Tensorflow.js tf.layers.dense() Function (original) (raw)

Last Updated : 26 May, 2021

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()

`