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TensorFlow 2.0 Quick Start Guide

You're reading from   TensorFlow 2.0 Quick Start Guide Get up to speed with the newly introduced features of TensorFlow 2.0

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789530759
Length 196 pages
Edition 1st Edition
Languages
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Author (1):
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Tony Holdroyd Tony Holdroyd
Author Profile Icon Tony Holdroyd
Tony Holdroyd
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to TensorFlow 2.00 Alpha
2. Introducing TensorFlow 2 FREE CHAPTER 3. Keras, a High-Level API for TensorFlow 2 4. ANN Technologies Using TensorFlow 2 5. Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
6. Supervised Machine Learning Using TensorFlow 2 7. Unsupervised Learning Using TensorFlow 2 8. Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
9. Recognizing Images with TensorFlow 2 10. Neural Style Transfer Using TensorFlow 2 11. Recurrent Neural Networks Using TensorFlow 2 12. TensorFlow Estimators and TensorFlow Hub 13. Converting from tf1.12 to tf2
14. Other Books You May Enjoy

One-hot encoding

One-hot encoding (OHE) is where a tensor is constructed from the data labels with a 1 in each of the elements corresponding to a label's value, and 0 everywhere else; that is, one of the bits in the tensor is hot (1).

OHE example 1

In this example, we are converting a decimal value of 5 to a one-hot encoded value of 0000100000 using the tf.one_hot() method:

y = 5
y_train_ohe = tf.one_hot(y, depth=10).numpy()
print(y, "is ",y_train_ohe,"when one-hot encoded with a depth of 10")
# 5 is 00000100000 when one-hot encoded with a depth of 10

OHE example 2

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