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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

Model building in Keras

We're finally ready to start building our model in Keras. As a reminder, the model architecture that we're going to use is shown in the previous section.

Importing data

First, let's import the dataset. The IMDb movie reviews dataset is already provided in Keras, so we can import it directly:

from keras.datasets import imdb

The imdb class has a load_data main function, which takes in the following important argument:

  • num_words: This is defined as the maximum number of unique words to be loaded. Only the n most common unique words (as they appear in the dataset) will be loaded. If n is small, the training time will be faster at the expense of accuracy. Let's set num_words = 10000...
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