## K-Means for image compression

I recently finished Andrew Ng’s CS229 course remotely. The course was extremely challenging and covered a wide range of Machine Learning concepts. Even if you have worked with, and used Machine Learning algorithms, he introduced concepts in novel and interesting ways I didn’t expect. One of the things that struct a chord with me was how we worked on K-Means clustering.

The algorithm is quite simple. Courtesy of CS229…

Given a training set, if we want to group it into n clusters

1. Initialize cluster centroids randomly

2. Repeat until convergence

For every i set

For each j set

Here an example of running K-means on the Iris dataset.

But an alternate use for K-Means is image compression. Given an image with the standard RGB colors we have 256x256x256=16.77M colors. We can use K-Means to compress these into less colors. The idea is similar to the above, with a few differences.

1. We take an image of dimension (m,n,3) (3=R,G,B) and resize it to (mxn,3)
2. For k clusters, we randomly sample k points from this data
3. We assign colors closest to the k points we’ve selected to that cluster
4. The new cluster points are the mean R,G,B points for each cluster
5. We repeat this process until convergence (points don’t change, or we reach a threshold)

This is what the code looks like

We can run this on a few images, reducing them from 16.77m colors to 16 colors to see how good the compression is

## Character level language models using Recurrent Neural Networks

In recent years Recurrent Neural Networks have shown great results in NLP tasks – generating text, neural machine translation, question answering, and a lot more.

In this post we will explore text generation – teaching computers to write in a certain style. This is based off (and a recreation of) Andrej Karpathy’s famous article The Unreasonable Effectiveness of Recurrent Neural Networks.

Predicting the next character in a sentence is a language model problem. Traditionally these were done using n-gram models. For example a unigram model would be the distribution of individual characters. At each time step we would predict a character using that probability distribution. A bigram model would take the probability distribution of 2 characters (for example, given the first letter a, what is the probability of the second letter is n). Mathematically

Doing this at a word level has a disadvantage – how to handle out of vocabulary words. Character models don’t have this problem since they learn general distributions of the underlying text. However, the challenge with n-gram models (word and character) is that the memory required grows exponentially with each additional n. We therefore have a limit to how far back in a sequence we can look. In our example we use an alphabet size of 98 characters (small case and capital letters, and special characters like space, parenthesis etc). A bigram model would take have 9,604 possible letter pairs. With a trigram model it grows to 941,192 possible triplets. In our example we go back 30 characters. That would require us to store 5.46e59 possible combinations.

This is where we can leverage the use of RNNs. I’m assuming you have an understanding of LSTMs and I will only describe the network architecture here. There is an excellent article by Christopher Olah on understanding RNNs and LSTMs that goes into the details of the underlying math.

For this problem we take data in sequences of 30 characters and try to predict the next character for each letter. We are using stateful LSTMs – the data is fed in batches but each batch is a continuation of the previous one. We also save the state of the LSTM at the end of each batch and use this as the initial state for the next batch. The benefit of doing this is that the system can learn longer term dependencies like closing an open parenthesis or bracket, ending a sentence with a period, etc. The code is available on my GitHub, and you can tweak the model parameters to see how the results look.

The model is agnostic to the data. I ran it on 3 different datasets – Shakespeare, Aesop’s fables and a crawl of Paul Graham‘s website. The same code learns to write in each style after a few epochs. In each case, it learns formatting, which words are commonly used, to close open quotes and parenthesis, etc.

We generate sample data as follows – we sample a capital letter (“L” in our case) and then ask the RNN to predict the next letter. we take the n highest probabilities (2 in these examples, but its a parameter that can be adjusted) and generate the next letter. Using that letter we generate the next one, and so on. Here are samples of the data for each dataset.

Shakespeare – we can see that the model learns quickly. At the end of the first epoch its already learned to format the text, close parenthesis (past the 30 character input) and add titles and scenes. After 5 epochs it gets even better and at 60 epochs it generates very “Shakespeare like” text.

Paul Graham posts – we have about 80% less data compared to Shakespeare and his writing style is more “diverse” so the model doesn’t do as well after the first epoch. Words are often incomplete. After 5 epochs we see a significant improvement – most words and the language structure are correct. The writing style is starting to resemble Paul Graham. After 60 epochs we see a big improvement overall but still have issues with some nonsensical words.

Aesop’s fables – the dataset is quite small so the model takes a lot longer to train. But it also gives us an insight into how the RNN is learning. After 1 epoch it only learns the more common letters in the language. It took 15 epochs for it to start to put words together. After 60 epochs it does better, but still has non English words. But it does learn the writing style (animal names in capital, different formatting from the above examples, etc).

The source code is available on my GitHub for anyone who wants to play with it. Please make sure you have a GPU with CUDA and CUDNN installed, otherwise it will take forever to train. The model parameters can be changed using command line arguments.

I also added a file in the git called n-gram_model.py that lets you try the same exercise with n-grams to compare how well the deep learning method does vs different n-gram sizes (both speed and accuracy).