Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

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_...