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

Generating text with RNNs

Now let's look at our first example of using an RNN for an interesting task. In this exercise, we will be using an RNN to generate a fairy tale story! This is a one-to-one RNN problem. We will train a single layer RNN on a collection of fairy tales and ask the RNN to generate a new story. For this task, we will use a small text corpus of 20 different tales (which we will increase later). This example also will highlight one of the crucial limitations of RNNs: the lack of persisting long-term memory. This exercise is available in rnn_language_bigram.ipynb in the ch6 folder.

Defining hyperparameters

First, we will define several hyperparameters needed for our RNN, as shown here:

  • The number of unrolls to perform at one time step. This is the number of steps that the input unrolled for, as discussed in the TBPTT method (T in the Truncated BPTT – training RNNs efficiently section). The higher this number is, the longer the RNN's memory is. However, due to the vanishing...