Pytorch Recurrent Variational Autoencoder with Dilated Convolutions

Model:

This is the implementation of Zichao Yang's Improved Variational Autoencoders for Text Modeling using Dilated Convolutions with Kim's Character-Aware Neural Language Models embedding for tokens

Most of the implementations about the recurrent variational autoencoder are adapted from analvikingur/pytorch_RVAE

Usage

Before model training it is necessary to train word embeddings:

$ python train_word_embeddings.py

This script train word embeddings defined in Mikolov et al. Distributed Representations of Words and Phrases

Parameters:

--use-cuda

--num-iterations

--batch-size

--num-sample –– number of sampled from noise tokens

To train model use:

$ python train.py

Parameters:

--use-cuda

--num-iterations

--batch-size

--learning-rate

--dropout –– probability of units to be zeroed in decoder input

--use-trained –– use trained before model

To sample data after training use:

$ python sample.py

Parameters:

--use-cuda

--num-sample