## 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).

## Image recognition on the CIFAR-10 dataset using deep learning

CIFAR-10 is an established computer vision dataset used for image recognition. Its a subset of 80 million tiny images collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. This is the link to the website.

The CIFAR-10 dataset consists of 60,000 32×32 color images of 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.

As a first post, I wanted to write a deep learning algorithm to identify images in the CIFAR-10 database. This topic has been very widely covered – in fact Google’s Tensorflow tutorials cover this very well. However, they do a few things that made it difficult for me to follow their code

1. They split the code across multiple files making it difficult to follow.
2. They use a binary version of the file and a file stream to feed Tensorflow.

I downloaded the python version of the data and loaded all the variables into memory. There is some image manipulation done in the tensorflow tutorial that I recreated in the numpy arrays directly and we will discuss it below.

Prerequisites for this tutorial:
Other than Python (obviously!)

• numpy
• pickle
• sklearn
• tensorflow

For TensorFlow I strongly recommend the GPU version if you have the set-up for it. The code takes 6 hours on my dual GTX Titan X machine and running it on a CPU will probably take days or weeks!
Assuming you have everything working, lets get started!

Declare some global variables we will use. In our code we are using GradientDescentOptimizer with learning rate decay. I have tested the same code with the AdamOptimizer. Adam runs faster but gives slightly worse results. If you do decide to use the AdamOptimizer, drop the learning rate to 0.0001. This is the link to the paper on Adam optimization.

Create data directory and download data if it doesn’t exist – this code will not run if we have already downloaded the data.

Load data into numpy arrays. The code below loads the labels from the batches.meta file, and the training and test data. The training data is split across 5 files. We also one hot encode the labels.

Having more training data can improve our algorithms. Since we are confined to 50,000 training images (5,000 for each category) we can “manufacture” more images using small image manipulations. We do 3 transformations – flip the image horizontally, randomly adjust the brightness and randomly adjust the contrast. We also normalize the data. Note that there are different ways to do this, but standardization works best for image. However rescaling can be an option as well.

Now comes the fun part. This is what our network looks like.

Lets define the various layers of the network. The last line of code (logits=tf.identity(final_output,name=’logits’)) is done in case you want to view the model in TensorBoard.

Now we define our cross entropy and optimization function. If you want to use the AdamOptomizer, uncomment that line, comment the generation_run, model_learning_rate and train_step lines and adjust the learning rate to something lower like 0.0001. Otherwise the model will not converge.

Now we define some functions to run through our batch. For large networks memory tends to be a big constraint. We run through our training data in batches. One epoch is one run through our complete training set (in multiple batches). After each epoch we randomly shuffle our data. This helps improve how our algorithm learns. We run through each batch of data and train our algorithm. We also check for accuracy every 1st, 2nd,…,10th, 20th,…, 100th,… step. Lastly we calculate the final accuracy of the model and save it so we can use the calculated weights on test data without having to re-run it.

The model gives around 81% accuracy on the test set. I have an iPython notebook on my GitHub site that lets you load the saved model and run it on random samples on the test set. It outputs the image vs the softmax probabilities of the top n predictions.