Nate Parrott
Jan 7, 2017 · 4 min read

We live in the future. Computers drive cars, fight parking tickets and raise children. Why not let machines name our children, too? What if a computer program could find the ideal baby name. Maybe it’s a perfect combination of both parents’ names—or maybe it’s a name that’s completely unique.

I trained a neural network on a list of 7500 popular American baby names, forcing it to turn each name into a mathematical representation called an embedding. Once I had a model that could translate between names and their embeddings, I could generate new names, blend existing names together, do arithmetic on names, and more.


Embeddings are an important machine learning technique. Facial-recognition algorithms are trained to convert images of faces into face embeddings—sequences of say, 16 numbers, which can be compared to find similar faces. Word-embedding networks turn words into vectors of numbers whose values map to their semantic meaning in interesting ways.

I trained an algorithm to generate name embeddings for the 7500 common baby names using a neural network called an autoencoder—a neural network trained to reconstruct its input after the data has been squeezed through a bottleneck (called a latent vector) that allows a limited amount of data through. The bottleneck forces the network to learn only the most important features of a name, compressing it by stripping superfluous information.

My network took 10-character names as input (shorter names were padded with a special <NULL> character), ran an LSTM over them, and generated a vector of 64 floating-point numbers that roughly fit a gaussian distribution. It took this embedding vector and attempted to reconstruct the input name’s characters.

The model seems to learn how to reconstruct the name’s length, first—then, it gradually gets better at reconstructing the specific letters.

The model took around a 30 minutes running on a GPU to train to a reasonable level of accuracy — as it trains, you can see the model slowly getting better at modeling and reconstructing names:

Playing with embeddings: doing math

Once we’ve converted words into vectors, we can add, subtract and multiply them. I’ve noticed a few interesting properties:

When names differ by a simple feature (like an extra “a”, you can subtract out that feature and add it onto other names:

It doesn’t always work, though:

You can “multiply” names by constants, which has some strange effects:

Blending names— for couples who can’t pick just one!

If you can do simple arithmetic on names, you can also linearly blend them, taking a weighted sum of two name embeddings and generating intermediate names from those

Generating random names

I built the embedding network as a variational autoencoder—a network that encourages the embeddings to have a normal distribution, rather than whatever crazy unpredictable distribution just happens to work best. This means it should be possible to randomly sample from a gaussian distribution to generate random embeddings that should yield plausible names:

Some of them definitely don’t make much sense (“P” or “Hhrsrrrrr”) but I kind of like a couple (“Pruliaa?” “Halden?” “Aradey?”)

If this post gets 1,000 stars, I will name my first-born child using this code. Please like and share!

💻 Check out the code!

🌟 I’ve been writing about my other adventures in deep learning here~

Nate Parrott

Written by

coding and designing and things, somewhere

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