text-rnn-tensorflow
Tutorial: Multi-layer Recurrent Neural Networks (LSTM) for text models in Python using TensorFlow.
Before going through this tutorial, I suggest to read the very very good blog note from Andrej Karpathy: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
This project takes also a lot from : https://github.com/hunkim/word-rnn-tensorflow by hunkim. (honestly: almost every thing, this word-rnn-tensorflow project is great)
Requirements
Training Tutorial
The project has a Train_RNN.ipynb notebook. Open it using Jupyter.
It describes the following activities:
- Load and prepare the data:
- Load Data
- Vocabulary creation
- Tensor creation
- Batches creation
- Model set-up
- Model training:
- train the model
- save it to local file
Text generation Tutorial
The project has a Generate_text_ipynb notebook. Open it using Jupyter.
It describes the following activities:
- Retrieve saved model
- Predict text
simple_model.py
This python script embeds the definition of a class for the model:
- in order to train one RNN,
- and to use a saved RNN.
It's a 'simplification' of the word-rnn-tensorflow project, with a lot of comments inside to describe its steps.
To Go further
Model training and text generation is done through notebooks in this tutorial. If you want to use a more strengthened code, a more optimized code, embedding more features, I suggest to use the word-rnn-tensorflow project
additional notes
The project comes with two types of input:
- data/tinyshakespeare/input.txt:
- a small condensate of Shakespeare books
- data/Artistes_et_Phalanges-David_campion/input.txt:
- The complete text of a french fantasy book "Artistes et Phalanges", by David Campion
- This file book is under the following licence: Licence Creative Commons CC BY-NC-ND