Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Setting up a data generator

We are just missing one key ingredient before we try our framework out on a difficult test task. The previous recipe presented a TabularTransformer that can effectively turn a pandas DataFrame into numerical arrays that a DNN can process. Yet, the recipe can only deal with all the data at once. The next step is to provide a way to create batches of the data of different sizes. This could be accomplished using tf.data or a Keras generator and, since previously in the book we have already explored quite a few examples with tf.data, this time we will prepare the code for a Keras generator that's capable of generating random batches on the fly when our DNN is learning.

Getting ready

Our generator will inherit from the Sequence class:

from tensorflow.keras.utils import Sequence

The Sequence class is the base object for fitting a sequence of data and it requires you to implement custom __getitem__ (which will return a complete batch) and...