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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 cats and dogs dataset

Now that we understand the theory behind CNNs, let's dive into data exploration. The cats and dogs dataset is provided by Microsoft. The instructions for the downloading and setting up of the dataset can be found in the Technical requirements section of this chapter.

Let's plot the images to better understand the kind of data we're working with. To do that, we can simply run the following code:

from matplotlib import pyplot as plt
import os
import random

# Get list of file names
_, _, cat_images = next(os.walk('Dataset/PetImages/Cat'))

# Prepare a 3x3 plot (total of 9 images)
fig, ax = plt.subplots(3,3, figsize=(20,10))

# Randomly select and plot an image
for idx, img in enumerate(random.sample(cat_images, 9)):
img_read = plt.imread('Dataset/PetImages/Cat/'+img)
ax[int(idx/3), idx%3].imshow(img_read)
ax[int(idx/3),...
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