Book Image

TensorFlow Machine Learning Cookbook

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook

By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (19 chapters)
TensorFlow Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Implementing an Advanced CNN


It is important to be able to extend CNN models for image recognition so that we understand how to increase the depth of the network. This may increase the accuracy of our predictions if we have enough data. Extending the depth of CNN networks is done in a standard fashion: we just repeat the convolution, maxpool, ReLU series until we are satisfied with the depth. Many of the more accurate image recognition networks operate in this fashion.

Getting ready

In this recipe, we will implement a more advanced method of reading image data and use a larger CNN to do image recognition on the CIFAR10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html). This dataset has 60,000 32x32 images that fall into exactly one of ten possible classes. The potential classes for the images are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. You can also refer to the first bullet point of the See also section.

Most image datasets will be too large to fit into...