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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

Creating a Siamese neural network in Keras

We are finally ready to start creating a Siamese neural network in Keras. In the previous sections, we looked at the theory and the high-level structure of a Siamese neural network. Let's now look at the architecture of a Siamese neural network in greater detail.

The following diagram shows the detailed architecture of the Siamese neural network we'll build in this chapter:

Let's start by creating the shared convolutional network (boxed in the preceding diagram) in Keras. By now, you should be familiar with the Conv layer, Pooling layer, and Dense layer. If you need a refresher, feel free to refer to Chapter 4, Cats Versus Dogs – Image Classification Using CNNs, for their definitions.

Let's define a function that builds this shared convolutional network using the Sequential class in Keras:

from keras.models...
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