How to do padding using keras?

This recipe helps you do padding using keras

Recipe Objective

How to do padding using keras? Padding is a parameter that is used to control the number of features at the output with respect to input featues.

Step 1- Importing Libraries.

import keras from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from tensorflow.keras import layers

Step 2-Creating a sample input.

We will create a sample input to show the working of the model.

sample_data = [[1, 2, 3, 4],[5, 6, 7, 86, 985],[8, 92, 92837, 7591, 251638, 29386, 188361],] output = keras.preprocessing.sequence.pad_sequences( inputs, padding="post" ) print(output)
[[     1      2      3      4      0      0      0]
 [     5      6      7     86    985      0      0]
 [     8     92  92837   7591 251638  29386 188361]]

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Build a Multimodal RAG System using AWS Bedrock and FAISS
In this LLM RAG Project, you will learn to build a Multimodal RAG system for a restaurant aggregator app, integrating text and visuals to deliver personalized food recommendations using advanced technologies like Amazon S3, Amazon Bedrock, and FAISS.

Many-to-One LSTM for Sentiment Analysis and Text Generation
In this LSTM Project , you will build develop a sentiment detection model using many-to-one LSTMs for accurate prediction of sentiment labels in airline text reviews. Additionally, we will also train many-to-one LSTMs on 'Alice's Adventures in Wonderland' to generate contextually relevant text.

Autogen Project to Build an Intelligent AI Personal Assistant
Build a multi-agent AI personal assistant using Autogen that can handle tasks like managing calendars, emails, reminders, messaging, research, and weather updates, automating everyday workflows with LLMs and tool integrations.

Learn to Build an End-to-End Machine Learning Pipeline - Part 3
This machine learning project integrates model monitoring, CI/CD practices and Amazon Sagemaker pipelines into the logistics-oriented machine learning pipeline to streamline workflow orchestration for scalable and reliable deployment of ML models in logistics.

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 a Autoregressive and Moving Average Time Series Model
In this time series project, you will learn to build Autoregressive and Moving Average Time Series Models to forecast future readings, optimize performance, and harness the power of predictive analytics for sensor data.