the output 
output.print()

Output:

Reference: https://js.tensorflow.org/api/latest/#layers.dense

Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, zambiatek Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we’ve already empowered, and we’re here to do the same for you. Don’t miss out – check it out now!