Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Length 308 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
James Loy James Loy
Author Profile Icon James Loy
James Loy
Arrow right icon
View More author details
Toc

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

Consolidating our code

At this point, it would be useful to consolidate our code. We have written a lot of code so far, including helper functions. Let's consolidate the helper functions into a utils.py file as follows.

First, we import the necessary libraries:

import numpy as np
import random
import os
import cv2
from keras.models import Sequential
from keras.layers import Flatten, Dense, Conv2D, MaxPooling2D
from keras import backend as K
from keras.preprocessing.image import load_img, img_to_array

We include the euclidean_distance, contrastive_loss, and accuracy functions needed to train a Siamese neural network in the utils.py file:

def euclidean_distance(vectors):
vector1, vector2 = vectors
sum_square = K.sum(K.square(vector1 - vector2), axis=1, keepdims=True)
return K.sqrt(K.maximum(sum_square, K.epsilon()))

def contrastive_loss(Y_true, D):
margin = 1
return...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image