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

Performing the style transfer

The function that performs style_transfer is quite long so we will present it in sections. Its signature is as follows:

def run_style_transfer(content_path,
style_path,
number_of_iterations=1000,
content_weight=1e3,
style_weight=1e-2):

Since we don't want to actually train any layers in our model, just use the output values from the layers as described previously; we set their trainable properties accordingly:

model = get_model() 
for layer in model.layers:
layer.trainable = False

Next, we get the style_features and content_features representations from the layers of our model, using the function previously defined:

style_features, content_features = get_feature_representations(model, content_path, style_path)

And gram_style_features, using a loop over style_features...