TensorFlow Machine Learning Cookbook - Second Edition

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 prediction of the cosine distance is just the sign of the output (negative means dissimilar, positive means similar). We can use this network to do record matching by taking the reference address that scores the highest on the cosine ...