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Keras 2.x Projects

You're reading from   Keras 2.x Projects 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789536645
Length 394 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Keras FREE CHAPTER 2. Modeling Real Estate Using Regression Analysis 3. Heart Disease Classification with Neural Networks 4. Concrete Quality Prediction Using Deep Neural Networks 5. Fashion Article Recognition Using Convolutional Neural Networks 6. Movie Reviews Sentiment Analysis Using Recurrent Neural Networks 7. Stock Volatility Forecasting Using Long Short-Term Memory 8. Reconstruction of Handwritten Digit Images Using Autoencoders 9. Robot Control System Using Deep Reinforcement Learning 10. Reuters Newswire Topics Classifier in Keras 11. What is Next? 12. Other Books You May Enjoy

Implementing a DNN to label sentences

To labeling sentences, we'll use the Reuters newswire topics dataset. This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics, published by Reuters in 1986. As with the IMDB dataset used in Chapter 6, Movie Reviews Sentiment Analysis Using Recurrent Neural Network, each wire is encoded as a sequence of word indexes.

Just as MNIST, Fashion-MNIST, and IMDB already used in the previous chapters, the Reuters dataset comes packaged as part of the Keras distribution, where there's also a detailed description of its content, as shown at the following link: https://keras.io/datasets/.

To import the Reuters dataset in the Python environment, the following code must be used:

from keras.datasets import reuters
(XTrain, YTrain), (XTest, YTest) = reuters.load_data(path="reuters.npz",
...
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