Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow

Captions generated for test images

Let's see what sort of captions are generated for the test images.

After 100 steps, the only thing that our model has learned is that the caption starts with an SOS token, and there are some words followed by a bunch of EOS tokens (see Figure 9.11):

Figure 9.11: Captions generated after 100 steps

After 1,000 steps, our model knows to generate slightly semantic phrases and recognizes objects in some images correctly (for example, a man holding a tennis racket, shown in Figure 9.12). However, the text seems to be short and vague, and in addition, several images are described incorrectly:

Figure 9.12: Captions generated after 1,000 steps

After 2,000 steps, our model has become quite good at generating expressive phrases composed of proper grammar (see Figure 9.13). Images are not described with small and vague phrases as we saw in step 1,000 before:

Figure 9.13: Captions generated after 2,000 steps

After 5,000 steps, our model now recognizes most of the images correctly...