You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Architecting a CNN for classification

Putting the three types of convolutional-related layers together, along with the fully connected layer(s), we can structure the CNN model for classification as follows:

Figure 12.6: CNN architecture

In this example, the input images are first fed into a convolutional layer (with ReLU activation) composed of a bunch of filters. The coefficients of the convolutional filters are trainable. A well-trained initial convolutional layer is able to derive good low-level representations of the input images, which will be critical to downstream convolutional layers if there are any, and also downstream classification tasks. Each resulting feature map is then downsampled by the pooling layer.

Next, the aggregated feature maps are fed into the second convolutional layer. Similarly, the second pooling layer reduces the size of the output feature maps. You can chain as many pairs of convolutional and pooling layers as you want. The second (or...