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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

The IMDb movie reviews dataset

At this point, let's take a quick look at the IMDb movie reviews dataset before we start building our model. It is always a good practice to understand our data before we build our model.

The IMDb movie reviews dataset is a corpus of movie reviews posted on the popular movie reviews website https://www.imdb.com/. Each movie review has a label indicating whether the review is positive (1) or negative (0).

The IMDb movie reviews dataset is provided in Keras, and we can import it by simply calling the following code:

from keras.datasets import imdb
training_set, testing_set = imdb.load_data(index_from = 3)
X_train, y_train = training_set
X_test, y_test = testing_set

We can print out the first movie review as follows:

print(X_train[0])

We'll see the following output:

[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36...
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