Hi all !
As I am trying to undestand RNN and LSTM I started a small project to create a model that will learn to generate names or first names based on a given corpus (tested on Lord of the Ring character list and real first names):
Being quite happy with the results I would like to share it here to get some reviews. Every comment is welcome (coding style, clarity, project structure, ML approach, etc.) !
The project is a Jupyter Notebook based on Tensorflow/Keras.
As a wrap up:
- I feel that the task to learn is quite easy (compared to text generation based on books like here.
- The main difficulty was to generate short fix-sized input sequences. I add some padding characters according to the choosen sequence size to do that easily and to generate names without seeds. But Iām not sure it is the right way to do it.
- Very small RNN (single layer, down to 16 hiddent units) are ables to do quite well.
- LSTM cells are probably overkill for that.
- But it allows me to keep a ālargeā character dictionnary (didnāt convert upper case or weird diacritic) and generate fun LOTR names.
What do you think of this project and what could have been done differently ?