Generating tweets using a Recurrent Neural Net (torch-rnn)

Even if you’re not actively following recent trends in AI and Machine Learning, you may have come across articles by a researcher who experiments with training neural nets to generate interesting things such as:

Brown salmon in oil. Add creamed meat and another deep mixture

  • Chocolate Pickle Sauce
  • Completely Meat Chocolate Pie

So what’s going on here? What’s being used is something called a Recurrent Neural Net to generate text in a specific style. It’s trained with input data which it analyzes to recognizes patterns in the text, constructing a model of that data. It can then generate new text following the same patterns, sometimes with rather curious and amusing results.

A commonly referred to article on this topic is by Andrej Karpathy, titled “The Unreasonable Effectiveness of Recurrent Neural Networks” – it’s well worth a read to get an understanding of the theory and approach.

There’s many RNN implementations you can download and start training with any input data you can imagine. Here’s a few to take a look at:

So it occurred to me, what would happen if you trained a RNN with all your past Twitter tweets, and then used it to generate new tweets? Let’s find out 🙂

Let’s try it out with torch-rnn – the following is a summary of install steps from https://github.com/jcjohnson/torch-rnn:

sudo apt-get -y install python2.7-dev
sudo apt-get install libhdf5-dev

Install torch, from http://torch.ch/docs/getting-started.html#_ :

git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; bash install-deps;
./install.sh
#source new PATH for first time usage in current shell
source ~/.bashrc

Now clone the torch-rnn repo:

git clone https://github.com/jcjohnson/torch-rnn.git

Install torch deps:

luarocks install torch
luarocks install nn
luarocks install optim
luarocks install lua-cjson

Install torch-hdf5:

git clone https://github.com/deepmind/torch-hdf5
cd torch-hdf5
luarocks make hdf5-0-0.rockspec

Install pip to install python deps:

sudo apt-get install python-pip

From inside torch-rnn dir:

pip install -r requirements.txt

Now following steps from docs to preprocess your text input:

python scripts/preprocess.py \
  --input_txt my_data.txt \
  --output_h5 my_data.h5 \
  --output_json my_data.json

For my input tweet text this looks like:

python scripts/preprocess.py \
  --input_txt ~/tweet-text/tweet-text.txt  \
  --output_h5 ~/tweet-text/tweet-text.h5 \
  --output_json ~/tweet-text/tweet-text.json

This gives me:

Total vocabulary size: 182

Total tokens in file: 313709

  Training size: 250969

  Val size: 31370

  Test size: 31370

Now to train the model:

th train.lua \
  -input_h5 my_data.h5 
  -input_json my_data.json

For my input file containing my tweet text this looks like:

th train.lua 
  -input_h5 ~/tweet-text/tweet-text.h5 
  -input_json ~/tweet-text/tweet-text.json

This gave me this error:

init.lua:389: module 'cutorch' not found:No LuaRocks module found for cutorch

 no field package.preload['cutorch']

Trying to manually install cutorch I got errors about cuda toolkit:

CMake Error at /usr/share/cmake-3.5/Modules/FindCUDA.cmake:617 (message):

  Specify CUDA_TOOLKIT_ROOT_DIR

Checking the docs:

By default this will run in GPU mode using CUDA; to run in CPU-only mode, add the flag -gpu -1

… so adding -gpu -1 and trying again, now I’ve got this output as it runs:

Epoch 1.44 / 50, i = 44 / 5000, loss = 3.493316

… one line every few seconds.

After some time it completes a run, and you’ll find files like this in your cv dir beneath where you ran the previous script:

checkpoint_1000.json
checkpoint_1000.t7
checkpoint_2000.json
checkpoint_2000.t7
checkpoint_3000.json
checkpoint_3000.t7
checkpoint_4000.json
checkpoint_4000.t7
checkpoint_5000.json
checkpoint_5000.t7

Now to run and get some generated text:

th sample.lua -checkpoint cv/checkpoint_5000.t7 -length 500 -gpu -1 -temperature 0.4

Breaking this down:

-checkpoint : as the model training runs, it saves these point in time snapshots of the model. You can run the generation against any of these files, but it seems the last file it generates gives you the best results

-length : how many characters to generate from the model

-gpu -1 : turn off the gpu usage

-temperature : this ranges from 0.1 to 1 and with values closest to zero the generation is less creative, closer to 1 the generated output is, let’s say, more creative

Let’s run a couple of example. Let’s do 140 chars are -temperature 0.1:

The programming to softting the some the programming to something the computer the computer the computer to a computer the com

and now lets crank it up to  1.0:

z&loDOps be sumpriting sor’s a porriquilefore AR2 vanerone as dathing 201lus: It’s buct. Z) https://t.co/gEDr9Er24N Amatere. PEs’me tha

Now we’ve some pretty random stuff including a randomly generated shortened url too.

Using a value towards the middle, like 0.4 to 0.5 gets some reasonably interesting results that are not too random, but somewhat similar to my typical tweet style. What’s interesting is my regular retweets of software development quotes from @CodeWisdom have heavily influenced the model, so based on my 3000+ tweets it generates text like:

RT @CodeWisdom followed by random generated stuff

Given that the following text is clearly not content from @CodeWisdom, it wouldn’t be appropriate to use this text as-is and post it as a new tweet. Since I’m looking to take this text and use it as input for an automated Twitter-bot, as interesting as this generated pattern is in that it does look like the majority of my tweets, I’ve filtered out anything that starts with ‘RT @text’

I’ve already implemented a first attempt at a Twitter bot using this content with an AWS Lambda running on a timed schedule, you can check it out here:

 


I’ll be following up with some additional posts on the implementation of my AWS Lambda soon.

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