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

Implementation – loading weights and inferencing with VGG-

sard/post/vgg16/ provides the weights as a dictionary of NumPy arrays. There are 16 weight values and 16 bias values corresponding to the 16 layers of VGG-16. They are saved under the keys as follows:

conv1_1_W, conv1_1_b, conv1_2_W, conv1_2_b, conv2_1_W, conv2_1_b…

First, download the file from the website and place it in the ch9/image_caption_data folder. Now we will discuss the implementation, from loading the downloaded CNN to making predictions with the pretrained CNN we'll use. First, we will discuss how to create necessary TensorFlow variables and load them with the downloaded weights. Next, we will define an input reading pipeline to read in images as inputs to the CNN and also several preprocessing steps. Then we will define the inference operations for the CNN to get predictions for the inputs. Then we will define calculations to get the class, along with the prediction for that class which the CNN thinks that it suits the...