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)

Automated image captioning in action!

Evaluating on our test dataset was a good way to test the model's performance, but how do we start using the model in the real world and caption completely new photos? This is where we need some knowledge of building an end-to-end system, which takes in any image as an input and gives us a free-text natural-language caption as the output.

Here are the major components and functions for our automated caption generator:

  • Caption model and metadata initializer
  • Image feature extraction model initializer
  • Transfer learning-based feature extractor
  • Caption generator

To make this generic, we built a class that makes use of several utility functions we mentioned in the previous sections:

from keras.preprocessing import image 
from keras.applications.vgg16 import preprocess_input as preprocess_vgg16_input 
from keras.applications import vgg16 ...