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Generating Text with an LSTM


What is this?

During the time that I was writing my bachelor's thesis Sequence-to-Sequence Learning of Financial Time Series in Algorithmic Trading (in which I used LSTM-based RNNs for modeling the thesis problem), I became interested in natural language processing. After reading Andrej Karpathy's blog post titled The Unreasonable Effectiveness of Recurrent Neural Networks, I decided to give text generation using LSTMs for NLP a go. Although slightly trivial, the project still comprises an interesting program and demo, and gives really interesting (and sometimes very funny) results.

I implemented the program over the course of a weekend in Hy (a LISP built on top of Python) using Keras and TensorFlow. You can train the model on any text sources you like. Remember to give it enough time to go over at least fifty epochs, otherwise the generated text will not be very interesting, rather seemingly random garbage.

The LSTM is trained character-by-character (in contrast to word-by-word) which means that it learns to write one single character at a time to construct words. This has the peculiar effect of the LSTM making up words during generation, though still producing coherent sentences (with enough training)!


Running the program

  1. Install prerequisites:
    pip install h5py hy keras numpy tensorflow
        NOTE: If you want to perform computations on your graphics card, first install CUDA and cuDNN, then install tensorflow-gpu instead of tensorflow above.
  2. Clone this repository:
    git clone
  3. Change working directory to it:
    cd LSTM-Text-Generation
  4. Run the program and specify sources:
    ./lstm.hy --sources corpus.txt

After each completed epoch, the program will save the model to a file. You can then use it to generate text by invoking the program with the --generate switch and providing a seed text:

./lstm.hy --generate "hello world"

Type ./lstm.hy --help to see more information on how to use the program.


If you have Anaconda, you can use the following sequence of commands to clone this repository, set up an environment, install dependencies and train the model on Shakespeare's texts:

git clone
cd LSTM-Text-Generation
conda create -n lstm_shakespeare python=3 -y
source activate lstm_shakespeare
pip install h5py hy keras numpy tensorflow
mkdir data models
wget -O data/shakespeare.txt
./lstm.hy --batch-size 256 --layers 512,dropout:0.2,512 --learning-rate 0.001 --lookback 40 --model models/shakespeare --sources data/shakespeare.txt --stride 7

Let it run for at least fifty epochs (or until it reaches a categorical accuracy of at least 0.5—the higher the better), then hit ctrl-c to exit. Then, use the following command to generate text using the newly trained model:

./lstm.hy --generate "For thy sweet love remembered such wealth brings" --model models/shakespeare


There are various settings to play with in the program. For the purpose of this program, there are no "optimal" settings- Rather, you should go ahead and experiment to come up with different, interesting results. If you want to see all available settings, type ./lstm.hy --help in your terminal, with the working directory set to the path where the lstm.hy program is located.

Batch size

The batch size is set like this: --batch-size 256. The default is 128.

Disabling GPU acceleration

If you only want to do computations on the CPU (despite having installed GPU-enabled TensorFlow), specify the --cpu flag.


Specifying the --generate command line argument and providing a seed text starts the program in text generation mode using the specified text as the initial seed for generation. You must train a model on a body of text before using the --generate command line argument. Example:

--generate "hello world"

Configuring layers

The program defaults to a single 128-cell LSTM layer. You can specify custom layers using the --layers argument. For example, if we wanted to LSTM layers with 128 cells in the first and 64 in the last, with a dropout layer (with a dropout probability of 20%) inbetween, we would specify the following command line argument to the program:

--layers lstm:128,dropout:0.2,lstm:64
    NOTE: The last layer must not be a dropout layer.

Learning rate

The learning rate (which defaults to 0.01) can be set the following way:

--learning-rate 0.001


You can use the --lookback command line argument to specify the size (in number of characters) of the sliding window during training. The program defaults to a lookback value of 32 characters, but you can set it to anything you like (although a greater lookback value requires more memory). For example, if you want to take the last 50 charactesr into account during training, specify the following command line argument:

--lookback 50


Use the --model command line argument to specify the model name. This tells the program the name of the model to load or save. For example, to save to (or load from, if the --generate flag has been specified) a model named my_model:

--model my_model


Using the ---stride command line argument lets you set how many characters to move the sliding window forward after each training iteration. This setting can be thought of as a way to reduce the memory footprint for large corpora. The default is 3. Example:

--stride 7

Word-by-word training (not yet implemented)

Specify the --word-by-word flag to train the model on one word at a time (i.e. training it to predict words) rather than one character at a time (i.e. training it to predict characters). Although this might result in more coherent generated sentences, this reduces the creative capability of the trained model—it will not be able to come up with new names or words.


  • The model seems unable to learn
    The neural network model might have become unstable because of too high learning rate. You can try lowering the learning rate. For example, the default is 0.01, so you could try specifying 0.001: --learning-rate 0.001


Below are a few interesting results attained by running the program on various corpora.

WARNING: There is some seriously offensive language ahead, please stop reading here if you are a sensitive person. The texts below have been generated by models trained on various corpora.

King James bible

I trained a model on the King James Bible for about a day on my Nvidia GeForce GTX 1080. The results were pretty funny and interesting:

9:7 "And the man of vily made mad the land of Egypt."

12:26 "And God said now, Seruily, I will judge the mighty servants, five kindreds which the souls of thy bread people."

18:34 "For your feet, O Ishmel."

119:6 "The children of Israel said unto him, Fear upon the seven commandment, I will go us."

Snoop Dogg

Taking this a little further, I trained a model on Snoop Dogg song lyrics for a while and then let it generate new songs inspired by Snoop Dogg's work. Funny... and offensive!

[Chorus: Amh Rock Jaz (Justin life]
If you feel ya bitch, you know what it abuse?
I'm all alone in the ride or you gon' change
One of 'em smoke and be the motherfuckin' round
Let me hear you get back to your
Niggas gotta come with the big nickulaf
So I'm gon' be? I make it lose it!
Ahh, yeah, I'm the way you hit the pound of courser
I can't be was that shit and I'm smokin' like O.I
Gotta mather now!
Niggas busta like a nigga in some real shit

Now this is a sexual times
But baby flip it, rollin around the party
Cuz they know it make the whole world go bound
And the rearte for my homies at this shit
West dude, I'm a good hard, them boys
See I'ma stuff that's the smoke
With the wrong time, happens all the dirt
Bounced in the black 18?3 or a trip
Did you feel nothing butch chillin', and I'mma little back
You know the clock where I problem, let's check
Ya cuz just say, Yeah, we just light? Fuck me
Delivery nigga

King James Bible + Snoop Dogg

Let's have some fun and combine King James Bible with Snoop Dogg song lyrics. Here, I have let the program train a model on the the two sources for a while. The results are... well, judge for yourself. Offensiveness ahead.

16:21 "Saying, The priest speak my soul in my motherfucker."

22:13 "And he shall not burn his weed for the stranger, when I can't call this shit was that taw it?"

38:18 "Ay you love out the pimpeth not die, which I cake bitches and a plocked before their sin"

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