Book Image

What's New in TensorFlow 2.0

By : Ajay Baranwal, Alizishaan Khatri, Tanish Baranwal
Book Image

What's New in TensorFlow 2.0

By: Ajay Baranwal, Alizishaan Khatri, Tanish Baranwal

Overview of this book

TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features. What's New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You'll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis. By the end of the book, you'll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.
Table of Contents (13 chapters)
Title Page

Transforming datasets

Once the dataset objects have been created, they need to be transformed based on the model's requirements. The following diagram shows the flow of dataset transformation:

Some of the most important transformations are as follows:

  • Data rearrangements: These might be needed to select a portion of data instead of taking the entire dataset. They can be useful for doing experiments with a subset of data.
  • Data cleanups: These are extremely important. It could just be as simple as cleaning up a date format, such as from YYYY/MM/DD to MM-DD-YYYY, or removing data that has missing values or incorrect numbers. Other examples of data cleansing is removing stop words from text files for an NLP module.
  • Data standardization and normalization: These are crucial for data where one or more features are coming from various sources and have different units and scales...