Keras implementation of LSTM variational autoencoder based on the code in twairball's repo. Totally rewritten. Doesn't follow the paper exactly, but the main ideas are implemented.
To run a toy example, download this small dataset and
unpack fra.txt into the the folder ./data/. Then run
$ python3 train.py- Building Autoencoders in Keras
- Generating-Sentences-from-a-Continuous-Space paper
- Architecture fixed and inference implemented thanks to this article on seq2seq in Keras
MIT
- Dropout and other tricks from the paper
- Initialization with word2vec/GloVE/whatever using the Embedding layer and its weights matrix
Not neccessary, but is greatly appreciated, if you use this work.
@misc{Alekseev2018lstmvaekeras,
author = {Alekseev~A.M.},
title = {Generating Sentences from a Continuous Space, Keras implementation.},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/alexeyev/Keras-Generating-Sentences-from-a-Continuous-Space}},
commit = {the latest commit of the codebase you have used}
}
Travelling the space:
1000 samples, 40 epochs, toy example: train data
== i 'm lucky . ==
1.00 i 'm lucky
0.83 i 'm lucky
0.67 i 'm tough
0.50 i 'm well
0.33 i won .
0.17 go calm
0.00 slow down
== slow down . ==
3000 samples, 40 epochs, toy example: train data
== it was long . ==
1.00 it was long
0.83 it was long
0.67 it was new
0.50 it was new
0.33 it was wrong
0.17 is that
0.00 is that
== is that so ? ==
== i was ready . ==
1.00 i was ready
0.83 i was ready
0.67 do n't die
0.50 do n't die
0.33 do n't lie
0.17 he is here
0.00 he is here
== he is here ! ==
== i feel cold . ==
1.00 i feel cold
0.83 i feel cold
0.67 i feel .
0.50 feel this
0.33 bring wine
0.17 say goodbye
0.00 say goodbye
== say goodbye . ==