Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, 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 our image language encoder-decoder deep learning model

We have all the essential components and utilities needed to build our model now. As we mentioned earlier, we will be using an encoder-decoder deep learning model architecture to build our image-captioning system.

The following code helps us build the architecture for this model, where we take pairs of image features and caption sequences as input to predict the next possible word in the caption at each time-step:

from keras.models import Sequential, Model 
from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector, Activation, Flatten, concatenate 
 
DENSE_DIM = 256 
EMBEDDING_DIM = 256 
MAX_CAPTION_SIZE = max_caption_size 
VOCABULARY_SIZE = vocab_size 
 
image_model = Sequential() 
image_model.add(Dense(DENSE_DIM, input_dim=4096, activation='relu')) 
image_model.add(RepeatVector(MAX_CAPTION_SIZE...