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

Comparing TFLite and TF

As we mentioned previously, TFLite models are quite different from the normal TF models. TFLite models are much faster, smaller in size, and less computationally expensive. This distinction comes from the special way TFLite models are stored and interpreted.

The first speed increase comes from the fundamental format the model is stored in. The .tflite model file is stored in a FlatBuffer format, containing a reduced and binary representation of the model. FlatBuffer is an efficient cross-platform serialization library for many popular languages and was created by Google for game development and other performance-critical applications. The FlatBuffer format plays an essential role in effectively serializing model data and providing quick access to that data while maintaining a small binary size. This is useful for model storage due to the huge...