Book Image

TensorFlow 2.0 Quick Start Guide

By : Tony Holdroyd
Book Image

TensorFlow 2.0 Quick Start Guide

By: Tony Holdroyd

Overview of this book

TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Introduction to TensorFlow 2.00 Alpha
5
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
7
Unsupervised Learning Using TensorFlow 2
8
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
13
Converting from tf1.12 to tf2

TensorFlow Estimators

tf.estimator is a high-level API for TensorFlow. It is used to simplify machine learning programming by providing the means for the straightforward training, evaluation, predicting, and exporting of models for serving.

Estimators confer many advantages on the TensorFlow developer. It is easier and more intuitive to develop models with Estimators than with low-level APIs. In particular, the same model can be run on a local machine or on a distributed multi-server system. The model is also agnostic to the processor it finds itself on, that is, either CPUs, GPUs, or TPUs. Estimators also simplify the development process by making it easier for model developers to share implementations and, being built on Keras layers, make customization simpler.

Estimators take care of all of the background plumbing that goes into working with a TensorFlow model. They support...