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.
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...