Machine Learnng Text Generation

This is an implementation of text generation with tensorflow using RNN, LSTM, and GRU neural nets. There are two examples including an 'ai dictionary' word definition generator and an 'ai lovecraft' lovecraftian horror generator.

lib.py is one big file to make it easy to copy into a collab notebook, it can be used to trian a model on an input text dataset.

ai_dictionary.py and ai_lovecraft.py are CLI text generation programs, each trained on Webster's Unabridged Dictionary and H.P. Lovecraft's Corpus respectively.

Getting Started

• fork and clone this repo
• this project requires a virtual enviroment, and you can run pip3 install virtualenv if you don't have the virtualenv package
• cd into the repo directory and run virtualEnv venv to create the virtual enviroment
• run source venv/bin/activate to start the virtual enviroment
• run pip3 install -r requirements.txt to install the required packages
• due to github file size limits, you will have to download the trained models from this google drive and unzip them in the models folder

the file structure should look like this:
.
├── models
  ├── lovecraft_model.hdf5
  ├── websters_model.hdf5

• run python3 ai_dictionary.py to interact with the ai dictionary

• run python3 ai_lovecraft.py to interact with the ai dictionary

The texts used for training are stored in the texts directory and were retrived from Project Gutenberg

Additional models can be trained by using lib.py

further project ideas:

• themed lorem ipsum rest api that generates the text on request.

• twitter bot that imitates the twitter persona of a well known personality.

• dictionary that makes a definition for a supplied word.

todo list

• create model prediciton loop that checks predicted words against known words from the training text, and rejects unknown words (for character based generation)
• compare LSTM and GRU layers for speed and accuaracy
• create data pipeline for tekenizing full words, treating punctuation as individual words
  • clean text of punctaion, new lines and trailing spaces better
• create pipeline for sentance by sentance fitting
  • find longest sentance and pad the rest
  • compensate for extra trailing spaces with the prediciton loop