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Tensorflow.js tf.layers.dense() Function

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  • Difficulty Level : Expert
  • Last Updated : 26 May, 2021
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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


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