What are stacking recurrent layers in neural networks

This recipe explains what are stacking recurrent layers in neural networks

Recipe Objective - What are Stacking Recurrent layers in neural network?

Stacked recurrent layers are defined as the Recurrent Neural Network model that is comprised of multiple Recurrent Neural Network layers. The Recurrent neural network layer provides a sequence to the output rather than a single value output to Recurrent neural network layer. Stacking recurrent layers are a way of building more powerful recurrent neural networks. Stacking recurrent layers are used to increase the capacity of a neural network until overfitting becomes a primary obstacle that is even after using dropout to mitigate the overfitting. Stacking recurrent layers increases the representational power of the neural network at a cost of higher computational loads.

This recipe explains what are staking recurrent layers, how it is beneficial for neural network models and how it can be executed.

A Deep Dive into the Types of Neural Networks

Explanation of Stacking recurrent layers.

Stacking recurrent layers on the top of each other in Keras, all the intermediate layers should return their full sequence of the outputs that is a 3D tensor, rather than their output at last timestep. This is done by specifying return_sequences = TRUE command.

Stacking recurrent layers can be executed using the Keras framework easily. It increases the complexity of the neural network model. It decreases the validation loss and helps in improving model accuracy.

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Build Piecewise and Spline Regression Models in Python
In this Regression Project, you will learn how to build a piecewise and spline regression model from scratch in Python to predict the points scored by a sports team.

Recommender System Machine Learning Project for Beginners-3
Content Based Recommender System Project - Building a Content-Based Product Recommender App with Streamlit

NLP and Deep Learning For Fake News Classification in Python
In this project you will use Python to implement various machine learning methods( RNN, LSTM, GRU) for fake news classification.

Learn to Build an End-to-End Machine Learning Pipeline - Part 1
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, addressing a major challenge in the logistics industry.

Hands-On Approach to Regression Discontinuity Design Python
In this machine learning project, you will learn to implement Regression Discontinuity Design Example in Python to determine the effect of age on Mortality Rate in Python.

Build CNN for Image Colorization using Deep Transfer Learning
Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Build Deep Autoencoders Model for Anomaly Detection in Python
In this deep learning project , you will build and deploy a deep autoencoders model using Flask.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Build Real Estate Price Prediction Model with NLP and FastAPI
In this Real Estate Price Prediction Project, you will learn to build a real estate price prediction machine learning model and deploy it on Heroku using FastAPI Framework.