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

Viewing the original images

Next, we use calls to the two preceding functions to display our content and style images, remembering that the image pixels need to be of type unsigned 8-bit integer. The plt.subplot(1,2,1) function means use a grid of one row and two columns at position one; plt.subplot(1,2,2) means use a grid of one row and two columns at position two:

channel_means = [103.939, 116.779, 123.68] # means of the BGR channels, for VGG processing

plt.figure(figsize=(10,10))

content_image = load_image(content_path).astype('uint8')
style_image = load_image(style_path).astype('uint8')

plt.subplot(1, 2, 1)
show_image(content_image, 'Content Image')

plt.subplot(1, 2, 2)
show_image(style_image, 'Style Image')

plt.show()

The output is shown in the following screenshot:

There follows a function to load the image. As we are going to use this, as...