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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure
3.7 (18)
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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

3.7 (18)
By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (13 chapters)
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12
Index

Training a Siamese Similarity Measure

A great property of RNN models, as compared to many other models, is that they can deal with sequences of various lengths. Taking advantage of this fact and that they can generalize to sequences not seen before, we can create a way to measure how similar sequences of inputs are to each other. In this recipe, we will train a Siamese similarity RNN to measure the similarity between addresses for record matching.

Getting ready

In this recipe, we will build a bidirectional RNN model that feeds into a fully connected layer that outputs a fixed length numerical vector. We create a bidirectional RNN layer for both input addresses and feed the outputs into a fully connected layer that outputs a fixed length numerical vector (length 100). We then compare the two vector outputs with the cosine distance, which is bounded between -1 and 1. We denote input data to be similar with a target of 1, and different with a target of -1. The predictions of the cosine distance...

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