Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh

Overview of this book

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Table of Contents (14 chapters)

Style transfer in action

The final piece of the puzzle is to use all the building blocks and perform style transfer in action! The art/style and content images are available from the data directory for reference. The following snippet outlines how loss and gradients are evaluated. We also write back outputs after regular intervals/iterations (5, 10, and so on) to understand how the process of neural style transfer transforms the images in consideration after a certain number of iterations as depicted in the following snippet:

from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
from imageio import imwrite
import time

result_prefix = 'st_res_'+TARGET_IMG.split('.')[0]
iterations = 20

# Run scipy-based optimization (L-BFGS) over the pixels of the
# generated image
# so as to minimize the neural style loss.
# This is our initial state: the target image...