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
Contributors
Preface
Index

Defining the LSTM


Now that we have defined the data generator to output a batch of data, starting with a batch of image feature vectors followed by the caption for the respective images word by word, we will define the LSTM cell. The definition of the LSTM and the training procedure is similar to what we observed in the previous chapter.

We will first define the parameters of the LSTM cell. Two sets of weights and a bias for input gate, forget gate, output gate, and for calculating the candidate value:

# Input gate (i_t) - How much memory to write to cell state
# Connects the current input to the input gate
ix = tf.Variable(tf.truncated_normal([embedding_size, num_nodes], stddev=0.01))
# Connects the previous hidden state to the input gate
im = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], stddev=0.01))
# Bias of the input gate
ib = tf.Variable(tf.random_uniform([1, num_nodes],0.0, 0.01))

# Forget gate (f_t) - How much memory to discard from cell state
# Connects the current input...