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)

Building an image caption dataset generator

One of the most essential steps in any complex deep learning system that consumes large amounts of data is to build an efficient dataset generator. This is very relevant in our system, especially because we will be dealing with image and text data. Besides that, we will be dealing with sequence models where we have to pass the same data multiple times to our model during training. Unpacking all the data in lists, pre-building datasets would be the most in-efficient way to tackle this problem. Hence we will be leveraging the power of generators for our system.

To start with, we will load up our image features learned from transfer learning, along with our vocabulary metadata, using the following code:

from sklearn.externals import joblib 
tl_img_feature_map = joblib.load('transfer_learn_img_features.pkl')