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Neural Network Projects with Python

You're reading from   Neural Network Projects with Python The ultimate guide to using Python to explore the true power of neural networks through six projects

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Length 308 pages
Edition 1st Edition
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Author (1):
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James Loy James Loy
Author Profile Icon James Loy
James Loy
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Table of Contents (10) Chapters Close

Preface 1. Machine Learning and Neural Networks 101 FREE CHAPTER 2. Predicting Diabetes with Multilayer Perceptrons 3. Predicting Taxi Fares with Deep Feedforward Networks 4. Cats Versus Dogs - Image Classification Using CNNs 5. Removing Noise from Images Using Autoencoders 6. Sentiment Analysis of Movie Reviews Using LSTM 7. Implementing a Facial Recognition System with Neural Networks 8. What's Next? 9. Other Books You May Enjoy

Managing image data for Keras

One common problem encountered in neural network projects for image classification is that most computers do not have sufficient RAM to load the entire set of data into memory. Even for relatively modern and powerful computers, it would be far too slow to load the entire set of images into memory and to train a CNN from there.

To alleviate this problem, Keras provides a useful flow_from_directory method that takes as an input the path to the images, and generates batches of data as output. The batches of data are loaded into memory, as required before model training. This way, we can train a deep neural network on a huge number of images without worrying about memory issues. Furthermore, the flow_from_directory method allows us to perform image preprocessing steps such as resizing and other image augmentation techniques by simply passing an argument...

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