Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By : Sujit Pal, Nick McClure
Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By: Sujit Pal, 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 allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Working with a linear SVM

For this example, we will create a linear separator from the iris dataset. We know from prior chapters that the sepal length and petal width create a linear separable binary dataset for predicting whether a flower is I. setosa or not.

Getting ready

To implement a soft separable SVM in TensorFlow, we will implement the specific loss function, as follows:

Here, A is the vector of partial slopes, b is the intercept, is a vector of inputs, is the actual class, (-1 or 1), and is the soft separability regularization parameter.

How to do it...