Text Generation with Python and TensorFlow/Keras

Introduction Are you interested in using a neural network to generate text? TensorFlow and Keras can be used for some amazing applications of natural language processing techniques, including the generation of text. In this tutorial, we’ll cover the theory behind text generation using a Recurrent Neural Networks , specifically a Long Short-Term Memory Network , implement this network in Python, and use it to generate some text. Defining Terms To begin with, let’s start by defining our terms. It may prove difficult to understand why certain lines of code are being executed unless you have a decent understanding of the concepts that are being brought together. TensorFlow TensorFlow is one of the most commonly used machine learning libraries in Python, specializing in the creation of deep neural networks. Deep neural networks excel at tasks like image recognition and recognizing patterns in speech. TensorFlow was designed by Google Brain, and its power lies in its ability to join together many different processing nodes. Keras Meanwhile, Keras is an application programming interface or API. Keras makes use of TensorFlow’s functions and abilities, but it streamlines the implementation of TensorFlow functions, making building a neural network much simpler and easier. Keras’ foundational principles are modularity and user-friendliness, meaning that while Keras is quite powerful, it is easy to use and scale. Natural Language Processing Natural Language Processing (NLP) is exactly what it sounds like, the techniques used to enable computers to understand natural human language, rather than having to interface with people through programming languages. Natural language processing is necessary for tasks like the classification of word documents or the creation of a chatbot. Corpus A corpus is a large collection of text, and in the machine learning sense a corpus can be thought of as your model’s input data. The corpus […]

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