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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 3. Deep Learning with Keras 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers 5. Improving Model Accuracy 6. Model Evaluation 7. Computer Vision with Convolutional Neural Networks 8. Transfer Learning and Pre-Trained Models 9. Sequential Modeling with Recurrent Neural Networks Appendix

L1 and L2 Regularization

The most common type of regularization for deep learning models is the one that keeps the weights of the network small. This type of regularization is called weight regularization and has two different variations: L2 regularization and L1 regularization. In this section, you will learn about these regularization methods in detail, along with how to implement them in Keras. Additionally, you will practice applying them to real-life problems and observe how they can improve the performance of a model.

L1 and L2 Regularization Formulation

In weight regularization, a penalizing term is added to the loss function. This term is either the L2 norm (the sum of the squared values) of the weights or the L1 norm (the sum of the absolute values) of the weights. If the L1 norm is used, then it will be called L1 regularization. If the L2 norm is used, then it will be called L2 regularization. In each case, the sum is multiplied by a hyperparameter called a regularization...

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