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 CNN models from scratch

Let's start building our image categorization classifier. Our approach will be to build models on our training dataset and validate it on our validation dataset. In the end, we will test the performance of all our models on the test dataset. Before we jump into modeling, let's load and prepare our datasets. To start with, we load up some basic dependencies:

import glob 
import numpy as np 
import matplotlib.pyplot as plt 
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img 
%matplotlib inline 

Let's now load our datasets, using the following code snippet:

IMG_DIM = (150, 150) 
train_files = glob.glob('training_data/*') 
train_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img  
in train_files] train_imgs = np.array(train_imgs) train_labels = ...