Tensorflow.js tf.layers.gru() Function

Last Updated : 17 Jun, 2021

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.

Tensorflow.js tf.layers.gru() function is used to create a RNN layer which consists of only one GRUCell and the apply method of this layer operates on a sequence of input tensors. The shape of input tensor must be atleast 2D and the first dimension must be time steps. gru is Gated Recurrent Unit.

Syntax:

tf.layers.gru(args)

Parameters:

args: It specifies the given config object.
1. recurrentActivation: It specifies the activation function which will be used for the recurrent step. The default value of this parameter is hard sigmoid.
2. implementation: It specifies the implementation mode. It can be either 1 or 2. For superior performance implementation is recommended.

Return value: It returns a tf.layers.Layer

Example 1:

Javascript

// Importing the tensorflow.js library  
const tf = require("@tensorflow/tfjs");  
  
// Create a RNN model with gru Layer 
const RNN = tf.layers.gru({units: 8, returnSequences: true}); 
  
// Create an input which will have 5 time steps 
const input = tf.input({shape: [5, 10]}); 
const output = RNN.apply(input); 
  
console.log(JSON.stringify(output.shape));

Output:

[null, 5, 8]

Example 2:

Javascript

// Importing the tensorflow.js library  
const tf = require("@tensorflow/tfjs");  
  
// Create a new model with gru Layer 
const rnn = tf.layers.gru({units: 4, returnSequences: true}); 
  
// Create a 3d tensor 
const x = tf.tensor3d([ 
    [ 
        [1, 2], 
        [3, 4], 
    ], 
    [ 
        [5, 6], 
        [7, 8], 
    ], 
]); 
  
// Apply gru layer to x 
const output = rnn.apply(x); 
  
// Print output 
output.print()