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)

Evaluating our deep learning models

We will now evaluate the five different models we built so far by first testing them on a sample test image, then visualizing how a CNN model actually tries to analyze and extract features from the image, and finally by testing each model's performance on our test dataset. The code for this section is available in the Model Performance Evaluations.ipynb Jupyter Notebook in case you want to execute the code and follow along with the chapter. We have also built a nifty utility module called model_evaluation_utils, which we will be using to evaluate the performance of our deep learning models. Let's load up the following dependencies before getting started:

import glob 
import numpy as np
import matplotlib.pyplot as plt
from keras.preprocessing.image import load_img, img_to_array, array_to_img
from keras.models import load_model