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

Quick Draw – image classification using TensorFlow

We will be using images taken from Google's Quick Draw! dataset. This is a public, that is, open source, the dataset of 50 million images in 345 categories, all of which were drawn in 20 seconds or less by over 15 million users taking part in the challenge. We will train on 10,000 images in 10 categories, some of which were chosen to be similar so that we can test the discriminatory power of the model. You can see examples of these images at https://quickdraw.withgoogle.com/data. The images are available in a variety of formats, all of which are described at https://github.com/googlecreativelab/quickdraw-dataset.

Here, we will use the images that have been stored as .npy files. The public dataset of .npy files is hosted at https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap?pli=1. From...