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)

Image feature extraction with transfer learning

The first step for our model is to leverage a pretrained DCNN model, using principles of transfer learning to extract the right features from our source images. To keep things simple, we will not be fine-tuning or connecting the VGG-16 model to the rest of our model architecture. We will be extracting the bottleneck features from all our images beforehand to speed up training later, since building a sequence model with several LSTMs will take a lot of training time even on GPUs, as we will see shortly.

To get started, we will load up all the source image filenames and their corresponding captions from the Flickr8k_text folder in the source dataset. Also we will combine the dev and train dataset images together, as we mentioned before:

import pandas as pd 
import numpy as np 
# read train image file names 
with open('../Flickr8k_text...