In this project, I generate a Simpsons TV script using a recursive neural network (RNN). The model uses part of the Simpsons dataset scripts from 27 seasons. The RNN generates a new TV script for a scene at Moe's Tavern.
The data is available at links posted above. I use a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..
- TensorFlow
- Keras
- Python 3.6
For more detailed list of python packages the requirements file is accurate (ha ha maybe, good luck). TensorFlow is a new technology so expect changes regularly, i.e. every week this model will need some updates. To avoid problems and show repeatibility of results I used a very stable version of TensorFlow and Keras. This model is not very good but provides the essential techniques for generating a tv script. Any queries seeking detailed instructions for reproduceabilty precision will be discarded and immediately passed around for a laugh.
Clone project:
git clone https://github.com/udacity/deep-learning.git
Stay up to date with any changes we make by pulling the changes to your local repository with git pull.
Activate anaconda environment
source activate tf_tvScript
Open jupyter notebook
jupyter notebook
Save changes for version control.
echo "# tv-script-generation" >> README.md
git init
touch .gitignore
git add -A
git commit -m "first commit"
Only need remote add for the first time connecting to git
git remote add origin https://github.com/prfrl/tv-script-generation.git
git push -u origin master
Refresh .gitignore cache Do not forget period at end of cached.
git rm -r --cached .
Package the "dlnd_tv_script_generation.ipynb", "helper.py", "problem_unittests.py", and the HTML file into a zip archive, or push the files from your GitHub repo.