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Python Deep Learning Cookbook

You're reading from   Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787125193
Length 330 pages
Edition 1st Edition
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Author (1):
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Indra den Bakker Indra den Bakker
Author Profile Icon Indra den Bakker
Indra den Bakker
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Toc

Table of Contents (15) Chapters Close

Preface 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks FREE CHAPTER 2. Feed-Forward Neural Networks 3. Convolutional Neural Networks 4. Recurrent Neural Networks 5. Reinforcement Learning 6. Generative Adversarial Networks 7. Computer Vision 8. Natural Language Processing 9. Speech Recognition and Video Analysis 10. Time Series and Structured Data 11. Game Playing Agents and Robotics 12. Hyperparameter Selection, Tuning, and Neural Network Learning 13. Network Internals 14. Pretrained Models

Transferring styles to images


In the last couple of years, styles from one image to another has had an enormous boost thanks to deep learning. Many people have experimented with a certain style, often from a well-known painter, to a photo. The resulting images are often interesting to see because they show a mix between the painter's style and the original image. In the following recipe, we will show you how to use pretrained weights from VGG16 to transfer the style of one image to another. 

How to do it...

  1. We start importing all the libraries as follows:
from keras.preprocessing.image import load_img, img_to_array
from scipy.misc import imsave
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import time
import argparse

from keras.applications import vgg16
from keras import backend as K
  1. Next, we load the two images that we will use for style transfer and plot them:
base_image_path = 'Data/golden_gate.jpg'
style_reference_image_path = 'Data/starry_night.jpg'
result_prefix = 'result_...
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