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TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide

By : Holdroyd
2.3 (3)
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TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide

2.3 (3)
By: 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)
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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

The code for our RNN example

This application is based on one provided by Google under an Apache 2 license.

As usual, we will break the code down into snippets and refer you to the repository for the license and the full working version. Firstly, we have module imports, as follows:

import tensorflow as tf
import numpy as np
import os
import time

Next, we have the download link for the text file.

You can easily change this to any text you wish by specifying the file name in file and the full URL of the file in url:

file='1400-0.txt'
url='https://www.gutenberg.org/files/1400/1400-0.txt' # Great Expectations by Charles Dickens

And then we set up the Keras get_file() utility for that file, shown as follows:

path = tf.keras.utils.get_file(file,url)

Then, we open and read the file and see how long it is, in characters:

text = open(path).read()
print ('Length of text...

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