What is exponential decay in tf

This recipe explains what is exponential decay in tf

Recipe Objective

What is exponential decay in tf?

This is achieved by using "tf.compat.v1.train.exponential_decay" function which will apply exponential decay to the learning rate. It is often recommended to lower the learning rate as the training progress when we are training a model. An exponential decay function is applied to a provided initial learning rate. A global_step value is required to compute the decayed learning rate. At each training step we can just pass a TensorFlow variable that we increment.

Step 1 - Import library

import tensorflow as tf

Step 2 - Define decayed learning rate

def decayed(My_step): return initial_learning_rate * decay_rate ^ (My_step / decay_steps)

Step 3 - Define learning rate

learning_rate = 0.2

Step 4 - Exponential Decay

rate_schedule = tf.keras.optimizers.schedules.ExponentialDecay(learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True) rate_schedule

 

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Multi-Class Text Classification with Deep Learning using BERT
In this deep learning project, you will implement one of the most popular state of the art Transformer models, BERT for Multi-Class Text Classification

Deploying Machine Learning Models with Flask for Beginners
In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.

MLOps Project to Build Search Relevancy Algorithm with SBERT
In this MLOps SBERT project you will learn to build and deploy an accurate and scalable search algorithm on AWS using SBERT and ANNOY to enhance search relevancy in news articles.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Build OCR from Scratch Python using YOLO and Tesseract
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

A/B Testing Approach for Comparing Performance of ML Models
The objective of this project is to compare the performance of BERT and DistilBERT models for building an efficient Question and Answering system. Using A/B testing approach, we explore the effectiveness and efficiency of both models and determine which one is better suited for Q&A tasks.

Build a Logistic Regression Model in Python from Scratch
Regression project to implement logistic regression in python from scratch on streaming app data.

Build a Hybrid Recommender System in Python using LightFM
In this Recommender System project, you will build a hybrid recommender system in Python using LightFM .